#!pip install ANNarchyANN-to-SNN conversion - CNN
This notebook demonstrates how to transform a CNN trained using tensorflow/keras into an SNN network usable in ANNarchy.
The CNN is adapted from the original model used in:
Diehl et al. (2015) “Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing” Proceedings of IJCNN. doi: 10.1109/IJCNN.2015.7280696
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
print(f"Tensorflow {tf.__version__}")2026-01-05 13:49:00.215815: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.
2026-01-05 13:49:00.218299: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.
2026-01-05 13:49:00.227507: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1767617340.243377 121409 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1767617340.248113 121409 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2026-01-05 13:49:00.263586: I tensorflow/core/platform/cpu_feature_guard.cc:210] 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.Tensorflow 2.18.0# Download data
(X_train, t_train), (X_test, t_test) = tf.keras.datasets.mnist.load_data()
# Normalize inputs
X_train = X_train.astype('float32') / 255.
X_test = X_test.astype('float32') / 255.
# One-hot output vectors
T_train = tf.keras.utils.to_categorical(t_train, 10)
T_test = tf.keras.utils.to_categorical(t_test, 10)Training an ANN in tensorflow/keras
The tensorflow.keras convolutional network is built using the functional API.
The CNN has three 5*5 convolutional layers with ReLU, each followed by 2*2 max-pooling, no bias, dropout at 0.25, and a softmax output layer with 10 neurons. We use the standard SGD optimizer and the categorical crossentropy loss for classification.
def create_cnn():
inputs = tf.keras.Input(shape = (28, 28, 1))
x = tf.keras.layers.Conv2D(
16,
kernel_size=(5,5),
activation='relu',
padding = 'same',
use_bias=False)(inputs)
x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(x)
x = tf.keras.layers.Conv2D(
64,
kernel_size=(5,5),
activation='relu',
padding = 'same',
use_bias=False)(x)
x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(x)
x = tf.keras.layers.Conv2D(
64,
kernel_size=(5,5),
activation='relu',
padding = 'same',
use_bias=False)(x)
x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(x)
x = tf.keras.layers.Dropout(0.25)(x)
x = tf.keras.layers.Flatten()(x)
x = tf.keras.layers.Dense(
10,
activation='softmax',
use_bias=False)(x)
# Create functional model
model= tf.keras.Model(inputs, x)
optimizer = tf.keras.optimizers.SGD(learning_rate=0.01)
# Loss function
model.compile(
loss='categorical_crossentropy', # loss function
optimizer=optimizer, # learning rule
metrics=['accuracy'] # show accuracy
)
print(model.summary())
return model# Create model
model = create_cnn()
# Train model
history = model.fit(
X_train, T_train, # training data
batch_size=128, # batch size
epochs=20, # Maximum number of epochs
validation_split=0.1, # Percentage of training data used for validation
)
model.save("runs/cnn.keras")
# Test model
predictions_keras = model.predict(X_test, verbose=0)
test_loss, test_accuracy = model.evaluate(X_test, T_test, verbose=0)
print(f"Test accuracy: {test_accuracy}")W0000 00:00:1767617341.902178 121409 gpu_device.cc:2344] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...Model: "functional"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ │ input_layer (InputLayer) │ (None, 28, 28, 1) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ conv2d (Conv2D) │ (None, 28, 28, 16) │ 400 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ max_pooling2d (MaxPooling2D) │ (None, 14, 14, 16) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ conv2d_1 (Conv2D) │ (None, 14, 14, 64) │ 25,600 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ max_pooling2d_1 (MaxPooling2D) │ (None, 7, 7, 64) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ conv2d_2 (Conv2D) │ (None, 7, 7, 64) │ 102,400 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ max_pooling2d_2 (MaxPooling2D) │ (None, 3, 3, 64) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dropout (Dropout) │ (None, 3, 3, 64) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ flatten (Flatten) │ (None, 576) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense (Dense) │ (None, 10) │ 5,760 │ └─────────────────────────────────┴────────────────────────┴───────────────┘
Total params: 134,160 (524.06 KB)
Trainable params: 134,160 (524.06 KB)
Non-trainable params: 0 (0.00 B)
None
Epoch 1/202026-01-05 13:49:02.005832: W external/local_xla/xla/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 169344000 exceeds 10% of free system memory.1/422 ━━━━━━━━━━━━━━━━━━━━ 2:36 373ms/step - accuracy: 0.1016 - loss: 2.3052 3/422 ━━━━━━━━━━━━━━━━━━━━ 18s 43ms/step - accuracy: 0.1172 - loss: 2.2999 5/422 ━━━━━━━━━━━━━━━━━━━━ 17s 43ms/step - accuracy: 0.1171 - loss: 2.2988 7/422 ━━━━━━━━━━━━━━━━━━━━ 17s 42ms/step - accuracy: 0.1164 - loss: 2.2989 9/422 ━━━━━━━━━━━━━━━━━━━━ 17s 42ms/step - accuracy: 0.1155 - loss: 2.2993 11/422 ━━━━━━━━━━━━━━━━━━━━ 17s 42ms/step - accuracy: 0.1143 - loss: 2.2996 13/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.1145 - loss: 2.2995 15/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.1151 - loss: 2.2992 17/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.1157 - loss: 2.2989 19/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.1167 - loss: 2.2984 21/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.1176 - loss: 2.2979 23/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.1187 - loss: 2.2973 25/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.1200 - loss: 2.2967 27/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.1211 - loss: 2.2962 29/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.1224 - loss: 2.2958 31/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.1235 - loss: 2.2953 33/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.1247 - loss: 2.2949 35/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.1260 - loss: 2.2944 37/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.1272 - loss: 2.2940 39/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.1284 - loss: 2.2935 41/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.1296 - loss: 2.2930 43/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.1309 - loss: 2.2925 45/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.1322 - loss: 2.2920 47/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.1335 - loss: 2.2915 49/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.1347 - loss: 2.2910 51/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.1360 - loss: 2.2905 53/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.1372 - loss: 2.2900 55/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.1384 - loss: 2.2896 57/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.1396 - loss: 2.2890 59/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.1408 - loss: 2.2885 61/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.1420 - loss: 2.2880 63/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.1433 - loss: 2.2875 65/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.1445 - loss: 2.2870 67/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.1457 - loss: 2.2865 69/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.1470 - loss: 2.2860 71/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.1483 - loss: 2.2854 73/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.1495 - loss: 2.2849 75/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.1507 - loss: 2.2843 77/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.1519 - loss: 2.2838 79/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.1532 - loss: 2.2833 81/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.1544 - loss: 2.2827 83/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.1557 - loss: 2.2822 85/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.1569 - loss: 2.2816 87/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.1582 - loss: 2.2810 89/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.1595 - loss: 2.2805 91/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.1608 - loss: 2.2799 93/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.1621 - loss: 2.2793 95/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.1634 - loss: 2.2787 97/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.1647 - loss: 2.2781 99/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.1660 - loss: 2.2775 101/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.1673 - loss: 2.2768 103/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.1686 - loss: 2.2762 105/422 ━━━━━━━━━━━━━━━━━━━━ 12s 41ms/step - accuracy: 0.1699 - loss: 2.2756 107/422 ━━━━━━━━━━━━━━━━━━━━ 12s 41ms/step - accuracy: 0.1712 - loss: 2.2749 109/422 ━━━━━━━━━━━━━━━━━━━━ 12s 41ms/step - accuracy: 0.1725 - loss: 2.2743 111/422 ━━━━━━━━━━━━━━━━━━━━ 12s 41ms/step - accuracy: 0.1739 - loss: 2.2736 113/422 ━━━━━━━━━━━━━━━━━━━━ 12s 41ms/step - accuracy: 0.1752 - loss: 2.2729 115/422 ━━━━━━━━━━━━━━━━━━━━ 12s 41ms/step - accuracy: 0.1766 - loss: 2.2722 117/422 ━━━━━━━━━━━━━━━━━━━━ 12s 41ms/step - accuracy: 0.1780 - loss: 2.2715 119/422 ━━━━━━━━━━━━━━━━━━━━ 12s 41ms/step - accuracy: 0.1794 - loss: 2.2708 121/422 ━━━━━━━━━━━━━━━━━━━━ 12s 41ms/step - accuracy: 0.1808 - loss: 2.2701 123/422 ━━━━━━━━━━━━━━━━━━━━ 12s 41ms/step - accuracy: 0.1822 - loss: 2.2693 125/422 ━━━━━━━━━━━━━━━━━━━━ 12s 41ms/step - accuracy: 0.1836 - loss: 2.2686 127/422 ━━━━━━━━━━━━━━━━━━━━ 12s 41ms/step - accuracy: 0.1850 - loss: 2.2678 129/422 ━━━━━━━━━━━━━━━━━━━━ 11s 41ms/step - accuracy: 0.1865 - 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loss: 1.9910 373/422 ━━━━━━━━━━━━━━━━━━━━ 2s 41ms/step - accuracy: 0.3524 - loss: 1.9882 375/422 ━━━━━━━━━━━━━━━━━━━━ 1s 41ms/step - accuracy: 0.3535 - loss: 1.9853 377/422 ━━━━━━━━━━━━━━━━━━━━ 1s 41ms/step - accuracy: 0.3547 - loss: 1.9824 379/422 ━━━━━━━━━━━━━━━━━━━━ 1s 41ms/step - accuracy: 0.3558 - loss: 1.9795 381/422 ━━━━━━━━━━━━━━━━━━━━ 1s 41ms/step - accuracy: 0.3569 - loss: 1.9767 383/422 ━━━━━━━━━━━━━━━━━━━━ 1s 41ms/step - accuracy: 0.3580 - loss: 1.9738 385/422 ━━━━━━━━━━━━━━━━━━━━ 1s 41ms/step - accuracy: 0.3592 - loss: 1.9710 387/422 ━━━━━━━━━━━━━━━━━━━━ 1s 41ms/step - accuracy: 0.3603 - loss: 1.9681 389/422 ━━━━━━━━━━━━━━━━━━━━ 1s 41ms/step - accuracy: 0.3614 - loss: 1.9653 391/422 ━━━━━━━━━━━━━━━━━━━━ 1s 41ms/step - accuracy: 0.3625 - loss: 1.9624 393/422 ━━━━━━━━━━━━━━━━━━━━ 1s 41ms/step - accuracy: 0.3636 - loss: 1.9596 395/422 ━━━━━━━━━━━━━━━━━━━━ 1s 41ms/step - accuracy: 0.3647 - loss: 1.9568 397/422 ━━━━━━━━━━━━━━━━━━━━ 1s 41ms/step - accuracy: 0.3658 - loss: 1.9539 399/422 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step - accuracy: 0.3668 - loss: 1.9511 401/422 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step - accuracy: 0.3679 - loss: 1.9483 403/422 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step - accuracy: 0.3690 - loss: 1.9455 405/422 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step - accuracy: 0.3701 - loss: 1.9427 407/422 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step - accuracy: 0.3711 - loss: 1.9399 409/422 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step - accuracy: 0.3722 - loss: 1.9371 411/422 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step - accuracy: 0.3733 - loss: 1.9343 413/422 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step - accuracy: 0.3743 - loss: 1.9316 415/422 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step - accuracy: 0.3754 - loss: 1.9288 417/422 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step - accuracy: 0.3764 - loss: 1.9260 419/422 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step - accuracy: 0.3775 - loss: 1.9233 421/422 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step - accuracy: 0.3785 - loss: 1.9205 422/422 ━━━━━━━━━━━━━━━━━━━━ 18s 43ms/step - accuracy: 0.3796 - loss: 1.9178 - val_accuracy: 0.9077 - val_loss: 0.3333 Epoch 2/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 25s 61ms/step - accuracy: 0.7969 - loss: 0.5830 3/422 ━━━━━━━━━━━━━━━━━━━━ 17s 41ms/step - accuracy: 0.8212 - loss: 0.5568 5/422 ━━━━━━━━━━━━━━━━━━━━ 17s 41ms/step - accuracy: 0.8318 - loss: 0.5367 7/422 ━━━━━━━━━━━━━━━━━━━━ 17s 41ms/step - accuracy: 0.8338 - loss: 0.5300 9/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.8344 - loss: 0.5238 11/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.8357 - loss: 0.5179 13/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.8361 - loss: 0.5147 15/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.8371 - loss: 0.5107 17/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.8379 - loss: 0.5073 19/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.8387 - loss: 0.5044 21/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.8396 - loss: 0.5015 23/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.8404 - loss: 0.4991 25/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.8412 - loss: 0.4969 27/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.8420 - loss: 0.4953 29/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.8429 - loss: 0.4937 31/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.8437 - loss: 0.4923 33/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.8444 - loss: 0.4910 35/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.8452 - loss: 0.4896 37/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.8458 - loss: 0.4883 39/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.8464 - loss: 0.4870 41/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.8469 - loss: 0.4858 43/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.8474 - loss: 0.4846 45/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.8479 - loss: 0.4833 47/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.8483 - loss: 0.4819 49/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.8488 - loss: 0.4806 51/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.8493 - loss: 0.4793 53/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.8498 - loss: 0.4779 55/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.8503 - loss: 0.4764 57/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.8509 - loss: 0.4750 59/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.8514 - loss: 0.4736 61/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.8520 - loss: 0.4721 63/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.8525 - loss: 0.4708 65/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.8530 - loss: 0.4694 67/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.8535 - loss: 0.4682 69/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.8539 - loss: 0.4671 71/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.8543 - loss: 