Stat. 654 Quiz keras python

Clone the TFPlayground Github repository into your R Project folder.

To clone the repository you can use RStudio

File > New Project > Version Control > Git

and paste the URL of the repository into the Git Repository URL box. Then select a folder to clone the repository into.

Click the Green button and copy the ulr: https://github.com/hyounesy/TFPlaygroundPSA.git

Then paste the URL into the Git Repository URL box. Select a folder to clone the repository into. Click the Create Project button.

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

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
from tensorflow import keras
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder

WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1776444926.442898   84416 cpu_feature_guard.cc:227] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.

Load the data

input = pd.read_csv("data/tiny/xor_25/input.txt", 
     sep="\t", escapechar="\\", 
     header=0)
     
print(input.head())

   pid        X1        X2  X1Squared  X2Squared       X1X2     sinX1  \
0    0 -2.821787  2.515420   7.962482   6.327340  -7.097981 -0.314382   
1    1  3.490298  1.194088  12.182181   1.425846   4.167723 -0.341681   
2    2 -3.775428 -2.684004  14.253855   7.203879  10.133265  0.592239   
3    3  2.217170  4.140828   4.915844  17.146453   9.180920  0.798273   
4    4  3.635186  0.969978  13.214575   0.940857   3.526050 -0.473793   

      sinX2  label  
0  0.586047     -1  
1  0.929881      1  
2 -0.441786      1  
3 -0.841057      1  
4  0.824873      1

input = input.rename(columns=lambda x: x.strip())
input = input.drop(columns=['pid'])
input['label'] = np.where(input['label'] == -1, 0, 1)
input['label'] = input['label'].astype(int)
input = pd.DataFrame(input)
print(input.head())

         X1        X2  X1Squared  X2Squared       X1X2     sinX1     sinX2  \
0 -2.821787  2.515420   7.962482   6.327340  -7.097981 -0.314382  0.586047   
1  3.490298  1.194088  12.182181   1.425846   4.167723 -0.341681  0.929881   
2 -3.775428 -2.684004  14.253855   7.203879  10.133265  0.592239 -0.441786   
3  2.217170  4.140828   4.915844  17.146453   9.180920  0.798273 -0.841057   
4  3.635186  0.969978  13.214575   0.940857   3.526050 -0.473793  0.824873   

   label  
0      0  
1      1  
2      1  
3      1  
4      1

Split the data into training and testing sets

n = input.shape[0]
input_parts = train_test_split(input, test_size=0.2, random_state=42)
train = input_parts[0]
test = input_parts[1]
print("Number of rows in training set: ", train.shape[0])
print("Number of rows in testing set: ", test.shape[0])

Number of rows in training set:  160
Number of rows in testing set:  40

Visualize the data

sns.scatterplot(data=train, x='X1', y='X2', hue='label')
plt.show()

sns.scatterplot(data=test, x='X1', y='X2', hue='label')
plt.show()

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.

print(train.head())

x_train = train.drop(columns=['label']).values
y_train = train['label'].values
x_test = test.drop(columns=['label']).values
y_test = test['label'].values
print(x_train.shape)
print(x_test.shape)
print(y_train.shape)
print(y_test.shape)

           X1        X2  X1Squared  X2Squared       X1X2     sinX1     sinX2  \
79  -5.204244 -3.877999  27.084159  15.038878  20.182056  0.881458  0.671630   
197 -2.440170  1.917264   5.954432   3.675902  -4.678451 -0.645305  0.940578   
38   1.098392 -0.720133   1.206466   0.518591  -0.790988  0.890477 -0.659485   
24  -1.972084  0.886488   3.889115   0.785861  -1.748229 -0.920559  0.774856   
122 -4.660882  0.500449  21.723825   0.250449  -2.332533  0.998674  0.479819   

     label  
79       1  
197      0  
38       1  
24       0  
122      1  
(160, 7)
(40, 7)
(160,)
(40,)

Set Architecture

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

keras activation

Use all 7 variables.

model = keras.Sequential()
model.add(keras.layers.Dense(units=8, activation='relu', input_shape=(7,)))
model.add(keras.layers.Dense(units=3, activation='relu'))
model.add(keras.layers.Dense(units=1, activation='sigmoid'))
model.summary()

/home/esuess/.virtualenvs/r-keras/lib/python3.10/site-packages/keras/src/layers/core/dense.py:95: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
I0000 00:00:1776444928.283704   84416 gpu_device.cc:2043] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 7280 MB memory:  -> device: 0, name: NVIDIA GeForce GTX 1080, pci bus id: 0000:01:00.0, compute capability: 6.1
I0000 00:00:1776444928.284304   84416 gpu_device.cc:2043] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 5497 MB memory:  -> device: 1, name: NVIDIA P106-100, pci bus id: 0000:06:00.0, compute capability: 6.1

Model: "sequential"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ dense (Dense)                   │ (None, 8)              │            64 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_1 (Dense)                 │ (None, 3)              │            27 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_2 (Dense)                 │ (None, 1)              │             4 │
└─────────────────────────────────┴────────────────────────┴───────────────┘

 Total params: 95 (380.00 B)

 Trainable params: 95 (380.00 B)

 Non-trainable params: 0 (0.00 B)

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

model.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.

history = model.fit(x_train, y_train, 
                    epochs=400, 
                    batch_size=20, 
                    validation_split=0.2)

Epoch 1/400

I0000 00:00:1776444929.403379   84501 service.cc:153] XLA service 0x7f926402ef70 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1776444929.403407   84501 service.cc:161]   StreamExecutor [0]: NVIDIA GeForce GTX 1080, Compute Capability 6.1 (Driver: 13.0.0; Runtime: 12.9.0; Toolkit: 12.5.0; DNN: 9.21.0)
I0000 00:00:1776444929.403411   84501 service.cc:161]   StreamExecutor [1]: NVIDIA P106-100, Compute Capability 6.1 (Driver: 13.0.0; Runtime: 12.9.0; Toolkit: 12.5.0; DNN: 9.21.0)
I0000 00:00:1776444929.419138   84501 dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
I0000 00:00:1776444929.509551   84501 cuda_dnn.cc:461] Loaded cuDNN version 92100
I0000 00:00:1776444929.514631   84501 dot_merger.cc:481] Merging Dots in computation: a_inference_one_step_on_data_958__.9

1/7 ━━━━━━━━━━━━━━━━━━━━ 6s 1s/step - accuracy: 0.8000 - loss: 0.4040

I0000 00:00:1776444930.085325   84501 device_compiler.h:208] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.
I0000 00:00:1776444930.190751   84501 dot_merger.cc:481] Merging Dots in computation: a_inference_one_step_on_data_958__.9


7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 78ms/step - accuracy: 0.7689 - loss: 0.4523

7/7 ━━━━━━━━━━━━━━━━━━━━ 2s 180ms/step - accuracy: 0.7578 - loss: 0.4789 - val_accuracy: 0.9062 - val_loss: 0.3129

Epoch 2/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 0.7500 - loss: 0.5295

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.7656 - loss: 0.4186 - val_accuracy: 0.9375 - val_loss: 0.2794

Epoch 3/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.5500 - loss: 0.7496

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.7812 - loss: 0.3808 - val_accuracy: 0.9062 - val_loss: 0.2596

Epoch 4/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.7500 - loss: 0.3245

