Building a simple neural network using Keras and Tensorflow

Thank you

A big thank you to Leon Jessen for posting his code on github.

Building a simple neural network using Keras and Tensorflow

I have forked his project on github and put his code into an R Notebook so we can run it in class.

Motivation

The following is a minimal example for building your first simple artificial neural network using Keras and TensorFlow for R.

TensorFlow for R by Rstudio lives here.

Gettings started - Install Keras and TensorFlow for R

You can install the Keras for R package from CRAN as follows:

# install.packages("keras")

TensorFlow is the default backend engine. TensorFlow and Keras can be installed as follows:

# library(keras)
# install_keras()

Naturally, we will also need Tidyverse:

# Install from CRAN
# install.packages("tidyverse")

# Or the development version from GitHub
# install.packages("devtools")
# devtools::install_github("hadley/tidyverse")

Once installed, we simply load the libraries

```r
library(\keras\)
suppressMessages(library(\tidyverse\))

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Artificial Neural Network Using the Iris Data Set
-------------------------------------------------

Right, let's get to it!

### Data

The famous (Fisher's or Anderson's) `iris` data set contains a total of 150 observations of 4 input features `Sepal.Length`, `Sepal.Width`, `Petal.Length` and `Petal.Width` and 3 output classes `setosa` `versicolor` and `virginica`, with 50 observations in each class. The distributions of the feature values looks like so:


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```r
```r
iris %>% as_tibble %>% gather(feature, value, -Species) %>%
  ggplot(aes(x = feature, y = value, fill = Species)) +
  geom_violin(alpha = 0.5, scale = \width\) +
  theme_bw()

