Reconstructing input image from layers of a CNN2019 Community Moderator ElectionHow to adapt the softmax layer for multiple labels?How to improve the neural art algorithm?Accuracy drops if more layers trainable - weirdUsing deconvolution in practiceHow to input & pre-process images for a Deep Convolutional Neural Network?What does “Model recursive loss convergence” mean?Image features (produced by VGG19) do not properly train an ANN in KerasSubsequent convolution layersKeras Attention Guided CNN problemRemedies to CNN-LSTM overfitting on relatively small image dataset
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Reconstructing input image from layers of a CNN
2019 Community Moderator ElectionHow to adapt the softmax layer for multiple labels?How to improve the neural art algorithm?Accuracy drops if more layers trainable - weirdUsing deconvolution in practiceHow to input & pre-process images for a Deep Convolutional Neural Network?What does “Model recursive loss convergence” mean?Image features (produced by VGG19) do not properly train an ANN in KerasSubsequent convolution layersKeras Attention Guided CNN problemRemedies to CNN-LSTM overfitting on relatively small image dataset
$begingroup$
I've been trying to implement neural style transfer as described in this paper here According to the paper,
we can visualise the information at different processing stages in the
CNN by reconstructing the input image from only knowing the network’s
responses in a particular layer.
My question is, how exactly does one go about reconstructing image from a single layer?
I'm implementing this in pytorch. I've the output from layer conv4_2 stored in a tensor of shape [1,512,50,50] but how do I visualize this?
Here's a part of my code, if that helps.
vgg = models.vgg19(pretrained=True).features
for param in vgg.parameters():
param.requires_grad_(False)
device = torch.device("cpu")
vgg.to(device)
content_img = Image.open("image3.jpg").convert('RGB')
style_img = Image.open("image5.jpg").convert('RGB')
content_img = transformation(content_img).to(device)
style_img = transformation(style_img).to(device)
def get_features(image, model):
layers = '0': 'conv1_1', '5': 'conv2_1', '10': 'conv3_1',
'19': 'conv4_1', '21': 'conv4_2', '28': 'conv5_1'
x = image
features =
for name, layer in model._modules.items():
x = layer(x)
if name in layers:
features[layers[name]] = x
return features
content_img_features = get_features(content_img, vgg)
style_img_features = get_features(style_img, vgg)
target_content = content_img_features['conv4_2']
How do I reconstruct the image from the output of conv4_2?
neural-network cnn convolution pytorch neural-style-transfer
asked Mar 26 at 5:11
Judy T RajJudy T Raj
1211
$endgroup$
add a comment |
$begingroup$
I've been trying to implement neural style transfer as described in this paper here According to the paper,
we can visualise the information at different processing stages in the
CNN by reconstructing the input image from only knowing the network’s
responses in a particular layer.
My question is, how exactly does one go about reconstructing image from a single layer?
I'm implementing this in pytorch. I've the output from layer conv4_2 stored in a tensor of shape [1,512,50,50] but how do I visualize this?
Here's a part of my code, if that helps.
vgg = models.vgg19(pretrained=True).features
for param in vgg.parameters():
param.requires_grad_(False)
device = torch.device("cpu")
vgg.to(device)
content_img = Image.open("image3.jpg").convert('RGB')
style_img = Image.open("image5.jpg").convert('RGB')
content_img = transformation(content_img).to(device)
style_img = transformation(style_img).to(device)
def get_features(image, model):
layers = '0': 'conv1_1', '5': 'conv2_1', '10': 'conv3_1',
'19': 'conv4_1', '21': 'conv4_2', '28': 'conv5_1'
x = image
features =
for name, layer in model._modules.items():
x = layer(x)
if name in layers:
features[layers[name]] = x
return features
content_img_features = get_features(content_img, vgg)
style_img_features = get_features(style_img, vgg)
target_content = content_img_features['conv4_2']
How do I reconstruct the image from the output of conv4_2?
neural-network cnn convolution pytorch neural-style-transfer
asked Mar 26 at 5:11
Judy T RajJudy T Raj
1211
$endgroup$
add a comment |
$begingroup$
I've been trying to implement neural style transfer as described in this paper here According to the paper,
we can visualise the information at different processing stages in the
CNN by reconstructing the input image from only knowing the network’s
responses in a particular layer.
My question is, how exactly does one go about reconstructing image from a single layer?
I'm implementing this in pytorch. I've the output from layer conv4_2 stored in a tensor of shape [1,512,50,50] but how do I visualize this?
Here's a part of my code, if that helps.
vgg = models.vgg19(pretrained=True).features
for param in vgg.parameters():
param.requires_grad_(False)
device = torch.device("cpu")
vgg.to(device)
content_img = Image.open("image3.jpg").convert('RGB')
style_img = Image.open("image5.jpg").convert('RGB')
content_img = transformation(content_img).to(device)
style_img = transformation(style_img).to(device)
def get_features(image, model):
layers = '0': 'conv1_1', '5': 'conv2_1', '10': 'conv3_1',
'19': 'conv4_1', '21': 'conv4_2', '28': 'conv5_1'
x = image
features =
for name, layer in model._modules.items():
x = layer(x)
if name in layers:
features[layers[name]] = x
return features
content_img_features = get_features(content_img, vgg)
style_img_features = get_features(style_img, vgg)
target_content = content_img_features['conv4_2']
How do I reconstruct the image from the output of conv4_2?
neural-network cnn convolution pytorch neural-style-transfer
asked Mar 26 at 5:11
Judy T RajJudy T Raj
1211
$endgroup$
I've been trying to implement neural style transfer as described in this paper here According to the paper,
we can visualise the information at different processing stages in the
CNN by reconstructing the input image from only knowing the network’s
responses in a particular layer.
My question is, how exactly does one go about reconstructing image from a single layer?
I'm implementing this in pytorch. I've the output from layer conv4_2 stored in a tensor of shape [1,512,50,50] but how do I visualize this?
Here's a part of my code, if that helps.
vgg = models.vgg19(pretrained=True).features
for param in vgg.parameters():
param.requires_grad_(False)
device = torch.device("cpu")
vgg.to(device)
content_img = Image.open("image3.jpg").convert('RGB')
style_img = Image.open("image5.jpg").convert('RGB')
content_img = transformation(content_img).to(device)
style_img = transformation(style_img).to(device)
def get_features(image, model):
layers = '0': 'conv1_1', '5': 'conv2_1', '10': 'conv3_1',
'19': 'conv4_1', '21': 'conv4_2', '28': 'conv5_1'
x = image
features =
for name, layer in model._modules.items():
x = layer(x)
if name in layers:
features[layers[name]] = x
return features
content_img_features = get_features(content_img, vgg)
style_img_features = get_features(style_img, vgg)
target_content = content_img_features['conv4_2']
How do I reconstruct the image from the output of conv4_2?
neural-network cnn convolution pytorch neural-style-transfer
neural-network cnn convolution pytorch neural-style-transfer
asked Mar 26 at 5:11
Judy T RajJudy T Raj
1211
asked Mar 26 at 5:11
Judy T RajJudy T Raj
1211
asked Mar 26 at 5:11
Judy T RajJudy T Raj
1211
asked Mar 26 at 5:11
Judy T RajJudy T Raj
1211
asked Mar 26 at 5:11
Judy T RajJudy T Raj
1211
1211
add a comment |
add a comment |
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