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.8047 - loss: 0.3581 - val_accuracy: 0.9062 - val_loss: 0.2440

Epoch 5/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.7500 - loss: 0.4581

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.8203 - loss: 0.3396 - val_accuracy: 0.9062 - val_loss: 0.2296

Epoch 6/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - accuracy: 0.8000 - loss: 0.4080

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.8203 - loss: 0.3211 - val_accuracy: 0.9062 - val_loss: 0.2173

Epoch 7/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.8500 - loss: 0.2736

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.8438 - loss: 0.3073 - val_accuracy: 0.9688 - val_loss: 0.2068

Epoch 8/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.8500 - loss: 0.3405

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.8750 - loss: 0.2949 - val_accuracy: 0.9688 - val_loss: 0.1971

Epoch 9/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.8000 - loss: 0.3259

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.8906 - loss: 0.2811 - val_accuracy: 0.9688 - val_loss: 0.1868

Epoch 10/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.8500 - loss: 0.2762

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.8906 - loss: 0.2700 - val_accuracy: 0.9688 - val_loss: 0.1772

Epoch 11/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.1395

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.8984 - loss: 0.2580 - val_accuracy: 0.9688 - val_loss: 0.1697

Epoch 12/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 0.8500 - loss: 0.3275

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9062 - loss: 0.2471 - val_accuracy: 0.9688 - val_loss: 0.1615

Epoch 13/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9000 - loss: 0.1636

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9062 - loss: 0.2373 - val_accuracy: 0.9688 - val_loss: 0.1555

Epoch 14/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.2300

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9297 - loss: 0.2289 - val_accuracy: 0.9688 - val_loss: 0.1491

Epoch 15/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9000 - loss: 0.3145

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9141 - loss: 0.2222 - val_accuracy: 0.9688 - val_loss: 0.1429

Epoch 16/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.1766

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9297 - loss: 0.2154 - val_accuracy: 0.9688 - val_loss: 0.1373

Epoch 17/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 0.8500 - loss: 0.2259

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9375 - loss: 0.2064 - val_accuracy: 1.0000 - val_loss: 0.1322

Epoch 18/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.1074

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9375 - loss: 0.1999 - val_accuracy: 1.0000 - val_loss: 0.1279

Epoch 19/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.1553

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9375 - loss: 0.1936 - val_accuracy: 1.0000 - val_loss: 0.1234

Epoch 20/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.2150

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9453 - loss: 0.1875 - val_accuracy: 0.9688 - val_loss: 0.1190

Epoch 21/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9000 - loss: 0.2974

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9453 - loss: 0.1826 - val_accuracy: 0.9688 - val_loss: 0.1155

Epoch 22/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9000 - loss: 0.2695

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9453 - loss: 0.1769 - val_accuracy: 0.9688 - val_loss: 0.1119

Epoch 23/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.1667

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9453 - loss: 0.1730 - val_accuracy: 0.9688 - val_loss: 0.1089

Epoch 24/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.1749

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9453 - loss: 0.1683 - val_accuracy: 0.9688 - val_loss: 0.1064

Epoch 25/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 0.9500 - loss: 0.1737

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9453 - loss: 0.1639 - val_accuracy: 0.9688 - val_loss: 0.1045

Epoch 26/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.8000 - loss: 0.3483

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9453 - loss: 0.1605 - val_accuracy: 0.9688 - val_loss: 0.1023

Epoch 27/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.1842

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9453 - loss: 0.1572 - val_accuracy: 0.9688 - val_loss: 0.1006

Epoch 28/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9000 - loss: 0.2243

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9453 - loss: 0.1543 - val_accuracy: 0.9688 - val_loss: 0.0995

Epoch 29/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.1094

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9453 - loss: 0.1521 - val_accuracy: 0.9688 - val_loss: 0.0976

Epoch 30/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9000 - loss: 0.1104

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9453 - loss: 0.1492 - val_accuracy: 0.9688 - val_loss: 0.0956

Epoch 31/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step - accuracy: 1.0000 - loss: 0.0339

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9453 - loss: 0.1466 - val_accuracy: 0.9688 - val_loss: 0.0938

Epoch 32/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0652

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9453 - loss: 0.1442 - val_accuracy: 0.9688 - val_loss: 0.0918

Epoch 33/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.1365

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9531 - loss: 0.1416 - val_accuracy: 0.9688 - val_loss: 0.0906

Epoch 34/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.1012

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9531 - loss: 0.1405 - val_accuracy: 0.9688 - val_loss: 0.0896

Epoch 35/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 0.9500 - loss: 0.1019

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9531 - loss: 0.1382 - val_accuracy: 0.9688 - val_loss: 0.0886

Epoch 36/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.1600

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9531 - loss: 0.1364 - val_accuracy: 0.9688 - val_loss: 0.0876

Epoch 37/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 0.9500 - loss: 0.1237

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9531 - loss: 0.1347 - val_accuracy: 0.9688 - val_loss: 0.0877

Epoch 38/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0252

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.9531 - loss: 0.1326 - val_accuracy: 0.9688 - val_loss: 0.0864

Epoch 39/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 1.0000 - loss: 0.1115

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9531 - loss: 0.1314 - val_accuracy: 0.9688 - val_loss: 0.0857

Epoch 40/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.0843

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9531 - loss: 0.1299 - val_accuracy: 0.9688 - val_loss: 0.0847

Epoch 41/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0574

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9531 - loss: 0.1279 - val_accuracy: 0.9688 - val_loss: 0.0838

Epoch 42/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.1679

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9531 - loss: 0.1266 - val_accuracy: 0.9688 - val_loss: 0.0840

Epoch 43/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0184

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9531 - loss: 0.1238 - val_accuracy: 0.9688 - val_loss: 0.0831

Epoch 44/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.0890

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9531 - loss: 0.1226 - val_accuracy: 0.9688 - val_loss: 0.0825

Epoch 45/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0478

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9531 - loss: 0.1212 - val_accuracy: 0.9688 - val_loss: 0.0818

Epoch 46/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0881

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9531 - loss: 0.1191 - val_accuracy: 0.9688 - val_loss: 0.0811

Epoch 47/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9000 - loss: 0.1329

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9531 - loss: 0.1185 - val_accuracy: 0.9688 - val_loss: 0.0807

Epoch 48/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 0.9500 - loss: 0.0925

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9531 - loss: 0.1163 - val_accuracy: 0.9688 - val_loss: 0.0798

Epoch 49/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.0746

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9531 - loss: 0.1152 - val_accuracy: 0.9688 - val_loss: 0.0798

Epoch 50/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 0.9500 - loss: 0.1148

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9531 - loss: 0.1138 - val_accuracy: 0.9688 - val_loss: 0.0792

Epoch 51/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.0900

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9531 - loss: 0.1122 - val_accuracy: 0.9688 - val_loss: 0.0784

Epoch 52/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.1576

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9531 - loss: 0.1113 - val_accuracy: 0.9688 - val_loss: 0.0780

Epoch 53/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.1518

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9531 - loss: 0.1107 - val_accuracy: 0.9688 - val_loss: 0.0777

Epoch 54/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.1205

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.9531 - loss: 0.1095 - val_accuracy: 0.9688 - val_loss: 0.0769

Epoch 55/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.9500 - loss: 0.0984

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9531 - loss: 0.1088 - val_accuracy: 0.9688 - val_loss: 0.0766