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JnVBoASKioquFyu9G+/EoxtzI2tzYYaC5mRkSEQCPwjElQIUF44HM4/YnJGRgYMAAJgeJA06LaOjg5TCnWos9L1Gzo7OzUYETAgBQUFODzOykvJpAEhZOVNH2rW5f3792kO7la2DkpXrhCv4JfYbLbMKaAAGDhIGnRba2srxWrIt2ySkbGxqQXMJQNq8vjxY4tRVKLJ4HfH5GHja19SUsLlcl88lZeX5+IToUJ0inF0CyQZGefl5WmsRQB0ESQNuq2xsXH4j2KWNvZNTU0aiwcYDolEkpubaxs4yGZp8rPxdxAKhfn5+QOO9/T0NDU12Y3yUaVyheAJRJqDR0VFhcZaBEAXQdKgw3p7e5ubm63pw20URKU719TUaCwkYDgqKyvb29ttg5xUqcTC2caIYvLizlXS7rGhdmJTEytbB9jgDYDhQdKgwyorK8ViMd3BY5gyNAf3Z8+ewX48AHOZmZl4IsHGT6W/6zg8zjbQITMzc8BxDoeDEDI2MVelckUZGZsOeqMEANAHkgYdVlJSgsPjGc7DdeHaOfv29PTU1tZqLCpgIDIyMqh+dgRjkor1MMJcnj17NmC1BiMjI4SQQNCrYuUKEQkFJJKq3w4A+g2SBh2Wl5dna+dGNh5uMT4n9yAcDgfDuwC2mExmfn4+I9xV9arooc44PC49Pb3/QVtbW4QQu6tV9frlx+xsptEUXg8bAIMCSYOukkgkDx8+dPWVMbzc1MKa5uABO/8CbKWnp4vEYrsxbqpXRTI3pvo53Lp1q/9BKpVKpVKba0tVr19OErG4tb7c09NTYy0CoIsgadBV5eXlbW1tHgHRMkt6BETfv39fJBJpICpgIG7evGnlQTOxxWbMgX2k2+PHjwcsKBIeHv6s6D4m9cujoaqQx2GHh4drrEUAdBEkDboqLS3NyNjU1XeMzJLeIROZTGZubq4GogKGgM1mZ2dn20e6Y1Wh/Vh3CZLcvn27/8G4uLjWhoq2xmdYtTK8wuwUKyur0NBQzTQHgI6CpEEnSSSSGzdueAfHEElkmYWd3IMtrOk3btzQQGDAEGRkZPD5fPsozJIGsrWptY9dampq/4OxsbGWlpY5t85i1cow+L2cJ1nXpk+fTiQSNdAcALoLkgadlJ+fX1dXFxQ1XZ7CODw+KHJaamoqTCcDmEhNTbV0p5nSKRjW6RDlkZub23+fFCMjo3nz5uVnXn7ObMewoUHl3j4n5HMXLlyo7oYA0HWQNOikCxcuUKwZ7gFRcpYPGT+bw+H88ccfao0KGAIOh5OVlYXhvQkpu7FuIrH4zp07/Q8uXrzYiES4e+0Qtm0N0Mt9fv/GsenTpzs4aGifCwB0FyQNuqezszM1NTUsZi4OJ++Pj8pwdvUdc/r0abUGBgzBvXv3+Hy+/VgM5k30Z0w1s/KgDZhDYWVltWzZskfpFzpa1Liq6b3rh4V8zmuvvaa+JgDQG5A06J4zZ86IxCgsZq5CV42JX/j06VOYewlUlJ6ebu5oZeYw5IbsSrMb45adnS1dC7LP0qVLbW2oqWe/w7w5qe72huybJxctWgTdDADIA5IGHcPhcM6ePTt63AxTC2uFLvQKmmBj53rs2DE1BQYMgVgsvnfvHiPMVR2VM8Jd+Hx+dnZ2/4MmJibr1q0rL8h4VjhwqWlM/HHmX5YUi9WrV6ujcgD0DyQNOubChQssFjtqyjJFL8Th8eOmrrh//35xcbE6AgOGoLCwkMVi0UOH2yNNaRajqCa25i/uQzFt2rTg4OA/Tn8jFgmxbbGq5MHTvDvr1q0zMxtuWVUAQB9IGnQJj8c7fvx4YORU6rA7Ww4lKHKala3DwYMHMQ8MGIisrCyiiZG1j52a6qeFOGdlZQ04iMPhNm7c2NVW9/DWGQzbkkjEf5z+V0BAQGJiIobVAqDfsE8aGhoaPvrooyVLlmzevLmrqwvz+g3ZuXPnOju7JiSuUe5yPIE4fvrqjIwM6GwAynn48KGNvz2eSFBT/bRgp8bGxoaGhgHH/f39p0+fnnHlILeHhVVbjzOS25sqN2zYgMfDZycA5IXxb4tEItm2bVtSUtKxY8dGjRp19OhRbOs3ZBwO5+jRo4GRU20YLkpXMjp6hqWNw7///W8MAwMGgsfjFRYW2gQ4qq8JGz97hMMNunrp2rVrJaLe+zewGZQjFPRmXDkQFxcXHByMSYUAGAiMk4aCggJbW9vw8HACgbBmzZo1a5T8TAxedPLkSSaLHTPzdVUqwROIMTNfz8zMhH0vgaIKCgoEAoGNn736mjCimJg7WuXk5Lx4ik6nv/rqqzm3TvewOl88q6jcO+d7WB1vvfWW6lUBYFAwXjO1sbGRTCZ/8sknVVVVrq6u69ats7CwkJ5qbW3tW3DN29vbxcWlo6MD29a1jUQi4XA4A6aQKYfNZh87diwwcrqxuY2KCzt6jo61sXP77rvvvvnmG9UD037d3d04HG6ko9AH9+/fJ5iQjOzM5H8FSiQShJBCr1grH8ajR48GfXOYOXPm2bNnM28cnzBDpdRZKORnphyNjY2lUCiG8C4kEol6e3tHOhD14vF4YrF4pKMwCBgnDT09Pc+ePfv0009dXFwuXbr03Xff7d69W3rKzMxsxYoV0sd1dXVkMtnU1BTb1rUNh8MhkUgkEkn1qo4ePdrLF7404/8wqS1m5usXD3z05MmTyMhI1WvTWiKRiMfjmZiYwE1rTBQXF1O97UhGRvJfIhKJxGKxQi9aGz/7/JulPT09NBptwClTU9PExMRrKcnjp68yIiv/7lHyMIXD7lyxYoXevwUhhLhcLh6PJ5Nlb1Kj04hEInw20AyMkwYqlRoYGOjl5YUQSkhI6L8EYf+k4ejRo2Qy2cTEBNvWtY00aVD922xrazt//vyYuPnWttisP+MfMTnrz+O//PLLxIkT9fg3TSAQ8Hg8MpkMuxCpTiQSFRcXu8wOUujJlEgkYrFYoUto/o4IodLSUmdn5xfPLl++PDk5uTg7JSJuvvx1DpB7+0xkZGRAQIDSNeiQ3t5eIpGo92+2kDRoDMafwEJDQ0tKSp4+fcrn869fv+7v749t/Ybp0KFDCEccN20lVhXicLj4V9Y9ffp0wL6CAAylrKyMy+Wqb7JlH2MbcxNb86HG3Dg5OUVHR+feOa90/fXP8lvqy+fPVz7nAMCQYZw0WFtbv/76699+++2qVavKy8vfffddbOs3QI2NjcnJyZEJS0zMLDGs1s1vrKtPxP79++FGIJBHXl4ejoC38qBroC1rH7v8/Pyhzs6bN6+t8VlD5RPlKn+ckUyn0ydMmKBsdAAYNOy7baOioqKi5N19Ech06NAhAskkMmEJ5jXHznnryK41KSkp06fLtcU2MGQFBQUUVxsCWRM3eqg+dsX3MzkczqBjDsaPH29jY5OfednRPUjRmvk8Tsmjm0sXL4RhLgAoB35ztFpTU9OVK1eipiwlG2O/zK2Tx2g3v8hDhw5BZwOQqaCggOqt9nsTUtbedmKxuKioaNCzBAJh2rRpxTl/ioR8RWsufXyLz+PMmDFD5RgBMFCQNGi148ePE41MxsQvUFP9ExLXVFdXD9iPGIAB2traWltbrTw1cW8CIURxpuKNCEMlDQihxMREHof9ND9d0ZqfZF0NDg4edIglAEAekDRoLxaL9fvvv4dPTFJHN4OUi3eYk0fwiRMn1FQ/0AZisZj1dwKBQKEaSkpKEEKaGdCAEMIR8JautGEWO/fy8vL29n6SdU2hatldrdWlOXAzDgBVwFQ07ZWcnMwXCFWZWiaPyMmLz+/fVFRUZCAz0AzQpk2b0tLS+h/x9vb+7bff5K+hoqKCQCaZMihYhzYkigu1oqximAKJiYnf7/2hh9VpRqHKWeeTB9dIJOKUKVOwCBAAAwU9DVpKIpEkJyf7hMRaWA1c4gZbPqFx5pa2Fy9eVGsrYKQIBILMzMwZEWHbFy+Q/lsRG/P06dPGxkb5K6mtrTWzt8ThNTcP3szeqqGhQSQSDVVg6tSpOCQpfHBd/jrz712eMGEChaK51AcA/QNJg5Z68uRJbW1tyPhZ6m4IjycERU1PTU3l8xUeVga0X2lpKY/HmxEeOikoQPrv1XGRCCGFNh9paWkxpVmoLcZBmNIsRCJRe3v7UAVsbGzGjx+fd++SnBXWVeR1tNTMnj0bowABMFCQNGiptLQ0EzNLN39NLPMcMPbl58+fP3jwQANtAQ0rKSnB43C+jv9bS9TWwsLOylI6TEFOTCaTZK7RdYhJFmSEEIs13EbYc+bMaWusrH825IoO/T1Kv8BgMKKjo7GJDwBDBUmDlsrMzPQMGo/HEzTQlt0oHwtremZmpgbaAhpWXl7uTLM1/vvuD1729uXl5fJXIhKJ8ESNvlfgCXiEkFAoHKbMhAkTGAyGPKtDcnuYxTmps2fPhuUZAFAR/Appo+7u7qqqKlefCI216OoT8ejRI401BzTm2bNnHoyBsx487BgVFcMNMxzA2NhYxBvu7zfmpM0Nv2MCHo+fN29eSW6qzM2y8+5ewiHxK6+8gmWIABgkSBq0UWlpqUQicXDT3HQGR7fAqqoqvd8/19CIxeKKigpP+4GLMnnY0bu7u1tbW+Wsh8FgcNrZWEc3HG7HcxwOR6fLmOQ5Z84cPA49zhhuGK9EIs69cy4+Pv7FbTMBAIqCpEEb1dTU4HB4Kl1zS9DY2ruJxeK6ujqNtQg0oLKyksPh+DoM3BzV39ERIVRYWChnPZ6enqzqDolYgnF8Q+uubLO3t5e5dTWVSp08efKj9PNi0ZAdIWV5d7rbGxcsUNcKaQAYFEgatFFLS4u5lS2BSJJdFCMUqh1CSP6PnkAnPHjwgEggjHZzGXDc0YZqZ2WZnZ0tZz0RERFCLr+rrBnrAIfUllcXESHX7bmFCxeyulpLH6UNVeDhzVN+fn6jR4/GLjoADBckDdqIxWKZYrqnpUzSLTSZTKYmGwXq9ueff4a7u5kaGb14aryvT1pa2jALIfQXEhJiY2NTn/4U6wAH11nSxGllTZo0SZ7C/v7+wcHB2WmnBj3b2lBR8zR34cKFmAYIgOGCpEEb8fl8IkmjM9yMyMbSdjXZKFCrsrKywsLC6WEhg56dER7a2dk5YKXIoeDx+Dlz5tTffdrbzcE0xsFVXi1wcHCQf3rkwoUL658VNNeWvngq59YZa2vrhIQETAMEwHBB0gAQQkgi0dztaqAZhw8fpltS4oMGH07r5+QY5DzqyJEjcv7oFy1aZEwkPz2Xg2mMg+h62tycU71y5Ur5p0fGxcXZ2Njk3j434Hgvr6cwO2XOnDlGg/W1AACUAEmDNjIyMhIqvu2vKgT8Xmm7mmwUqE9ZWVlaWtryiS+RCEMu9bFmUpy0mDwVWllZrVq1quZmSfczNQ58kYglhYfuuru5KbR0I4lEmjVrVlHOH/zev3WEFD1IEfJ5MNMSAAxB0qCNTE1Ne7nPNdkin9eDEDIzU9d2mkDDfvzxR4YlZfbY4cYSRnl7hrm77tu3T86RDUuXLnV3dcv/922xQK7ySnh26TG7pvPjjz8mDJ3rDGrWrFmCXm5JTmr/g/mZlyMiIhxemDwCAFAaJA3ayMLCgsfR6LR4aXPm5uaabBSoyaNHj+7fv/96wqRhuhmk1r6cUFNTc/XqVXmqJZFIn332GbeRVfJbFhZhDtRd0Vp2NmfhwoUhIYOPwxjGqFGjgoODC7NT+o50ttY1VBUmJiZiGiMAhg6SBm1kaWnZy30uFqvr89yLuD1MhJCVlZXGWgTqc+jQIVc67eWQYJklA51Hjff1Pnz4sFgslqdmf3//N998s+p6YfPDKpXD/BvB897c7/709vRat26dcjVMmTKlujSHw+6Sfln66KaRkVFsbCxmIQIAIGnQTpaWlhKJhNcz3G492OL2sBBCsGuwHqipqXnw4MGSl8bjcXLtZL00ZkJdXZ38O48sX758/Lhx+T/f7mnGbIKuRCLJ25dG4Eq++uorpQfWxMbGIiSpKLwn/fJpfvrYsWPhjhsA2IKkQRtZWFig/94y0AweB5IGPXHt2jUTI6OE0UFylqEToUkAACAASURBVA91c3W2tb127Zqc5fF4/BdffEGztMn99k9RLzYbUlRcfNz6qHbLli3OzsqvgspgMDw8PCqLshBCPA67obJw3LhxmIQHAOgDSYM2ko4t6OX1aKxFPo9DIpFg9oQeyMjIiPb2HLCt5fDigwIyMzPlHA6JEKJQKLt27eI1sp78J0OpGP+m/UlD2dmHy5Yti4+PV7GqMWPG1DzNRQjVlj+WSMRjxoxRPTwAQH+QNGgjY2NjhJBQoLntowR8nrRRoNM6OzvLy8ujvb0Uuira2/P58+dFRUXyX+Ln57dx48b6O2V1twZZUkl+vV2cRz+khoWEvv3226rUIxUcHMzubmN1tTRUFVIoFFdXV9XrBAD0B0mDNiISiQghTQ6EFItFik5yA1ooJydHIpGEe7grdFXAKCcTIyP5t6KQmjt37ssvv1x4+F5Pk5KDGyQSSd6+W2Z44+3bt2Py8vP19UUINdeWtdQ99fX1xck3qgMAID9IGrQRrM8IlHPnzh1nW1t7a8VmwRAJhHB3t/T0dEWb++ijj+hU27x9t5TbALPmz+K2grrNmzdjtWm1o6MjmUzuaK7uaK52c3PDpE4AQH+QNGgjoVCIECIQiBprkUAgShsFuquzs/P27dtT5B4C2V/C6KDi4uKysjKFrjI3N9+8eXN3eUvNHwrc2pDidfaU/Jo1Y8aMmJgYRa8dCh6Pd3Bw6GytY3Y0OTk5YVUtAKAPJA3aqLe3FyGkyT2riCSytFGguw4cOICXSOZGjVXi2vigAHtrqx9++EHRC8eOHZuYmFh25iGfzVPowpJfs8zJpuvXr1e0xeHRaLSWujKxWESn07GtGQCAIGnQTlwuFyFEIptorEUS2UQgEEBng+7KzMw8f/786vhYqrkyKxOQCIR3p0/Nyso6c+aMote+/fbbBBGuIvmx/JewqtsbMytef/11S0uMt4C3srJqb65GsFIZAOoBSYM24nA4CCEjI80lDUZkk752gc4pKyv7+OOPw91dl8SMV7qSuED/2WMj/vWvf2VkKDaRkkajzZ8/v+bPYsFzeTurKi7l0Wn0uXPnKh6mDObm5nweB8FGKgCoByQN2ojH4yGEiEaamwMpbUvaLtAtpaWla9eutbcw37F4oZyrQA7ln7MSx7i7ffjhh4rmDYsWLcKJJLVpJfIU5nX1NGU9W7hwIUmRxSTkRCaTBzwAAGAIkgZtJBAIEEJEkuaWWpK2JW0X6JCcnJw33niDYWb6w5qVFiaqZplEAmHXskVj3F3/+c9/yr9GJEKIRqPFxMTU33kqT+GGjHIigThr1ixlwxwOHv/XexpMIQZAHSBpAAghhEMwo133pKamvvvuu9502r7XVlmZmWJSpxGRuHPpokmB/lu3bj1+/Lj8F06dOpVd3/m8vktmyaasyujoaMxHMwwA85YBUAdIGrSRdDlnkZCvsRYFfF5fu0AnXLp06eOPPx7v7fn96uXmmK7mSSIQPps/b8G4qO+//37fvn1yXhUVFWVkZNTyuGb4Ynwmt7uybeLEiSqHObi+wbwwqhcAddDcSgAGgs/n940MYLPZIpGor89f/u2gpB/COOxuY1MN7SDFed6Nw+HU/eEPYCUlJWX79u3TQkd/Mm+OiuMYBoXD4d6fMY1iavLLoUMkEum1116TeYmpqeno0aOrnzR4zAwZpljbk3okkURFRWEX7N/0zRyGKcQAqAMkDVhqbW1NSkoaag7C/PnzP/jgA3nqsbOzQwh1tdVTGcpv+qeQ7vZGa2tr6GnQCWVlZdu2bZsUGKCmjKHP6vjYXoHwl19+8fb2lqdvIDQ09NGxw0gsQfgho+osa3Z0dGQwGFgG2g+PxzMim/B7uTCqFwB1gKQBS4cOHeIjYdh7k3F4HEKIz+cTCEQCAY8Qan/ScOHChaVLlzo4OMisx9nZmUwmN9eVeQRqaG/f5tpST09PzbQFVLR9+3ZHa6tPk15Ra8Yg9eaUSWWNjV999VVUVJTM+QjBwcEinoBV10lxsRmqTFdZ8/hgZZafkhOHwzG1oPJ7G6SLnQAAsAVjGjBTW1ubnJzsnhjsMM7TPsrDPsqDPsbVLtJN+th/WTTelPTzzz/LUxWBQAgKCqoufajumKVEQn59ZUFoaKhmmgOqKCoqKi4uXvtyApmkiYwfh8O9N31qe3v7rVu3ZBb29/fH4XDMyrahCoj5InZdp7+/P6Yx/g2XyzW3tEUI9fRobmd5AAwHJA2Y2bt3L8HcyH3G6EHPEoxJXnPDUlJSCgsL5aktJiam5ukjDlv2WHTVVRRm8nkcDLcAAOpTXFyMx+EivTw01qIbg25vbVVcXCyzJIVCcXJy6qpoHaoAs7pdIpYEBARgGuDf9PT0WFjZov8uqwoAwNbI3J4Qi8VCoVCfVgW4f//+7du3g9+ciDciiMXivuNisbjvS+fJfrWpxbt27Tp48GDfbPKhxMfH792793FGcvTUFWqMGyGEUO6dc56enu7u7vr0E0H/HT8vFAr1afadRCKRICTq97rSAJFYLJFI5Hl5+Pr6Zj/L64tN+sz3fdlV0UIgENzc3NT3SuNwOFSaFQ6HZ7PZevZ6Vo5EIhGLxXr/VIjFYn36NddmIzamQSQS6c2cKB6Pt2fPHmsfO4cJngPeyqW/sX99gUN+K8Zl77h25syZpKSk4eu0tLScNGnS3bRTYbGvktS5NGRzbWlV8YMPP/xQb34cfUQikfR/fXo38fLykkgk2eUV4328NdNiTVt7K5Pl7e0tzyvE19c3Ne2mkCfAGxHQC0lDd0Wrm5sbkajGLVV5PB6JZEwyMuZwOPr3klaC9C1I758KTebQBm5kkgY8Hk8mk01MNLe3glrt27evua3lpQ3ziH9fFlcgEBAIBCLxf08yPXjUqFifAwcOTJo0SeaIyDfeeCM1NTXn1qmXEv9PLXEjhBC6nfyTo6PjnDlz+sepHwQCAY/HI5PJ+vSthYSEeHl5nUi/F+Pvh1P/QEiE0ImMe5aWlpMnT5bnFzYsLEwiEj+v7aL62qH//sXqe/67y1ujX0pQ6y8+n883IpuQjIxFIpHevMOoore3l0gk6v1TQSQSsfp1OHHixN69e0tLS83MzEJDQ7ds2aK+GcK6CMY0qCo3N/f06dNe88ItnKjylPdfFi02xm3dulVmauzs7Pzqq6/eu3a4q60ei0gHUZR9o7r04bvvvqtPf1b1Gw6He+edd57U1iVn52iguYcVlSl5Ba+99pqcf3V8fHyMjIy6yppfPNXbxeG0skaPHnzQD1YEAgGBSCIQSXrfIQ/UYd++fWvWrElKSkpLSzt58qSdnV1sbGx+fj5W9dfU1OBwuP3792NVoeZB0qASFou1ZcsWS0+652x5px6QzMij34p9nJd35MgRmYXfeustqrXllaPbJGrofOthddw49XVMTEx8fDzmlQP1GTdu3IwZM76/mlLZMuSQQ0x0Pu/ZdvZ8SEjI/Pnz5bzEyMgoMDCwNa+WWdnGrGxjVbWzqjukj+vvPkUIqXuSjkgkes7qkEg0OuYD6I0ffvjh/fff/+CDDyIiImJjYw8dOhQZGfmf//wHq/otLS03bdoUEjLcAmhaDpIG5Ukkki+++KKD1RX6Tjxu6NVsXkQLHuU2LWj//v0FBQXDlzQzM9u8eXNt+eN7KUdUivUFErH498OfGRHRxx9/jG3NQAM++OADO0fHD0+cZHPVtYSRUCT65LfTAjzhyy+/lDlut7/IyMiO4saMj85nfHT+/uZLDz+7In1cciLL2dnZ3t5eTQFLkcnknFtn2N1tsFIZUEJzc/OAeTfff//9woULEUK5ubk0Gu3evXvR0dG2trbx8fElJX9t68pkMt98800XFxdLS8tZs2ZVVFRIj7e2ti5YsIBGo9nb27/33nt8Pt/KymrPnj3SISZDXXXlypWwsDBTU1M3N7fvv/9ec9+8fKBTWnmnT5++detW2HsJpnSFF3v2WxLZVdb88ccf//rrr8Mv3hwdHb148eKTJ/c7e4U6e2H2KS3zxtHK4qxvvvnG1tYWqzqBxpiamn799dcrVqz47My5r5cvUccqT3uv3civqf3xxx8VXb1x+fLl48ePlw6B5HK5AoGgbwF1Op2OeZwDHD9+vKurCyHk7u6u7raA/pkxY8bPP//c1ta2cuXKmJgYExOT/r0CbDb7gw8+OHXqFJ1O37ZtW0xMTGVlpYWFxZw5cyQSybFjx0xMTL799tuYmJji4mIKhZKQkODo6Pj7779XVFRs2LCBQqF88cUXfbUNehWbzZ43b97777+/f//+tLS0999/PzIyUqsGVUDSoKQnT558//33zpP9HcYpM2MeTySEvT85Y9P5LVu2fPvtt8N/klu3bl1BQcGFXz5a88lxCyuasiH/T1VJ9u3knxcvXgxrM+guNze3zz///IMPPjiUdvv/JsVhW3lKXv6ZzKz33ntv7FiFV28kkUi+vr7Sxz09Pb29vVSqXMN9MGFnZyddhR0AJRw8eNDHx+fkyZNTp04lk8njx49ftWrV0qVLpWd7e3u3bNni4uKCENq+ffuZM2eOHTsWERFx7969lpYWa2trhNCJEydcXV0zMjLweHxlZeWdO3esrKyio6M5HE5mZmZfQw8ePBj0KhMTE4FAsHbtWhcXlzFjxnh6emog1VYI3J5QRnd396ZNm0ydLANXjle6ElM6ZfRbsfcyMw8fPjx8SRKJtGvXLiOC+Pz+D0VCVYd3dbc3XjzwcWhoyLvvvqtiVWBkxcXFrVy58tDN29kVzzCstrq1bdfFy5MnT+57rwTAQJDJ5E8//bSoqKihoeHAgQPW1tavvfba+vXr+wqMGTNG+gCPx4eHhxcXF5eUlAgEAjqdTiKRSCSSsbFxfX19RUVFYWFhQECAlZWVtPwbb7xx9OjRvnqGuioqKmrMmDGBgYHLli07duzY9OnTta3PDJIGhYnF4k8//bSzpztiw8t4EkGVquzGuHnMHL1///7s7OzhS9Lp9J07dzbXFN849bUqLQr4vLM/b7QwI+/cuZNAUCl4oA3eeuutkNDQz8+c7+4ZfJs0RfGFws2nztoyGJs3b9bMlE4AtERRUVFCQoJ0TIODg8OyZcvOnTv3008//fjjj3w+X1qm/wBbgUAgFostLS2pVKqgH4lEsn79eoFAMMystKGuMjc3f/DgwfXr1y0sLLZt2+bs7Hz9+nV1f+MKgaRBYQcPHsx68CBkbbwJzUL12nwXRVr5MD755JPWVhkj4cPCwv7xj388Sr/wKP2C0s1dOfZFd2vN119/rckeY6A+eDz+iy++4CPc15cuY1LhobQ7la1tX375pZmZGSYVAqAr7O3tU1NTr1692v+gSCTqv9ZLenq69AGXy83MzPTz8wsICOjs7OzbH6C9vX3OnDnFxcV+fn6FhYXPnz+XHj958uSUKVP6qh3qqjt37uzZs2fChAn79u0rLy+Pjo4+cOCAWr9rRcGYBsU8fPjw4MGDnrNDGOEumFSIw+PC30tI33Tuk08+2b9///CDG+bPn19aWnr11NcMJy9H9yBF23qQ+ltR9o3PP/9crTsGAQ1jMBjr16/ftm3b9NKn431VWiayqrX1RPrdZcuWqXV7CAC0E5VKfe+991atWlVYWDh+/HgSiZSTk7N9+/Z333237515/fr1RCKRTqfv2LFDJBKtXLmSQqHMnTt3yZIl3333HZFI3LFjR2Vlpbe3t5eXF41GW7p06aefflpfX//xxx/Pnj27ry1vb+9Br2poaPjwww/JZPLEiROrqqqKioqWL18+Qs/H4KCnQQHd3d1btmyx8qb7LMByb1+ytWnoukl5+XkHDx6UWXjTpk3eXp7n92/iPO9WqJW6iryb5/fOnz8/MTFR2UiBlpo5c2ZISMh3V68LRSJV6vnX5Wt0BuO1117DKjAAdMuePXt27979+++/JyUlzZs37+zZs3v27Pnyyy/7Cvz888+bN2+eMmUKm81OT0+Xzgw6fvz4+PHjly9fPnPmTDKZnJKSQiQSSSRSWloaQmjKlClr166dPXv2zp07+7c16FUJCQk7d+789ttvx44d+84778ydO1fbZsXjRmRZ/qNHjwYFBYWFhWm+aVV8+OGH6Vl3Y3a/amJrLk95LpdLIpHkXGyx5Les6itPDh06JPNDXmNj49KlS+nOQQve+U7Ou87cHuYv2xa5ONIPHDhA+vta13pMIBAwmUwrKytDWO+yuLh4xYoVH86ZOWdshHI1PKx49s5/ju7YsaN/J6qKND97AgzQ3d1NJBLNzeV6y9Jdu3btWrZsmcy1+VWRm5sbEREhFAoNfDQY9DTIKy0t7ebNmwErx8uZMSjKZ/4YMyerbdu2ydxaxsHBYfPmzRWF93JunZaz8qvHvpQIODt27DCcjMHQ+Pv7x8bGHr2drnRnw6G0O15eXgkJCdgGBgDQJ5A0yIXL5e7Zs4cWPMopRl1bC+KJhODXJ1ZWVZ48eVJm4bi4uFdeeeXm+R86W2plFn6Sda308a1NmzapNQ0HI27VqlVNXd1phcVKXFtS3/C4qnrFihUwYwIAMAxIGuRy6tSptva2ABVWZZCHlSfdaaLPoUOH2Gy2zMLr16+n2VKvHP9y+BtMHHbXH6e/iYuLmzp1KnaRAm3k7+8fHBx87v4DJa49l5VNo9EmT56MeVQA6Ifw8HCJRGLg9yYQJA3y6O3t/fXXX50m+pg7Wqm7LZ9XI3q4nLNnz8osaWpq+s9//rP26aOi7JRhit26+BMeCT/44APsYgTaKykpqaCmtkrBjaye83g3Cwr1cnt0AAC2IGmQLTU1tZvJ9Jip3i19pYxtzO2jPS5cuCDPHn0xMTHjx4+/dfEnkZA/aIHWhoq8e7+vWbOGRsNg8Wmg/SZNmmRubn71UZ5CV6U9KeoVCmfOnKmmqAAAegOSBtlSU1Mt3W3NHa0109yoiT7Nzc19i34M791332V3tw613FP67/vpdJp0izZgCMhkcnx8/B95BWJFZkVdf5wfGhoKQ14AADJB0iCDWCzOzc1lhGGzlJM8bPztCWRiTk6OPIU9PDwmTZp0/8ZxsWjgnIv2pqqyvNsrV66EPYINytSpU1tZrPzqGjnLtzJZ+dU1L7/8slqjAgDoB0gaZGhqauJwOJZumuvexxHwFqOofXury7Rs2TJWV0vJo7QBx7PTTllZWc2aNQvrAIFWi4iIoFKpaYVFcpa/XVSMw+Pj4+PVGhUAQD/AuCcZ2tvbEULGVI2uw29sYyZtVx7+/v4BAQGZKUeYHU39jxc9SFkwfx6ZTFZDgEB74fH42NjYOzdT/zFjujzzJ28VFoeFhUn35wXAkO3Zs+fJkyeq1GBvbz9g2Uf9A0mDDAKBACFEUG03S0URSEQhV8YST/0tXbp0586dOalH+x+0pJjNnTsX69CADoiPj79w4UJxfUPAKKfhS3Y+f55fXbPx1fmaCQwAbVZdXd1WXxfu7qbc5UV1DfLMltd1kDTIYGpqihAScHo12aiQKzA1tZG/fEJCAizkB/pERERYWFjcKiyWmTTcLSmTIBQbG6uRuADQdt729lvnz1Pu2j2/X81uaJJdTsfBmAYZGAwGQojb3qPJRrntbDqdrskWgT4hEokvvfTSrSLZS0OmFRYFBgbCiw0AICdIGmSwsbGhUCis2g6NtSgWip43dru5KdlFBgBCaPLkyQ0dnSX1DcOU6erpyXlWBX1UmiEUClksFovFev78+UjHAoDy4PaEbP7+/hUVdRprjlXdIRaIZO51CcAwoqOjraysrj7K83NyHKrMH3kFEoQw3NMSDGPevHkNDX/lcBs2bFi0aNHIxgOAcmT3NHR3d1++fPnbb79FCBUXF8vcg1H/hIWFdT1tlYhkL9GIiY7iRiMjI0gagCpIJFJiYuKNvHxO7+DDcSQSSfLD3PHjx9va2mo4NgPEYrEaGhpmjQnf9MosB6p1SUnJSEcEgJJkJA3l5eV+fn5r1679xz/+gRD6+OOPAwMDq6urNRGa1oiIiBD1CrqetmimufaihuDgYJgqCVQ0b968nl7+tSGWlM56WlHV0vrqq69qOCrDJO1jmB4WMmdshK+DQ2Nj40hHBLSRSCQyMTEZ6ShkkJE0rF+/Pi4urqamRrqTzcGDB93c3KQJhOEICAgwNzdvLxzu9jBWxAJRZ0nz2LFjNdAW0G/Ozs4TJ078NSNTKBK9ePbYnQwvL6+oqCjNB2aApEmDg7U1QsiBal1fXz/SEQGgJBlJQ3p6+vvvv4/H/1XM1tb2o48+un37ttrj0iYEAiE8PLytQBO/513lLaJeQWRkpAbaAnpv9erVzd3dH/16eufF3/v/+/zM+cdV1atWrZJn9Segurq6OmMSiUaxQAg52VA7Ojq4XO5IBwU0RygUrl69mk6n29nZ7d27V3rwxo0bQUFBrq6uq1ev7u7uRgjNmzevt7dX+v5//PhxLy8vBweHlStXSgfPDlrJnj177O3tqVRqXFxcS4smusNlJA2WlpY8Hq//EaFQaG5urs6QtFFERET3s1YhT6DuhjqKGs3MzHx9fdXdEDAEfn5+ixYtake4MnZP/3/VPH5CQsLkyZNHOkBDUVdX52RrI03RRtnaSCSSujrNja0GI+7s2bOlpaVVVVVpaWkbN27s6elpaWlZtGjRb7/9VlFRQSQSpf3358+fJ5PJDx48KC0t3bhx45UrV2pqang83o4dOwatpLa2dtu2bVlZWU1NTTQa7fDhwxr4XmTMnpg+ffrWrVsvXrwo/bKwsPD99983wC10Q0NDJSJxd0WrbeCQY9Ex0VnWHBwcTCBodAFKoMcM7Waidqqurnax/Wu5NmdbW+kRb2/vEQ0KaI5IJOrq6iovLw8JCeHz+QihkydPxsXFBQUFIYS2bt0aFxfXv/zly5cXLFjg4+ODENq0adPixYt37NjxYiVEIvHp06d2dnZMJpNMJjOZTA18LzJ6Gnbv3o0QotPpQqGQSqUGBQV5eXl9/fXXGohMq3h5eZHJ5O6KVrW2IhFLup+1BgYGqrUVAIAmSSSSqqoqV/pfm97RKBYWJsZVVVUjGxXQpAULFixbtmzKlCkeHh7//ve/EUINDQ2ZmZmhoaGhoaEzZsygUqn9y7e0tDg5/bWc66hRo5qamgathEgk7tu3LzQ0NCEhoby8XDPfi4yeBktLy1u3bj148KCkpMTS0jIwMNDLy0szkWkVAoHg6enZVS3vJlLK4baxhRw+3JsAQJ+0tLSw2WwPO0bfEQ8GQ2Nv8UAbtLW1vf766x988EF6evqKFSvGjh1Lp9Pnzp37008/IYT4fH7fGh5SdnZ2fTewGhoapAsTv1hJaWlpSkrKtWvX7O3tv/76687OTg18LzJ6GphMJpPJ9PX1feWVV+Lj4+l0OpPJNMwVzby8vNh1XWptQrrupGGmZQDoq9LSUoSQj7193xFvB3vpQWAgDh8+vGjRIrFYHBERYWZm1tXVNW3atIsXL1ZUVAiFwo8++mjr1q3SkkKhUCwWJyYmnj59urKyUiQS7dq1Szok4MVKmpqaXFxc7O3t6+rqTp06xeFwNPC9yEgarAZjmOvOuru7P2/qVusST88bukxMTOzs7NTXBABAwwoLCy1NTRyo/9t83M/Jsbm5ub1dvT2XQHu89dZbeDze3t7ex8dn1qxZ8fHxrq6ue/funTFjhqOjY3l5uXT5RAKBMGnSpMDAQD8/v927d0+ZMsXZ2RmPx2/ZsmXQSpYvXy7NG5YuXbphw4a0tLTc3Fx1fy8ybk/0T4e5XG5OTs5PP/30n//8R81RaSNXV1eJSNzTzDR3tJZdWinP67tdXFz6JrgCAPRAXl5esItL/9mto12cEUL5+fmTJk0aubiA5lCp1Bs3bgw4mJSUlJSUNOBgSkqK9MHy5cuXL18+fCU0Gu3u3bt9Xy5evBiziIcmI2mQjt7sExIS4uzs/M4779y8eVOdUWkjd3d3hBC7vkt9SQO7vjPYD5Z1AkB/cDicoqKit1/+2+xWB6q1vbV1dnY2JA1A5yj8odbR0fHhw4fqCEXLMRgMCoXCUttYSIlIzK7v8vT0VFP9AADNy87OFggEYz09Bhwf6+WRmZk5IiEBoAoZPQ1FRUX9v+zq6vrqq6/6poIYFBwO5+vrW/WsSU31s2o6xAKRn5+fmuoHAGjerVu3HG2o7gz6gOMxfr6XsnNKSkrgVx7oFhlJw4trBtDp9OPHj8usNycn5+DBg9K5pHojJCQk99hjiUiMI2A/7KCztIlEIsEiDQDoDS6Xe+vWrVfHRrx4aqyXB8XE5Pr165A0AN0iI2l4cXalmZmZzEpbW1v37dtHIpGUj0srRUVF/fLLLx3FjbZB2Pe1tOTWBAcHa/8WZwAAOf35559cLjcxPOTFUyQCIWF00LVr19atW2dkZKT52MCgnjY1fX7mvHLXFtU1IFNTbOPRQoMnDTKnbYSHhw91SigUfvPNN4sXLz579mz/4zwer2+cZ1tbm0Ag6O3tVTDaEebl5UWj0RruVVj728sujRBCSCwWiwbbY3CA3m5Oe3Hjsvfm69xzos2kzzyfz5fnRwDUQSQSSSQSg31Vnzx5MszN1dHaetBX4Owx4eezsq9cuZKYmKi+GCQSiUgk0vsfgfSVpmIlrq6ubDa7li9U7nILBsPeXt4/Dbpr8KQhImKQ/rT+hvnxHDx4MCYm5sVl1bu7uzdt2iR9HBISEh4ezmazFQlVK8TFxZ3//YL7q6EEE7n6UYRCoVAo+yVYfbOYRCBGRUXp4nOi5TSz4AkYhmG+qnNycsrLy79a9OpQf7CdrCwj3N1OnDgxYcIEtU60FolEAoHaN9sbWQKBQCxWdREdIyMjY2NjVWogk8kqxqD9Bk8a5Pk7N6iMjAw2m52YmFhbWzvgFJ1OT0tLkz4+c+YMhUKxsbFRrpURtGTJknPnzrVl1bgnBssszOVyiUSizNs0YoGoMe1pQkKCdFYnwIpAIGCx//Ay0gAAIABJREFUWJaWlkSijNtwQE04HE5vb6+1tbpmKWuz06dPe9nbxQYFDrP/+JrJcW/9cujRo0cvv/yymsJgMplEIlGe28o6zdjYWPV9/p4+fVr4rITqo+Tyeszq9ra2NhVj0H6Dv5kO8+wXFBR88sknly9fHvRsZmbm48ePly5dKhKJOBzO0qVLf/rpJ0tLS4QQHo+nUCjSYiQSCYfDDfO7pLUcHR2nTJly6/d0l8n+BLLsP0XyfI+1aSW93Zzly5fr4hOizaTPp46+0vSJAT7/d+7cefLkydfLFw//vYe6uY7xdN+/f//kyZPVmtoa4I9AOVYetPB/TFHu2sJDd8UlLGzj0UIy+sTy8/MjIyNp/YwdO3aYF/eHH3546tSpEydO7Nq1y87O7sSJE9KMQZ+89tprQnZv1bUCTGoT8QRPz+cmJCTACg0A6AeRSPTjjz8Guzi/5Cd787m1LyfU19dfuHBBA4EBoDoZScO7775rbGz83Xff4fH43bt3f/XVV7a2tnv27NFMcNrJxcVl7ty5FcmPeV09qtdWfvER4orefvtt1asCAGiD5OTk6urqd6bJdcfBz8kxITjwwIEDhrkRINA5MpKGR48eff7550uWLJk+fbqtre3//d//bd26tW884zCcnZ3379+PUZBa54033jA1Mik+fl/FenoauyuvFixevNjR0RGTwAAAI4vD4ezfv39SUECQyyg5L3nz5ck9bPbhw4fVGhgAmJCRNODxeOmQ1LCwsIyMDIRQfHz87du3NRCZNrOysnr77bcbM591FDXILj20J4fv0qi2a9aswSowAMDIOnLkCJvJXPuyAlsBO1hbzx8XdfLkyebmZvUFBgAmZCQNkZGRX375ZWVl5ejRoy9evNjb23v//n3VB6nqgVdeecXfz+/Jf+6KhUquAdCYWdFeUL9hwwZY0AkA/dDe3v7bb7/Nix7bfyNseSyPfcmYQNDj3lmgebm5uS+99BLm1cpIGn744Qcmk3nu3Lno6GgajWZjY7Ns2bI333wT8zh0Dh6P37RpE6eJWXX9iRKXi3oFxcfvjxs3Lj4+HvPYAAAj4tChQwSJZGXsREUvpJiYrIiNuXr1alVVlToC04yOjo7jx48f/a9jx44ZwhREdRi0z+nq1as0Gk0zbQ1DRtLg7u6em5v7z3/+k0Qipaamnj59+s6dO5999pnyAeoRf3//WbNmlZ9/1MvkKnptxaU8EZu/YcMGdQQGANC8pqamixcvLnppnJWZMmsJJ0VH2pib6XRnQ0pKyt69ew8eOir998MPP1y7dm2kg9IKkyZN+vXXX6WP169f/+mnnyKEbty4ERQU5Orqunr16u7uboRQfn5+YmLirFmzXn31VaFQuHr1ajqdbmdnt3fvXoSQqalpe/tf2ywfOXLEy8uLTqevX79eeuT48eNeXl4ODg4rV658cVDti2f7t6XQ9yIjaXBwcHjvvfceP34sjTgxMTEmJkahBvTb2rVrjXDE8vMyVt0eoLebU3mlICkpycXFRU2BAQA07ODBgyZE4sLx0cpdTiYRV8TGpKWlPX36FNvANIbH45FNzDd8myb9Z2phzeUq/IFKL82ePbsvf/r999/nz5/f0tKyaNGi3377raKigkgk/uMf/5CevXnz5pQpU27cuHH27NnS0tKqqqq0tLSNGzf29PRQqdSoqCiE0JMnT7Zu3Xr79u0nT56kpKRcvXq1tLR048aNV65cqamp4fF4O3bs6N/6UGf72lLoe5GRNHz++ef5+fljxowJDAz8+uuvGxsbFapd71Gp1CVLltSmlfA6FZh++ez3PDKBtHr1avUFBgDQpNra2itXriyJGW+uwjrEs8aEMywpurs5MB6PF/fbZUMsEql1eWwdMnv27Bs3bohEoqKiIiKRGBwcfPny5bi4uKCgICKRuHXr1r6NmRgMxrp160xNTUUiUVdXV3l5ub+/P5/P77+m57lz55YvX+7o6MhgMI4cOeLk5HT58uUFCxb4+PiQSKRNmzYlJyf3b32os31tKfS9yPiJrl279vbt2w0NDW+99daVK1dcXV2nTp3622+/KdSGflu0aJExiVx5JV/O8oLnvTWpJUlJSYa5ti4Aeunf//63pYnx/HFRqlRiRCSujo9NT08vKMBm7TgNMzU1FQh4kv/uAcHv5Sj6B0lfubi4ODk5ZWdnS7sZEEINDQ2ZmZmhoaGhoaEzZsygUqnSknZ2fy1ivWDBgmXLlk2ZMsXDw2NAHtnQ0DBq1F8TeiMjI0ePHt3S0uLk9Nfey6NGjWpqaupffqizfW0pRK400M7O7u2337558+bhw4dzc3OXLFmiREv6ikKhzJ49u+52mahXrg07am+V4ESShQsXqjswAIBmFBYW/vnnn6vjY01U3uR6eliIG4P+3Xffqb5no+ZRKBSJWNzLe44Q4vM4IqFA/1YEVpr0DkVf0kCn0+fOnfv48ePHjx8/ePDg5MmT0mJ9fTNtbW2vv/56Y2PjgQMHtm/f/ujRo76q6HR6S0uL9HFOTk5mZqadnV1Dw1/z/xsaGhgMRv+mhzqrXD+Q7Gu4XO7vv/++atUqBoPx+uuvx8XFnTt3TomW9NjcuXMFPb3N2XINe667XfbSSy/R6XR1RwUA0ACxWLx7925XOm3OWBmbA8uDgMe/O/3lgoKC69evq16bhklTBA67GyHE7WH2HQEIodmzZ//6669MJjMoKAghNG3atIsXL1ZUVAiFwo8++mjr1q0Dyh8+fHjRokVisTgiIsLMzKyrq6vv1KxZs44cOdLS0sJkMt98883a2trExMTTp09XVlaKRKJdu3bNnDmzf1XDn1WUjKQhKSmJRqMtWLCAxWLt27evra3tzJkz8+bNU6VJ/ePm5ubr69uY9Uxmyef1Xc/ru6ZNm6aBqAAAGnDu3LmSkpINM6cTMVrAJtrbK8bf97vvvmMymZhUqDEWFhYIoV7uc4QQj8PuOwIQQmFhYUKhMCkpSfqlq6vr3r17Z8yY4ejoWF5e/u233w4o/9Zbb+HxeHt7ex8fn1mzZvWfnB8VFbV+/fqoqChfX98JEyYsWLDAz89v9+7dU6ZMcXZ2xuPxW7Zs6V/V8GcVJWNfNaFQ+Msvv8ycORN+9sOLjY09cPigWCjCE4d742h5VGNkZBQdreT4agCAVmloaPjxxx+nh4VEeGC5r/3G2TMW/euH3bt3b9++HcNq1c3Y2BghJBD0IoSEgl6EEJlMHuGYtEltbW3/L5OSkvpyCKnRo0ffv//X7gRUKnXAvIb+Z9etW7du3br+Z5cvX758+fL+R8LDw6XrOA96tn9tCpHR05CcnLx48WLIGGSKiooS9Qq7y1uHL9b+pCEkJASWgARAD4jF4s8++8ycSHw/EeO+QzqF8v6MaTdu3Pjjjz+wrVmt/jYOAzbj1lMwHwYbvr6+xsbGHaVNw5SRiCVd5S0hISEaiwoAoD5Hjx7Ny8v79NVXLEyUn2Y5lJkRYS/5+ezcubO1VcZHEe0hXTWIbGyGEDIim/YdAfoEkgZsEIlEHx8fZuVwa6b2NDGFXL6/v7/GogIAqMnTp09/+eWX+eOixnp6qKmJj+bOJorF27Zt05WZFNIFiS1t7Pr+HzD3D+gBSBow4+3tzarpGKYAq6ZdWkxTEQEA1EIsFn/xxRf2lpYK7WapKKq5+YevzMzKyrp8+bL6WsFQcXExxZpubEpBCBmRTa1sHUpKSkY6KIAxGQMhgfw8PDy459livghvNPhYyOcN3RQKBSZbAqDrkpOTS0tL9722ikxS71voRH+/uED/H3/8cfLkydq/UFJmZqar75i+L119x2RmZkokEpzujG9g1XQ8OZCu3LUdpU1UvP6P/4OeBsy4ublJxJLnTd1DFXje0OXq6qrBiAAA2BOJRIcOHYoN8At1c9VAc+9Me5nFZJ49e1YDbakiPz+/trbWN2xS3xG/sEmNjY39VyXScg4ODg6WDGJVr3L/GGSqIbzDQ08DZqS7Tz1v6KK42Axa4Hljd3gwDGgAQLdlZWU1Nzd/MUelFXLk50C1jgvwT05OXrFihWZaVM6RI0esbB08g8b3HXH3j6IynI8cORIeHj6CgcmPSqUqt7JyH1tbW6yC0VqQNGDG1tbWzMysp2nw9VgkYklPM9NlJmxrCYBue/DgAY1iEejspLEWYwP9//ztdGNjo4ODg8YaVcjDhw8zMjJmrNiCx//v5iwOj4+Z8VryfzZnZmaOGzduBMOTU2FhYUFRmb2Ln3KXtzdV1dXVYRuSFoKkAUsuLi7MxsFvT3Dbn4t6hbAXNgC6rra21p3B0OR9ek87BkKorq5OO5MGLpe7Y8cOB9eA0dEzBpwKGDs15/a5nTt3njp1SvvHZCCEHN0CF6wbuDijnFJO7m59loVtPFoIkgYsubq63i3OHvTU88YuaQGNBgQAwJpIJCISNDoajIjHS9uV/5LU/2/vvuOautoHgJ8kJEDYK2GDgEwVQRQQXBWrIALirOKkoiK2oFjrKmprVRTrbt2KolZRrFXcoqIMB7JEQARk7xkSyPz9kffl5SeIIdwkEJ7vp59+kptzz/MkN1cfb8495+HD69evI4TYbDYOhyMQCAQCISgoyMLCAvP0IiIiysorv9+8F9dpASQcDjdt0ZYTv84PDw/funUr5qGB+MFASCyZmJi0lDcgbhc3VdNKG4hEYvt6pgCAfkpLS6u8/ovjnUWhrL4e9eT3ciaTuW/fvvdlecX42lKZuhJCXTG+NiU77ciRI5jnFhsbe+PGjUmzgrV0u56vQkPb+Ns5a2/duvXPP/9gHh2IH1xpwJKJiQmnjd1S2aSg8/nabs3FdUZGRjIy8IED0L/Z2Njc+vffhha6qoKYrrenfSoik8kmJoIubxETE1NVXT1290xlI43W1lY8Hk8ikYrjshP+epKenj5s2DCsEnv//v2OHTusR37rMGF2N83sx/oW56Xu3r3b1NR0yJAhWEUHEgFXGrBkZmaGEGoq6mKKp6ZPtfxXAQD9mqurKw+hp++yxBbxccY7JycnAf/J0dzcfPLkSV1nk89u49IfZ6Gop3rgwAGs5pesqalZu3atKsXIc+GWrzaeumCTho5paGhoP5oVG3QJigYs6ejoKCkpdZ4XksflNRfXwVyQAEgBKpVqZ2d3681b8YR7X1KaX1nl7i7omlhHjhxpojdbzf98KV0cHmez0CUtLQ2T+SXb2trWrl3bwmDPDowgyX59BT4ZouzswIhWFgoJCWEwGL1PAEgKFA1YwuFwgwcPbir8vGigldZzWRwoGgCQDj4+PhlFxR8rKsUQK+blaw0NjTFjxgjS+OXLl9euXTOf6SCvqdj5Va3hBrrOpn/88UdlZa8y53K5v/zyS3bOh1mBe1U0dATcS0mNMmfVvo/5hVu2bOFyub1JQArcvn1bS0ur4xb+yh2CtBT8VVGAogFjFhYWjYU1n21sLKjhvySJjAAAGHNzc1NRUYl5+VrUgVra2h6mZ3p7ewvy20RdXV1YWJiqOdXU0/ZLbYYsdWUSOJs3b+7RvRifOXLkyOPHj70Wh+mb9mx4hI6xtffS7U+fPTtw4IDQ0fujiRMnRkVF8R+HhIRs3ryZTCbX1NQghNLS0qZOnerl5TVr1iyE0JEjR4yMjCwsLDZt2jRp0iSEUMeWU6ZM2bZtm5eXl42NzevXrzu+ihA6e/bs4MGDKRRKSEgIf8vevXt1dHTU1dUnTJjQy0qxHRQNGLO0tGytpTGbWztubCyooVAoampqksoKAIAhEonk4eFxPzWd1Yu/egXxOOMdg8n09vb+aksWi/Xzzz83MJrtf3BD+C/OIUFSlrcL+uZtWuoffwg5G8H169fPnTs3zmuFzajJQuxuNWLiBJ9VUVFRV65cES6B/sjb2zs2Npb/+ObNm7Nnz1ZXV3dycuJvefTo0bfffnvv3r3Xr1/v3bs3MTExOTn59u3b/Fc7toyLi5s8efLNmzcXLFgQERHR8dWMjIywsLAnT55kZGTcvXv39u3bRUVF27dvT0pKKi8v19LSOnPmDCbvBYoGjPEvJzQW/L81spsKa+AyAwDSxMPDo4nBSMr9INIo99MybG1t9fT0um/G4/F27NjxNi11RPCkLn+Y6EhzqL71fKfLly///fffPc0nPj5+9+7dti5erlP9e7pvu9FTFtmP9d27d29cXJzQnfQv3t7e9+7d43A47969k5GR+ewGFiqVGhQURCaTr169unTpUl1dXVVV1ZUrV3bux8jIiF8iODg40Gi0ji9FR0cvXLhQT0+PSqWePXtWX1+fSqXm5uYaGRm1trbKyso2NnY9W3FPQdGAMWNjYxKJ1HFYA4/Ha/pUC0UDANLEyspKX1//yTsRLv3cxGCk5Be4ubl9teUff/xx6/btYcvGag79SnnBZ+Jpa/StTURERPs/fwWRmpq6YcOGQVZOUxdsEnyvLrnP/9lsqOumTZv60XJWvWFkZKSvr//y5Uv+ZYbPXm1f8KKsrKy9QNTX72KecnV1df6DzhOSlpaWts8D5OjoaGtrKyMjc/ToUTs7u0mTJn34gFl1C0UDxmRkZExMTDreddlaQ2O1tMEoSACkzNixYxOyc7kY3cHYWUJOLofLHTt2bPfNDh48ePHiRav5jgYTLAXvfOgSV21nk61bt969e1eQ9tnZ2SEhIZp65jNW7O64wIRwcDi8b8BObSObkJCQrCzx3bwqQfxfKLosGvD/nUmTSqWWlZXxH5eXl3fupJvJyykUSvuohdevXyckJPz99993796NjY19+fLljBkzMHgP/Gyx6gi0MzMzay6qa3/aVFSH/juFAwBAari4uNS3tLwvKRVR/4k5H0xMTLpZb4LL5YaHh0dGRlrNczKdNrxnveNxdkETtZ1Nfvnll6/O1Zifn7969Wqyqs6coP1EklzPAn2BDFF2TtAfypqGq1evzsvLw6TPvszb2zsqKqqxsXHo0KHdtDlz5kx1dXVzc/Px48d71L+Xl9fZs2crKysbGxtXrFhRVFRUXl5uZGSko6NTXFx8+fJlOp3e6zeBEBQNomBqakora+D9dzJpWkm9rKxsl9eaAAD9l52dnYKCQvz7bFF0zuZwEnM+uLq6frEBmx0WFnbl6lXrhaNNvXtYMSCEEMLhcfZBE3XHDP7tt98iIyO/1Ky0tHTVqlUEOdV5Px6WV1AWItCXyMorfvfjIZKCRmBgYFFREYY990H29vZsNnvmzJndtBkzZszy5cvt7OzGjx/v5+fXzcThJBLJ0/P/LQ/m5OQUEhLi5ORkaWnp6uo6Z86chQsX8usGPz+/tWvXPn78+M2bN71/IzCrMfZMTEy4LA69ohGvJosQai6tNzIywndaygUA0K+RSKQxY8Y8fPVq+aSJmC96mfQhr4nB+Oabb7p8lcFgrF+/PjEpcfjK8frjejFeCo+zXTmeqCB78ODBurq6H3/88bM3Ul9fv2rVKhaPuDD4iIKyuvCBvoCsqOq35s9z4d8HBQWdOXNGQ0Pj6/v0Wx0LI1tb28TExI4PEELJyck4HK6kpAQhtHnzZgcHhy+1HDNmDH/qjo4bg4KCgoKC2kNoaWk9f/68/em8efMweRfwNxn2+EtZ0sr/s6RNS1kDLG4JgFTy9PQsqa199TEf855jkl8NGjSoy5UampqaAgMDk9+8dAid0quKASGEEA6Hs1k02vI7xwtRUVu3bu04fwOHw1m/fn1tffO8Hw8rq1F6GehLFJQ15gUfbqS1rlu3js1miyhKvzB06NCMjAxLS8vhw4cXFBSsWLFC0hl1AYoG7Ono6JBIpJby/9zfQitvMDQ0lGxKAABRcHR0NDExOf3oCbbdZpeWJeR8+O677zq/VFdXt3z58uz8XKdNntQRRlhFNPOxs10+7vad2A0bNrT/zX3+/Pm3qam+ATvVqaL9E0xVU883YFdGRiZWcwn0U2QyOTIyMjs7OzU1NSoqSkFBQdIZdQGKBuzh8XhdXd2WikaEEKuljUVrgxWxAZBKOBxu1apVqYWf7qWmY9Unl8eL+Pe2gYHBtGnTPnuJf43hU0Wx8y/T1Cy0sYrIZzDBcsSPbnFPn2zcuJHL5dJotNOnT9uP8R1kNQrbQF0yMrd3mDA7MjKyqalJDOGA0KBoEAldXV16ZTNCiF7ZhBD66twsAIB+aty4cePGjYu4ebu8vgGTDs89eZZZVPLzzz8TicSO29lsdmho6KfyYqfNnsrGXxwi1xs6TqZ2qyfGPYmLiIh48eIFnU53nrxAFIG65Dx5QWtra3x8vNgiAiFIZiAkh8Oh0+nNzc0SiS4GVCo1ISHh0eJz/KeqqqpS/Gb7LP6iOC0tLTAKVVLYbDaPx5P6L39wcHBAQMBP5y8eWrqQTCL1pquEnA8nHjyeM2eOlZXVZ5/bX3/99Tb17Yj1k+V0lZlMpoAd8ng8LpcreHvNEQbmc0f+ffHvyspKObIyWVlL8H17SU5BjayompeXJ8QXhsViYbIIVnXZxwdX9gm3b3FeqizGw2H7IskUDXg8Xl5eXlHxK9Od9l/+/v6WlpZ0Op1IJGppaRkZYfbTIxAci8VisVhkMlmQxX6AKNDp9La2Nik+0/kUFRUjIiKWL18eduX6noXzSMJ+3zKKirdFxzg5O4eEhHxW6X748OHq1auDfUdoDzfuUZ+tra14PJ7Uk1JmsLd9Q07ly5cv2Uy2DAGPJ4jp9OHxuMw2hrKyshBfGCKR2Pt/G2hpaRUXF5e8fyrc7jIIaQ+Ai8qS+cMU918SiS4G2traM2bMqK2tJZPJ8vJfX2weiAL/Cybd37R+YSB8/tbW1rt37167du3mS1d2zJtDJPR4zsSsktKQM+fNLCx27dpF6LT7hQsXSKryZj52GOX7FTaLXB4HX0Jc3qfcN4OsHMUTtPhDKovZOmzYMEl9YcLCwiQSt3+By7YAAICB0aNH79q1K/HDx/XnLzF7eOtg+qei1SfPGpqYHDx4kEwmf/Yql8uNj4/XH2uBJ/Z2/mYBkanKGtY6srKyr+OuiiciQuhV3BVtbW07OzEVRkA4UDQAAAA2xo0bFx4e/rqgcM3ZC/S2NgH3epX38cfTkaYWFkePHlVSUurcoK6urrm5WWWQWCc+UjWhkEik3LSnlcW5YghXXZaf/fbx/PnzYQRSHweHBwAAMDNmzJj9+/e/KysPPnO+RYC6ITHnw9pzUTbDhh0+fLivDf6Qk5OjUqlPb/4lhljP/j2mqaHh6+srhligN6BoAAAALI0aNergwYN51TXBpyPp3d56kJj7Yf2FSyNGjjxw4EDnXyXaqaurKygoNBfXiyDZL2oqqjU0NPz+++8/pMeXF4p2IcrK4tzslMf+/v6ysrIiDQR6D4oGAADAmJ2d3aFDhz7W1K47F/Wl8Q2phZ9+vnBpxMiRERER3f9licfjR40aVZb0UTTJdoFFa6t9V+bo6Ojp6amrq/v0356tuNhTz/49TqVSvb29RRoFYAKKBgAAwJ6tre3evXvTi0t+v9bFwtPFNbU/RV60tLbZs2ePIPdDent700rqq9OLRZBpFz49eIfn4Tw9PWVkZPz9/fMynovuYkNFcU5u2tMlS5Z8NpkV6JugaAAAAJEYNWrUpk2b7qamXUlI6ri9jcVef+GSkrr6vn375OTkBOnKxcXF3Nw8+9JLHpcnmmT/h9nc+vHfNE9PTwqFghDy8PAwMDB4HHNYROGexBzV0dHx8vISUf8AW1A0AACAqHh6evr6+h6+c7+opqZ945/3HpTUN4SHh6uoqAjYDw6HW7NmTWN+ddGj96LJ9H/eRyUReYT2JRZlZGQCAwML3r/MTXuGeayPmQl5mS9WrFgBlxn6CygaAABAhIKDg9U1Nf+4dYf/tKCy6mpi8pIlSywseraqtYODg7u7+/uLSa11LSJI8z9qMkuLn+SsXLlSU/N/y1u4ubmNGDHi3qXwNgYNw1jMVvqdi7uGDx/u7u6OYbdApKBoAAAAESKTyYGBgYk5HzKLihFCZ+KeamhqLly4UIiu1q5dqySrkH5cyHmOv4rdykr768nQIUPmzJnTcTsOh9u0aROT0Xj3UjiG4e7/vZfRXLt58+aBMGeo1ICiAQAARGvy5MlUKjUm+VV9S8vjzKw5c+YId2+hqqrqTz/9VPW2qOSZSCZcyr6UzGls++WXXzrPsGRoaLh27dqMpNi0FzcxiZWRFJv64mZwcLCxsTEmHQLxgKIBAABEi0AguLu7P3uf/ezdew6X6+HhIXRXkyZN+uabb96dS2A2MTDMECFUn1vx6f67ZcuWDRo0qMsGPj4+Hh4edy7u7v0ckdVlH+9E7Zw0adKsWbN62RUQMygaAABA5JydnZsZrVHxCWZmZlpaWr3pat26dSQe4f3FZKxyQwghLi/j1HNTE9MFCxZ002rDhg1GhvrRf/3Um8ENzDZ69J8/6epQt2zZInQnQFKgaAAAAJGzsrLC4XBFNTXW1ta97EpLSysgIKDkaU5TYc3XWwum6El2U2HN+vXru19HXl5efvfu3W0tdbEXfhc61p2oXS2NleHh4d1Mggn6LCgaAABA5Mhksrq6OkLIwMCg973Nnj1bR1sn58qr3neFEOKyObnRb8aNGyfICpPGxsbr1q179+p+ZvIdIWJlvX6QkRS7Zs0aExMTIXYHEgdFAwAAiIOGhgZCiF869BKRSFyyZEllSlFzcV3veyt7kddaSwsICBCw/bRp08aPH3/v8t6W5p5Fp9Ma7l4Kd3V1hYWp+i8oGgAAQBz4kz8KOAXkV02dOlVNVbXw/rved1VwL9PBwaFH80Zs2LBBBs998Pe+HgV6eHU/jsvcuHFjDxMEfQgUDQAAIA78+xg7380oHBKJ5O7uXpaQx2VxetNPc0ld48dqHx+fHu2loaGxevXqzJd3C96/FHCXotyUjKTbq1at4s9ODfopKBoAAEAcWCxW+/8xMWXKFBatrSaztDedlCfly8vLjx8/vqezBJK6AAAgAElEQVQ7+vj4DB069E7UTjar7auN2Szm7Qu/W1lZzZw5U5gsQZ/R3UBZAMCXtLS0bNu2LScnR8D2VCp127ZtOjo6Is0K9GUMBqP9/5iwsrKiUqmVrwspdoZCd1L5utDJyUmIH03wePymTZv8/Pye/vPXxJk/dt/4+e2TDdXFByMisbrQAiQFigYAeqy+vv6HH374WPDJfowvnkAQZJfM5Lv+/v6HDx+GQeMDVlNTE0KoubkZqw5xOJyrq2vs03tC98BsZDR9qnX1cxVudzMzs6VLl544cdLCbry+qe2XmpUWZCbcPbd48aKeLrcB+iAoGgDomfLy8lWrVtXUNfmt+VPX2EbAvUaMn3Vxf9D3339/4MCBoUOHijRD0AfxeLzGxkaEUENDA4bdjh49+tq1a80ldUr6wtyUUZVahHjI2dlZ6ASWLl0aHx9/49QvAWGXSLJdzLvAbGP8c/oXMzPTZcuWCR0F9B1wpQiAHigsLPT392+kMRf9dFLwigEhpKKuveinkwrq+oGBgS9fCjp2DEgNGo3GH81QX1+PYbcjR44kkUhVKUXC7V71tsjMzKw3IxNlZGS2b9/OaK750p0Uj6IP0Oortm/fDotfSwcoGgAQVH5+fkBAABsnt/Cnkxraxj3dnayoumDNX9rGQ4ODgxMSEkSQIOi7+LUCSUamrg6DmRXakclke3v7ypRPQuzLZXOq0orHjBnTyxwGDRq0evXqt89vfHyX+NlLhdmvUp5dW7lypZmZWS+jgD4CigYABFJWVhYYGEiQU1207oSKurZwnZDkyHOC9hsMHrFu3bq3b99imyHoy/hFg76GOrY/TyCEJkyYUJ9T0Vrf0tMdq9NK2HSmEPdNdDZ79uzhw4ffidrFZjHbN3LYrNionTY2NvPnz+99CNBHQNEAwNfR6fTg4GAWjzgv+IiCskZvupIhkmau3KOlbxEaGlpWVoZVhqCP4w9oMNTUEEXRgMfhy17k9XTH0ue5+vr6VlZWvc8Bj8evX7++qa78zdPo9o1v42Maqkt+/vlnuGNCmsCxBODrDhw4UFRcOjswQkm1V+sT8hFJcrMDI7h42bCwMB6P1/sOQd/X0tKCEKKoqPAfYEhdXd3FxaXocXaPvkssWmvFq8Jp06bhcDhM0hg8eLCbm9vLhxd5PC5CiMflJj2IGj9+vKWlJSb9gz4CigYAviI/Pz8mJmbstACqgTlWfZKV1KYu2PT27dvHjx9j1Sfoy1pbWxFCqgrktravT4XUUzNnzqSV1tdm9GCWp08PswgI7+3tjW0ajXUVn3JTEEIl+ekNNaUwlZP0gaIBgK+4fv26HFl51MS52HZrNsRFz2RodHT015uC/o9/GeD4g8dcLhfzzp2dnU1MTPJupgrYnsvk5MdmfPvtt5qamhimMXz4cBUVlYKsZIRQflayoqLiiBEjMOwf9AUwTwMAX5GUlGRuO1aGKIt5z9YOk+KuH2xra5OVxb5z0Ke4ubnh8Xgul0ulUjHvHIfDLVmyZMuWLbXvyzSsdL/avvDBO3Zz2+LFi7FNA4/H29jYlH96jxAq//Te2tqaINjUZ6AfgSsNAHSHy+UWFxdT9AeLonOKnhmbzS4pKRFF56BPUVZW9vHx8fX1dXFxEUX/kydPNjExyb6Y/NWRDWw6My8mZcqUKYMGDcI8DUNDw/rqEoRQQ02pgYEB5v0DiYOiAYDu0Gg0DodDVlITRef8bvnj6gHoDTweHxQUVJ9bWfmysPuWH26koDbuihUrRJGGlpZWS1MtQojWWKOlhcGoYdDXQNEAQHf4w9ZkZEii6JxIlG0PAUAvjR07duTIkVlRiVz2FxfLplc1F8RmzJ07V1f3679iCEFFRYXZxrh9fkcbg6aqqiqKEECyoGgAoDv/KRpIIhlzQCCSEEJMJvOrLQEQRHBwcGs1rfDeuy81yL6UpKKovHTpUhElYG1tbWxsXJX/0tDQ0NraWkRRgATBQEgAusNfmVBOXkkUncsrKCP4eQJgx8LCwsPD4971BwbjLYgKn1e6jfnVZYn5P61bp6ioKKIEzM3N4YYg6QZXGgDoTmlpKUJIWdh5o7tHkiXLKyjDvJAAQ8uXL8cxeQWxGZ1fyv77pZ6urq+vr/izAlIDigYAupOdnS2voKKsJvwygN3T0jPLzs4WUedgANLR0fHy8iq4k8FpZXXc3lRYU51a7O/vLyMDF5iB8KBoAKA7b968MbIQ4QQ1RuYjUlNT2Wy26EKAgWbBggUcBqv4aU7Hjfm307W0tNzd3SWVFZAOUDQA8EW1tbVZWVkm1k6iC2Fq40Sj0WDFS4AhPT290aNHFz16376FTWeWJX2cPn06kUiUYGJACmBfNDx69CggIMDPz2/r1q2VlZWY9w+A2Dx+/JiHkMXwcaILoTdoqKKK5qNHj0QXAgxAnp6eTZ9qaSX1/KcVrwp4LK6np6dkswJSAOOioays7M8//9y2bdvp06cNDQ0PHjyIbf8AiNPdu3eNBtv3ci3s7uHweKsREx8+fAi/UAAMubi4yMrKVrwu5D+tfPPJ0tJSRHMzgAEF4xExFRUVEydO1NHRQQh9++23mzZtan+prq5u8+bN/MdkMtnExETq7zTj8Xitra1wF76k8OfTpdFowi3+W1pamp6ePnneBlFPvmRh7/bq8d8PHjwYPXq0SAOJH4fD4XK5Un+m9022trZpdzIq04oQQk151RNnz5XiA8FkMkWxEhjoDOOiwd7e3t7eHiHEZrMvX74solnWARCDe/fuyZDkLOwmiDqQjpG1hvag2NhY6SsagARNnz6de43L4XAQQiQ7Qzc3N0lnBKSBSO69SUhIOH/+vJOT04IFC9o3qqurHz16lP/43LlzCgoKKioqoojed9TW1srJycnLy0s6kQGKxWI1NjYqKioKcY8Zl8t98OCBjcMkJWWRrDrxGfux0x9fO8BisbBdqljiWlpa2trapP5M75smTZo0adKkhoYGGRkZ0c3m1EeQSCQ8Hsb1iwPGnzKPxzt06NCtW7e2bNmyaNEiOIqgn0pKSqqqqrJ18RJPuKGO7giHv3PnjnjCAQCAcDC+0vDq1avCwsLdu3fzV45HCEm8bigrK+u49LCioiLMiA6+Ki4uTkVDR9/UVjzhyEpqgyxHxcXFdbw4BwAAfQ3GRUNWVtaHDx/apylVVla+cOECtiF6atOmTRkZ/29G1UePHsH1UtC9tLS0QVajhBtBKZxB1o6Prx1gMpkkkkhW1AQAgN7DuGhYvHjx4sWLse2zl/T19Ss+Ff4ZsBSPwx978Cgxv1BJSSSLDwFpUldXp29DFWdEZTUqh8NpamqSsmENAABpIv1jDry9vaubmguqapTJ8vFZ2dOmTZP4Lyag79PU1KyrLBJnxLqqIhKJpKysLM6gAADQI9L/16eDg4OJicnD9IyEnA90JnPOnDmSzgj0A998803228e1FYXiCddKb3795Kqrqyv8NgEA6Mukv2hACGlqajYxGE10Oh6PV1dXl3Q6QBx27tw5Y8aMxYsX+/r6HjhwoKe7+/n5aVMpfx9Z01xfJYr0OmK20qP/XMdltqxevVrUsQAAoDekv2h4+vTp69evR5ubOw42wyG0b98+/kSBQIo1NzffuHGDqY4jWCq3KvOuXr3a03k5yWTywYMHCVzGmV2LSz6miShPhFB9dcm5Pd9XfMrcs2ePgYGB6AIBAEDvSXnRcOXKlfXr17tYmM9wHmWgqRHi6R4TE7Np0yYGgyHp1IAIJSQkcDgcm6WuFoucrBY6t7a2Jicn97QTQ0PDs2fP6utoRu4JeHLjKJuF8XTgPB4v5dn1E9vn4dnNJ06cGDVqFLb9AwAA5qS2aKDT6Rs3bgwPD59qP/z3+XPwOBxCaKaz4y+zfJ/GPV64cGFBQYGkcwSicuvWLWUjDQUdFYSQirGGgrbK7du3heiHSqWeOnVq0aKFiffOnfh1XlFuClYZ1pQXXIhYHnvhd7eJ4y9evGhlZYVVzwAAIDrSWTRUVlYuXbr0WVzcL7N8N0z3IhII7S+529meWhnAbm5atGhRQkKCBJMEIpKZmZmUlGQ8Zch/nuNwxpNtHj9+/PHjRyF6IxKJq1atioyMpKqTz0csv3lmK53W0Jv02Ky2uBtHT/w6j0WrOHDgwPbt2+EeYABAfyGFRUNRUZG/v39TddWJlcs87Id3bmCmo31m1fIhujpr1qy5f/+++DMEotPS0rJt2zZFfTWDcRbtG43cbOS0FMPCwoRer9LCwuLs2bOhoaEf0+P+Cpv17pWQX5vivNTj279Lvh/pN3/elStXYEU3AED/Im1Fw/v375ctW0ZgMf9a7j9YR/tLzRTl5CIW+42zsti8eXN0dLQ4MwSiQ6fTQ0JCistL7FZPxBH+993Gkwj2P7jlfvywbt06oesGPB4/Z86cq1evjhg+JObExn9O/8Js68HIGB6P+/SfvyL3BFDVFc6fP7969Wo5OTnhMgEAAEmRqqLhzp07y5YtUycRjy3311X7yuKERALh1+9meznY79q1a9euXT0dXQ/6mqKioqVLl6ZnZYz62UPZSOOzV1XNKA5rJye9Sl62bFlZWZnQUSgUyh9//LF58+bct4/Ohfs3N1QLsheb1XblyNoXd04vWbL4zJkzgwcPFjoBAACQICkpGhgMxm+//bZlyxaXwabHlvtrCvYjMR6H+3m6V4in+42YGH9//6Iisc4ACLDC5XKvXLkyf/780sbK0dt91K10umxGsTN0DvPKr/g0b968f/75pzd33vr4+Jw6dYpNr72wbyW9ub77xhw26+/DIUU5L8PDwwMDA4VYpxsAAPoIaSga0tPT/fz8Ym/dCvWaumPeHLKsbI92n+Pi/FfA0oaKCj8/v2vXrsEsDv1LVlbWkiVLwvfs0XQ0HBc+q/M1ho7UBlPH7ZmlPIz666+/BgQEfPjwQei4lpaWx44dYzPqr5/YyONyu2n5MHp/8YeUffv2jR8/XuhwAADQF/TvoqGuru7333///vvvScy2M6uWz3R2FK6fIYYG539YOc5i8M6dO7///vusrCxs8wSiUFdX9+uvvy5evPhTQ6nTpqm2KyfIkL8+BzNRUc7+x0mj1rvnlH2cP39+eHh4U1OTcAkYGxtv27atMPtVRvKdL7UpK8x6/eTqypUrHR2F/HICAEDf0V+vlDY1NZ0/f/7y5ct4LjdwsttcF2eZDvdVCkFRTi5s9oxvbYf9cevOokWLJkyYsHz5clNTU6wSBhji8XgxMTGHDx+ms1ot5zsOch/acdijICj2RuOH6uffSrsWE/PgwYPg4OCpU6cKkYmrq+vo0aNf3Dkz1Mmjy3W0E+6e1dHWnj9/vhCdAwBAX9P/igYOh3Px4sVTp04xW1t9nUYuGj9WTUEBq86dLQaPNDO59ebt6UdPvnvyxMPD44cffoDlKvqUurq6LVu2JCcn67qYOS4YLatGFq4fPJFgNt1ef6x55pkXYWFhjx492rp1qxCLTM6ePTs4OLj8U5ausc1nL7XSmz+kxweuXAHjGAAA0qGf/VlGp9ODg4NT3771dLD/fuIEigr26wjLEAg+oxw87IdfT3516tHDhISEw4cPm5ubYx4ICKGoqCgwMLCupWHkT+7UEUa971BOQ9EhdHJZQl7CiWeLFy8+cuSIjk7X4yi/xNHRUUFBIS/jReeioTD7FYfN+uabb3qfJwAA9AX9bEzD8ePHszIyDixdtNHXWxQVQzuSjMxcF+eoH4PUSMSNGzfC6Mi+oK6uLjAwsBnHGLNzBiYVQzvd0WZjdvhW0+tWrVrV3Nzco32JRKKDg0N+VhdrW+S/T9bX14dlqAAAUqOfFQ1JSUkThw0ZaWYinnAUFWX/ieMLCwsrKirEExF0Y8+ePbW0eqdNnvJa2M+7rKCr6rhxallV+f79+3u6r6OjY1lBZiv98wGVHzMTYBkqAIA06WdFA4PBUBLvPHr8cK2treIMCjrLzMx8+PCh5XeOoqgY+BT11MxnOfz777/5+fk92tHFxYXL5eRlvOi4sbI4t7G23NXVFdMcAQBAkvpZ0aCmplZa95W5dLBVWlePw+FUVVXFGRR0dvz4cTJV2fAb0a4GaTx5CElF/sSJEz3aS09Pz9zcPDP5bkNNaft/6Ym3yGQy3GkJAJAm/Wwg5LRp03bu3PnX/UffTxzfy3ssBRH/PufwnfsuLi5qX5uUGohUSkpKQkLC8FXf4PBd3NaIITyRYDbd7tHZR9nZ2ZaWloLvOHny5EOHDh3e6N1xo4eHh2wPpxoDAIC+rJ8VDb6+vjU1NadOnXqUkRkwaeLEoTb4rm6O772csvJj9x8l5OQ6Ojpu375dFCGAgHg83oEDB5SNNfVcxbFkg+FEq4LYjIMHDx49elTwvebNm2dl9flVkB6VHQAA0Pf1s6IBh8MtX7587NixBw8e3HLpyulHFP+J4ycOtelyXh3hfCivOP7g0fPsXB0dne3bt7u7u2PYORBCSkrKu3fvRv7kLurLDHx4GYL5LIeXh3p2sYFIJMKYRwCA1OtnYxr4rKys/vzzz+PHj6sbGGy+dGXJkWNvCwp7321lY+O2K9cWHfrzY2Pzxo0br1+/7uHR9TR/QJxevXpFVJSl2BmKLaKOkwmeSHj16pXYIgIAQL/Qz640dGRvb3/s2LHk5OQDBw6sPH564lCbVe7ffnVF7C61slgXnj6/8Oy5LJm8Zu3aGTNmEIlEzBMGwpGXl+cw2Ww6k6gopvEBrOY2LocrJ977dAAAoO/rl1caOnJ0dLxw4cKmTZvelpbP3Xfo8J37TQyG4Ltzebxbr1Nm7z1w9mm8z4wZMTExc+fOhYqhT3F3d5eTkU05+JDTxhJDODad+ebAA2VFJTc3NzGEAwCAfqTfFw0IITweP3369JiYmHl+fleTX83cs//yi0Q2h/PVHZM/5PkdOPLbtRuWw4dfvnw5NDRUiKUHgKhRKJRdu3Y1ZVfFb7zeWFAj0lj1uRXxG6+1fmrcu3cv3DIDAACfkYaigU9BQSEoKCgmJmacm9vB2HsLD/2ZU1b+pcZNDMaWS1d/PB1J1qKcPHly3759xsbGYkwW9Mzo0aPPnDmjRVR9vvHau3MJbAYT8xAsWlvGqfiEsH/0lKjnzp2zt7fHPAQAAPR30lM08FEolLCwsDNnzuAUlZb9eeJhembnNsU1tYsO/ZmYX7Bly5YzZ84MHz5c/HmCnjI3N4+Kilr2/bLSh9lxIZfLXuRh1TOPxyuOy44LvlQVn78qcFVkZOSgQYOw6hwAAKSJtBUNfDY2NpGRkWPGjQv7Ozop9//97VJHo60+dVZGUen8+fPe3t5wc0Q/QiKRAgICrl69OtLGPuXgw5e7YpmNPRi/0qXWWlryb7fS/nriOnJ0dHT04sWLYRlrAAD4EuksGhBCsrKyv//+u/2IEb9GX29pa2vfHnHzNp3LO3LkCKw92E/p6ent379/586dzIKmZ+ujmwqFH+XQkFf17OdrvPLWffv2hYeHU6lUDPMEAADpI7VFA0KIQCBs2bKlqbXtWuJL/paPFZWPM7OCgoJ0dXUlmxvopUmTJkVFRRlo6ib9dotRQxOih5byxqQdtwYbmFy6dGns2LGYZwgAANJHmosGhJCuru64cePupaXzn95LS1dSUvL09JRsVgATVCr1yJEjSiSF9xeThNg963yClorGoUOH1NXVMc8NAACkkpQXDQghZ2fn/Moq/uQNb/MLR4wYQSKRJJ0UwIa6uvqMGTOq3nzicXk92pHL5FSlFs+dOxdusgUAAMFJf9FgZmbG4/GKqmsQQkU1NaamppLOCGBJT0+P3crq6bxPTForj8PV09MTUVYAACCVpL9o0NLSQgjV0mgsDqeJ0cp/CqRGW1sbQghP7Nk66fz2bR1GyAIAAPgq6S8aFBUVEUI0RmtLaxuPx1NSUpJ0RgBL/7lpltvD3bi8/+0LAABAMJK5JZ33X2KIJScnJyMjk5T7obqpCSGkoKAgnrh8YnubA5aZmRlCKPfaG3ktxc9e4nK5bDabSCR2Lg5ayhoQQqampnB0xAA+ZImDQwCwIpmigcvlMhgMGk2YO+WEYGpq+iA980F6pqysLJVKFVtcHo/HZDI5AqyCAYRmbGxsY2Pz7kZKT3ccPXo0hUIR25dhYGKz2TweDz5kCeJyuSwWS+oPAYvF4nJ7er0RCEMyRQOBQCCTyWL7pSAqKko8gT7DZDJlZWXl5eUlEn3gOHfuXJfbWSxWY2OjqqoqTPIoKS0tLW1tbfCboAQ1NDTIyMjwf6WVYkQiEY+X/l/b+wL4lAEAAAAgECgaAAAAACAQKBoAAAAAIBAoGgAAAAAgECgaAAAAACAQKBoAAAAAIBAoGgAAAAAgECgaAAAAACAQKBoAAAAAIBAoGgAAAAAgECgaAAAAACAQKBoAAAAAIBAoGgAAAAAgECgaAAAAACAQKBoAAAAAIBAZSScABoS0tLRLly6RSKTQ0FBlZWVJpwMAAEAYcKUBiMObN28ePnwYGxtbUlIi6VwAAAAICYoGIA5mZmYIIQKBYGJiIulcAAAACAmKBiAOKioqCCFZWVk5OTlJ5wIAAEBIUDQAcfjw4QMOh6PT6WVlZZLOBQAAgJCgaADiUFZWxuPx+A8knQsAAAAhQdEAxKGxsVGOSOQ/kHQuAAAAhARFAxCtxsbGffv2/fvvv3NcnC31dHfs2BETE8NmsyWdFwAAgB6DogGICpfLPXv2rJeX17WrV5ZOGLf824n7lywcYai/Y8eOWbNmJSQkSDpBAAAAPQNFAxCV69evHz58+Nsh1tGhId+7TcDjcKoK5J3z555etVyDgA8JCSkqKpJ0jgAAAHoAigYgKmpqagihqk6DGNgcTi2NJicnJy8vL4m8AAAACAmKBiAqEydO3LVrV1Zl9crjp7k8Hn/jx4rKoJNnFbUop0+f1tLSkmyGAAAAegSKBiBCpqam2tra9TQam8Phb6lvaWGy2YMHD6ZSqZLNDQAAQE9B0QBE5fXr135+fg0VFfuXLiTJ/GdpNAdTk1Cvqffv3PHz82toaJBshgAAAHoEigYgKvfv3yfweJGrVw41NOi4faaz4+/z55SUlLx9+1ZSuQEAABACFA0Ae2w2Ozs7u7W1lc5kltTWdm6QU1aOEKqoqIBFLwEAoB+RkXQCoH9ra2srLS0tLy8vKysrKysrLS399OlTUVERi8Uiych4j3IYrKPdeS93u+EvP3zct29fRESEgoKCsbGxkZGRnp6erq6urq6ujo4OlUolEAjifzsAAAC6AUUD6JmKiornz5/n5OQUFhYWFRXV/vdCAg6H01JW0lNXt9HUmGphZq6rM8TQgD91dGfaqip/Biyto7WkfyrKK6/4VF1dkJ72PC6uicHgNyAQCNra2oaGhiYmJjY2Nq6urmQyWUzvEAAAwBdA0QB6oL6+fsaMGW1tbQghA00NN0vzQRQtHTVVXTU1qqpK+2hHAakrKoy3sRpvY9W+paWtrbyuvqy+oby+Pru0LDE9LTExESFka2t76tQpbN8LAACAnoKiAfSAioqKp6dnXFxcXV1dcU3tldokTSVFbVVVHTVVHTU1HTVVXXU1Aw0NqqoKHocTsE8mm11SW1dSW1deX19W31BR31De0FBR39B+1cHIyGjatGkie08AAAAEBUUD6AE8Hr9hw4YNGzZUVVUVFhYWFxf/Z0BDefmbjHftP1WQZGSMtDQtdHUdzc3GWlnKEj//mlU3NT/JzEopKMgpK6+ob+BP/UQgEKhUqo6OjrmJ6XhdXT09PQMDg0GDBikpKYn7fQIAAOgKFA1AGBQKhUKhjBo1quNG/qDIkpKSwsLCjx8/ZmRm3nqTYqCpcW71SjKJ1N4s41Nx0KkzbC7P2tra1W2SiYmJoaGhvr4+DH4EAIA+DooGgBlZWVkTExMTE5OxY8fyt9y/f3/jxo17bvyrqfy/qwXpn4o5PHTv3j1VVVUJZQoAAEAYUDQAEbKwsDA0NEyvqkFVNR23Dx06FCoGAADod7AvGlJTU0+cOEGj0ZycnAICAuCC80BmZGR0/fp1SWcBAAAAGxjPCMlgMPbs2RMcHHzixInKysrbt29j2z8AAAAAJAXjKw1v3rwxNzcfPHgwQsjb2/vy5cteXl78l7hcLo1G4z9msVg8Ho/33+WSpdgAeZt9E/+Th0MgcfD5SxwcAoAVjIuGmpqa9iWPqVRqTc3/fsmuqqry9PTkPx4+fLipqWltV6sSSBk6nU6n0yWdxYDW2Ngo6RQGuoFwpvdlbDa7tbVV0lmIVmtrK4fDkXQWAwL2Yxpw/53V57N/4amqqu7atYv/ODU1lUwmS/399zQajUQikTrcbQjEicPh0Ol0MpkMA2skpa2tjcViKSoqSjqRgYtOp+PxeDk5OUknIlpEIhGPh/UXxQHjokFDQyMjI4P/uKamRlNTs/0lOTk5Nzc3/uPS0lIikSgrK4tt9L6GRqPJyMhI/dvss1gsFkKIRCLJ9HB+a4AVNpvNZrPhFJAgBoNBIBCk/hAQCAScwLPQgt7AuDQbMWJEbm5uaWkpj8e7e/eui4sLtv0DAAAAQFIw/hcYmUwOCQnZtWsXi8UaNmxY+yAGAAAAAPR32F+2tbe3t7e3x7xbAAAAAEgWjBwBAAAAgECgaAAAAACAQKBoAAAAAIBAoGgAAAAAgECgaAAAAACAQKBoAAAAAIBAJDZT3vv379lstqSii0dTU5OsrKzUz8XWZ7HZ7JaWFkVFRZhGWlJaW1tZLJbUTxjfl9FoNAKBIC8vL+lERKuyslLSKQwUOImsfpaZmfnmzRvxxxWzxMREfX19AwMDSScyQDU2Nqanp9vZ2cHaB5JSUFBQVVXl6Ogo6UQGrpSUFCUlJf7Kw9JtxowZcKaLgWSKhgFi7NixS5YsWbJkiaQTGaDevn27bNmyixcvmpubSzqXAerIkSO3b9+OjY2VdCID17x586ytrTdv3p/Ax60AAAq0SURBVCzpRICUgDENAAAAABAIFA0AAAAAEAj8PCFCZWVlSkpKMApMUphMZnV1NYVCIRKJks5lgGpqaqLT6dra2pJOZOCqqqoiEolqamqSTgRICSgaAAAAACAQ+HkCAAAAAAIZuEXDd9995+vrO3PmzBkzZgQGBiYnJ3+pJZfLnTlz5pdezcvL+/nnnztuodPpXl5eXC4Xw2zbcygoKFi3bh2GPfcFojsW+/fvP3bsGP9xWVmZl5dXSkoK/+nNmzfXr1/fZT9FRUVr1qxBUv2xf/r06ZdffvHz81u8ePG+ffsaGxt73yecC8IRz7GAcwFgYuAWDQihQ4cORUdHX7x40cvLKyIigsFgSDqjgUtEx2LIkCE5OTn8x2lpabKysqmpqfynOTk5NjY2r1+/9vPzwyRWP8Llcrdv3+7o6Hjy5Mnjx48rKioePXpU0kkNUGI7FnAuAExIbEbIvkNWVnbKlCmnT58uLy83MTFJSUk5c+YMg8EYNmyYv7+/goLCzp07WSxWaGjo3r17Y2Jibty4wWKxBg0atG7dOlVVVcEDde65oKDg3LlzFhYWHz9+LC8vDwkJMTMzQwjdvn37+vXrJBJp9OjRubm5v/76a3sOq1at4nA4x48ff/XqlYyMzLp160xMTET22Ygb5sfCxsbmyJEjTCaTRCKlp6d7enq2/+sqNzd34sSJRCKxqamJvyU6OvrWrVtycnJjxozhb5HWj72mpqahoWHKlCn8uTK/++67yMhI/kudP/MnT55kZWW1tLTU1NSQSKQffvhBS0sLIQTnAibEdizgXACYGNBXGviYTObDhw95PJ62tnZDQ8PevXtDQ0OPHTtGIBBOnTqFENqwYQORSNy7d291dfXly5f37Nlz7tw5ZWXlhw8fCh6ly54RQunp6fb29ps3b54wYUJMTAxCKC8vLyYmZs+ePXv37n39+jW/WXsO/AbDhg07fvy4ra3tzZs3Mf44JArzY6Gjo6OsrJyXl8fj8TIzM6dPn15TU9PY2NjY2FhdXW1paamoqGhhYYEQevv27d27d8PDw/ft2/f27Vv+7tL6sWtpaampqe3evTsjI4PD4SgpKa1atQp9+Vv68OHDOXPm7N6928HB4fDhwwghOBewIrZjAecCwMSAL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/>