0.4660 73/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.8547 - loss: 0.4649 75/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.8551 - loss: 0.4638 77/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.8555 - loss: 0.4627 79/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.8559 - loss: 0.4617 81/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.8563 - 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accuracy: 0.8774 - loss: 0.4014 324/422 ━━━━━━━━━━━━━━━━━━━━ 4s 41ms/step - accuracy: 0.8776 - loss: 0.4011 326/422 ━━━━━━━━━━━━━━━━━━━━ 3s 41ms/step - accuracy: 0.8777 - loss: 0.4008 328/422 ━━━━━━━━━━━━━━━━━━━━ 3s 41ms/step - accuracy: 0.8778 - loss: 0.4005 330/422 ━━━━━━━━━━━━━━━━━━━━ 3s 41ms/step - accuracy: 0.8779 - loss: 0.4002 332/422 ━━━━━━━━━━━━━━━━━━━━ 3s 41ms/step - accuracy: 0.8780 - loss: 0.3999 334/422 ━━━━━━━━━━━━━━━━━━━━ 3s 41ms/step - accuracy: 0.8781 - loss: 0.3995 336/422 ━━━━━━━━━━━━━━━━━━━━ 3s 41ms/step - accuracy: 0.8782 - loss: 0.3992 338/422 ━━━━━━━━━━━━━━━━━━━━ 3s 41ms/step - accuracy: 0.8783 - loss: 0.3989 340/422 ━━━━━━━━━━━━━━━━━━━━ 3s 41ms/step - accuracy: 0.8784 - loss: 0.3986 342/422 ━━━━━━━━━━━━━━━━━━━━ 3s 41ms/step - accuracy: 0.8785 - loss: 0.3983 344/422 ━━━━━━━━━━━━━━━━━━━━ 3s 41ms/step - accuracy: 0.8786 - loss: 0.3980 346/422 ━━━━━━━━━━━━━━━━━━━━ 3s 41ms/step - accuracy: 0.8787 - loss: 0.3977 348/422 ━━━━━━━━━━━━━━━━━━━━ 3s 41ms/step - accuracy: 0.8788 - loss: 0.3974 350/422 ━━━━━━━━━━━━━━━━━━━━ 2s 41ms/step - accuracy: 0.8789 - loss: 0.3971 352/422 ━━━━━━━━━━━━━━━━━━━━ 2s 41ms/step - accuracy: 0.8790 - loss: 0.3968 354/422 ━━━━━━━━━━━━━━━━━━━━ 2s 41ms/step - accuracy: 0.8791 - loss: 0.3965 356/422 ━━━━━━━━━━━━━━━━━━━━ 2s 41ms/step - accuracy: 0.8792 - loss: 0.3962 358/422 ━━━━━━━━━━━━━━━━━━━━ 2s 41ms/step - accuracy: 0.8793 - loss: 0.3959 360/422 ━━━━━━━━━━━━━━━━━━━━ 2s 41ms/step - accuracy: 0.8794 - loss: 0.3956 362/422 ━━━━━━━━━━━━━━━━━━━━ 2s 41ms/step - accuracy: 0.8794 - loss: 0.3953 364/422 ━━━━━━━━━━━━━━━━━━━━ 2s 41ms/step - accuracy: 0.8795 - loss: 0.3951 366/422 ━━━━━━━━━━━━━━━━━━━━ 2s 41ms/step - accuracy: 0.8796 - loss: 0.3948 368/422 ━━━━━━━━━━━━━━━━━━━━ 2s 41ms/step - accuracy: 0.8797 - loss: 0.3945 370/422 ━━━━━━━━━━━━━━━━━━━━ 2s 41ms/step - accuracy: 0.8798 - loss: 0.3942 372/422 ━━━━━━━━━━━━━━━━━━━━ 2s 41ms/step - accuracy: 0.8799 - loss: 0.3939 374/422 ━━━━━━━━━━━━━━━━━━━━ 1s 41ms/step - accuracy: 0.8800 - loss: 0.3937 376/422 ━━━━━━━━━━━━━━━━━━━━ 1s 41ms/step - accuracy: 0.8801 - loss: 0.3934 378/422 ━━━━━━━━━━━━━━━━━━━━ 1s 41ms/step - accuracy: 0.8802 - loss: 0.3931 380/422 ━━━━━━━━━━━━━━━━━━━━ 1s 41ms/step - accuracy: 0.8802 - loss: 0.3929 382/422 ━━━━━━━━━━━━━━━━━━━━ 1s 41ms/step - accuracy: 0.8803 - loss: 0.3926 384/422 ━━━━━━━━━━━━━━━━━━━━ 1s 41ms/step - accuracy: 0.8804 - loss: 0.3924 386/422 ━━━━━━━━━━━━━━━━━━━━ 1s 41ms/step - accuracy: 0.8805 - loss: 0.3921 388/422 ━━━━━━━━━━━━━━━━━━━━ 1s 41ms/step - accuracy: 0.8806 - loss: 0.3918 390/422 ━━━━━━━━━━━━━━━━━━━━ 1s 41ms/step - accuracy: 0.8807 - loss: 0.3916 392/422 ━━━━━━━━━━━━━━━━━━━━ 1s 41ms/step - accuracy: 0.8808 - loss: 0.3913 394/422 ━━━━━━━━━━━━━━━━━━━━ 1s 41ms/step - accuracy: 0.8808 - loss: 0.3911 396/422 ━━━━━━━━━━━━━━━━━━━━ 1s 41ms/step - accuracy: 0.8809 - loss: 0.3908 398/422 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step - accuracy: 0.8810 - loss: 0.3906 400/422 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step - accuracy: 0.8811 - loss: 0.3903 402/422 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step - accuracy: 0.8812 - loss: 0.3901 404/422 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step - accuracy: 0.8812 - loss: 0.3898 406/422 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step - accuracy: 0.8813 - loss: 0.3896 408/422 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step - accuracy: 0.8814 - loss: 0.3893 410/422 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step - accuracy: 0.8815 - loss: 0.3891 412/422 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step - accuracy: 0.8816 - loss: 0.3888 414/422 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step - accuracy: 0.8816 - loss: 0.3886 416/422 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step - accuracy: 0.8817 - loss: 0.3883 418/422 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step - accuracy: 0.8818 - loss: 0.3881 420/422 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step - accuracy: 0.8819 - loss: 0.3879 422/422 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step - accuracy: 0.8820 - loss: 0.3876 422/422 ━━━━━━━━━━━━━━━━━━━━ 18s 43ms/step - accuracy: 0.8820 - loss: 0.3875 - val_accuracy: 0.9553 - val_loss: 0.1703 Epoch 3/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 26s 63ms/step - accuracy: 0.9375 - loss: 0.2224 3/422 ━━━━━━━━━━━━━━━━━━━━ 17s 41ms/step - accuracy: 0.9349 - loss: 0.2339 5/422 ━━━━━━━━━━━━━━━━━━━━ 17s 41ms/step - accuracy: 0.9311 - loss: 0.2482 7/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9277 - loss: 0.2609 9/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9262 - loss: 0.2646 11/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9252 - loss: 0.2678 13/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9246 - loss: 0.2697 15/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9238 - loss: 0.2715 17/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9232 - loss: 0.2721 19/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9226 - loss: 0.2728 21/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9223 - loss: 0.2726 23/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9220 - loss: 0.2723 25/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9220 - loss: 0.2718 27/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9218 - loss: 0.2715 29/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9218 - 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loss: 0.2488 299/422 ━━━━━━━━━━━━━━━━━━━━ 5s 41ms/step - accuracy: 0.9264 - loss: 0.2487 301/422 ━━━━━━━━━━━━━━━━━━━━ 4s 41ms/step - accuracy: 0.9264 - loss: 0.2486 303/422 ━━━━━━━━━━━━━━━━━━━━ 4s 41ms/step - accuracy: 0.9264 - loss: 0.2486 305/422 ━━━━━━━━━━━━━━━━━━━━ 4s 41ms/step - accuracy: 0.9264 - loss: 0.2485 307/422 ━━━━━━━━━━━━━━━━━━━━ 4s 41ms/step - accuracy: 0.9265 - loss: 0.2484 309/422 ━━━━━━━━━━━━━━━━━━━━ 4s 41ms/step - accuracy: 0.9265 - loss: 0.2483 311/422 ━━━━━━━━━━━━━━━━━━━━ 4s 41ms/step - accuracy: 0.9265 - loss: 0.2482 313/422 ━━━━━━━━━━━━━━━━━━━━ 4s 41ms/step - accuracy: 0.9265 - loss: 0.2481 315/422 ━━━━━━━━━━━━━━━━━━━━ 4s 41ms/step - accuracy: 0.9266 - loss: 0.2480 317/422 ━━━━━━━━━━━━━━━━━━━━ 4s 41ms/step - accuracy: 0.9266 - loss: 0.2479 319/422 ━━━━━━━━━━━━━━━━━━━━ 4s 41ms/step - accuracy: 0.9266 - loss: 0.2478 321/422 ━━━━━━━━━━━━━━━━━━━━ 4s 41ms/step - accuracy: 0.9266 - loss: 0.2477 323/422 ━━━━━━━━━━━━━━━━━━━━ 4s 41ms/step - accuracy: 0.9266 - loss: 0.2477 325/422 ━━━━━━━━━━━━━━━━━━━━ 3s 41ms/step - 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accuracy: 0.9273 - loss: 0.2451 381/422 ━━━━━━━━━━━━━━━━━━━━ 1s 41ms/step - accuracy: 0.9274 - loss: 0.2450 383/422 ━━━━━━━━━━━━━━━━━━━━ 1s 41ms/step - accuracy: 0.9274 - loss: 0.2449 385/422 ━━━━━━━━━━━━━━━━━━━━ 1s 41ms/step - accuracy: 0.9274 - loss: 0.2448 387/422 ━━━━━━━━━━━━━━━━━━━━ 1s 41ms/step - accuracy: 0.9274 - loss: 0.2447 389/422 ━━━━━━━━━━━━━━━━━━━━ 1s 41ms/step - accuracy: 0.9275 - loss: 0.2446 391/422 ━━━━━━━━━━━━━━━━━━━━ 1s 41ms/step - accuracy: 0.9275 - loss: 0.2445 393/422 ━━━━━━━━━━━━━━━━━━━━ 1s 41ms/step - accuracy: 0.9275 - loss: 0.2444 395/422 ━━━━━━━━━━━━━━━━━━━━ 1s 41ms/step - accuracy: 0.9275 - loss: 0.2443 397/422 ━━━━━━━━━━━━━━━━━━━━ 1s 41ms/step - accuracy: 0.9276 - loss: 0.2442 399/422 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step - accuracy: 0.9276 - loss: 0.2441 401/422 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step - accuracy: 0.9276 - loss: 0.2440 403/422 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step - accuracy: 0.9276 - loss: 0.2439 405/422 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step - accuracy: 0.9277 - loss: 0.2438 407/422 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step - accuracy: 0.9277 - loss: 0.2437 409/422 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step - accuracy: 0.9277 - loss: 0.2436 411/422 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step - accuracy: 0.9277 - loss: 0.2435 413/422 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step - accuracy: 0.9278 - loss: 0.2434 415/422 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step - accuracy: 0.9278 - loss: 0.2433 417/422 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step - accuracy: 0.9278 - loss: 0.2432 419/422 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step - accuracy: 0.9278 - loss: 0.2431 421/422 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step - accuracy: 0.9279 - loss: 0.2430 422/422 ━━━━━━━━━━━━━━━━━━━━ 18s 42ms/step - accuracy: 0.9279 - loss: 0.2430 - val_accuracy: 0.9667 - val_loss: 0.1241 Epoch 4/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 25s 61ms/step - accuracy: 0.9766 - loss: 0.1075 3/422 ━━━━━━━━━━━━━━━━━━━━ 17s 41ms/step - accuracy: 0.9553 - loss: 0.1519 5/422 ━━━━━━━━━━━━━━━━━━━━ 17s 41ms/step - accuracy: 0.9520 - loss: 0.1565 7/422 ━━━━━━━━━━━━━━━━━━━━ 17s 41ms/step - accuracy: 0.9500 - loss: 0.1624 9/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9472 - loss: 0.1666 11/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9455 - loss: 0.1703 13/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9444 - loss: 0.1726 15/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9436 - loss: 0.1746 17/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9433 - loss: 0.1756 19/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9434 - loss: 0.1759 21/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9432 - loss: 0.1766 23/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9430 - loss: 0.1773 25/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9429 - loss: 0.1775 27/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9429 - loss: 0.1776 29/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9429 - loss: 0.1778 31/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9429 - loss: 0.1780 33/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9429 - 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loss: 0.1514 419/422 ━━━━━━━━━━━━━━━━━━━━ 0s 57ms/step - accuracy: 0.9544 - loss: 0.1514 421/422 ━━━━━━━━━━━━━━━━━━━━ 0s 56ms/step - accuracy: 0.9544 - loss: 0.1513 422/422 ━━━━━━━━━━━━━━━━━━━━ 24s 58ms/step - accuracy: 0.9544 - loss: 0.1513 - val_accuracy: 0.9757 - val_loss: 0.0884 Epoch 6/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 25s 62ms/step - accuracy: 0.9531 - loss: 0.1473 3/422 ━━━━━━━━━━━━━━━━━━━━ 17s 42ms/step - accuracy: 0.9562 - loss: 0.1520 5/422 ━━━━━━━━━━━━━━━━━━━━ 17s 41ms/step - accuracy: 0.9522 - loss: 0.1555 7/422 ━━━━━━━━━━━━━━━━━━━━ 17s 41ms/step - accuracy: 0.9529 - loss: 0.1496 9/422 ━━━━━━━━━━━━━━━━━━━━ 17s 41ms/step - accuracy: 0.9541 - loss: 0.1443 11/422 ━━━━━━━━━━━━━━━━━━━━ 17s 41ms/step - accuracy: 0.9552 - loss: 0.1406 13/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9555 - loss: 0.1386 15/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9555 - loss: 0.1377 17/422 ━━━━━━━━━━━━━━━━━━━━ 16s 42ms/step - accuracy: 0.9557 - loss: 0.1366 19/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9560 - loss: 0.1359 21/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9560 - loss: 0.1363 23/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9561 - loss: 0.1368 25/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9561 - loss: 0.1372 27/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9561 - loss: 0.1376 29/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9562 - loss: 0.1378 31/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9561 - loss: 0.1381 33/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9561 - loss: 0.1387 35/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9561 - loss: 0.1392 37/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9560 - loss: 0.1396 39/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9559 - loss: 0.1401 41/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9559 - loss: 0.1405 43/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9558 - loss: 0.1408 45/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9558 - loss: 0.1410 47/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9558 - loss: 0.1412 49/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9558 - loss: 0.1413 51/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9558 - loss: 0.1414 53/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9558 - loss: 0.1415 55/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9558 - loss: 0.1415 57/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9558 - loss: 0.1416 59/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9559 - loss: 0.1416 61/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9559 - loss: 0.1416 63/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9559 - loss: 0.1416 65/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9560 - loss: 0.1416 67/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9560 - loss: 0.1416 69/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9560 - loss: 0.1416 71/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9561 - loss: 0.1416 73/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9561 - loss: 0.1415 75/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9561 - loss: 0.1415 77/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9562 - loss: 0.1414 79/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9562 - loss: 0.1413 81/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9563 - loss: 0.1412 83/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9563 - loss: 0.1411 85/422 ━━━━━━━━━━━━━━━━━━━━ 13s 42ms/step - accuracy: 0.9563 - loss: 0.1411 87/422 ━━━━━━━━━━━━━━━━━━━━ 13s 42ms/step - accuracy: 0.9564 - loss: 0.1410 89/422 ━━━━━━━━━━━━━━━━━━━━ 13s 42ms/step - accuracy: 0.9564 - loss: 0.1410 91/422 ━━━━━━━━━━━━━━━━━━━━ 13s 42ms/step - accuracy: 0.9564 - loss: 0.1409 93/422 ━━━━━━━━━━━━━━━━━━━━ 13s 42ms/step - accuracy: 0.9564 - loss: 0.1409 95/422 ━━━━━━━━━━━━━━━━━━━━ 13s 42ms/step - accuracy: 0.9564 - loss: 0.1408 97/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.9564 - loss: 0.1408 99/422 ━━━━━━━━━━━━━━━━━━━━ 13s 42ms/step - accuracy: 0.9564 - 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accuracy: 0.9580 - loss: 0.1370 382/422 ━━━━━━━━━━━━━━━━━━━━ 2s 59ms/step - accuracy: 0.9580 - loss: 0.1370 384/422 ━━━━━━━━━━━━━━━━━━━━ 2s 58ms/step - accuracy: 0.9580 - loss: 0.1370 