Feed-Forward Neural Network for the Google Tensorflow Playground XOR Data
Clone the TFPlayground Github repository into your R Project folder.
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Click the Green button and copy the ulr: https://github.com/hyounesy/TFPlaygroundPSA.git
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Use the data in ../data/tiny/xor_25/input.txt to create a feed-forward neural network to classify the data. Use the keras package to create the model.
Load the required libraries
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ ggplot2 3.5.1 ✔ tibble 3.2.1
✔ lubridate 1.9.4 ✔ tidyr 1.3.1
✔ purrr 1.0.4
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
── Attaching packages ────────────────────────────────────── tidymodels 1.3.0 ──
✔ broom 1.0.7 ✔ rsample 1.2.1.9000
✔ dials 1.4.0.9000 ✔ tune 1.3.0.9000
✔ infer 1.0.7 ✔ workflows 1.2.0.9000
✔ modeldata 1.4.0 ✔ workflowsets 1.1.0
✔ parsnip 1.3.0.9000 ✔ yardstick 1.3.2
✔ recipes 1.1.1.9000
── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ──
✖ scales::discard() masks purrr::discard()
✖ dplyr::filter() masks stats::filter()
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✖ recipes::step() masks stats::step()
library (readr)library (janitor)
Attaching package: 'janitor'
The following objects are masked from 'package:stats':
chisq.test, fisher.test
library (forcats)library (keras)
Attaching package: 'keras'
The following object is masked from 'package:yardstick':
get_weights
Load the data
<- read_delim ("data/tiny/xor_25/input.txt" , delim = " \t " , escape_double = FALSE , trim_ws = TRUE )
Rows: 200 Columns: 9
── Column specification ────────────────────────────────────────────────────────
Delimiter: "\t"
dbl (9): pid, X1, X2, X1Squared, X2Squared, X1X2, sinX1, sinX2, label
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
<- input |> clean_names () |> mutate (label = if_else (label == - 1 , 0 , 1 ) ) |> # or use 0L, !L mutate (label = as.integer (label)) |> tibble ()head (input)
# A tibble: 6 × 9
pid x1 x2 x1squared x2squared x1x2 sin_x1 sin_x2 label
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
1 0 -2.82 2.52 7.96 6.33 -7.10 -0.314 0.586 0
2 1 3.49 1.19 12.2 1.43 4.17 -0.342 0.930 1
3 2 -3.78 -2.68 14.3 7.20 10.1 0.592 -0.442 1
4 3 2.22 4.14 4.92 17.1 9.18 0.798 -0.841 1
5 4 3.64 0.970 13.2 0.941 3.53 -0.474 0.825 1
6 5 2.22 0.788 4.95 0.621 1.75 0.794 0.709 1
Split the data into training and testing sets
<- nrow (input)<- input |> initial_split (prop = 0.8 )<- input_parts |> training ()<- input_parts |> testing ()list (train, test) |> map_int (nrow)
Visualize the data
|> ggplot (aes (x = x1, y = x2, color = factor (label))) + geom_point ()
|> ggplot (aes (x = x1, y = x2, color = factor (label))) + geom_point ()
Using keras and tensorflow
Note that the functions in the keras package are expecting the data to be in a matrix object and not a tibble. So as.matrix is added at the end of each line.
Do not forget to remove the ID variable pid.
<- train %>% select (- pid, - label) |> select (x1, x2) |> as.matrix ()<- train %>% select (label) %>% as.matrix () %>% to_categorical () <- test %>% select (- pid, - label) |> select (x1, x2) |> as.matrix ()<- test %>% select (label) %>% as.matrix () %>% to_categorical () dim (x_train)
Set Architecture
With the data in place, we now set the architecture of our neural network.
keras activation
<- keras_model_sequential ()|> layer_dense (units = 8 , activation = 'relu' , input_shape = 2 ) |> layer_dense (units = 3 , activation = 'relu' ) |> layer_dense (units = 2 , activation = 'softmax' )|> summary ()
Model: "sequential"
________________________________________________________________________________
Layer (type) Output Shape Param #
================================================================================
dense_2 (Dense) (None, 8) 24
dense_1 (Dense) (None, 3) 27
dense (Dense) (None, 2) 8
================================================================================
Total params: 59
Trainable params: 59
Non-trainable params: 0
________________________________________________________________________________
Next, the architecture set in the model needs to be compiled.
%>% compile (optimizer = "rmsprop" ,loss = "binary_crossentropy" ,metrics = "accuracy"
Train the Artificial Neural Network
Lastly we fit the model and save the training progress in the history object.
Try changing the validation_split from 0 to 0.2 to see the validation_loss .
<- model %>% fit (x = x_train, y = y_train,epochs = 400 ,batch_size = 20 ,validation_split = 0.2
Epoch 1/400
7/7 - 1s - loss: 0.8102 - accuracy: 0.4297 - val_loss: 0.6826 - val_accuracy: 0.6250 - 1s/epoch - 181ms/step
Epoch 2/400
7/7 - 0s - loss: 0.7814 - accuracy: 0.4844 - val_loss: 0.6769 - val_accuracy: 0.5938 - 52ms/epoch - 7ms/step
Epoch 3/400
7/7 - 0s - loss: 0.7616 - accuracy: 0.5312 - val_loss: 0.6735 - val_accuracy: 0.6250 - 38ms/epoch - 5ms/step
Epoch 4/400
7/7 - 0s - loss: 0.7459 - accuracy: 0.5469 - val_loss: 0.6708 - val_accuracy: 0.6250 - 36ms/epoch - 5ms/step
Epoch 5/400
7/7 - 0s - loss: 0.7330 - accuracy: 0.5859 - val_loss: 0.6685 - val_accuracy: 0.6250 - 36ms/epoch - 5ms/step
Epoch 6/400
7/7 - 0s - loss: 0.7194 - accuracy: 0.6406 - val_loss: 0.6661 - val_accuracy: 0.6562 - 35ms/epoch - 5ms/step
Epoch 7/400
7/7 - 0s - loss: 0.7073 - accuracy: 0.6797 - val_loss: 0.6641 - val_accuracy: 0.6250 - 33ms/epoch - 5ms/step
Epoch 8/400
7/7 - 0s - loss: 0.6978 - accuracy: 0.7109 - val_loss: 0.6630 - val_accuracy: 0.6250 - 33ms/epoch - 5ms/step
Epoch 9/400
7/7 - 0s - loss: 0.6894 - accuracy: 0.7266 - val_loss: 0.6627 - val_accuracy: 0.5938 - 33ms/epoch - 5ms/step
Epoch 10/400
7/7 - 0s - loss: 0.6824 - accuracy: 0.7578 - val_loss: 0.6613 - val_accuracy: 0.5938 - 33ms/epoch - 5ms/step
Epoch 11/400
7/7 - 0s - loss: 0.6768 - accuracy: 0.7656 - val_loss: 0.6602 - val_accuracy: 0.6250 - 32ms/epoch - 5ms/step
Epoch 12/400
7/7 - 0s - loss: 0.6720 - accuracy: 0.8203 - val_loss: 0.6591 - val_accuracy: 0.6562 - 32ms/epoch - 5ms/step
Epoch 13/400
7/7 - 0s - loss: 0.6678 - accuracy: 0.8359 - val_loss: 0.6578 - val_accuracy: 0.6875 - 33ms/epoch - 5ms/step
Epoch 14/400
7/7 - 0s - loss: 0.6644 - accuracy: 0.8438 - val_loss: 0.6554 - val_accuracy: 0.6875 - 33ms/epoch - 5ms/step
Epoch 15/400
7/7 - 0s - loss: 0.6621 - accuracy: 0.8438 - val_loss: 0.6540 - val_accuracy: 0.6875 - 32ms/epoch - 5ms/step
Epoch 16/400
7/7 - 0s - loss: 0.6595 - accuracy: 0.8516 - val_loss: 0.6515 - val_accuracy: 0.7188 - 34ms/epoch - 5ms/step
Epoch 17/400
7/7 - 0s - loss: 0.6570 - accuracy: 0.8516 - val_loss: 0.6491 - val_accuracy: 0.7188 - 33ms/epoch - 5ms/step
Epoch 18/400
7/7 - 0s - loss: 0.6543 - accuracy: 0.8594 - val_loss: 0.6467 - val_accuracy: 0.7188 - 33ms/epoch - 5ms/step
