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Basically I have a 2d array and I want to do this nice numpy-like thing
noise_spec[:rows,:cols]
in Tensorflow. Here rows and cols are just two integers.
951
You can use tf. slice on higher dimensional tensors as well. You can also use tf. strided_slice to extract slices of tensors by 'striding' over the tensor dimensions.
Used in the notebooksThis operation extracts a slice of size size from a tensor input_ starting at the location specified by begin . The slice size is represented as a tensor shape, where size[i] is the number of elements of the 'i'th dimension of input_ that you want to slice.
To flatten the tensor, we're going to use the TensorFlow reshape operation. So tf. reshape, we pass in our tensor currently represented by tf_initial_tensor_constant, and then the shape that we're going to give it is a -1 inside of a Python list.
Ragged tensors are the TensorFlow equivalent of nested variable-length lists. They make it easy to store and process data with non-uniform shapes, including: Variable-length features, such as the set of actors in a movie. Batches of variable-length sequential inputs, such as sentences or video clips.
Indeed, TensorFlow now has better support for slicing, so you can use the exact same syntax as NumPy:
result = noise_spec[:rows, :cols]
found out, it's
tf.slice(noise_spec, [0,0],[rows, cols])
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