cnn.py 文件源码

def new(self, image_dim, final_vec_dim):

    input_dim  = (None,) + image_dim

    # Create initial nets, one per final vector element
    self.nets = []
    self.input_layers = []
    for i in range(final_vec_dim):
      l_input = layers.InputLayer(shape=input_dim)
      l_conv0 = layers.Conv2DLayer(l_input, 64, (5,5))
      l_max0  = layers.MaxPool2DLayer(l_conv0, (5,5), stride=3)
      l_conv1 = layers.Conv2DLayer(l_max0, 32, (5,5))
      l_max1  = layers.MaxPool2DLayer(l_conv1, (5,5), stride=2)
      l_conv2 = layers.Conv2DLayer(l_conv1, 32, (3,3))
      l_pool  = layers.MaxPool2DLayer(l_conv2, (3,3), stride=1)
      l_1d1   = layers.DenseLayer(l_pool, 24)
      l_1d2   = layers.DenseLayer(l_1d1, 8)
      l_1d3   = layers.DenseLayer(l_1d2, 1)

      self.nets.append(l_1d3)
      self.input_layers.append(l_input)

  # Train the neural net
  # @param {Matrix} trainset X
  # @param {Vector} trainset y
  # @param {Matrix} validation set X
  # @param {Vector} validation set y
  # @param {int} batch size
  # @param {int} number of epochs to run
  # @param {list[double]} learning rates (non-negative, non-zero)
  # @param {str} path to save model