386/422 ━━━━━━━━━━━━━━━━━━━━ 2s 58ms/step - accuracy: 0.9580 - loss: 0.1369 388/422 ━━━━━━━━━━━━━━━━━━━━ 1s 58ms/step - accuracy: 0.9580 - loss: 0.1369 390/422 ━━━━━━━━━━━━━━━━━━━━ 1s 58ms/step - accuracy: 0.9580 - loss: 0.1369 392/422 ━━━━━━━━━━━━━━━━━━━━ 1s 58ms/step - accuracy: 0.9580 - loss: 0.1369 394/422 ━━━━━━━━━━━━━━━━━━━━ 1s 58ms/step - accuracy: 0.9580 - loss: 0.1369 396/422 ━━━━━━━━━━━━━━━━━━━━ 1s 58ms/step - accuracy: 0.9580 - loss: 0.1369 398/422 ━━━━━━━━━━━━━━━━━━━━ 1s 58ms/step - accuracy: 0.9580 - loss: 0.1368 400/422 ━━━━━━━━━━━━━━━━━━━━ 1s 58ms/step - accuracy: 0.9580 - loss: 0.1368 402/422 ━━━━━━━━━━━━━━━━━━━━ 1s 58ms/step - accuracy: 0.9581 - loss: 0.1368 404/422 ━━━━━━━━━━━━━━━━━━━━ 1s 58ms/step - accuracy: 0.9581 - loss: 0.1368 406/422 ━━━━━━━━━━━━━━━━━━━━ 0s 57ms/step - accuracy: 0.9581 - loss: 0.1368 408/422 ━━━━━━━━━━━━━━━━━━━━ 0s 57ms/step - accuracy: 0.9581 - loss: 0.1368 410/422 ━━━━━━━━━━━━━━━━━━━━ 0s 57ms/step - accuracy: 0.9581 - loss: 0.1368 412/422 ━━━━━━━━━━━━━━━━━━━━ 0s 57ms/step - accuracy: 0.9581 - loss: 0.1367 414/422 ━━━━━━━━━━━━━━━━━━━━ 0s 57ms/step - accuracy: 0.9581 - loss: 0.1367 416/422 ━━━━━━━━━━━━━━━━━━━━ 0s 57ms/step - accuracy: 0.9581 - loss: 0.1367 418/422 ━━━━━━━━━━━━━━━━━━━━ 0s 57ms/step - accuracy: 0.9581 - loss: 0.1367 420/422 ━━━━━━━━━━━━━━━━━━━━ 0s 57ms/step - accuracy: 0.9581 - loss: 0.1367 422/422 ━━━━━━━━━━━━━━━━━━━━ 0s 57ms/step - accuracy: 0.9581 - loss: 0.1367 422/422 ━━━━━━━━━━━━━━━━━━━━ 25s 58ms/step - accuracy: 0.9581 - loss: 0.1366 - val_accuracy: 0.9777 - val_loss: 0.0814 Epoch 7/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 26s 64ms/step - accuracy: 0.9375 - loss: 0.2171 3/422 ━━━━━━━━━━━━━━━━━━━━ 16s 40ms/step - accuracy: 0.9440 - loss: 0.1813 5/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9479 - loss: 0.1680 7/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9484 - loss: 0.1694 9/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9489 - loss: 0.1685 11/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9498 - loss: 0.1653 13/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9507 - loss: 0.1625 15/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9515 - loss: 0.1597 17/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9521 - loss: 0.1577 19/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9526 - loss: 0.1559 21/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9531 - loss: 0.1542 23/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9536 - loss: 0.1527 25/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9540 - loss: 0.1515 27/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9544 - loss: 0.1504 29/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9548 - loss: 0.1493 31/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9552 - loss: 0.1483 33/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9555 - loss: 0.1473 35/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9559 - loss: 0.1464 37/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9562 - loss: 0.1454 39/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9565 - loss: 0.1446 41/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9567 - loss: 0.1438 43/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9570 - loss: 0.1430 45/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9572 - loss: 0.1423 47/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9574 - loss: 0.1416 49/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9577 - loss: 0.1409 51/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9579 - loss: 0.1403 53/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9581 - loss: 0.1398 55/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9582 - loss: 0.1394 57/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9584 - loss: 0.1389 59/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9586 - loss: 0.1384 61/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9587 - loss: 0.1380 63/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9588 - loss: 0.1377 65/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9590 - loss: 0.1373 67/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9591 - loss: 0.1369 69/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9592 - loss: 0.1366 71/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9593 - loss: 0.1363 73/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9593 - loss: 0.1360 75/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9594 - loss: 0.1358 77/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9595 - loss: 0.1355 79/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.9595 - loss: 0.1352 81/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.9596 - loss: 0.1350 83/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.9597 - loss: 0.1347 85/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.9598 - loss: 0.1344 87/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.9598 - 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accuracy: 0.9620 - loss: 0.1271 307/422 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - accuracy: 0.9620 - loss: 0.1271 308/422 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - accuracy: 0.9620 - loss: 0.1270 309/422 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - accuracy: 0.9620 - loss: 0.1270 310/422 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - accuracy: 0.9620 - loss: 0.1270 311/422 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - accuracy: 0.9620 - loss: 0.1270 312/422 ━━━━━━━━━━━━━━━━━━━━ 6s 56ms/step - accuracy: 0.9620 - loss: 0.1270 313/422 ━━━━━━━━━━━━━━━━━━━━ 6s 56ms/step - accuracy: 0.9620 - loss: 0.1270 314/422 ━━━━━━━━━━━━━━━━━━━━ 6s 56ms/step - accuracy: 0.9620 - loss: 0.1270 315/422 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - accuracy: 0.9620 - loss: 0.1269 316/422 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - accuracy: 0.9620 - loss: 0.1269 317/422 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - accuracy: 0.9620 - loss: 0.1269 318/422 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - accuracy: 0.9620 - loss: 0.1269 319/422 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - accuracy: 0.9620 - 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accuracy: 0.9621 - loss: 0.1266 340/422 ━━━━━━━━━━━━━━━━━━━━ 5s 61ms/step - accuracy: 0.9621 - loss: 0.1265 342/422 ━━━━━━━━━━━━━━━━━━━━ 4s 61ms/step - accuracy: 0.9621 - loss: 0.1265 344/422 ━━━━━━━━━━━━━━━━━━━━ 4s 61ms/step - accuracy: 0.9621 - loss: 0.1265 346/422 ━━━━━━━━━━━━━━━━━━━━ 4s 61ms/step - accuracy: 0.9621 - loss: 0.1264 348/422 ━━━━━━━━━━━━━━━━━━━━ 4s 61ms/step - accuracy: 0.9621 - loss: 0.1264 350/422 ━━━━━━━━━━━━━━━━━━━━ 4s 61ms/step - accuracy: 0.9621 - loss: 0.1264 352/422 ━━━━━━━━━━━━━━━━━━━━ 4s 61ms/step - accuracy: 0.9621 - loss: 0.1263 354/422 ━━━━━━━━━━━━━━━━━━━━ 4s 61ms/step - accuracy: 0.9621 - loss: 0.1263 356/422 ━━━━━━━━━━━━━━━━━━━━ 3s 60ms/step - accuracy: 0.9621 - loss: 0.1263 358/422 ━━━━━━━━━━━━━━━━━━━━ 3s 60ms/step - accuracy: 0.9621 - loss: 0.1262 360/422 ━━━━━━━━━━━━━━━━━━━━ 3s 60ms/step - accuracy: 0.9622 - loss: 0.1262 362/422 ━━━━━━━━━━━━━━━━━━━━ 3s 60ms/step - accuracy: 0.9622 - loss: 0.1262 364/422 ━━━━━━━━━━━━━━━━━━━━ 3s 60ms/step - accuracy: 0.9622 - loss: 0.1261 366/422 ━━━━━━━━━━━━━━━━━━━━ 3s 60ms/step - accuracy: 0.9622 - loss: 0.1261 368/422 ━━━━━━━━━━━━━━━━━━━━ 3s 60ms/step - accuracy: 0.9622 - loss: 0.1260 370/422 ━━━━━━━━━━━━━━━━━━━━ 3s 60ms/step - accuracy: 0.9622 - loss: 0.1260 372/422 ━━━━━━━━━━━━━━━━━━━━ 2s 60ms/step - accuracy: 0.9622 - loss: 0.1260 374/422 ━━━━━━━━━━━━━━━━━━━━ 2s 60ms/step - accuracy: 0.9622 - loss: 0.1259 376/422 ━━━━━━━━━━━━━━━━━━━━ 2s 59ms/step - accuracy: 0.9622 - loss: 0.1259 378/422 ━━━━━━━━━━━━━━━━━━━━ 2s 59ms/step - accuracy: 0.9622 - loss: 0.1259 380/422 ━━━━━━━━━━━━━━━━━━━━ 2s 59ms/step - accuracy: 0.9622 - loss: 0.1258 382/422 ━━━━━━━━━━━━━━━━━━━━ 2s 59ms/step - accuracy: 0.9622 - loss: 0.1258 384/422 ━━━━━━━━━━━━━━━━━━━━ 2s 59ms/step - accuracy: 0.9622 - loss: 0.1258 386/422 ━━━━━━━━━━━━━━━━━━━━ 2s 59ms/step - accuracy: 0.9622 - loss: 0.1257 388/422 ━━━━━━━━━━━━━━━━━━━━ 2s 59ms/step - accuracy: 0.9622 - loss: 0.1257 390/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9623 - loss: 0.1257 392/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9623 - loss: 0.1256 394/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9623 - loss: 0.1256 396/422 ━━━━━━━━━━━━━━━━━━━━ 1s 58ms/step - accuracy: 0.9623 - loss: 0.1256 398/422 ━━━━━━━━━━━━━━━━━━━━ 1s 58ms/step - accuracy: 0.9623 - loss: 0.1255 400/422 ━━━━━━━━━━━━━━━━━━━━ 1s 58ms/step - accuracy: 0.9623 - loss: 0.1255 402/422 ━━━━━━━━━━━━━━━━━━━━ 1s 58ms/step - accuracy: 0.9623 - loss: 0.1255 404/422 ━━━━━━━━━━━━━━━━━━━━ 1s 58ms/step - accuracy: 0.9623 - loss: 0.1254 406/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9623 - loss: 0.1254 408/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9623 - loss: 0.1254 410/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9623 - loss: 0.1253 412/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9623 - loss: 0.1253 414/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9623 - loss: 0.1253 416/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9623 - loss: 0.1252 418/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9624 - loss: 0.1252 420/422 ━━━━━━━━━━━━━━━━━━━━ 0s 57ms/step - accuracy: 0.9624 - loss: 0.1252 422/422 ━━━━━━━━━━━━━━━━━━━━ 0s 57ms/step - accuracy: 0.9624 - loss: 0.1251 422/422 ━━━━━━━━━━━━━━━━━━━━ 25s 59ms/step - accuracy: 0.9624 - loss: 0.1251 - val_accuracy: 0.9777 - val_loss: 0.0772 Epoch 8/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 26s 63ms/step - accuracy: 0.9766 - loss: 0.0699 3/422 ━━━━━━━━━━━━━━━━━━━━ 17s 41ms/step - accuracy: 0.9666 - loss: 0.0908 5/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9632 - loss: 0.1030 7/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9633 - loss: 0.1064 9/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9620 - loss: 0.1122 11/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9611 - loss: 0.1160 13/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9604 - loss: 0.1184 15/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9599 - loss: 0.1202 17/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9595 - loss: 0.1218 19/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9591 - loss: 0.1231 21/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9589 - loss: 0.1238 23/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9588 - loss: 0.1245 25/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9587 - loss: 0.1250 27/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9588 - loss: 0.1252 29/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9588 - loss: 0.1255 31/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9588 - loss: 0.1257 33/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9589 - loss: 0.1259 35/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9590 - loss: 0.1259 37/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9591 - loss: 0.1258 39/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9592 - loss: 0.1256 41/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9594 - loss: 0.1253 43/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9595 - loss: 0.1250 45/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9597 - loss: 0.1247 47/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9598 - loss: 0.1243 49/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9600 - loss: 0.1241 51/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9601 - loss: 0.1238 53/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9602 - loss: 0.1236 55/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9603 - loss: 0.1234 57/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9604 - loss: 0.1232 59/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9605 - loss: 0.1230 61/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9606 - loss: 0.1228 63/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9607 - loss: 0.1227 65/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9608 - loss: 0.1224 67/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9609 - loss: 0.1222 69/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9611 - loss: 0.1220 71/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9612 - loss: 0.1218 73/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9613 - loss: 0.1216 75/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9613 - loss: 0.1214 77/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9614 - loss: 0.1212 79/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9615 - loss: 0.1211 81/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.9616 - loss: 0.1209 83/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.9617 - loss: 0.1207 85/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.9618 - loss: 0.1205 87/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.9619 - loss: 0.1203 89/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.9620 - loss: 0.1201 91/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.9621 - loss: 0.1199 93/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.9622 - loss: 0.1197 95/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.9623 - loss: 0.1195 97/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.9623 - loss: 0.1193 99/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.9624 - loss: 0.1191 101/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.9625 - loss: 0.1190 103/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.9626 - loss: 0.1188 105/422 ━━━━━━━━━━━━━━━━━━━━ 12s 41ms/step - accuracy: 0.9627 - loss: 0.1186 107/422 ━━━━━━━━━━━━━━━━━━━━ 12s 41ms/step - accuracy: 0.9627 - loss: 0.1185 109/422 ━━━━━━━━━━━━━━━━━━━━ 12s 41ms/step - accuracy: 0.9628 - loss: 0.1184 111/422 ━━━━━━━━━━━━━━━━━━━━ 12s 41ms/step - accuracy: 0.9628 - loss: 0.1183 113/422 ━━━━━━━━━━━━━━━━━━━━ 12s 41ms/step - accuracy: 0.9629 - loss: 0.1182 115/422 ━━━━━━━━━━━━━━━━━━━━ 12s 41ms/step - accuracy: 0.9629 - loss: 0.1181 117/422 ━━━━━━━━━━━━━━━━━━━━ 12s 41ms/step - accuracy: 0.9630 - loss: 0.1180 119/422 ━━━━━━━━━━━━━━━━━━━━ 12s 41ms/step - accuracy: 0.9630 - loss: 0.1180 121/422 ━━━━━━━━━━━━━━━━━━━━ 12s 41ms/step - accuracy: 0.9631 - loss: 0.1179 123/422 ━━━━━━━━━━━━━━━━━━━━ 12s 41ms/step - accuracy: 0.9631 - loss: 0.1178 125/422 ━━━━━━━━━━━━━━━━━━━━ 12s 41ms/step - accuracy: 0.9631 - 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loss: 0.1134 405/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9653 - loss: 0.1133 407/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9653 - loss: 0.1133 409/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9653 - loss: 0.1133 