Epoch 56/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 0.9000 - loss: 0.2054

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9609 - loss: 0.1068 - val_accuracy: 0.9688 - val_loss: 0.0760

Epoch 57/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0212

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9609 - loss: 0.1057 - val_accuracy: 0.9688 - val_loss: 0.0752

Epoch 58/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - accuracy: 1.0000 - loss: 0.0788

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9531 - loss: 0.1047 - val_accuracy: 0.9688 - val_loss: 0.0741

Epoch 59/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - accuracy: 0.9500 - loss: 0.1264

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9609 - loss: 0.1041 - val_accuracy: 0.9688 - val_loss: 0.0738

Epoch 60/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 1.0000 - loss: 0.1073

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9609 - loss: 0.1036 - val_accuracy: 0.9688 - val_loss: 0.0732

Epoch 61/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.1371

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9531 - loss: 0.1022 - val_accuracy: 0.9688 - val_loss: 0.0728

Epoch 62/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.8500 - loss: 0.1968

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9531 - loss: 0.1012 - val_accuracy: 0.9688 - val_loss: 0.0719

Epoch 63/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0511

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9531 - loss: 0.1001 - val_accuracy: 0.9688 - val_loss: 0.0715

Epoch 64/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0067

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9531 - loss: 0.0997 - val_accuracy: 0.9688 - val_loss: 0.0711

Epoch 65/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9000 - loss: 0.1619

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9531 - loss: 0.0992 - val_accuracy: 0.9688 - val_loss: 0.0710

Epoch 66/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 1.0000 - loss: 0.0305

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9531 - loss: 0.0987 - val_accuracy: 0.9688 - val_loss: 0.0704

Epoch 67/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0445

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9531 - loss: 0.0975 - val_accuracy: 0.9688 - val_loss: 0.0709

Epoch 68/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.1050

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9531 - loss: 0.0968 - val_accuracy: 0.9688 - val_loss: 0.0706

Epoch 69/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0577

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9531 - loss: 0.0968 - val_accuracy: 0.9688 - val_loss: 0.0703

Epoch 70/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 0.9000 - loss: 0.1775

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9531 - loss: 0.0963 - val_accuracy: 0.9688 - val_loss: 0.0701

Epoch 71/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 0.9500 - loss: 0.0609

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9531 - loss: 0.0953 - val_accuracy: 0.9688 - val_loss: 0.0699

Epoch 72/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0604

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9531 - loss: 0.0949 - val_accuracy: 0.9688 - val_loss: 0.0696

Epoch 73/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.0477

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9531 - loss: 0.0949 - val_accuracy: 0.9688 - val_loss: 0.0697

Epoch 74/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.1044

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9531 - loss: 0.0942 - val_accuracy: 0.9688 - val_loss: 0.0700

Epoch 75/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0950

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9531 - loss: 0.0943 - val_accuracy: 0.9688 - val_loss: 0.0703

Epoch 76/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0287

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9531 - loss: 0.0937 - val_accuracy: 0.9688 - val_loss: 0.0702

Epoch 77/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 0.8500 - loss: 0.2014

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9531 - loss: 0.0924 - val_accuracy: 0.9688 - val_loss: 0.0701

Epoch 78/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9000 - loss: 0.1499

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9531 - loss: 0.0920 - val_accuracy: 0.9688 - val_loss: 0.0693

Epoch 79/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 0.9500 - loss: 0.1612

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9531 - loss: 0.0924 - val_accuracy: 0.9688 - val_loss: 0.0692

Epoch 80/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9000 - loss: 0.1768

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9531 - loss: 0.0913 - val_accuracy: 0.9688 - val_loss: 0.0690

Epoch 81/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.0749

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9531 - loss: 0.0903 - val_accuracy: 0.9688 - val_loss: 0.0688

Epoch 82/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0389

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9531 - loss: 0.0909 - val_accuracy: 0.9688 - val_loss: 0.0692

Epoch 83/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 0.9000 - loss: 0.1272

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9531 - loss: 0.0903 - val_accuracy: 0.9688 - val_loss: 0.0690

Epoch 84/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 0.8500 - loss: 0.2067

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9531 - loss: 0.0892 - val_accuracy: 0.9688 - val_loss: 0.0689

Epoch 85/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.1282

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9609 - loss: 0.0897 - val_accuracy: 0.9688 - val_loss: 0.0684

Epoch 86/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0364

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9609 - loss: 0.0892 - val_accuracy: 0.9688 - val_loss: 0.0684

Epoch 87/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9000 - loss: 0.1231

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9531 - loss: 0.0887 - val_accuracy: 0.9688 - val_loss: 0.0680

Epoch 88/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9000 - loss: 0.1378

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9531 - loss: 0.0887 - val_accuracy: 0.9688 - val_loss: 0.0685

Epoch 89/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 0.9000 - loss: 0.1372

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9609 - loss: 0.0872 - val_accuracy: 0.9688 - val_loss: 0.0683

Epoch 90/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 0.9000 - loss: 0.1214

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9531 - loss: 0.0863 - val_accuracy: 0.9688 - val_loss: 0.0683

Epoch 91/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - accuracy: 0.9500 - loss: 0.0939

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9531 - loss: 0.0853 - val_accuracy: 0.9688 - val_loss: 0.0691

Epoch 92/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0178

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9609 - loss: 0.0839 - val_accuracy: 0.9688 - val_loss: 0.0691

Epoch 93/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9000 - loss: 0.1348

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9609 - loss: 0.0833 - val_accuracy: 0.9688 - val_loss: 0.0690

Epoch 94/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 0.9000 - loss: 0.1246

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9609 - loss: 0.0823 - val_accuracy: 0.9688 - val_loss: 0.0693

Epoch 95/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.8500 - loss: 0.2133

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9609 - loss: 0.0813 - val_accuracy: 0.9688 - val_loss: 0.0695

Epoch 96/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0433

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9609 - loss: 0.0809 - val_accuracy: 0.9688 - val_loss: 0.0697

Epoch 97/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 1.0000 - loss: 0.0887

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9609 - loss: 0.0798 - val_accuracy: 0.9688 - val_loss: 0.0698

Epoch 98/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.1217

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9609 - loss: 0.0792 - val_accuracy: 0.9688 - val_loss: 0.0699

Epoch 99/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.8500 - loss: 0.1346

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9609 - loss: 0.0776 - val_accuracy: 0.9688 - val_loss: 0.0683

Epoch 100/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 0.9000 - loss: 0.1594

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9609 - loss: 0.0776 - val_accuracy: 0.9688 - val_loss: 0.0683

Epoch 101/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0705

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9609 - loss: 0.0781 - val_accuracy: 0.9688 - val_loss: 0.0679

Epoch 102/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.1244

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9609 - loss: 0.0775 - val_accuracy: 0.9688 - val_loss: 0.0677

Epoch 103/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.0584

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9609 - loss: 0.0776 - val_accuracy: 0.9688 - val_loss: 0.0675

Epoch 104/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.1043

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9609 - loss: 0.0764 - val_accuracy: 0.9688 - val_loss: 0.0673

Epoch 105/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0870

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9609 - loss: 0.0756 - val_accuracy: 0.9688 - val_loss: 0.0676

Epoch 106/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - accuracy: 0.9000 - loss: 0.1354