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Our aim is to connect the 4 input features to the correct output class using an artificial neural network. For this task, we have chosen the following simple architecture with one input layer with 4 neurons (one for each feature), one hidden layer with 4 neurons and one output layer with 3 neurons (one for each class), all fully connected:

![architecture_visualisation.png](./img/architecture_visualisation.png)

Our artificial neural network will have a total of 35 parameters: 4 for each input neuron connected to the hidden layer, plus an additional 4 for the associated first bias neuron and 3 for each of the hidden neurons connected to the output layer, plus an additional 3 for the associated second bias neuron. I.e. $4 \times 4+4+4 \ times 3+3=35$

### Prepare data

We start with slightly wrangling the iris data set by renaming and scaling the features and converting character labels to numeric:


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```r
```r
set.seed(265509)
nn_dat <- iris %>% as_tibble %>%
  mutate(sepal_length = scale(Sepal.Length),
         sepal_width  = scale(Sepal.Width),
         petal_length = scale(Petal.Length),
         petal_width  = scale(Petal.Width),          
         class_label  = as.numeric(Species) - 1) %>% 
    select(sepal_length, sepal_width, petal_length, petal_width, class_label)

nn_dat %>% head(3)

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<div data-pagedtable="false">
  <script data-pagedtable-source type="application/json">
{"columns":[{"label":["sepal_length"],"name":[1],"type":["dbl"],"align":["right"]},{"label":["sepal_width"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["petal_length"],"name":[3],"type":["dbl"],"align":["right"]},{"label":["petal_width"],"name":[4],"type":["dbl"],"align":["right"]},{"label":["class_label"],"name":[5],"type":["dbl"],"align":["right"]}],"data":[{"1":"-0.8976739","2":"1.0156020","3":"-1.335752","4":"-1.311052","5":"0"},{"1":"-1.1392005","2":"-0.1315388","3":"-1.335752","4":"-1.311052","5":"0"},{"1":"-1.3807271","2":"0.3273175","3":"-1.392399","4":"-1.311052","5":"0"}],"options":{"columns":{"min":{},"max":[10],"total":[5]},"rows":{"min":[10],"max":[10],"total":[3]},"pages":{}}}
  </script>
</div>

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Then, we create indices for splitting the iris data into a training and a test data set. We set aside 20% of the data for testing:


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```r
```r
test_fraction   <- 0.20
n_total_samples <- nrow(nn_dat)
n_train_samples <- ceiling((1 - test_fraction) * n_total_samples)
train_indices   <- sample(n_total_samples, n_train_samples)
n_test_samples  <- n_total_samples - n_train_samples
test_indices    <- setdiff(seq(1, n_train_samples), train_indices)

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Based on the indices, we can now create training and test data


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```r
```r
x_train <- nn_dat %>% select(-class_label) %>% as.matrix %>% .[train_indices,]
y_train <- nn_dat %>% pull(class_label) %>% .[train_indices] %>% to_categorical(3)
x_test  <- nn_dat %>% select(-class_label) %>% as.matrix %>% .[test_indices,]
y_test  <- nn_dat %>% pull(class_label) %>% .[test_indices] %>% to_categorical(3)

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### Set Architecture

With the data in place, we now set the architecture of our artificical neural network:


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```r
```r
model <- keras_model_sequential()
model %>% 
  layer_dense(units = 4, activation = 'relu', input_shape = 4) %>% 
  layer_dense(units = 3, activation = 'softmax')
model %>% summary

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Model: 2
________________________________________________________________________________________________ Layer (type) Output Shape Param #
================================================================================================= dense_5 (Dense) (None, 4) 20
_________________________________________________________________________________________________ dense_4 (Dense) (None, 3) 15
================================================================================================= Total params: 35 Trainable params: 35 Non-trainable params: 0 _________________________________________________________________________________________________

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Next, the architecture set in the model needs to be compiled:


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```r
```r
model %>% compile(
  loss      = 'categorical_crossentropy',
  optimizer = optimizer_rmsprop(),
  metrics   = c('accuracy')
)

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### Train the Artificial Neural Network

Lastly we fit the model and save the training progres in the `history` object:


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```r
```r
history <- model %>% fit(
  x = x_train, y = y_train,
  epochs = 200,
  batch_size = 20,
  validation_split = 0
)

