q_network.py 文件源码

def _convolutional_layer(self, input, patch_size, stride, input_channels, output_channels, bias_init_value, scope_name):
        with tf.variable_scope(scope_name) as scope:
            weights = tf.get_variable(name='weights',
                                  shape=[patch_size, patch_size, input_channels, output_channels],
                                  initializer=tf.contrib.layers.xavier_initializer_conv2d())
            biases = tf.Variable(name='biases', initial_value=tf.constant(value=bias_init_value, shape=[output_channels]))
            conv = tf.nn.conv2d(input, weights, [1, stride, stride, 1], padding='SAME')

            linear_rectification_bias = tf.nn.bias_add(conv, biases)
            output = tf.nn.relu(linear_rectification_bias, name=scope.name)

            grid_x = output_channels // 4
            grid_y = 4 * input_channels
            kernels_image_grid = self._create_kernels_image_grid(weights, (grid_x, grid_y))
            tf.image_summary(scope_name + '/features', kernels_image_grid, max_images=1)

            if "_conv1" in scope_name:
                x_min = tf.reduce_min(weights)
                x_max = tf.reduce_max(weights)
                weights_0_to_1 = (weights - x_min) / (x_max - x_min)
                weights_0_to_255_uint8 = tf.image.convert_image_dtype(weights_0_to_1, dtype=tf.uint8)

                # to tf.image_summary format [batch_size, height, width, channels]
                weights_transposed = tf.transpose(weights_0_to_255_uint8, [3, 0, 1, 2])

                tf.image_summary(scope_name + '/features', weights_transposed[:,:,:,0:1], max_images=32)

        return output