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9609 - loss: 0.0743 - val_accuracy: 0.9688 - val_loss: 0.0679

Epoch 107/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - accuracy: 0.9500 - loss: 0.1028

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9609 - loss: 0.0750 - val_accuracy: 0.9688 - val_loss: 0.0674

Epoch 108/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 0.9500 - loss: 0.1132

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9609 - loss: 0.0740 - val_accuracy: 0.9688 - val_loss: 0.0674

Epoch 109/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0685

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9688 - loss: 0.0737 - val_accuracy: 0.9688 - val_loss: 0.0674

Epoch 110/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.0846

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9609 - loss: 0.0728 - val_accuracy: 0.9688 - val_loss: 0.0679

Epoch 111/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0434

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9688 - loss: 0.0726 - val_accuracy: 0.9688 - val_loss: 0.0691

Epoch 112/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.0859

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9688 - loss: 0.0726 - val_accuracy: 0.9688 - val_loss: 0.0691

Epoch 113/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0290

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9609 - loss: 0.0725 - val_accuracy: 0.9688 - val_loss: 0.0684

Epoch 114/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 0.9500 - loss: 0.0902

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9609 - loss: 0.0715 - val_accuracy: 0.9688 - val_loss: 0.0688

Epoch 115/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0447

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9609 - loss: 0.0713 - val_accuracy: 0.9688 - val_loss: 0.0696

Epoch 116/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 0.9000 - loss: 0.1143

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9609 - loss: 0.0704 - val_accuracy: 0.9688 - val_loss: 0.0702

Epoch 117/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0329

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9609 - loss: 0.0712 - val_accuracy: 0.9688 - val_loss: 0.0710

Epoch 118/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.0627

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9688 - loss: 0.0703 - val_accuracy: 0.9688 - val_loss: 0.0705

Epoch 119/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 0.9000 - loss: 0.1592

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9688 - loss: 0.0688 - val_accuracy: 0.9688 - val_loss: 0.0712

Epoch 120/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0431

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9688 - loss: 0.0695 - val_accuracy: 0.9688 - val_loss: 0.0718

Epoch 121/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - accuracy: 1.0000 - loss: 0.0269

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9688 - loss: 0.0686 - val_accuracy: 0.9688 - val_loss: 0.0719

Epoch 122/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0160

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9688 - loss: 0.0679 - val_accuracy: 0.9688 - val_loss: 0.0716

Epoch 123/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.0835

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9688 - loss: 0.0677 - val_accuracy: 0.9688 - val_loss: 0.0717

Epoch 124/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0783

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9688 - loss: 0.0680 - val_accuracy: 0.9688 - val_loss: 0.0717

Epoch 125/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0597

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9688 - loss: 0.0668 - val_accuracy: 0.9688 - val_loss: 0.0720

Epoch 126/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0338

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9688 - loss: 0.0659 - val_accuracy: 0.9688 - val_loss: 0.0724

Epoch 127/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0427

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9688 - loss: 0.0664 - val_accuracy: 0.9688 - val_loss: 0.0721

Epoch 128/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 0.9500 - loss: 0.0667

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9688 - loss: 0.0660 - val_accuracy: 0.9688 - val_loss: 0.0713

Epoch 129/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 0.9500 - loss: 0.1033

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9688 - loss: 0.0649 - val_accuracy: 0.9688 - val_loss: 0.0717

Epoch 130/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.0589

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9688 - loss: 0.0644 - val_accuracy: 0.9688 - val_loss: 0.0710

Epoch 131/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0718

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9688 - loss: 0.0642 - val_accuracy: 0.9688 - val_loss: 0.0721

Epoch 132/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 0.9500 - loss: 0.0781

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9688 - loss: 0.0644 - val_accuracy: 0.9688 - val_loss: 0.0728

Epoch 133/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0183

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9688 - loss: 0.0637 - val_accuracy: 0.9688 - val_loss: 0.0724

Epoch 134/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0105

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9688 - loss: 0.0631 - val_accuracy: 0.9688 - val_loss: 0.0723

Epoch 135/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0218

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9688 - loss: 0.0626 - val_accuracy: 0.9688 - val_loss: 0.0724

Epoch 136/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 0.9500 - loss: 0.0417

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9688 - loss: 0.0621 - val_accuracy: 0.9688 - val_loss: 0.0728

Epoch 137/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0644

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9766 - loss: 0.0626 - val_accuracy: 0.9688 - val_loss: 0.0729

Epoch 138/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0593

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9688 - loss: 0.0623 - val_accuracy: 0.9688 - val_loss: 0.0725

Epoch 139/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0292

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9688 - loss: 0.0617 - val_accuracy: 0.9688 - val_loss: 0.0729

Epoch 140/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step - accuracy: 0.9500 - loss: 0.0664

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9688 - loss: 0.0621 - val_accuracy: 0.9688 - val_loss: 0.0732

Epoch 141/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.0782

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9688 - loss: 0.0608 - val_accuracy: 0.9688 - val_loss: 0.0733

Epoch 142/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 0.9500 - loss: 0.0604

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9688 - loss: 0.0609 - val_accuracy: 0.9688 - val_loss: 0.0735

Epoch 143/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0478

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9688 - loss: 0.0606 - val_accuracy: 0.9688 - val_loss: 0.0740

Epoch 144/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0020

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9688 - loss: 0.0595 - val_accuracy: 0.9688 - val_loss: 0.0751

Epoch 145/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 1.0000 - loss: 0.0472

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9688 - loss: 0.0604 - val_accuracy: 0.9688 - val_loss: 0.0761

Epoch 146/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.0610

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9688 - loss: 0.0598 - val_accuracy: 0.9688 - val_loss: 0.0760

Epoch 147/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0394

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9766 - loss: 0.0593 - val_accuracy: 0.9688 - val_loss: 0.0757

Epoch 148/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9000 - loss: 0.1090

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9688 - loss: 0.0587 - val_accuracy: 0.9688 - val_loss: 0.0757

Epoch 149/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.0746

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9688 - loss: 0.0590 - val_accuracy: 0.9688 - val_loss: 0.0757

Epoch 150/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - accuracy: 1.0000 - loss: 0.0562

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9688 - loss: 0.0586 - val_accuracy: 0.9688 - val_loss: 0.0759

Epoch 151/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0084

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9688 - loss: 0.0576 - val_accuracy: 0.9688 - val_loss: 0.0767

Epoch 152/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 1.0000 - loss: 0.0307

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9766 - loss: 0.0576 - val_accuracy: 0.9688 - val_loss: 0.0760

Epoch 153/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.0667

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9688 - loss: 0.0576 - val_accuracy: 0.9688 - val_loss: 0.0763

Epoch 154/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0113

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9766 - loss: 0.0566 - val_accuracy: 0.9688 - val_loss: 0.0762

Epoch 155/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0077

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9766 - loss: 0.0573 - val_accuracy: 0.9688 - val_loss: 0.0764

Epoch 156/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.1023

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9766 - loss: 0.0572 - val_accuracy: 0.9688 - val_loss: 0.0766

Epoch 157/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0563

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9766 - loss: 0.0561 - val_accuracy: 0.9688 - val_loss: 0.0773

Epoch 158/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0169

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9766 - loss: 0.0557 - val_accuracy: 0.9688 - val_loss: 0.0758