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<!-- rnb-output-begin {"data":"Epoch 1/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 1.3146 - accuracy: 0.4500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 632us/step - loss: 1.3092 - accuracy: 0.3333\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 1s 85ms/step - loss: 1.3092 - accuracy: 0.3333 \nEpoch 2/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 1.1826 - accuracy: 0.3500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 586us/step - loss: 1.2631 - accuracy: 0.3500\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 1.2631 - accuracy: 0.3500 \nEpoch 3/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 1.4383 - accuracy: 0.2000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 3ms/step - loss: 1.2305 - accuracy: 0.3583\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 23ms/step - loss: 1.2305 - accuracy: 0.3583\nEpoch 4/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.9543 - accuracy: 0.4500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 2ms/step - loss: 1.2025 - accuracy: 0.3583\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 22ms/step - loss: 1.2025 - accuracy: 0.3583\nEpoch 5/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 1.2216 - accuracy: 0.3500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 1ms/step - loss: 1.1776 - accuracy: 0.3667\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 20ms/step - loss: 1.1776 - accuracy: 0.3667\nEpoch 6/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 1.2356 - accuracy: 0.2000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 754us/step - loss: 1.1545 - accuracy: 0.3667\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 1.1545 - accuracy: 0.3667 \nEpoch 7/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.8686 - accuracy: 0.5000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 2ms/step - loss: 1.1328 - accuracy: 0.3667\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 22ms/step - loss: 1.1328 - accuracy: 0.3667\nEpoch 8/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 1.1982 - accuracy: 0.1500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 522us/step - loss: 1.1122 - accuracy: 0.3667\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 18ms/step - loss: 1.1122 - accuracy: 0.3667 \nEpoch 9/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 1.2572 - accuracy: 0.1500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 581us/step - loss: 1.0929 - accuracy: 0.3750\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 20ms/step - loss: 1.0929 - accuracy: 0.3750 \nEpoch 10/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 1.0138 - accuracy: 0.3500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 838us/step - loss: 1.0745 - accuracy: 0.3833\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 1.0745 - accuracy: 0.3833 \nEpoch 11/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 1.0590 - accuracy: 0.3000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 3ms/step - loss: 1.0570 - accuracy: 0.3833\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 23ms/step - loss: 1.0570 - accuracy: 0.3833\nEpoch 12/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 1.0460 - accuracy: 0.3000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 2ms/step - loss: 1.0403 - accuracy: 0.3833\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 23ms/step - loss: 1.0403 - accuracy: 0.3833\nEpoch 13/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 1.1162 - accuracy: 0.2500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 2ms/step - loss: 1.0245 - accuracy: 0.3917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 21ms/step - loss: 1.0245 - accuracy: 0.3917\nEpoch 14/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.8707 - accuracy: 0.5000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 3ms/step - loss: 1.0090 - accuracy: 0.4083\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 23ms/step - loss: 1.0090 - accuracy: 0.4083\nEpoch 15/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.9713 - accuracy: 0.4000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 1ms/step - loss: 0.9941 - accuracy: 0.4083\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 20ms/step - loss: 0.9941 - accuracy: 0.4083\nEpoch 16/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.9151 - accuracy: 0.5000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 579us/step - loss: 0.9794 - accuracy: 0.4083\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 18ms/step - loss: 0.9794 - accuracy: 0.4083 \nEpoch 17/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.8932 - accuracy: 0.5000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 595us/step - loss: 0.9656 - accuracy: 0.4083\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.9656 - accuracy: 0.4083 \nEpoch 18/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 1.1059 - accuracy: 0.3000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 2ms/step - loss: 0.9522 - accuracy: 0.4083\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 22ms/step - loss: 0.9522 - accuracy: 0.4083\nEpoch 19/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.9085 - accuracy: 0.4500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 2ms/step - loss: 0.9392 - accuracy: 0.4333\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 21ms/step - loss: 0.9392 - accuracy: 0.4333\nEpoch 20/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.8883 - accuracy: 0.4500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 790us/step - loss: 0.9266 - accuracy: 0.4500\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.9266 - accuracy: 0.4500 \nEpoch 21/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 1.0031 - accuracy: 0.4500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 1ms/step - loss: 0.9145 - accuracy: 0.4417\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 20ms/step - loss: 0.9145 - accuracy: 0.4417\nEpoch 22/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.7275 - accuracy: 0.5500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 2ms/step - loss: 0.9029 - accuracy: 0.4583\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 22ms/step - loss: 0.9029 - accuracy: 0.4583\nEpoch 23/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.9503 - accuracy: 0.4000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 1ms/step - loss: 0.8918 - accuracy: 0.4500\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 21ms/step - loss: 0.8918 - accuracy: 0.4500\nEpoch 24/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.7148 - accuracy: 0.5000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 625us/step - loss: 0.8813 - accuracy: 0.4500\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 18ms/step - loss: 0.8813 - accuracy: 0.4500 \nEpoch 25/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 1.2577 - accuracy: 0.3000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 627us/step - loss: 0.8710 - accuracy: 0.4500\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.8710 - accuracy: 0.4500 \nEpoch 26/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.7393 - accuracy: 0.6500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 552us/step - loss: 0.8613 - accuracy: 0.4500\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 18ms/step - loss: 0.8613 - accuracy: 0.4500 \nEpoch 27/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.7891 - accuracy: 0.4500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 786us/step - loss: 0.8516 - accuracy: 0.4583\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.8516 - accuracy: 0.4583 \nEpoch 28/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.7014 - accuracy: 0.5000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 1ms/step - loss: 0.8423 - accuracy: 0.4667\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 21ms/step - loss: 0.8423 - accuracy: 0.4667\nEpoch 29/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 1.0145 - accuracy: 0.4000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 1ms/step - loss: 0.8333 - accuracy: 0.4917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 20ms/step - loss: 0.8333 - accuracy: 0.4917\nEpoch 30/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.9247 - accuracy: 0.4500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 2ms/step - loss: 0.8244 - accuracy: 0.5083\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 21ms/step - loss: 0.8244 - accuracy: 0.5083\nEpoch 31/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6738 - accuracy: 0.6500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 1ms/step - loss: 0.8158 - accuracy: 0.5083\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 20ms/step - loss: 0.8158 - accuracy: 0.5083\nEpoch 32/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.8393 - accuracy: 0.5500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 629us/step - loss: 0.8075 - accuracy: 0.5083\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.8075 - accuracy: 0.5083 \nEpoch 33/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6671 - accuracy: 0.5000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 754us/step - loss: 0.7995 - accuracy: 0.5000\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.7995 - accuracy: 0.5000 \nEpoch 34/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.8134 - accuracy: 0.4500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 2ms/step - loss: 0.7917 - accuracy: 0.4917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 22ms/step - loss: 0.7917 - accuracy: 0.4917\nEpoch 35/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6892 - accuracy: 0.5500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 3ms/step - loss: 0.7845 - accuracy: 0.5083\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 23ms/step - loss: 0.7845 - accuracy: 0.5083\nEpoch 36/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6879 - accuracy: 0.6000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 585us/step - loss: 0.7772 - accuracy: 0.5333\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.7772 - accuracy: 0.5333 \nEpoch 37/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6998 - accuracy: 0.6000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 2ms/step - loss: 0.7703 - accuracy: 0.5500\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 23ms/step - loss: 0.7703 - accuracy: 0.5500\nEpoch 38/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6709 - accuracy: 0.7000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 2ms/step - loss: 0.7634 - accuracy: 0.5500\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 22ms/step - loss: 0.7634 - accuracy: 0.5500\nEpoch 39/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.7880 - accuracy: 0.6000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 2ms/step - loss: 0.7567 - accuracy: 0.5750\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 22ms/step - loss: 0.7567 - accuracy: 0.5750\nEpoch 40/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.7314 - accuracy: 0.7000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 617us/step - loss: 0.7501 - accuracy: 0.5833\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.7501 - accuracy: 0.5833 \nEpoch 41/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.7269 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 618us/step - loss: 0.7439 - accuracy: 0.5833\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.7439 - accuracy: 0.5833 \nEpoch 42/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.7133 - accuracy: 0.6000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 2ms/step - loss: 0.7378 - accuracy: 0.5833\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 21ms/step - loss: 0.7378 - accuracy: 0.5833\nEpoch 43/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6314 - accuracy: 0.5500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 702us/step - loss: 0.7320 - accuracy: 0.5833\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.7320 - accuracy: 0.5833 \nEpoch 44/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6208 - accuracy: 0.5500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 2ms/step - loss: 0.7264 - accuracy: 0.6000\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 22ms/step - loss: 0.7264 - accuracy: 0.6000\nEpoch 45/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.4508 - accuracy: 0.7000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 847us/step - loss: 0.7211 - accuracy: 0.5917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 21ms/step - loss: 0.7211 - accuracy: 0.5917 \nEpoch 46/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.7018 - accuracy: 0.6500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 617us/step - loss: 0.7158 - accuracy: 0.5917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.7158 - accuracy: 0.5917 \nEpoch 47/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6888 - accuracy: 0.5500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 592us/step - loss: 0.7108 - accuracy: 0.6000\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.7108 - accuracy: 0.6000 \nEpoch 48/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.7136 - accuracy: 0.6000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 728us/step - loss: 0.7059 - accuracy: 0.6167\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.7059 - accuracy: 0.6167 \nEpoch 49/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.7264 - accuracy: 0.6000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 761us/step - loss: 0.7011 - accuracy: 0.6250\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.7011 - accuracy: 0.6250 \nEpoch 50/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.7306 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 783us/step - loss: 0.6966 - accuracy: 0.6333\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.6966 - accuracy: 0.6333 \nEpoch 51/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.4879 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 835us/step - loss: 0.6925 - accuracy: 0.6333\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.6925 - accuracy: 0.6333 \nEpoch 52/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6134 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 2ms/step - loss: 0.6882 - accuracy: 0.6417\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 22ms/step - loss: 0.6882 - accuracy: 0.6417\nEpoch 53/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5804 - accuracy: 0.7000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 1ms/step - loss: 0.6841 - accuracy: 0.6417\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 20ms/step - loss: 0.6841 - accuracy: 0.6417\nEpoch 54/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5657 - accuracy: 0.8000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 3ms/step - loss: 0.6803 - accuracy: 0.6583\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 24ms/step - loss: 0.6803 - accuracy: 0.6583\nEpoch 55/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.8458 - accuracy: 0.6000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 2ms/step - loss: 0.6765 - accuracy: 0.6583\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 21ms/step - loss: 0.6765 - accuracy: 0.6583\nEpoch 56/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.8174 - accuracy: 0.5500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 597us/step - loss: 0.6729 - accuracy: 0.6583\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 18ms/step - loss: 0.6729 - accuracy: 0.6583 \nEpoch 57/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6018 - accuracy: 0.5000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 695us/step - loss: 0.6695 - accuracy: 0.6583\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.6695 - accuracy: 0.6583 \nEpoch 58/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6955 - accuracy: 0.6000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 2ms/step - loss: 0.6661 - accuracy: 0.6583\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 21ms/step - loss: 0.6661 - accuracy: 0.6583\nEpoch 59/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.7480 - accuracy: 0.8000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 649us/step - loss: 0.6630 - accuracy: 0.6583\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.6630 - accuracy: 0.6583 \nEpoch 60/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6712 - accuracy: 0.7000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 1ms/step - loss: 0.6598 - accuracy: 0.6583\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 21ms/step - loss: 0.6598 - accuracy: 0.6583\nEpoch 61/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.7638 - accuracy: 0.7000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 2ms/step - loss: 0.6568 - accuracy: 0.6583\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 22ms/step - loss: 0.6568 - accuracy: 0.6583\nEpoch 62/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.7733 - accuracy: 0.7000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 1ms/step - loss: 0.6539 - accuracy: 0.6583\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 21ms/step - loss: 0.6539 - accuracy: 0.6583\nEpoch 63/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.7415 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 3ms/step - loss: 0.6510 - accuracy: 0.6583\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 23ms/step - loss: 0.6510 - accuracy: 0.6583\nEpoch 64/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6543 - accuracy: 0.7000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 579us/step - loss: 0.6482 - accuracy: 0.6583\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 18ms/step - loss: 0.6482 - accuracy: 0.6583 \nEpoch 65/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6206 - accuracy: 0.6500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 624us/step - loss: 0.6454 - accuracy: 0.6583\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.6454 - accuracy: 0.6583 \nEpoch 66/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6605 - accuracy: 0.6000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 2ms/step - loss: 0.6427 - accuracy: 0.6583\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 22ms/step - loss: 0.6427 - accuracy: 0.6583\nEpoch 67/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5182 - accuracy: 0.6500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 2ms/step - loss: 0.6403 - accuracy: 0.6583\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 22ms/step - loss: 0.6403 - accuracy: 0.6583\nEpoch 68/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6439 - accuracy: 0.8000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 1ms/step - loss: 0.6379 - accuracy: 0.6583\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 23ms/step - loss: 0.6379 - accuracy: 0.6583\nEpoch 69/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5915 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 2ms/step - loss: 0.6357 - accuracy: 0.6667\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 22ms/step - loss: 0.6357 - accuracy: 0.6667\nEpoch 70/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6438 - accuracy: 0.8000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 1ms/step - loss: 0.6336 - accuracy: 0.6667\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 21ms/step - loss: 0.6336 - accuracy: 0.6667\nEpoch 71/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5953 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 1ms/step - loss: 0.6316 - accuracy: 0.6667\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 21ms/step - loss: 0.6316 - accuracy: 0.6667\nEpoch 72/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.7936 - accuracy: 0.5500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 585us/step - loss: 0.6296 - accuracy: 0.6667\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.6296 - accuracy: 0.6667 \nEpoch 73/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5576 - accuracy: 0.6500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 1ms/step - loss: 0.6275 - accuracy: 0.6667\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 21ms/step - loss: 0.6275 - accuracy: 0.6667\nEpoch 74/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5583 - accuracy: 0.6500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 721us/step - loss: 0.6255 - accuracy: 0.6833\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.6255 - accuracy: 0.6833 \nEpoch 75/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.7650 - accuracy: 0.6500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 1ms/step - loss: 0.6236 - accuracy: 0.6833\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 20ms/step - loss: 0.6236 - accuracy: 0.6833\nEpoch 76/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5352 - accuracy: 0.8000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 1ms/step - loss: 0.6217 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 20ms/step - loss: 0.6217 - accuracy: 0.6917\nEpoch 77/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5358 - accuracy: 0.8500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 1ms/step - loss: 0.6198 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 20ms/step - loss: 0.6198 - accuracy: 0.6917\nEpoch 78/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6879 - accuracy: 0.5500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 713us/step - loss: 0.6180 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.6180 - accuracy: 0.6917 \nEpoch 79/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5994 - accuracy: 0.6000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 3ms/step - loss: 0.6164 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 23ms/step - loss: 0.6164 - accuracy: 0.6917\nEpoch 80/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.7281 - accuracy: 0.6000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 740us/step - loss: 0.6147 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.6147 - accuracy: 0.6917 \nEpoch 81/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5808 - accuracy: 0.7000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 648us/step - loss: 0.6131 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 47ms/step - loss: 0.6131 - accuracy: 0.6917 \nEpoch 82/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5839 - accuracy: 0.7000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 2ms/step - loss: 0.6115 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 22ms/step - loss: 0.6115 - accuracy: 0.6917\nEpoch 83/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6340 - accuracy: 0.6000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 532us/step - loss: 0.6100 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 18ms/step - loss: 0.6100 - accuracy: 0.6917 \nEpoch 84/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6165 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 3ms/step - loss: 0.6085 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 23ms/step - loss: 0.6085 - accuracy: 0.6917\nEpoch 85/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6126 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 1ms/step - loss: 0.6070 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 20ms/step - loss: 0.6070 - accuracy: 0.6917\nEpoch 86/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5778 - accuracy: 0.7000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 777us/step - loss: 0.6056 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.6056 - accuracy: 0.6917 \nEpoch 87/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5722 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 588us/step - loss: 0.6042 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.6042 - accuracy: 0.6917 \nEpoch 88/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.7331 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 2ms/step - loss: 0.6029 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 23ms/step - loss: 0.6029 - accuracy: 0.6917\nEpoch 89/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5736 - accuracy: 0.7000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 1ms/step - loss: 0.6015 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 20ms/step - loss: 0.6015 - accuracy: 0.6917\nEpoch 90/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5786 - accuracy: 0.6500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 1ms/step - loss: 0.6002 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 20ms/step - loss: 0.6002 - accuracy: 0.6917\nEpoch 91/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6256 - accuracy: 0.6000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 2ms/step - loss: 0.5988 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 22ms/step - loss: 0.5988 - accuracy: 0.6917\nEpoch 92/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6103 - accuracy: 0.7000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 1ms/step - loss: 0.5975 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 20ms/step - loss: 0.5975 - accuracy: 0.6917\nEpoch 93/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.7444 - accuracy: 0.6000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 1ms/step - loss: 0.5963 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 20ms/step - loss: 0.5963 - accuracy: 0.6917\nEpoch 94/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6956 - accuracy: 0.6500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 717us/step - loss: 0.5950 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5950 - accuracy: 0.6917 \nEpoch 95/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5259 - accuracy: 0.7000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 700us/step - loss: 0.5938 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5938 - accuracy: 0.6917 \nEpoch 96/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.7107 - accuracy: 0.5000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 1ms/step - loss: 0.5926 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 20ms/step - loss: 0.5926 - accuracy: 0.6917\nEpoch 97/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.4441 - accuracy: 0.7000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 902us/step - loss: 0.5914 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5914 - accuracy: 0.6917 \nEpoch 98/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5224 - accuracy: 0.7000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 1ms/step - loss: 0.5903 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 20ms/step - loss: 0.5903 - accuracy: 0.6917\nEpoch 99/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6222 - accuracy: 0.8500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 962us/step - loss: 0.5892 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5892 - accuracy: 0.6917 \nEpoch 100/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.7337 - accuracy: 0.6000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 2ms/step - loss: 0.5881 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 23ms/step - loss: 0.5881 - accuracy: 0.6917\nEpoch 101/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5195 - accuracy: 0.7000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 718us/step - loss: 0.5871 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5871 - accuracy: 0.6917 \nEpoch 102/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6514 - accuracy: 0.6000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 656us/step - loss: 0.5861 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5861 - accuracy: 0.6917 \nEpoch 103/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5375 - accuracy: 0.8500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 596us/step - loss: 0.5851 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5851 - accuracy: 0.6917 \nEpoch 104/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.3722 - accuracy: 0.9000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 586us/step - loss: 0.5841 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5841 - accuracy: 0.6917 \nEpoch 105/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6097 - accuracy: 0.6000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 1ms/step - loss: 0.5832 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 21ms/step - loss: 0.5832 - accuracy: 0.6917\nEpoch 106/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6803 - accuracy: 0.6500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 944us/step - loss: 0.5822 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 20ms/step - loss: 0.5822 - accuracy: 0.6917 \nEpoch 107/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6398 - accuracy: 0.6500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 861us/step - loss: 0.5814 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5814 - accuracy: 0.6917 \nEpoch 108/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5460 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 1ms/step - loss: 0.5803 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 20ms/step - loss: 0.5803 - accuracy: 0.6917\nEpoch 109/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5922 - accuracy: 0.7000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 805us/step - loss: 0.5794 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5794 - accuracy: 0.6917 \nEpoch 110/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6299 - accuracy: 0.7000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 1ms/step - loss: 0.5784 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 20ms/step - loss: 0.5784 - accuracy: 0.6917\nEpoch 111/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.7738 - accuracy: 0.5000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 678us/step - loss: 0.5775 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5775 - accuracy: 0.6917 \nEpoch 112/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6669 - accuracy: 0.7000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 587us/step - loss: 0.5765 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 18ms/step - loss: 0.5765 - accuracy: 0.6917 \nEpoch 113/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.4115 - accuracy: 0.8500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 2ms/step - loss: 0.5756 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 22ms/step - loss: 0.5756 - accuracy: 0.6917\nEpoch 114/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5871 - accuracy: 0.7000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 697us/step - loss: 0.5746 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5746 - accuracy: 0.6917 \nEpoch 115/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5014 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 2ms/step - loss: 0.5737 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 22ms/step - loss: 0.5737 - accuracy: 0.6917\nEpoch 116/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6300 - accuracy: 0.6500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 657us/step - loss: 0.5727 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5727 - accuracy: 0.6917 \nEpoch 117/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.4466 - accuracy: 0.8500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 3ms/step - loss: 0.5718 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 24ms/step - loss: 0.5718 - accuracy: 0.6917\nEpoch 118/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5294 - accuracy: 0.8000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 2ms/step - loss: 0.5710 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 22ms/step - loss: 0.5710 - accuracy: 0.6917\nEpoch 119/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5448 - accuracy: 0.7000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 702us/step - loss: 0.5700 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5700 - accuracy: 0.6917 \nEpoch 120/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6877 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 584us/step - loss: 0.5691 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 18ms/step - loss: 0.5691 - accuracy: 0.6917 \nEpoch 121/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6489 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 765us/step - loss: 0.5682 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5682 - accuracy: 0.6917 \nEpoch 122/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5650 - accuracy: 0.5500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 1ms/step - loss: 0.5674 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 20ms/step - loss: 0.5674 - accuracy: 0.6917\nEpoch 123/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6406 - accuracy: 0.5500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 687us/step - loss: 0.5665 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5665 - accuracy: 0.6917 \nEpoch 124/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6143 - accuracy: 0.7000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 989us/step - loss: 0.5655 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 20ms/step - loss: 0.5655 - accuracy: 0.6917 \nEpoch 125/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6083 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 848us/step - loss: 0.5646 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 20ms/step - loss: 0.5646 - accuracy: 0.6917 \nEpoch 126/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6228 - accuracy: 0.6500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 588us/step - loss: 0.5637 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5637 - accuracy: 0.6917 \nEpoch 127/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5465 - accuracy: 0.6500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 612us/step - loss: 0.5628 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5628 - accuracy: 0.6917 \nEpoch 128/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5298 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 570us/step - loss: 0.5619 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5619 - accuracy: 0.6917 \nEpoch 129/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.4281 - accuracy: 0.8500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 935us/step - loss: 0.5611 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 20ms/step - loss: 0.5611 - accuracy: 0.6917 \nEpoch 130/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6695 - accuracy: 0.5500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 601us/step - loss: 0.5601 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5601 - accuracy: 0.6917 \nEpoch 131/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5811 - accuracy: 0.6500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 962us/step - loss: 0.5593 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 20ms/step - loss: 0.5593 - accuracy: 0.6917 \nEpoch 132/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6018 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 689us/step - loss: 0.5584 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5584 - accuracy: 0.6917 \nEpoch 133/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6543 - accuracy: 0.6500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 1ms/step - loss: 0.5575 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 21ms/step - loss: 0.5575 - accuracy: 0.6917\nEpoch 134/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.4383 - accuracy: 0.8000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 693us/step - loss: 0.5565 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5565 - accuracy: 0.6917 \nEpoch 135/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6316 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 974us/step - loss: 0.5556 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 20ms/step - loss: 0.5556 - accuracy: 0.6917 \nEpoch 136/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5152 - accuracy: 0.8000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 623us/step - loss: 0.5547 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5547 - accuracy: 0.6917 \nEpoch 137/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6035 - accuracy: 0.6500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 615us/step - loss: 0.5538 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5538 - accuracy: 0.6917 \nEpoch 138/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5472 - accuracy: 0.6000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 1ms/step - loss: 0.5529 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 21ms/step - loss: 0.5529 - accuracy: 0.6917\nEpoch 139/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6686 - accuracy: 0.5000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 809us/step - loss: 0.5520 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5520 - accuracy: 0.6917 \nEpoch 140/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5201 - accuracy: 0.5000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 1ms/step - loss: 0.5511 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 21ms/step - loss: 0.5511 - accuracy: 0.6917\nEpoch 141/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6928 - accuracy: 0.6000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 720us/step - loss: 0.5501 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5501 - accuracy: 0.6917 \nEpoch 142/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5260 - accuracy: 0.8500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 1ms/step - loss: 0.5494 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 20ms/step - loss: 0.5494 - accuracy: 0.6917\nEpoch 143/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6178 - accuracy: 0.6000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 737us/step - loss: 0.5484 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5484 - accuracy: 0.6917 \nEpoch 144/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.4089 - accuracy: 0.7000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 668us/step - loss: 0.5475 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5475 - accuracy: 0.6917 \nEpoch 145/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5860 - accuracy: 0.5500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 610us/step - loss: 0.5465 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5465 - accuracy: 0.6917 \nEpoch 146/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5515 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 675us/step - loss: 0.5456 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5456 - accuracy: 0.6917 \nEpoch 147/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5169 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 654us/step - loss: 0.5446 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5446 - accuracy: 0.6917 \nEpoch 148/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5611 - accuracy: 0.7000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 598us/step - loss: 0.5436 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5436 - accuracy: 0.6917 \nEpoch 149/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.4876 - accuracy: 0.7000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 838us/step - loss: 0.5427 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5427 - accuracy: 0.6917 \nEpoch 150/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5597 - accuracy: 0.6500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 1ms/step - loss: 0.5418 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 20ms/step - loss: 0.5418 - accuracy: 0.6917\nEpoch 151/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6531 - accuracy: 0.6500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 904us/step - loss: 0.5408 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 20ms/step - loss: 0.5408 - accuracy: 0.6917 \nEpoch 152/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.4758 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 804us/step - loss: 0.5399 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 21ms/step - loss: 0.5399 - accuracy: 0.6917 \nEpoch 153/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6072 - accuracy: 0.5500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 645us/step - loss: 0.5389 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5389 - accuracy: 0.6917 \nEpoch 154/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5891 - accuracy: 0.7000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 605us/step - loss: 0.5379 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 18ms/step - loss: 0.5379 - accuracy: 0.6917 \nEpoch 155/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5705 - accuracy: 0.6000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 2ms/step - loss: 0.5368 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 22ms/step - loss: 0.5368 - accuracy: 0.6917\nEpoch 156/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5169 - accuracy: 0.7000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 830us/step - loss: 0.5357 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5357 - accuracy: 0.6917 \nEpoch 157/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.4144 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 926us/step - loss: 0.5346 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 20ms/step - loss: 0.5346 - accuracy: 0.6917 \nEpoch 158/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5302 - accuracy: 0.6000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 677us/step - loss: 0.5336 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5336 - accuracy: 0.6917 \nEpoch 159/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5873 - accuracy: 0.6500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 713us/step - loss: 0.5325 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5325 - accuracy: 0.6917 \nEpoch 160/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5733 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 598us/step - loss: 0.5314 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5314 - accuracy: 0.6917 \nEpoch 161/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.4812 - accuracy: 0.7000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 721us/step - loss: 0.5303 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5303 - accuracy: 0.6917 \nEpoch 162/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6660 - accuracy: 0.5500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 591us/step - loss: 0.5292 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 18ms/step - loss: 0.5292 - accuracy: 0.6917 \nEpoch 163/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5114 - accuracy: 0.7000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 579us/step - loss: 0.5281 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5281 - accuracy: 0.6917 \nEpoch 164/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6130 - accuracy: 0.6000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 597us/step - loss: 0.5270 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5270 - accuracy: 0.6917 \nEpoch 165/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5206 - accuracy: 0.6500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 814us/step - loss: 0.5259 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5259 - accuracy: 0.6917 \nEpoch 166/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5998 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 1ms/step - loss: 0.5248 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 20ms/step - loss: 0.5248 - accuracy: 0.6917\nEpoch 167/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5556 - accuracy: 0.5500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 840us/step - loss: 0.5236 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5236 - accuracy: 0.6917 \nEpoch 168/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.4537 - accuracy: 0.6000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 744us/step - loss: 0.5224 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5224 - accuracy: 0.6917 \nEpoch 169/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5130 - accuracy: 0.7000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 2ms/step - loss: 0.5213 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 22ms/step - loss: 0.5213 - accuracy: 0.6917\nEpoch 170/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5373 - accuracy: 0.6000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 678us/step - loss: 0.5201 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5201 - accuracy: 0.6917 \nEpoch 171/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.4222 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 568us/step - loss: 0.5189 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 43ms/step - loss: 0.5189 - accuracy: 0.6917 \nEpoch 172/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5436 - accuracy: 0.7000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 3ms/step - loss: 0.5178 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 23ms/step - loss: 0.5178 - accuracy: 0.6917\nEpoch 173/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5871 - accuracy: 0.6000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 888us/step - loss: 0.5166 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 20ms/step - loss: 0.5166 - accuracy: 0.6917 \nEpoch 174/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5113 - accuracy: 0.7000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 658us/step - loss: 0.5154 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5154 - accuracy: 0.6917 \nEpoch 175/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.3877 - accuracy: 0.8500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 757us/step - loss: 0.5142 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5142 - accuracy: 0.6917 \nEpoch 176/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5053 - accuracy: 0.6000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 839us/step - loss: 0.5130 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5130 - accuracy: 0.6917 \nEpoch 177/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.4341 - accuracy: 0.8000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 634us/step - loss: 0.5116 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5116 - accuracy: 0.6917 \nEpoch 178/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.4135 - accuracy: 0.7000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 603us/step - loss: 0.5103 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 18ms/step - loss: 0.5103 - accuracy: 0.6917 \nEpoch 179/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.4641 - accuracy: 0.6500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 563us/step - loss: 0.5091 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5091 - accuracy: 0.6917 \nEpoch 180/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.4914 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 1ms/step - loss: 0.5077 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 21ms/step - loss: 0.5077 - accuracy: 0.6917\nEpoch 181/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.4681 - accuracy: 0.6500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 572us/step - loss: 0.5065 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5065 - accuracy: 0.6917 \nEpoch 182/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.4002 - accuracy: 0.7000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 738us/step - loss: 0.5053 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5053 - accuracy: 0.6917 \nEpoch 183/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.3638 - accuracy: 0.8500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 618us/step - loss: 0.5040 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5040 - accuracy: 0.6917 \nEpoch 184/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.4062 - accuracy: 0.8500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 644us/step - loss: 0.5029 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5029 - accuracy: 0.6917 \nEpoch 185/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5250 - accuracy: 0.6000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 662us/step - loss: 0.5017 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5017 - accuracy: 0.6917 \nEpoch 186/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6363 - accuracy: 0.5500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 654us/step - loss: 0.5003 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.5003 - accuracy: 0.6917 \nEpoch 187/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.3830 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 555us/step - loss: 0.4990 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 18ms/step - loss: 0.4990 - accuracy: 0.6917 \nEpoch 188/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5892 - accuracy: 0.6000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 1ms/step - loss: 0.4976 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 20ms/step - loss: 0.4976 - accuracy: 0.6917\nEpoch 189/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.4722 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 628us/step - loss: 0.4962 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.4962 - accuracy: 0.6917 \nEpoch 190/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.4150 - accuracy: 0.8000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 592us/step - loss: 0.4948 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.4948 - accuracy: 0.6917 \nEpoch 191/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.4167 - accuracy: 0.8000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 858us/step - loss: 0.4933 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.4933 - accuracy: 0.6917 \nEpoch 192/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5952 - accuracy: 0.6000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 672us/step - loss: 0.4918 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.4918 - accuracy: 0.6917 \nEpoch 193/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.4614 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 577us/step - loss: 0.4901 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.4901 - accuracy: 0.6917 \nEpoch 194/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6146 - accuracy: 0.6000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 625us/step - loss: 0.4885 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 18ms/step - loss: 0.4885 - accuracy: 0.6917 \nEpoch 195/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5188 - accuracy: 0.6500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 702us/step - loss: 0.4870 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.4870 - accuracy: 0.6917 \nEpoch 196/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.4562 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 615us/step - loss: 0.4854 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 18ms/step - loss: 0.4854 - accuracy: 0.6917 \nEpoch 197/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5204 - accuracy: 0.6500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 683us/step - loss: 0.4839 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.4839 - accuracy: 0.6917 \nEpoch 198/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.3824 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 603us/step - loss: 0.4825 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.4825 - accuracy: 0.6917 \nEpoch 199/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.5074 - accuracy: 0.6000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 859us/step - loss: 0.4811 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.4811 - accuracy: 0.6917 \nEpoch 200/200\n\n1/6 [====>.........................] - ETA: 0s - loss: 0.6141 - accuracy: 0.5500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 657us/step - loss: 0.4797 - accuracy: 0.6917\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n6/6 [==============================] - 0s 19ms/step - loss: 0.4797 - accuracy: 0.6917 \n"} -->

Epoch 1/200

1/6 [====>…………………….] - ETA: 0s - loss: 1.3146 - accuracy: 0.4500 6/6 [==============================] - 0s 632us/step - loss: 1.3092 - accuracy: 0.3333  6/6 [==============================] - 1s 85ms/step - loss: 1.3092 - accuracy: 0.3333 Epoch 2/200

1/6 [====>…………………….] - ETA: 0s - loss: 1.1826 - accuracy: 0.3500 6/6 [==============================] - 0s 586us/step - loss: 1.2631 - accuracy: 0.3500  6/6 [==============================] - 0s 19ms/step - loss: 1.2631 - accuracy: 0.3500 Epoch 3/200

1/6 [====>…………………….] - ETA: 0s - loss: 1.4383 - accuracy: 0.2000 6/6 [==============================] - 0s 3ms/step - loss: 1.2305 - accuracy: 0.3583  6/6 [==============================] - 0s 23ms/step - loss: 1.2305 - accuracy: 0.3583 Epoch 4/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.9543 - accuracy: 0.4500 6/6 [==============================] - 0s 2ms/step - loss: 1.2025 - accuracy: 0.3583  6/6 [==============================] - 0s 22ms/step - loss: 1.2025 - accuracy: 0.3583 Epoch 5/200

1/6 [====>…………………….] - ETA: 0s - loss: 1.2216 - accuracy: 0.3500 6/6 [==============================] - 0s 1ms/step - loss: 1.1776 - accuracy: 0.3667  6/6 [==============================] - 0s 20ms/step - loss: 1.1776 - accuracy: 0.3667 Epoch 6/200

1/6 [====>…………………….] - ETA: 0s - loss: 1.2356 - accuracy: 0.2000 6/6 [==============================] - 0s 754us/step - loss: 1.1545 - accuracy: 0.3667  6/6 [==============================] - 0s 19ms/step - loss: 1.1545 - accuracy: 0.3667 Epoch 7/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.8686 - accuracy: 0.5000 6/6 [==============================] - 0s 2ms/step - loss: 1.1328 - accuracy: 0.3667  6/6 [==============================] - 0s 22ms/step - loss: 1.1328 - accuracy: 0.3667 Epoch 8/200

1/6 [====>…………………….] - ETA: 0s - loss: 1.1982 - accuracy: 0.1500 6/6 [==============================] - 0s 522us/step - loss: 1.1122 - accuracy: 0.3667  6/6 [==============================] - 0s 18ms/step - loss: 1.1122 - accuracy: 0.3667 Epoch 9/200

1/6 [====>…………………….] - ETA: 0s - loss: 1.2572 - accuracy: 0.1500 6/6 [==============================] - 0s 581us/step - loss: 1.0929 - accuracy: 0.3750  6/6 [==============================] - 0s 20ms/step - loss: 1.0929 - accuracy: 0.3750 Epoch 10/200

1/6 [====>…………………….] - ETA: 0s - loss: 1.0138 - accuracy: 0.3500 6/6 [==============================] - 0s 838us/step - loss: 1.0745 - accuracy: 0.3833  6/6 [==============================] - 0s 19ms/step - loss: 1.0745 - accuracy: 0.3833 Epoch 11/200

1/6 [====>…………………….] - ETA: 0s - loss: 1.0590 - accuracy: 0.3000 6/6 [==============================] - 0s 3ms/step - loss: 1.0570 - accuracy: 0.3833  6/6 [==============================] - 0s 23ms/step - loss: 1.0570 - accuracy: 0.3833 Epoch 12/200

1/6 [====>…………………….] - ETA: 0s - loss: 1.0460 - accuracy: 0.3000 6/6 [==============================] - 0s 2ms/step - loss: 1.0403 - accuracy: 0.3833  6/6 [==============================] - 0s 23ms/step - loss: 1.0403 - accuracy: 0.3833 Epoch 13/200

1/6 [====>…………………….] - ETA: 0s - loss: 1.1162 - accuracy: 0.2500 6/6 [==============================] - 0s 2ms/step - loss: 1.0245 - accuracy: 0.3917  6/6 [==============================] - 0s 21ms/step - loss: 1.0245 - accuracy: 0.3917 Epoch 14/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.8707 - accuracy: 0.5000 6/6 [==============================] - 0s 3ms/step - loss: 1.0090 - accuracy: 0.4083  6/6 [==============================] - 0s 23ms/step - loss: 1.0090 - accuracy: 0.4083 Epoch 15/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.9713 - accuracy: 0.4000 6/6 [==============================] - 0s 1ms/step - loss: 0.9941 - accuracy: 0.4083  6/6 [==============================] - 0s 20ms/step - loss: 0.9941 - accuracy: 0.4083 Epoch 16/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.9151 - accuracy: 0.5000 6/6 [==============================] - 0s 579us/step - loss: 0.9794 - accuracy: 0.4083  6/6 [==============================] - 0s 18ms/step - loss: 0.9794 - accuracy: 0.4083 Epoch 17/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.8932 - accuracy: 0.5000 6/6 [==============================] - 0s 595us/step - loss: 0.9656 - accuracy: 0.4083  6/6 [==============================] - 0s 19ms/step - loss: 0.9656 - accuracy: 0.4083 Epoch 18/200

