HenryAI/KerasBERTv1-Data · Datasets at Hugging Face

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x = data_augmentation(inputs) # Apply random data augmentation

# Pre-trained Xception weights requires that input be normalized
# from (0, 255) to a range (-1., +1.), the normalization layer
# does the following, outputs = (inputs - mean) / sqrt(var)
norm_layer = keras.layers.experimental.preprocessing.Normalization()
mean = np.array([127.5] * 3)
var = mean ** 2
# Scale inputs to [-1, +1]
x = norm_layer(x)
norm_layer.set_weights([mean, var])

# The base model contains batchnorm layers. We want to keep them in inference mode
# when we unfreeze the base model for fine-tuning, so we make sure that the
# base_model is running in inference mode here.
x = base_model(x, training=False)
x = keras.layers.GlobalAveragePooling2D()(x)
x = keras.layers.Dropout(0.2)(x) # Regularize with dropout
outputs = keras.layers.Dense(1)(x)
model = keras.Model(inputs, outputs)

model.summary()
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_5 (InputLayer) [(None, 150, 150, 3)] 0
_________________________________________________________________
sequential_3 (Sequential) (None, 150, 150, 3) 0
_________________________________________________________________
normalization (Normalization (None, 150, 150, 3) 7
_________________________________________________________________
xception (Model) (None, 5, 5, 2048) 20861480
_________________________________________________________________
global_average_pooling2d (Gl (None, 2048) 0
_________________________________________________________________
dropout (Dropout) (None, 2048) 0
_________________________________________________________________
dense_7 (Dense) (None, 1) 2049
=================================================================
Total params: 20,863,536
Trainable params: 2,049
Non-trainable params: 20,861,487
_________________________________________________________________
Train the top layer
model.compile(
optimizer=keras.optimizers.Adam(),
loss=keras.losses.BinaryCrossentropy(from_logits=True),
metrics=[keras.metrics.BinaryAccuracy()],
)

epochs = 20
model.fit(train_ds, epochs=epochs, validation_data=validation_ds)
Epoch 1/20
291/291 [==============================] - 24s 83ms/step - loss: 0.1639 - binary_accuracy: 0.9276 - val_loss: 0.0883 - val_binary_accuracy: 0.9652
Epoch 2/20
291/291 [==============================] - 22s 76ms/step - loss: 0.1202 - binary_accuracy: 0.9491 - val_loss: 0.0855 - val_binary_accuracy: 0.9686
Epoch 3/20
291/291 [==============================] - 23s 80ms/step - loss: 0.1076 - binary_accuracy: 0.9546 - val_loss: 0.0802 - val_binary_accuracy: 0.9682
Epoch 4/20
291/291 [==============================] - 23s 80ms/step - loss: 0.1127 - binary_accuracy: 0.9539 - val_loss: 0.0798 - val_binary_accuracy: 0.9682
Epoch 5/20
291/291 [==============================] - 23s 78ms/step - loss: 0.1072 - binary_accuracy: 0.9558 - val_loss: 0.0807 - val_binary_accuracy: 0.9695
Epoch 6/20
291/291 [==============================] - 23s 79ms/step - loss: 0.1073 - binary_accuracy: 0.9565 - val_loss: 0.0746 - val_binary_accuracy: 0.9733
Epoch 7/20
291/291 [==============================] - 23s 79ms/step - loss: 0.1037 - binary_accuracy: 0.9562 - val_loss: 0.0738 - val_binary_accuracy: 0.9712
Epoch 8/20
291/291 [==============================] - 23s 79ms/step - loss: 0.1061 - binary_accuracy: 0.9580 - val_loss: 0.0764 - val_binary_accuracy: 0.9738
Epoch 9/20
291/291 [==============================] - 23s 78ms/step - loss: 0.0959 - binary_accuracy: 0.9612 - val_loss: 0.0823 - val_binary_accuracy: 0.9673
Epoch 10/20
291/291 [==============================] - 23s 79ms/step - loss: 0.0956 - binary_accuracy: 0.9600 - val_loss: 0.0736 - val_binary_accuracy: 0.9725
Epoch 11/20
291/291 [==============================] - 23s 79ms/step - loss: 0.0944 - binary_accuracy: 0.9603 - val_loss: 0.0781 - val_binary_accuracy: 0.9716
Epoch 12/20
291/291 [==============================] - 23s 79ms/step - loss: 0.0960 - binary_accuracy: 0.9615 - val_loss: 0.0720 - val_binary_accuracy: 0.9725
Epoch 13/20
291/291 [==============================] - 23s 79ms/step - loss: 0.0987 - binary_accuracy: 0.9614 - val_loss: 0.0791 - val_binary_accuracy: 0.9708
Epoch 14/20
291/291 [==============================] - 23s 79ms/step - loss: 0.0930 - binary_accuracy: 0.9636 - val_loss: 0.0780 - val_binary_accuracy: 0.9690
Epoch 15/20
291/291 [==============================] - 23s 78ms/step - loss: 0.0954 - binary_accuracy: 0.9624 - val_loss: 0.0772 - val_binary_accuracy: 0.9678
Epoch 16/20
291/291 [==============================] - 23s 78ms/step - loss: 0.0963 - binary_accuracy: 0.9598 - val_loss: 0.0781 - val_binary_accuracy: 0.9695
Epoch 17/20
291/291 [==============================] - 23s 78ms/step - loss: 0.1006 - binary_accuracy: 0.9585 - val_loss: 0.0832 - val_binary_accuracy: 0.9699
Epoch 18/20
291/291 [==============================] - 23s 78ms/step - loss: 0.0942 - binary_accuracy: 0.9615 - val_loss: 0.0761 - val_binary_accuracy: 0.9703
Epoch 19/20
291/291 [==============================] - 23s 79ms/step - loss: 0.0950 - binary_accuracy: 0.9613 - val_loss: 0.0817 - val_binary_accuracy: 0.9690
Epoch 20/20
291/291 [==============================] - 23s 79ms/step - loss: 0.0906 - binary_accuracy: 0.9624 - val_loss: 0.0755 - val_binary_accuracy: 0.9712

