title: VGG: ValueError: too many values to unpack (expected 2)
body: <p>I defined VGG net and made test tasks: def net get the VGG model parameters, so I can use mnist data for a test task.</p>
  
  <p>Here are the error information:</p>
  
  <p>Traceback (most recent call last):</p>
  
  <blockquote>
  <pre><code> File "D:\Users\2015randongmei\workspace\tensor\VGG.py", line 73, in &lt;module&gt;
      nets, mean_pixel, all_layers = net(VGG_PATH, image)
    File "D:\Users\2015randongmei\workspace\tensor\VGG.py", line 47, in net
      kernels, bias = weights[i][0][0][0][0]
  ValueError: too many values to unpack (expected 2)
  </code></pre>
  </blockquote>
  
  <p>Here is the code:</p>
  
  <pre><code># -*- coding:utf-8 -*- 
  import os
  import numpy as np
  import scipy.io
  import tensorflow as tf
  from scipy.misc import imread
  import matplotlib.pyplot as plt
  #import ast
  #source
  def _conv_layer(input, weights, bias):
      #权重w转换为tensorlow支持的格式：
      tf.constant(weights)
      conv = tf.nn.conv2d(input, tf.constant(weights), strides=(1, 1, 1, 1), padding='SAME')
      return tf.nn.bias_add(conv, bias)
  
  def _pool_layer(input):
          return tf.nn.max_pool(input, ksize=(1, 2, 2, 1), strides=(1, 2, 2, 1), padding='SAME')
  
  def preprocess(image, mean_pixel):
       return image - mean_pixel
  
  #VGG 19层网络，
  #net完成所有层的前向传播,计算结果保存在net字典中
  def net(data_path, input_image):
      layers = {
          'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1',
          'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2',
          'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3',
          'relu3_3', 'conv3_4', 'relu3_4', 'pool3',
          'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3',
          'relu4_3', 'conv4_4', 'relu4_4', 'pool4',
          'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3',
          'relu5_3', 'conv5_4', 'relu5_4'
          }
      data = scipy.io.loadmat(data_path)
      mean = data['normalization'][0][0][0]
      mean_pixel = np.mean(mean, axis=(0, 1))
      weights = data['layers'][0]
      #保存每一层的输出
      net = {}
      current = input_image
      for i, name in enumerate(layers):
          #neme的前四个字母
          kind = name[:4]
          if kind == 'conv':
              kernels, bias = weights[i][0][0][0][0]
              # matconvnet: weights are [width, height, in_channels, out_channels]
              # tensorflow: weights are [height, width, in_channels, out_channels]
              kernels = np.transpose(kernels, (1, 0, 2, 3))
              #bias只有一个维度
              bias = bias.reshape(-1)
              #对卷积层进行前向传播，current为当前输入
              current = _conv_layer(current, kernels, bias)
          elif kind == 'relu':
              current = tf.nn.relu(current)
          elif kind == 'pool':
              current = _pool_layer(current)
              net[name] = current
      assert len(net) == len(layers)
      return net, mean_pixel, layers
  print ("Network for VGG ready")
  
  
  #将网络结果进行可视化操作
  cwd = os.getcwd()
  VGG_PATH = cwd+"\\data\\imagenet-vgg-verydeep-19.mat"
  IMG_PATH = cwd + "\\data\\cat.jpg"
  input_image = imread(IMG_PATH)
  shape =(1,input_image.shape[0],input_image.shape[1],input_image.shape[2])
  with tf.Session() as sess:
      image = tf.placeholder('float', shape=shape)
      nets, mean_pixel, all_layers = net(VGG_PATH, image)
      input_image_pre = np.array([preprocess(input_image, mean_pixel)])
      layers = all_layers # For all layers 
      # layers = ('relu2_1', 'relu3_1', 'relu4_1')
      for i, layer in enumerate(layers):
          print ("[%d/%d] %s" % (i+1, len(layers), layer))
          features = nets[layer].eval(feed_dict={image: input_image_pre})
          print (" Type of 'features' is ", type(features))
          print (" Shape of 'features' is %s" % (features.shape,))
          # Plot response 
          if 1:
              plt.figure(i+1, figsize=(10, 5))
              plt.matshow(features[0, :, :, 0], cmap=plt.cm.gray, fignum=i+1)
              plt.title("" + layer)
              plt.colorbar()
              plt.show()
  </code></pre>