Tensorflow：卷积中的无效参数错误

Question

我正在尝试运行这段 Python 代码，但似乎无法解决错误：

tf.nn.conv2d(tf.reshape(x, [5, 5]), tf.reshape(wt, [3, 3]), strides=[1, 1],  padding='SAME')

这里，x 是来自 (5,5) numpy 数组的 tf.Variable，w 是来自 (3,3) numpy 数组的 tf.Variable。

我得到的错误是：

---------------------------------------------------------------------------
InvalidArgumentError                      Traceback (most recent call last)
C:\Anaconda3\lib\site-packages\tensorflow\python\framework\common_shapes.py in _call_cpp_shape_fn_impl(op, input_tensors_needed, input_tensors_as_shapes_needed, debug_python_shape_fn, require_shape_fn)
    669           node_def_str, input_shapes, input_tensors, input_tensors_as_shapes,
--> 670           status)
    671   except errors.InvalidArgumentError as err:

C:\Anaconda3\lib\contextlib.py in __exit__(self, type, value, traceback)
     65             try:
---> 66                 next(self.gen)
     67             except StopIteration:

C:\Anaconda3\lib\site-packages\tensorflow\python\framework\errors_impl.py in raise_exception_on_not_ok_status()
    468           compat.as_text(pywrap_tensorflow.TF_Message(status)),
--> 469           pywrap_tensorflow.TF_GetCode(status))
    470   finally:

InvalidArgumentError: Shape must be rank 4 but is rank 2 for 'Conv2D_19' (op: 'Conv2D') with input shapes: [5,5], [3,3].

Answer 1

为了使用tf.nn.conv2d。您的输入和过滤器都应转换为 4D。此外，strides 应该是 1-D of length 4（为输入的每个维度滑动 window）。以下摘自documentation:

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:

Flattens the filter to a 2-D matrix with shape [filter_height * filter_width * in_channels, output_channels]. 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]. For each patch, right-multiplies the filter matrix and the image patch vector.

您可以采用：tf.reshape(x, [1, 5, 5, 1]) 用于数据，tf.reshape(wt, [3, 3, 1, 1]) 用于过滤器，以及 strides=[1, 1, 1, 1]。这导致：

tf.nn.conv2d(tf.reshape(x, [1, 5, 5, 1]), tf.reshape(wt, [3, 3, 1, 1]), strides=[1, 1, 1, 1],  padding='SAME')

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