Epoch 19/400
7/7 - 0s - loss: 0.6516 - accuracy: 0.8594 - val_loss: 0.6441 - val_accuracy: 0.7500 - 35ms/epoch - 5ms/step
Epoch 20/400
7/7 - 0s - loss: 0.6493 - accuracy: 0.8594 - val_loss: 0.6414 - val_accuracy: 0.7500 - 34ms/epoch - 5ms/step
Epoch 21/400
7/7 - 0s - loss: 0.6468 - accuracy: 0.8594 - val_loss: 0.6379 - val_accuracy: 0.7812 - 33ms/epoch - 5ms/step
Epoch 22/400
7/7 - 0s - loss: 0.6441 - accuracy: 0.8672 - val_loss: 0.6342 - val_accuracy: 0.8125 - 34ms/epoch - 5ms/step
Epoch 23/400
7/7 - 0s - loss: 0.6415 - accuracy: 0.8672 - val_loss: 0.6295 - val_accuracy: 0.8125 - 33ms/epoch - 5ms/step
Epoch 24/400
7/7 - 0s - loss: 0.6386 - accuracy: 0.8672 - val_loss: 0.6251 - val_accuracy: 0.8438 - 33ms/epoch - 5ms/step
Epoch 25/400
7/7 - 0s - loss: 0.6361 - accuracy: 0.8750 - val_loss: 0.6215 - val_accuracy: 0.8438 - 32ms/epoch - 5ms/step
Epoch 26/400
7/7 - 0s - loss: 0.6334 - accuracy: 0.8828 - val_loss: 0.6182 - val_accuracy: 0.8438 - 34ms/epoch - 5ms/step
Epoch 27/400
7/7 - 0s - loss: 0.6308 - accuracy: 0.8828 - val_loss: 0.6134 - val_accuracy: 0.8438 - 33ms/epoch - 5ms/step
Epoch 28/400
7/7 - 0s - loss: 0.6278 - accuracy: 0.8906 - val_loss: 0.6099 - val_accuracy: 0.8438 - 33ms/epoch - 5ms/step
Epoch 29/400
7/7 - 0s - loss: 0.6250 - accuracy: 0.8906 - val_loss: 0.6062 - val_accuracy: 0.8438 - 33ms/epoch - 5ms/step
Epoch 30/400
7/7 - 0s - loss: 0.6224 - accuracy: 0.8984 - val_loss: 0.6022 - val_accuracy: 0.8750 - 33ms/epoch - 5ms/step
Epoch 31/400
7/7 - 0s - loss: 0.6196 - accuracy: 0.9141 - val_loss: 0.5987 - val_accuracy: 0.8750 - 33ms/epoch - 5ms/step
Epoch 32/400
7/7 - 0s - loss: 0.6170 - accuracy: 0.9141 - val_loss: 0.5947 - val_accuracy: 0.8750 - 33ms/epoch - 5ms/step
Epoch 33/400
7/7 - 0s - loss: 0.6143 - accuracy: 0.9141 - val_loss: 0.5906 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 34/400
7/7 - 0s - loss: 0.6115 - accuracy: 0.9141 - val_loss: 0.5874 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 35/400
7/7 - 0s - loss: 0.6088 - accuracy: 0.9219 - val_loss: 0.5836 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 36/400
7/7 - 0s - loss: 0.6057 - accuracy: 0.9219 - val_loss: 0.5794 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 37/400
7/7 - 0s - loss: 0.6028 - accuracy: 0.9297 - val_loss: 0.5757 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 38/400
7/7 - 0s - loss: 0.6000 - accuracy: 0.9219 - val_loss: 0.5723 - val_accuracy: 0.9062 - 31ms/epoch - 4ms/step
Epoch 39/400
7/7 - 0s - loss: 0.5974 - accuracy: 0.9219 - val_loss: 0.5680 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 40/400
7/7 - 0s - loss: 0.5941 - accuracy: 0.9453 - val_loss: 0.5644 - val_accuracy: 0.9062 - 34ms/epoch - 5ms/step
Epoch 41/400
7/7 - 0s - loss: 0.5913 - accuracy: 0.9375 - val_loss: 0.5605 - val_accuracy: 0.9062 - 32ms/epoch - 5ms/step
Epoch 42/400
7/7 - 0s - loss: 0.5883 - accuracy: 0.9453 - val_loss: 0.5577 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 43/400
7/7 - 0s - loss: 0.5857 - accuracy: 0.9531 - val_loss: 0.5542 - val_accuracy: 0.9062 - 34ms/epoch - 5ms/step
Epoch 44/400
7/7 - 0s - loss: 0.5832 - accuracy: 0.9531 - val_loss: 0.5519 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 45/400
7/7 - 0s - loss: 0.5807 - accuracy: 0.9531 - val_loss: 0.5489 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 46/400
7/7 - 0s - loss: 0.5781 - accuracy: 0.9531 - val_loss: 0.5455 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 47/400
7/7 - 0s - loss: 0.5756 - accuracy: 0.9609 - val_loss: 0.5418 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 48/400
7/7 - 0s - loss: 0.5730 - accuracy: 0.9688 - val_loss: 0.5385 - val_accuracy: 0.9062 - 34ms/epoch - 5ms/step
Epoch 49/400
7/7 - 0s - loss: 0.5703 - accuracy: 0.9688 - val_loss: 0.5349 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 50/400
7/7 - 0s - loss: 0.5676 - accuracy: 0.9688 - val_loss: 0.5324 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 51/400
7/7 - 0s - loss: 0.5650 - accuracy: 0.9688 - val_loss: 0.5286 - val_accuracy: 0.9062 - 32ms/epoch - 5ms/step
Epoch 52/400
7/7 - 0s - loss: 0.5627 - accuracy: 0.9688 - val_loss: 0.5249 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 53/400
7/7 - 0s - loss: 0.5598 - accuracy: 0.9609 - val_loss: 0.5225 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 54/400
7/7 - 0s - loss: 0.5572 - accuracy: 0.9688 - val_loss: 0.5186 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 55/400
7/7 - 0s - loss: 0.5547 - accuracy: 0.9609 - val_loss: 0.5152 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 56/400
7/7 - 0s - loss: 0.5521 - accuracy: 0.9609 - val_loss: 0.5122 - val_accuracy: 0.9062 - 34ms/epoch - 5ms/step
Epoch 57/400
7/7 - 0s - loss: 0.5494 - accuracy: 0.9609 - val_loss: 0.5087 - val_accuracy: 0.9062 - 34ms/epoch - 5ms/step
Epoch 58/400
7/7 - 0s - loss: 0.5467 - accuracy: 0.9609 - val_loss: 0.5053 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 59/400
7/7 - 0s - loss: 0.5442 - accuracy: 0.9453 - val_loss: 0.5011 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 60/400
7/7 - 0s - loss: 0.5413 - accuracy: 0.9453 - val_loss: 0.4981 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 61/400
7/7 - 0s - loss: 0.5389 - accuracy: 0.9375 - val_loss: 0.4953 - val_accuracy: 0.9062 - 35ms/epoch - 5ms/step
Epoch 62/400
7/7 - 0s - loss: 0.5362 - accuracy: 0.9453 - val_loss: 0.4911 - val_accuracy: 0.9062 - 40ms/epoch - 6ms/step
Epoch 63/400
7/7 - 0s - loss: 0.5335 - accuracy: 0.9375 - val_loss: 0.4872 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 64/400
7/7 - 0s - loss: 0.5309 - accuracy: 0.9375 - val_loss: 0.4851 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 65/400
7/7 - 0s - loss: 0.5285 - accuracy: 0.9375 - val_loss: 0.4821 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 66/400
7/7 - 0s - loss: 0.5258 - accuracy: 0.9375 - val_loss: 0.4781 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 67/400
7/7 - 0s - loss: 0.5232 - accuracy: 0.9375 - val_loss: 0.4742 - val_accuracy: 0.9062 - 40ms/epoch - 6ms/step
Epoch 68/400
7/7 - 0s - loss: 0.5207 - accuracy: 0.9375 - val_loss: 0.4716 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 69/400
7/7 - 0s - loss: 0.5180 - accuracy: 0.9375 - val_loss: 0.4678 - val_accuracy: 0.9062 - 34ms/epoch - 5ms/step
Epoch 70/400
7/7 - 0s - loss: 0.5153 - accuracy: 0.9375 - val_loss: 0.4643 - val_accuracy: 0.8750 - 35ms/epoch - 5ms/step
Epoch 71/400
7/7 - 0s - loss: 0.5128 - accuracy: 0.9375 - val_loss: 0.4616 - val_accuracy: 0.8750 - 34ms/epoch - 5ms/step
Epoch 72/400
7/7 - 0s - loss: 0.5101 - accuracy: 0.9375 - val_loss: 0.4584 - val_accuracy: 0.8750 - 34ms/epoch - 5ms/step
Epoch 73/400
7/7 - 0s - loss: 0.5075 - accuracy: 0.9375 - val_loss: 0.4548 - val_accuracy: 0.8750 - 33ms/epoch - 5ms/step
Epoch 74/400
