I manually implement the batchnormalize
layer. But the code in the initial function to create the nontrainable
variables seems not to work. code:
import tensorflow as tf
class batchNormalization(tf.keras.layers.Layer):
def __init__(self, shape, Trainable, **kwargs):
super(batchNormalization, self).__init__(**kwargs)
self.shape = shape
self.Trainable = Trainable
self.beta = tf.Variable(initial_value=tf.zeros(shape), trainable=Trainable)
self.gamma = tf.Variable(initial_value=tf.ones(shape), trainable=Trainable)
self.moving_mean = tf.Variable(initial_value=tf.zeros(self.shape), trainable=False)
self.moving_var = tf.Variable(initial_value=tf.ones(self.shape), trainable=False)
def update_var(self,inputs):
wu, sigma = tf.nn.moments(inputs, axes=[0, 1, 2], shift=None, keepdims=False, name=None)
var = tf.math.sqrt(sigma)
self.moving_mean = self.moving_mean * 0.09 + wu * 0.01
self.moving_var = self.moving_var * 0.09 + var * 0.01
return wu,var
def call(self, inputs):
wu, var = self.update_var(inputs)
return tf.nn.batch_normalization(inputs, wu, var, self.beta,
self.gamma, variance_epsilon=0.001)
@tf.function
def train_step(model, inputs, label,optimizer):
with tf.GradientTape(persistent=False) as tape:
predictions = model(inputs, training=1)
loss = tf.keras.losses.mean_squared_error(predictions,label)
grads = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables))
if __name__=='__main__':
f=tf.ones([2,256,256,8])
label=tf.ones([2,256,256,8])
inputs = tf.keras.Input(shape=(256,256,8))
outputs=batchNormalization([8],True)(inputs)
Model = tf.keras.Model(inputs=inputs, outputs=outputs)
Layer = batchNormalization([8],True)
print(len(Model.variables))
print(len(Model.trainable_variables))
print(len(Layer.variables))
print(len(Layer.trainable_variables))
optimizer=tf.keras.optimizers.Adam(learning_rate=0.001)
for i in range(0,100):
train_step(Layer, f, label,optimizer)
# train_step(Model,f,label,optimizer)
when training, another error was raised:
TypeError: An op outside of the function building code is being passed a "Graph" tensor.
It is possible to have Graph tensors leak out of the function building context by including a tf.init_scope in your function building code.
question from:
https://stackoverflow.com/questions/65661130/cant-create-non-trainable-variables-in-tensorflow-v2