<tensorflow.python.keras.callbacks.History at 0x7f3fa4cdab00>
Do a round of fine-tuning of the entire model
Finally, let's unfreeze the base model and train the entire model end-to-end with a low learning rate.

Importantly, although the base model becomes trainable, it is still running in inference mode since we passed training=False when calling it when we built the model. This means that the batch normalization layers inside won't update their batch statistics. If they did, they would wreck havoc on the representations learned by the model so far.

x = data_augmentation(inputs) # Apply random data augmentation

# Pre-trained Xception weights requires that input be normalized

# from (0, 255) to a range (-1., +1.), the normalization layer

# does the following, outputs = (inputs - mean) / sqrt(var)

norm_layer = keras.layers.experimental.preprocessing.Normalization()

mean = np.array([127.5] * 3)

var = mean ** 2

# Scale inputs to [-1, +1]

x = norm_layer(x)

norm_layer.set_weights([mean, var])

# The base model contains batchnorm layers. We want to keep them in inference mode

# when we unfreeze the base model for fine-tuning, so we make sure that the

# base_model is running in inference mode here.

x = base_model(x, training=False)

x = keras.layers.GlobalAveragePooling2D()(x)

x = keras.layers.Dropout(0.2)(x) # Regularize with dropout

outputs = keras.layers.Dense(1)(x)

model = keras.Model(inputs, outputs)

model.summary()

Model: "model"

_________________________________________________________________

Layer (type) Output Shape Param #

=================================================================

input_5 (InputLayer) [(None, 150, 150, 3)] 0

_________________________________________________________________

sequential_3 (Sequential) (None, 150, 150, 3) 0

_________________________________________________________________

normalization (Normalization (None, 150, 150, 3) 7

_________________________________________________________________

xception (Model) (None, 5, 5, 2048) 20861480

_________________________________________________________________

global_average_pooling2d (Gl (None, 2048) 0

_________________________________________________________________

dropout (Dropout) (None, 2048) 0

_________________________________________________________________

dense_7 (Dense) (None, 1) 2049

=================================================================

Total params: 20,863,536

Trainable params: 2,049

Non-trainable params: 20,861,487

_________________________________________________________________

Train the top layer

model.compile(

optimizer=keras.optimizers.Adam(),

loss=keras.losses.BinaryCrossentropy(from_logits=True),

metrics=[keras.metrics.BinaryAccuracy()],

)

epochs = 20

model.fit(train_ds, epochs=epochs, validation_data=validation_ds)

Epoch 1/20

291/291 [==============================] - 24s 83ms/step - loss: 0.1639 - binary_accuracy: 0.9276 - val_loss: 0.0883 - val_binary_accuracy: 0.9652

