Train VGG network for last two layer(Transfer Learning)

from keras.models import Sequential
from keras.optimizers import SGD
from keras.layers import Input, Dense, Convolution2D, MaxPooling2D, AveragePooling2D, ZeroPadding2D, Dropout, Flatten, merge, Reshape, Activation

from sklearn.metrics import log_loss



def vgg16_model(img_rows, img_cols, channel=1, num_classes=None):
    """VGG 16 Model for Keras
    Model Schema is based on 
    https://gist.github.com/baraldilorenzo/07d7802847aaad0a35d3
    ImageNet Pretrained Weights 
    https://drive.google.com/file/d/0Bz7KyqmuGsilT0J5dmRCM0ROVHc/view?usp=sharing
    Parameters:
      img_rows, img_cols - resolution of inputs
      channel - 1 for grayscale, 3 for color 
      num_classes - number of categories for our classification task
    """
    model = Sequential()
    model.add(ZeroPadding2D((1, 1), input_shape=(channel, img_rows, img_cols)))
    model.add(Convolution2D(64, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1, 1)))
    model.add(Convolution2D(64, 3, 3, activation='relu'))
    model.add(MaxPooling2D((2, 2), strides=(2, 2)))

    model.add(ZeroPadding2D((1, 1)))
    model.add(Convolution2D(128, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1, 1)))
    model.add(Convolution2D(128, 3, 3, activation='relu'))
    model.add(MaxPooling2D((2, 2), strides=(2, 2)))

    model.add(ZeroPadding2D((1, 1)))
    model.add(Convolution2D(256, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1, 1)))
    model.add(Convolution2D(256, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1, 1)))
    model.add(Convolution2D(256, 3, 3, activation='relu'))
    model.add(MaxPooling2D((2, 2), strides=(2, 2)))

    model.add(ZeroPadding2D((1, 1)))
    model.add(Convolution2D(512, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1, 1)))
    model.add(Convolution2D(512, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1, 1)))
    model.add(Convolution2D(512, 3, 3, activation='relu'))
    model.add(MaxPooling2D((2, 2), strides=(2, 2)))

    model.add(ZeroPadding2D((1, 1)))
    model.add(Convolution2D(512, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1, 1)))
    model.add(Convolution2D(512, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1, 1)))
    model.add(Convolution2D(512, 3, 3, activation='relu'))
    model.add(MaxPooling2D((2, 2), strides=(2, 2)))

    # Add Fully Connected Layer
    model.add(Flatten())
    model.add(Dense(4096, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(4096, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(1000, activation='softmax'))

    # Loads ImageNet pre-trained data
    model.load_weights('imagenet_models/vgg16_weights.h5')

    # Truncate and replace softmax layer for transfer learning
    model.layers.pop()
    model.outputs = [model.layers[-1].output]
    model.layers[-1].outbound_nodes = []
    model.add(Dense(num_classes, activation='softmax'))

    # Uncomment below to set the first 10 layers to non-trainable (weights will not be updated)
    #for layer in model.layers[:10]:
    #    layer.trainable = False

    # Learning rate is changed to 0.001
    sgd = SGD(lr=1e-3, decay=1e-6, momentum=0.9, nesterov=True)
    model.compile(optimizer=sgd, loss='categorical_crossentropy', metrics=['accuracy'])

    return model


if __name__ == '__main__':

    # Example to fine-tune on 3000 samples from Cifar10

    img_rows, img_cols = 200, 200 # Resolution of inputs
    channel = 3
    num_classes = 5
    batch_size = 16 
    nb_epoch = 10

    # Load Cifar10 data. Please implement your own load_data() module for your own dataset
    X_train, Y_train, X_valid, Y_valid = load_data(img_rows, img_cols)

    # Load our model
    model = vgg16_model(img_rows, img_cols, channel, num_classes)

    # Start Fine-tuning
    model.fit(X_train, Y_train,
              batch_size=batch_size,
              nb_epoch=nb_epoch,
              shuffle=True,
              verbose=1,
              validation_data=(X_valid, Y_valid),
              )

    # Make predictions
    predictions_valid = model.predict(X_valid, batch_size=batch_size, verbose=1)

    # Cross-entropy loss score
score = log_loss(Y_valid, predictions_valid)