Datasets:
HenryAI
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KerasCodeExamples.txt

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# First convolution block without batch normalization.
x = layers.Conv2D(filters=filters, kernel_size=7, strides=1, padding=\"same\")(inputs)
x = layers.LeakyReLU(0.2)(x)
# Second convolution block with batch normalization.
x = layers.Conv2D(filters=filters, kernel_size=1, strides=1, padding=\"same\")(x)
x = layers.LeakyReLU(0.2)(x)
x = layers.BatchNormalization()(x)
# Intermediate resizing as a bottleneck.
bottleneck = layers.Resizing(
*TARGET_SIZE, interpolation=interpolation
)(x)
# Residual passes.
for _ in range(num_res_blocks):
x = res_block(bottleneck)
# Projection.
x = layers.Conv2D(
filters=filters, kernel_size=3, strides=1, padding=\"same\", use_bias=False
)(x)
x = layers.BatchNormalization()(x)
# Skip connection.
x = layers.Add()([bottleneck, x])
# Final resized image.
x = layers.Conv2D(filters=3, kernel_size=7, strides=1, padding=\"same\")(x)
final_resize = layers.Add()([naive_resize, x])
return tf.keras.Model(inputs, final_resize, name=\"learnable_resizer\")
learnable_resizer = get_learnable_resizer()
Visualize the outputs of the learnable resizing module
Here, we visualize how the resized images would look like after being passed through the random weights of the resizer.
sample_images, _ = next(iter(train_ds))
plt.figure(figsize=(16, 10))
for i, image in enumerate(sample_images[:6]):
image = image / 255
ax = plt.subplot(3, 4, 2 * i + 1)
plt.title(\"Input Image\")
plt.imshow(image.numpy().squeeze())
plt.axis(\"off\")
ax = plt.subplot(3, 4, 2 * i + 2)
resized_image = learnable_resizer(image[None, ...])
plt.title(\"Resized Image\")
plt.imshow(resized_image.numpy().squeeze())
plt.axis(\"off\")
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
png
Model building utility
def get_model():
backbone = tf.keras.applications.DenseNet121(
weights=None,
include_top=True,
classes=2,
input_shape=((TARGET_SIZE[0], TARGET_SIZE[1], 3)),
)
backbone.trainable = True
inputs = layers.Input((INP_SIZE[0], INP_SIZE[1], 3))
x = layers.Rescaling(scale=1.0 / 255)(inputs)
x = learnable_resizer(x)
outputs = backbone(x)
return tf.keras.Model(inputs, outputs)
The structure of the learnable image resizer module allows for flexible integrations with different vision models.
Compile and train our model with learnable resizer
model = get_model()
model.compile(
loss=keras.losses.CategoricalCrossentropy(label_smoothing=0.1),
optimizer=\"sgd\",
metrics=[\"accuracy\"],
)
model.fit(train_ds, validation_data=validation_ds, epochs=EPOCHS)
Epoch 1/5
146/146 [==============================] - 49s 247ms/step - loss: 0.6956 - accuracy: 0.5697 - val_loss: 0.6958 - val_accuracy: 0.5103
Epoch 2/5
146/146 [==============================] - 33s 216ms/step - loss: 0.6685 - accuracy: 0.6117 - val_loss: 0.6955 - val_accuracy: 0.5387
Epoch 3/5
146/146 [==============================] - 33s 216ms/step - loss: 0.6542 - accuracy: 0.6190 - val_loss: 0.7410 - val_accuracy: 0.5684
Epoch 4/5
146/146 [==============================] - 33s 216ms/step - loss: 0.6357 - accuracy: 0.6576 - val_loss: 0.9322 - val_accuracy: 0.5314
Epoch 5/5
146/146 [==============================] - 33s 215ms/step - loss: 0.6224 - accuracy: 0.6745 - val_loss: 0.6526 - val_accuracy: 0.6672