vgg-net

问题 I wrote a image vgg classification model with pytorch's pretrained vgg16 model. import matplotlib.pyplot as plt import numpy as np import torch from PIL import Image import urllib from skimage.transform import resize from skimage import io import yaml # Downloading imagenet 1000 classes list file = urllib. request. urlopen("https://gist.githubusercontent.com/yrevar/942d3a0ac09ec9e5eb3a/raw/238f720ff059c1f82f368259d1ca4ffa5dd8f9f5/imagenet1000_clsidx_to_labels.txt") classes = '' for f in file:

问题 I wrote a image vgg classification model with pytorch's pretrained vgg16 model. import matplotlib.pyplot as plt import numpy as np import torch from PIL import Image import urllib from skimage.transform import resize from skimage import io import yaml # Downloading imagenet 1000 classes list file = urllib. request. urlopen("https://gist.githubusercontent.com/yrevar/942d3a0ac09ec9e5eb3a/raw/238f720ff059c1f82f368259d1ca4ffa5dd8f9f5/imagenet1000_clsidx_to_labels.txt") classes = '' for f in file:

问题 Let's say I have a ResNet50 model and I wish to connect the output layer of this model to the input layer of a VGG model. This is the ResNet model and the output tensor of ResNet50: img_shape = (164, 164, 3) resnet50_model = ResNet50(include_top=False, input_shape=img_shape, weights = None) print(resnet50_model.output.shape) I get the output: TensorShape([Dimension(None), Dimension(6), Dimension(6), Dimension(2048)]) Now I want a new layer where I reshape this output tensor to (64,64,18) Then

问题 Let's say I have a ResNet50 model and I wish to connect the output layer of this model to the input layer of a VGG model. This is the ResNet model and the output tensor of ResNet50: img_shape = (164, 164, 3) resnet50_model = ResNet50(include_top=False, input_shape=img_shape, weights = None) print(resnet50_model.output.shape) I get the output: TensorShape([Dimension(None), Dimension(6), Dimension(6), Dimension(2048)]) Now I want a new layer where I reshape this output tensor to (64,64,18) Then

问题 I'm fine-tuning VGG-16 for my task. The idea is that I load the pretrained weights, remove the last layer (which is softmax with 1000 outputs) and replace it with a softmax with a few outputs. Then I freeze all the layers but the last and train the model. Here is the code that builds the original model and loads the weights. def VGG_16(weights_path=None): model = Sequential() model.add(ZeroPadding2D((1,1),input_shape=(224,224,3))) model.add(Conv2D(64, (3, 3), activation='relu')) model.add

问题 I am relatively new to DL and Keras. I am trying to implement perceptual loss using the pretrained VGG16 in Keras but have some troubles. I already found that question but I am still struggling :/ A short explanation of what my network should do: I have a CNN (subsequent called mainModel) that gets grayscale images as input (#TrainData, 512, 512, 1) and outputs grayscale images with the same size. The network should reduce artifacts in the images - but I think it is not that important for

问题 My code works well with VGG19 loaded from a .mat file and is used in the following function like this (I use tensorflow 1.14.0) : #initial method : load VGG19 via file and function conv2D_relu VGG19 = scipy.io.loadmat('imagenet-vgg-verydeep-19.mat') VGG19_layers = VGG19['layers'][0] def conv2d_relu_old(prev_layer, n_layer, layer_name,VGG19_layers): # get weights for this layer: weights = VGG19_layers[n_layer][0][0][2][0][0] W = tf.constant(weights) bias = VGG19_layers[n_layer][0][0][2][0][1]

问题 I have the weight and bias values for each layer of the VGG model saved as a .h5 file. I got the file from: https://github.com/fchollet/deep-learning-models/releases/tag/v0.1 Now let's say I want to change a few values in that file. With help from How to overwrite array inside h5 file using h5py, I am trying to do the same as follows: import h5py file_name = "vgg.h5" f = h5py.File(file_name, 'r+') # List all groups print("Keys: %s" % f.keys()) # Get the data data = (f['block2_conv1']['block2

问题 I have the weight and bias values for each layer of the VGG model saved as a .h5 file. I got the file from: https://github.com/fchollet/deep-learning-models/releases/tag/v0.1 Now let's say I want to change a few values in that file. With help from How to overwrite array inside h5 file using h5py, I am trying to do the same as follows: import h5py file_name = "vgg.h5" f = h5py.File(file_name, 'r+') # List all groups print("Keys: %s" % f.keys()) # Get the data data = (f['block2_conv1']['block2

问题 I have the weight and bias values for each layer of the VGG model saved as a .h5 file. I got the file from: https://github.com/fchollet/deep-learning-models/releases/tag/v0.1 Now let's say I want to change a few values in that file. With help from How to overwrite array inside h5 file using h5py, I am trying to do the same as follows: import h5py file_name = "vgg.h5" f = h5py.File(file_name, 'r+') # List all groups print("Keys: %s" % f.keys()) # Get the data data = (f['block2_conv1']['block2