Source code for tensorlayerx.nn.layers.linear.base_linear

#! /usr/bin/python
# -*- coding: utf-8 -*-

import tensorlayerx as tlx
from tensorlayerx import logging
from tensorlayerx.nn.core import Module

__all__ = [

[docs]class Linear(Module): """Applies a linear transformation to the incoming data: :math:`y = xA^T + b` Parameters ---------- out_features : int The number of units of this layer. act : activation function The activation function of this layer. W_init : initializer or str The initializer for the weight matrix. b_init : initializer or None or str The initializer for the bias vector. If None, skip biases. in_features: int The number of channels of the previous layer. If None, it will be automatically detected when the layer is forwarded for the first time. name : None or str A unique layer name. If None, a unique name will be automatically generated. Examples -------- With TensorLayerx >>> net = tlx.nn.Input([100, 50], name='input') >>> linear = tlx.nn.Linear(out_features=800, act=tlx.ReLU, in_features=50, name='linear_1') >>> tensor = tlx.nn.Linear(out_features=800, act=tlx.ReLU, name='linear_2')(net) Notes ----- If the layer input has more than two axes, it needs to be flatten by using :class:`Flatten`. """ def __init__( self, out_features, act=None, W_init='truncated_normal', b_init='constant', in_features=None, name=None, # 'linear', ): super(Linear, self).__init__(name, act=act) self.out_features = out_features self.W_init = self.str_to_init(W_init) self.b_init = self.str_to_init(b_init) self.in_features = in_features if self.in_features is not None: self._built = True "Linear %s: %d %s" % (, self.out_features, self.act.__class__.__name__ if self.act is not None else 'No Activation') ) def __repr__(self): actstr = self.act.__class__.__name__ if self.act is not None else 'No Activation' s = ('{classname}(out_features={out_features}, ' + actstr) if self.in_features is not None: s += ', in_features=\'{in_features}\'' if is not None: s += ', name=\'{name}\'' s += ')' return s.format(classname=self.__class__.__name__, **self.__dict__) def build(self, inputs_shape): if self.in_features is None and len(inputs_shape) < 2: raise AssertionError("The dimension of input should not be less than 2") if self.in_features: shape = [self.out_features, self.in_features] else: self.in_features = inputs_shape[-1] shape = [self.out_features, self.in_features] self.weights = self._get_weights("weights", shape=tuple(shape), init=self.W_init) self.biases = None self.b_init_flag = False if self.b_init: self.biases = self._get_weights("biases", shape=(self.out_features,), init=self.b_init) self.b_init_flag = True self.act_init_flag = False if self.act: self.act_init_flag = True def forward(self, inputs): if self._forward_state == False: if self._built == False: self._built = True self._forward_state = True z = tlx.ops.linear(inputs, self.weights, self.biases) if self.act_init_flag: z = self.act(z) if not self._nodes_fixed and self._build_graph: self._add_node(inputs, z) self._nodes_fixed = True return z