411/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9653 - loss: 0.1133 413/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9653 - loss: 0.1133 415/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9653 - loss: 0.1133 417/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9653 - loss: 0.1133 419/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9653 - loss: 0.1133 421/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9653 - loss: 0.1132 422/422 ━━━━━━━━━━━━━━━━━━━━ 25s 59ms/step - accuracy: 0.9653 - loss: 0.1132 - val_accuracy: 0.9788 - val_loss: 0.0751 Epoch 9/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 26s 62ms/step - accuracy: 0.9453 - loss: 0.0967 3/422 ━━━━━━━━━━━━━━━━━━━━ 17s 41ms/step - accuracy: 0.9553 - loss: 0.0993 5/422 ━━━━━━━━━━━━━━━━━━━━ 17s 41ms/step - accuracy: 0.9609 - loss: 0.0954 7/422 ━━━━━━━━━━━━━━━━━━━━ 17s 41ms/step - accuracy: 0.9632 - loss: 0.0936 9/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9648 - loss: 0.0925 11/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9655 - loss: 0.0939 13/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9661 - loss: 0.0943 15/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9663 - loss: 0.0950 17/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9665 - loss: 0.0954 19/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9666 - loss: 0.0955 21/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9667 - loss: 0.0956 23/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9666 - loss: 0.0957 25/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9666 - loss: 0.0957 27/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9666 - loss: 0.0958 29/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9667 - loss: 0.0957 31/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9668 - 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accuracy: 0.9676 - loss: 0.1035 394/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9676 - loss: 0.1036 396/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9676 - loss: 0.1036 398/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9676 - loss: 0.1036 400/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9676 - loss: 0.1036 402/422 ━━━━━━━━━━━━━━━━━━━━ 1s 58ms/step - accuracy: 0.9676 - loss: 0.1036 404/422 ━━━━━━━━━━━━━━━━━━━━ 1s 58ms/step - accuracy: 0.9676 - loss: 0.1036 406/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9676 - loss: 0.1036 408/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9676 - loss: 0.1036 410/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9676 - loss: 0.1036 412/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9676 - loss: 0.1036 414/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9676 - loss: 0.1036 416/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9676 - loss: 0.1036 418/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9676 - loss: 0.1036 420/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9676 - loss: 0.1036 422/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9676 - loss: 0.1036 422/422 ━━━━━━━━━━━━━━━━━━━━ 25s 59ms/step - accuracy: 0.9676 - loss: 0.1036 - val_accuracy: 0.9803 - val_loss: 0.0702 Epoch 10/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 28s 68ms/step - accuracy: 0.9844 - loss: 0.0593 3/422 ━━━━━━━━━━━━━━━━━━━━ 18s 44ms/step - accuracy: 0.9748 - loss: 0.0896 5/422 ━━━━━━━━━━━━━━━━━━━━ 18s 44ms/step - accuracy: 0.9734 - loss: 0.0947 7/422 ━━━━━━━━━━━━━━━━━━━━ 18s 44ms/step - accuracy: 0.9728 - loss: 0.0956 9/422 ━━━━━━━━━━━━━━━━━━━━ 18s 44ms/step - accuracy: 0.9723 - loss: 0.0958 11/422 ━━━━━━━━━━━━━━━━━━━━ 17s 43ms/step - accuracy: 0.9718 - loss: 0.0964 13/422 ━━━━━━━━━━━━━━━━━━━━ 17s 43ms/step - accuracy: 0.9717 - loss: 0.0964 15/422 ━━━━━━━━━━━━━━━━━━━━ 17s 43ms/step - accuracy: 0.9715 - loss: 0.0964 17/422 ━━━━━━━━━━━━━━━━━━━━ 17s 43ms/step - accuracy: 0.9712 - loss: 0.0968 19/422 ━━━━━━━━━━━━━━━━━━━━ 17s 42ms/step - accuracy: 0.9711 - loss: 0.0970 21/422 ━━━━━━━━━━━━━━━━━━━━ 17s 42ms/step - accuracy: 0.9708 - loss: 0.0974 23/422 ━━━━━━━━━━━━━━━━━━━━ 16s 42ms/step - accuracy: 0.9706 - loss: 0.0978 25/422 ━━━━━━━━━━━━━━━━━━━━ 16s 42ms/step - accuracy: 0.9705 - loss: 0.0981 27/422 ━━━━━━━━━━━━━━━━━━━━ 16s 42ms/step - accuracy: 0.9704 - loss: 0.0982 29/422 ━━━━━━━━━━━━━━━━━━━━ 16s 43ms/step - accuracy: 0.9702 - loss: 0.0985 31/422 ━━━━━━━━━━━━━━━━━━━━ 16s 43ms/step - accuracy: 0.9702 - loss: 0.0988 32/422 ━━━━━━━━━━━━━━━━━━━━ 16s 43ms/step - accuracy: 0.9701 - loss: 0.0989 34/422 ━━━━━━━━━━━━━━━━━━━━ 16s 43ms/step - accuracy: 0.9701 - loss: 0.0990 36/422 ━━━━━━━━━━━━━━━━━━━━ 16s 43ms/step - accuracy: 0.9701 - loss: 0.0991 38/422 ━━━━━━━━━━━━━━━━━━━━ 16s 43ms/step - accuracy: 0.9701 - loss: 0.0991 40/422 ━━━━━━━━━━━━━━━━━━━━ 16s 43ms/step - accuracy: 0.9701 - loss: 0.0991 42/422 ━━━━━━━━━━━━━━━━━━━━ 16s 43ms/step - accuracy: 0.9702 - loss: 0.0990 44/422 ━━━━━━━━━━━━━━━━━━━━ 16s 44ms/step - accuracy: 0.9702 - loss: 0.0988 46/422 ━━━━━━━━━━━━━━━━━━━━ 16s 44ms/step - accuracy: 0.9703 - loss: 0.0987 48/422 ━━━━━━━━━━━━━━━━━━━━ 16s 44ms/step - accuracy: 0.9704 - loss: 0.0986 50/422 ━━━━━━━━━━━━━━━━━━━━ 16s 44ms/step - accuracy: 0.9704 - loss: 0.0986 52/422 ━━━━━━━━━━━━━━━━━━━━ 16s 44ms/step - accuracy: 0.9704 - loss: 0.0985 54/422 ━━━━━━━━━━━━━━━━━━━━ 16s 44ms/step - accuracy: 0.9704 - loss: 0.0985 56/422 ━━━━━━━━━━━━━━━━━━━━ 16s 44ms/step - accuracy: 0.9705 - loss: 0.0984 58/422 ━━━━━━━━━━━━━━━━━━━━ 16s 44ms/step - accuracy: 0.9705 - loss: 0.0985 60/422 ━━━━━━━━━━━━━━━━━━━━ 15s 44ms/step - accuracy: 0.9705 - loss: 0.0985 62/422 ━━━━━━━━━━━━━━━━━━━━ 15s 44ms/step - accuracy: 0.9705 - loss: 0.0985 64/422 ━━━━━━━━━━━━━━━━━━━━ 15s 44ms/step - accuracy: 0.9705 - loss: 0.0984 66/422 ━━━━━━━━━━━━━━━━━━━━ 15s 44ms/step - accuracy: 0.9705 - loss: 0.0984 68/422 ━━━━━━━━━━━━━━━━━━━━ 15s 44ms/step - accuracy: 0.9705 - loss: 0.0983 70/422 ━━━━━━━━━━━━━━━━━━━━ 15s 44ms/step - accuracy: 0.9706 - loss: 0.0982 72/422 ━━━━━━━━━━━━━━━━━━━━ 15s 44ms/step - accuracy: 0.9706 - loss: 0.0981 74/422 ━━━━━━━━━━━━━━━━━━━━ 15s 44ms/step - accuracy: 0.9707 - loss: 0.0980 76/422 ━━━━━━━━━━━━━━━━━━━━ 15s 44ms/step - accuracy: 0.9707 - loss: 0.0979 78/422 ━━━━━━━━━━━━━━━━━━━━ 15s 44ms/step - accuracy: 0.9708 - loss: 0.0978 80/422 ━━━━━━━━━━━━━━━━━━━━ 15s 44ms/step - accuracy: 0.9708 - loss: 0.0977 82/422 ━━━━━━━━━━━━━━━━━━━━ 14s 44ms/step - accuracy: 0.9709 - loss: 0.0976 84/422 ━━━━━━━━━━━━━━━━━━━━ 14s 44ms/step - accuracy: 0.9709 - loss: 0.0975 86/422 ━━━━━━━━━━━━━━━━━━━━ 14s 44ms/step - accuracy: 0.9709 - loss: 0.0974 88/422 ━━━━━━━━━━━━━━━━━━━━ 14s 44ms/step - accuracy: 0.9709 - loss: 0.0974 90/422 ━━━━━━━━━━━━━━━━━━━━ 14s 44ms/step - accuracy: 0.9710 - loss: 0.0973 92/422 ━━━━━━━━━━━━━━━━━━━━ 14s 44ms/step - accuracy: 0.9710 - loss: 0.0973 94/422 ━━━━━━━━━━━━━━━━━━━━ 14s 44ms/step - accuracy: 0.9710 - loss: 0.0972 96/422 ━━━━━━━━━━━━━━━━━━━━ 14s 43ms/step - accuracy: 0.9710 - loss: 0.0972 98/422 ━━━━━━━━━━━━━━━━━━━━ 14s 43ms/step - accuracy: 0.9710 - 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loss: 0.0951 374/422 ━━━━━━━━━━━━━━━━━━━━ 2s 61ms/step - accuracy: 0.9711 - loss: 0.0951 376/422 ━━━━━━━━━━━━━━━━━━━━ 2s 60ms/step - accuracy: 0.9711 - loss: 0.0951 378/422 ━━━━━━━━━━━━━━━━━━━━ 2s 60ms/step - accuracy: 0.9711 - loss: 0.0951 380/422 ━━━━━━━━━━━━━━━━━━━━ 2s 60ms/step - accuracy: 0.9711 - loss: 0.0951 382/422 ━━━━━━━━━━━━━━━━━━━━ 2s 60ms/step - accuracy: 0.9711 - loss: 0.0951 384/422 ━━━━━━━━━━━━━━━━━━━━ 2s 60ms/step - accuracy: 0.9711 - loss: 0.0951 386/422 ━━━━━━━━━━━━━━━━━━━━ 2s 60ms/step - accuracy: 0.9711 - loss: 0.0951 388/422 ━━━━━━━━━━━━━━━━━━━━ 2s 60ms/step - accuracy: 0.9711 - loss: 0.0951 390/422 ━━━━━━━━━━━━━━━━━━━━ 1s 60ms/step - accuracy: 0.9711 - loss: 0.0951 392/422 ━━━━━━━━━━━━━━━━━━━━ 1s 60ms/step - accuracy: 0.9711 - loss: 0.0951 394/422 ━━━━━━━━━━━━━━━━━━━━ 1s 60ms/step - accuracy: 0.9711 - loss: 0.0951 396/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9711 - loss: 0.0951 398/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9711 - loss: 0.0951 400/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9711 - loss: 0.0951 402/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9711 - loss: 0.0951 404/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9711 - loss: 0.0951 406/422 ━━━━━━━━━━━━━━━━━━━━ 0s 59ms/step - accuracy: 0.9711 - loss: 0.0951 408/422 ━━━━━━━━━━━━━━━━━━━━ 0s 59ms/step - accuracy: 0.9711 - loss: 0.0950 410/422 ━━━━━━━━━━━━━━━━━━━━ 0s 59ms/step - accuracy: 0.9711 - loss: 0.0950 412/422 ━━━━━━━━━━━━━━━━━━━━ 0s 59ms/step - accuracy: 0.9711 - loss: 0.0950 414/422 ━━━━━━━━━━━━━━━━━━━━ 0s 59ms/step - accuracy: 0.9711 - loss: 0.0950 416/422 ━━━━━━━━━━━━━━━━━━━━ 0s 59ms/step - accuracy: 0.9711 - loss: 0.0950 418/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9711 - loss: 0.0950 420/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9711 - loss: 0.0950 422/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9711 - loss: 0.0950 422/422 ━━━━━━━━━━━━━━━━━━━━ 25s 60ms/step - accuracy: 0.9711 - loss: 0.0950 - val_accuracy: 0.9808 - val_loss: 0.0678 Epoch 11/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 25s 61ms/step - accuracy: 0.9922 - loss: 0.0533 3/422 ━━━━━━━━━━━━━━━━━━━━ 17s 41ms/step - accuracy: 0.9852 - loss: 0.0804 5/422 ━━━━━━━━━━━━━━━━━━━━ 17s 41ms/step - accuracy: 0.9818 - loss: 0.0867 7/422 ━━━━━━━━━━━━━━━━━━━━ 17s 41ms/step - accuracy: 0.9809 - loss: 0.0884 9/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9798 - loss: 0.0910 11/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9792 - loss: 0.0925 13/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9786 - loss: 0.0931 15/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9783 - loss: 0.0930 17/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9779 - loss: 0.0931 19/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9773 - loss: 0.0942 21/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9767 - loss: 0.0952 23/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9762 - loss: 0.0960 25/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9758 - loss: 0.0966 27/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9756 - loss: 0.0968 29/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9754 - loss: 0.0969 31/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9752 - loss: 0.0970 33/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9750 - loss: 0.0970 35/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9749 - loss: 0.0969 37/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9748 - loss: 0.0967 39/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9746 - loss: 0.0966 41/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9746 - loss: 0.0965 43/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9744 - loss: 0.0964 45/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9743 - loss: 0.0963 47/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9742 - loss: 0.0963 49/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9741 - loss: 0.0962 51/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9740 - loss: 0.0962 53/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9739 - loss: 0.0961 55/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9738 - loss: 0.0960 57/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9737 - loss: 0.0959 59/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9736 - loss: 0.0958 61/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9736 - loss: 0.0958 63/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9735 - loss: 0.0958 65/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9734 - loss: 0.0958 67/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9734 - loss: 0.0958 69/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9734 - loss: 0.0957 71/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9733 - loss: 0.0957 73/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9733 - loss: 0.0957 75/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9732 - loss: 0.0956 77/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9732 - loss: 0.0956 79/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9732 - loss: 0.0956 81/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9731 - 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accuracy: 0.9726 - loss: 0.0910 385/422 ━━━━━━━━━━━━━━━━━━━━ 2s 60ms/step - accuracy: 0.9726 - loss: 0.0910 387/422 ━━━━━━━━━━━━━━━━━━━━ 2s 60ms/step - accuracy: 0.9726 - loss: 0.0910 389/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9726 - loss: 0.0910 391/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9726 - loss: 0.0909 393/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9726 - loss: 0.0909 395/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9726 - loss: 0.0909 397/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9726 - loss: 0.0909 399/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9726 - loss: 0.0909 401/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9726 - loss: 0.0909 403/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9726 - loss: 0.0909 405/422 ━━━━━━━━━━━━━━━━━━━━ 0s 59ms/step - accuracy: 0.9726 - loss: 0.0909 407/422 ━━━━━━━━━━━━━━━━━━━━ 0s 59ms/step - accuracy: 0.9726 - loss: 0.0909 409/422 ━━━━━━━━━━━━━━━━━━━━ 0s 59ms/step - accuracy: 0.9726 - loss: 0.0909 411/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9726 - loss: 0.0909 413/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9726 - loss: 0.0908 415/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9726 - loss: 0.0908 417/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9726 - loss: 0.0908 419/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9726 - loss: 0.0908 421/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9726 - loss: 0.0908 422/422 ━━━━━━━━━━━━━━━━━━━━ 25s 59ms/step - accuracy: 0.9726 - loss: 0.0908 - val_accuracy: 0.9803 - val_loss: 0.0622 Epoch 12/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 26s 63ms/step - accuracy: 0.9688 - loss: 0.0969 3/422 ━━━━━━━━━━━━━━━━━━━━ 17s 41ms/step - accuracy: 0.9774 - loss: 0.0787 5/422 ━━━━━━━━━━━━━━━━━━━━ 17s 41ms/step - accuracy: 0.9791 - loss: 0.0757 7/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9798 - loss: 0.0763 9/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9796 - loss: 0.0780 11/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9794 - loss: 0.0795 13/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9790 - loss: 0.0804 15/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9787 - loss: 0.0806 17/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9784 - loss: 0.0806 19/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9780 - loss: 0.0813 21/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9777 - loss: 0.0818 