tf.nn.depthwise_conv2d_backprop_filter

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Computes the gradients of depthwise convolution with respect to the filter.

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Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.nn.depthwise_conv2d_backprop_filter, tf.compat.v1.nn.depthwise_conv2d_native_backprop_filter

tf.nn.depthwise_conv2d_backprop_filter(
    input, filter_sizes, out_backprop, strides, padding,
    data_format='NHWC', dilations=[1, 1, 1, 1], name=None
)

Args
input	A Tensor. Must be one of the following types: half, bfloat16, float32, float64. 4-D with shape based on data_format. For example, if data_format is 'NHWC' then input is a 4-D [batch, in_height, in_width, in_channels] tensor.
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, depthwise_multiplier] tensor.
out_backprop	A Tensor. Must have the same type as input. 4-D with shape based on data_format. For example, if data_format is 'NHWC' then out_backprop shape is [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.
padding	Controls how to pad the image before applying the convolution. Can be 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]].
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, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, 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).

Returns
A Tensor. Has the same type as input.