Created
August 9, 2019 23:47
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Set features and hyperparams for neural style transfer.
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| # get content and style features only once before training | |
| content_features = get_features(content, vgg) | |
| style_features = get_features(style, vgg) | |
| # calculate the gram matrices for each layer of our style representation | |
| style_grams = {layer: gram_matrix(style_features[layer]) for layer in style_features} | |
| #initialize the target image as the content image | |
| target = content.clone().requires_grad_(True).to(device) | |
| style_weights = {'conv1_1': 1., | |
| 'conv2_1': 0.75, | |
| 'conv3_1': 0.2, | |
| 'conv4_1': 0.2, | |
| 'conv5_1': 0.2} | |
| content_weight = 1 | |
| style_weight = 1e6 | |
| # iteration hyperparameters | |
| optimizer = optim.Adam([target], lr=0.003) | |
| steps = 2000 # decide how many iterations to update your image (5000) |
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