Vgg16 attention code

Here is an example code for implementing attention on top of VGG16 architecture in Keras:

from keras.models import Model
from keras.layers import Input, Dense, Dropout, Flatten, Conv2D, MaxPooling2D, GlobalMaxPooling2D, GlobalAveragePooling2D, Concatenate, Multiply

# Define input shape
input_shape = (224, 224, 3)

# Load VGG16 model with pre-trained weights
vgg16 = VGG16(weights='imagenet', include_top=False, input_shape=input_shape)

# Freeze all layers in VGG16
for layer in vgg16.layers:
    layer.trainable = False

# Add attention layer
x = GlobalMaxPooling2D()(vgg16.output)
a = Dense(512, activation='relu')(x)
a = Dropout(0.5)(a)
a = Dense(1, activation='sigmoid')(a)
a = Multiply()([a, x])
a = Concatenate()([a, GlobalAveragePooling2D()(vgg16.output)])

# Add classification layers
y = Dense(512, activation='relu')(a)
y = Dropout(0.5)(y)
y = Dense(10, activation='softmax')(y)

# Create model
model = Model(inputs=vgg16.input, outputs=y)

# Compile model
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

# Train model
model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_val, y_val))

In this example, we first load the pre-trained VGG16 model and freeze all its layers to prevent any changes to the pre-trained weights. We then add an attention layer on top of the VGG16 output, which consists of a dense layer followed by a dropout layer and a sigmoid activation layer. We multiply this attention vector with the GlobalMaxPooling2D output of VGG16 and concatenate it with the GlobalAveragePooling2D output. Finally, we add classification layers on top of the attention layer and compile and train the model.