未使用渐变带和 apply_gradients() 更新权重

Question

我正在构建一个带有自定义损失函数的 DNN，我正在 TensorFlow.kerasenter code here 中使用渐变带训练这个 DNN。代码运行没有任何错误，但是，据我检查 DNN 的权重，权重根本没有更新。我完全按照 TensorFlow 网站上的建议进行了搜索并搜索了答案，但仍然不明白是什么原因。这是我的代码：

import numpy as np

import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, LeakyReLU, Concatenate
from tensorflow.keras.models import Model
from tensorflow.keras import backend as K
from tensorflow.keras import optimizers

# Generate a random train data
c0_train = np.array([30 * np.random.uniform() for i in range(10000)])

# Build a simple DNN
c0_input = Input(shape=(1,), name='c0')
hidden_1 = Dense(100)(c0_input)
activation_1 = LeakyReLU(alpha=0.1)(hidden_1)
hidden_2 = Dense(100)(activation_1)
activation_2 = LeakyReLU(alpha=0.1)(hidden_2)
hidden_3 = Dense(100)(activation_2)
activation_3 = LeakyReLU(alpha=0.1)(hidden_3)
x0_output = Dense(1, name='x0')(activation_3)

model = Model(inputs=c0_input, outputs=x0_output)

# Calculating the loss function 
def cal_loss(c0_input):
  x0_output = model(c0_input)
  loss = tf.reduce_mean(
      tf.multiply(c0_input, tf.square(tf.subtract(x0_output, c0_input))))
  return loss

# Compute the gradient calculation
@tf.function
def compute_loss_grads(c0_input):
  with tf.GradientTape() as tape:
    loss = cal_loss(c0_input)
  grads = tape.gradient(loss, model.trainable_variables)
  return loss, grads

# Optimizer
opt = optimizers.Adam(learning_rate=0.01)

# Start looping
for epoch in range(50):
  print('Epoch = ', epoch)
  # Compute the loss and gradients
  [loss, grads] = compute_loss_grads(tf.cast(c0_train, tf.float32))
  # Adjust the weights of the model
  opt.apply_gradients(zip(grads, model.trainable_variables))

我已经使用 model.get_weights() 检查了模型的权重，它们在运行循环前后看起来完全一样。那么这里的问题是什么？还有一个问题，我怎样才能打印出每个时期的损失？

Answer 1

体重确实发生了变化。您可以按以下方式检查；建立模型后保存您的权重文件（这些是初始权重）。

model = Model(inputs=c0_input, outputs=x0_output)
a_weg = model.get_weights()

现在，运行你的训练循环。训练完成后，得到新的权重如下，前后对比

b_weg = model.get_weights()

a_weg[:1]
[array([[ 0.03541631, -0.02134866,  0.17080751,  0.10538128,  0.1361396 ,
          0.08645812,  0.114059  ,  0.216836  , -0.22464292, -0.21979895,
         -0.23927784, -0.00685263,  0.2167016 ,  0.09989142, -0.17772573,
          0.16095945, -0.10120587, -0.22456157, -0.22947621,  0.04009536,
          0.01029667, -0.18134505, -0.11318983,  0.10220072,  0.10100928,

b_weg[:1]
[array([[ 0.05140253,  0.00969543,  0.15155758,  0.07171137,  0.15917814,
          0.10883425,  0.11428417,  0.17012525, -0.25049415, -0.20693016,
         -0.20231842,  0.005939  ,  0.19197173,  0.07405043, -0.14260964,
          0.12490476, -0.11532102, -0.24605738, -0.25135723,  0.01863468,
          0.0311144 , -0.20050383, -0.11864465,  0.07961675,  0.11557189,

这就是您在每个时期打印损失分数的方法。

# Start looping
for epoch in range(5):
  # Compute the loss and gradients
  [loss, grads] = compute_loss_grads(tf.cast(c0_train, tf.float32))
  # Adjust the weights of the model
  opt.apply_gradients(zip(grads, model.trainable_variables))
  print('Epoch = ', epoch, ' - loss = ', loss.numpy())

Epoch =  0  - loss =  5962.977
Epoch =  1  - loss =  3042.2874
Epoch =  2  - loss =  2877.9978
Epoch =  3  - loss =  2607.5347
Epoch =  4  - loss =  2173.3213

未使用渐变带和 apply_gradients() 更新权重

Weights were not updated using Gradient Tape and apply_gradients()

tensorflow

loss-function

tf.keras

tensorflow2.0

gradienttape