I want to know what is the difference between a these two. For me, they are the same function, so I do not see the reason of existance two same functions.
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In TensorFlow 2, they accept different arguments:
tf.nn.convolution(input,...)Computes sums of N-D convolutions (actually cross-correlation).
input: An (N+2)-D Tensor of type T, of shape [batch_size] + input_spatial_shape + [in_channels]
While the second one is more specific:
tf.nn.conv1d(input,...)Computes a 1-D convolution given 3-D input and filter tensors.
input: A 3D Tensor.input: A 3D Tensor.
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$\begingroup$ Thank you for your answer. So, basically, both in some case, e.g. 3D tensor input, are computing the same thing? $\endgroup$– CepelineJan 29 '20 at 15:15
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$\begingroup$ I don't know for sure. I believe you can check it by feeding a 3D tensor to both. $\endgroup$ Jan 30 '20 at 11:38