Epoch 159/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0115

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9766 - loss: 0.0552 - val_accuracy: 0.9688 - val_loss: 0.0757

Epoch 160/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.0712

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9766 - loss: 0.0545 - val_accuracy: 0.9688 - val_loss: 0.0764

Epoch 161/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step - accuracy: 0.9500 - loss: 0.0794

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9766 - loss: 0.0544 - val_accuracy: 0.9688 - val_loss: 0.0767

Epoch 162/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.0897

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9766 - loss: 0.0534 - val_accuracy: 0.9688 - val_loss: 0.0766

Epoch 163/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0307

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9766 - loss: 0.0540 - val_accuracy: 0.9688 - val_loss: 0.0783

Epoch 164/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0306

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9766 - loss: 0.0532 - val_accuracy: 0.9688 - val_loss: 0.0780

Epoch 165/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 0.9500 - loss: 0.0628

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9766 - loss: 0.0529 - val_accuracy: 0.9688 - val_loss: 0.0796

Epoch 166/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0855

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9766 - loss: 0.0525 - val_accuracy: 0.9688 - val_loss: 0.0790

Epoch 167/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0217

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9766 - loss: 0.0520 - val_accuracy: 0.9688 - val_loss: 0.0816

Epoch 168/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0027

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9844 - loss: 0.0512 - val_accuracy: 0.9688 - val_loss: 0.0816

Epoch 169/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0711

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9844 - loss: 0.0516 - val_accuracy: 0.9688 - val_loss: 0.0813

Epoch 170/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0054

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9844 - loss: 0.0512 - val_accuracy: 0.9688 - val_loss: 0.0807

Epoch 171/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0370

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9922 - loss: 0.0504 - val_accuracy: 0.9688 - val_loss: 0.0809

Epoch 172/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0452

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9844 - loss: 0.0507 - val_accuracy: 0.9688 - val_loss: 0.0822

Epoch 173/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0585

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0497 - val_accuracy: 0.9688 - val_loss: 0.0803

Epoch 174/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0490

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0498 - val_accuracy: 0.9688 - val_loss: 0.0809

Epoch 175/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0263

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9766 - loss: 0.0488 - val_accuracy: 0.9688 - val_loss: 0.0823

Epoch 176/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0552

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9844 - loss: 0.0494 - val_accuracy: 0.9688 - val_loss: 0.0804

Epoch 177/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0336

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9844 - loss: 0.0482 - val_accuracy: 0.9688 - val_loss: 0.0815

Epoch 178/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0122

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9844 - loss: 0.0484 - val_accuracy: 0.9688 - val_loss: 0.0825

Epoch 179/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0386

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0479 - val_accuracy: 0.9688 - val_loss: 0.0817

Epoch 180/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0510

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9766 - loss: 0.0480 - val_accuracy: 0.9688 - val_loss: 0.0813

Epoch 181/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0268

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0469 - val_accuracy: 0.9688 - val_loss: 0.0822

Epoch 182/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0060

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0466 - val_accuracy: 0.9688 - val_loss: 0.0826

Epoch 183/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0406

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0462 - val_accuracy: 0.9688 - val_loss: 0.0820

Epoch 184/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0174

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0459 - val_accuracy: 0.9688 - val_loss: 0.0833

Epoch 185/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.1184

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9922 - loss: 0.0457 - val_accuracy: 0.9688 - val_loss: 0.0836

Epoch 186/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0671

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9844 - loss: 0.0459 - val_accuracy: 0.9688 - val_loss: 0.0833

Epoch 187/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0625

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9844 - loss: 0.0460 - val_accuracy: 0.9688 - val_loss: 0.0839

Epoch 188/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.1099

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9922 - loss: 0.0449 - val_accuracy: 0.9688 - val_loss: 0.0841

Epoch 189/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0683

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9844 - loss: 0.0448 - val_accuracy: 0.9688 - val_loss: 0.0842

Epoch 190/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.0378

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9844 - loss: 0.0444 - val_accuracy: 0.9688 - val_loss: 0.0844

Epoch 191/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0354

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0440 - val_accuracy: 0.9375 - val_loss: 0.0855

Epoch 192/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0814

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9844 - loss: 0.0437 - val_accuracy: 0.9375 - val_loss: 0.0869

Epoch 193/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0495

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9844 - loss: 0.0436 - val_accuracy: 0.9688 - val_loss: 0.0840

Epoch 194/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0644

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0429 - val_accuracy: 0.9688 - val_loss: 0.0837

Epoch 195/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0496

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0425 - val_accuracy: 0.9688 - val_loss: 0.0824

Epoch 196/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - accuracy: 0.9500 - loss: 0.0829

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0429 - val_accuracy: 0.9688 - val_loss: 0.0824

Epoch 197/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0148

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0424 - val_accuracy: 0.9688 - val_loss: 0.0841

Epoch 198/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0153

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9922 - loss: 0.0421 - val_accuracy: 0.9688 - val_loss: 0.0859

Epoch 199/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0421

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9922 - loss: 0.0418 - val_accuracy: 0.9688 - val_loss: 0.0864

Epoch 200/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0794

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0413 - val_accuracy: 0.9375 - val_loss: 0.0872

Epoch 201/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0095

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9844 - loss: 0.0422 - val_accuracy: 0.9375 - val_loss: 0.0881

Epoch 202/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0102

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0413 - val_accuracy: 0.9375 - val_loss: 0.0878

Epoch 203/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0110

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0412 - val_accuracy: 0.9375 - val_loss: 0.0880

Epoch 204/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0344

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9844 - loss: 0.0414 - val_accuracy: 0.9375 - val_loss: 0.0885

Epoch 205/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0500

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0403 - val_accuracy: 0.9688 - val_loss: 0.0855

Epoch 206/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0156

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0413 - val_accuracy: 0.9375 - val_loss: 0.0888

Epoch 207/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0472

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0402 - val_accuracy: 0.9375 - val_loss: 0.0894

Epoch 208/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0274

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0398 - val_accuracy: 0.9375 - val_loss: 0.0882

Epoch 209/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0175

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9922 - loss: 0.0402 - val_accuracy: 0.9375 - val_loss: 0.0878

Epoch 210/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0688

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0402 - val_accuracy: 0.9375 - val_loss: 0.0885

Epoch 211/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0056

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0394 - val_accuracy: 0.9688 - val_loss: 0.0879

Epoch 212/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 1.0000 - loss: 0.0110

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0395 - val_accuracy: 0.9375 - val_loss: 0.0893

Epoch 213/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0383

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9922 - loss: 0.0391 - val_accuracy: 0.9375 - val_loss: 0.0912

Epoch 214/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 1.0000 - loss: 0.0295

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9922 - loss: 0.0392 - val_accuracy: 0.9375 - val_loss: 0.0907

Epoch 215/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0381

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0388 - val_accuracy: 0.9375 - val_loss: 0.0910

Epoch 216/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0030

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0389 - val_accuracy: 0.9375 - val_loss: 0.0921

Epoch 217/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0620

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9844 - loss: 0.0388 - val_accuracy: 0.9375 - val_loss: 0.0924

Epoch 218/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0545

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9844 - loss: 0.0385 - val_accuracy: 0.9375 - val_loss: 0.0906

Epoch 219/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0213

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9844 - loss: 0.0381 - val_accuracy: 0.9688 - val_loss: 0.0890