7/7 - 0s - loss: 0.5048 - accuracy: 0.9375 - val_loss: 0.4521 - val_accuracy: 0.8750 - 35ms/epoch - 5ms/step
Epoch 75/400
7/7 - 0s - loss: 0.5024 - accuracy: 0.9375 - val_loss: 0.4486 - val_accuracy: 0.8750 - 33ms/epoch - 5ms/step
Epoch 76/400
7/7 - 0s - loss: 0.4997 - accuracy: 0.9375 - val_loss: 0.4458 - val_accuracy: 0.8750 - 35ms/epoch - 5ms/step
Epoch 77/400
7/7 - 0s - loss: 0.4970 - accuracy: 0.9375 - val_loss: 0.4431 - val_accuracy: 0.8750 - 34ms/epoch - 5ms/step
Epoch 78/400
7/7 - 0s - loss: 0.4944 - accuracy: 0.9375 - val_loss: 0.4401 - val_accuracy: 0.8750 - 33ms/epoch - 5ms/step
Epoch 79/400
7/7 - 0s - loss: 0.4917 - accuracy: 0.9375 - val_loss: 0.4362 - val_accuracy: 0.8750 - 32ms/epoch - 5ms/step
Epoch 80/400
7/7 - 0s - loss: 0.4889 - accuracy: 0.9375 - val_loss: 0.4341 - val_accuracy: 0.8750 - 34ms/epoch - 5ms/step
Epoch 81/400
7/7 - 0s - loss: 0.4862 - accuracy: 0.9375 - val_loss: 0.4306 - val_accuracy: 0.8750 - 34ms/epoch - 5ms/step
Epoch 82/400
7/7 - 0s - loss: 0.4837 - accuracy: 0.9375 - val_loss: 0.4269 - val_accuracy: 0.8750 - 33ms/epoch - 5ms/step
Epoch 83/400
7/7 - 0s - loss: 0.4808 - accuracy: 0.9375 - val_loss: 0.4241 - val_accuracy: 0.8750 - 34ms/epoch - 5ms/step
Epoch 84/400
7/7 - 0s - loss: 0.4781 - accuracy: 0.9297 - val_loss: 0.4209 - val_accuracy: 0.8750 - 32ms/epoch - 5ms/step
Epoch 85/400
7/7 - 0s - loss: 0.4755 - accuracy: 0.9297 - val_loss: 0.4174 - val_accuracy: 0.8750 - 33ms/epoch - 5ms/step
Epoch 86/400
7/7 - 0s - loss: 0.4730 - accuracy: 0.9297 - val_loss: 0.4143 - val_accuracy: 0.8750 - 33ms/epoch - 5ms/step
Epoch 87/400
7/7 - 0s - loss: 0.4704 - accuracy: 0.9375 - val_loss: 0.4118 - val_accuracy: 0.8750 - 34ms/epoch - 5ms/step
Epoch 88/400
7/7 - 0s - loss: 0.4676 - accuracy: 0.9375 - val_loss: 0.4092 - val_accuracy: 0.8750 - 34ms/epoch - 5ms/step
Epoch 89/400
7/7 - 0s - loss: 0.4652 - accuracy: 0.9375 - val_loss: 0.4066 - val_accuracy: 0.8750 - 34ms/epoch - 5ms/step
Epoch 90/400
7/7 - 0s - loss: 0.4625 - accuracy: 0.9375 - val_loss: 0.4041 - val_accuracy: 0.8750 - 33ms/epoch - 5ms/step
Epoch 91/400
7/7 - 0s - loss: 0.4599 - accuracy: 0.9375 - val_loss: 0.4014 - val_accuracy: 0.8750 - 34ms/epoch - 5ms/step
Epoch 92/400
7/7 - 0s - loss: 0.4571 - accuracy: 0.9453 - val_loss: 0.3985 - val_accuracy: 0.8750 - 33ms/epoch - 5ms/step
Epoch 93/400
7/7 - 0s - loss: 0.4547 - accuracy: 0.9531 - val_loss: 0.3950 - val_accuracy: 0.8750 - 34ms/epoch - 5ms/step
Epoch 94/400
7/7 - 0s - loss: 0.4518 - accuracy: 0.9453 - val_loss: 0.3926 - val_accuracy: 0.8750 - 33ms/epoch - 5ms/step
Epoch 95/400
7/7 - 0s - loss: 0.4496 - accuracy: 0.9531 - val_loss: 0.3901 - val_accuracy: 0.8750 - 34ms/epoch - 5ms/step
Epoch 96/400
7/7 - 0s - loss: 0.4471 - accuracy: 0.9531 - val_loss: 0.3866 - val_accuracy: 0.8750 - 32ms/epoch - 5ms/step
Epoch 97/400
7/7 - 0s - loss: 0.4442 - accuracy: 0.9531 - val_loss: 0.3844 - val_accuracy: 0.8750 - 33ms/epoch - 5ms/step
Epoch 98/400
7/7 - 0s - loss: 0.4415 - accuracy: 0.9531 - val_loss: 0.3814 - val_accuracy: 0.8750 - 33ms/epoch - 5ms/step
Epoch 99/400
7/7 - 0s - loss: 0.4391 - accuracy: 0.9453 - val_loss: 0.3795 - val_accuracy: 0.8750 - 33ms/epoch - 5ms/step
Epoch 100/400
7/7 - 0s - loss: 0.4366 - accuracy: 0.9453 - val_loss: 0.3760 - val_accuracy: 0.8750 - 34ms/epoch - 5ms/step
Epoch 101/400
7/7 - 0s - loss: 0.4340 - accuracy: 0.9531 - val_loss: 0.3734 - val_accuracy: 0.8750 - 33ms/epoch - 5ms/step
Epoch 102/400
7/7 - 0s - loss: 0.4314 - accuracy: 0.9531 - val_loss: 0.3704 - val_accuracy: 0.8750 - 33ms/epoch - 5ms/step
Epoch 103/400
7/7 - 0s - loss: 0.4288 - accuracy: 0.9531 - val_loss: 0.3670 - val_accuracy: 0.8750 - 33ms/epoch - 5ms/step
Epoch 104/400
7/7 - 0s - loss: 0.4262 - accuracy: 0.9531 - val_loss: 0.3651 - val_accuracy: 0.8750 - 34ms/epoch - 5ms/step
Epoch 105/400
7/7 - 0s - loss: 0.4234 - accuracy: 0.9609 - val_loss: 0.3623 - val_accuracy: 0.8750 - 34ms/epoch - 5ms/step
Epoch 106/400
7/7 - 0s - loss: 0.4209 - accuracy: 0.9609 - val_loss: 0.3594 - val_accuracy: 0.8750 - 38ms/epoch - 5ms/step
Epoch 107/400
7/7 - 0s - loss: 0.4185 - accuracy: 0.9609 - val_loss: 0.3575 - val_accuracy: 0.8750 - 34ms/epoch - 5ms/step
Epoch 108/400
7/7 - 0s - loss: 0.4159 - accuracy: 0.9609 - val_loss: 0.3550 - val_accuracy: 0.8750 - 33ms/epoch - 5ms/step
Epoch 109/400
7/7 - 0s - loss: 0.4134 - accuracy: 0.9609 - val_loss: 0.3530 - val_accuracy: 0.8750 - 34ms/epoch - 5ms/step
Epoch 110/400
7/7 - 0s - loss: 0.4109 - accuracy: 0.9609 - val_loss: 0.3507 - val_accuracy: 0.8750 - 33ms/epoch - 5ms/step
Epoch 111/400
7/7 - 0s - loss: 0.4084 - accuracy: 0.9609 - val_loss: 0.3479 - val_accuracy: 0.8750 - 34ms/epoch - 5ms/step
Epoch 112/400
7/7 - 0s - loss: 0.4062 - accuracy: 0.9609 - val_loss: 0.3454 - val_accuracy: 0.8750 - 34ms/epoch - 5ms/step
Epoch 113/400
7/7 - 0s - loss: 0.4039 - accuracy: 0.9531 - val_loss: 0.3431 - val_accuracy: 0.8750 - 34ms/epoch - 5ms/step
Epoch 114/400
7/7 - 0s - loss: 0.4013 - accuracy: 0.9609 - val_loss: 0.3403 - val_accuracy: 0.8750 - 35ms/epoch - 5ms/step
Epoch 115/400
7/7 - 0s - loss: 0.3987 - accuracy: 0.9609 - val_loss: 0.3379 - val_accuracy: 0.8750 - 33ms/epoch - 5ms/step
Epoch 116/400
7/7 - 0s - loss: 0.3964 - accuracy: 0.9609 - val_loss: 0.3357 - val_accuracy: 0.8750 - 33ms/epoch - 5ms/step
Epoch 117/400
7/7 - 0s - loss: 0.3940 - accuracy: 0.9609 - val_loss: 0.3342 - val_accuracy: 0.8750 - 33ms/epoch - 5ms/step
Epoch 118/400
7/7 - 0s - loss: 0.3915 - accuracy: 0.9531 - val_loss: 0.3313 - val_accuracy: 0.8750 - 33ms/epoch - 5ms/step
Epoch 119/400
7/7 - 0s - loss: 0.3891 - accuracy: 0.9531 - val_loss: 0.3281 - val_accuracy: 0.8750 - 34ms/epoch - 5ms/step
Epoch 120/400
7/7 - 0s - loss: 0.3868 - accuracy: 0.9531 - val_loss: 0.3256 - val_accuracy: 0.8750 - 36ms/epoch - 5ms/step
Epoch 121/400
7/7 - 0s - loss: 0.3843 - accuracy: 0.9531 - val_loss: 0.3245 - val_accuracy: 0.8750 - 33ms/epoch - 5ms/step
Epoch 122/400
7/7 - 0s - loss: 0.3822 - accuracy: 0.9609 - val_loss: 0.3215 - val_accuracy: 0.8750 - 35ms/epoch - 5ms/step
Epoch 123/400
7/7 - 0s - loss: 0.3796 - accuracy: 0.9609 - val_loss: 0.3190 - val_accuracy: 0.8750 - 35ms/epoch - 5ms/step
Epoch 124/400
7/7 - 0s - loss: 0.3773 - accuracy: 0.9609 - val_loss: 0.3180 - val_accuracy: 0.8750 - 35ms/epoch - 5ms/step
Epoch 125/400
7/7 - 0s - loss: 0.3753 - accuracy: 0.9609 - val_loss: 0.3162 - val_accuracy: 0.8750 - 33ms/epoch - 5ms/step
Epoch 126/400
7/7 - 0s - loss: 0.3727 - accuracy: 0.9531 - val_loss: 0.3137 - val_accuracy: 0.8750 - 35ms/epoch - 5ms/step
Epoch 127/400
7/7 - 0s - loss: 0.3704 - accuracy: 0.9531 - val_loss: 0.3115 - val_accuracy: 0.8750 - 34ms/epoch - 5ms/step
Epoch 128/400
7/7 - 0s - loss: 0.3684 - accuracy: 0.9531 - val_loss: 0.3102 - val_accuracy: 0.9062 - 34ms/epoch - 5ms/step
Epoch 129/400
7/7 - 0s - loss: 0.3659 - accuracy: 0.9531 - val_loss: 0.3084 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 130/400
7/7 - 0s - loss: 0.3636 - accuracy: 0.9609 - val_loss: 0.3065 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 131/400
7/7 - 0s - loss: 0.3613 - accuracy: 0.9531 - val_loss: 0.3044 - val_accuracy: 0.9062 - 34ms/epoch - 5ms/step
Epoch 132/400
7/7 - 0s - loss: 0.3592 - accuracy: 0.9531 - val_loss: 0.3023 - val_accuracy: 0.9062 - 34ms/epoch - 5ms/step
Epoch 133/400
7/7 - 0s - loss: 0.3571 - accuracy: 0.9609 - val_loss: 0.3003 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 134/400
7/7 - 0s - loss: 0.3548 - accuracy: 0.9609 - val_loss: 0.2981 - val_accuracy: 0.9062 - 34ms/epoch - 5ms/step
Epoch 135/400
7/7 - 0s - loss: 0.3528 - accuracy: 0.9609 - val_loss: 0.2967 - val_accuracy: 0.9062 - 36ms/epoch - 5ms/step
Epoch 136/400
7/7 - 0s - loss: 0.3505 - accuracy: 0.9609 - val_loss: 0.2943 - val_accuracy: 0.9062 - 34ms/epoch - 5ms/step
Epoch 137/400
7/7 - 0s - loss: 0.3486 - accuracy: 0.9531 - val_loss: 0.2936 - val_accuracy: 0.9062 - 34ms/epoch - 5ms/step
Epoch 138/400
7/7 - 0s - loss: 0.3462 - accuracy: 0.9609 - val_loss: 0.2915 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 139/400
7/7 - 0s - loss: 0.3441 - accuracy: 0.9609 - val_loss: 0.2893 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 140/400
7/7 - 0s - loss: 0.3422 - accuracy: 0.9609 - val_loss: 0.2883 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 141/400
7/7 - 0s - loss: 0.3403 - accuracy: 0.9609 - val_loss: 0.2874 - val_accuracy: 0.9062 - 34ms/epoch - 5ms/step
Epoch 142/400
7/7 - 0s - loss: 0.3380 - accuracy: 0.9609 - val_loss: 0.2852 - val_accuracy: 0.9062 - 34ms/epoch - 5ms/step
Epoch 143/400
7/7 - 0s - loss: 0.3364 - accuracy: 0.9609 - val_loss: 0.2842 - val_accuracy: 0.9062 - 34ms/epoch - 5ms/step
Epoch 144/400
7/7 - 0s - loss: 0.3343 - accuracy: 0.9609 - val_loss: 0.2823 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 145/400
7/7 - 0s - loss: 0.3323 - accuracy: 0.9609 - val_loss: 0.2812 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 146/400
7/7 - 0s - loss: 0.3301 - accuracy: 0.9609 - val_loss: 0.2787 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 147/400
7/7 - 0s - loss: 0.3283 - accuracy: 0.9609 - val_loss: 0.2765 - val_accuracy: 0.9062 - 35ms/epoch - 5ms/step
Epoch 148/400
7/7 - 0s - loss: 0.3262 - accuracy: 0.9609 - val_loss: 0.2748 - val_accuracy: 0.9062 - 34ms/epoch - 5ms/step
Epoch 149/400
7/7 - 0s - loss: 0.3242 - accuracy: 0.9609 - val_loss: 0.2739 - val_accuracy: 0.9062 - 34ms/epoch - 5ms/step
Epoch 150/400
7/7 - 0s - loss: 0.3225 - accuracy: 0.9609 - val_loss: 0.2721 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 151/400
7/7 - 0s - loss: 0.3203 - accuracy: 0.9609 - val_loss: 0.2702 - val_accuracy: 0.9062 - 34ms/epoch - 5ms/step
Epoch 152/400
7/7 - 0s - loss: 0.3188 - accuracy: 0.9609 - val_loss: 0.2690 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 153/400
7/7 - 0s - loss: 0.3164 - accuracy: 0.9609 - val_loss: 0.2673 - val_accuracy: 0.9062 - 36ms/epoch - 5ms/step
Epoch 154/400
7/7 - 0s - loss: 0.3148 - accuracy: 0.9609 - val_loss: 0.2654 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 155/400
7/7 - 0s - loss: 0.3133 - accuracy: 0.9609 - val_loss: 0.2637 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 156/400
7/7 - 0s - loss: 0.3114 - accuracy: 0.9609 - val_loss: 0.2626 - val_accuracy: 0.9062 - 34ms/epoch - 5ms/step
Epoch 157/400
7/7 - 0s - loss: 0.3095 - accuracy: 0.9609 - val_loss: 0.2610 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 158/400
7/7 - 0s - loss: 0.3078 - accuracy: 0.9609 - val_loss: 0.2599 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 159/400
7/7 - 0s - loss: 0.3062 - accuracy: 0.9609 - val_loss: 0.2582 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 160/400
7/7 - 0s - loss: 0.3042 - accuracy: 0.9609 - val_loss: 0.2570 - val_accuracy: 0.9062 - 34ms/epoch - 5ms/step
Epoch 161/400
7/7 - 0s - loss: 0.3027 - accuracy: 0.9609 - val_loss: 0.2550 - val_accuracy: 0.9062 - 36ms/epoch - 5ms/step
Epoch 162/400
7/7 - 0s - loss: 0.3009 - accuracy: 0.9609 - val_loss: 0.2540 - val_accuracy: 0.9062 - 36ms/epoch - 5ms/step
Epoch 163/400
7/7 - 0s - loss: 0.2993 - accuracy: 0.9609 - val_loss: 0.2537 - val_accuracy: 0.9062 - 34ms/epoch - 5ms/step
Epoch 164/400
7/7 - 0s - loss: 0.2975 - accuracy: 0.9609 - val_loss: 0.2519 - val_accuracy: 0.9062 - 34ms/epoch - 5ms/step
Epoch 165/400
7/7 - 0s - loss: 0.2959 - accuracy: 0.9609 - val_loss: 0.2518 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 166/400
7/7 - 0s - loss: 0.2945 - accuracy: 0.9609 - val_loss: 0.2506 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 167/400
7/7 - 0s - loss: 0.2927 - accuracy: 0.9609 - val_loss: 0.2497 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 168/400
7/7 - 0s - loss: 0.2916 - accuracy: 0.9609 - val_loss: 0.2480 - val_accuracy: 0.9062 - 35ms/epoch - 5ms/step
Epoch 169/400
7/7 - 0s - loss: 0.2895 - accuracy: 0.9609 - val_loss: 0.2472 - val_accuracy: 0.9062 - 36ms/epoch - 5ms/step
Epoch 170/400
7/7 - 0s - loss: 0.2878 - accuracy: 0.9609 - val_loss: 0.2456 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 171/400
7/7 - 0s - loss: 0.2863 - accuracy: 0.9609 - val_loss: 0.2449 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 172/400
7/7 - 0s - loss: 0.2848 - accuracy: 0.9609 - val_loss: 0.2437 - val_accuracy: 0.9062 - 34ms/epoch - 5ms/step
Epoch 173/400
7/7 - 0s - loss: 0.2829 - accuracy: 0.9609 - val_loss: 0.2426 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 174/400
7/7 - 0s - loss: 0.2819 - accuracy: 0.9609 - val_loss: 0.2423 - val_accuracy: 0.9062 - 34ms/epoch - 5ms/step
Epoch 175/400
7/7 - 0s - loss: 0.2798 - accuracy: 0.9609 - val_loss: 0.2409 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 176/400
7/7 - 0s - loss: 0.2787 - accuracy: 0.9609 - val_loss: 0.2399 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 177/400
7/7 - 0s - loss: 0.2767 - accuracy: 0.9609 - val_loss: 0.2394 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 178/400
7/7 - 0s - loss: 0.2754 - accuracy: 0.9609 - val_loss: 0.2381 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 179/400
7/7 - 0s - loss: 0.2741 - accuracy: 0.9609 - val_loss: 0.2369 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 180/400
7/7 - 0s - loss: 0.2726 - accuracy: 0.9609 - val_loss: 0.2354 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 181/400
7/7 - 0s - loss: 0.2714 - accuracy: 0.9609 - val_loss: 0.2345 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 182/400
7/7 - 0s - loss: 0.2701 - accuracy: 0.9609 - val_loss: 0.2342 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 183/400
7/7 - 0s - loss: 0.2685 - accuracy: 0.9609 - val_loss: 0.2339 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 184/400
