pyplot.imshow(paintingStyleImage)
inputImage = image.imread("../data/grandcentral.jpg")
pyplot.imshow(inputImage)
outputWidth = 800
outputHeight = 600
# Beta constant
beta = 5
# Alpha constant
alpha = 100
# Noise ratio
noiseRatio = 0.6
nodes = vggverydeep19.load('../data/imagenet-vgg-verydeep-19.mat', (600, 800))
# Mean VGG-19 image
meanImage19 = np.array([103.939, 116.779, 123.68]).reshape((1, 1, 1, 3)) #pylint: disable=no-member
# Squared-error loss of content between the two feature representations
def sqErrorLossContent(sess, modelGraph, layer):
p = session.run(modelGraph[layer])
#pylint: disable=maybe-no-member
N = p.shape[3]
M = p.shape[1] * p.shape[2]
return (1 / (4 * N * M)) * tf.reduce_sum(
input_tensor=tf.pow(modelGraph[layer] -
sess.run(modelGraph[layer]), 2))
pyplot.imshow(paintingStyleImage)
inputImage = image.imread("../data/grandcentral.jpg")
pyplot.imshow(inputImage)
outputWidth = 800
outputHeight = 600
# Beta constant
beta = 5
# Alpha constant
alpha = 100
# Noise ratio
noiseRatio = 0.6
nodes = vggverydeep19.load('../data/imagenet-vgg-verydeep-19.mat', (600, 800))
# Mean VGG-19 image
meanImage19 = np.array([103.939, 116.779, 123.68]).reshape((1,1,1,3)) #pylint: disable=no-member
# Squared-error loss of content between the two feature representations
def sqErrorLossContent(sess, modelGraph, layer):
p = session.run(modelGraph[layer])
#pylint: disable=maybe-no-member
N = p.shape[3]
M = p.shape[1] * p.shape[2]
return (1 / (4 * N * M)) * tf.reduce_sum(tf.pow(modelGraph[layer] - sess.run(modelGraph[layer]), 2))
# Squared-error loss of style between the two feature representations