Epoch 220/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.0928

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0374 - val_accuracy: 0.9375 - val_loss: 0.0899

Epoch 221/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0782

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0376 - val_accuracy: 0.9375 - val_loss: 0.0892

Epoch 222/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0447

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0377 - val_accuracy: 0.9375 - val_loss: 0.0912

Epoch 223/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 1.0000 - loss: 0.0098

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9922 - loss: 0.0372 - val_accuracy: 0.9375 - val_loss: 0.0913

Epoch 224/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0010

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9922 - loss: 0.0372 - val_accuracy: 0.9375 - val_loss: 0.0895

Epoch 225/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0203

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9922 - loss: 0.0369 - val_accuracy: 0.9375 - val_loss: 0.0906

Epoch 226/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0547

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9922 - loss: 0.0372 - val_accuracy: 0.9688 - val_loss: 0.0895

Epoch 227/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0422

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9922 - loss: 0.0366 - val_accuracy: 0.9688 - val_loss: 0.0896

Epoch 228/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0088

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9922 - loss: 0.0362 - val_accuracy: 0.9375 - val_loss: 0.0942

Epoch 229/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0262

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9922 - loss: 0.0361 - val_accuracy: 0.9375 - val_loss: 0.0943

Epoch 230/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0239

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0361 - val_accuracy: 0.9375 - val_loss: 0.0953

Epoch 231/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0287

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0362 - val_accuracy: 0.9375 - val_loss: 0.0938

Epoch 232/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0031

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0357 - val_accuracy: 0.9375 - val_loss: 0.0945

Epoch 233/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 1.0000 - loss: 0.0207

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9922 - loss: 0.0358 - val_accuracy: 0.9375 - val_loss: 0.0941

Epoch 234/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0264

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0355 - val_accuracy: 0.9375 - val_loss: 0.0929

Epoch 235/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0244

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0354 - val_accuracy: 0.9375 - val_loss: 0.0921

Epoch 236/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0349

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9922 - loss: 0.0350 - val_accuracy: 0.9375 - val_loss: 0.0939

Epoch 237/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 1.0000 - loss: 0.0025

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0346 - val_accuracy: 0.9375 - val_loss: 0.0957

Epoch 238/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0298

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0351 - val_accuracy: 0.9375 - val_loss: 0.0963

Epoch 239/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0131

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0348 - val_accuracy: 0.9375 - val_loss: 0.0939

Epoch 240/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.0783

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0345 - val_accuracy: 0.9375 - val_loss: 0.0942

Epoch 241/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0209

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0344 - val_accuracy: 0.9375 - val_loss: 0.0965

Epoch 242/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0069

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0345 - val_accuracy: 0.9375 - val_loss: 0.0964

Epoch 243/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0347

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9922 - loss: 0.0344 - val_accuracy: 0.9375 - val_loss: 0.0952

Epoch 244/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0082

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9922 - loss: 0.0339 - val_accuracy: 0.9375 - val_loss: 0.0964

Epoch 245/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0125

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9922 - loss: 0.0339 - val_accuracy: 0.9375 - val_loss: 0.0973

Epoch 246/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0177

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9922 - loss: 0.0334 - val_accuracy: 0.9375 - val_loss: 0.0981

Epoch 247/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0669

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0336 - val_accuracy: 0.9375 - val_loss: 0.0973

Epoch 248/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0021

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0333 - val_accuracy: 0.9062 - val_loss: 0.0996

Epoch 249/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step - accuracy: 1.0000 - loss: 0.0052

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0331 - val_accuracy: 0.9062 - val_loss: 0.0989

Epoch 250/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0189

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0328 - val_accuracy: 0.9375 - val_loss: 0.0944

Epoch 251/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.0392

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0327 - val_accuracy: 0.9375 - val_loss: 0.0949

Epoch 252/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0451

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0328 - val_accuracy: 0.9375 - val_loss: 0.0951

Epoch 253/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0029

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0330 - val_accuracy: 0.9375 - val_loss: 0.0942

Epoch 254/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.0636

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0327 - val_accuracy: 0.9375 - val_loss: 0.0952

Epoch 255/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - accuracy: 1.0000 - loss: 0.0049

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0324 - val_accuracy: 0.9375 - val_loss: 0.0965

Epoch 256/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0079

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0320 - val_accuracy: 0.9375 - val_loss: 0.0973

Epoch 257/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0211

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0321 - val_accuracy: 0.9375 - val_loss: 0.0981

Epoch 258/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0564

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0317 - val_accuracy: 0.9375 - val_loss: 0.0971

Epoch 259/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0360

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0319 - val_accuracy: 0.9375 - val_loss: 0.0979

Epoch 260/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0024

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0313 - val_accuracy: 0.9375 - val_loss: 0.0977

Epoch 261/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.0727

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0313 - val_accuracy: 0.9375 - val_loss: 0.0990

Epoch 262/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0418

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9922 - loss: 0.0313 - val_accuracy: 0.9062 - val_loss: 0.0999

Epoch 263/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0211

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0312 - val_accuracy: 0.9375 - val_loss: 0.0987

Epoch 264/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0123

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - accuracy: 0.9922 - loss: 0.0310 - val_accuracy: 0.9375 - val_loss: 0.0995

Epoch 265/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0016

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0307 - val_accuracy: 0.9062 - val_loss: 0.1001

Epoch 266/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0072

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9922 - loss: 0.0306 - val_accuracy: 0.9375 - val_loss: 0.1005

Epoch 267/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.0574

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0308 - val_accuracy: 0.9375 - val_loss: 0.0996

Epoch 268/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0215

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0303 - val_accuracy: 0.9375 - val_loss: 0.0984

Epoch 269/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 1.0000 - loss: 0.0381

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0305 - val_accuracy: 0.9062 - val_loss: 0.1005

Epoch 270/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.0682

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0302 - val_accuracy: 0.9062 - val_loss: 0.1007

Epoch 271/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0619

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0298 - val_accuracy: 0.9375 - val_loss: 0.1000

Epoch 272/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0031

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0300 - val_accuracy: 0.9375 - val_loss: 0.1002

Epoch 273/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0108

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9922 - loss: 0.0300 - val_accuracy: 0.9375 - val_loss: 0.1009

Epoch 274/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 1.0000 - loss: 0.0217

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0299 - val_accuracy: 0.9375 - val_loss: 0.0981

Epoch 275/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0205

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9922 - loss: 0.0291 - val_accuracy: 0.9375 - val_loss: 0.0979

Epoch 276/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0384

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.9922 - loss: 0.0294 - val_accuracy: 0.9375 - val_loss: 0.0999

Epoch 277/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0338

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0293 - val_accuracy: 0.9375 - val_loss: 0.1006

Epoch 278/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0027

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0292 - val_accuracy: 0.9062 - val_loss: 0.1025

Epoch 279/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0134

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0290 - val_accuracy: 0.9062 - val_loss: 0.1045

Epoch 280/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0059

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0286 - val_accuracy: 0.9375 - val_loss: 0.1009

Epoch 281/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 1.0000 - loss: 0.0074

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9922 - loss: 0.0291 - val_accuracy: 0.9375 - val_loss: 0.1013

Epoch 282/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0160

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0290 - val_accuracy: 0.9062 - val_loss: 0.1024

Epoch 283/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step - accuracy: 1.0000 - loss: 0.0022

