Datasets:
HenryAI
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KerasCodeExamples.txt

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dpr = [x for x in np.linspace(0, stochastic_depth_rate, transformer_layers)]
# Create multiple layers of the Transformer block.
for i in range(transformer_layers):
# Layer normalization 1.
x1 = layers.LayerNormalization(epsilon=1e-5)(encoded_patches)
# Create a multi-head attention layer.
attention_output = layers.MultiHeadAttention(
num_heads=num_heads, key_dim=projection_dim, dropout=0.1
)(x1, x1)
# Skip connection 1.
attention_output = StochasticDepth(dpr[i])(attention_output)
x2 = layers.Add()([attention_output, encoded_patches])
# Layer normalization 2.
x3 = layers.LayerNormalization(epsilon=1e-5)(x2)
# MLP.
x3 = mlp(x3, hidden_units=transformer_units, dropout_rate=0.1)
# Skip connection 2.
x3 = StochasticDepth(dpr[i])(x3)
encoded_patches = layers.Add()([x3, x2])
# Apply sequence pooling.
representation = layers.LayerNormalization(epsilon=1e-5)(encoded_patches)
attention_weights = tf.nn.softmax(layers.Dense(1)(representation), axis=1)
weighted_representation = tf.matmul(
attention_weights, representation, transpose_a=True
)
weighted_representation = tf.squeeze(weighted_representation, -2)
# Classify outputs.
logits = layers.Dense(num_classes)(weighted_representation)
# Create the Keras model.
model = keras.Model(inputs=inputs, outputs=logits)
return model
Model training and evaluation
def run_experiment(model):
optimizer = tfa.optimizers.AdamW(learning_rate=0.001, weight_decay=0.0001)
model.compile(
optimizer=optimizer,
loss=keras.losses.CategoricalCrossentropy(
from_logits=True, label_smoothing=0.1
),
metrics=[
keras.metrics.CategoricalAccuracy(name=\"accuracy\"),
keras.metrics.TopKCategoricalAccuracy(5, name=\"top-5-accuracy\"),
],
)
checkpoint_filepath = \"/tmp/checkpoint\"
checkpoint_callback = keras.callbacks.ModelCheckpoint(
checkpoint_filepath,
monitor=\"val_accuracy\",
save_best_only=True,
save_weights_only=True,
)
history = model.fit(
x=x_train,
y=y_train,
batch_size=batch_size,
epochs=num_epochs,
validation_split=0.1,
callbacks=[checkpoint_callback],
)
model.load_weights(checkpoint_filepath)
_, accuracy, top_5_accuracy = model.evaluate(x_test, y_test)
print(f\"Test accuracy: {round(accuracy * 100, 2)}%\")
print(f\"Test top 5 accuracy: {round(top_5_accuracy * 100, 2)}%\")
return history
cct_model = create_cct_model()
history = run_experiment(cct_model)
Epoch 1/30
352/352 [==============================] - 10s 18ms/step - loss: 1.9181 - accuracy: 0.3277 - top-5-accuracy: 0.8296 - val_loss: 1.7123 - val_accuracy: 0.4250 - val_top-5-accuracy: 0.9028
Epoch 2/30
352/352 [==============================] - 6s 16ms/step - loss: 1.5725 - accuracy: 0.5010 - top-5-accuracy: 0.9295 - val_loss: 1.5026 - val_accuracy: 0.5530 - val_top-5-accuracy: 0.9364
Epoch 3/30
352/352 [==============================] - 6s 16ms/step - loss: 1.4492 - accuracy: 0.5633 - top-5-accuracy: 0.9476 - val_loss: 1.3744 - val_accuracy: 0.6038 - val_top-5-accuracy: 0.9558
Epoch 4/30
352/352 [==============================] - 6s 16ms/step - loss: 1.3658 - accuracy: 0.6055 - top-5-accuracy: 0.9576 - val_loss: 1.3258 - val_accuracy: 0.6148 - val_top-5-accuracy: 0.9648
Epoch 5/30
352/352 [==============================] - 6s 16ms/step - loss: 1.3142 - accuracy: 0.6302 - top-5-accuracy: 0.9640 - val_loss: 1.2723 - val_accuracy: 0.6468 - val_top-5-accuracy: 0.9710
Epoch 6/30
352/352 [==============================] - 6s 16ms/step - loss: 1.2729 - accuracy: 0.6489 - top-5-accuracy: 0.9684 - val_loss: 1.2490 - val_accuracy: 0.6640 - val_top-5-accuracy: 0.9704
Epoch 7/30
352/352 [==============================] - 6s 16ms/step - loss: 1.2371 - accuracy: 0.6664 - top-5-accuracy: 0.9711 - val_loss: 1.1822 - val_accuracy: 0.6906 - val_top-5-accuracy: 0.9744
Epoch 8/30
352/352 [==============================] - 6s 16ms/step - loss: 1.1899 - accuracy: 0.6942 - top-5-accuracy: 0.9735 - val_loss: 1.1799 - val_accuracy: 0.6982 - val_top-5-accuracy: 0.9768
Epoch 9/30
352/352 [==============================] - 6s 16ms/step - loss: 1.1706 - accuracy: 0.6972 - top-5-accuracy: 0.9767 - val_loss: 1.1390 - val_accuracy: 0.7148 - val_top-5-accuracy: 0.9768
Epoch 10/30