7/7 - 0s - loss: 0.2673 - accuracy: 0.9609 - val_loss: 0.2331 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 185/400
7/7 - 0s - loss: 0.2655 - accuracy: 0.9609 - val_loss: 0.2319 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 186/400
7/7 - 0s - loss: 0.2643 - accuracy: 0.9609 - val_loss: 0.2306 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 187/400
7/7 - 0s - loss: 0.2628 - accuracy: 0.9609 - val_loss: 0.2306 - val_accuracy: 0.9375 - 35ms/epoch - 5ms/step
Epoch 188/400
7/7 - 0s - loss: 0.2619 - accuracy: 0.9609 - val_loss: 0.2297 - val_accuracy: 0.9375 - 35ms/epoch - 5ms/step
Epoch 189/400
7/7 - 0s - loss: 0.2603 - accuracy: 0.9609 - val_loss: 0.2283 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 190/400
7/7 - 0s - loss: 0.2589 - accuracy: 0.9609 - val_loss: 0.2281 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 191/400
7/7 - 0s - loss: 0.2576 - accuracy: 0.9609 - val_loss: 0.2269 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 192/400
7/7 - 0s - loss: 0.2562 - accuracy: 0.9609 - val_loss: 0.2263 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 193/400
7/7 - 0s - loss: 0.2556 - accuracy: 0.9609 - val_loss: 0.2260 - val_accuracy: 0.9375 - 35ms/epoch - 5ms/step
Epoch 194/400
7/7 - 0s - loss: 0.2536 - accuracy: 0.9609 - val_loss: 0.2255 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 195/400
7/7 - 0s - loss: 0.2522 - accuracy: 0.9609 - val_loss: 0.2247 - val_accuracy: 0.9375 - 35ms/epoch - 5ms/step
Epoch 196/400
7/7 - 0s - loss: 0.2512 - accuracy: 0.9688 - val_loss: 0.2245 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 197/400
7/7 - 0s - loss: 0.2503 - accuracy: 0.9688 - val_loss: 0.2236 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 198/400
7/7 - 0s - loss: 0.2488 - accuracy: 0.9688 - val_loss: 0.2224 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 199/400
7/7 - 0s - loss: 0.2475 - accuracy: 0.9688 - val_loss: 0.2223 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 200/400
7/7 - 0s - loss: 0.2463 - accuracy: 0.9609 - val_loss: 0.2216 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 201/400
7/7 - 0s - loss: 0.2452 - accuracy: 0.9688 - val_loss: 0.2209 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 202/400
7/7 - 0s - loss: 0.2439 - accuracy: 0.9688 - val_loss: 0.2208 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 203/400
7/7 - 0s - loss: 0.2426 - accuracy: 0.9688 - val_loss: 0.2202 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 204/400
7/7 - 0s - loss: 0.2414 - accuracy: 0.9688 - val_loss: 0.2194 - val_accuracy: 0.9375 - 37ms/epoch - 5ms/step
Epoch 205/400
7/7 - 0s - loss: 0.2401 - accuracy: 0.9688 - val_loss: 0.2184 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 206/400
7/7 - 0s - loss: 0.2392 - accuracy: 0.9688 - val_loss: 0.2181 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 207/400
7/7 - 0s - loss: 0.2381 - accuracy: 0.9688 - val_loss: 0.2180 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 208/400
7/7 - 0s - loss: 0.2368 - accuracy: 0.9688 - val_loss: 0.2173 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 209/400
7/7 - 0s - loss: 0.2354 - accuracy: 0.9688 - val_loss: 0.2167 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 210/400
7/7 - 0s - loss: 0.2348 - accuracy: 0.9688 - val_loss: 0.2161 - val_accuracy: 0.9375 - 39ms/epoch - 6ms/step
Epoch 211/400
7/7 - 0s - loss: 0.2334 - accuracy: 0.9688 - val_loss: 0.2154 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 212/400
7/7 - 0s - loss: 0.2326 - accuracy: 0.9688 - val_loss: 0.2152 - val_accuracy: 0.9375 - 32ms/epoch - 5ms/step
Epoch 213/400
7/7 - 0s - loss: 0.2309 - accuracy: 0.9688 - val_loss: 0.2144 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 214/400
7/7 - 0s - loss: 0.2302 - accuracy: 0.9688 - val_loss: 0.2138 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 215/400
7/7 - 0s - loss: 0.2293 - accuracy: 0.9688 - val_loss: 0.2134 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 216/400
7/7 - 0s - loss: 0.2279 - accuracy: 0.9688 - val_loss: 0.2126 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 217/400
7/7 - 0s - loss: 0.2270 - accuracy: 0.9688 - val_loss: 0.2125 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 218/400
7/7 - 0s - loss: 0.2257 - accuracy: 0.9688 - val_loss: 0.2120 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 219/400
7/7 - 0s - loss: 0.2249 - accuracy: 0.9688 - val_loss: 0.2116 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 220/400
7/7 - 0s - loss: 0.2237 - accuracy: 0.9688 - val_loss: 0.2109 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 221/400
7/7 - 0s - loss: 0.2230 - accuracy: 0.9688 - val_loss: 0.2110 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 222/400
7/7 - 0s - loss: 0.2216 - accuracy: 0.9688 - val_loss: 0.2099 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 223/400
7/7 - 0s - loss: 0.2209 - accuracy: 0.9688 - val_loss: 0.2104 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 224/400
7/7 - 0s - loss: 0.2198 - accuracy: 0.9688 - val_loss: 0.2101 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 225/400
7/7 - 0s - loss: 0.2188 - accuracy: 0.9688 - val_loss: 0.2103 - val_accuracy: 0.9375 - 35ms/epoch - 5ms/step
Epoch 226/400
7/7 - 0s - loss: 0.2177 - accuracy: 0.9688 - val_loss: 0.2096 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 227/400
7/7 - 0s - loss: 0.2169 - accuracy: 0.9688 - val_loss: 0.2088 - val_accuracy: 0.9375 - 35ms/epoch - 5ms/step
Epoch 228/400
7/7 - 0s - loss: 0.2163 - accuracy: 0.9688 - val_loss: 0.2084 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 229/400
7/7 - 0s - loss: 0.2152 - accuracy: 0.9688 - val_loss: 0.2082 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 230/400
7/7 - 0s - loss: 0.2145 - accuracy: 0.9688 - val_loss: 0.2078 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 231/400
7/7 - 0s - loss: 0.2135 - accuracy: 0.9688 - val_loss: 0.2077 - val_accuracy: 0.9375 - 36ms/epoch - 5ms/step
Epoch 232/400
7/7 - 0s - loss: 0.2125 - accuracy: 0.9688 - val_loss: 0.2073 - val_accuracy: 0.9375 - 36ms/epoch - 5ms/step
Epoch 233/400
7/7 - 0s - loss: 0.2117 - accuracy: 0.9688 - val_loss: 0.2069 - val_accuracy: 0.9375 - 47ms/epoch - 7ms/step
Epoch 234/400
7/7 - 0s - loss: 0.2108 - accuracy: 0.9688 - val_loss: 0.2066 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 235/400
7/7 - 0s - loss: 0.2098 - accuracy: 0.9688 - val_loss: 0.2067 - val_accuracy: 0.9375 - 35ms/epoch - 5ms/step
Epoch 236/400
7/7 - 0s - loss: 0.2093 - accuracy: 0.9766 - val_loss: 0.2056 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 237/400
7/7 - 0s - loss: 0.2080 - accuracy: 0.9688 - val_loss: 0.2053 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 238/400