23/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9774 - loss: 0.0821 25/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9772 - loss: 0.0823 27/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9771 - loss: 0.0823 29/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9770 - loss: 0.0822 31/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9770 - loss: 0.0820 33/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9770 - loss: 0.0818 35/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9770 - loss: 0.0815 37/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9769 - loss: 0.0814 39/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9768 - loss: 0.0813 41/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9767 - loss: 0.0814 43/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9767 - loss: 0.0814 45/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9766 - loss: 0.0815 47/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9765 - loss: 0.0817 49/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9763 - loss: 0.0819 51/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9762 - loss: 0.0822 53/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9760 - loss: 0.0825 55/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9759 - loss: 0.0827 57/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9758 - loss: 0.0829 59/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9757 - loss: 0.0832 61/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9756 - loss: 0.0834 63/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9754 - loss: 0.0836 65/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9753 - loss: 0.0838 67/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9752 - loss: 0.0840 69/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9751 - loss: 0.0843 71/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9750 - loss: 0.0845 73/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9749 - loss: 0.0846 75/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9748 - loss: 0.0847 77/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9747 - loss: 0.0849 79/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9746 - loss: 0.0850 81/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.9746 - loss: 0.0852 83/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.9745 - loss: 0.0853 85/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.9744 - loss: 0.0855 87/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.9743 - loss: 0.0856 89/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.9742 - loss: 0.0857 91/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.9742 - loss: 0.0858 93/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.9741 - loss: 0.0859 95/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.9741 - loss: 0.0860 97/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.9740 - loss: 0.0860 99/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.9740 - loss: 0.0861 101/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.9739 - loss: 0.0862 103/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.9739 - loss: 0.0862 105/422 ━━━━━━━━━━━━━━━━━━━━ 12s 41ms/step - accuracy: 0.9739 - loss: 0.0862 107/422 ━━━━━━━━━━━━━━━━━━━━ 12s 41ms/step - accuracy: 0.9739 - loss: 0.0863 109/422 ━━━━━━━━━━━━━━━━━━━━ 12s 41ms/step - accuracy: 0.9738 - loss: 0.0863 111/422 ━━━━━━━━━━━━━━━━━━━━ 12s 41ms/step - accuracy: 0.9738 - loss: 0.0863 113/422 ━━━━━━━━━━━━━━━━━━━━ 12s 41ms/step - accuracy: 0.9738 - loss: 0.0863 115/422 ━━━━━━━━━━━━━━━━━━━━ 12s 41ms/step - accuracy: 0.9738 - loss: 0.0864 117/422 ━━━━━━━━━━━━━━━━━━━━ 12s 41ms/step - accuracy: 0.9738 - loss: 0.0864 119/422 ━━━━━━━━━━━━━━━━━━━━ 12s 41ms/step - accuracy: 0.9737 - loss: 0.0864 121/422 ━━━━━━━━━━━━━━━━━━━━ 12s 41ms/step - accuracy: 0.9737 - loss: 0.0865 123/422 ━━━━━━━━━━━━━━━━━━━━ 12s 41ms/step - accuracy: 0.9737 - loss: 0.0865 125/422 ━━━━━━━━━━━━━━━━━━━━ 12s 41ms/step - accuracy: 0.9737 - loss: 0.0866 127/422 ━━━━━━━━━━━━━━━━━━━━ 12s 41ms/step - accuracy: 0.9737 - loss: 0.0866 129/422 ━━━━━━━━━━━━━━━━━━━━ 11s 41ms/step - accuracy: 0.9736 - loss: 0.0866 131/422 ━━━━━━━━━━━━━━━━━━━━ 11s 41ms/step - accuracy: 0.9736 - loss: 0.0867 133/422 ━━━━━━━━━━━━━━━━━━━━ 11s 41ms/step - accuracy: 0.9736 - loss: 0.0867 135/422 ━━━━━━━━━━━━━━━━━━━━ 11s 41ms/step - accuracy: 0.9736 - loss: 0.0867 137/422 ━━━━━━━━━━━━━━━━━━━━ 11s 41ms/step - accuracy: 0.9736 - loss: 0.0867 139/422 ━━━━━━━━━━━━━━━━━━━━ 11s 41ms/step - accuracy: 0.9736 - loss: 0.0868 141/422 ━━━━━━━━━━━━━━━━━━━━ 11s 41ms/step - accuracy: 0.9735 - loss: 0.0868 143/422 ━━━━━━━━━━━━━━━━━━━━ 11s 41ms/step - accuracy: 0.9735 - loss: 0.0868 145/422 ━━━━━━━━━━━━━━━━━━━━ 11s 41ms/step - accuracy: 0.9735 - loss: 0.0869 147/422 ━━━━━━━━━━━━━━━━━━━━ 11s 41ms/step - accuracy: 0.9735 - loss: 0.0869 149/422 ━━━━━━━━━━━━━━━━━━━━ 11s 41ms/step - accuracy: 0.9735 - loss: 0.0870 151/422 ━━━━━━━━━━━━━━━━━━━━ 11s 41ms/step - accuracy: 0.9734 - loss: 0.0870 153/422 ━━━━━━━━━━━━━━━━━━━━ 11s 41ms/step - accuracy: 0.9734 - loss: 0.0870 155/422 ━━━━━━━━━━━━━━━━━━━━ 10s 41ms/step - accuracy: 0.9734 - loss: 0.0871 157/422 ━━━━━━━━━━━━━━━━━━━━ 10s 41ms/step - accuracy: 0.9734 - loss: 0.0871 159/422 ━━━━━━━━━━━━━━━━━━━━ 10s 41ms/step - accuracy: 0.9734 - loss: 0.0871 161/422 ━━━━━━━━━━━━━━━━━━━━ 10s 41ms/step - accuracy: 0.9734 - loss: 0.0871 163/422 ━━━━━━━━━━━━━━━━━━━━ 10s 41ms/step - accuracy: 0.9734 - loss: 0.0872 165/422 ━━━━━━━━━━━━━━━━━━━━ 10s 41ms/step - accuracy: 0.9733 - loss: 0.0872 167/422 ━━━━━━━━━━━━━━━━━━━━ 10s 41ms/step - accuracy: 0.9733 - loss: 0.0872 169/422 ━━━━━━━━━━━━━━━━━━━━ 10s 41ms/step - accuracy: 0.9733 - loss: 0.0872 171/422 ━━━━━━━━━━━━━━━━━━━━ 10s 41ms/step - 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accuracy: 0.9731 - loss: 0.0875 394/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9731 - loss: 0.0875 396/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9731 - loss: 0.0875 398/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9731 - loss: 0.0875 400/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9731 - loss: 0.0875 402/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9732 - loss: 0.0874 404/422 ━━━━━━━━━━━━━━━━━━━━ 1s 58ms/step - accuracy: 0.9732 - loss: 0.0874 406/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9732 - loss: 0.0874 408/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9732 - loss: 0.0874 410/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9732 - loss: 0.0874 412/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9732 - loss: 0.0874 414/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9732 - loss: 0.0874 416/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9732 - loss: 0.0874 418/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9732 - loss: 0.0873 420/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9732 - loss: 0.0873 422/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9732 - loss: 0.0873 422/422 ━━━━━━━━━━━━━━━━━━━━ 25s 59ms/step - accuracy: 0.9732 - loss: 0.0873 - val_accuracy: 0.9820 - val_loss: 0.0603 Epoch 13/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 26s 63ms/step - accuracy: 0.9688 - loss: 0.0669 3/422 ━━━━━━━━━━━━━━━━━━━━ 17s 41ms/step - accuracy: 0.9627 - loss: 0.0975 5/422 ━━━━━━━━━━━━━━━━━━━━ 17s 41ms/step - accuracy: 0.9650 - loss: 0.0981 7/422 ━━━━━━━━━━━━━━━━━━━━ 17s 41ms/step - accuracy: 0.9676 - loss: 0.0941 9/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9692 - loss: 0.0915 11/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9701 - loss: 0.0897 13/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9707 - loss: 0.0889 15/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9712 - loss: 0.0881 17/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9715 - loss: 0.0875 19/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9718 - loss: 0.0869 21/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9720 - loss: 0.0866 23/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9722 - loss: 0.0863 25/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9723 - loss: 0.0862 27/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9723 - loss: 0.0862 29/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9723 - loss: 0.0862 31/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9722 - loss: 0.0863 33/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9722 - loss: 0.0863 35/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9722 - loss: 0.0863 37/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9722 - loss: 0.0864 39/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9721 - loss: 0.0866 41/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9721 - loss: 0.0868 43/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9720 - loss: 0.0870 45/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9720 - loss: 0.0871 47/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9719 - loss: 0.0872 49/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9719 - loss: 0.0874 51/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9719 - loss: 0.0875 53/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9719 - loss: 0.0876 55/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9718 - loss: 0.0876 57/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9719 - loss: 0.0876 59/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9719 - loss: 0.0876 61/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9719 - loss: 0.0875 63/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9720 - loss: 0.0875 65/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9720 - loss: 0.0874 67/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9720 - loss: 0.0874 69/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9720 - loss: 0.0874 71/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9721 - loss: 0.0873 73/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - 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accuracy: 0.9737 - loss: 0.0828 295/422 ━━━━━━━━━━━━━━━━━━━━ 8s 65ms/step - accuracy: 0.9737 - loss: 0.0828 297/422 ━━━━━━━━━━━━━━━━━━━━ 8s 64ms/step - accuracy: 0.9737 - loss: 0.0828 299/422 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - accuracy: 0.9737 - loss: 0.0827 301/422 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - accuracy: 0.9737 - loss: 0.0827 303/422 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - accuracy: 0.9737 - loss: 0.0827 305/422 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - accuracy: 0.9737 - loss: 0.0827 307/422 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - accuracy: 0.9737 - loss: 0.0826 309/422 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - accuracy: 0.9737 - loss: 0.0826 311/422 ━━━━━━━━━━━━━━━━━━━━ 7s 63ms/step - accuracy: 0.9737 - loss: 0.0826 313/422 ━━━━━━━━━━━━━━━━━━━━ 6s 63ms/step - accuracy: 0.9737 - loss: 0.0826 315/422 ━━━━━━━━━━━━━━━━━━━━ 6s 63ms/step - accuracy: 0.9738 - loss: 0.0825 317/422 ━━━━━━━━━━━━━━━━━━━━ 6s 63ms/step - accuracy: 0.9738 - loss: 0.0825 319/422 ━━━━━━━━━━━━━━━━━━━━ 6s 63ms/step - accuracy: 0.9738 - 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accuracy: 0.9741 - loss: 0.0817 403/422 ━━━━━━━━━━━━━━━━━━━━ 1s 58ms/step - accuracy: 0.9741 - loss: 0.0817 405/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9741 - loss: 0.0817 407/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9741 - loss: 0.0817 409/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9741 - loss: 0.0817 411/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9741 - loss: 0.0817 413/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9741 - loss: 0.0817 415/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9741 - loss: 0.0817 417/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9741 - loss: 0.0817 419/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9741 - loss: 0.0816 421/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9741 - loss: 0.0816 422/422 ━━━━━━━━━━━━━━━━━━━━ 25s 59ms/step - accuracy: 0.9741 - loss: 0.0816 - val_accuracy: 0.9823 - val_loss: 0.0594 Epoch 14/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 27s 64ms/step - accuracy: 0.9766 - loss: 0.1025 3/422 ━━━━━━━━━━━━━━━━━━━━ 17s 42ms/step - accuracy: 0.9701 - loss: 0.1089 5/422 ━━━━━━━━━━━━━━━━━━━━ 17s 42ms/step - accuracy: 0.9684 - loss: 0.1059 7/422 ━━━━━━━━━━━━━━━━━━━━ 17s 42ms/step - accuracy: 0.9700 - loss: 0.0995 9/422 ━━━━━━━━━━━━━━━━━━━━ 17s 41ms/step - accuracy: 0.9715 - loss: 0.0946 11/422 ━━━━━━━━━━━━━━━━━━━━ 17s 42ms/step - accuracy: 0.9731 - loss: 0.0908 13/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9740 - loss: 0.0893 15/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9748 - loss: 0.0879 17/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9753 - loss: 0.0869 19/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9755 - loss: 0.0865 21/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9757 - loss: 0.0859 23/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9761 - loss: 0.0850 25/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9763 - loss: 0.0844 27/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9766 - loss: 0.0838 29/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9768 - loss: 0.0833 31/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9770 - loss: 0.0828 33/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9772 - loss: 0.0825 35/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9773 - loss: 0.0823 37/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9773 - loss: 0.0822 39/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9774 - loss: 0.0821 41/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9774 - loss: 0.0820 43/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9775 - loss: 0.0818 45/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9775 - loss: 0.0816 47/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9775 - loss: 0.0815 49/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9776 - loss: 0.0813 51/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9776 - loss: 0.0811 53/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9776 - loss: 0.0810 55/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9776 - loss: 0.0809 57/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9776 - loss: 0.0808 59/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9776 - loss: 0.0806 61/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9776 - loss: 0.0805 63/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9776 - loss: 0.0804 65/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9777 - loss: 0.0803 67/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9777 - loss: 0.0802 69/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9777 - loss: 0.0800 71/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9777 - loss: 0.0799 73/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9777 - loss: 0.0798 75/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9777 - loss: 0.0797 77/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9777 - loss: 0.0796 79/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9777 - loss: 0.0795 81/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9777 - loss: 0.0795 83/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.9777 - 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accuracy: 0.9778 - loss: 0.0779 138/422 ━━━━━━━━━━━━━━━━━━━━ 12s 43ms/step - accuracy: 0.9778 - loss: 0.0779 139/422 ━━━━━━━━━━━━━━━━━━━━ 12s 45ms/step - accuracy: 0.9778 - loss: 0.0778 140/422 ━━━━━━━━━━━━━━━━━━━━ 12s 46ms/step - accuracy: 0.9778 - loss: 0.0778 141/422 ━━━━━━━━━━━━━━━━━━━━ 13s 47ms/step - accuracy: 0.9778 - loss: 0.0778 142/422 ━━━━━━━━━━━━━━━━━━━━ 13s 48ms/step - accuracy: 0.9778 - loss: 0.0778 143/422 ━━━━━━━━━━━━━━━━━━━━ 13s 49ms/step - accuracy: 0.9778 - loss: 0.0778 144/422 ━━━━━━━━━━━━━━━━━━━━ 13s 50ms/step - accuracy: 0.9778 - loss: 0.0778 145/422 ━━━━━━━━━━━━━━━━━━━━ 14s 51ms/step - accuracy: 0.9778 - loss: 0.0777 146/422 ━━━━━━━━━━━━━━━━━━━━ 14s 52ms/step - accuracy: 0.9778 - loss: 0.0777 147/422 ━━━━━━━━━━━━━━━━━━━━ 14s 53ms/step - accuracy: 0.9778 - loss: 0.0777 148/422 ━━━━━━━━━━━━━━━━━━━━ 14s 54ms/step - accuracy: 0.9778 - loss: 0.0777 149/422 ━━━━━━━━━━━━━━━━━━━━ 15s 55ms/step - accuracy: 0.9778 - loss: 0.0777 150/422 ━━━━━━━━━━━━━━━━━━━━ 15s 56ms/step - 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loss: 0.0769 386/422 ━━━━━━━━━━━━━━━━━━━━ 2s 59ms/step - accuracy: 0.9773 - loss: 0.0769 388/422 ━━━━━━━━━━━━━━━━━━━━ 2s 59ms/step - accuracy: 0.9773 - loss: 0.0769 390/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9773 - loss: 0.0769 392/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9773 - loss: 0.0769 394/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9773 - loss: 0.0769 396/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9773 - loss: 0.0769 398/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9773 - loss: 0.0769 400/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9773 - loss: 0.0769 402/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9773 - loss: 0.0769 404/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9772 - loss: 0.0769 406/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9772 - loss: 0.0769 408/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9772 - loss: 0.0769 410/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9772 - loss: 0.0769 412/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9772 - loss: 0.0769 414/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9772 - loss: 0.0769 416/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9772 - loss: 0.0769 418/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9772 - loss: 0.0769 420/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9772 - loss: 0.0769 422/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9772 - loss: 0.0769 422/422 ━━━━━━━━━━━━━━━━━━━━ 25s 59ms/step - accuracy: 0.9772 - loss: 0.0769 - val_accuracy: 0.9848 - val_loss: 0.0549 Epoch 15/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 25s 61ms/step - accuracy: 0.9922 - loss: 0.0441 3/422 ━━━━━━━━━━━━━━━━━━━━ 17s 41ms/step - accuracy: 0.9865 - loss: 0.0640 5/422 ━━━━━━━━━━━━━━━━━━━━ 17s 41ms/step - accuracy: 0.9828 - loss: 0.0712 7/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9816 - loss: 0.0722 9/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9811 - loss: 0.0725 11/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9809 - loss: 0.0726 13/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9809 - loss: 0.0722 15/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9810 - loss: 0.0712 17/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9811 - loss: 0.0703 19/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9812 - loss: 0.0696 21/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9811 - loss: 0.0694 23/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9810 - loss: 0.0692 25/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9810 - loss: 0.0689 27/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9809 - loss: 0.0687 29/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9808 - loss: 0.0686 31/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9808 - loss: 0.0685 33/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9807 - loss: 0.0685 35/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9806 - loss: 0.0685 37/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9805 - loss: 0.0687 39/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9804 - loss: 0.0689 41/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9803 - loss: 0.0690 43/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9802 - loss: 0.0691 45/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9801 - loss: 0.0692 47/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9801 - loss: 0.0693 49/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9800 - loss: 0.0693 51/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9800 - loss: 0.0693 53/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9800 - loss: 0.0694 55/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9799 - loss: 0.0694 57/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9799 - loss: 0.0695 59/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9798 - loss: 0.0695 61/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9798 - loss: 0.0696 63/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9798 - loss: 0.0696 65/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9797 - loss: 0.0697 67/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - 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accuracy: 0.9781 - loss: 0.0722 261/422 ━━━━━━━━━━━━━━━━━━━━ 10s 68ms/step - accuracy: 0.9781 - loss: 0.0722 263/422 ━━━━━━━━━━━━━━━━━━━━ 10s 68ms/step - accuracy: 0.9781 - loss: 0.0722 265/422 ━━━━━━━━━━━━━━━━━━━━ 10s 68ms/step - accuracy: 0.9781 - loss: 0.0722 267/422 ━━━━━━━━━━━━━━━━━━━━ 10s 68ms/step - accuracy: 0.9781 - loss: 0.0722 269/422 ━━━━━━━━━━━━━━━━━━━━ 10s 67ms/step - accuracy: 0.9780 - loss: 0.0722 271/422 ━━━━━━━━━━━━━━━━━━━━ 10s 67ms/step - accuracy: 0.9780 - loss: 0.0722 273/422 ━━━━━━━━━━━━━━━━━━━━ 9s 67ms/step - accuracy: 0.9780 - loss: 0.0723 275/422 ━━━━━━━━━━━━━━━━━━━━ 9s 67ms/step - accuracy: 0.9780 - loss: 0.0723 277/422 ━━━━━━━━━━━━━━━━━━━━ 9s 67ms/step - accuracy: 0.9780 - loss: 0.0723 279/422 ━━━━━━━━━━━━━━━━━━━━ 9s 66ms/step - accuracy: 0.9780 - loss: 0.0723 281/422 ━━━━━━━━━━━━━━━━━━━━ 9s 66ms/step - accuracy: 0.9780 - loss: 0.0723 283/422 ━━━━━━━━━━━━━━━━━━━━ 9s 66ms/step - accuracy: 0.9780 - loss: 0.0723 285/422 ━━━━━━━━━━━━━━━━━━━━ 9s 66ms/step - accuracy: 0.9780 - 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accuracy: 0.9780 - loss: 0.0724 315/422 ━━━━━━━━━━━━━━━━━━━━ 6s 64ms/step - accuracy: 0.9780 - loss: 0.0724 317/422 ━━━━━━━━━━━━━━━━━━━━ 6s 63ms/step - accuracy: 0.9780 - loss: 0.0724 319/422 ━━━━━━━━━━━━━━━━━━━━ 6s 63ms/step - accuracy: 0.9780 - loss: 0.0724 321/422 ━━━━━━━━━━━━━━━━━━━━ 6s 63ms/step - accuracy: 0.9780 - loss: 0.0724 323/422 ━━━━━━━━━━━━━━━━━━━━ 6s 63ms/step - accuracy: 0.9780 - loss: 0.0724 325/422 ━━━━━━━━━━━━━━━━━━━━ 6s 63ms/step - accuracy: 0.9780 - loss: 0.0724 327/422 ━━━━━━━━━━━━━━━━━━━━ 5s 63ms/step - accuracy: 0.9780 - loss: 0.0725 329/422 ━━━━━━━━━━━━━━━━━━━━ 5s 63ms/step - accuracy: 0.9780 - loss: 0.0725 331/422 ━━━━━━━━━━━━━━━━━━━━ 5s 62ms/step - accuracy: 0.9780 - loss: 0.0725 333/422 ━━━━━━━━━━━━━━━━━━━━ 5s 62ms/step - accuracy: 0.9780 - loss: 0.0725 335/422 ━━━━━━━━━━━━━━━━━━━━ 5s 62ms/step - accuracy: 0.9780 - loss: 0.0725 337/422 ━━━━━━━━━━━━━━━━━━━━ 5s 62ms/step - accuracy: 0.9780 - loss: 0.0725 339/422 ━━━━━━━━━━━━━━━━━━━━ 5s 62ms/step - accuracy: 0.9780 - 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accuracy: 0.9779 - loss: 0.0726 369/422 ━━━━━━━━━━━━━━━━━━━━ 3s 60ms/step - accuracy: 0.9779 - loss: 0.0726 371/422 ━━━━━━━━━━━━━━━━━━━━ 3s 60ms/step - accuracy: 0.9779 - loss: 0.0726 373/422 ━━━━━━━━━━━━━━━━━━━━ 2s 60ms/step - accuracy: 0.9779 - loss: 0.0726 375/422 ━━━━━━━━━━━━━━━━━━━━ 2s 60ms/step - accuracy: 0.9779 - loss: 0.0726 377/422 ━━━━━━━━━━━━━━━━━━━━ 2s 60ms/step - accuracy: 0.9779 - loss: 0.0726 379/422 ━━━━━━━━━━━━━━━━━━━━ 2s 60ms/step - accuracy: 0.9779 - loss: 0.0726 381/422 ━━━━━━━━━━━━━━━━━━━━ 2s 60ms/step - accuracy: 0.9779 - loss: 0.0726 383/422 ━━━━━━━━━━━━━━━━━━━━ 2s 60ms/step - accuracy: 0.9779 - loss: 0.0727 385/422 ━━━━━━━━━━━━━━━━━━━━ 2s 59ms/step - accuracy: 0.9779 - loss: 0.0727 387/422 ━━━━━━━━━━━━━━━━━━━━ 2s 59ms/step - accuracy: 0.9779 - loss: 0.0727 389/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9779 - loss: 0.0727 391/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9779 - loss: 0.0727 393/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9778 - loss: 0.0727 395/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9778 - loss: 0.0727 397/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9778 - loss: 0.0727 399/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9778 - loss: 0.0727 401/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9778 - loss: 0.0727 403/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9778 - loss: 0.0727 405/422 ━━━━━━━━━━━━━━━━━━━━ 0s 59ms/step - accuracy: 0.9778 - loss: 0.0727 407/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9778 - loss: 0.0727 409/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9778 - loss: 0.0727 411/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9778 - loss: 0.0728 413/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9778 - loss: 0.0728 415/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9778 - loss: 0.0728 417/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9778 - loss: 0.0728 419/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9778 - loss: 0.0728 421/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9778 - loss: 0.0728 422/422 ━━━━━━━━━━━━━━━━━━━━ 25s 59ms/step - accuracy: 0.9778 - loss: 0.0728 - val_accuracy: 0.9845 - val_loss: 0.0539 Epoch 16/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 26s 64ms/step - accuracy: 0.9766 - loss: 0.0758 3/422 ━━━━━━━━━━━━━━━━━━━━ 17s 41ms/step - accuracy: 0.9787 - loss: 0.0689 5/422 ━━━━━━━━━━━━━━━━━━━━ 17s 41ms/step - accuracy: 0.9789 - loss: 0.0686 7/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9772 - loss: 0.0732 9/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9767 - loss: 0.0742 11/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9760 - loss: 0.0754 13/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9758 - loss: 0.0760 15/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9755 - loss: 0.0764 17/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9755 - loss: 0.0764 19/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9756 - loss: 0.0760 21/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9756 - loss: 0.0756 23/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9758 - loss: 0.0751 25/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9759 - loss: 0.0748 27/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9759 - loss: 0.0746 29/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9759 - loss: 0.0744 31/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9759 - loss: 0.0743 33/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9759 - loss: 0.0743 35/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9760 - loss: 0.0743 37/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9760 - loss: 0.0743 39/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9761 - loss: 0.0742 41/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9762 - loss: 0.0741 43/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9763 - loss: 0.0740 45/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9763 - loss: 0.0739 47/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9764 - loss: 0.0738 49/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9765 - loss: 0.0737 51/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9765 - loss: 0.0736 53/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9766 - loss: 0.0735 55/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9767 - loss: 0.0733 57/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9768 - loss: 0.0732 59/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9769 - loss: 0.0731 61/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9770 - loss: 0.0730 63/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9770 - loss: 0.0730 65/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9771 - loss: 0.0729 67/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9771 - loss: 0.0729 69/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9771 - loss: 0.0728 71/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9772 - loss: 0.0727 73/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9772 - loss: 0.0726 75/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9773 - loss: 0.0725 77/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9773 - loss: 0.0725 79/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9773 - loss: 0.0725 81/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.9773 - loss: 0.0724 83/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.9773 - loss: 0.0724 85/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.9773 - loss: 0.0723 87/422 ━━━━━━━━━━━━━━━━━━━━ 13s 41ms/step - accuracy: 0.9774 - loss: 0.0723 89/422 ━━━━━━━━━━━━━━━━━━━━ 14s 43ms/step - accuracy: 0.9774 - loss: 0.0722 90/422 ━━━━━━━━━━━━━━━━━━━━ 14s 45ms/step - accuracy: 0.9774 - loss: 0.0722 91/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9774 - loss: 0.0721 92/422 ━━━━━━━━━━━━━━━━━━━━ 15s 48ms/step - accuracy: 0.9774 - loss: 0.0721 93/422 ━━━━━━━━━━━━━━━━━━━━ 16s 50ms/step - accuracy: 0.9774 - loss: 0.0721 94/422 ━━━━━━━━━━━━━━━━━━━━ 16s 51ms/step - accuracy: 0.9774 - loss: 0.0720 95/422 ━━━━━━━━━━━━━━━━━━━━ 17s 53ms/step - accuracy: 0.9774 - loss: 0.0720 96/422 ━━━━━━━━━━━━━━━━━━━━ 17s 54ms/step - accuracy: 0.9775 - 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accuracy: 0.9779 - loss: 0.0704 380/422 ━━━━━━━━━━━━━━━━━━━━ 2s 60ms/step - accuracy: 0.9779 - loss: 0.0704 382/422 ━━━━━━━━━━━━━━━━━━━━ 2s 60ms/step - accuracy: 0.9779 - loss: 0.0704 384/422 ━━━━━━━━━━━━━━━━━━━━ 2s 59ms/step - accuracy: 0.9779 - loss: 0.0704 386/422 ━━━━━━━━━━━━━━━━━━━━ 2s 59ms/step - accuracy: 0.9779 - loss: 0.0703 388/422 ━━━━━━━━━━━━━━━━━━━━ 2s 59ms/step - accuracy: 0.9779 - loss: 0.0703 390/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9779 - loss: 0.0703 392/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9779 - loss: 0.0703 394/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9779 - loss: 0.0703 396/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9779 - loss: 0.0703 398/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9779 - loss: 0.0703 400/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9779 - loss: 0.0703 402/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9779 - loss: 0.0703 404/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9779 - loss: 0.0703 406/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9779 - loss: 0.0703 408/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9779 - loss: 0.0703 410/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9779 - loss: 0.0703 412/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9779 - loss: 0.0703 414/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9779 - loss: 0.0703 416/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9779 - loss: 0.0703 418/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9779 - loss: 0.0703 420/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9779 - loss: 0.0703 422/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9779 - loss: 0.0703 422/422 ━━━━━━━━━━━━━━━━━━━━ 25s 59ms/step - accuracy: 0.9779 - loss: 0.0703 - val_accuracy: 0.9848 - val_loss: 0.0525 Epoch 17/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 27s 64ms/step - accuracy: 0.9844 - loss: 0.0762 3/422 ━━━━━━━━━━━━━━━━━━━━ 17s 42ms/step - accuracy: 0.9822 - loss: 0.0701 5/422 ━━━━━━━━━━━━━━━━━━━━ 17s 41ms/step - accuracy: 0.9827 - loss: 0.0637 7/422 ━━━━━━━━━━━━━━━━━━━━ 17s 41ms/step - accuracy: 0.9816 - loss: 0.0658 9/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9804 - loss: 0.0687 11/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9800 - loss: 0.0694 13/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9798 - loss: 0.0695 15/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9797 - loss: 0.0698 17/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9797 - loss: 0.0701 19/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9797 - loss: 0.0703 21/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9796 - loss: 0.0705 23/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9796 - loss: 0.0706 25/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9797 - loss: 0.0706 27/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9797 - loss: 0.0705 29/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9798 - loss: 0.0704 31/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9799 - loss: 0.0703 33/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9799 - loss: 0.0701 35/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9800 - loss: 0.0699 37/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9800 - loss: 0.0698 39/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9800 - loss: 0.0696 41/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9801 - loss: 0.0695 43/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9801 - loss: 0.0693 45/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9802 - loss: 0.0693 47/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9802 - loss: 0.0693 49/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9802 - loss: 0.0693 51/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9802 - loss: 0.0693 53/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9802 - loss: 0.0692 55/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9802 - loss: 0.0691 57/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9802 - loss: 0.0691 59/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - 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loss: 0.0669 386/422 ━━━━━━━━━━━━━━━━━━━━ 2s 60ms/step - accuracy: 0.9797 - loss: 0.0669 388/422 ━━━━━━━━━━━━━━━━━━━━ 2s 60ms/step - accuracy: 0.9797 - loss: 0.0669 390/422 ━━━━━━━━━━━━━━━━━━━━ 1s 60ms/step - accuracy: 0.9797 - loss: 0.0669 392/422 ━━━━━━━━━━━━━━━━━━━━ 1s 60ms/step - accuracy: 0.9797 - loss: 0.0669 394/422 ━━━━━━━━━━━━━━━━━━━━ 1s 60ms/step - accuracy: 0.9797 - loss: 0.0669 396/422 ━━━━━━━━━━━━━━━━━━━━ 1s 60ms/step - accuracy: 0.9797 - loss: 0.0669 398/422 ━━━━━━━━━━━━━━━━━━━━ 1s 60ms/step - accuracy: 0.9797 - loss: 0.0669 400/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9797 - loss: 0.0669 402/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9797 - loss: 0.0669 404/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9797 - loss: 0.0669 406/422 ━━━━━━━━━━━━━━━━━━━━ 0s 59ms/step - accuracy: 0.9797 - loss: 0.0669 408/422 ━━━━━━━━━━━━━━━━━━━━ 0s 59ms/step - accuracy: 0.9797 - loss: 0.0669 410/422 ━━━━━━━━━━━━━━━━━━━━ 0s 59ms/step - accuracy: 0.9797 - loss: 0.0669 412/422 ━━━━━━━━━━━━━━━━━━━━ 0s 59ms/step - accuracy: 0.9797 - loss: 0.0669 414/422 ━━━━━━━━━━━━━━━━━━━━ 0s 59ms/step - accuracy: 0.9797 - loss: 0.0669 416/422 ━━━━━━━━━━━━━━━━━━━━ 0s 59ms/step - accuracy: 0.9797 - loss: 0.0669 418/422 ━━━━━━━━━━━━━━━━━━━━ 0s 59ms/step - accuracy: 0.9797 - loss: 0.0669 420/422 ━━━━━━━━━━━━━━━━━━━━ 0s 59ms/step - accuracy: 0.9797 - loss: 0.0669 422/422 ━━━━━━━━━━━━━━━━━━━━ 0s 59ms/step - accuracy: 0.9797 - loss: 0.0669 422/422 ━━━━━━━━━━━━━━━━━━━━ 25s 60ms/step - accuracy: 0.9797 - loss: 0.0669 - val_accuracy: 0.9857 - val_loss: 0.0501 Epoch 18/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 28s 67ms/step - accuracy: 0.9688 - loss: 0.0573 3/422 ━━━━━━━━━━━━━━━━━━━━ 18s 45ms/step - accuracy: 0.9740 - loss: 0.0540 5/422 ━━━━━━━━━━━━━━━━━━━━ 18s 44ms/step - accuracy: 0.9757 - loss: 0.0527 7/422 ━━━━━━━━━━━━━━━━━━━━ 17s 43ms/step - accuracy: 0.9764 - loss: 0.0554 9/422 ━━━━━━━━━━━━━━━━━━━━ 17s 43ms/step - accuracy: 0.9777 - loss: 0.0558 11/422 ━━━━━━━━━━━━━━━━━━━━ 17s 42ms/step - accuracy: 0.9785 - loss: 0.0557 13/422 ━━━━━━━━━━━━━━━━━━━━ 17s 42ms/step - accuracy: 0.9789 - loss: 0.0558 15/422 ━━━━━━━━━━━━━━━━━━━━ 17s 42ms/step - accuracy: 0.9791 - loss: 0.0561 17/422 ━━━━━━━━━━━━━━━━━━━━ 16s 42ms/step - accuracy: 0.9794 - loss: 0.0563 19/422 ━━━━━━━━━━━━━━━━━━━━ 16s 42ms/step - accuracy: 0.9795 - loss: 0.0564 21/422 ━━━━━━━━━━━━━━━━━━━━ 16s 42ms/step - accuracy: 0.9796 - loss: 0.0565 23/422 ━━━━━━━━━━━━━━━━━━━━ 16s 42ms/step - accuracy: 0.9796 - loss: 0.0568 25/422 ━━━━━━━━━━━━━━━━━━━━ 16s 42ms/step - accuracy: 0.9795 - loss: 0.0572 27/422 ━━━━━━━━━━━━━━━━━━━━ 16s 42ms/step - accuracy: 0.9794 - loss: 0.0578 29/422 ━━━━━━━━━━━━━━━━━━━━ 16s 42ms/step - accuracy: 0.9793 - loss: 0.0585 31/422 ━━━━━━━━━━━━━━━━━━━━ 16s 42ms/step - accuracy: 0.9792 - loss: 0.0589 33/422 ━━━━━━━━━━━━━━━━━━━━ 16s 42ms/step - accuracy: 0.9791 - loss: 0.0593 35/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9791 - loss: 0.0595 37/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9791 - loss: 0.0597 39/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9790 - loss: 0.0599 41/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9790 - loss: 0.0601 43/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9790 - loss: 0.0603 45/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9790 - loss: 0.0604 47/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9789 - loss: 0.0606 49/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9789 - loss: 0.0607 51/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9789 - loss: 0.0609 53/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9789 - loss: 0.0610 55/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9789 - loss: 0.0611 57/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - accuracy: 0.9789 - loss: 0.0613 59/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9789 - loss: 0.0615 61/422 ━━━━━━━━━━━━━━━━━━━━ 14s 41ms/step - accuracy: 0.9789 - loss: 0.0617 62/422 ━━━━━━━━━━━━━━━━━━━━ 15s 43ms/step - accuracy: 0.9788 - loss: 0.0618 63/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9788 - loss: 0.0619 64/422 ━━━━━━━━━━━━━━━━━━━━ 17s 48ms/step - accuracy: 0.9788 - loss: 0.0620 65/422 ━━━━━━━━━━━━━━━━━━━━ 18s 51ms/step - accuracy: 0.9788 - loss: 0.0621 66/422 ━━━━━━━━━━━━━━━━━━━━ 18s 53ms/step - accuracy: 0.9788 - loss: 0.0622 67/422 ━━━━━━━━━━━━━━━━━━━━ 19s 55ms/step - accuracy: 0.9788 - loss: 0.0623 68/422 ━━━━━━━━━━━━━━━━━━━━ 20s 57ms/step - accuracy: 0.9788 - loss: 0.0624 69/422 ━━━━━━━━━━━━━━━━━━━━ 21s 60ms/step - accuracy: 0.9788 - loss: 0.0625 70/422 ━━━━━━━━━━━━━━━━━━━━ 21s 62ms/step - accuracy: 0.9788 - loss: 0.0625 71/422 ━━━━━━━━━━━━━━━━━━━━ 22s 64ms/step - accuracy: 0.9788 - loss: 0.0626 72/422 ━━━━━━━━━━━━━━━━━━━━ 22s 66ms/step - accuracy: 0.9788 - loss: 0.0627 73/422 ━━━━━━━━━━━━━━━━━━━━ 23s 67ms/step - accuracy: 0.9788 - loss: 0.0627 74/422 ━━━━━━━━━━━━━━━━━━━━ 24s 69ms/step - accuracy: 0.9788 - loss: 0.0628 75/422 ━━━━━━━━━━━━━━━━━━━━ 24s 71ms/step - accuracy: 0.9788 - loss: 0.0629 76/422 ━━━━━━━━━━━━━━━━━━━━ 25s 73ms/step - accuracy: 0.9788 - loss: 0.0629 77/422 ━━━━━━━━━━━━━━━━━━━━ 25s 75ms/step - accuracy: 0.9788 - 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accuracy: 0.9791 - loss: 0.0653 422/422 ━━━━━━━━━━━━━━━━━━━━ 25s 59ms/step - accuracy: 0.9791 - loss: 0.0653 - val_accuracy: 0.9848 - val_loss: 0.0492 Epoch 19/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 27s 65ms/step - accuracy: 0.9844 - loss: 0.0597 3/422 ━━━━━━━━━━━━━━━━━━━━ 17s 41ms/step - accuracy: 0.9865 - loss: 0.0472 5/422 ━━━━━━━━━━━━━━━━━━━━ 17s 41ms/step - accuracy: 0.9877 - loss: 0.0426 7/422 ━━━━━━━━━━━━━━━━━━━━ 17s 42ms/step - accuracy: 0.9886 - loss: 0.0398 9/422 ━━━━━━━━━━━━━━━━━━━━ 17s 42ms/step - accuracy: 0.9887 - loss: 0.0394 11/422 ━━━━━━━━━━━━━━━━━━━━ 17s 42ms/step - accuracy: 0.9889 - loss: 0.0392 13/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9889 - loss: 0.0400 15/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9887 - loss: 0.0407 17/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9886 - loss: 0.0410 19/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9885 - loss: 0.0414 21/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9883 - loss: 0.0419 23/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9883 - loss: 0.0422 25/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9882 - loss: 0.0423 27/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - accuracy: 0.9882 - loss: 0.0426 29/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.9881 - loss: 0.0428 30/422 ━━━━━━━━━━━━━━━━━━━━ 20s 53ms/step - accuracy: 0.9880 - loss: 0.0430 31/422 ━━━━━━━━━━━━━━━━━━━━ 22s 58ms/step - accuracy: 0.9880 - loss: 0.0432 32/422 ━━━━━━━━━━━━━━━━━━━━ 24s 62ms/step - accuracy: 0.9879 - loss: 0.0434 33/422 ━━━━━━━━━━━━━━━━━━━━ 25s 67ms/step - accuracy: 0.9878 - loss: 0.0435 34/422 ━━━━━━━━━━━━━━━━━━━━ 27s 71ms/step - accuracy: 0.9878 - loss: 0.0436 35/422 ━━━━━━━━━━━━━━━━━━━━ 28s 75ms/step - accuracy: 0.9877 - loss: 0.0438 36/422 ━━━━━━━━━━━━━━━━━━━━ 30s 78ms/step - accuracy: 0.9877 - loss: 0.0439 37/422 ━━━━━━━━━━━━━━━━━━━━ 31s 82ms/step - accuracy: 0.9877 - loss: 0.0440 38/422 ━━━━━━━━━━━━━━━━━━━━ 32s 85ms/step - accuracy: 0.9876 - loss: 0.0441 39/422 ━━━━━━━━━━━━━━━━━━━━ 33s 89ms/step - accuracy: 0.9876 - 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accuracy: 0.9836 - loss: 0.0539 192/422 ━━━━━━━━━━━━━━━━━━━━ 17s 78ms/step - accuracy: 0.9836 - loss: 0.0540 194/422 ━━━━━━━━━━━━━━━━━━━━ 17s 78ms/step - accuracy: 0.9835 - loss: 0.0540 196/422 ━━━━━━━━━━━━━━━━━━━━ 17s 77ms/step - accuracy: 0.9835 - loss: 0.0541 198/422 ━━━━━━━━━━━━━━━━━━━━ 17s 77ms/step - accuracy: 0.9835 - loss: 0.0541 200/422 ━━━━━━━━━━━━━━━━━━━━ 17s 77ms/step - accuracy: 0.9835 - loss: 0.0542 202/422 ━━━━━━━━━━━━━━━━━━━━ 16s 76ms/step - accuracy: 0.9835 - loss: 0.0542 204/422 ━━━━━━━━━━━━━━━━━━━━ 16s 76ms/step - accuracy: 0.9835 - loss: 0.0543 206/422 ━━━━━━━━━━━━━━━━━━━━ 16s 76ms/step - accuracy: 0.9835 - loss: 0.0543 208/422 ━━━━━━━━━━━━━━━━━━━━ 16s 75ms/step - accuracy: 0.9834 - loss: 0.0544 210/422 ━━━━━━━━━━━━━━━━━━━━ 15s 75ms/step - accuracy: 0.9834 - loss: 0.0544 212/422 ━━━━━━━━━━━━━━━━━━━━ 15s 75ms/step - accuracy: 0.9834 - loss: 0.0545 214/422 ━━━━━━━━━━━━━━━━━━━━ 15s 74ms/step - accuracy: 0.9834 - loss: 0.0545 216/422 ━━━━━━━━━━━━━━━━━━━━ 15s 74ms/step - 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accuracy: 0.9832 - loss: 0.0552 244/422 ━━━━━━━━━━━━━━━━━━━━ 12s 70ms/step - accuracy: 0.9832 - loss: 0.0553 246/422 ━━━━━━━━━━━━━━━━━━━━ 12s 70ms/step - accuracy: 0.9832 - loss: 0.0553 248/422 ━━━━━━━━━━━━━━━━━━━━ 12s 70ms/step - accuracy: 0.9831 - loss: 0.0554 250/422 ━━━━━━━━━━━━━━━━━━━━ 11s 69ms/step - accuracy: 0.9831 - loss: 0.0555 252/422 ━━━━━━━━━━━━━━━━━━━━ 11s 69ms/step - accuracy: 0.9831 - loss: 0.0555 254/422 ━━━━━━━━━━━━━━━━━━━━ 11s 69ms/step - accuracy: 0.9831 - loss: 0.0556 256/422 ━━━━━━━━━━━━━━━━━━━━ 11s 69ms/step - accuracy: 0.9831 - loss: 0.0556 258/422 ━━━━━━━━━━━━━━━━━━━━ 11s 69ms/step - accuracy: 0.9831 - loss: 0.0557 260/422 ━━━━━━━━━━━━━━━━━━━━ 11s 68ms/step - accuracy: 0.9831 - loss: 0.0557 262/422 ━━━━━━━━━━━━━━━━━━━━ 10s 68ms/step - accuracy: 0.9830 - loss: 0.0557 264/422 ━━━━━━━━━━━━━━━━━━━━ 10s 68ms/step - accuracy: 0.9830 - loss: 0.0558 266/422 ━━━━━━━━━━━━━━━━━━━━ 10s 68ms/step - accuracy: 0.9830 - loss: 0.0558 268/422 ━━━━━━━━━━━━━━━━━━━━ 10s 68ms/step - accuracy: 0.9830 - loss: 0.0559 270/422 ━━━━━━━━━━━━━━━━━━━━ 10s 67ms/step - accuracy: 0.9830 - loss: 0.0559 272/422 ━━━━━━━━━━━━━━━━━━━━ 10s 67ms/step - accuracy: 0.9830 - loss: 0.0560 274/422 ━━━━━━━━━━━━━━━━━━━━ 9s 67ms/step - accuracy: 0.9830 - loss: 0.0560 276/422 ━━━━━━━━━━━━━━━━━━━━ 9s 67ms/step - accuracy: 0.9830 - loss: 0.0560 278/422 ━━━━━━━━━━━━━━━━━━━━ 9s 67ms/step - accuracy: 0.9830 - loss: 0.0561 280/422 ━━━━━━━━━━━━━━━━━━━━ 9s 66ms/step - accuracy: 0.9829 - loss: 0.0561 282/422 ━━━━━━━━━━━━━━━━━━━━ 9s 66ms/step - accuracy: 0.9829 - loss: 0.0562 284/422 ━━━━━━━━━━━━━━━━━━━━ 9s 66ms/step - accuracy: 0.9829 - loss: 0.0562 286/422 ━━━━━━━━━━━━━━━━━━━━ 8s 66ms/step - accuracy: 0.9829 - loss: 0.0562 288/422 ━━━━━━━━━━━━━━━━━━━━ 8s 66ms/step - accuracy: 0.9829 - loss: 0.0563 290/422 ━━━━━━━━━━━━━━━━━━━━ 8s 66ms/step - accuracy: 0.9829 - loss: 0.0563 292/422 ━━━━━━━━━━━━━━━━━━━━ 8s 65ms/step - accuracy: 0.9829 - loss: 0.0563 294/422 ━━━━━━━━━━━━━━━━━━━━ 8s 65ms/step - accuracy: 0.9829 - loss: 0.0564 296/422 ━━━━━━━━━━━━━━━━━━━━ 8s 65ms/step - accuracy: 0.9829 - loss: 0.0564 298/422 ━━━━━━━━━━━━━━━━━━━━ 8s 65ms/step - accuracy: 0.9828 - loss: 0.0564 300/422 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - accuracy: 0.9828 - loss: 0.0565 302/422 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - accuracy: 0.9828 - loss: 0.0565 304/422 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - accuracy: 0.9828 - loss: 0.0565 306/422 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - accuracy: 0.9828 - loss: 0.0566 308/422 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - accuracy: 0.9828 - loss: 0.0566 310/422 