network.py 文件源码

def set_inpt(self, inpt, inpt_dropout, mini_batch_size):
        # Reshape the input to 2D
        self.inpt = inpt.reshape(self.image_shape)

        # Do convolution
        self.conv_out = conv2d(
            input=self.inpt, filters=self.w, filter_shape=self.filter_shape,
            input_shape=self.image_shape, border_mode=self.border_mode,
            subsample=self.stride)

        # Get the feature maps for this layer
        self.feature_maps = theano.function([self.inpt], self.conv_out)

        # Max pooling
        pooled_out = pool.pool_2d(input=self.conv_out, ds=self.poolsize,
                                  ignore_border=True, mode='max')

        # Apply bias and activation and set as output
        self.output = self.activation_fn(
            pooled_out + self.b.dimshuffle('x', 0, 'x', 'x'))
        self.output_dropout = self.output # no dropout in convolutional layers