7/7 - 0s - loss: 0.2074 - accuracy: 0.9688 - val_loss: 0.2049 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 239/400
7/7 - 0s - loss: 0.2064 - accuracy: 0.9766 - val_loss: 0.2047 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 240/400
7/7 - 0s - loss: 0.2055 - accuracy: 0.9766 - val_loss: 0.2044 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 241/400
7/7 - 0s - loss: 0.2047 - accuracy: 0.9766 - val_loss: 0.2044 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 242/400
7/7 - 0s - loss: 0.2037 - accuracy: 0.9766 - val_loss: 0.2040 - val_accuracy: 0.9375 - 35ms/epoch - 5ms/step
Epoch 243/400
7/7 - 0s - loss: 0.2031 - accuracy: 0.9766 - val_loss: 0.2038 - val_accuracy: 0.9375 - 35ms/epoch - 5ms/step
Epoch 244/400
7/7 - 0s - loss: 0.2022 - accuracy: 0.9766 - val_loss: 0.2033 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 245/400
7/7 - 0s - loss: 0.2012 - accuracy: 0.9766 - val_loss: 0.2037 - val_accuracy: 0.9375 - 35ms/epoch - 5ms/step
Epoch 246/400
7/7 - 0s - loss: 0.2003 - accuracy: 0.9766 - val_loss: 0.2033 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 247/400
7/7 - 0s - loss: 0.1997 - accuracy: 0.9766 - val_loss: 0.2032 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 248/400
7/7 - 0s - loss: 0.1986 - accuracy: 0.9766 - val_loss: 0.2026 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 249/400
7/7 - 0s - loss: 0.1980 - accuracy: 0.9766 - val_loss: 0.2026 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 250/400
7/7 - 0s - loss: 0.1971 - accuracy: 0.9766 - val_loss: 0.2021 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 251/400
7/7 - 0s - loss: 0.1963 - accuracy: 0.9766 - val_loss: 0.2018 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 252/400
7/7 - 0s - loss: 0.1952 - accuracy: 0.9766 - val_loss: 0.2021 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 253/400
7/7 - 0s - loss: 0.1949 - accuracy: 0.9766 - val_loss: 0.2017 - val_accuracy: 0.9375 - 35ms/epoch - 5ms/step
Epoch 254/400
7/7 - 0s - loss: 0.1939 - accuracy: 0.9766 - val_loss: 0.2013 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 255/400
7/7 - 0s - loss: 0.1934 - accuracy: 0.9766 - val_loss: 0.2011 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 256/400
7/7 - 0s - loss: 0.1923 - accuracy: 0.9766 - val_loss: 0.2009 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 257/400
7/7 - 0s - loss: 0.1915 - accuracy: 0.9766 - val_loss: 0.2008 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 258/400
7/7 - 0s - loss: 0.1905 - accuracy: 0.9766 - val_loss: 0.2007 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 259/400
7/7 - 0s - loss: 0.1902 - accuracy: 0.9766 - val_loss: 0.2005 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 260/400
7/7 - 0s - loss: 0.1893 - accuracy: 0.9766 - val_loss: 0.2006 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 261/400
7/7 - 0s - loss: 0.1883 - accuracy: 0.9766 - val_loss: 0.2002 - val_accuracy: 0.9375 - 35ms/epoch - 5ms/step
Epoch 262/400
7/7 - 0s - loss: 0.1876 - accuracy: 0.9766 - val_loss: 0.1997 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 263/400
7/7 - 0s - loss: 0.1868 - accuracy: 0.9766 - val_loss: 0.1995 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 264/400
7/7 - 0s - loss: 0.1858 - accuracy: 0.9766 - val_loss: 0.1995 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 265/400
7/7 - 0s - loss: 0.1857 - accuracy: 0.9766 - val_loss: 0.1997 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 266/400
7/7 - 0s - loss: 0.1844 - accuracy: 0.9766 - val_loss: 0.1998 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 267/400
7/7 - 0s - loss: 0.1835 - accuracy: 0.9766 - val_loss: 0.1992 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 268/400
7/7 - 0s - loss: 0.1830 - accuracy: 0.9766 - val_loss: 0.1990 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 269/400
7/7 - 0s - loss: 0.1828 - accuracy: 0.9766 - val_loss: 0.1991 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 270/400
7/7 - 0s - loss: 0.1817 - accuracy: 0.9766 - val_loss: 0.1986 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 271/400
7/7 - 0s - loss: 0.1811 - accuracy: 0.9766 - val_loss: 0.1987 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 272/400
7/7 - 0s - loss: 0.1803 - accuracy: 0.9766 - val_loss: 0.1985 - val_accuracy: 0.9375 - 35ms/epoch - 5ms/step
Epoch 273/400
7/7 - 0s - loss: 0.1795 - accuracy: 0.9766 - val_loss: 0.1982 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 274/400
7/7 - 0s - loss: 0.1789 - accuracy: 0.9766 - val_loss: 0.1981 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 275/400
7/7 - 0s - loss: 0.1783 - accuracy: 0.9766 - val_loss: 0.1983 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 276/400
7/7 - 0s - loss: 0.1772 - accuracy: 0.9766 - val_loss: 0.1982 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 277/400
7/7 - 0s - loss: 0.1769 - accuracy: 0.9766 - val_loss: 0.1981 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 278/400
7/7 - 0s - loss: 0.1763 - accuracy: 0.9766 - val_loss: 0.1978 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 279/400
7/7 - 0s - loss: 0.1754 - accuracy: 0.9766 - val_loss: 0.1977 - val_accuracy: 0.9375 - 36ms/epoch - 5ms/step
Epoch 280/400
7/7 - 0s - loss: 0.1750 - accuracy: 0.9766 - val_loss: 0.1977 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 281/400
7/7 - 0s - loss: 0.1745 - accuracy: 0.9766 - val_loss: 0.1976 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 282/400
7/7 - 0s - loss: 0.1735 - accuracy: 0.9766 - val_loss: 0.1977 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 283/400
7/7 - 0s - loss: 0.1730 - accuracy: 0.9766 - val_loss: 0.1973 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 284/400
7/7 - 0s - loss: 0.1723 - accuracy: 0.9766 - val_loss: 0.1974 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 285/400
7/7 - 0s - loss: 0.1718 - accuracy: 0.9766 - val_loss: 0.1976 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 286/400
7/7 - 0s - loss: 0.1708 - accuracy: 0.9766 - val_loss: 0.1973 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 287/400
7/7 - 0s - loss: 0.1708 - accuracy: 0.9766 - val_loss: 0.1979 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 288/400
7/7 - 0s - loss: 0.1695 - accuracy: 0.9766 - val_loss: 0.1976 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 289/400
7/7 - 0s - loss: 0.1690 - accuracy: 0.9766 - val_loss: 0.1976 - val_accuracy: 0.9375 - 35ms/epoch - 5ms/step
Epoch 290/400
7/7 - 0s - loss: 0.1684 - accuracy: 0.9766 - val_loss: 0.1975 - val_accuracy: 0.9375 - 35ms/epoch - 5ms/step
Epoch 291/400
7/7 - 0s - loss: 0.1676 - accuracy: 0.9766 - val_loss: 0.1976 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 292/400
7/7 - 0s - loss: 0.1672 - accuracy: 0.9766 - val_loss: 0.1971 - val_accuracy: 0.9375 - 35ms/epoch - 5ms/step
