npy文件讀取與fine tune

npy文件是numpy專用的二進制文件，npy內容由一個字典組成，字典中的每一個鍵對應一層網絡模型參數（包括權重w和偏置b）。
對於某些網絡模型參數保存爲.npy形式的文件，我們需要讀取這些文件的參數，並fine tune到當前網絡相應的變量。

def load_initial_weights(session):
  # Load the weights into memory
    weights_dict = np.load('C:\\Downloads\\bvlc_alexnet.npy', encoding = 'bytes').item()
  # Loop over all layer names stored in the weights dict
    for op_name in weights_dict:
    # Check if the layer is one of the layers that should be reinitialized
        if op_name not in ['fc6','fc7','fc8']:
            with tf.variable_scope('shared/'+op_name,reuse = True):
       # Loop over list of weights/biases and assign them to their corresponding tf variable
                for data in weights_dict[op_name]:
                  # Biases
                    if len(data.shape) == 1:
                        var = tf.get_variable('biases', trainable = True)
                        print(var.name)
                        session.run(var.assign(data))
                      # Weights
                    else:
                        var = tf.get_variable('weights', trainable = True)
                        session.run(var.assign(data))