#!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__}")2026-05-11 09:50:59.066168: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.
2026-05-11 09:50:59.069283: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.
2026-05-11 09:50:59.078972: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2026-05-11 09:50:59.093958: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2026-05-11 09:50:59.098477: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2026-05-11 09:50:59.109977: 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.
2026-05-11 09:50:59.831439: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRTTensorflow 2.17.0# 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}")2026-05-11 09:51:01.013527: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected
2026-05-11 09:51:01.013550: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:135] retrieving CUDA diagnostic information for host: twix
2026-05-11 09:51:01.013556: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:142] hostname: twix
2026-05-11 09:51:01.013651: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:166] libcuda reported version is: 520.61.5
2026-05-11 09:51:01.013670: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:170] kernel reported version is: 470.256.2
2026-05-11 09:51:01.013676: E external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:252] kernel version 470.256.2 does not match DSO version 520.61.5 -- cannot find working devices in this configurationModel: "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 1/202026-05-11 09:51:01.124759: W external/local_tsl/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 169344000 exceeds 10% of free system memory.1/422 ━━━━━━━━━━━━━━━━━━━━ 2:58 425ms/step - accuracy: 0.0547 - loss: 2.3147 2/422 ━━━━━━━━━━━━━━━━━━━━ 21s 50ms/step - accuracy: 0.0566 - loss: 2.3138 4/422 ━━━━━━━━━━━━━━━━━━━━ 19s 48ms/step - accuracy: 0.0605 - loss: 2.3125 6/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - accuracy: 0.0665 - loss: 2.3108 8/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - accuracy: 0.0711 - loss: 2.3098 10/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - accuracy: 0.0746 - loss: 2.3090 12/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - accuracy: 0.0781 - loss: 2.3083 14/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - accuracy: 0.0809 - loss: 2.3078 16/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - accuracy: 0.0833 - loss: 2.3074 18/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.0851 - loss: 2.3072 20/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.0868 - loss: 2.3069 22/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.0881 - loss: 2.3066 24/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.0893 - loss: 2.3064 25/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.0900 - loss: 2.3062 26/422 ━━━━━━━━━━━━━━━━━━━━ 18s 48ms/step - accuracy: 0.0906 - loss: 2.3061 27/422 ━━━━━━━━━━━━━━━━━━━━ 18s 48ms/step - accuracy: 0.0913 - loss: 2.3060 29/422 ━━━━━━━━━━━━━━━━━━━━ 18s 48ms/step - accuracy: 0.0926 - loss: 2.3057 31/422 ━━━━━━━━━━━━━━━━━━━━ 18s 48ms/step - accuracy: 0.0939 - loss: 2.3054 33/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.0953 - loss: 2.3051 35/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.0965 - loss: 2.3049 37/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.0976 - loss: 2.3046 38/422 ━━━━━━━━━━━━━━━━━━━━ 18s 48ms/step - accuracy: 0.0982 - loss: 2.3044 39/422 ━━━━━━━━━━━━━━━━━━━━ 18s 48ms/step - accuracy: 0.0987 - loss: 2.3043 40/422 ━━━━━━━━━━━━━━━━━━━━ 18s 48ms/step - accuracy: 0.0993 - loss: 2.3042 41/422 ━━━━━━━━━━━━━━━━━━━━ 18s 48ms/step - accuracy: 0.0999 - loss: 2.3040 42/422 ━━━━━━━━━━━━━━━━━━━━ 18s 48ms/step - accuracy: 0.1005 - loss: 2.3039 43/422 ━━━━━━━━━━━━━━━━━━━━ 18s 48ms/step - accuracy: 0.1012 - loss: 2.3038 44/422 ━━━━━━━━━━━━━━━━━━━━ 18s 48ms/step - accuracy: 0.1018 - loss: 2.3036 45/422 ━━━━━━━━━━━━━━━━━━━━ 18s 48ms/step - accuracy: 0.1025 - loss: 2.3035 46/422 ━━━━━━━━━━━━━━━━━━━━ 18s 49ms/step - accuracy: 0.1031 - loss: 2.3033 48/422 ━━━━━━━━━━━━━━━━━━━━ 18s 49ms/step - accuracy: 0.1045 - loss: 2.3031 50/422 ━━━━━━━━━━━━━━━━━━━━ 18s 48ms/step - accuracy: 0.1057 - loss: 2.3028 52/422 ━━━━━━━━━━━━━━━━━━━━ 17s 48ms/step - accuracy: 0.1069 - loss: 2.3025 54/422 ━━━━━━━━━━━━━━━━━━━━ 17s 48ms/step - accuracy: 0.1081 - loss: 2.3022 56/422 ━━━━━━━━━━━━━━━━━━━━ 17s 48ms/step - accuracy: 0.1092 - loss: 2.3019 58/422 ━━━━━━━━━━━━━━━━━━━━ 17s 48ms/step - accuracy: 0.1104 - loss: 2.3016 60/422 ━━━━━━━━━━━━━━━━━━━━ 17s 48ms/step - accuracy: 0.1115 - loss: 2.3013 61/422 ━━━━━━━━━━━━━━━━━━━━ 17s 48ms/step - accuracy: 0.1121 - loss: 2.3012 62/422 ━━━━━━━━━━━━━━━━━━━━ 17s 48ms/step - accuracy: 0.1126 - loss: 2.3010 63/422 ━━━━━━━━━━━━━━━━━━━━ 17s 48ms/step - accuracy: 0.1132 - loss: 2.3009 65/422 ━━━━━━━━━━━━━━━━━━━━ 17s 48ms/step - accuracy: 0.1143 - loss: 2.3006 66/422 ━━━━━━━━━━━━━━━━━━━━ 17s 48ms/step - accuracy: 0.1149 - loss: 2.3005 67/422 ━━━━━━━━━━━━━━━━━━━━ 17s 49ms/step - accuracy: 0.1155 - loss: 2.3003 69/422 ━━━━━━━━━━━━━━━━━━━━ 17s 48ms/step - accuracy: 0.1166 - loss: 2.3000 71/422 ━━━━━━━━━━━━━━━━━━━━ 16s 48ms/step - accuracy: 0.1177 - loss: 2.2997 73/422 ━━━━━━━━━━━━━━━━━━━━ 16s 48ms/step - accuracy: 0.1188 - loss: 2.2995 75/422 ━━━━━━━━━━━━━━━━━━━━ 16s 48ms/step - accuracy: 0.1199 - loss: 2.2992 77/422 ━━━━━━━━━━━━━━━━━━━━ 16s 48ms/step - accuracy: 0.1210 - loss: 2.2989 79/422 ━━━━━━━━━━━━━━━━━━━━ 16s 48ms/step - accuracy: 0.1221 - loss: 2.2986 81/422 ━━━━━━━━━━━━━━━━━━━━ 16s 48ms/step - accuracy: 0.1232 - loss: 2.2983 83/422 ━━━━━━━━━━━━━━━━━━━━ 16s 48ms/step - accuracy: 0.1243 - loss: 2.2980 85/422 ━━━━━━━━━━━━━━━━━━━━ 16s 48ms/step - accuracy: 0.1254 - 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accuracy: 0.2765 - loss: 2.1439 354/422 ━━━━━━━━━━━━━━━━━━━━ 3s 46ms/step - accuracy: 0.2776 - loss: 2.1415 356/422 ━━━━━━━━━━━━━━━━━━━━ 3s 46ms/step - accuracy: 0.2787 - loss: 2.1392 358/422 ━━━━━━━━━━━━━━━━━━━━ 2s 46ms/step - accuracy: 0.2798 - loss: 2.1368 360/422 ━━━━━━━━━━━━━━━━━━━━ 2s 46ms/step - accuracy: 0.2809 - loss: 2.1344 362/422 ━━━━━━━━━━━━━━━━━━━━ 2s 46ms/step - accuracy: 0.2820 - loss: 2.1320 364/422 ━━━━━━━━━━━━━━━━━━━━ 2s 46ms/step - accuracy: 0.2831 - loss: 2.1296 366/422 ━━━━━━━━━━━━━━━━━━━━ 2s 46ms/step - accuracy: 0.2842 - loss: 2.1272 368/422 ━━━━━━━━━━━━━━━━━━━━ 2s 46ms/step - accuracy: 0.2853 - loss: 2.1248 370/422 ━━━━━━━━━━━━━━━━━━━━ 2s 46ms/step - accuracy: 0.2864 - loss: 2.1224 372/422 ━━━━━━━━━━━━━━━━━━━━ 2s 46ms/step - accuracy: 0.2875 - loss: 2.1200 374/422 ━━━━━━━━━━━━━━━━━━━━ 2s 46ms/step - accuracy: 0.2886 - loss: 2.1176 376/422 ━━━━━━━━━━━━━━━━━━━━ 2s 46ms/step - accuracy: 0.2897 - loss: 2.1152 378/422 ━━━━━━━━━━━━━━━━━━━━ 2s 46ms/step - accuracy: 0.2908 - 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accuracy: 0.2993 - loss: 2.0933 395/422 ━━━━━━━━━━━━━━━━━━━━ 1s 47ms/step - accuracy: 0.2998 - loss: 2.0921 396/422 ━━━━━━━━━━━━━━━━━━━━ 1s 47ms/step - accuracy: 0.3004 - loss: 2.0909 397/422 ━━━━━━━━━━━━━━━━━━━━ 1s 47ms/step - accuracy: 0.3009 - loss: 2.0897 398/422 ━━━━━━━━━━━━━━━━━━━━ 1s 47ms/step - accuracy: 0.3014 - loss: 2.0884 400/422 ━━━━━━━━━━━━━━━━━━━━ 1s 47ms/step - accuracy: 0.3025 - loss: 2.0860 401/422 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.3030 - loss: 2.0848 403/422 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.3040 - loss: 2.0823 405/422 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.3051 - loss: 2.0799 406/422 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.3056 - loss: 2.0786 407/422 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.3061 - loss: 2.0774 408/422 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.3067 - loss: 2.0762 409/422 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.3072 - loss: 2.0750 411/422 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.3082 - loss: 2.0725 412/422 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.3087 - loss: 2.0713 414/422 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.3098 - loss: 2.0688 416/422 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.3108 - loss: 2.0664 418/422 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.3118 - loss: 2.0639 419/422 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.3123 - loss: 2.0627 420/422 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.3129 - loss: 2.0615 422/422 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.3139 - loss: 2.0590 422/422 ━━━━━━━━━━━━━━━━━━━━ 21s 49ms/step - accuracy: 0.5290 - loss: 1.5436 - val_accuracy: 0.9097 - val_loss: 0.3701 Epoch 2/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 25s 60ms/step - accuracy: 0.8438 - loss: 0.5488 3/422 ━━━━━━━━━━━━━━━━━━━━ 19s 46ms/step - accuracy: 0.8490 - loss: 0.5327 4/422 ━━━━━━━━━━━━━━━━━━━━ 19s 48ms/step - accuracy: 0.8506 - loss: 0.5257 5/422 ━━━━━━━━━━━━━━━━━━━━ 20s 49ms/step - accuracy: 0.8508 - loss: 0.5219 7/422 ━━━━━━━━━━━━━━━━━━━━ 20s 49ms/step - accuracy: 0.8508 - loss: 0.5151 9/422 ━━━━━━━━━━━━━━━━━━━━ 20s 49ms/step - accuracy: 0.8492 - loss: 0.5143 10/422 ━━━━━━━━━━━━━━━━━━━━ 20s 49ms/step - accuracy: 0.8483 - loss: 0.5142 11/422 ━━━━━━━━━━━━━━━━━━━━ 20s 49ms/step - accuracy: 0.8473 - loss: 0.5143 12/422 ━━━━━━━━━━━━━━━━━━━━ 20s 49ms/step - accuracy: 0.8464 - loss: 0.5149 14/422 ━━━━━━━━━━━━━━━━━━━━ 20s 49ms/step - accuracy: 0.8451 - loss: 0.5163 16/422 ━━━━━━━━━━━━━━━━━━━━ 20s 49ms/step - accuracy: 0.8436 - loss: 0.5180 17/422 ━━━━━━━━━━━━━━━━━━━━ 20s 50ms/step - accuracy: 0.8432 - loss: 0.5185 18/422 ━━━━━━━━━━━━━━━━━━━━ 20s 50ms/step - accuracy: 0.8430 - loss: 0.5185 19/422 ━━━━━━━━━━━━━━━━━━━━ 20s 50ms/step - accuracy: 0.8428 - loss: 0.5185 20/422 ━━━━━━━━━━━━━━━━━━━━ 19s 50ms/step - accuracy: 0.8427 - loss: 0.5185 22/422 ━━━━━━━━━━━━━━━━━━━━ 19s 50ms/step - accuracy: 0.8426 - loss: 0.5184 23/422 ━━━━━━━━━━━━━━━━━━━━ 19s 50ms/step - accuracy: 0.8425 - loss: 0.5185 25/422 ━━━━━━━━━━━━━━━━━━━━ 19s 50ms/step - accuracy: 0.8425 - loss: 0.5183 26/422 ━━━━━━━━━━━━━━━━━━━━ 19s 50ms/step - accuracy: 0.8425 - loss: 0.5182 27/422 ━━━━━━━━━━━━━━━━━━━━ 19s 50ms/step - accuracy: 0.8426 - loss: 0.5179 29/422 ━━━━━━━━━━━━━━━━━━━━ 19s 50ms/step - accuracy: 0.8427 - loss: 0.5171 30/422 ━━━━━━━━━━━━━━━━━━━━ 19s 50ms/step - accuracy: 0.8428 - loss: 0.5167 31/422 ━━━━━━━━━━━━━━━━━━━━ 19s 50ms/step - accuracy: 0.8430 - loss: 0.5164 32/422 ━━━━━━━━━━━━━━━━━━━━ 19s 50ms/step - accuracy: 0.8431 - loss: 0.5161 34/422 ━━━━━━━━━━━━━━━━━━━━ 19s 50ms/step - accuracy: 0.8435 - loss: 0.5154 35/422 ━━━━━━━━━━━━━━━━━━━━ 19s 50ms/step - accuracy: 0.8437 - loss: 0.5149 37/422 ━━━━━━━━━━━━━━━━━━━━ 19s 50ms/step - accuracy: 0.8441 - loss: 0.5140 39/422 ━━━━━━━━━━━━━━━━━━━━ 19s 50ms/step - accuracy: 0.8444 - loss: 0.5132 41/422 ━━━━━━━━━━━━━━━━━━━━ 19s 50ms/step - accuracy: 0.8447 - loss: 0.5125 42/422 ━━━━━━━━━━━━━━━━━━━━ 19s 50ms/step - accuracy: 0.8448 - loss: 0.5122 44/422 ━━━━━━━━━━━━━━━━━━━━ 18s 50ms/step - accuracy: 0.8452 - loss: 0.5114 45/422 ━━━━━━━━━━━━━━━━━━━━ 18s 50ms/step - 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loss: 0.4245 405/422 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.8736 - loss: 0.4242 407/422 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.8737 - loss: 0.4239 409/422 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.8738 - loss: 0.4236 411/422 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.8739 - loss: 0.4233 413/422 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.8740 - loss: 0.4230 415/422 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.8741 - loss: 0.4227 417/422 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.8742 - loss: 0.4224 419/422 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.8742 - loss: 0.4221 421/422 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.8743 - loss: 0.4219 422/422 ━━━━━━━━━━━━━━━━━━━━ 20s 48ms/step - accuracy: 0.8928 - loss: 0.3605 - val_accuracy: 0.9532 - val_loss: 0.1738 Epoch 3/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 25s 62ms/step - accuracy: 0.9141 - loss: 0.2594 3/422 ━━━━━━━━━━━━━━━━━━━━ 19s 46ms/step - accuracy: 0.9206 - loss: 0.2740 5/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - accuracy: 0.9197 - loss: 0.2786 7/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - accuracy: 0.9207 - loss: 0.2748 9/422 ━━━━━━━━━━━━━━━━━━━━ 19s 46ms/step - accuracy: 0.9208 - loss: 0.2730 11/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - accuracy: 0.9207 - loss: 0.2732 13/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - accuracy: 0.9210 - loss: 0.2719 15/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.9210 - loss: 0.2709 17/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.9212 - loss: 0.2693 19/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.9213 - loss: 0.2679 21/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.9211 - loss: 0.2672 23/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.9209 - loss: 0.2670 24/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.9207 - loss: 0.2669 26/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.9203 - loss: 0.2668 28/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.9201 - loss: 0.2665 30/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.9200 - loss: 0.2661 32/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.9199 - loss: 0.2659 34/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.9198 - loss: 0.2657 36/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9196 - loss: 0.2656 38/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9195 - loss: 0.2656 40/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9193 - loss: 0.2657 42/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9192 - loss: 0.2657 44/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9190 - loss: 0.2657 46/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9189 - loss: 0.2659 48/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9188 - loss: 0.2661 50/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9186 - loss: 0.2665 52/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9185 - loss: 0.2668 54/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9184 - loss: 0.2670 56/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9183 - loss: 0.2672 58/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9182 - loss: 0.2674 60/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9182 - loss: 0.2675 62/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9181 - loss: 0.2676 64/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9181 - loss: 0.2677 66/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9180 - loss: 0.2678 68/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9180 - loss: 0.2679 70/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9179 - loss: 0.2680 72/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9179 - loss: 0.2681 74/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9178 - loss: 0.2682 76/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9178 - loss: 0.2683 78/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9178 - loss: 0.2685 80/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9177 - loss: 0.2686 82/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9177 - loss: 0.2687 84/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9177 - loss: 0.2687 86/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9177 - loss: 0.2687 88/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9177 - loss: 0.2687 90/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9177 - loss: 0.2687 92/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9177 - loss: 0.2686 94/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9178 - loss: 0.2685 96/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9178 - loss: 0.2684 98/422 ━━━━━━━━━━━━━━━━━━━━ 14s 46ms/step - accuracy: 0.9179 - loss: 0.2683 100/422 ━━━━━━━━━━━━━━━━━━━━ 14s 46ms/step - accuracy: 0.9179 - loss: 0.2683 102/422 ━━━━━━━━━━━━━━━━━━━━ 14s 46ms/step - accuracy: 0.9179 - loss: 0.2682 104/422 ━━━━━━━━━━━━━━━━━━━━ 14s 46ms/step - accuracy: 0.9180 - loss: 0.2680 106/422 ━━━━━━━━━━━━━━━━━━━━ 14s 46ms/step - accuracy: 0.9180 - loss: 0.2679 108/422 ━━━━━━━━━━━━━━━━━━━━ 14s 46ms/step - accuracy: 0.9181 - loss: 0.2678 110/422 ━━━━━━━━━━━━━━━━━━━━ 14s 46ms/step - accuracy: 0.9181 - loss: 0.2676 112/422 ━━━━━━━━━━━━━━━━━━━━ 14s 46ms/step - 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accuracy: 0.9223 - loss: 0.2547 349/422 ━━━━━━━━━━━━━━━━━━━━ 3s 46ms/step - accuracy: 0.9223 - loss: 0.2546 351/422 ━━━━━━━━━━━━━━━━━━━━ 3s 46ms/step - accuracy: 0.9224 - loss: 0.2545 353/422 ━━━━━━━━━━━━━━━━━━━━ 3s 46ms/step - accuracy: 0.9224 - loss: 0.2544 355/422 ━━━━━━━━━━━━━━━━━━━━ 3s 46ms/step - accuracy: 0.9224 - loss: 0.2543 357/422 ━━━━━━━━━━━━━━━━━━━━ 3s 46ms/step - accuracy: 0.9224 - loss: 0.2542 359/422 ━━━━━━━━━━━━━━━━━━━━ 2s 46ms/step - accuracy: 0.9225 - loss: 0.2541 361/422 ━━━━━━━━━━━━━━━━━━━━ 2s 46ms/step - accuracy: 0.9225 - loss: 0.2540 363/422 ━━━━━━━━━━━━━━━━━━━━ 2s 46ms/step - accuracy: 0.9225 - loss: 0.2539 365/422 ━━━━━━━━━━━━━━━━━━━━ 2s 46ms/step - accuracy: 0.9226 - loss: 0.2538 367/422 ━━━━━━━━━━━━━━━━━━━━ 2s 46ms/step - accuracy: 0.9226 - loss: 0.2537 369/422 ━━━━━━━━━━━━━━━━━━━━ 2s 46ms/step - accuracy: 0.9226 - loss: 0.2536 371/422 ━━━━━━━━━━━━━━━━━━━━ 2s 46ms/step - accuracy: 0.9227 - loss: 0.2535 373/422 ━━━━━━━━━━━━━━━━━━━━ 2s 46ms/step - accuracy: 0.9227 - loss: 0.2534 375/422 ━━━━━━━━━━━━━━━━━━━━ 2s 46ms/step - accuracy: 0.9227 - loss: 0.2533 377/422 ━━━━━━━━━━━━━━━━━━━━ 2s 46ms/step - accuracy: 0.9228 - loss: 0.2532 378/422 ━━━━━━━━━━━━━━━━━━━━ 2s 46ms/step - accuracy: 0.9228 - loss: 0.2532 379/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9228 - loss: 0.2531 381/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9228 - loss: 0.2530 383/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9229 - loss: 0.2530 385/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9229 - loss: 0.2529 387/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9229 - loss: 0.2528 389/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9229 - loss: 0.2527 391/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9230 - loss: 0.2526 393/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9230 - loss: 0.2525 395/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9230 - loss: 0.2524 397/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9231 - loss: 0.2523 399/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9231 - loss: 0.2522 401/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9231 - loss: 0.2521 403/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9231 - loss: 0.2520 405/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9232 - loss: 0.2519 407/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9232 - loss: 0.2518 409/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9232 - loss: 0.2518 411/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9233 - loss: 0.2517 413/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9233 - loss: 0.2516 415/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9233 - loss: 0.2515 417/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9233 - loss: 0.2514 419/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9234 - loss: 0.2513 421/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9234 - loss: 0.2512 422/422 ━━━━━━━━━━━━━━━━━━━━ 20s 48ms/step - accuracy: 0.9294 - loss: 0.2319 - val_accuracy: 0.9667 - val_loss: 0.1266 Epoch 4/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 26s 62ms/step - accuracy: 0.9375 - loss: 0.1539 3/422 ━━━━━━━━━━━━━━━━━━━━ 20s 49ms/step - accuracy: 0.9314 - loss: 0.1984 4/422 ━━━━━━━━━━━━━━━━━━━━ 21s 52ms/step - accuracy: 0.9310 - loss: 0.2076 5/422 ━━━━━━━━━━━━━━━━━━━━ 21s 52ms/step - accuracy: 0.9314 - loss: 0.2114 6/422 ━━━━━━━━━━━━━━━━━━━━ 21s 51ms/step - accuracy: 0.9317 - loss: 0.2126 7/422 ━━━━━━━━━━━━━━━━━━━━ 21s 52ms/step - accuracy: 0.9321 - loss: 0.2123 9/422 ━━━━━━━━━━━━━━━━━━━━ 21s 51ms/step - accuracy: 0.9321 - loss: 0.2163 11/422 ━━━━━━━━━━━━━━━━━━━━ 20s 50ms/step - accuracy: 0.9325 - loss: 0.2174 13/422 ━━━━━━━━━━━━━━━━━━━━ 20s 49ms/step - accuracy: 0.9329 - loss: 0.2180 15/422 ━━━━━━━━━━━━━━━━━━━━ 19s 49ms/step - accuracy: 0.9335 - loss: 0.2178 17/422 ━━━━━━━━━━━━━━━━━━━━ 19s 49ms/step - accuracy: 0.9337 - loss: 0.2183 18/422 ━━━━━━━━━━━━━━━━━━━━ 19s 49ms/step - accuracy: 0.9338 - loss: 0.2183 19/422 ━━━━━━━━━━━━━━━━━━━━ 19s 49ms/step - accuracy: 0.9339 - loss: 0.2183 21/422 ━━━━━━━━━━━━━━━━━━━━ 19s 49ms/step - accuracy: 0.9341 - 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accuracy: 0.9410 - loss: 0.1957 362/422 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9410 - loss: 0.1957 363/422 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9410 - loss: 0.1956 364/422 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9410 - loss: 0.1956 365/422 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9410 - loss: 0.1956 366/422 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9410 - loss: 0.1955 367/422 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9410 - loss: 0.1955 368/422 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9411 - loss: 0.1955 369/422 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9411 - loss: 0.1954 370/422 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9411 - loss: 0.1954 371/422 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9411 - loss: 0.1954 372/422 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9411 - loss: 0.1953 373/422 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9411 - loss: 0.1953 374/422 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9411 - loss: 0.1953 375/422 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9411 - loss: 0.1952 376/422 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9411 - loss: 0.1952 377/422 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9411 - loss: 0.1952 378/422 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9412 - loss: 0.1951 379/422 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9412 - loss: 0.1951 380/422 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9412 - loss: 0.1951 381/422 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9412 - loss: 0.1951 382/422 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.9412 - loss: 0.1950 383/422 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.9412 - loss: 0.1950 384/422 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.9412 - loss: 0.1950 385/422 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.9412 - loss: 0.1949 386/422 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.9412 - loss: 0.1949 387/422 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9412 - loss: 0.1949 388/422 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9413 - loss: 0.1948 389/422 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9413 - loss: 0.1948 390/422 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9413 - loss: 0.1948 391/422 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9413 - loss: 0.1947 392/422 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9413 - loss: 0.1947 393/422 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9413 - loss: 0.1947 394/422 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9413 - loss: 0.1947 395/422 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9413 - loss: 0.1946 397/422 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9413 - loss: 0.1946 398/422 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9413 - loss: 0.1945 399/422 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9414 - loss: 0.1945 400/422 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9414 - loss: 0.1945 401/422 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9414 - loss: 0.1944 402/422 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9414 - 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accuracy: 0.9415 - loss: 0.1940 417/422 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9415 - loss: 0.1939 418/422 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9415 - loss: 0.1939 419/422 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9416 - loss: 0.1939 420/422 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9416 - loss: 0.1938 421/422 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9416 - loss: 0.1938 422/422 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9416 - loss: 0.1938 422/422 ━━━━━━━━━━━━━━━━━━━━ 22s 52ms/step - accuracy: 0.9458 - loss: 0.1807 - val_accuracy: 0.9713 - val_loss: 0.1065 Epoch 5/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 2:14:16 19s/step - accuracy: 0.9688 - loss: 0.1774 3/422 ━━━━━━━━━━━━━━━━━━━━ 19s 46ms/step - accuracy: 0.9583 - loss: 0.1832 5/422 ━━━━━━━━━━━━━━━━━━━━ 19s 46ms/step - accuracy: 0.9577 - loss: 0.1785 7/422 ━━━━━━━━━━━━━━━━━━━━ 19s 46ms/step - accuracy: 0.9567 - loss: 0.1765 9/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - accuracy: 0.9568 - loss: 0.1733 11/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - 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loss: 0.1565 383/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9533 - loss: 0.1565 385/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9533 - loss: 0.1565 387/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9533 - loss: 0.1565 389/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9533 - loss: 0.1565 391/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9533 - loss: 0.1565 393/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9533 - loss: 0.1564 395/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9533 - loss: 0.1564 397/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9533 - loss: 0.1564 399/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9533 - loss: 0.1564 401/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9533 - loss: 0.1564 403/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9533 - loss: 0.1564 405/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9533 - loss: 0.1564 407/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9533 - loss: 0.1563 409/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9533 - loss: 0.1563 411/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9533 - loss: 0.1563 413/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9533 - loss: 0.1563 415/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9533 - loss: 0.1563 416/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9533 - loss: 0.1563 417/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9533 - loss: 0.1563 418/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9533 - loss: 0.1563 419/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9533 - loss: 0.1562 421/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9533 - loss: 0.1562 422/422 ━━━━━━━━━━━━━━━━━━━━ 39s 48ms/step - accuracy: 0.9536 - loss: 0.1533 - val_accuracy: 0.9763 - val_loss: 0.0921 Epoch 6/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 26s 63ms/step - accuracy: 0.9453 - loss: 0.2331 3/422 ━━━━━━━━━━━━━━━━━━━━ 19s 46ms/step - accuracy: 0.9510 - loss: 0.2027 5/422 ━━━━━━━━━━━━━━━━━━━━ 19s 46ms/step - accuracy: 0.9552 - loss: 0.1818 7/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9582 - loss: 0.1700 9/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9592 - loss: 0.1658 11/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9594 - loss: 0.1633 13/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9594 - loss: 0.1619 14/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9596 - loss: 0.1611 16/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9598 - loss: 0.1595 18/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9598 - loss: 0.1588 20/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9597 - loss: 0.1582 22/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9595 - loss: 0.1578 24/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9594 - loss: 0.1572 26/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9594 - loss: 0.1564 28/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9594 - loss: 0.1556 30/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9594 - loss: 0.1549 32/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9595 - loss: 0.1542 34/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9596 - loss: 0.1534 36/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9596 - loss: 0.1527 38/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9597 - loss: 0.1520 40/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9599 - loss: 0.1512 42/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9600 - loss: 0.1505 44/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9601 - loss: 0.1498 46/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9601 - loss: 0.1491 48/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9602 - loss: 0.1486 50/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9603 - loss: 0.1480 52/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9603 - loss: 0.1475 54/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9603 - loss: 0.1471 56/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9603 - loss: 0.1468 58/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9604 - loss: 0.1465 60/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9604 - loss: 0.1462 62/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9603 - loss: 0.1460 64/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9603 - loss: 0.1459 66/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9603 - loss: 0.1457 68/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9603 - loss: 0.1455 70/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9603 - loss: 0.1453 72/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9603 - loss: 0.1450 74/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9603 - loss: 0.1448 76/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9603 - loss: 0.1446 78/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9603 - loss: 0.1445 80/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9603 - loss: 0.1443 82/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9603 - loss: 0.1442 84/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9602 - loss: 0.1440 86/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9602 - 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accuracy: 0.9599 - loss: 0.1426 116/422 ━━━━━━━━━━━━━━━━━━━━ 14s 46ms/step - accuracy: 0.9599 - loss: 0.1426 118/422 ━━━━━━━━━━━━━━━━━━━━ 14s 46ms/step - accuracy: 0.9598 - loss: 0.1426 120/422 ━━━━━━━━━━━━━━━━━━━━ 13s 46ms/step - accuracy: 0.9598 - loss: 0.1426 122/422 ━━━━━━━━━━━━━━━━━━━━ 13s 46ms/step - accuracy: 0.9598 - loss: 0.1425 123/422 ━━━━━━━━━━━━━━━━━━━━ 13s 46ms/step - accuracy: 0.9597 - loss: 0.1425 125/422 ━━━━━━━━━━━━━━━━━━━━ 13s 46ms/step - accuracy: 0.9597 - loss: 0.1425 127/422 ━━━━━━━━━━━━━━━━━━━━ 13s 46ms/step - accuracy: 0.9597 - loss: 0.1425 129/422 ━━━━━━━━━━━━━━━━━━━━ 13s 46ms/step - accuracy: 0.9596 - loss: 0.1424 131/422 ━━━━━━━━━━━━━━━━━━━━ 13s 46ms/step - accuracy: 0.9596 - loss: 0.1424 133/422 ━━━━━━━━━━━━━━━━━━━━ 13s 46ms/step - accuracy: 0.9596 - loss: 0.1424 135/422 ━━━━━━━━━━━━━━━━━━━━ 13s 46ms/step - accuracy: 0.9596 - loss: 0.1424 137/422 ━━━━━━━━━━━━━━━━━━━━ 13s 46ms/step - accuracy: 0.9595 - loss: 0.1424 139/422 ━━━━━━━━━━━━━━━━━━━━ 13s 46ms/step - 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loss: 0.1337 - val_accuracy: 0.9762 - val_loss: 0.0858 Epoch 7/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 25s 60ms/step - accuracy: 0.9609 - loss: 0.1349 3/422 ━━━━━━━━━━━━━━━━━━━━ 19s 46ms/step - accuracy: 0.9570 - loss: 0.1422 5/422 ━━━━━━━━━━━━━━━━━━━━ 19s 46ms/step - accuracy: 0.9606 - loss: 0.1334 7/422 ━━━━━━━━━━━━━━━━━━━━ 19s 46ms/step - accuracy: 0.9610 - loss: 0.1310 9/422 ━━━━━━━━━━━━━━━━━━━━ 19s 46ms/step - accuracy: 0.9611 - loss: 0.1288 11/422 ━━━━━━━━━━━━━━━━━━━━ 19s 46ms/step - accuracy: 0.9615 - loss: 0.1274 12/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - accuracy: 0.9616 - loss: 0.1271 13/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - accuracy: 0.9618 - loss: 0.1269 15/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - accuracy: 0.9620 - loss: 0.1267 17/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.9621 - loss: 0.1266 19/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.9623 - loss: 0.1266 21/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.9624 - loss: 0.1267 23/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.9626 - loss: 0.1264 25/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9627 - loss: 0.1261 27/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9628 - loss: 0.1258 29/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9629 - loss: 0.1255 