1/6 [====>…………………….] - ETA: 0s - loss: 1.1059 - accuracy: 0.3000 6/6 [==============================] - 0s 2ms/step - loss: 0.9522 - accuracy: 0.4083  6/6 [==============================] - 0s 22ms/step - loss: 0.9522 - accuracy: 0.4083 Epoch 19/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.9085 - accuracy: 0.4500 6/6 [==============================] - 0s 2ms/step - loss: 0.9392 - accuracy: 0.4333  6/6 [==============================] - 0s 21ms/step - loss: 0.9392 - accuracy: 0.4333 Epoch 20/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.8883 - accuracy: 0.4500 6/6 [==============================] - 0s 790us/step - loss: 0.9266 - accuracy: 0.4500  6/6 [==============================] - 0s 19ms/step - loss: 0.9266 - accuracy: 0.4500 Epoch 21/200

1/6 [====>…………………….] - ETA: 0s - loss: 1.0031 - accuracy: 0.4500 6/6 [==============================] - 0s 1ms/step - loss: 0.9145 - accuracy: 0.4417  6/6 [==============================] - 0s 20ms/step - loss: 0.9145 - accuracy: 0.4417 Epoch 22/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.7275 - accuracy: 0.5500 6/6 [==============================] - 0s 2ms/step - loss: 0.9029 - accuracy: 0.4583  6/6 [==============================] - 0s 22ms/step - loss: 0.9029 - accuracy: 0.4583 Epoch 23/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.9503 - accuracy: 0.4000 6/6 [==============================] - 0s 1ms/step - loss: 0.8918 - accuracy: 0.4500  6/6 [==============================] - 0s 21ms/step - loss: 0.8918 - accuracy: 0.4500 Epoch 24/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.7148 - accuracy: 0.5000 6/6 [==============================] - 0s 625us/step - loss: 0.8813 - accuracy: 0.4500  6/6 [==============================] - 0s 18ms/step - loss: 0.8813 - accuracy: 0.4500 Epoch 25/200

1/6 [====>…………………….] - ETA: 0s - loss: 1.2577 - accuracy: 0.3000 6/6 [==============================] - 0s 627us/step - loss: 0.8710 - accuracy: 0.4500  6/6 [==============================] - 0s 19ms/step - loss: 0.8710 - accuracy: 0.4500 Epoch 26/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.7393 - accuracy: 0.6500 6/6 [==============================] - 0s 552us/step - loss: 0.8613 - accuracy: 0.4500  6/6 [==============================] - 0s 18ms/step - loss: 0.8613 - accuracy: 0.4500 Epoch 27/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.7891 - accuracy: 0.4500 6/6 [==============================] - 0s 786us/step - loss: 0.8516 - accuracy: 0.4583  6/6 [==============================] - 0s 19ms/step - loss: 0.8516 - accuracy: 0.4583 Epoch 28/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.7014 - accuracy: 0.5000 6/6 [==============================] - 0s 1ms/step - loss: 0.8423 - accuracy: 0.4667  6/6 [==============================] - 0s 21ms/step - loss: 0.8423 - accuracy: 0.4667 Epoch 29/200

1/6 [====>…………………….] - ETA: 0s - loss: 1.0145 - accuracy: 0.4000 6/6 [==============================] - 0s 1ms/step - loss: 0.8333 - accuracy: 0.4917  6/6 [==============================] - 0s 20ms/step - loss: 0.8333 - accuracy: 0.4917 Epoch 30/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.9247 - accuracy: 0.4500 6/6 [==============================] - 0s 2ms/step - loss: 0.8244 - accuracy: 0.5083  6/6 [==============================] - 0s 21ms/step - loss: 0.8244 - accuracy: 0.5083 Epoch 31/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6738 - accuracy: 0.6500 6/6 [==============================] - 0s 1ms/step - loss: 0.8158 - accuracy: 0.5083  6/6 [==============================] - 0s 20ms/step - loss: 0.8158 - accuracy: 0.5083 Epoch 32/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.8393 - accuracy: 0.5500 6/6 [==============================] - 0s 629us/step - loss: 0.8075 - accuracy: 0.5083  6/6 [==============================] - 0s 19ms/step - loss: 0.8075 - accuracy: 0.5083 Epoch 33/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6671 - accuracy: 0.5000 6/6 [==============================] - 0s 754us/step - loss: 0.7995 - accuracy: 0.5000  6/6 [==============================] - 0s 19ms/step - loss: 0.7995 - accuracy: 0.5000 Epoch 34/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.8134 - accuracy: 0.4500 6/6 [==============================] - 0s 2ms/step - loss: 0.7917 - accuracy: 0.4917  6/6 [==============================] - 0s 22ms/step - loss: 0.7917 - accuracy: 0.4917 Epoch 35/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6892 - accuracy: 0.5500 6/6 [==============================] - 0s 3ms/step - loss: 0.7845 - accuracy: 0.5083  6/6 [==============================] - 0s 23ms/step - loss: 0.7845 - accuracy: 0.5083 Epoch 36/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6879 - accuracy: 0.6000 6/6 [==============================] - 0s 585us/step - loss: 0.7772 - accuracy: 0.5333  6/6 [==============================] - 0s 19ms/step - loss: 0.7772 - accuracy: 0.5333 Epoch 37/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6998 - accuracy: 0.6000 6/6 [==============================] - 0s 2ms/step - loss: 0.7703 - accuracy: 0.5500  6/6 [==============================] - 0s 23ms/step - loss: 0.7703 - accuracy: 0.5500 Epoch 38/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6709 - accuracy: 0.7000 6/6 [==============================] - 0s 2ms/step - loss: 0.7634 - accuracy: 0.5500  6/6 [==============================] - 0s 22ms/step - loss: 0.7634 - accuracy: 0.5500 Epoch 39/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.7880 - accuracy: 0.6000 6/6 [==============================] - 0s 2ms/step - loss: 0.7567 - accuracy: 0.5750  6/6 [==============================] - 0s 22ms/step - loss: 0.7567 - accuracy: 0.5750 Epoch 40/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.7314 - accuracy: 0.7000 6/6 [==============================] - 0s 617us/step - loss: 0.7501 - accuracy: 0.5833  6/6 [==============================] - 0s 19ms/step - loss: 0.7501 - accuracy: 0.5833 Epoch 41/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.7269 - accuracy: 0.7500 6/6 [==============================] - 0s 618us/step - loss: 0.7439 - accuracy: 0.5833  6/6 [==============================] - 0s 19ms/step - loss: 0.7439 - accuracy: 0.5833 Epoch 42/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.7133 - accuracy: 0.6000 6/6 [==============================] - 0s 2ms/step - loss: 0.7378 - accuracy: 0.5833  6/6 [==============================] - 0s 21ms/step - loss: 0.7378 - accuracy: 0.5833 Epoch 43/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6314 - accuracy: 0.5500 6/6 [==============================] - 0s 702us/step - loss: 0.7320 - accuracy: 0.5833  6/6 [==============================] - 0s 19ms/step - loss: 0.7320 - accuracy: 0.5833 Epoch 44/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6208 - accuracy: 0.5500 6/6 [==============================] - 0s 2ms/step - loss: 0.7264 - accuracy: 0.6000  6/6 [==============================] - 0s 22ms/step - loss: 0.7264 - accuracy: 0.6000 Epoch 45/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.4508 - accuracy: 0.7000 6/6 [==============================] - 0s 847us/step - loss: 0.7211 - accuracy: 0.5917  6/6 [==============================] - 0s 21ms/step - loss: 0.7211 - accuracy: 0.5917 Epoch 46/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.7018 - accuracy: 0.6500 6/6 [==============================] - 0s 617us/step - loss: 0.7158 - accuracy: 0.5917  6/6 [==============================] - 0s 19ms/step - loss: 0.7158 - accuracy: 0.5917 Epoch 47/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6888 - accuracy: 0.5500 6/6 [==============================] - 0s 592us/step - loss: 0.7108 - accuracy: 0.6000  6/6 [==============================] - 0s 19ms/step - loss: 0.7108 - accuracy: 0.6000 Epoch 48/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.7136 - accuracy: 0.6000 6/6 [==============================] - 0s 728us/step - loss: 0.7059 - accuracy: 0.6167  6/6 [==============================] - 0s 19ms/step - loss: 0.7059 - accuracy: 0.6167 Epoch 49/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.7264 - accuracy: 0.6000 6/6 [==============================] - 0s 761us/step - loss: 0.7011 - accuracy: 0.6250  6/6 [==============================] - 0s 19ms/step - loss: 0.7011 - accuracy: 0.6250 Epoch 50/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.7306 - accuracy: 0.7500 6/6 [==============================] - 0s 783us/step - loss: 0.6966 - accuracy: 0.6333  6/6 [==============================] - 0s 19ms/step - loss: 0.6966 - accuracy: 0.6333 Epoch 51/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.4879 - accuracy: 0.7500 6/6 [==============================] - 0s 835us/step - loss: 0.6925 - accuracy: 0.6333  6/6 [==============================] - 0s 19ms/step - loss: 0.6925 - accuracy: 0.6333 Epoch 52/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6134 - accuracy: 0.7500 6/6 [==============================] - 0s 2ms/step - loss: 0.6882 - accuracy: 0.6417  6/6 [==============================] - 0s 22ms/step - loss: 0.6882 - accuracy: 0.6417 Epoch 53/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5804 - accuracy: 0.7000 6/6 [==============================] - 0s 1ms/step - loss: 0.6841 - accuracy: 0.6417  6/6 [==============================] - 0s 20ms/step - loss: 0.6841 - accuracy: 0.6417 Epoch 54/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5657 - accuracy: 0.8000 6/6 [==============================] - 0s 3ms/step - loss: 0.6803 - accuracy: 0.6583  6/6 [==============================] - 0s 24ms/step - loss: 0.6803 - accuracy: 0.6583 Epoch 55/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.8458 - accuracy: 0.6000 6/6 [==============================] - 0s 2ms/step - loss: 0.6765 - accuracy: 0.6583  6/6 [==============================] - 0s 21ms/step - loss: 0.6765 - accuracy: 0.6583 Epoch 56/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.8174 - accuracy: 0.5500 6/6 [==============================] - 0s 597us/step - loss: 0.6729 - accuracy: 0.6583  6/6 [==============================] - 0s 18ms/step - loss: 0.6729 - accuracy: 0.6583 Epoch 57/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6018 - accuracy: 0.5000 6/6 [==============================] - 0s 695us/step - loss: 0.6695 - accuracy: 0.6583  6/6 [==============================] - 0s 19ms/step - loss: 0.6695 - accuracy: 0.6583 Epoch 58/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6955 - accuracy: 0.6000 6/6 [==============================] - 0s 2ms/step - loss: 0.6661 - accuracy: 0.6583  6/6 [==============================] - 0s 21ms/step - loss: 0.6661 - accuracy: 0.6583 Epoch 59/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.7480 - accuracy: 0.8000 6/6 [==============================] - 0s 649us/step - loss: 0.6630 - accuracy: 0.6583  6/6 [==============================] - 0s 19ms/step - loss: 0.6630 - accuracy: 0.6583 Epoch 60/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6712 - accuracy: 0.7000 6/6 [==============================] - 0s 1ms/step - loss: 0.6598 - accuracy: 0.6583  6/6 [==============================] - 0s 21ms/step - loss: 0.6598 - accuracy: 0.6583 Epoch 61/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.7638 - accuracy: 0.7000 6/6 [==============================] - 0s 2ms/step - loss: 0.6568 - accuracy: 0.6583  6/6 [==============================] - 0s 22ms/step - loss: 0.6568 - accuracy: 0.6583 Epoch 62/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.7733 - accuracy: 0.7000 6/6 [==============================] - 0s 1ms/step - loss: 0.6539 - accuracy: 0.6583  6/6 [==============================] - 0s 21ms/step - loss: 0.6539 - accuracy: 0.6583 Epoch 63/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.7415 - accuracy: 0.7500 6/6 [==============================] - 0s 3ms/step - loss: 0.6510 - accuracy: 0.6583  6/6 [==============================] - 0s 23ms/step - loss: 0.6510 - accuracy: 0.6583 Epoch 64/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6543 - accuracy: 0.7000 6/6 [==============================] - 0s 579us/step - loss: 0.6482 - accuracy: 0.6583  6/6 [==============================] - 0s 18ms/step - loss: 0.6482 - accuracy: 0.6583 Epoch 65/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6206 - accuracy: 0.6500 6/6 [==============================] - 0s 624us/step - loss: 0.6454 - accuracy: 0.6583  6/6 [==============================] - 0s 19ms/step - loss: 0.6454 - accuracy: 0.6583 Epoch 66/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6605 - accuracy: 0.6000 6/6 [==============================] - 0s 2ms/step - loss: 0.6427 - accuracy: 0.6583  6/6 [==============================] - 0s 22ms/step - loss: 0.6427 - accuracy: 0.6583 Epoch 67/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5182 - accuracy: 0.6500 6/6 [==============================] - 0s 2ms/step - loss: 0.6403 - accuracy: 0.6583  6/6 [==============================] - 0s 22ms/step - loss: 0.6403 - accuracy: 0.6583 Epoch 68/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6439 - accuracy: 0.8000 6/6 [==============================] - 0s 1ms/step - loss: 0.6379 - accuracy: 0.6583  6/6 [==============================] - 0s 23ms/step - loss: 0.6379 - accuracy: 0.6583 Epoch 69/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5915 - accuracy: 0.7500 6/6 [==============================] - 0s 2ms/step - loss: 0.6357 - accuracy: 0.6667  6/6 [==============================] - 0s 22ms/step - loss: 0.6357 - accuracy: 0.6667 Epoch 70/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6438 - accuracy: 0.8000 6/6 [==============================] - 0s 1ms/step - loss: 0.6336 - accuracy: 0.6667  6/6 [==============================] - 0s 21ms/step - loss: 0.6336 - accuracy: 0.6667 Epoch 71/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5953 - accuracy: 0.7500 6/6 [==============================] - 0s 1ms/step - loss: 0.6316 - accuracy: 0.6667  6/6 [==============================] - 0s 21ms/step - loss: 0.6316 - accuracy: 0.6667 Epoch 72/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.7936 - accuracy: 0.5500 6/6 [==============================] - 0s 585us/step - loss: 0.6296 - accuracy: 0.6667  6/6 [==============================] - 0s 19ms/step - loss: 0.6296 - accuracy: 0.6667 Epoch 73/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5576 - accuracy: 0.6500 6/6 [==============================] - 0s 1ms/step - loss: 0.6275 - accuracy: 0.6667  6/6 [==============================] - 0s 21ms/step - loss: 0.6275 - accuracy: 0.6667 Epoch 74/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5583 - accuracy: 0.6500 6/6 [==============================] - 0s 721us/step - loss: 0.6255 - accuracy: 0.6833  6/6 [==============================] - 0s 19ms/step - loss: 0.6255 - accuracy: 0.6833 Epoch 75/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.7650 - accuracy: 0.6500 6/6 [==============================] - 0s 1ms/step - loss: 0.6236 - accuracy: 0.6833  6/6 [==============================] - 0s 20ms/step - loss: 0.6236 - accuracy: 0.6833 Epoch 76/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5352 - accuracy: 0.8000 6/6 [==============================] - 0s 1ms/step - loss: 0.6217 - accuracy: 0.6917  6/6 [==============================] - 0s 20ms/step - loss: 0.6217 - accuracy: 0.6917 Epoch 77/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5358 - accuracy: 0.8500 6/6 [==============================] - 0s 1ms/step - loss: 0.6198 - accuracy: 0.6917  6/6 [==============================] - 0s 20ms/step - loss: 0.6198 - accuracy: 0.6917 Epoch 78/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6879 - accuracy: 0.5500 6/6 [==============================] - 0s 713us/step - loss: 0.6180 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.6180 - accuracy: 0.6917 Epoch 79/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5994 - accuracy: 0.6000 6/6 [==============================] - 0s 3ms/step - loss: 0.6164 - accuracy: 0.6917  6/6 [==============================] - 0s 23ms/step - loss: 0.6164 - accuracy: 0.6917 Epoch 80/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.7281 - accuracy: 0.6000 6/6 [==============================] - 0s 740us/step - loss: 0.6147 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.6147 - accuracy: 0.6917 Epoch 81/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5808 - accuracy: 0.7000 6/6 [==============================] - 0s 648us/step - loss: 0.6131 - accuracy: 0.6917  6/6 [==============================] - 0s 47ms/step - loss: 0.6131 - accuracy: 0.6917 Epoch 82/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5839 - accuracy: 0.7000 6/6 [==============================] - 0s 2ms/step - loss: 0.6115 - accuracy: 0.6917  6/6 [==============================] - 0s 22ms/step - loss: 0.6115 - accuracy: 0.6917 Epoch 83/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6340 - accuracy: 0.6000 6/6 [==============================] - 0s 532us/step - loss: 0.6100 - accuracy: 0.6917  6/6 [==============================] - 0s 18ms/step - loss: 0.6100 - accuracy: 0.6917 Epoch 84/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6165 - accuracy: 0.7500 6/6 [==============================] - 0s 3ms/step - loss: 0.6085 - accuracy: 0.6917  6/6 [==============================] - 0s 23ms/step - loss: 0.6085 - accuracy: 0.6917 Epoch 85/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6126 - accuracy: 0.7500 6/6 [==============================] - 0s 1ms/step - loss: 0.6070 - accuracy: 0.6917  6/6 [==============================] - 0s 20ms/step - loss: 0.6070 - accuracy: 0.6917 Epoch 86/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5778 - accuracy: 0.7000 6/6 [==============================] - 0s 777us/step - loss: 0.6056 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.6056 - accuracy: 0.6917 Epoch 87/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5722 - accuracy: 0.7500 6/6 [==============================] - 0s 588us/step - loss: 0.6042 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.6042 - accuracy: 0.6917 Epoch 88/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.7331 - accuracy: 0.7500 6/6 [==============================] - 0s 2ms/step - loss: 0.6029 - accuracy: 0.6917  6/6 [==============================] - 0s 23ms/step - loss: 0.6029 - accuracy: 0.6917 Epoch 89/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5736 - accuracy: 0.7000 6/6 [==============================] - 0s 1ms/step - loss: 0.6015 - accuracy: 0.6917  6/6 [==============================] - 0s 20ms/step - loss: 0.6015 - accuracy: 0.6917 Epoch 90/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5786 - accuracy: 0.6500 6/6 [==============================] - 0s 1ms/step - loss: 0.6002 - accuracy: 0.6917  6/6 [==============================] - 0s 20ms/step - loss: 0.6002 - accuracy: 0.6917 Epoch 91/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6256 - accuracy: 0.6000 6/6 [==============================] - 0s 2ms/step - loss: 0.5988 - accuracy: 0.6917  6/6 [==============================] - 0s 22ms/step - loss: 0.5988 - accuracy: 0.6917 Epoch 92/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6103 - accuracy: 0.7000 6/6 [==============================] - 0s 1ms/step - loss: 0.5975 - accuracy: 0.6917  6/6 [==============================] - 0s 20ms/step - loss: 0.5975 - accuracy: 0.6917 Epoch 93/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.7444 - accuracy: 0.6000 6/6 [==============================] - 0s 1ms/step - loss: 0.5963 - accuracy: 0.6917  6/6 [==============================] - 0s 20ms/step - loss: 0.5963 - accuracy: 0.6917 Epoch 94/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6956 - accuracy: 0.6500 6/6 [==============================] - 0s 717us/step - loss: 0.5950 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5950 - accuracy: 0.6917 Epoch 95/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5259 - accuracy: 0.7000 6/6 [==============================] - 0s 700us/step - loss: 0.5938 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5938 - accuracy: 0.6917 Epoch 96/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.7107 - accuracy: 0.5000 6/6 [==============================] - 0s 1ms/step - loss: 0.5926 - accuracy: 0.6917  6/6 [==============================] - 0s 20ms/step - loss: 0.5926 - accuracy: 0.6917 Epoch 97/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.4441 - accuracy: 0.7000 6/6 [==============================] - 0s 902us/step - loss: 0.5914 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5914 - accuracy: 0.6917 Epoch 98/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5224 - accuracy: 0.7000 6/6 [==============================] - 0s 1ms/step - loss: 0.5903 - accuracy: 0.6917  6/6 [==============================] - 0s 20ms/step - loss: 0.5903 - accuracy: 0.6917 Epoch 99/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6222 - accuracy: 0.8500 6/6 [==============================] - 0s 962us/step - loss: 0.5892 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5892 - accuracy: 0.6917 Epoch 100/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.7337 - accuracy: 0.6000 6/6 [==============================] - 0s 2ms/step - loss: 0.5881 - accuracy: 0.6917  6/6 [==============================] - 0s 23ms/step - loss: 0.5881 - accuracy: 0.6917 Epoch 101/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5195 - accuracy: 0.7000 6/6 [==============================] - 0s 718us/step - loss: 0.5871 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5871 - accuracy: 0.6917 Epoch 102/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6514 - accuracy: 0.6000 6/6 [==============================] - 0s 656us/step - loss: 0.5861 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5861 - accuracy: 0.6917 Epoch 103/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5375 - accuracy: 0.8500 6/6 [==============================] - 0s 596us/step - loss: 0.5851 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5851 - accuracy: 0.6917 Epoch 104/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.3722 - accuracy: 0.9000 6/6 [==============================] - 0s 586us/step - loss: 0.5841 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5841 - accuracy: 0.6917 Epoch 105/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6097 - accuracy: 0.6000 6/6 [==============================] - 0s 1ms/step - loss: 0.5832 - accuracy: 0.6917  6/6 [==============================] - 0s 21ms/step - loss: 0.5832 - accuracy: 0.6917 Epoch 106/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6803 - accuracy: 0.6500 6/6 [==============================] - 0s 944us/step - loss: 0.5822 - accuracy: 0.6917  6/6 [==============================] - 0s 20ms/step - loss: 0.5822 - accuracy: 0.6917 Epoch 107/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6398 - accuracy: 0.6500 6/6 [==============================] - 0s 861us/step - loss: 0.5814 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5814 - accuracy: 0.6917 Epoch 108/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5460 - accuracy: 0.7500 6/6 [==============================] - 0s 1ms/step - loss: 0.5803 - accuracy: 0.6917  6/6 [==============================] - 0s 20ms/step - loss: 0.5803 - accuracy: 0.6917 Epoch 109/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5922 - accuracy: 0.7000 6/6 [==============================] - 0s 805us/step - loss: 0.5794 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5794 - accuracy: 0.6917 Epoch 110/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6299 - accuracy: 0.7000 6/6 [==============================] - 0s 1ms/step - loss: 0.5784 - accuracy: 0.6917  6/6 [==============================] - 0s 20ms/step - loss: 0.5784 - accuracy: 0.6917 Epoch 111/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.7738 - accuracy: 0.5000 6/6 [==============================] - 0s 678us/step - loss: 0.5775 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5775 - accuracy: 0.6917 Epoch 112/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6669 - accuracy: 0.7000 6/6 [==============================] - 0s 587us/step - loss: 0.5765 - accuracy: 0.6917  6/6 [==============================] - 0s 18ms/step - loss: 0.5765 - accuracy: 0.6917 Epoch 113/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.4115 - accuracy: 0.8500 6/6 [==============================] - 0s 2ms/step - loss: 0.5756 - accuracy: 0.6917  6/6 [==============================] - 0s 22ms/step - loss: 0.5756 - accuracy: 0.6917 Epoch 114/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5871 - accuracy: 0.7000 6/6 [==============================] - 0s 697us/step - loss: 0.5746 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5746 - accuracy: 0.6917 Epoch 115/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5014 - accuracy: 0.7500 6/6 [==============================] - 0s 2ms/step - loss: 0.5737 - accuracy: 0.6917  6/6 [==============================] - 0s 22ms/step - loss: 0.5737 - accuracy: 0.6917 Epoch 116/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6300 - accuracy: 0.6500 6/6 [==============================] - 0s 657us/step - loss: 0.5727 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5727 - accuracy: 0.6917 Epoch 117/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.4466 - accuracy: 0.8500 6/6 [==============================] - 0s 3ms/step - loss: 0.5718 - accuracy: 0.6917  6/6 [==============================] - 0s 24ms/step - loss: 0.5718 - accuracy: 0.6917 Epoch 118/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5294 - accuracy: 0.8000 6/6 [==============================] - 0s 2ms/step - loss: 0.5710 - accuracy: 0.6917  6/6 [==============================] - 0s 22ms/step - loss: 0.5710 - accuracy: 0.6917 Epoch 119/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5448 - accuracy: 0.7000 6/6 [==============================] - 0s 702us/step - loss: 0.5700 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5700 - accuracy: 0.6917 Epoch 120/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6877 - accuracy: 0.7500 6/6 [==============================] - 0s 584us/step - loss: 0.5691 - accuracy: 0.6917  6/6 [==============================] - 0s 18ms/step - loss: 0.5691 - accuracy: 0.6917 Epoch 121/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6489 - accuracy: 0.7500 6/6 [==============================] - 0s 765us/step - loss: 0.5682 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5682 - accuracy: 0.6917 Epoch 122/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5650 - accuracy: 0.5500 6/6 [==============================] - 0s 1ms/step - loss: 0.5674 - accuracy: 0.6917  6/6 [==============================] - 0s 20ms/step - loss: 0.5674 - accuracy: 0.6917 Epoch 123/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6406 - accuracy: 0.5500 6/6 [==============================] - 0s 687us/step - loss: 0.5665 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5665 - accuracy: 0.6917 Epoch 124/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6143 - accuracy: 0.7000 6/6 [==============================] - 0s 989us/step - loss: 0.5655 - accuracy: 0.6917  6/6 [==============================] - 0s 20ms/step - loss: 0.5655 - accuracy: 0.6917 Epoch 125/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6083 - accuracy: 0.7500 6/6 [==============================] - 0s 848us/step - loss: 0.5646 - accuracy: 0.6917  6/6 [==============================] - 0s 20ms/step - loss: 0.5646 - accuracy: 0.6917 Epoch 126/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6228 - accuracy: 0.6500 6/6 [==============================] - 0s 588us/step - loss: 0.5637 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5637 - accuracy: 0.6917 Epoch 127/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5465 - accuracy: 0.6500 6/6 [==============================] - 0s 612us/step - loss: 0.5628 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5628 - accuracy: 0.6917 Epoch 128/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5298 - accuracy: 0.7500 6/6 [==============================] - 0s 570us/step - loss: 0.5619 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5619 - accuracy: 0.6917 Epoch 129/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.4281 - accuracy: 0.8500 6/6 [==============================] - 0s 935us/step - loss: 0.5611 - accuracy: 0.6917  6/6 [==============================] - 0s 20ms/step - loss: 0.5611 - accuracy: 0.6917 Epoch 130/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6695 - accuracy: 0.5500 6/6 [==============================] - 0s 601us/step - loss: 0.5601 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5601 - accuracy: 0.6917 Epoch 131/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5811 - accuracy: 0.6500 6/6 [==============================] - 0s 962us/step - loss: 0.5593 - accuracy: 0.6917  6/6 [==============================] - 0s 20ms/step - loss: 0.5593 - accuracy: 0.6917 Epoch 132/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6018 - accuracy: 0.7500 6/6 [==============================] - 0s 689us/step - loss: 0.5584 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5584 - accuracy: 0.6917 Epoch 133/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6543 - accuracy: 0.6500 6/6 [==============================] - 0s 1ms/step - loss: 0.5575 - accuracy: 0.6917  6/6 [==============================] - 0s 21ms/step - loss: 0.5575 - accuracy: 0.6917 Epoch 134/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.4383 - accuracy: 0.8000 6/6 [==============================] - 0s 693us/step - loss: 0.5565 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5565 - accuracy: 0.6917 Epoch 135/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6316 - accuracy: 0.7500 6/6 [==============================] - 0s 974us/step - loss: 0.5556 - accuracy: 0.6917  6/6 [==============================] - 0s 20ms/step - loss: 0.5556 - accuracy: 0.6917 Epoch 136/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5152 - accuracy: 0.8000 6/6 [==============================] - 0s 623us/step - loss: 0.5547 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5547 - accuracy: 0.6917 Epoch 137/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6035 - accuracy: 0.6500 6/6 [==============================] - 0s 615us/step - loss: 0.5538 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5538 - accuracy: 0.6917 Epoch 138/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5472 - accuracy: 0.6000 6/6 [==============================] - 0s 1ms/step - loss: 0.5529 - accuracy: 0.6917  6/6 [==============================] - 0s 21ms/step - loss: 0.5529 - accuracy: 0.6917 Epoch 139/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6686 - accuracy: 0.5000 6/6 [==============================] - 0s 809us/step - loss: 0.5520 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5520 - accuracy: 0.6917 Epoch 140/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5201 - accuracy: 0.5000 6/6 [==============================] - 0s 1ms/step - loss: 0.5511 - accuracy: 0.6917  6/6 [==============================] - 0s 21ms/step - loss: 0.5511 - accuracy: 0.6917 Epoch 141/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6928 - accuracy: 0.6000 6/6 [==============================] - 0s 720us/step - loss: 0.5501 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5501 - accuracy: 0.6917 Epoch 142/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5260 - accuracy: 0.8500 6/6 [==============================] - 0s 1ms/step - loss: 0.5494 - accuracy: 0.6917  6/6 [==============================] - 0s 20ms/step - loss: 0.5494 - accuracy: 0.6917 Epoch 143/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6178 - accuracy: 0.6000 6/6 [==============================] - 0s 737us/step - loss: 0.5484 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5484 - accuracy: 0.6917 Epoch 144/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.4089 - accuracy: 0.7000 6/6 [==============================] - 0s 668us/step - loss: 0.5475 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5475 - accuracy: 0.6917 Epoch 145/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5860 - accuracy: 0.5500 6/6 [==============================] - 0s 610us/step - loss: 0.5465 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5465 - accuracy: 0.6917 Epoch 146/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5515 - accuracy: 0.7500 6/6 [==============================] - 0s 675us/step - loss: 0.5456 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5456 - accuracy: 0.6917 Epoch 147/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5169 - accuracy: 0.7500 6/6 [==============================] - 0s 654us/step - loss: 0.5446 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5446 - accuracy: 0.6917 Epoch 148/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5611 - accuracy: 0.7000 6/6 [==============================] - 0s 598us/step - loss: 0.5436 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5436 - accuracy: 0.6917 Epoch 149/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.4876 - accuracy: 0.7000 6/6 [==============================] - 0s 838us/step - loss: 0.5427 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5427 - accuracy: 0.6917 Epoch 150/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5597 - accuracy: 0.6500 6/6 [==============================] - 0s 1ms/step - loss: 0.5418 - accuracy: 0.6917  6/6 [==============================] - 0s 20ms/step - loss: 0.5418 - accuracy: 0.6917 Epoch 151/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6531 - accuracy: 0.6500 6/6 [==============================] - 0s 904us/step - loss: 0.5408 - accuracy: 0.6917  6/6 [==============================] - 0s 20ms/step - loss: 0.5408 - accuracy: 0.6917 Epoch 152/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.4758 - accuracy: 0.7500 6/6 [==============================] - 0s 804us/step - loss: 0.5399 - accuracy: 0.6917  6/6 [==============================] - 0s 21ms/step - loss: 0.5399 - accuracy: 0.6917 Epoch 153/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6072 - accuracy: 0.5500 6/6 [==============================] - 0s 645us/step - loss: 0.5389 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5389 - accuracy: 0.6917 Epoch 154/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5891 - accuracy: 0.7000 6/6 [==============================] - 0s 605us/step - loss: 0.5379 - accuracy: 0.6917  6/6 [==============================] - 0s 18ms/step - loss: 0.5379 - accuracy: 0.6917 Epoch 155/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5705 - accuracy: 0.6000 6/6 [==============================] - 0s 2ms/step - loss: 0.5368 - accuracy: 0.6917  6/6 [==============================] - 0s 22ms/step - loss: 0.5368 - accuracy: 0.6917 Epoch 156/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5169 - accuracy: 0.7000 6/6 [==============================] - 0s 830us/step - loss: 0.5357 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5357 - accuracy: 0.6917 Epoch 157/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.4144 - accuracy: 0.7500 6/6 [==============================] - 0s 926us/step - loss: 0.5346 - accuracy: 0.6917  6/6 [==============================] - 0s 20ms/step - loss: 0.5346 - accuracy: 0.6917 Epoch 158/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5302 - accuracy: 0.6000 6/6 [==============================] - 0s 677us/step - loss: 0.5336 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5336 - accuracy: 0.6917 Epoch 159/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5873 - accuracy: 0.6500 6/6 [==============================] - 0s 713us/step - loss: 0.5325 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5325 - accuracy: 0.6917 Epoch 160/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5733 - accuracy: 0.7500 6/6 [==============================] - 0s 598us/step - loss: 0.5314 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5314 - accuracy: 0.6917 Epoch 161/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.4812 - accuracy: 0.7000 6/6 [==============================] - 0s 721us/step - loss: 0.5303 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5303 - accuracy: 0.6917 Epoch 162/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6660 - accuracy: 0.5500 6/6 [==============================] - 0s 591us/step - loss: 0.5292 - accuracy: 0.6917  6/6 [==============================] - 0s 18ms/step - loss: 0.5292 - accuracy: 0.6917 Epoch 163/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5114 - accuracy: 0.7000 6/6 [==============================] - 0s 579us/step - loss: 0.5281 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5281 - accuracy: 0.6917 Epoch 164/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6130 - accuracy: 0.6000 6/6 [==============================] - 0s 597us/step - loss: 0.5270 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5270 - accuracy: 0.6917 Epoch 165/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5206 - accuracy: 0.6500 6/6 [==============================] - 0s 814us/step - loss: 0.5259 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5259 - accuracy: 0.6917 Epoch 166/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5998 - accuracy: 0.7500 6/6 [==============================] - 0s 1ms/step - loss: 0.5248 - accuracy: 0.6917  6/6 [==============================] - 0s 20ms/step - loss: 0.5248 - accuracy: 0.6917 Epoch 167/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5556 - accuracy: 0.5500 6/6 [==============================] - 0s 840us/step - loss: 0.5236 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5236 - accuracy: 0.6917 Epoch 168/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.4537 - accuracy: 0.6000 6/6 [==============================] - 0s 744us/step - loss: 0.5224 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5224 - accuracy: 0.6917 Epoch 169/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5130 - accuracy: 0.7000 6/6 [==============================] - 0s 2ms/step - loss: 0.5213 - accuracy: 0.6917  6/6 [==============================] - 0s 22ms/step - loss: 0.5213 - accuracy: 0.6917 Epoch 170/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5373 - accuracy: 0.6000 6/6 [==============================] - 0s 678us/step - loss: 0.5201 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5201 - accuracy: 0.6917 Epoch 171/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.4222 - accuracy: 0.7500 6/6 [==============================] - 0s 568us/step - loss: 0.5189 - accuracy: 0.6917  6/6 [==============================] - 0s 43ms/step - loss: 0.5189 - accuracy: 0.6917 Epoch 172/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5436 - accuracy: 0.7000 6/6 [==============================] - 0s 3ms/step - loss: 0.5178 - accuracy: 0.6917  6/6 [==============================] - 0s 23ms/step - loss: 0.5178 - accuracy: 0.6917 Epoch 173/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5871 - accuracy: 0.6000 6/6 [==============================] - 0s 888us/step - loss: 0.5166 - accuracy: 0.6917  6/6 [==============================] - 0s 20ms/step - loss: 0.5166 - accuracy: 0.6917 Epoch 174/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5113 - accuracy: 0.7000 6/6 [==============================] - 0s 658us/step - loss: 0.5154 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5154 - accuracy: 0.6917 Epoch 175/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.3877 - accuracy: 0.8500 6/6 [==============================] - 0s 757us/step - loss: 0.5142 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5142 - accuracy: 0.6917 Epoch 176/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5053 - accuracy: 0.6000 6/6 [==============================] - 0s 839us/step - loss: 0.5130 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5130 - accuracy: 0.6917 Epoch 177/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.4341 - accuracy: 0.8000 6/6 [==============================] - 0s 634us/step - loss: 0.5116 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5116 - accuracy: 0.6917 Epoch 178/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.4135 - accuracy: 0.7000 6/6 [==============================] - 0s 603us/step - loss: 0.5103 - accuracy: 0.6917  6/6 [==============================] - 0s 18ms/step - loss: 0.5103 - accuracy: 0.6917 Epoch 179/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.4641 - accuracy: 0.6500 6/6 [==============================] - 0s 563us/step - loss: 0.5091 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5091 - accuracy: 0.6917 Epoch 180/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.4914 - accuracy: 0.7500 6/6 [==============================] - 0s 1ms/step - loss: 0.5077 - accuracy: 0.6917  6/6 [==============================] - 0s 21ms/step - loss: 0.5077 - accuracy: 0.6917 Epoch 181/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.4681 - accuracy: 0.6500 6/6 [==============================] - 0s 572us/step - loss: 0.5065 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5065 - accuracy: 0.6917 Epoch 182/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.4002 - accuracy: 0.7000 6/6 [==============================] - 0s 738us/step - loss: 0.5053 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5053 - accuracy: 0.6917 Epoch 183/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.3638 - accuracy: 0.8500 6/6 [==============================] - 0s 618us/step - loss: 0.5040 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5040 - accuracy: 0.6917 Epoch 184/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.4062 - accuracy: 0.8500 6/6 [==============================] - 0s 644us/step - loss: 0.5029 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5029 - accuracy: 0.6917 Epoch 185/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5250 - accuracy: 0.6000 6/6 [==============================] - 0s 662us/step - loss: 0.5017 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5017 - accuracy: 0.6917 Epoch 186/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6363 - accuracy: 0.5500 6/6 [==============================] - 0s 654us/step - loss: 0.5003 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.5003 - accuracy: 0.6917 Epoch 187/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.3830 - accuracy: 0.7500 6/6 [==============================] - 0s 555us/step - loss: 0.4990 - accuracy: 0.6917  6/6 [==============================] - 0s 18ms/step - loss: 0.4990 - accuracy: 0.6917 Epoch 188/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5892 - accuracy: 0.6000 6/6 [==============================] - 0s 1ms/step - loss: 0.4976 - accuracy: 0.6917  6/6 [==============================] - 0s 20ms/step - loss: 0.4976 - accuracy: 0.6917 Epoch 189/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.4722 - accuracy: 0.7500 6/6 [==============================] - 0s 628us/step - loss: 0.4962 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.4962 - accuracy: 0.6917 Epoch 190/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.4150 - accuracy: 0.8000 6/6 [==============================] - 0s 592us/step - loss: 0.4948 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.4948 - accuracy: 0.6917 Epoch 191/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.4167 - accuracy: 0.8000 6/6 [==============================] - 0s 858us/step - loss: 0.4933 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.4933 - accuracy: 0.6917 Epoch 192/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5952 - accuracy: 0.6000 6/6 [==============================] - 0s 672us/step - loss: 0.4918 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.4918 - accuracy: 0.6917 Epoch 193/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.4614 - accuracy: 0.7500 6/6 [==============================] - 0s 577us/step - loss: 0.4901 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.4901 - accuracy: 0.6917 Epoch 194/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6146 - accuracy: 0.6000 6/6 [==============================] - 0s 625us/step - loss: 0.4885 - accuracy: 0.6917  6/6 [==============================] - 0s 18ms/step - loss: 0.4885 - accuracy: 0.6917 Epoch 195/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5188 - accuracy: 0.6500 6/6 [==============================] - 0s 702us/step - loss: 0.4870 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.4870 - accuracy: 0.6917 Epoch 196/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.4562 - accuracy: 0.7500 6/6 [==============================] - 0s 615us/step - loss: 0.4854 - accuracy: 0.6917  6/6 [==============================] - 0s 18ms/step - loss: 0.4854 - accuracy: 0.6917 Epoch 197/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5204 - accuracy: 0.6500 6/6 [==============================] - 0s 683us/step - loss: 0.4839 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.4839 - accuracy: 0.6917 Epoch 198/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.3824 - accuracy: 0.7500 6/6 [==============================] - 0s 603us/step - loss: 0.4825 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.4825 - accuracy: 0.6917 Epoch 199/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.5074 - accuracy: 0.6000 6/6 [==============================] - 0s 859us/step - loss: 0.4811 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.4811 - accuracy: 0.6917 Epoch 200/200

