VGG16 + smaill MLP.py

import os
import numpy as np
from keras.models import Sequential
from keras.layers import Activation, Dropout, Flatten, Dense
from keras.preprocessing.image import ImageDataGenerator
from keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D
from keras import optimizers
from keras import applications
from keras.models import Model

model_vgg = applications.VGG16(include_top=False, weights='imagenet')

train_generator_bottleneck = datagen.flow_from_directory(
        train_data_dir,
        target_size=(img_width, img_height),
        batch_size=batch_size,
        class_mode=None,
        shuffle=False)

validation_generator_bottleneck = datagen.flow_from_directory(
        validation_data_dir,
        target_size=(img_width, img_height),
        batch_size=batch_size,
        class_mode=None,
        shuffle=False)

bottleneck_features_train = model_vgg.predict_generator(train_generator_bottleneck, train_samples // batch_size)
np.save(open('models/bottleneck_features_train.npy', 'wb'), bottleneck_features_train)

bottleneck_features_validation = model_vgg.predict_generator(validation_generator_bottleneck, validation_samples // batch_size)
np.save(open('models/bottleneck_features_validation.npy', 'wb'), bottleneck_features_validation)

#Now we can load it...
train_data = np.load(open('models/bottleneck_features_train.npy', 'rb'))
train_labels = np.array([0] * (train_samples // 2) + [1] * (train_samples // 2))

validation_data = np.load(open('models/bottleneck_features_validation.npy', 'rb'))
validation_labels = np.array([0] * (validation_samples // 2) + [1] * (validation_samples // 2))
#And define and train the custom fully connected neural network :

model_top = Sequential()
model_top.add(Flatten(input_shape=train_data.shape[1:]))
model_top.add(Dense(256, activation='relu'))
model_top.add(Dropout(0.5))
model_top.add(Dense(1, activation='sigmoid'))

model_top.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy'])

model_top.fit(train_data, train_labels,
        epochs=epochs, 
        batch_size=batch_size,
        validation_data=(validation_data, validation_labels))

model_top.save_weights('models/bottleneck_30_epochs.h5')

#Bottleneck model evaluation
#Loss and accuracy :
model_top.evaluate(validation_data, validation_labels)