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - accuracy: 0.9828 - loss: 0.0566 312/422 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - accuracy: 0.9828 - loss: 0.0566 314/422 ━━━━━━━━━━━━━━━━━━━━ 6s 64ms/step - accuracy: 0.9828 - loss: 0.0567 316/422 ━━━━━━━━━━━━━━━━━━━━ 6s 64ms/step - accuracy: 0.9828 - loss: 0.0567 318/422 ━━━━━━━━━━━━━━━━━━━━ 6s 63ms/step - accuracy: 0.9828 - loss: 0.0567 320/422 ━━━━━━━━━━━━━━━━━━━━ 6s 63ms/step - accuracy: 0.9828 - loss: 0.0567 322/422 ━━━━━━━━━━━━━━━━━━━━ 6s 63ms/step - accuracy: 0.9827 - loss: 0.0568 324/422 ━━━━━━━━━━━━━━━━━━━━ 6s 63ms/step - accuracy: 0.9827 - loss: 0.0568 326/422 ━━━━━━━━━━━━━━━━━━━━ 6s 63ms/step - accuracy: 0.9827 - loss: 0.0568 328/422 ━━━━━━━━━━━━━━━━━━━━ 5s 63ms/step - accuracy: 0.9827 - loss: 0.0568 330/422 ━━━━━━━━━━━━━━━━━━━━ 5s 63ms/step - accuracy: 0.9827 - loss: 0.0569 332/422 ━━━━━━━━━━━━━━━━━━━━ 5s 62ms/step - accuracy: 0.9827 - loss: 0.0569 334/422 ━━━━━━━━━━━━━━━━━━━━ 5s 62ms/step - accuracy: 0.9827 - loss: 0.0569 336/422 ━━━━━━━━━━━━━━━━━━━━ 5s 62ms/step - accuracy: 0.9827 - loss: 0.0569 338/422 ━━━━━━━━━━━━━━━━━━━━ 5s 62ms/step - accuracy: 0.9827 - loss: 0.0569 340/422 ━━━━━━━━━━━━━━━━━━━━ 5s 62ms/step - accuracy: 0.9827 - loss: 0.0570 342/422 ━━━━━━━━━━━━━━━━━━━━ 4s 62ms/step - accuracy: 0.9827 - loss: 0.0570 344/422 ━━━━━━━━━━━━━━━━━━━━ 4s 62ms/step - accuracy: 0.9827 - loss: 0.0570 346/422 ━━━━━━━━━━━━━━━━━━━━ 4s 62ms/step - accuracy: 0.9827 - loss: 0.0570 348/422 ━━━━━━━━━━━━━━━━━━━━ 4s 61ms/step - accuracy: 0.9827 - loss: 0.0570 350/422 ━━━━━━━━━━━━━━━━━━━━ 4s 61ms/step - accuracy: 0.9826 - loss: 0.0571 352/422 ━━━━━━━━━━━━━━━━━━━━ 4s 61ms/step - accuracy: 0.9826 - loss: 0.0571 354/422 ━━━━━━━━━━━━━━━━━━━━ 4s 61ms/step - accuracy: 0.9826 - loss: 0.0571 356/422 ━━━━━━━━━━━━━━━━━━━━ 4s 61ms/step - accuracy: 0.9826 - loss: 0.0571 358/422 ━━━━━━━━━━━━━━━━━━━━ 3s 61ms/step - accuracy: 0.9826 - loss: 0.0571 360/422 ━━━━━━━━━━━━━━━━━━━━ 3s 61ms/step - accuracy: 0.9826 - loss: 0.0572 362/422 ━━━━━━━━━━━━━━━━━━━━ 3s 61ms/step - accuracy: 0.9826 - loss: 0.0572 364/422 ━━━━━━━━━━━━━━━━━━━━ 3s 61ms/step - accuracy: 0.9826 - loss: 0.0572 366/422 ━━━━━━━━━━━━━━━━━━━━ 3s 60ms/step - accuracy: 0.9826 - loss: 0.0572 368/422 ━━━━━━━━━━━━━━━━━━━━ 3s 60ms/step - accuracy: 0.9826 - loss: 0.0572 370/422 ━━━━━━━━━━━━━━━━━━━━ 3s 60ms/step - accuracy: 0.9826 - loss: 0.0573 372/422 ━━━━━━━━━━━━━━━━━━━━ 3s 60ms/step - accuracy: 0.9826 - loss: 0.0573 374/422 ━━━━━━━━━━━━━━━━━━━━ 2s 60ms/step - accuracy: 0.9826 - loss: 0.0573 376/422 ━━━━━━━━━━━━━━━━━━━━ 2s 60ms/step - accuracy: 0.9826 - loss: 0.0573 378/422 ━━━━━━━━━━━━━━━━━━━━ 2s 60ms/step - accuracy: 0.9825 - loss: 0.0573 380/422 ━━━━━━━━━━━━━━━━━━━━ 2s 60ms/step - accuracy: 0.9825 - loss: 0.0574 382/422 ━━━━━━━━━━━━━━━━━━━━ 2s 60ms/step - accuracy: 0.9825 - loss: 0.0574 384/422 ━━━━━━━━━━━━━━━━━━━━ 2s 59ms/step - accuracy: 0.9825 - loss: 0.0574 386/422 ━━━━━━━━━━━━━━━━━━━━ 2s 59ms/step - accuracy: 0.9825 - loss: 0.0574 388/422 ━━━━━━━━━━━━━━━━━━━━ 2s 59ms/step - accuracy: 0.9825 - loss: 0.0574 390/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9825 - loss: 0.0575 392/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9825 - loss: 0.0575 394/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9825 - loss: 0.0575 396/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9825 - loss: 0.0575 398/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9825 - loss: 0.0575 400/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9825 - loss: 0.0575 402/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9824 - loss: 0.0576 404/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - accuracy: 0.9824 - loss: 0.0576 406/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9824 - loss: 0.0576 408/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9824 - loss: 0.0576 410/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9824 - loss: 0.0576 412/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9824 - loss: 0.0577 414/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9824 - loss: 0.0577 416/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9824 - loss: 0.0577 418/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9824 - loss: 0.0577 420/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9824 - loss: 0.0577 422/422 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step - accuracy: 0.9824 - loss: 0.0577 422/422 ━━━━━━━━━━━━━━━━━━━━ 25s 59ms/step - accuracy: 0.9824 - loss: 0.0577 - val_accuracy: 0.9853 - val_loss: 0.0489 Epoch 20/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 27s 66ms/step - accuracy: 0.9844 - loss: 0.0449 2/422 ━━━━━━━━━━━━━━━━━━━━ 1:24 200ms/step - accuracy: 0.9824 - loss: 0.0574 3/422 ━━━━━━━━━━━━━━━━━━━━ 1:24 203ms/step - accuracy: 0.9787 - loss: 0.0660 4/422 ━━━━━━━━━━━━━━━━━━━━ 1:24 202ms/step - accuracy: 0.9772 - loss: 0.0691 5/422 ━━━━━━━━━━━━━━━━━━━━ 1:23 201ms/step - accuracy: 0.9771 - loss: 0.0690 6/422 ━━━━━━━━━━━━━━━━━━━━ 1:23 201ms/step - accuracy: 0.9774 - loss: 0.0690 7/422 ━━━━━━━━━━━━━━━━━━━━ 1:23 201ms/step - accuracy: 0.9779 - loss: 0.0682 8/422 ━━━━━━━━━━━━━━━━━━━━ 1:23 201ms/step - accuracy: 0.9784 - loss: 0.0675 9/422 ━━━━━━━━━━━━━━━━━━━━ 1:22 201ms/step - accuracy: 0.9788 - loss: 0.0666 10/422 ━━━━━━━━━━━━━━━━━━━━ 1:22 200ms/step - accuracy: 0.9790 - loss: 0.0658 11/422 ━━━━━━━━━━━━━━━━━━━━ 1:22 201ms/step - accuracy: 0.9792 - loss: 0.0654 12/422 ━━━━━━━━━━━━━━━━━━━━ 1:22 201ms/step - accuracy: 0.9794 - loss: 0.0649 13/422 ━━━━━━━━━━━━━━━━━━━━ 1:22 200ms/step - accuracy: 0.9796 - loss: 0.0643 14/422 ━━━━━━━━━━━━━━━━━━━━ 1:21 200ms/step - accuracy: 0.9799 - loss: 0.0636 15/422 ━━━━━━━━━━━━━━━━━━━━ 1:21 200ms/step - accuracy: 0.9801 - loss: 0.0631 16/422 ━━━━━━━━━━━━━━━━━━━━ 1:21 200ms/step - accuracy: 0.9802 - loss: 0.0627 17/422 ━━━━━━━━━━━━━━━━━━━━ 1:21 200ms/step - accuracy: 0.9804 - loss: 0.0623 18/422 ━━━━━━━━━━━━━━━━━━━━ 1:20 200ms/step - accuracy: 0.9805 - loss: 0.0619 19/422 ━━━━━━━━━━━━━━━━━━━━ 1:20 200ms/step - accuracy: 0.9806 - loss: 0.0616 20/422 ━━━━━━━━━━━━━━━━━━━━ 1:20 200ms/step - accuracy: 0.9807 - loss: 0.0614 21/422 ━━━━━━━━━━━━━━━━━━━━ 1:20 200ms/step - accuracy: 0.9808 - loss: 0.0612 22/422 ━━━━━━━━━━━━━━━━━━━━ 1:20 200ms/step - accuracy: 0.9808 - loss: 0.0611 23/422 ━━━━━━━━━━━━━━━━━━━━ 1:19 200ms/step - accuracy: 0.9808 - loss: 0.0611 24/422 ━━━━━━━━━━━━━━━━━━━━ 1:19 200ms/step - accuracy: 0.9808 - loss: 0.0611 25/422 ━━━━━━━━━━━━━━━━━━━━ 1:19 200ms/step - accuracy: 0.9808 - loss: 0.0611 26/422 ━━━━━━━━━━━━━━━━━━━━ 1:19 200ms/step - accuracy: 0.9809 - loss: 0.0610 27/422 ━━━━━━━━━━━━━━━━━━━━ 1:19 200ms/step - accuracy: 0.9809 - loss: 0.0609 28/422 ━━━━━━━━━━━━━━━━━━━━ 1:18 200ms/step - accuracy: 0.9809 - loss: 0.0609 29/422 ━━━━━━━━━━━━━━━━━━━━ 1:18 201ms/step - accuracy: 0.9809 - loss: 0.0608 30/422 ━━━━━━━━━━━━━━━━━━━━ 1:18 201ms/step - accuracy: 0.9809 - loss: 0.0609 31/422 ━━━━━━━━━━━━━━━━━━━━ 1:18 201ms/step - accuracy: 0.9809 - loss: 0.0609 32/422 ━━━━━━━━━━━━━━━━━━━━ 1:18 201ms/step - accuracy: 0.9808 - loss: 0.0609 33/422 ━━━━━━━━━━━━━━━━━━━━ 1:18 201ms/step - accuracy: 0.9808 - loss: 0.0609 34/422 ━━━━━━━━━━━━━━━━━━━━ 1:18 201ms/step - accuracy: 0.9808 - loss: 0.0609 35/422 ━━━━━━━━━━━━━━━━━━━━ 1:17 201ms/step - accuracy: 0.9808 - loss: 0.0609 36/422 ━━━━━━━━━━━━━━━━━━━━ 1:17 201ms/step - accuracy: 0.9807 - loss: 0.0609 37/422 ━━━━━━━━━━━━━━━━━━━━ 1:17 201ms/step - accuracy: 0.9807 - loss: 0.0610 38/422 ━━━━━━━━━━━━━━━━━━━━ 1:17 201ms/step - accuracy: 0.9807 - loss: 0.0610 39/422 ━━━━━━━━━━━━━━━━━━━━ 1:17 201ms/step - accuracy: 0.9807 - loss: 0.0610 40/422 ━━━━━━━━━━━━━━━━━━━━ 1:16 201ms/step - accuracy: 0.9806 - loss: 0.0611 41/422 ━━━━━━━━━━━━━━━━━━━━ 1:16 201ms/step - 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accuracy: 0.9804 - loss: 0.0621 91/422 ━━━━━━━━━━━━━━━━━━━━ 39s 119ms/step - accuracy: 0.9804 - loss: 0.0621 93/422 ━━━━━━━━━━━━━━━━━━━━ 38s 118ms/step - accuracy: 0.9805 - loss: 0.0622 95/422 ━━━━━━━━━━━━━━━━━━━━ 37s 116ms/step - accuracy: 0.9805 - loss: 0.0622 97/422 ━━━━━━━━━━━━━━━━━━━━ 37s 114ms/step - accuracy: 0.9805 - loss: 0.0622 99/422 ━━━━━━━━━━━━━━━━━━━━ 36s 113ms/step - accuracy: 0.9805 - loss: 0.0622 101/422 ━━━━━━━━━━━━━━━━━━━━ 35s 112ms/step - accuracy: 0.9805 - loss: 0.0621 103/422 ━━━━━━━━━━━━━━━━━━━━ 35s 110ms/step - accuracy: 0.9806 - loss: 0.0621 105/422 ━━━━━━━━━━━━━━━━━━━━ 34s 109ms/step - accuracy: 0.9806 - loss: 0.0621 107/422 ━━━━━━━━━━━━━━━━━━━━ 33s 108ms/step - accuracy: 0.9806 - loss: 0.0621 109/422 ━━━━━━━━━━━━━━━━━━━━ 33s 106ms/step - accuracy: 0.9806 - loss: 0.0621 111/422 ━━━━━━━━━━━━━━━━━━━━ 32s 105ms/step - accuracy: 0.9807 - loss: 0.0621 113/422 ━━━━━━━━━━━━━━━━━━━━ 32s 104ms/step - accuracy: 0.9807 - loss: 0.0621 115/422 ━━━━━━━━━━━━━━━━━━━━ 31s 103ms/step - 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accuracy: 0.9816 - loss: 0.0602 417/422 ━━━━━━━━━━━━━━━━━━━━ 0s 65ms/step - accuracy: 0.9816 - loss: 0.0602 418/422 ━━━━━━━━━━━━━━━━━━━━ 0s 65ms/step - accuracy: 0.9816 - loss: 0.0602 419/422 ━━━━━━━━━━━━━━━━━━━━ 0s 66ms/step - accuracy: 0.9816 - loss: 0.0602 420/422 ━━━━━━━━━━━━━━━━━━━━ 0s 66ms/step - accuracy: 0.9816 - loss: 0.0602 421/422 ━━━━━━━━━━━━━━━━━━━━ 0s 67ms/step - accuracy: 0.9816 - loss: 0.0602 422/422 ━━━━━━━━━━━━━━━━━━━━ 0s 67ms/step - accuracy: 0.9816 - loss: 0.0602 422/422 ━━━━━━━━━━━━━━━━━━━━ 32s 75ms/step - accuracy: 0.9816 - loss: 0.0602 - val_accuracy: 0.9847 - val_loss: 0.0484 Test accuracy: 0.9864000082015991
plt.figure(figsize=(12, 6))
plt.subplot(121)
plt.plot(history.history['loss'], '-r', label="Training")
plt.plot(history.history['val_loss'], '-b', label="Validation")
plt.xlabel('Epoch #')
plt.ylabel('Loss')
plt.legend()
plt.subplot(122)
plt.plot(history.history['accuracy'], '-r', label="Training")
plt.plot(history.history['val_accuracy'], '-b', label="Validation")
plt.xlabel('Epoch #')
plt.ylabel('Accuracy')
plt.legend()
plt.show()
Initialize the ANN-to-SNN converter
We now create an instance of the ANN-to-SNN conversion object.
from ANNarchy.extensions.ann_to_snn_conversion import ANNtoSNNConverter
snn_converter = ANNtoSNNConverter(
input_encoding='IB',
hidden_neuron='IaF',
read_out='spike_count',
)ANNarchy 5.0 (5.0.0) on linux (posix).net = snn_converter.load_keras_model("runs/cnn.keras", show_info=True)WARNING: Dense representation is an experimental feature for spiking models, we greatly appreciate bug reports.
* Input layer: input_layer, (28, 28, 1)
* InputLayer skipped.
* Conv2D layer: conv2d, (28, 28, 16)
* MaxPooling2D layer: max_pooling2d, (14, 14, 16)
* Conv2D layer: conv2d_1, (14, 14, 64)
* MaxPooling2D layer: max_pooling2d_1, (7, 7, 64)
* Conv2D layer: conv2d_2, (7, 7, 64)
* MaxPooling2D layer: max_pooling2d_2, (3, 3, 64)
* Dropout skipped.
* Flatten skipped.
* Dense layer: dense, 10
weights: (10, 576)
mean -0.001368094002828002, std 0.06911402940750122
min -0.21231625974178314, max 0.2032238394021988
predictions_snn = snn_converter.predict(X_test[:300], duration_per_sample=200) 0%| | 0/300 [00:00<?, ?it/s] 0%|▎ | 1/300 [00:01<09:39, 1.94s/it] 1%|▌ | 2/300 [00:03<09:38, 1.94s/it] 1%|▊ | 3/300 [00:05<09:35, 1.94s/it] 1%|█ | 4/300 [00:07<09:33, 1.94s/it] 2%|█▎ | 5/300 [00:09<09:31, 1.94s/it] 2%|█▌ | 6/300 [00:11<09:28, 1.94s/it] 2%|█▉ | 7/300 [00:13<09:26, 1.94s/it] 3%|██▏ | 8/300 [00:15<09:24, 1.93s/it] 3%|██▍ | 9/300 [00:17<09:23, 1.94s/it] 3%|██▋ | 10/300 [00:19<09:23, 1.94s/it] 4%|██▉ | 11/300 [00:21<09:25, 1.96s/it] 4%|███▏ | 12/300 [00:23<09:24, 1.96s/it] 4%|███▍ | 13/300 [00:25<09:21, 1.96s/it] 5%|███▋ | 14/300 [00:27<09:20, 1.96s/it] 5%|████ | 15/300 [00:29<09:17, 1.96s/it] 5%|████▎ | 16/300 [00:31<09:15, 1.96s/it] 6%|████▌ | 17/300 [00:33<09:12, 1.95s/it] 6%|████▊ | 18/300 [00:35<09:10, 1.95s/it] 6%|█████ | 19/300 [00:37<09:08, 1.95s/it] 7%|█████▎ | 20/300 [00:38<09:05, 1.95s/it] 7%|█████▌ | 21/300 [00:40<09:03, 1.95s/it] 7%|█████▊ | 22/300 [00:42<09:00, 1.95s/it] 8%|██████▏ 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[09:08<00:32, 1.93s/it] 95%|██████████████████████████████████████████████████████████████████████████▊ | 284/300 [09:09<00:30, 1.93s/it] 95%|███████████████████████████████████████████████████████████████████████████ | 285/300 [09:11<00:28, 1.93s/it] 95%|███████████████████████████████████████████████████████████████████████████▎ | 286/300 [09:13<00:27, 1.93s/it] 96%|███████████████████████████████████████████████████████████████████████████▌ | 287/300 [09:15<00:25, 1.93s/it] 96%|███████████████████████████████████████████████████████████████████████████▊ | 288/300 [09:17<00:23, 1.93s/it] 96%|████████████████████████████████████████████████████████████████████████████ | 289/300 [09:19<00:21, 1.93s/it] 97%|████████████████████████████████████████████████████████████████████████████▎ | 290/300 [09:21<00:19, 1.93s/it] 97%|████████████████████████████████████████████████████████████████████████████▋ | 291/300 [09:23<00:17, 1.93s/it] 97%|████████████████████████████████████████████████████████████████████████████▉ | 292/300 [09:25<00:15, 1.94s/it] 98%|█████████████████████████████████████████████████████████████████████████████▏ | 293/300 [09:27<00:13, 1.94s/it] 98%|█████████████████████████████████████████████████████████████████████████████▍ | 294/300 [09:29<00:11, 1.95s/it] 98%|█████████████████████████████████████████████████████████████████████████████▋ | 295/300 [09:31<00:09, 1.95s/it] 99%|█████████████████████████████████████████████████████████████████████████████▉ | 296/300 [09:33<00:07, 1.94s/it] 99%|██████████████████████████████████████████████████████████████████████████████▏| 297/300 [09:35<00:05, 1.94s/it] 99%|██████████████████████████████████████████████████████████████████████████████▍| 298/300 [09:37<00:03, 1.94s/it]100%|██████████████████████████████████████████████████████████████████████████████▋| 299/300 [09:39<00:01, 1.93s/it]100%|███████████████████████████████████████████████████████████████████████████████| 300/300 [09:40<00:00, 1.93s/it]100%|███████████████████████████████████████████████████████████████████████████████| 300/300 [09:40<00:00, 1.94s/it]Using the recorded predictions, we can now compute the accuracy using scikit-learn for all presented samples.
from sklearn.metrics import classification_report, accuracy_score
print(classification_report(t_test[:300], predictions_snn))
print("Test accuracy of the SNN:", accuracy_score(t_test[:300], predictions_snn)) precision recall f1-score support
0 1.00 1.00 1.00 24
1 1.00 0.98 0.99 41
2 1.00 1.00 1.00 32
3 1.00 0.96 0.98 24
4 1.00 0.97 0.99 37
5 1.00 1.00 1.00 29
6 0.96 1.00 0.98 24
7 1.00 1.00 1.00 34
8 0.81 1.00 0.89 21
9 1.00 0.91 0.95 34
accuracy 0.98 300
macro avg 0.98 0.98 0.98 300
weighted avg 0.98 0.98 0.98 300
Test accuracy of the SNN: 0.98