31/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9629 - loss: 0.1252 33/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9630 - loss: 0.1251 35/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9630 - loss: 0.1249 37/422 ━━━━━━━━━━━━━━━━━━━━ 17s 47ms/step - accuracy: 0.9631 - loss: 0.1247 39/422 ━━━━━━━━━━━━━━━━━━━━ 17s 47ms/step - accuracy: 0.9632 - loss: 0.1244 41/422 ━━━━━━━━━━━━━━━━━━━━ 17s 47ms/step - accuracy: 0.9632 - loss: 0.1242 43/422 ━━━━━━━━━━━━━━━━━━━━ 17s 47ms/step - accuracy: 0.9633 - loss: 0.1240 45/422 ━━━━━━━━━━━━━━━━━━━━ 17s 47ms/step - accuracy: 0.9633 - loss: 0.1239 47/422 ━━━━━━━━━━━━━━━━━━━━ 17s 47ms/step - accuracy: 0.9633 - loss: 0.1238 49/422 ━━━━━━━━━━━━━━━━━━━━ 17s 47ms/step - accuracy: 0.9633 - 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accuracy: 0.9626 - loss: 0.1237 395/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9626 - loss: 0.1237 397/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9626 - loss: 0.1237 399/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9626 - loss: 0.1237 401/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9626 - loss: 0.1237 403/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9626 - loss: 0.1236 405/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9626 - loss: 0.1236 407/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9627 - loss: 0.1236 409/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9627 - loss: 0.1236 411/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9627 - loss: 0.1236 413/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9627 - loss: 0.1236 415/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9627 - loss: 0.1236 417/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9627 - loss: 0.1236 419/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9627 - loss: 0.1236 421/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9627 - loss: 0.1236 422/422 ━━━━━━━━━━━━━━━━━━━━ 20s 48ms/step - accuracy: 0.9631 - loss: 0.1221 - val_accuracy: 0.9783 - val_loss: 0.0781 Epoch 8/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 26s 63ms/step - accuracy: 0.9688 - loss: 0.1015 3/422 ━━━━━━━━━━━━━━━━━━━━ 19s 46ms/step - accuracy: 0.9657 - loss: 0.1054 5/422 ━━━━━━━━━━━━━━━━━━━━ 19s 46ms/step - accuracy: 0.9663 - loss: 0.1034 7/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9663 - loss: 0.1035 9/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9658 - loss: 0.1043 11/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9656 - loss: 0.1058 13/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9654 - loss: 0.1068 15/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9655 - loss: 0.1071 17/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9658 - loss: 0.1070 19/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9661 - loss: 0.1068 21/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - 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accuracy: 0.9670 - loss: 0.1075 394/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9670 - loss: 0.1075 396/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9670 - loss: 0.1076 398/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9670 - loss: 0.1076 400/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9670 - loss: 0.1076 402/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9670 - loss: 0.1076 404/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9670 - loss: 0.1076 405/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9670 - loss: 0.1076 406/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9670 - loss: 0.1076 407/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9670 - loss: 0.1076 408/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9670 - loss: 0.1076 409/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9670 - loss: 0.1076 411/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9670 - loss: 0.1076 413/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9670 - loss: 0.1077 415/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9670 - loss: 0.1077 417/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9670 - loss: 0.1077 419/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9670 - loss: 0.1077 420/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9670 - loss: 0.1077 422/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9670 - loss: 0.1077 422/422 ━━━━━━━━━━━━━━━━━━━━ 20s 48ms/step - accuracy: 0.9666 - loss: 0.1098 - val_accuracy: 0.9770 - val_loss: 0.0772 Epoch 9/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 25s 60ms/step - accuracy: 0.9531 - loss: 0.1334 3/422 ━━━━━━━━━━━━━━━━━━━━ 19s 46ms/step - accuracy: 0.9592 - loss: 0.1302 5/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - accuracy: 0.9605 - loss: 0.1318 7/422 ━━━━━━━━━━━━━━━━━━━━ 19s 46ms/step - accuracy: 0.9625 - loss: 0.1286 9/422 ━━━━━━━━━━━━━━━━━━━━ 19s 46ms/step - accuracy: 0.9641 - loss: 0.1247 11/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - accuracy: 0.9652 - loss: 0.1210 13/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9661 - loss: 0.1180 15/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9667 - loss: 0.1161 17/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9671 - loss: 0.1150 19/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9674 - loss: 0.1142 21/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9673 - loss: 0.1142 23/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9672 - loss: 0.1142 25/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.9671 - loss: 0.1142 27/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.9672 - loss: 0.1140 29/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.9672 - loss: 0.1137 31/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.9673 - loss: 0.1135 33/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.9673 - loss: 0.1133 35/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9674 - loss: 0.1131 37/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9674 - loss: 0.1129 39/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9675 - loss: 0.1126 41/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9675 - loss: 0.1124 43/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9676 - loss: 0.1122 45/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9677 - loss: 0.1120 47/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9677 - loss: 0.1118 49/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9678 - loss: 0.1116 51/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9679 - loss: 0.1114 53/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9679 - loss: 0.1113 55/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9679 - loss: 0.1112 57/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9680 - loss: 0.1111 59/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9680 - loss: 0.1109 61/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9681 - loss: 0.1107 63/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9681 - loss: 0.1106 65/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9682 - loss: 0.1105 67/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9682 - loss: 0.1104 69/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9682 - loss: 0.1103 71/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9683 - loss: 0.1102 73/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9683 - loss: 0.1101 75/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9683 - loss: 0.1100 77/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9684 - loss: 0.1099 79/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9684 - loss: 0.1098 81/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9684 - loss: 0.1097 83/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9685 - loss: 0.1096 85/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9685 - loss: 0.1095 87/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9685 - loss: 0.1093 89/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9685 - loss: 0.1092 91/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9686 - loss: 0.1091 93/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9686 - 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accuracy: 0.9689 - loss: 0.1045 406/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9689 - loss: 0.1045 408/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9689 - loss: 0.1045 410/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9689 - loss: 0.1044 412/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9689 - loss: 0.1044 414/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9689 - loss: 0.1044 416/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9689 - loss: 0.1044 417/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9689 - loss: 0.1044 419/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9689 - loss: 0.1044 421/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9689 - loss: 0.1044 422/422 ━━━━━━━━━━━━━━━━━━━━ 20s 48ms/step - accuracy: 0.9688 - loss: 0.1023 - val_accuracy: 0.9783 - val_loss: 0.0737 Epoch 10/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 26s 62ms/step - accuracy: 0.9531 - loss: 0.1232 3/422 ━━━━━━━━━━━━━━━━━━━━ 19s 46ms/step - accuracy: 0.9531 - loss: 0.1253 5/422 ━━━━━━━━━━━━━━━━━━━━ 19s 46ms/step - accuracy: 0.9559 - loss: 0.1196 7/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9583 - loss: 0.1189 9/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9607 - loss: 0.1173 10/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - accuracy: 0.9618 - loss: 0.1159 12/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - accuracy: 0.9633 - loss: 0.1139 14/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - accuracy: 0.9644 - loss: 0.1119 16/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.9653 - loss: 0.1101 18/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9661 - loss: 0.1081 20/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9669 - loss: 0.1061 22/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9672 - loss: 0.1050 24/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9675 - loss: 0.1043 26/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9677 - loss: 0.1037 28/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9679 - loss: 0.1034 30/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9681 - 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accuracy: 0.9701 - loss: 0.0993 57/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9701 - loss: 0.0992 59/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9702 - loss: 0.0991 60/422 ━━━━━━━━━━━━━━━━━━━━ 16s 47ms/step - accuracy: 0.9702 - loss: 0.0991 62/422 ━━━━━━━━━━━━━━━━━━━━ 16s 47ms/step - accuracy: 0.9703 - loss: 0.0990 64/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9704 - loss: 0.0988 66/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9705 - loss: 0.0987 68/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9705 - loss: 0.0986 69/422 ━━━━━━━━━━━━━━━━━━━━ 16s 47ms/step - accuracy: 