1/6 [====>…………………….] - ETA: 0s - loss: 0.6141 - accuracy: 0.5500 6/6 [==============================] - 0s 657us/step - loss: 0.4797 - accuracy: 0.6917  6/6 [==============================] - 0s 19ms/step - loss: 0.4797 - accuracy: 0.6917

<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucGxvdChoaXN0b3J5KSArXG4gIGdndGl0bGUoXFxUcmFpbmluZyBhIG5ldXJhbCBuZXR3b3JrIGJhc2VkIGNsYXNzaWZpZXIgb24gdGhlIGlyaXMgZGF0YSBzZXRcXCkgK1xuICB0aGVtZV9idygpXG5gYGBcbmBgYCJ9 -->

```r
```r
plot(history) +
  ggtitle(\Training a neural network based classifier on the iris data set\) +
  theme_bw()

<!-- rnb-source-end -->

<!-- rnb-plot-begin -->

<img 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" />

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### Evaluate Network Performance

The final performance can be obtained like so:


<!-- rnb-text-end -->


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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucGVyZiA8LSBtb2RlbCAlPiUgZXZhbHVhdGUoeF90ZXN0LCB5X3Rlc3QpXG5gYGBcbmBgYCJ9 -->

```r
```r
perf <- model %>% evaluate(x_test, y_test)

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG4xLzEgWz09PT09PT09PT09PT09PT09PT09PT09PT09PT09PV0gLSAwcyAxMHVzL3N0ZXAgLSBsb3NzOiAwLjMwODggLSBhY2N1cmFjeTogMC44NTAwXG5cYlxiXGJcYlxiXGJcYlxiXGJcYlxiXGJcYlxiXGJcYlxiXGJcYlxiXGJcYlxiXGJcYlxiXGJcYlxiXGJcYlxiXGJcYlxiXGJcYlxiXGJcYlxiXGJcYlxiXGJcYlxiXGJcYlxiXGJcYlxiXGJcYlxiXGJcYlxiXGJcYlxiXGJcYlxiXGJcYlxiXGJcYlxiXGJcYlxiXGJcYlxiXGJcYlxiXGJcYlxiXGJcYlxuMS8xIFs9PT09PT09PT09PT09PT09PT09PT09PT09PT09PT1dIC0gMHMgMjIwdXMvc3RlcCAtIGxvc3M6IDAuMzA4OCAtIGFjY3VyYWN5OiAwLjg1MDBcbiJ9 -->

1/1 [==============================] - 0s 10us/step - loss: 0.3088 - accuracy: 0.8500  1/1 [==============================] - 0s 220us/step - loss: 0.3088 - accuracy: 0.8500

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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucHJpbnQocGVyZilcbmBgYFxuYGBgIn0= -->

```r
```r
print(perf)

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiICAgICBsb3NzICBhY2N1cmFjeSBcbjAuMzA4NzYzMSAwLjg1MDAwMDAgXG4ifQ== -->

 loss  accuracy 

0.3087631 0.8500000

<!-- rnb-output-end -->

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<!-- rnb-text-begin -->



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```r
```r
classes <- iris %>% as_tibble %>% pull(Species) %>% unique
y_pred  <- model %>% predict_classes(x_test)
y_true  <- nn_dat %>% pull(class_label) %>% .[test_indices]

tibble(y_true = classes[y_true + 1], y_pred = classes[y_pred + 1],
       Correct = ifelse(y_true == y_pred, \Yes\, \No\) %>% factor) %>% 
  ggplot(aes(x = y_true, y = y_pred, colour = Correct)) +
  geom_jitter() +
  theme_bw() +
  ggtitle(label = \Classification Performance of Artificial Neural Network\,
          subtitle = str_c(\Accuracy = \,round(perf[2],3)*100,\%\)) +
  xlab(label = \True iris class\) +
  ylab(label = \Predicted iris class\)

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/>

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```r
```r
library(gmodels)

CrossTable(y_pred, y_true,
           prop.chisq = FALSE, prop.t = FALSE, prop.r = FALSE,
           dnn = c('predicted', 'actual'))

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Cell Contents |————————-| | N | | N / Col Total | |————————-|

Total Observations in Table: 20

         | actual 

predicted	0	1	2	Row Total
0	12	0	0	12
1.000	0.000	0.000
————-	———–	———–	———–	———–
1	0	5	3	8
0.000	1.000	1.000
————-	———–	———–	———–	———–
Column Total	12	5	3	20
0.600	0.250	0.150
————-	———–	———–	———–	———–

```

Conclusion

I hope this illustrated just how easy it is to get started building artificial neural network using Keras and TensorFlow in R. With relative ease, we created a 3-class predictor with an accuracy of 100%. This was a basic minimal example. The network can be expanded to create Deep Learning networks and also the entire TensorFlow API is available.

Enjoy and Happy Learning!

Leon

Thanks again Leon, this was awsome!!!