Function conv2d_backprop_input

tensorflow/python/ops/nn_ops.py:2116–2176

r"""Computes the gradients of convolution with respect to the input. Args: input_sizes: A `Tensor` of type `int32`. An integer vector representing the shape of `input`, where `input` is a 4-D `[batch, height, width, channels]` tensor. filter: A `Tensor`. Must be one of the fol

(  # pylint: disable=redefined-builtin,dangerous-default-value
    input_sizes,
    filter=None,
    out_backprop=None,
    strides=None,
    padding=None,
    use_cudnn_on_gpu=True,
    data_format="NHWC",
    dilations=[1, 1, 1, 1],
    name=None,
    filters=None)

@tf_export(v1=["nn.conv2d_backprop_input"])
def conv2d_backprop_input(  # pylint: disable=redefined-builtin,dangerous-default-value
    input_sizes,
    filter=None,
    out_backprop=None,
    strides=None,
    padding=None,
    use_cudnn_on_gpu=True,
    data_format="NHWC",
    dilations=[1, 1, 1, 1],
    name=None,
    filters=None):
  r"""Computes the gradients of convolution with respect to the input.

  Args:
    input_sizes: A `Tensor` of type `int32`.
      An integer vector representing the shape of `input`,
      where `input` is a 4-D `[batch, height, width, channels]` tensor.
    filter: A `Tensor`. Must be one of the following types:
      `half`, `bfloat16`, `float32`, `float64`.
      4-D with shape
      `[filter_height, filter_width, in_channels, out_channels]`.
    out_backprop: A `Tensor`. Must have the same type as `filter`.
      4-D with shape `[batch, out_height, out_width, out_channels]`.
      Gradients w.r.t. the output of the convolution.
    strides: A list of `ints`.
      The stride of the sliding window for each dimension of the input
      of the convolution. Must be in the same order as the dimension specified
      with format.
    padding: Either the `string `"SAME"` or `"VALID"` indicating the type of
      padding algorithm to use, or a list indicating the explicit paddings at
      the start and end of each dimension. When explicit padding is used and
      data_format is `"NHWC"`, this should be in the form `[[0, 0], [pad_top,
      pad_bottom], [pad_left, pad_right], [0, 0]]`. When explicit padding used
      and data_format is `"NCHW"`, this should be in the form `[[0, 0], [0, 0],
      [pad_top, pad_bottom], [pad_left, pad_right]]`.
    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].
    dilations: An optional list of `ints`. Defaults to `[1, 1, 1, 1]`.
      1-D tensor of length 4.  The dilation factor for each dimension of
      `input`. If set to k > 1, there will be k-1 skipped cells between each
      filter element on that dimension. The dimension order is determined by
      the value of `data_format`, see above for details. Dilations in the batch
      and depth dimensions must be 1.
    name: A name for the operation (optional).
    filters: Alias for filter.

  Returns:
    A `Tensor`. Has the same type as `filter`.
  """
  filter = deprecation.deprecated_argument_lookup(
      "filters", filters, "filter", filter)
  padding, explicit_paddings = _convert_padding(padding)