0.9706 - loss: 0.0985 71/422 ━━━━━━━━━━━━━━━━━━━━ 16s 47ms/step - accuracy: 0.9706 - loss: 0.0984 73/422 ━━━━━━━━━━━━━━━━━━━━ 16s 47ms/step - accuracy: 0.9707 - loss: 0.0983 75/422 ━━━━━━━━━━━━━━━━━━━━ 16s 47ms/step - accuracy: 0.9707 - loss: 0.0982 77/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9707 - loss: 0.0982 79/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9707 - 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loss: 0.0959 - val_accuracy: 0.9778 - val_loss: 0.0701 Epoch 11/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 25s 60ms/step - accuracy: 0.9688 - loss: 0.1017 3/422 ━━━━━━━━━━━━━━━━━━━━ 18s 45ms/step - accuracy: 0.9748 - loss: 0.0882 5/422 ━━━━━━━━━━━━━━━━━━━━ 18s 45ms/step - accuracy: 0.9762 - loss: 0.0856 7/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9774 - loss: 0.0844 9/422 ━━━━━━━━━━━━━━━━━━━━ 19s 46ms/step - accuracy: 0.9778 - loss: 0.0854 11/422 ━━━━━━━━━━━━━━━━━━━━ 19s 46ms/step - accuracy: 0.9773 - loss: 0.0870 13/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - accuracy: 0.9771 - loss: 0.0877 15/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9771 - loss: 0.0876 17/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9770 - loss: 0.0876 19/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9768 - loss: 0.0879 21/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9767 - loss: 0.0882 23/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9765 - loss: 0.0885 25/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - 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loss: 0.0897 398/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9729 - loss: 0.0897 399/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9729 - loss: 0.0897 401/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9729 - loss: 0.0897 403/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9729 - loss: 0.0897 405/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9729 - loss: 0.0897 407/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9729 - loss: 0.0896 409/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9729 - loss: 0.0896 411/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9729 - loss: 0.0896 413/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9729 - loss: 0.0896 415/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9729 - loss: 0.0896 417/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9729 - loss: 0.0896 419/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9729 - loss: 0.0896 421/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9729 - loss: 0.0896 422/422 ━━━━━━━━━━━━━━━━━━━━ 20s 47ms/step - accuracy: 0.9729 - loss: 0.0891 - val_accuracy: 0.9807 - val_loss: 0.0655 Epoch 12/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 25s 61ms/step - accuracy: 0.9531 - loss: 0.0961 3/422 ━━━━━━━━━━━━━━━━━━━━ 19s 46ms/step - accuracy: 0.9640 - loss: 0.0860 5/422 ━━━━━━━━━━━━━━━━━━━━ 19s 46ms/step - accuracy: 0.9682 - loss: 0.0813 7/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9703 - loss: 0.0793 9/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9716 - loss: 0.0781 11/422 ━━━━━━━━━━━━━━━━━━━━ 19s 46ms/step - accuracy: 0.9721 - loss: 0.0777 13/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9723 - loss: 0.0777 15/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9724 - loss: 0.0776 17/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9726 - loss: 0.0774 19/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9726 - loss: 0.0775 21/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9726 - loss: 0.0775 23/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9727 - loss: 0.0774 25/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9729 - loss: 0.0774 27/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9730 - loss: 0.0773 29/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9731 - loss: 0.0774 31/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9732 - loss: 0.0776 33/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9732 - loss: 0.0777 35/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9734 - loss: 0.0777 37/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9735 - loss: 0.0777 39/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9736 - loss: 0.0777 41/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9736 - loss: 0.0778 42/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9737 - loss: 0.0778 44/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9737 - loss: 0.0778 46/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9738 - loss: 0.0778 48/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9739 - loss: 0.0778 50/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9739 - loss: 0.0778 52/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9740 - loss: 0.0778 54/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9740 - loss: 0.0778 56/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9740 - loss: 0.0779 58/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9741 - loss: 0.0781 60/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9741 - loss: 0.0781 62/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9741 - loss: 0.0782 64/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9742 - loss: 0.0783 66/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9742 - loss: 0.0784 68/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9742 - loss: 0.0785 70/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9742 - loss: 0.0785 72/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9743 - loss: 0.0786 74/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9743 - loss: 0.0787 76/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9743 - loss: 0.0788 78/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9743 - loss: 0.0789 80/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9744 - loss: 0.0789 82/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9744 - loss: 0.0790 84/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9744 - loss: 0.0791 86/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9744 - loss: 0.0791 88/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9744 - loss: 0.0792 90/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9744 - loss: 0.0793 92/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9744 - loss: 0.0793 94/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9744 - loss: 0.0794 96/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9744 - loss: 0.0795 98/422 ━━━━━━━━━━━━━━━━━━━━ 14s 46ms/step - accuracy: 0.9744 - loss: 0.0796 100/422 ━━━━━━━━━━━━━━━━━━━━ 14s 46ms/step - accuracy: 0.9744 - loss: 0.0797 102/422 ━━━━━━━━━━━━━━━━━━━━ 14s 46ms/step - accuracy: 0.9744 - loss: 0.0798 104/422 ━━━━━━━━━━━━━━━━━━━━ 14s 46ms/step - accuracy: 0.9744 - 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loss: 0.0839 419/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9741 - loss: 0.0839 421/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9741 - loss: 0.0839 422/422 ━━━━━━━━━━━━━━━━━━━━ 20s 48ms/step - accuracy: 0.9741 - loss: 0.0844 - val_accuracy: 0.9812 - val_loss: 0.0632 Epoch 13/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 25s 61ms/step - accuracy: 1.0000 - loss: 0.0369 3/422 ━━━━━━━━━━━━━━━━━━━━ 19s 46ms/step - accuracy: 0.9974 - loss: 0.0439 5/422 ━━━━━━━━━━━━━━━━━━━━ 19s 46ms/step - accuracy: 0.9913 - loss: 0.0558 7/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9864 - loss: 0.0652 9/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9838 - loss: 0.0695 11/422 ━━━━━━━━━━━━━━━━━━━━ 19s 46ms/step - accuracy: 0.9819 - loss: 0.0729 13/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9807 - loss: 0.0745 15/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9799 - loss: 0.0761 17/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.9792 - loss: 0.0775 19/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9785 - loss: 0.0786 21/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9779 - loss: 0.0797 23/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9774 - loss: 0.0804 25/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9770 - loss: 0.0809 27/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9768 - loss: 0.0811 29/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9766 - loss: 0.0812 31/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9766 - loss: 0.0811 33/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9765 - loss: 0.0811 35/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9765 - loss: 0.0811 37/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9765 - loss: 0.0811 39/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9764 - loss: 0.0812 41/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9764 - loss: 0.0813 43/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9763 - loss: 0.0814 45/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9763 - 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accuracy: 0.9743 - loss: 0.0837 335/422 ━━━━━━━━━━━━━━━━━━━━ 4s 47ms/step - accuracy: 0.9743 - loss: 0.0837 337/422 ━━━━━━━━━━━━━━━━━━━━ 3s 47ms/step - accuracy: 0.9743 - loss: 0.0837 339/422 ━━━━━━━━━━━━━━━━━━━━ 3s 47ms/step - accuracy: 0.9743 - loss: 0.0837 341/422 ━━━━━━━━━━━━━━━━━━━━ 3s 47ms/step - accuracy: 0.9743 - loss: 0.0836 343/422 ━━━━━━━━━━━━━━━━━━━━ 3s 47ms/step - accuracy: 0.9743 - loss: 0.0836 345/422 ━━━━━━━━━━━━━━━━━━━━ 3s 47ms/step - accuracy: 0.9743 - loss: 0.0836 347/422 ━━━━━━━━━━━━━━━━━━━━ 3s 47ms/step - accuracy: 0.9743 - loss: 0.0836 349/422 ━━━━━━━━━━━━━━━━━━━━ 3s 47ms/step - accuracy: 0.9743 - loss: 0.0836 351/422 ━━━━━━━━━━━━━━━━━━━━ 3s 47ms/step - accuracy: 0.9743 - loss: 0.0835 353/422 ━━━━━━━━━━━━━━━━━━━━ 3s 47ms/step - accuracy: 0.9743 - loss: 0.0835 355/422 ━━━━━━━━━━━━━━━━━━━━ 3s 47ms/step - accuracy: 0.9743 - loss: 0.0835 357/422 ━━━━━━━━━━━━━━━━━━━━ 3s 47ms/step - accuracy: 0.9743 - loss: 0.0835 359/422 ━━━━━━━━━━━━━━━━━━━━ 2s 47ms/step - accuracy: 0.9743 - 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accuracy: 0.9744 - loss: 0.0832 389/422 ━━━━━━━━━━━━━━━━━━━━ 1s 47ms/step - accuracy: 0.9744 - loss: 0.0832 390/422 ━━━━━━━━━━━━━━━━━━━━ 1s 47ms/step - accuracy: 0.9744 - loss: 0.0832 392/422 ━━━━━━━━━━━━━━━━━━━━ 1s 47ms/step - accuracy: 0.9744 - loss: 0.0832 394/422 ━━━━━━━━━━━━━━━━━━━━ 1s 47ms/step - accuracy: 0.9744 - loss: 0.0832 396/422 ━━━━━━━━━━━━━━━━━━━━ 1s 47ms/step - accuracy: 0.9744 - loss: 0.0831 397/422 ━━━━━━━━━━━━━━━━━━━━ 1s 47ms/step - accuracy: 0.9744 - loss: 0.0831 399/422 ━━━━━━━━━━━━━━━━━━━━ 1s 47ms/step - accuracy: 0.9744 - loss: 0.0831 401/422 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.9744 - loss: 0.0831 403/422 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.9744 - loss: 0.0831 405/422 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.9744 - loss: 0.0831 407/422 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.9744 - loss: 0.0831 409/422 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.9744 - loss: 0.0831 411/422 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.9744 - loss: 0.0830 413/422 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.9744 - loss: 0.0830 415/422 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.9744 - loss: 0.0830 417/422 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.9744 - loss: 0.0830 419/422 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.9744 - loss: 0.0830 421/422 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.9744 - loss: 0.0830 422/422 ━━━━━━━━━━━━━━━━━━━━ 20s 48ms/step - accuracy: 0.9749 - loss: 0.0803 - val_accuracy: 0.9828 - val_loss: 0.0583 Epoch 14/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 25s 61ms/step - accuracy: 0.9609 - loss: 0.0632 3/422 ━━━━━━━━━━━━━━━━━━━━ 19s 46ms/step - accuracy: 0.9701 - loss: 0.0678 5/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - accuracy: 0.9747 - loss: 0.0650 7/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - accuracy: 0.9759 - loss: 0.0646 9/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - accuracy: 0.9765 - loss: 0.0650 11/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - accuracy: 0.9770 - loss: 0.0647 13/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - 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loss: 0.0761 - val_accuracy: 0.9822 - val_loss: 0.0571 Epoch 15/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 26s 62ms/step - accuracy: 0.9922 - loss: 0.0348 3/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - accuracy: 0.9839 - loss: 0.0478 4/422 ━━━━━━━━━━━━━━━━━━━━ 20s 48ms/step - accuracy: 0.9811 - loss: 0.0553 6/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - accuracy: 0.9803 - loss: 0.0602 8/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - accuracy: 0.9798 - loss: 0.0637 10/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - accuracy: 0.9788 - loss: 0.0668 12/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - accuracy: 0.9781 - loss: 0.0689 14/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9776 - loss: 0.0703 16/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.9774 - loss: 0.0707 18/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.9773 - loss: 0.0711 20/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.9773 - loss: 0.0713 22/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.9772 - loss: 0.0717 24/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.9772 - loss: 0.0721 26/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.9772 - loss: 0.0723 28/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.9773 - loss: 0.0722 30/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.9773 - loss: 0.0722 32/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9774 - loss: 0.0722 34/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9774 - loss: 0.0721 36/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9774 - loss: 0.0720 38/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9775 - loss: 0.0719 40/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9775 - loss: 0.0718 42/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9774 - loss: 0.0719 44/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9774 - loss: 0.0720 45/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9773 - loss: 0.0721 47/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9773 - loss: 0.0722 49/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9773 - 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accuracy: 0.9780 - loss: 0.0723 396/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9780 - loss: 0.0723 398/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9780 - loss: 0.0723 400/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9780 - loss: 0.0723 402/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9780 - loss: 0.0723 404/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9780 - loss: 0.0723 406/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9780 - loss: 0.0723 408/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9780 - loss: 0.0723 410/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9780 - loss: 0.0723 412/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9780 - loss: 0.0723 414/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9780 - loss: 0.0723 416/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9780 - loss: 0.0723 418/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9780 - loss: 0.0723 420/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9780 - loss: 0.0723 422/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9780 - loss: 0.0723 422/422 ━━━━━━━━━━━━━━━━━━━━ 20s 48ms/step - accuracy: 0.9776 - loss: 0.0721 - val_accuracy: 0.9815 - val_loss: 0.0568 Epoch 16/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 25s 61ms/step - accuracy: 0.9688 - loss: 0.0575 3/422 ━━━━━━━━━━━━━━━━━━━━ 19s 48ms/step - accuracy: 0.9670 - loss: 0.0877 4/422 ━━━━━━━━━━━━━━━━━━━━ 20s 49ms/step - accuracy: 0.9684 - loss: 0.0877 6/422 ━━━━━━━━━━━━━━━━━━━━ 19s 48ms/step - accuracy: 0.9705 - loss: 0.0860 8/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - accuracy: 0.9713 - loss: 0.0842 10/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - accuracy: 0.9718 - loss: 0.0831 12/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - accuracy: 0.9721 - loss: 0.0822 14/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9728 - loss: 0.0809 16/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9731 - loss: 0.0799 18/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9731 - loss: 0.0793 20/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9733 - loss: 0.0787 22/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9735 - loss: 0.0781 24/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9737 - loss: 0.0776 26/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9738 - loss: 0.0772 28/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9740 - loss: 0.0766 30/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9741 - loss: 0.0761 32/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9742 - loss: 0.0758 34/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9741 - loss: 0.0758 36/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9741 - loss: 0.0758 38/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9741 - loss: 0.0757 40/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9740 - loss: 0.0758 42/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9740 - loss: 0.0758 44/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9739 - loss: 0.0759 46/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9738 - 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val_accuracy: 0.9837 - val_loss: 0.0543 Epoch 17/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 25s 61ms/step - accuracy: 0.9844 - loss: 0.0655 3/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - accuracy: 0.9787 - loss: 0.0804 5/422 ━━━━━━━━━━━━━━━━━━━━ 20s 48ms/step - accuracy: 0.9758 - loss: 0.0866 7/422 ━━━━━━━━━━━━━━━━━━━━ 19s 48ms/step - accuracy: 0.9758 - loss: 0.0850 9/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - accuracy: 0.9766 - loss: 0.0830 11/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - accuracy: 0.9772 - loss: 0.0810 13/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - accuracy: 0.9777 - loss: 0.0792 15/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - accuracy: 0.9781 - loss: 0.0783 17/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.9783 - loss: 0.0776 19/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.9785 - loss: 0.0771 21/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.9786 - loss: 0.0769 23/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.9788 - loss: 0.0766 25/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.9789 - loss: 0.0761 27/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.9791 - loss: 0.0757 29/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.9792 - loss: 0.0753 31/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.9792 - loss: 0.0750 33/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.9793 - loss: 0.0748 35/422 ━━━━━━━━━━━━━━━━━━━━ 18s 47ms/step - accuracy: 0.9793 - loss: 0.0748 37/422 ━━━━━━━━━━━━━━━━━━━━ 17s 47ms/step - accuracy: 0.9793 - loss: 0.0748 39/422 ━━━━━━━━━━━━━━━━━━━━ 17s 47ms/step - accuracy: 0.9793 - loss: 0.0748 41/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9793 - loss: 0.0748 43/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9793 - loss: 0.0748 45/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9793 - loss: 0.0748 47/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9793 - loss: 0.0748 49/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9792 - loss: 0.0749 51/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9792 - loss: 0.0749 53/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9791 - loss: 0.0749 55/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9791 - loss: 0.0750 57/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9790 - loss: 0.0750 59/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9790 - loss: 0.0750 61/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9789 - loss: 0.0749 63/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9789 - loss: 0.0749 65/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9789 - loss: 0.0748 67/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9788 - loss: 0.0748 69/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9788 - loss: 0.0748 71/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9787 - loss: 0.0747 73/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9787 - loss: 0.0747 75/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9787 - loss: 0.0746 77/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9787 - loss: 0.0745 79/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9786 - loss: 0.0744 81/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9786 - loss: 0.0743 83/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9786 - loss: 0.0743 85/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9786 - loss: 0.0742 87/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9786 - loss: 0.0741 89/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9785 - loss: 0.0740 91/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9785 - loss: 0.0740 93/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9785 - loss: 0.0739 95/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9785 - loss: 0.0738 97/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9785 - loss: 0.0738 99/422 ━━━━━━━━━━━━━━━━━━━━ 14s 46ms/step - accuracy: 0.9784 - loss: 0.0737 101/422 ━━━━━━━━━━━━━━━━━━━━ 14s 46ms/step - accuracy: 0.9784 - loss: 0.0736 103/422 ━━━━━━━━━━━━━━━━━━━━ 14s 46ms/step - accuracy: 0.9784 - loss: 0.0735 105/422 ━━━━━━━━━━━━━━━━━━━━ 14s 46ms/step - accuracy: 0.9784 - 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accuracy: 0.9785 - loss: 0.0677 - val_accuracy: 0.9848 - val_loss: 0.0522 Epoch 18/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 25s 60ms/step - accuracy: 0.9688 - loss: 0.0854 3/422 ━━━━━━━━━━━━━━━━━━━━ 19s 46ms/step - accuracy: 0.9714 - loss: 0.0858 5/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - accuracy: 0.9735 - loss: 0.0779 7/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - accuracy: 0.9747 - loss: 0.0743 9/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - accuracy: 0.9752 - loss: 0.0728 11/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - accuracy: 0.9752 - loss: 0.0727 13/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9749 - loss: 0.0736 15/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9750 - loss: 0.0736 17/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9752 - loss: 0.0734 19/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9751 - loss: 0.0741 21/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9751 - loss: 0.0744 23/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9752 - loss: 0.0744 25/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9753 - loss: 0.0743 27/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9755 - loss: 0.0743 29/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9756 - loss: 0.0742 31/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9758 - loss: 0.0742 33/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9759 - loss: 0.0741 35/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9761 - loss: 0.0740 37/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9762 - loss: 0.0739 39/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9764 - loss: 0.0737 41/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9766 - loss: 0.0735 43/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9767 - loss: 0.0733 45/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9768 - loss: 0.0731 47/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9770 - loss: 0.0729 49/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9771 - loss: 0.0726 51/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9772 - 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accuracy: 0.9799 - loss: 0.0655 379/422 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9799 - loss: 0.0655 380/422 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9799 - loss: 0.0655 381/422 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9799 - loss: 0.0655 382/422 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.9799 - loss: 0.0655 383/422 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.9799 - loss: 0.0655 384/422 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.9799 - loss: 0.0655 385/422 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.9799 - loss: 0.0655 386/422 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.9799 - loss: 0.0655 387/422 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.9799 - loss: 0.0655 388/422 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.9799 - loss: 0.0655 389/422 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.9799 - loss: 0.0655 390/422 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.9799 - loss: 0.0655 391/422 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.9799 - 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loss: 0.0654 419/422 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9799 - loss: 0.0654 420/422 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9799 - loss: 0.0654 421/422 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9799 - loss: 0.0654 422/422 ━━━━━━━━━━━━━━━━━━━━ 22s 51ms/step - accuracy: 0.9802 - loss: 0.0641 - val_accuracy: 0.9847 - val_loss: 0.0513 Epoch 19/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 28s 68ms/step - accuracy: 0.9766 - loss: 0.0700 2/422 ━━━━━━━━━━━━━━━━━━━━ 23s 56ms/step - accuracy: 0.9746 - loss: 0.0748 3/422 ━━━━━━━━━━━━━━━━━━━━ 22s 54ms/step - accuracy: 0.9753 - loss: 0.0725 4/422 ━━━━━━━━━━━━━━━━━━━━ 22s 54ms/step - accuracy: 0.9761 - loss: 0.0707 5/422 ━━━━━━━━━━━━━━━━━━━━ 22s 53ms/step - accuracy: 0.9765 - loss: 0.0684 6/422 ━━━━━━━━━━━━━━━━━━━━ 21s 53ms/step - accuracy: 0.9767 - loss: 0.0668 7/422 ━━━━━━━━━━━━━━━━━━━━ 21s 53ms/step - accuracy: 0.9770 - loss: 0.0653 8/422 ━━━━━━━━━━━━━━━━━━━━ 21s 53ms/step - accuracy: 0.9772 - loss: 0.0647 9/422 ━━━━━━━━━━━━━━━━━━━━ 21s 52ms/step - accuracy: 0.9776 - loss: 0.0639 10/422 ━━━━━━━━━━━━━━━━━━━━ 21s 52ms/step - accuracy: 0.9779 - loss: 0.0636 11/422 ━━━━━━━━━━━━━━━━━━━━ 21s 52ms/step - accuracy: 0.9781 - loss: 0.0636 12/422 ━━━━━━━━━━━━━━━━━━━━ 21s 53ms/step - accuracy: 0.9784 - loss: 0.0633 13/422 ━━━━━━━━━━━━━━━━━━━━ 21s 52ms/step - accuracy: 0.9788 - loss: 0.0628 14/422 ━━━━━━━━━━━━━━━━━━━━ 21s 52ms/step - accuracy: 0.9790 - loss: 0.0625 15/422 ━━━━━━━━━━━━━━━━━━━━ 21s 52ms/step - accuracy: 0.9791 - loss: 0.0623 16/422 ━━━━━━━━━━━━━━━━━━━━ 21s 52ms/step - accuracy: 0.9792 - loss: 0.0622 17/422 ━━━━━━━━━━━━━━━━━━━━ 21s 53ms/step - accuracy: 0.9794 - loss: 0.0620 18/422 ━━━━━━━━━━━━━━━━━━━━ 21s 53ms/step - accuracy: 0.9795 - loss: 0.0619 19/422 ━━━━━━━━━━━━━━━━━━━━ 21s 53ms/step - accuracy: 0.9795 - loss: 0.0619 20/422 ━━━━━━━━━━━━━━━━━━━━ 21s 53ms/step - accuracy: 0.9796 - loss: 0.0618 21/422 ━━━━━━━━━━━━━━━━━━━━ 21s 53ms/step - accuracy: 0.9797 - loss: 0.0617 22/422 ━━━━━━━━━━━━━━━━━━━━ 21s 53ms/step - accuracy: 0.9798 - 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loss: 0.0625 422/422 ━━━━━━━━━━━━━━━━━━━━ 21s 50ms/step - accuracy: 0.9813 - loss: 0.0613 - val_accuracy: 0.9845 - val_loss: 0.0489 Epoch 20/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 25s 61ms/step - accuracy: 0.9922 - loss: 0.0250 3/422 ━━━━━━━━━━━━━━━━━━━━ 19s 46ms/step - accuracy: 0.9909 - loss: 0.0326 5/422 ━━━━━━━━━━━━━━━━━━━━ 19s 46ms/step - accuracy: 0.9876 - loss: 0.0385 7/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9859 - loss: 0.0410 9/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9858 - loss: 0.0409 11/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9861 - loss: 0.0405 13/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9864 - loss: 0.0401 15/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9867 - loss: 0.0398 17/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9867 - loss: 0.0401 19/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9868 - loss: 0.0404 21/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9868 - loss: 0.0409 23/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9868 - loss: 0.0413 25/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9869 - loss: 0.0415 27/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9869 - loss: 0.0418 29/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - accuracy: 0.9868 - loss: 0.0421 31/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9868 - loss: 0.0424 33/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9867 - loss: 0.0427 35/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9867 - loss: 0.0429 37/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9866 - loss: 0.0431 39/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9866 - loss: 0.0433 41/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9865 - loss: 0.0435 43/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9865 - loss: 0.0436 45/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9865 - loss: 0.0437 47/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9864 - loss: 0.0439 49/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9864 - loss: 0.0440 51/422 ━━━━━━━━━━━━━━━━━━━━ 17s 46ms/step - accuracy: 0.9863 - loss: 0.0442 53/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9862 - loss: 0.0443 55/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9862 - loss: 0.0445 57/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9861 - loss: 0.0446 59/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9860 - loss: 0.0448 61/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9860 - loss: 0.0450 63/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9859 - loss: 0.0451 65/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9858 - loss: 0.0453 67/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9858 - loss: 0.0454 69/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9857 - loss: 0.0456 71/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9857 - loss: 0.0457 73/422 ━━━━━━━━━━━━━━━━━━━━ 16s 46ms/step - accuracy: 0.9856 - loss: 0.0459 75/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9855 - loss: 0.0460 77/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9854 - loss: 0.0462 79/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9854 - loss: 0.0464 81/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9853 - loss: 0.0465 83/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9852 - loss: 0.0467 85/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9852 - loss: 0.0468 87/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9851 - loss: 0.0470 89/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9851 - loss: 0.0471 91/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9850 - loss: 0.0473 93/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9850 - loss: 0.0474 95/422 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.9849 - loss: 0.0476 97/422 ━━━━━━━━━━━━━━━━━━━━ 14s 46ms/step - accuracy: 0.9849 - loss: 0.0477 99/422 ━━━━━━━━━━━━━━━━━━━━ 14s 46ms/step - accuracy: 0.9848 - loss: 0.0479 101/422 ━━━━━━━━━━━━━━━━━━━━ 14s 46ms/step - accuracy: 0.9847 - loss: 0.0481 103/422 ━━━━━━━━━━━━━━━━━━━━ 14s 46ms/step - accuracy: 0.9847 - loss: 0.0482 105/422 ━━━━━━━━━━━━━━━━━━━━ 14s 46ms/step - accuracy: 0.9846 - loss: 0.0484 107/422 ━━━━━━━━━━━━━━━━━━━━ 14s 46ms/step - accuracy: 0.9846 - loss: 0.0485 109/422 ━━━━━━━━━━━━━━━━━━━━ 14s 46ms/step - accuracy: 0.9845 - loss: 0.0487 110/422 ━━━━━━━━━━━━━━━━━━━━ 14s 46ms/step - accuracy: 0.9845 - loss: 0.0488 112/422 ━━━━━━━━━━━━━━━━━━━━ 14s 46ms/step - accuracy: 0.9844 - loss: 0.0490 114/422 ━━━━━━━━━━━━━━━━━━━━ 14s 46ms/step - accuracy: 0.9844 - loss: 0.0491 116/422 ━━━━━━━━━━━━━━━━━━━━ 14s 46ms/step - accuracy: 0.9843 - loss: 0.0493 118/422 ━━━━━━━━━━━━━━━━━━━━ 14s 46ms/step - accuracy: 0.9842 - loss: 0.0495 120/422 ━━━━━━━━━━━━━━━━━━━━ 13s 46ms/step - accuracy: 0.9842 - loss: 0.0496 122/422 ━━━━━━━━━━━━━━━━━━━━ 13s 46ms/step - accuracy: 0.9841 - loss: 0.0498 124/422 ━━━━━━━━━━━━━━━━━━━━ 13s 46ms/step - accuracy: 0.9841 - loss: 0.0500 126/422 ━━━━━━━━━━━━━━━━━━━━ 13s 46ms/step - accuracy: 0.9840 - loss: 0.0501 128/422 ━━━━━━━━━━━━━━━━━━━━ 13s 46ms/step - accuracy: 0.9840 - 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loss: 0.0527 182/422 ━━━━━━━━━━━━━━━━━━━━ 11s 46ms/step - accuracy: 0.9831 - loss: 0.0527 184/422 ━━━━━━━━━━━━━━━━━━━━ 10s 46ms/step - accuracy: 0.9831 - loss: 0.0528 186/422 ━━━━━━━━━━━━━━━━━━━━ 10s 46ms/step - accuracy: 0.9831 - loss: 0.0529 187/422 ━━━━━━━━━━━━━━━━━━━━ 10s 46ms/step - accuracy: 0.9831 - loss: 0.0529 189/422 ━━━━━━━━━━━━━━━━━━━━ 10s 46ms/step - accuracy: 