ValueError: No gradients provided for any variable？

Question

在使用卷积神经网络+全连接神经网络计算句子相似度训练模型出现无梯度的问题。
以下是源代码

import numpy as np
import tensorflow as tf
from tensorflow.keras import datasets, layers, optimizers, Sequential, metrics
import os
import math
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
#构建输入的向量
sentence_x = np.random.randn(1000, 38, 300)
sentence_x = tf.cast(tf.reshape(sentence_x, [1000, 38, 300, 1]), dtype=tf.float32)

sentence_y = np.random.randn(1000, 38, 300)
sentence_y = tf.cast(tf.reshape(sentence_y, [1000, 38, 300, 1]), dtype=tf.float32)

label = np.random.randint(0, 2, (1, 1000))
label = tf.reshape(label, [1000])

train_db = tf.data.Dataset.from_tensor_slices((sentence_x, sentence_y, label))
train_db = train_db.shuffle(100).batch(20)


#卷积层
conv_layers = [ # 5 units of 2 * conv +maxpooling
    # unit 1
    layers.Conv2D(3, kernel_size=[2, 2], strides=[2, 2], padding='same', activation = tf.nn.relu),
    layers.Conv2D(3, kernel_size=[2, 2], padding='same', activation = tf.nn.relu),
    layers.MaxPool2D(pool_size=[2, 2], strides= 2, padding='same'),
    # unit 2
    layers.Conv2D(3, kernel_size=[2, 2], strides=[2, 2], padding='same', activation = tf.nn.relu),
    layers.Conv2D(3, kernel_size=[2, 2], padding='same', activation = tf.nn.relu),
    layers.MaxPool2D(pool_size=[2, 2], strides= 2, padding='same'),

]

fc_net = Sequential([
        layers.Dense(150, activation = tf.nn.relu),
        layers.Dense(80, activation = tf.nn.relu),
        layers.Dense(20, activation = None),
    ])
conv_net = Sequential(conv_layers)
conv_net.build(input_shape = [None, 38, 300, 1])
fc_net.build(input_shape = [None, 171])
optimizer = tf.keras.optimizers.Adam(1e-3)
variables = conv_net.trainable_variables + fc_net.trainable_variables
def main():
    for epoch in range(50):
        for step, (sentence_x, sentence_y, label) in enumerate(train_db):
            with tf.GradientTape() as tape:
                out1 = conv_net(sentence_x)
                out2 = conv_net(sentence_y)
                fc_input_x = tf.reshape(out1, [-1, 171])
                fc_input_y = tf.reshape(out2, [-1, 171])
                vec_x = fc_net(fc_input_x)
                vec_y = fc_net(fc_input_y)
                #对输出的句向量进行计算相似度值
                output = tf.exp(-tf.reduce_sum(tf.abs(vec_x - vec_y), axis=1))
                output = tf.reshape(output, [-1])

                output = tf.math.ceil(output)
                output1 = tf.one_hot(tf.cast(output, dtype=tf.int32), depth=2)
                label = tf.cast(label, dtype=tf.int32)
                label= tf.one_hot(label, depth=2)
                print("output1", output1)
                print("label", label)
                loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=output1, labels=label))
                #loss = tf.reduce_sum(tf.square(output1-label))
            grad = tape.gradient(loss, variables)
            optimizer.apply_gradients(zip(grad, variables))

            if step % 10 == 0:
                print("epoch={0}, step = {1}, loss={2}".format(epoch, step, loss))



if __name__ == '__main__':
    main()

希望大佬们能指点一下，本人入门级小白。

Answer 1

不知道你这个问题是否已经解决, 如果还没有解决的话:

• 关于该问题，我找了一篇非常好的博客，你可以看看是否有帮助，链接：ValueError: No gradients provided for any variable（TensorFlow2！绝对详细！）

如果你已经解决了该问题, 非常希望你能够分享一下解决方案, 写成博客, 将相关链接放在评论区, 以帮助更多的人 ^-^