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@pranjal-joshi
Created April 24, 2017 10:34
loss = K.variable(0.0)
layer_features = output_dict['block4_conv2']
b_img_features = layer_features[0,:,:,:]
final_features = layer_features[2,:,:,:]
loss += content_wt * content_loss(b_img_features,final_features)
feature_layers = ['block1_conv1','block2_conv1','block3_conv1','block4_conv1','block5_conv1']
for layer in feature_layers:
layer_features = output_dict[layer]
style_features = layer_features[1,:,:,:]
final_features = layer_features[2,:,:,:]
sl = style_loss(style_features,final_features)
loss += (style_wt / len(feature_layers))*sl
loss += total_var_wt*total_var_loss(final_img)
grads = K.gradients(loss,final_img)
outputs = [loss]
outputs.append(grads)
f_outputs = K.function([final_img],outputs)
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