vgg_model.py

from __future__ import print_function

import numpy as np
import time
from keras.preprocessing.image import save_img
from keras.applications import vgg16
from keras import backend as K
from keras.models import load_model


# dimensions of the generated pictures for each filter.
img_width = 128
img_height = 128

# the name of the layer we want to visualize
# (see model definition at keras/applications/vgg16.py)
layer_name = 'block5_conv1'

# util function to convert a tensor into a valid image


def deprocess_image(x):
    # normalize tensor: center on 0., ensure std is 0.1
    x -= x.mean()
    x /= (x.std() + K.epsilon())
    x *= 0.1

    # clip to [0, 1]
    x += 0.5
    x = np.clip(x, 0, 1)

    # convert to RGB array
    x *= 255
    if K.image_data_format() == 'channels_first':
        x = x.transpose((1, 2, 0))
    x = np.clip(x, 0, 255).astype('uint8')
    return x


# build the VGG16 network with ImageNet weights
model = vgg16.VGG16(weights='imagenet', include_top=False)
print('Model loaded.')

model.summary()