Epoch 293/400
7/7 - 0s - loss: 0.1664 - accuracy: 0.9766 - val_loss: 0.1972 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 294/400
7/7 - 0s - loss: 0.1661 - accuracy: 0.9766 - val_loss: 0.1970 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 295/400
7/7 - 0s - loss: 0.1657 - accuracy: 0.9766 - val_loss: 0.1970 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 296/400
7/7 - 0s - loss: 0.1648 - accuracy: 0.9766 - val_loss: 0.1969 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 297/400
7/7 - 0s - loss: 0.1640 - accuracy: 0.9766 - val_loss: 0.1968 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 298/400
7/7 - 0s - loss: 0.1634 - accuracy: 0.9766 - val_loss: 0.1970 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 299/400
7/7 - 0s - loss: 0.1630 - accuracy: 0.9766 - val_loss: 0.1970 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 300/400
7/7 - 0s - loss: 0.1625 - accuracy: 0.9766 - val_loss: 0.1972 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 301/400
7/7 - 0s - loss: 0.1618 - accuracy: 0.9766 - val_loss: 0.1972 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 302/400
7/7 - 0s - loss: 0.1609 - accuracy: 0.9766 - val_loss: 0.1970 - val_accuracy: 0.9375 - 35ms/epoch - 5ms/step
Epoch 303/400
7/7 - 0s - loss: 0.1609 - accuracy: 0.9766 - val_loss: 0.1972 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 304/400
7/7 - 0s - loss: 0.1595 - accuracy: 0.9766 - val_loss: 0.1970 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 305/400
7/7 - 0s - loss: 0.1593 - accuracy: 0.9766 - val_loss: 0.1970 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 306/400
7/7 - 0s - loss: 0.1586 - accuracy: 0.9766 - val_loss: 0.1970 - val_accuracy: 0.9375 - 35ms/epoch - 5ms/step
Epoch 307/400
7/7 - 0s - loss: 0.1583 - accuracy: 0.9766 - val_loss: 0.1971 - val_accuracy: 0.9375 - 35ms/epoch - 5ms/step
Epoch 308/400
7/7 - 0s - loss: 0.1573 - accuracy: 0.9766 - val_loss: 0.1976 - val_accuracy: 0.9375 - 35ms/epoch - 5ms/step
Epoch 309/400
7/7 - 0s - loss: 0.1570 - accuracy: 0.9766 - val_loss: 0.1977 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 310/400
7/7 - 0s - loss: 0.1565 - accuracy: 0.9766 - val_loss: 0.1979 - val_accuracy: 0.9375 - 32ms/epoch - 5ms/step
Epoch 311/400
7/7 - 0s - loss: 0.1556 - accuracy: 0.9766 - val_loss: 0.1979 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 312/400
7/7 - 0s - loss: 0.1554 - accuracy: 0.9766 - val_loss: 0.1977 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 313/400
7/7 - 0s - loss: 0.1546 - accuracy: 0.9766 - val_loss: 0.1981 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 314/400
7/7 - 0s - loss: 0.1542 - accuracy: 0.9766 - val_loss: 0.1983 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 315/400
7/7 - 0s - loss: 0.1537 - accuracy: 0.9766 - val_loss: 0.1981 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 316/400
7/7 - 0s - loss: 0.1531 - accuracy: 0.9766 - val_loss: 0.1981 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 317/400
7/7 - 0s - loss: 0.1524 - accuracy: 0.9766 - val_loss: 0.1983 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 318/400
7/7 - 0s - loss: 0.1519 - accuracy: 0.9766 - val_loss: 0.1982 - val_accuracy: 0.9375 - 36ms/epoch - 5ms/step
Epoch 319/400
7/7 - 0s - loss: 0.1515 - accuracy: 0.9766 - val_loss: 0.1982 - val_accuracy: 0.9375 - 35ms/epoch - 5ms/step
Epoch 320/400
7/7 - 0s - loss: 0.1509 - accuracy: 0.9766 - val_loss: 0.1983 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 321/400
7/7 - 0s - loss: 0.1508 - accuracy: 0.9766 - val_loss: 0.1983 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 322/400
7/7 - 0s - loss: 0.1500 - accuracy: 0.9766 - val_loss: 0.1983 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 323/400
7/7 - 0s - loss: 0.1494 - accuracy: 0.9766 - val_loss: 0.1982 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 324/400
7/7 - 0s - loss: 0.1489 - accuracy: 0.9766 - val_loss: 0.1982 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 325/400
7/7 - 0s - loss: 0.1486 - accuracy: 0.9766 - val_loss: 0.1977 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 326/400
7/7 - 0s - loss: 0.1481 - accuracy: 0.9766 - val_loss: 0.1977 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 327/400
7/7 - 0s - loss: 0.1476 - accuracy: 0.9766 - val_loss: 0.1975 - val_accuracy: 0.9375 - 32ms/epoch - 5ms/step
Epoch 328/400
7/7 - 0s - loss: 0.1468 - accuracy: 0.9766 - val_loss: 0.1976 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 329/400
7/7 - 0s - loss: 0.1463 - accuracy: 0.9766 - val_loss: 0.1976 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 330/400
7/7 - 0s - loss: 0.1459 - accuracy: 0.9766 - val_loss: 0.1976 - val_accuracy: 0.9375 - 35ms/epoch - 5ms/step
Epoch 331/400
7/7 - 0s - loss: 0.1454 - accuracy: 0.9766 - val_loss: 0.1978 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 332/400
7/7 - 0s - loss: 0.1452 - accuracy: 0.9766 - val_loss: 0.1978 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 333/400
7/7 - 0s - loss: 0.1446 - accuracy: 0.9766 - val_loss: 0.1975 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 334/400
7/7 - 0s - loss: 0.1445 - accuracy: 0.9766 - val_loss: 0.1981 - val_accuracy: 0.9375 - 32ms/epoch - 5ms/step
Epoch 335/400
7/7 - 0s - loss: 0.1433 - accuracy: 0.9766 - val_loss: 0.1982 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 336/400
7/7 - 0s - loss: 0.1427 - accuracy: 0.9766 - val_loss: 0.1983 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 337/400
7/7 - 0s - loss: 0.1423 - accuracy: 0.9766 - val_loss: 0.1985 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 338/400
7/7 - 0s - loss: 0.1422 - accuracy: 0.9766 - val_loss: 0.1982 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 339/400
7/7 - 0s - loss: 0.1418 - accuracy: 0.9766 - val_loss: 0.1985 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 340/400
7/7 - 0s - loss: 0.1412 - accuracy: 0.9766 - val_loss: 0.1991 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 341/400
7/7 - 0s - loss: 0.1408 - accuracy: 0.9766 - val_loss: 0.1989 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 342/400
7/7 - 0s - loss: 0.1400 - accuracy: 0.9766 - val_loss: 0.1990 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 343/400
7/7 - 0s - loss: 0.1398 - accuracy: 0.9766 - val_loss: 0.1991 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 344/400
7/7 - 0s - loss: 0.1398 - accuracy: 0.9766 - val_loss: 0.1989 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 345/400
7/7 - 0s - loss: 0.1393 - accuracy: 0.9766 - val_loss: 0.1986 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 346/400
7/7 - 0s - loss: 0.1390 - accuracy: 0.9766 - val_loss: 0.1990 - val_accuracy: 0.9375 - 35ms/epoch - 5ms/step
Epoch 347/400
