convolution¶

dragon.vm.tensorflow.nn.convolution(
  input,
  filters,
  strides=1,
  padding='VALID',
  data_format='NHWC',
  dilations=1,
  name=None,
  **kwargs
)[source]¶

Apply the n-dimension convolution.

• If data_format is 'NCHW', excepts input shape \((N, C_{\text{in}}, D1, D2, ...)\), filters shape \((C_{\text{out}}, C_{\text{in}}, D1_{\text{f}}, D2_{\text{f}}, ...)\), and output shape is \((N, C_{\text{out}}, D1_{\text{out}}, D2_{\text{out}}, ...)\).
• If data_format is 'NHWC', excepts input shape \((N, D1, D2, ..., C_{\text{in}})\), filters shape \((C_{\text{out}}, D1_{\text{f}}, D2_{\text{f}}, ..., C_{\text{in}})\), and output shape is \((N, D1_{\text{out}}, D2_{\text{out}}, ..., C_{\text{out}})\).
• padding could be 'VALID', 'SAME' or explicit padding size.

Examples:

for i in range(3):
    ndim = i + 1
    x = tf.ones((1,) + (2,) * ndim + (2,))
    filters = tf.ones((3,) + (1,) * ndim + (2,))
    y = tf.nn.convolution(x, filters)
    assert y.shape == (1,) + (2,) * ndim + (3,)

Parameters:

• input (dragon.Tensor) – The input tensor.
• filters (dragon.Tensor) – The filters tensor.
• strides (Union[int, Sequence[int]], optional, default=1) – The stride of convolution window.
• padding (Union[int, Sequence[int], str], optional, default='VALID') – The padding algorithm or size.
• data_format (str, optional, default='NHWC') – 'NCHW' or 'NHWC'.
• dilations (Union[int, Sequence[int]], optional, default=1) – The rate of dilated filters.
• name (str, optional) – The operation name.

Returns:

dragon.Tensor – The output tensor.