network_builder.py 文件源码

def conv_layer(self, input_tensor, layer_number):
        """Build a convolution layer ended with an activation function.

        Args:
            input_tensor: The output from the layer before.
            layer_number (int): The number of the layer in the network.

        Returns:
            tensor: The activated output.
        """
        inchannels, input_tensor = self._ensure_2d(input_tensor)

        layer_spec = self.network_spec['layers'][layer_number]
        filter_shape = (layer_spec['filter']['height'],
                        layer_spec['filter']['width'],
                        inchannels,
                        layer_spec['filter']['outchannels'])
        filter_strides = (layer_spec['strides']['inchannels'],
                          layer_spec['strides']['x'],
                          layer_spec['strides']['y'],
                          layer_spec['strides']['batch'])
        with tf.name_scope('conv' + str(layer_number)):
            w = self._weight_variable(filter_shape, name='W')
            b = self._bias_variable([layer_spec['filter']['outchannels']], name='b')
            conv = tf.nn.conv2d(input_tensor, w, strides=filter_strides, padding='SAME')
            activation = getattr(tf.nn, layer_spec['activation_function'])(conv + b, name='activation')
        return activation