## tx.math

### sparse_dense_multiply

source

.sparse_dense_multiply(
sp_tensor, dense_tensor, name = 'sparse_multiply_dense'
)


element-wise sparse_multiply_dense

Info

Uses sparse_dense_cwise_mul from Tensorflow but returns a dense result and reshapes the result to match the shape of sp_tensor

Args

• sp_tensor (SparseTensor) : a sparse tensor
• dense_tensor (Tensor) : a dense tensor
• name (str) : op name

Returns

• tensor (Tensor) : the result for the multiplication between the sparse and dense tensors

### rms

source

.rms(
x
)


Root mean square (RMS)

Also known as quadratic mean is defined as:

$x_{\mathrm{RMS}}=\sqrt{\frac{x_{1}^{2}+x_{2}^{2}+\ldots+x_{n}^{2}}{n}}$

In estimation theory, the root-mean-square deviation of an estimator is a measure of the imperfection of the fit of the estimator to the data.

Args

• x (Tensor) : input tensor

Returns

• result (Tensor) : scalar tensor with the result of applying the root mean square to the input tensor