LostTech.TensorFlow : API Documentation

Type uniform_unit_scaling_initializer

Namespace tensorflow

Parent Initializer

Interfaces Iuniform_unit_scaling_initializer

Initializer that generates tensors without scaling variance.

When initializing a deep network, it is in principle advantageous to keep the scale of the input variance constant, so it does not explode or diminish by reaching the final layer. If the input is `x` and the operation `x * W`, and we want to initialize `W` uniformly at random, we need to pick `W` from

[-sqrt(3) / sqrt(dim), sqrt(3) / sqrt(dim)]

to keep the scale intact, where `dim = W.shape[0]` (the size of the input). A similar calculation for convolutional networks gives an analogous result with `dim` equal to the product of the first 3 dimensions. When nonlinearities are present, we need to multiply this by a constant `factor`. See (Sussillo et al., 2014) for deeper motivation, experiments and the calculation of constants. In section 2.3 there, the constants were numerically computed: for a linear layer it's 1.0, relu: ~1.43, tanh: ~1.15.


Public properties

DType dtype get; set;

double factor get; set;

object PythonObject get;

Nullable<int> seed get; set;