Datasets:
HenryAI
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KerasCodeExamples.txt

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Extract the encoder model along with other layers
# Extract the augmentation layers.
train_augmentation_model = mae_model.train_augmentation_model
test_augmentation_model = mae_model.test_augmentation_model
# Extract the patchers.
patch_layer = mae_model.patch_layer
patch_encoder = mae_model.patch_encoder
patch_encoder.downstream = True # Swtich the downstream flag to True.
# Extract the encoder.
encoder = mae_model.encoder
# Pack as a model.
downstream_model = keras.Sequential(
[
layers.Input((IMAGE_SIZE, IMAGE_SIZE, 3)),
patch_layer,
patch_encoder,
encoder,
layers.BatchNormalization(), # Refer to A.1 (Linear probing).
layers.GlobalAveragePooling1D(),
layers.Dense(NUM_CLASSES, activation=\"softmax\"),
],
name=\"linear_probe_model\",
)
# Only the final classification layer of the `downstream_model` should be trainable.
for layer in downstream_model.layers[:-1]:
layer.trainable = False
downstream_model.summary()
Model: \"linear_probe_model\"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
patches_1 (Patches) (None, 64, 108) 0
patch_encoder_1 (PatchEncod (None, 64, 128) 22252
er)
mae_encoder (Functional) (None, None, 128) 1981696
batch_normalization (BatchN (None, 64, 128) 512
ormalization)
global_average_pooling1d (G (None, 128) 0
lobalAveragePooling1D)
dense_19 (Dense) (None, 10) 1290
=================================================================
Total params: 2,005,750
Trainable params: 1,290
Non-trainable params: 2,004,460
_________________________________________________________________
We are using average pooling to extract learned representations from the MAE encoder. Another approach would be to use a learnable dummy token inside the encoder during pretraining (resembling the [CLS] token). Then we can extract representations from that token during the downstream tasks.
Prepare datasets for linear probing
def prepare_data(images, labels, is_train=True):
if is_train:
augmentation_model = train_augmentation_model
else:
augmentation_model = test_augmentation_model
dataset = tf.data.Dataset.from_tensor_slices((images, labels))
if is_train:
dataset = dataset.shuffle(BUFFER_SIZE)
dataset = dataset.batch(BATCH_SIZE).map(
lambda x, y: (augmentation_model(x), y), num_parallel_calls=AUTO
)
return dataset.prefetch(AUTO)
train_ds = prepare_data(x_train, y_train)
val_ds = prepare_data(x_train, y_train, is_train=False)
test_ds = prepare_data(x_test, y_test, is_train=False)
Perform linear probing
linear_probe_epochs = 50
linear_prob_lr = 0.1
warm_epoch_percentage = 0.1
steps = int((len(x_train) // BATCH_SIZE) * linear_probe_epochs)
warmup_steps = int(steps * warm_epoch_percentage)
scheduled_lrs = WarmUpCosine(
learning_rate_base=linear_prob_lr,
total_steps=steps,
warmup_learning_rate=0.0,
warmup_steps=warmup_steps,
)
optimizer = keras.optimizers.SGD(learning_rate=scheduled_lrs, momentum=0.9)
downstream_model.compile(
optimizer=optimizer, loss=\"sparse_categorical_crossentropy\", metrics=[\"accuracy\"]
)
downstream_model.fit(train_ds, validation_data=val_ds, epochs=linear_probe_epochs)
loss, accuracy = downstream_model.evaluate(test_ds)
accuracy = round(accuracy * 100, 2)