gistfile1.txt

vgg_mean = np.array([123.68, 116.779, 103.939], dtype=np.float32).reshape((3,1,1))


def addConvBlock(model, layers, filters):
        
    for i in range(layers):
        model.add(ZeroPadding2D((1, 1)))
        model.add(Convolution2D(filters, 3, 3, activation='relu'))
    model.add(MaxPooling2D((2, 2), strides=(2, 2)))
        
def addFCBlock(model):
    model.add(Dense(4096, activation='relu'))
#     model.add(BatchNormalization())
    model.add(Dropout(0.5))

    
def create():
    model = Sequential()
    model.add(Lambda(vgg_preprocess, input_shape=(3,224,224)))

    addConvBlock(model,2, 64)
    addConvBlock(model,2, 128)
    addConvBlock(model,3, 256)
    addConvBlock(model,3, 512)
    addConvBlock(model,3, 512)

    model.add(Flatten())
    addFCBlock(model)
    addFCBlock(model)
    model.add(Dense(1000, activation='softmax'))
    model.load_weights("/home/harsh/.keras/models/vgg16.h5")
    return model

def vgg_preprocess(x):
    x = x - vgg_mean
    return x[:, ::-1] # reverse axis rgb->bgr