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import numpy as np |
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from core.leras import nn |
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tf = nn.tf |
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class Dense(nn.LayerBase): |
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def __init__(self, in_ch, out_ch, use_bias=True, use_wscale=False, maxout_ch=0, kernel_initializer=None, bias_initializer=None, trainable=True, dtype=None, **kwargs ): |
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""" |
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use_wscale enables weight scale (equalized learning rate) |
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if kernel_initializer is None, it will be forced to random_normal |
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maxout_ch https://link.springer.com/article/10.1186/s40537-019-0233-0 |
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typical 2-4 if you want to enable DenseMaxout behaviour |
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""" |
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self.in_ch = in_ch |
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self.out_ch = out_ch |
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self.use_bias = use_bias |
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self.use_wscale = use_wscale |
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self.maxout_ch = maxout_ch |
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self.kernel_initializer = kernel_initializer |
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self.bias_initializer = bias_initializer |
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self.trainable = trainable |
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if dtype is None: |
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dtype = nn.floatx |
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self.dtype = dtype |
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super().__init__(**kwargs) |
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def build_weights(self): |
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if self.maxout_ch > 1: |
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weight_shape = (self.in_ch,self.out_ch*self.maxout_ch) |
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else: |
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weight_shape = (self.in_ch,self.out_ch) |
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kernel_initializer = self.kernel_initializer |
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if self.use_wscale: |
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gain = 1.0 |
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fan_in = np.prod( weight_shape[:-1] ) |
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he_std = gain / np.sqrt(fan_in) |
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self.wscale = tf.constant(he_std, dtype=self.dtype ) |
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if kernel_initializer is None: |
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kernel_initializer = tf.initializers.random_normal(0, 1.0, dtype=self.dtype) |
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if kernel_initializer is None: |
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kernel_initializer = tf.initializers.glorot_uniform(dtype=self.dtype) |
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self.weight = tf.get_variable("weight", weight_shape, dtype=self.dtype, initializer=kernel_initializer, trainable=self.trainable ) |
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if self.use_bias: |
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bias_initializer = self.bias_initializer |
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if bias_initializer is None: |
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bias_initializer = tf.initializers.zeros(dtype=self.dtype) |
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self.bias = tf.get_variable("bias", (self.out_ch,), dtype=self.dtype, initializer=bias_initializer, trainable=self.trainable ) |
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def get_weights(self): |
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weights = [self.weight] |
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if self.use_bias: |
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weights += [self.bias] |
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return weights |
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def forward(self, x): |
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weight = self.weight |
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if self.use_wscale: |
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weight = weight * self.wscale |
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x = tf.matmul(x, weight) |
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if self.maxout_ch > 1: |
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x = tf.reshape (x, (-1, self.out_ch, self.maxout_ch) ) |
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x = tf.reduce_max(x, axis=-1) |
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if self.use_bias: |
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x = tf.add(x, tf.reshape(self.bias, (1,self.out_ch) ) ) |
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return x |
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nn.Dense = Dense |
