/TensorFlow Python


    dilations=[1, 1, 1, 1],

Defined in tensorflow/python/ops/gen_nn_ops.py.

See the guide: Neural Network > Convolution

Computes the gradients of convolution with respect to the filter.


  • input: A Tensor. Must be one of the following types: half, bfloat16, float32, float64. 4-D with shape [batch, in_height, in_width, in_channels].
  • filter_sizes: A Tensor of type int32. An integer vector representing the tensor shape of filter, where filter is a 4-D [filter_height, filter_width, in_channels, out_channels] tensor.
  • out_backprop: A Tensor. Must have the same type as input. 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: 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].
  • 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).


A Tensor. Has the same type as input.

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Code samples licensed under the Apache 2.0 License.