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0287 - val_accuracy: 0.9375 - val_loss: 0.1009

Epoch 284/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 1.0000 - loss: 0.0074

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0285 - val_accuracy: 0.9375 - val_loss: 0.1000

Epoch 285/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0522

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0287 - val_accuracy: 0.9062 - val_loss: 0.1018

Epoch 286/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0021

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0283 - val_accuracy: 0.9375 - val_loss: 0.1018

Epoch 287/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0275

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0284 - val_accuracy: 0.9375 - val_loss: 0.0993

Epoch 288/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0263

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0276 - val_accuracy: 0.9688 - val_loss: 0.0968

Epoch 289/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 1.0000 - loss: 0.0095

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0281 - val_accuracy: 0.9375 - val_loss: 0.0982

Epoch 290/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0073

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0277 - val_accuracy: 0.9375 - val_loss: 0.1000

Epoch 291/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.0481

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0278 - val_accuracy: 0.9375 - val_loss: 0.1007

Epoch 292/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.0713

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0274 - val_accuracy: 0.9062 - val_loss: 0.1033

Epoch 293/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0263

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0275 - val_accuracy: 0.9688 - val_loss: 0.0982

Epoch 294/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.1018

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0274 - val_accuracy: 0.9375 - val_loss: 0.1012

Epoch 295/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0111

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0275 - val_accuracy: 0.9375 - val_loss: 0.1008

Epoch 296/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - accuracy: 1.0000 - loss: 1.8448e-04

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.9922 - loss: 0.0266 - val_accuracy: 0.9062 - val_loss: 0.1036

Epoch 297/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0221

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0268 - val_accuracy: 0.9062 - val_loss: 0.1034

Epoch 298/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 1.0000 - loss: 0.0499

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0267 - val_accuracy: 0.9375 - val_loss: 0.1026

Epoch 299/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 8.6736e-04

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9922 - loss: 0.0269 - val_accuracy: 0.9688 - val_loss: 0.0986

Epoch 300/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 1.0000 - loss: 0.0431

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0265 - val_accuracy: 0.9375 - val_loss: 0.1008

Epoch 301/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0025

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0265 - val_accuracy: 0.9062 - val_loss: 0.1051

Epoch 302/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0535

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0265 - val_accuracy: 0.9375 - val_loss: 0.1021

Epoch 303/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0311

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0263 - val_accuracy: 0.9375 - val_loss: 0.1030

Epoch 304/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 1.0000 - loss: 0.0314

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0261 - val_accuracy: 0.9375 - val_loss: 0.1026

Epoch 305/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0231

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9922 - loss: 0.0264 - val_accuracy: 0.9688 - val_loss: 0.0982

Epoch 306/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0039

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0261 - val_accuracy: 0.9375 - val_loss: 0.1027

Epoch 307/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0131

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9922 - loss: 0.0257 - val_accuracy: 0.9688 - val_loss: 0.1001

Epoch 308/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0232

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9922 - loss: 0.0254 - val_accuracy: 0.9688 - val_loss: 0.0984

Epoch 309/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 0.9500 - loss: 0.0645

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.9922 - loss: 0.0258 - val_accuracy: 0.9688 - val_loss: 0.0975

Epoch 310/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0355

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0258 - val_accuracy: 0.9688 - val_loss: 0.0989

Epoch 311/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0227

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0252 - val_accuracy: 0.9688 - val_loss: 0.1033

Epoch 312/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9500 - loss: 0.0607

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0252 - val_accuracy: 0.9688 - val_loss: 0.1015

Epoch 313/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0012

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0254 - val_accuracy: 0.9688 - val_loss: 0.1019

Epoch 314/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0236

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9922 - loss: 0.0252 - val_accuracy: 0.9688 - val_loss: 0.0991

Epoch 315/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 1.0000 - loss: 0.0453

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0247 - val_accuracy: 0.9375 - val_loss: 0.1050

Epoch 316/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - accuracy: 1.0000 - loss: 0.0494

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0251 - val_accuracy: 0.9688 - val_loss: 0.1039

Epoch 317/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0298

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0250 - val_accuracy: 0.9688 - val_loss: 0.0984

Epoch 318/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0028

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0247 - val_accuracy: 0.9688 - val_loss: 0.1025

Epoch 319/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0306

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 1.0000 - loss: 0.0246 - val_accuracy: 0.9688 - val_loss: 0.1011

Epoch 320/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0161

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0245 - val_accuracy: 0.9688 - val_loss: 0.1014

Epoch 321/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0462

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9922 - loss: 0.0246 - val_accuracy: 0.9688 - val_loss: 0.1042

Epoch 322/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 7.1721e-04

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0244 - val_accuracy: 0.9688 - val_loss: 0.1024

Epoch 323/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 1.0000 - loss: 0.0385

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9922 - loss: 0.0242 - val_accuracy: 0.9688 - val_loss: 0.0985

Epoch 324/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0013

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9922 - loss: 0.0245 - val_accuracy: 0.9688 - val_loss: 0.1043

Epoch 325/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0348

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0239 - val_accuracy: 0.9688 - val_loss: 0.1038

Epoch 326/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 1.0000 - loss: 3.0978e-04

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0239 - val_accuracy: 0.9688 - val_loss: 0.1049

Epoch 327/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0070

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 1.0000 - loss: 0.0238 - val_accuracy: 0.9688 - val_loss: 0.1038

Epoch 328/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0499

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0238 - val_accuracy: 0.9688 - val_loss: 0.1024

Epoch 329/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0069

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0235 - val_accuracy: 0.9688 - val_loss: 0.1032

Epoch 330/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0186

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0236 - val_accuracy: 0.9688 - val_loss: 0.1078

Epoch 331/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 1.0000 - loss: 0.0055

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0235 - val_accuracy: 0.9688 - val_loss: 0.1071

Epoch 332/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0511

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0234 - val_accuracy: 0.9688 - val_loss: 0.1085

Epoch 333/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0273

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0233 - val_accuracy: 0.9062 - val_loss: 0.1106

Epoch 334/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0354

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0232 - val_accuracy: 0.9375 - val_loss: 0.1094

Epoch 335/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0230

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0231 - val_accuracy: 0.9375 - val_loss: 0.1080

Epoch 336/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0180

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0232 - val_accuracy: 0.9688 - val_loss: 0.1073

Epoch 337/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0266

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0232 - val_accuracy: 0.9688 - val_loss: 0.1075

Epoch 338/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0381

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0231 - val_accuracy: 0.9688 - val_loss: 0.1070

Epoch 339/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step - accuracy: 1.0000 - loss: 0.0066

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 1.0000 - loss: 0.0225 - val_accuracy: 0.9688 - val_loss: 0.1051

Epoch 340/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0101

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0231 - val_accuracy: 0.9688 - val_loss: 0.1051

Epoch 341/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0212

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0226 - val_accuracy: 0.9688 - val_loss: 0.1035

Epoch 342/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - accuracy: 1.0000 - loss: 0.0392

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0227 - val_accuracy: 0.9688 - val_loss: 0.1025

Epoch 343/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0399

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 1.0000 - loss: 0.0225 - val_accuracy: 0.9688 - val_loss: 0.1039

Epoch 344/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0487

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0223 - val_accuracy: 0.9688 - val_loss: 0.1028

Epoch 345/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0391

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0221 - val_accuracy: 0.9688 - val_loss: 0.1026

Epoch 346/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0114

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0223 - val_accuracy: 0.9688 - val_loss: 0.1032

Epoch 347/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0329

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 1.0000 - loss: 0.0218 - val_accuracy: 0.9688 - val_loss: 0.1030

Epoch 348/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0209

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0221 - val_accuracy: 0.9688 - val_loss: 0.0996

Epoch 349/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0062

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0221 - val_accuracy: 0.9688 - val_loss: 0.1024

Epoch 350/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - accuracy: 1.0000 - loss: 0.0371

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0218 - val_accuracy: 0.9688 - val_loss: 0.1036

Epoch 351/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0501

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0217 - val_accuracy: 0.9688 - val_loss: 0.1034

Epoch 352/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0080

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0218 - val_accuracy: 0.9688 - val_loss: 0.1000

Epoch 353/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0306

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0216 - val_accuracy: 0.9688 - val_loss: 0.0999

Epoch 354/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0067

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0212 - val_accuracy: 0.9688 - val_loss: 0.1003

Epoch 355/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0229

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 1.0000 - loss: 0.0211 - val_accuracy: 0.9688 - val_loss: 0.0983

Epoch 356/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 1.0000 - loss: 0.0152

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0216 - val_accuracy: 0.9688 - val_loss: 0.0974

Epoch 357/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 3.0081e-04

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9922 - loss: 0.0208 - val_accuracy: 0.9688 - val_loss: 0.0982

Epoch 358/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0180

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9922 - loss: 0.0209 - val_accuracy: 0.9688 - val_loss: 0.1057

Epoch 359/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - accuracy: 1.0000 - loss: 0.0326

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0211 - val_accuracy: 0.9688 - val_loss: 0.1018

Epoch 360/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 1.0000 - loss: 0.0049

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 1.0000 - loss: 0.0212 - val_accuracy: 0.9688 - val_loss: 0.1008

Epoch 361/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0043

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0204 - val_accuracy: 0.9688 - val_loss: 0.1003

Epoch 362/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0053

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 1.0000 - loss: 0.0212 - val_accuracy: 0.9688 - val_loss: 0.1013

Epoch 363/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0145

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0209 - val_accuracy: 0.9688 - val_loss: 0.0998

Epoch 364/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0219

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0201 - val_accuracy: 0.9688 - val_loss: 0.1006

Epoch 365/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - accuracy: 1.0000 - loss: 0.0267

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0203 - val_accuracy: 0.9688 - val_loss: 0.0999

Epoch 366/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 5.0055e-04

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0207 - val_accuracy: 0.9688 - val_loss: 0.0979

Epoch 367/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 5.5602e-04

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0205 - val_accuracy: 0.9688 - val_loss: 0.0991

Epoch 368/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0051

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 1.0000 - loss: 0.0205 - val_accuracy: 0.9688 - val_loss: 0.0983

Epoch 369/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0061

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0198 - val_accuracy: 0.9688 - val_loss: 0.0981

Epoch 370/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0051

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0204 - val_accuracy: 0.9688 - val_loss: 0.1036

Epoch 371/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 1.0000 - loss: 0.0142

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0200 - val_accuracy: 0.9688 - val_loss: 0.1013

Epoch 372/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 1.0000 - loss: 0.0481

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0198 - val_accuracy: 0.9688 - val_loss: 0.1059

Epoch 373/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 1.0000 - loss: 0.0220

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0198 - val_accuracy: 0.9688 - val_loss: 0.1082

Epoch 374/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0071

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0200 - val_accuracy: 0.9688 - val_loss: 0.1037

Epoch 375/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0080

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0192 - val_accuracy: 0.9688 - val_loss: 0.1024

Epoch 376/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0539

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0195 - val_accuracy: 0.9688 - val_loss: 0.1031

Epoch 377/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0122

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0194 - val_accuracy: 0.9688 - val_loss: 0.0984

Epoch 378/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0049

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0197 - val_accuracy: 0.9688 - val_loss: 0.0988

Epoch 379/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0165

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0194 - val_accuracy: 0.9688 - val_loss: 0.1001

Epoch 380/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 1.1155e-04

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0194 - val_accuracy: 0.9688 - val_loss: 0.0984

Epoch 381/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0326

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0193 - val_accuracy: 0.9688 - val_loss: 0.0969

Epoch 382/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0046

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0191 - val_accuracy: 0.9688 - val_loss: 0.1002

Epoch 383/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 8.7104e-04

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0192 - val_accuracy: 0.9688 - val_loss: 0.1034

Epoch 384/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0063

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0188 - val_accuracy: 0.9688 - val_loss: 0.1007

Epoch 385/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step - accuracy: 1.0000 - loss: 0.0149

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0191 - val_accuracy: 0.9688 - val_loss: 0.1026

Epoch 386/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0195

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0188 - val_accuracy: 0.9688 - val_loss: 0.0977

Epoch 387/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0337

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0187 - val_accuracy: 0.9688 - val_loss: 0.1001

Epoch 388/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0410

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0193 - val_accuracy: 0.9688 - val_loss: 0.1021

Epoch 389/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 1.0000 - loss: 0.0203

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0184 - val_accuracy: 0.9688 - val_loss: 0.1009

Epoch 390/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0672

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0187 - val_accuracy: 0.9688 - val_loss: 0.1013

Epoch 391/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0048

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 1.0000 - loss: 0.0184 - val_accuracy: 0.9688 - val_loss: 0.0989

Epoch 392/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0123

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0185 - val_accuracy: 0.9688 - val_loss: 0.1001

Epoch 393/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 6.8494e-04

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0184 - val_accuracy: 0.9688 - val_loss: 0.0989

Epoch 394/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0383

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 1.0000 - loss: 0.0179 - val_accuracy: 0.9688 - val_loss: 0.0982

Epoch 395/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 1.0000 - loss: 0.0054

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0181 - val_accuracy: 0.9688 - val_loss: 0.0987

Epoch 396/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0198

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0181 - val_accuracy: 0.9688 - val_loss: 0.0999

Epoch 397/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0048

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0181 - val_accuracy: 0.9688 - val_loss: 0.1004

Epoch 398/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 4.3215e-04

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 1.0000 - loss: 0.0175 - val_accuracy: 0.9688 - val_loss: 0.1013

Epoch 399/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 1.0000 - loss: 0.0364

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0180 - val_accuracy: 0.9688 - val_loss: 0.1009

Epoch 400/400


1/7 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 1.1657e-05

7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 1.0000 - loss: 0.0182 - val_accuracy: 0.9688 - val_loss: 0.1036

Evaluate Network Performance

The final performance can be obtained like so.

perf = model.evaluate(x_train, y_train)
print(perf)

1/5 ━━━━━━━━━━━━━━━━━━━━ 0s 218ms/step - accuracy: 1.0000 - loss: 0.0230

5/5 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9937 - loss: 0.0344  

[0.03438631817698479, 0.9937499761581421]

perf = model.evaluate(x_test, y_test)
print(perf)

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9062 - loss: 0.3878

2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 173ms/step - accuracy: 0.9156 - loss: 0.3525

2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 184ms/step - accuracy: 0.9250 - loss: 0.3173

[0.3172706365585327, 0.925000011920929]

Feed-Forward Neural Network for the Google Tensorflow Playground XOR Data