# calculate the targets that are output at each layer style_layers_outputs = [K.variable(y) for y in style_model.predict(style_img)] # we will assume the weight of the content loss is 1 # and only weight the style losses style_weights = [0.2,0.4,0.3,0.5,0.2] # create the total loss which is the sum of content + style loss loss = K.mean(K.square(content_model.output - content_target)) for w, symbolic, actual in zip(style_weights, symbolic_conv_outputs, style_layers_outputs): # gram_matrix() expects a (H, W, C) as input loss += w * style_loss(symbolic[0], actual[0]) # once again, create the gradients and loss + grads function # note: it doesn't matter which model's input you use # they are both pointing to the same keras Input layer in memory grads = K.gradients(loss, vgg.input) # just like theano.function get_loss_and_grads = K.function( inputs=[vgg.input], outputs=[loss] + grads ) def get_loss_and_grads_wrapper(x_vec):
style_model = Model(vgg.input, symbolic_conv_outputs)
# calculate the targets that are output at each layer
style_layers_outputs = [K.variable(y) for y in style_model.predict(style_img)]
# we will assume the weight of the content loss is 1
# and only weight the style losses
style_weights = [0.2, 0.4, 0.3, 0.5, 0.2]
# create the total loss which is the sum of content + style loss
loss = K.mean(K.square(content_model.output - content_target))
for w, symbolic, actual in zip(style_weights, symbolic_conv_outputs,
style_layers_outputs):
# gram_matrix() expects a (H, W, C) as input
loss += w * style_loss(symbolic[0], actual[0])
# once again, create the gradients and loss + grads function
# note: it doesn't matter which model's input you use
# they are both pointing to the same keras Input layer in memory
grads = K.gradients(loss, vgg.input)
# just like theano.function
get_loss_and_grads = K.function(inputs=[vgg.input], outputs=[loss] + grads)
def get_loss_and_grads_wrapper(x_vec):
l, g = get_loss_and_grads([x_vec.reshape(*batch_shape)])
return l.astype(np.float64), g.flatten().astype(np.float64)
def transferStyle(content_path, style_path):
content_img = load_img_and_preprocess(
# '../large_files/caltech101/101_ObjectCategories/elephant/image_0002.jpg',
# 'batman.jpg',
content_path,
# (225, 300),
)
# resize the style image
# since we don't care too much about warping it
h, w = content_img.shape[1:3]
style_img = load_img_and_preprocess(
style_path,
# 'styles/flowercarrier.jpg',
# 'styles/monalisa.jpg',
# 'style/lesdemoisellesdavignon.jpg',
(h, w))
# we'll use this throughout the rest of the script
batch_shape = content_img.shape
shape = content_img.shape[1:]
# we want to make only 1 VGG here
# as you'll see later, the final model needs
# to have a common input
vgg = VGG16_AvgPool(shape)
# create the content model
# we only want 1 output
# remember you can call vgg.summary() to see a list of layers
# 1,2,4,5,7-9,11-13,15-17
content_model = keras.models.Model(vgg.input,
vgg.layers[13].get_output_at(0))
content_target = K.variable(content_model.predict(content_img))
# create the style model
# we want multiple outputs
# we will take the same approach as in style_transfer2.py
symbolic_conv_outputs = [
layer.get_output_at(1) for layer in vgg.layers \
if layer.name.endswith('conv1')
]
# make a big model that outputs multiple layers' outputs
style_model = keras.models.Model(vgg.input, symbolic_conv_outputs)
# calculate the targets that are output at each layer
style_layers_outputs = [
K.variable(y) for y in style_model.predict(style_img)
]
# we will assume the weight of the content loss is 1
# and only weight the style losses
style_weights = 0.05 * np.array([0.2, 0.4, 0.3, 0.5, 0.2])
# create the total loss which is the sum of content + style loss
loss = K.mean(K.square(content_model.output - content_target))
for w, symbolic, actual in zip(style_weights, symbolic_conv_outputs,
style_layers_outputs):
# gram_matrix() expects a (H, W, C) as input
loss += w * style_loss(symbolic[0], actual[0])
# once again, create the gradients and loss + grads function
# note: it doesn't matter which model's input you use
# they are both pointing to the same keras Input layer in memory
grads = K.gradients(loss, vgg.input)
# just like theano.function
get_loss_and_grads = K.function(inputs=[vgg.input], outputs=[loss] + grads)
def get_loss_and_grads_wrapper(x_vec):
l, g = get_loss_and_grads([x_vec.reshape(*batch_shape)])
return l.astype(np.float64), g.flatten().astype(np.float64)
return minimize(get_loss_and_grads_wrapper, 10, batch_shape)