0.9830 - loss: 0.0529 191/422 ━━━━━━━━━━━━━━━━━━━━ 10s 46ms/step - accuracy: 0.9830 - loss: 0.0530 193/422 ━━━━━━━━━━━━━━━━━━━━ 10s 46ms/step - accuracy: 0.9830 - loss: 0.0531 195/422 ━━━━━━━━━━━━━━━━━━━━ 10s 46ms/step - accuracy: 0.9830 - loss: 0.0531 197/422 ━━━━━━━━━━━━━━━━━━━━ 10s 46ms/step - accuracy: 0.9830 - loss: 0.0532 199/422 ━━━━━━━━━━━━━━━━━━━━ 10s 46ms/step - accuracy: 0.9829 - loss: 0.0533 201/422 ━━━━━━━━━━━━━━━━━━━━ 10s 46ms/step - accuracy: 0.9829 - loss: 0.0533 203/422 ━━━━━━━━━━━━━━━━━━━━ 10s 46ms/step - accuracy: 0.9829 - loss: 0.0534 205/422 ━━━━━━━━━━━━━━━━━━━━ 10s 46ms/step - accuracy: 0.9829 - 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accuracy: 0.9827 - loss: 0.0542 235/422 ━━━━━━━━━━━━━━━━━━━━ 8s 46ms/step - accuracy: 0.9826 - loss: 0.0542 237/422 ━━━━━━━━━━━━━━━━━━━━ 8s 46ms/step - accuracy: 0.9826 - loss: 0.0543 239/422 ━━━━━━━━━━━━━━━━━━━━ 8s 46ms/step - accuracy: 0.9826 - loss: 0.0543 241/422 ━━━━━━━━━━━━━━━━━━━━ 8s 46ms/step - accuracy: 0.9826 - loss: 0.0543 243/422 ━━━━━━━━━━━━━━━━━━━━ 8s 46ms/step - accuracy: 0.9826 - loss: 0.0544 245/422 ━━━━━━━━━━━━━━━━━━━━ 8s 46ms/step - accuracy: 0.9826 - loss: 0.0544 247/422 ━━━━━━━━━━━━━━━━━━━━ 8s 46ms/step - accuracy: 0.9826 - loss: 0.0545 249/422 ━━━━━━━━━━━━━━━━━━━━ 7s 46ms/step - accuracy: 0.9825 - loss: 0.0545 251/422 ━━━━━━━━━━━━━━━━━━━━ 7s 46ms/step - accuracy: 0.9825 - loss: 0.0545 253/422 ━━━━━━━━━━━━━━━━━━━━ 7s 46ms/step - accuracy: 0.9825 - loss: 0.0546 255/422 ━━━━━━━━━━━━━━━━━━━━ 7s 46ms/step - accuracy: 0.9825 - loss: 0.0546 257/422 ━━━━━━━━━━━━━━━━━━━━ 7s 46ms/step - accuracy: 0.9825 - loss: 0.0547 259/422 ━━━━━━━━━━━━━━━━━━━━ 7s 46ms/step - accuracy: 0.9825 - loss: 0.0547 261/422 ━━━━━━━━━━━━━━━━━━━━ 7s 46ms/step - accuracy: 0.9825 - loss: 0.0547 263/422 ━━━━━━━━━━━━━━━━━━━━ 7s 46ms/step - accuracy: 0.9824 - loss: 0.0548 265/422 ━━━━━━━━━━━━━━━━━━━━ 7s 46ms/step - accuracy: 0.9824 - loss: 0.0548 267/422 ━━━━━━━━━━━━━━━━━━━━ 7s 46ms/step - accuracy: 0.9824 - loss: 0.0549 269/422 ━━━━━━━━━━━━━━━━━━━━ 7s 46ms/step - accuracy: 0.9824 - loss: 0.0549 271/422 ━━━━━━━━━━━━━━━━━━━━ 6s 46ms/step - accuracy: 0.9824 - loss: 0.0549 273/422 ━━━━━━━━━━━━━━━━━━━━ 6s 46ms/step - accuracy: 0.9824 - loss: 0.0550 275/422 ━━━━━━━━━━━━━━━━━━━━ 6s 46ms/step - accuracy: 0.9824 - loss: 0.0550 277/422 ━━━━━━━━━━━━━━━━━━━━ 6s 46ms/step - accuracy: 0.9824 - loss: 0.0550 279/422 ━━━━━━━━━━━━━━━━━━━━ 6s 46ms/step - accuracy: 0.9824 - loss: 0.0551 281/422 ━━━━━━━━━━━━━━━━━━━━ 6s 46ms/step - accuracy: 0.9823 - loss: 0.0551 283/422 ━━━━━━━━━━━━━━━━━━━━ 6s 46ms/step - accuracy: 0.9823 - loss: 0.0551 285/422 ━━━━━━━━━━━━━━━━━━━━ 6s 46ms/step - accuracy: 0.9823 - loss: 0.0552 287/422 ━━━━━━━━━━━━━━━━━━━━ 6s 46ms/step - accuracy: 0.9823 - loss: 0.0552 289/422 ━━━━━━━━━━━━━━━━━━━━ 6s 46ms/step - accuracy: 0.9823 - loss: 0.0552 291/422 ━━━━━━━━━━━━━━━━━━━━ 6s 46ms/step - accuracy: 0.9823 - loss: 0.0553 293/422 ━━━━━━━━━━━━━━━━━━━━ 5s 46ms/step - accuracy: 0.9823 - loss: 0.0553 295/422 ━━━━━━━━━━━━━━━━━━━━ 5s 46ms/step - accuracy: 0.9823 - loss: 0.0553 297/422 ━━━━━━━━━━━━━━━━━━━━ 5s 46ms/step - accuracy: 0.9823 - loss: 0.0553 299/422 ━━━━━━━━━━━━━━━━━━━━ 5s 46ms/step - accuracy: 0.9823 - loss: 0.0554 301/422 ━━━━━━━━━━━━━━━━━━━━ 5s 46ms/step - accuracy: 0.9823 - loss: 0.0554 303/422 ━━━━━━━━━━━━━━━━━━━━ 5s 46ms/step - accuracy: 0.9823 - loss: 0.0555 305/422 ━━━━━━━━━━━━━━━━━━━━ 5s 46ms/step - accuracy: 0.9822 - loss: 0.0555 307/422 ━━━━━━━━━━━━━━━━━━━━ 5s 46ms/step - accuracy: 0.9822 - loss: 0.0555 309/422 ━━━━━━━━━━━━━━━━━━━━ 5s 46ms/step - accuracy: 0.9822 - loss: 0.0555 311/422 ━━━━━━━━━━━━━━━━━━━━ 5s 46ms/step - accuracy: 0.9822 - loss: 0.0556 313/422 ━━━━━━━━━━━━━━━━━━━━ 5s 46ms/step - accuracy: 0.9822 - loss: 0.0556 315/422 ━━━━━━━━━━━━━━━━━━━━ 4s 46ms/step - accuracy: 0.9822 - loss: 0.0556 317/422 ━━━━━━━━━━━━━━━━━━━━ 4s 46ms/step - accuracy: 0.9822 - loss: 0.0557 319/422 ━━━━━━━━━━━━━━━━━━━━ 4s 46ms/step - accuracy: 0.9822 - loss: 0.0557 321/422 ━━━━━━━━━━━━━━━━━━━━ 4s 46ms/step - accuracy: 0.9822 - loss: 0.0557 323/422 ━━━━━━━━━━━━━━━━━━━━ 4s 46ms/step - accuracy: 0.9822 - loss: 0.0558 325/422 ━━━━━━━━━━━━━━━━━━━━ 4s 46ms/step - accuracy: 0.9822 - loss: 0.0558 326/422 ━━━━━━━━━━━━━━━━━━━━ 4s 46ms/step - accuracy: 0.9822 - loss: 0.0558 328/422 ━━━━━━━━━━━━━━━━━━━━ 4s 46ms/step - accuracy: 0.9822 - loss: 0.0558 329/422 ━━━━━━━━━━━━━━━━━━━━ 4s 46ms/step - accuracy: 0.9822 - loss: 0.0558 330/422 ━━━━━━━━━━━━━━━━━━━━ 4s 46ms/step - accuracy: 0.9822 - loss: 0.0559 332/422 ━━━━━━━━━━━━━━━━━━━━ 4s 46ms/step - accuracy: 0.9821 - loss: 0.0559 334/422 ━━━━━━━━━━━━━━━━━━━━ 4s 46ms/step - accuracy: 0.9821 - loss: 0.0559 336/422 ━━━━━━━━━━━━━━━━━━━━ 3s 46ms/step - accuracy: 0.9821 - loss: 0.0559 338/422 ━━━━━━━━━━━━━━━━━━━━ 3s 46ms/step - accuracy: 0.9821 - loss: 0.0560 340/422 ━━━━━━━━━━━━━━━━━━━━ 3s 46ms/step - accuracy: 0.9821 - loss: 0.0560 342/422 ━━━━━━━━━━━━━━━━━━━━ 3s 46ms/step - accuracy: 0.9821 - loss: 0.0560 344/422 ━━━━━━━━━━━━━━━━━━━━ 3s 46ms/step - accuracy: 0.9821 - loss: 0.0560 346/422 ━━━━━━━━━━━━━━━━━━━━ 3s 46ms/step - accuracy: 0.9821 - loss: 0.0561 348/422 ━━━━━━━━━━━━━━━━━━━━ 3s 46ms/step - accuracy: 0.9821 - loss: 0.0561 350/422 ━━━━━━━━━━━━━━━━━━━━ 3s 46ms/step - accuracy: 0.9821 - loss: 0.0561 352/422 ━━━━━━━━━━━━━━━━━━━━ 3s 46ms/step - accuracy: 0.9821 - loss: 0.0561 354/422 ━━━━━━━━━━━━━━━━━━━━ 3s 46ms/step - accuracy: 0.9821 - loss: 0.0562 356/422 ━━━━━━━━━━━━━━━━━━━━ 3s 46ms/step - accuracy: 0.9821 - loss: 0.0562 358/422 ━━━━━━━━━━━━━━━━━━━━ 2s 46ms/step - accuracy: 0.9821 - loss: 0.0562 360/422 ━━━━━━━━━━━━━━━━━━━━ 2s 46ms/step - accuracy: 0.9821 - loss: 0.0562 362/422 ━━━━━━━━━━━━━━━━━━━━ 2s 46ms/step - accuracy: 0.9821 - loss: 0.0562 364/422 ━━━━━━━━━━━━━━━━━━━━ 2s 46ms/step - accuracy: 0.9820 - loss: 0.0563 366/422 ━━━━━━━━━━━━━━━━━━━━ 2s 46ms/step - accuracy: 0.9820 - loss: 0.0563 368/422 ━━━━━━━━━━━━━━━━━━━━ 2s 46ms/step - accuracy: 0.9820 - loss: 0.0563 370/422 ━━━━━━━━━━━━━━━━━━━━ 2s 46ms/step - accuracy: 0.9820 - loss: 0.0563 372/422 ━━━━━━━━━━━━━━━━━━━━ 2s 46ms/step - accuracy: 0.9820 - loss: 0.0563 374/422 ━━━━━━━━━━━━━━━━━━━━ 2s 46ms/step - accuracy: 0.9820 - loss: 0.0564 376/422 ━━━━━━━━━━━━━━━━━━━━ 2s 46ms/step - accuracy: 0.9820 - loss: 0.0564 378/422 ━━━━━━━━━━━━━━━━━━━━ 2s 46ms/step - accuracy: 0.9820 - loss: 0.0564 380/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9820 - loss: 0.0564 382/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9820 - loss: 0.0564 384/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9820 - loss: 0.0565 386/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9820 - loss: 0.0565 388/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9820 - loss: 0.0565 390/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9820 - loss: 0.0565 392/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9820 - loss: 0.0565 394/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9820 - loss: 0.0566 396/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9820 - loss: 0.0566 398/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9820 - loss: 0.0566 400/422 ━━━━━━━━━━━━━━━━━━━━ 1s 46ms/step - accuracy: 0.9820 - loss: 0.0566 402/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9820 - loss: 0.0566 404/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9820 - loss: 0.0566 406/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9820 - loss: 0.0567 408/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9820 - loss: 0.0567 410/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9820 - loss: 0.0567 412/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9820 - loss: 0.0567 414/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9820 - loss: 0.0567 416/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9820 - loss: 0.0567 418/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9820 - loss: 0.0568 420/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9820 - loss: 0.0568 422/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - accuracy: 0.9820 - loss: 0.0568 422/422 ━━━━━━━━━━━━━━━━━━━━ 20s 48ms/step - accuracy: 0.9816 - loss: 0.0597 - val_accuracy: 0.9845 - val_loss: 0.0495 Test accuracy: 0.9854999780654907
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 (5.0.2) 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 -0.00016470116679556668, std 0.06922455132007599
min -0.30074018239974976, max 0.1865146905183792
predictions_snn = snn_converter.predict(X_test[:300], duration_per_sample=200) 0%| | 0/300 [00:00<?, ?it/s] 0%|▎ | 1/300 [00:01<08:11, 1.64s/it] 1%|▌ | 2/300 [00:03<08:08, 1.64s/it] 1%|▊ | 3/300 [00:04<08:05, 1.63s/it] 1%|█ | 4/300 [00:06<08:05, 1.64s/it] 2%|█▎ | 5/300 [00:08<08:04, 1.64s/it] 2%|█▌ | 6/300 [00:09<08:01, 1.64s/it] 2%|█▉ | 7/300 [00:11<08:01, 1.64s/it] 3%|██▏ | 8/300 [00:13<07:58, 1.64s/it] 3%|██▍ | 9/300 [00:14<07:56, 1.64s/it] 3%|██▋ | 10/300 [00:16<07:55, 1.64s/it] 4%|██▉ | 11/300 [00:18<07:52, 1.64s/it] 4%|███▏ | 12/300 [00:19<07:52, 1.64s/it] 4%|███▍ | 13/300 [00:21<07:49, 1.64s/it] 5%|███▋ | 14/300 [00:22<07:48, 1.64s/it] 5%|████ | 15/300 [00:24<07:46, 1.64s/it] 5%|████▎ | 16/300 [00:26<07:46, 1.64s/it] 6%|████▌ | 17/300 [00:27<07:44, 1.64s/it] 6%|████▊ | 18/300 [00:29<07:43, 1.64s/it] 6%|█████ | 19/300 [00:31<07:41, 1.64s/it] 7%|█████▎ | 20/300 [00:32<07:39, 1.64s/it] 7%|█████▌ | 21/300 [00:34<07:38, 1.64s/it] 7%|█████▊ | 22/300 [00:36<07:36, 1.64s/it] 8%|██████▏ 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97%|████████████████████████████████████████████████████████████████████████████▉ | 292/300 [07:59<00:13, 1.64s/it] 98%|█████████████████████████████████████████████████████████████████████████████▏ | 293/300 [08:00<00:11, 1.64s/it] 98%|█████████████████████████████████████████████████████████████████████████████▍ | 294/300 [08:02<00:09, 1.64s/it] 98%|█████████████████████████████████████████████████████████████████████████████▋ | 295/300 [08:03<00:08, 1.64s/it] 99%|█████████████████████████████████████████████████████████████████████████████▉ | 296/300 [08:05<00:06, 1.64s/it] 99%|██████████████████████████████████████████████████████████████████████████████▏| 297/300 [08:07<00:04, 1.64s/it] 99%|██████████████████████████████████████████████████████████████████████████████▍| 298/300 [08:08<00:03, 1.64s/it]100%|██████████████████████████████████████████████████████████████████████████████▋| 299/300 [08:10<00:01, 1.64s/it]100%|███████████████████████████████████████████████████████████████████████████████| 300/300 [08:12<00:00, 1.64s/it]100%|███████████████████████████████████████████████████████████████████████████████| 300/300 [08:12<00:00, 1.64s/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 0.96 1.00 0.98 24
1 1.00 0.98 0.99 41
2 0.97 1.00 0.98 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 1.00 0.96 0.98 24
7 1.00 1.00 1.00 34
8 0.84 1.00 0.91 21
9 1.00 0.94 0.97 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