def build(self, input_shape):
# input_shape (None,40)
print(input_shape)
input_dim = input_shape[1]
self.kernel = self.add_weight(shape=(input_dim, self.units),
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
if self.H == 'Glorot':
self.H = np.float32(np.sqrt(1.5 / (int(input_dim) + self.units)))
# print('Glorot H: {}'.format(self.H))
if self.kernel_lr_multiplier == 'Glorot':
self.kernel_lr_multiplier = np.float32(
1. / np.sqrt(1.5 / (int(input_dim) + self.units)))
# print('Glorot learning rate multiplier: {}'.format(self.kernel_lr_multiplier))
if self.use_bias:
pass
else:
self.lr_multipliers = [self.kernel_lr_multiplier]
self.bias = None
self.input_spec = InputSpec(min_ndim=2, axes={-1: input_dim})
self.built = True
self.binary = binarize(self.kernel, H=self.H)
def build(self, input_shape):
# input_shape (None,40)
print(input_shape)
input_dim = input_shape[1]
if self.H == 'Glorot':
self.H = np.float32(np.sqrt(1.5 / (int(input_dim) + self.units)))
#print('Glorot H: {}'.format(self.H))
if self.w_lr_multiplier == 'Glorot':
self.w_lr_multiplier = np.float32(
1. / np.sqrt(1.5 / (int(input_dim) + self.units)))
#print('Glorot learning rate multiplier: {}'.format(self.kernel_lr_multiplier))
self.w_constraint = Clip(-self.H, self.H)
#self.w_initializer = initializers.RandomUniform(-self.H, self.H)
self.w_initializer = initializers.Ones()
self.w_regularizer = regularizers.l2(0.01)
self.w = self.add_weight(shape=(input_dim, self.units),
initializer=self.w_initializer,
name='w',
regularizer=self.w_regularizer,
constraint=self.w_constraint)
#self.bw=self.add_weight(shape=(input_dim,self.units),
# initializer=self.w_initializer,
# name='bw',
# regularizer=self.w_regularizer,
# constraint=self.w_constraint)
#self.bw = binarize(self.w, H=self.H)
if self.use_bias:
self.lr_multipliers = [
self.w_lr_multiplier, self.bias_lr_multiplier
]
self.bias = self.add_weight(
shape=(self.units,
), # is this shape right??? # 假设这个weight每一个都不一样好了先！
initializer=self.w_initializer,
name='bias')
#regularizer=self.bias_regularizer,
#constraint=self.bias_constraint)
else:
self.lr_multipliers = [self.w_lr_multiplier]
self.bias = None
self.input_spec = InputSpec(min_ndim=2, axes={-1: input_dim})
self.built = True
self.binary = binarize(self.w, H=self.H)