fcnn_vgg.py

model2 = Sequential()
model2.add(Flatten(input_shape=(7,7,512)))
model2.add(Dense(100, activation='relu'))
model2.add(Dropout(0.5))
model2.add(BatchNormalization())
model2.add(Dense(10, activation='softmax'))

# compile the model
model2.compile(optimizer='adam', metrics=['accuracy'], loss='categorical_crossentropy')

model2.summary()

# train model using features generated from VGG16 model
model2.fit(vgg_features_train, train_target, epochs=50, batch_size=128, validation_data=(vgg_features_val, val_target))