7/7 - 0s - loss: 0.1382 - accuracy: 0.9766 - val_loss: 0.1992 - val_accuracy: 0.9375 - 35ms/epoch - 5ms/step
Epoch 348/400
7/7 - 0s - loss: 0.1380 - accuracy: 0.9766 - val_loss: 0.1995 - val_accuracy: 0.9375 - 35ms/epoch - 5ms/step
Epoch 349/400
7/7 - 0s - loss: 0.1378 - accuracy: 0.9766 - val_loss: 0.1995 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 350/400
7/7 - 0s - loss: 0.1373 - accuracy: 0.9766 - val_loss: 0.1997 - val_accuracy: 0.9375 - 35ms/epoch - 5ms/step
Epoch 351/400
7/7 - 0s - loss: 0.1367 - accuracy: 0.9766 - val_loss: 0.1998 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 352/400
7/7 - 0s - loss: 0.1363 - accuracy: 0.9766 - val_loss: 0.1995 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 353/400
7/7 - 0s - loss: 0.1359 - accuracy: 0.9766 - val_loss: 0.1996 - val_accuracy: 0.9375 - 38ms/epoch - 5ms/step
Epoch 354/400
7/7 - 0s - loss: 0.1356 - accuracy: 0.9766 - val_loss: 0.1999 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 355/400
7/7 - 0s - loss: 0.1349 - accuracy: 0.9766 - val_loss: 0.2003 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 356/400
7/7 - 0s - loss: 0.1345 - accuracy: 0.9766 - val_loss: 0.2002 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 357/400
7/7 - 0s - loss: 0.1345 - accuracy: 0.9766 - val_loss: 0.2002 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 358/400
7/7 - 0s - loss: 0.1337 - accuracy: 0.9766 - val_loss: 0.2003 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 359/400
7/7 - 0s - loss: 0.1335 - accuracy: 0.9766 - val_loss: 0.2004 - val_accuracy: 0.9375 - 36ms/epoch - 5ms/step
Epoch 360/400
7/7 - 0s - loss: 0.1332 - accuracy: 0.9766 - val_loss: 0.2006 - val_accuracy: 0.9375 - 36ms/epoch - 5ms/step
Epoch 361/400
7/7 - 0s - loss: 0.1324 - accuracy: 0.9766 - val_loss: 0.2010 - val_accuracy: 0.9375 - 37ms/epoch - 5ms/step
Epoch 362/400
7/7 - 0s - loss: 0.1323 - accuracy: 0.9766 - val_loss: 0.2011 - val_accuracy: 0.9375 - 35ms/epoch - 5ms/step
Epoch 363/400
7/7 - 0s - loss: 0.1323 - accuracy: 0.9766 - val_loss: 0.2012 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 364/400
7/7 - 0s - loss: 0.1317 - accuracy: 0.9766 - val_loss: 0.2012 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 365/400
7/7 - 0s - loss: 0.1313 - accuracy: 0.9766 - val_loss: 0.2017 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 366/400
7/7 - 0s - loss: 0.1309 - accuracy: 0.9766 - val_loss: 0.2016 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 367/400
7/7 - 0s - loss: 0.1300 - accuracy: 0.9766 - val_loss: 0.2013 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 368/400
7/7 - 0s - loss: 0.1299 - accuracy: 0.9766 - val_loss: 0.2016 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 369/400
7/7 - 0s - loss: 0.1301 - accuracy: 0.9766 - val_loss: 0.2018 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 370/400
7/7 - 0s - loss: 0.1295 - accuracy: 0.9766 - val_loss: 0.2017 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 371/400
7/7 - 0s - loss: 0.1288 - accuracy: 0.9766 - val_loss: 0.2024 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 372/400
7/7 - 0s - loss: 0.1288 - accuracy: 0.9766 - val_loss: 0.2023 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 373/400
7/7 - 0s - loss: 0.1281 - accuracy: 0.9766 - val_loss: 0.2025 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 374/400
7/7 - 0s - loss: 0.1287 - accuracy: 0.9766 - val_loss: 0.2027 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 375/400
7/7 - 0s - loss: 0.1278 - accuracy: 0.9766 - val_loss: 0.2026 - val_accuracy: 0.9375 - 40ms/epoch - 6ms/step
Epoch 376/400
7/7 - 0s - loss: 0.1275 - accuracy: 0.9766 - val_loss: 0.2030 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 377/400
7/7 - 0s - loss: 0.1271 - accuracy: 0.9766 - val_loss: 0.2032 - val_accuracy: 0.9375 - 35ms/epoch - 5ms/step
Epoch 378/400
7/7 - 0s - loss: 0.1263 - accuracy: 0.9766 - val_loss: 0.2034 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 379/400
7/7 - 0s - loss: 0.1267 - accuracy: 0.9766 - val_loss: 0.2033 - val_accuracy: 0.9375 - 35ms/epoch - 5ms/step
Epoch 380/400
7/7 - 0s - loss: 0.1260 - accuracy: 0.9766 - val_loss: 0.2033 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 381/400
7/7 - 0s - loss: 0.1252 - accuracy: 0.9766 - val_loss: 0.2034 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 382/400
7/7 - 0s - loss: 0.1249 - accuracy: 0.9766 - val_loss: 0.2034 - val_accuracy: 0.9375 - 35ms/epoch - 5ms/step
Epoch 383/400
7/7 - 0s - loss: 0.1247 - accuracy: 0.9766 - val_loss: 0.2037 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 384/400
7/7 - 0s - loss: 0.1246 - accuracy: 0.9766 - val_loss: 0.2042 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 385/400
7/7 - 0s - loss: 0.1247 - accuracy: 0.9766 - val_loss: 0.2040 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 386/400
7/7 - 0s - loss: 0.1239 - accuracy: 0.9766 - val_loss: 0.2043 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 387/400
7/7 - 0s - loss: 0.1236 - accuracy: 0.9766 - val_loss: 0.2041 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 388/400
7/7 - 0s - loss: 0.1235 - accuracy: 0.9766 - val_loss: 0.2045 - val_accuracy: 0.9375 - 37ms/epoch - 5ms/step
Epoch 389/400
7/7 - 0s - loss: 0.1231 - accuracy: 0.9766 - val_loss: 0.2052 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 390/400
7/7 - 0s - loss: 0.1225 - accuracy: 0.9766 - val_loss: 0.2052 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 391/400
7/7 - 0s - loss: 0.1226 - accuracy: 0.9766 - val_loss: 0.2052 - val_accuracy: 0.9375 - 35ms/epoch - 5ms/step
Epoch 392/400
7/7 - 0s - loss: 0.1220 - accuracy: 0.9766 - val_loss: 0.2055 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 393/400
7/7 - 0s - loss: 0.1217 - accuracy: 0.9766 - val_loss: 0.2063 - val_accuracy: 0.9062 - 35ms/epoch - 5ms/step
Epoch 394/400
7/7 - 0s - loss: 0.1215 - accuracy: 0.9766 - val_loss: 0.2062 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 395/400
7/7 - 0s - loss: 0.1209 - accuracy: 0.9766 - val_loss: 0.2061 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
Epoch 396/400
7/7 - 0s - loss: 0.1207 - accuracy: 0.9766 - val_loss: 0.2066 - val_accuracy: 0.9062 - 33ms/epoch - 5ms/step
Epoch 397/400
7/7 - 0s - loss: 0.1207 - accuracy: 0.9766 - val_loss: 0.2066 - val_accuracy: 0.9062 - 34ms/epoch - 5ms/step
Epoch 398/400
7/7 - 0s - loss: 0.1203 - accuracy: 0.9766 - val_loss: 0.2066 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 399/400
7/7 - 0s - loss: 0.1198 - accuracy: 0.9766 - val_loss: 0.2064 - val_accuracy: 0.9375 - 33ms/epoch - 5ms/step
Epoch 400/400
7/7 - 0s - loss: 0.1196 - accuracy: 0.9766 - val_loss: 0.2065 - val_accuracy: 0.9375 - 34ms/epoch - 5ms/step
plot (history) + ggtitle ("Training a neural network based classifier on the iris data set" ) + theme_bw ()
