tf.sparse_segment_sum()

tf.sparse_segment_sum(data, indices, segment_ids, name=None) Computes the sum along sparse segments of a tensor.

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tf.batch_matmul()

tf.batch_matmul(x, y, adj_x=None, adj_y=None, name=None) Multiplies slices of two tensors in batches. Multiplies

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tf.reduce_mean()

tf.reduce_mean(input_tensor, reduction_indices=None, keep_dims=False, name=None) Computes the mean of elements across dimensions

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tf.add()

tf.add(x, y, name=None) Returns x + y element-wise. NOTE: Add supports broadcasting

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tf.sqrt()

tf.sqrt(x, name=None) Computes square root of x element-wise. I.e., (y = \sqrt{x} = x^{1/2}).

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tf.einsum()

tf.einsum(axes, *inputs) A generalized contraction between tensors of arbitrary dimension. Like

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tf.reduce_logsumexp()

tf.reduce_logsumexp(input_tensor, reduction_indices=None, keep_dims=False, name=None) Computes log(sum(exp(elements across dimensions

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tf.matmul()

tf.matmul(a, b, transpose_a=False, transpose_b=False, a_is_sparse=False, b_is_sparse=False, name=None) Multiplies matrix a

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tf.cumprod()

tf.cumprod(x, axis=0, exclusive=False, reverse=False, name=None) Compute the cumulative product of the tensor x along

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tf.floor()

tf.floor(x, name=None) Returns element-wise largest integer not greater than x. Args:

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