讀者可能還記得本系列部落格（二）和（六）中 tf.nn 模組，其中最關心的是 conv2d 這個函式。

首先將部落格（二） MNIST 例程中 convolutional.py 關鍵原始碼列出：

  def model(data, train=False):
    """The Model definition."""
    # 2D convolution, with 'SAME' padding (i.e. the output feature map has
    # the same size as the input). Note that {strides} is a 4D array whose
    # shape matches the data layout: [image index, y, x, depth].
    conv = tf.nn.conv2d(data,
                        conv1_weights,
                        strides=[1, 1, 1, 1],
                        padding='SAME')
    # Bias and rectified linear non-linearity.
    relu = tf.nn.relu(tf.nn.bias_add(conv, conv1_biases))
 

看到第一個卷積層的實現使用 tf.nn.conv2d( input_tensor, weight_tensor, strides_param, padding_method) 這個函式。追蹤至 tensorflow/tensorflow/python/ops/gen_nn_ops.py 這個檔案中，將程式碼列出：

def conv2d(input, filter, strides, padding, use_cudnn_on_gpu=None,
           data_format=None, name=None):
  r"""Computes a 2-D convolution given 4-D `input` and `filter` tensors.

  Given an input tensor of shape `[batch, in_height, in_width, in_channels]`
  and a filter / kernel tensor of shape
  `[filter_height, filter_width, in_channels, out_channels]`, this op
  performs the following:

  1. Flattens the filter to a 2-D matrix with shape
     `[filter_height * filter_width * in_channels, output_channels]`.
  2. Extracts image patches from the input tensor to form a *virtual*
     tensor of shape `[batch, out_height, out_width,
     filter_height * filter_width * in_channels]`.
  3. For each patch, right-multiplies the filter matrix and the image patch
     vector.

  In detail, with the default NHWC format,

      output[b, i, j, k] =
          sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] *
                          filter[di, dj, q, k]

  Must have `strides[0] = strides[3] = 1`.  For the most common case of the same
  horizontal and vertices strides, `strides = [1, stride, stride, 1]`.

  Args:
    input: A `Tensor`. Must be one of the following types: `float32`, `float64`.
    filter: A `Tensor`. Must have the same type as `input`.
    strides: A list of `ints`.
      1-D of length 4.  The stride of the sliding window for each dimension
      of `input`. Must be in the same order as the dimension specified with format.
    padding: A `string` from: `"SAME", "VALID"`.
      The type of padding algorithm to use.
    use_cudnn_on_gpu: An optional `bool`. Defaults to `True`.
    data_format: An optional `string` from: `"NHWC", "NCHW"`. Defaults to `"NHWC"`.
      Specify the data format of the input and output data. With the
      default format "NHWC", the data is stored in the order of:
          [batch, in_height, in_width, in_channels].
      Alternatively, the format could be "NCHW", the data storage order of:
          [batch, in_channels, in_height, in_width].
    name: A name for the operation (optional).

  Returns:
    A `Tensor`. Has the same type as `input`.
  """
  return _op_def_lib.apply_op("Conv2D", input=input, filter=filter,
                              strides=strides, padding=padding,
                              use_cudnn_on_gpu=use_cudnn_on_gpu,
                              data_format=data_format, name=name)
 

該檔案內容為編譯時自動生成。生成器的原始碼位於 tensorflow/tensorflow/python/framework/python_op_gen.h 和 python_op_gen.cc。

_op_def_lib 是這樣構建的：

def _InitOpDefLibrary():
  op_list = op_def_pb2.OpList()
  text_format.Merge(_InitOpDefLibrary.op_list_ascii, op_list)
  op_def_registry.register_op_list(op_list)
  op_def_lib = op_def_library.OpDefLibrary()
  op_def_lib.add_op_list(op_list)
  return op_def_lib


_InitOpDefLibrary.op_list_ascii = """%s"""


_op_def_lib = _InitOpDefLibrary()
 

看到 _op_def_lib 實際上是個 op_def_pb2.OpList 物件，實現了記錄 TensorFlow 支援全部運算的列表。