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update leras/layers.py

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This commit is contained in:
Colombo
2020-02-24 22:23:08 +04:00
parent d65ff888cd
commit 0324592b67
1 changed files with 10 additions and 6 deletions
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core/leras/layers.py
+7 -3
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@@ -109,7 +109,7 @@ def initialize_layers(nn):
class Conv2D(LayerBase): class Conv2D(LayerBase):
""" """
use_wscale bool enables equalized learning rate, kernel_initializer will be forced to random_normal use_wscale bool enables equalized learning rate, if kernel_initializer is None, it will be forced to random_normal
""" """
@@ -171,6 +171,7 @@ def initialize_layers(nn):
fan_in = self.kernel_size*self.kernel_size*self.in_ch fan_in = self.kernel_size*self.kernel_size*self.in_ch
he_std = gain / np.sqrt(fan_in) # He init he_std = gain / np.sqrt(fan_in) # He init
self.wscale = tf.constant(he_std, dtype=self.dtype ) self.wscale = tf.constant(he_std, dtype=self.dtype )
if kernel_initializer is None:
kernel_initializer = tf.initializers.random_normal(0, 1.0, dtype=self.dtype) kernel_initializer = tf.initializers.random_normal(0, 1.0, dtype=self.dtype)
if kernel_initializer is None: if kernel_initializer is None:
@@ -217,7 +218,7 @@ def initialize_layers(nn):
class Conv2DTranspose(LayerBase): class Conv2DTranspose(LayerBase):
""" """
use_wscale enables weight scale (equalized learning rate) use_wscale enables weight scale (equalized learning rate)
kernel_initializer will be forced to random_normal if kernel_initializer is None, it will be forced to random_normal
""" """
def __init__(self, in_ch, out_ch, kernel_size, strides=2, padding='SAME', use_bias=True, use_wscale=False, kernel_initializer=None, bias_initializer=None, trainable=True, dtype=None, **kwargs ): def __init__(self, in_ch, out_ch, kernel_size, strides=2, padding='SAME', use_bias=True, use_wscale=False, kernel_initializer=None, bias_initializer=None, trainable=True, dtype=None, **kwargs ):
if not isinstance(strides, int): if not isinstance(strides, int):
@@ -247,7 +248,9 @@ def initialize_layers(nn):
fan_in = self.kernel_size*self.kernel_size*self.in_ch fan_in = self.kernel_size*self.kernel_size*self.in_ch
he_std = gain / np.sqrt(fan_in) # He init he_std = gain / np.sqrt(fan_in) # He init
self.wscale = tf.constant(he_std, dtype=self.dtype ) self.wscale = tf.constant(he_std, dtype=self.dtype )
if kernel_initializer is None:
kernel_initializer = tf.initializers.random_normal(0, 1.0, dtype=self.dtype) kernel_initializer = tf.initializers.random_normal(0, 1.0, dtype=self.dtype)
if kernel_initializer is None: if kernel_initializer is None:
kernel_initializer = tf.initializers.glorot_uniform(dtype=self.dtype) kernel_initializer = tf.initializers.glorot_uniform(dtype=self.dtype)
self.weight = tf.get_variable("weight", (self.kernel_size,self.kernel_size,self.out_ch,self.in_ch), dtype=self.dtype, initializer=kernel_initializer, trainable=self.trainable ) self.weight = tf.get_variable("weight", (self.kernel_size,self.kernel_size,self.out_ch,self.in_ch), dtype=self.dtype, initializer=kernel_initializer, trainable=self.trainable )
@@ -367,7 +370,7 @@ def initialize_layers(nn):
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 ): 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 ):
""" """
use_wscale enables weight scale (equalized learning rate) use_wscale enables weight scale (equalized learning rate)
kernel_initializer will be forced to random_normal if kernel_initializer is None, it will be forced to random_normal
maxout_ch https://link.springer.com/article/10.1186/s40537-019-0233-0 maxout_ch https://link.springer.com/article/10.1186/s40537-019-0233-0
typical 2-4 if you want to enable DenseMaxout behaviour typical 2-4 if you want to enable DenseMaxout behaviour
@@ -399,6 +402,7 @@ def initialize_layers(nn):
fan_in = np.prod( weight_shape[:-1] ) fan_in = np.prod( weight_shape[:-1] )
he_std = gain / np.sqrt(fan_in) # He init he_std = gain / np.sqrt(fan_in) # He init
self.wscale = tf.constant(he_std, dtype=self.dtype ) self.wscale = tf.constant(he_std, dtype=self.dtype )
if kernel_initializer is None:
kernel_initializer = tf.initializers.random_normal(0, 1.0, dtype=self.dtype) kernel_initializer = tf.initializers.random_normal(0, 1.0, dtype=self.dtype)
if kernel_initializer is None: if kernel_initializer is None: