Global average pooling solution for Dogs vs. Cats

sol_02_global_average_pooling

# Define model
from keras import models
from keras import layers
from keras import optimizers

epochs = 100

model = models.Sequential()
model.add(layers.GlobalAveragePooling2D(input_shape=(7,7,512)))
model.add(layers.Dense(1, activation='sigmoid'))
model.summary()

# Compile model
model.compile(optimizer=optimizers.Adam(),
              loss='binary_crossentropy',
              metrics=['acc'])
              
# Train model
history = model.fit(train_features, train_labels,
                    epochs=epochs,
                    batch_size=batch_size, 
                    validation_data=(validation_features, validation_labels))