Epoch 2/20

291/291 [==============================] - 22s 76ms/step - loss: 0.1202 - binary_accuracy: 0.9491 - val_loss: 0.0855 - val_binary_accuracy: 0.9686

Epoch 3/20

291/291 [==============================] - 23s 80ms/step - loss: 0.1076 - binary_accuracy: 0.9546 - val_loss: 0.0802 - val_binary_accuracy: 0.9682

Epoch 4/20

291/291 [==============================] - 23s 80ms/step - loss: 0.1127 - binary_accuracy: 0.9539 - val_loss: 0.0798 - val_binary_accuracy: 0.9682

Epoch 5/20

291/291 [==============================] - 23s 78ms/step - loss: 0.1072 - binary_accuracy: 0.9558 - val_loss: 0.0807 - val_binary_accuracy: 0.9695

Epoch 6/20

291/291 [==============================] - 23s 79ms/step - loss: 0.1073 - binary_accuracy: 0.9565 - val_loss: 0.0746 - val_binary_accuracy: 0.9733

Epoch 7/20

291/291 [==============================] - 23s 79ms/step - loss: 0.1037 - binary_accuracy: 0.9562 - val_loss: 0.0738 - val_binary_accuracy: 0.9712

Epoch 8/20

291/291 [==============================] - 23s 79ms/step - loss: 0.1061 - binary_accuracy: 0.9580 - val_loss: 0.0764 - val_binary_accuracy: 0.9738

Epoch 9/20

291/291 [==============================] - 23s 78ms/step - loss: 0.0959 - binary_accuracy: 0.9612 - val_loss: 0.0823 - val_binary_accuracy: 0.9673

Epoch 10/20

291/291 [==============================] - 23s 79ms/step - loss: 0.0956 - binary_accuracy: 0.9600 - val_loss: 0.0736 - val_binary_accuracy: 0.9725

Epoch 11/20

291/291 [==============================] - 23s 79ms/step - loss: 0.0944 - binary_accuracy: 0.9603 - val_loss: 0.0781 - val_binary_accuracy: 0.9716

Epoch 12/20

291/291 [==============================] - 23s 79ms/step - loss: 0.0960 - binary_accuracy: 0.9615 - val_loss: 0.0720 - val_binary_accuracy: 0.9725

Epoch 13/20

291/291 [==============================] - 23s 79ms/step - loss: 0.0987 - binary_accuracy: 0.9614 - val_loss: 0.0791 - val_binary_accuracy: 0.9708

Epoch 14/20

291/291 [==============================] - 23s 79ms/step - loss: 0.0930 - binary_accuracy: 0.9636 - val_loss: 0.0780 - val_binary_accuracy: 0.9690

Epoch 15/20

291/291 [==============================] - 23s 78ms/step - loss: 0.0954 - binary_accuracy: 0.9624 - val_loss: 0.0772 - val_binary_accuracy: 0.9678

Epoch 16/20

291/291 [==============================] - 23s 78ms/step - loss: 0.0963 - binary_accuracy: 0.9598 - val_loss: 0.0781 - val_binary_accuracy: 0.9695

Epoch 17/20

291/291 [==============================] - 23s 78ms/step - loss: 0.1006 - binary_accuracy: 0.9585 - val_loss: 0.0832 - val_binary_accuracy: 0.9699

Epoch 18/20

291/291 [==============================] - 23s 78ms/step - loss: 0.0942 - binary_accuracy: 0.9615 - val_loss: 0.0761 - val_binary_accuracy: 0.9703

Epoch 19/20

291/291 [==============================] - 23s 79ms/step - loss: 0.0950 - binary_accuracy: 0.9613 - val_loss: 0.0817 - val_binary_accuracy: 0.9690

Epoch 20/20

291/291 [==============================] - 23s 79ms/step - loss: 0.0906 - binary_accuracy: 0.9624 - val_loss: 0.0755 - val_binary_accuracy: 0.9712

<tensorflow.python.keras.callbacks.History at 0x7f3fa4cdab00>

Do a round of fine-tuning of the entire model

Finally, let's unfreeze the base model and train the entire model end-to-end with a low learning rate.

Importantly, although the base model becomes trainable, it is still running in inference mode since we passed training=False when calling it when we built the model. This means that the batch normalization layers inside won't update their batch statistics. If they did, they would wreck havoc on the representations learned by the model so far.