Tensorflow梯度始终为零
[英]Tensorflow gradients are always zero
问题描述
Tensorflow gradients are always zero with respect to conv layers that are after first conv layer. 
相对于第一个conv层之后的conv层，Tensorflow梯度始终为零。 I've tried different ways to check that but gradients are always zero! 我尝试了不同的方法来检查梯度，但梯度始终为零！ Here is the small reproducible code that can be run to check that. 这是可运行的小的可复制代码，可以检查该代码。
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import numpy as np
import math
import os
import random
import tflearn
batch_size = 100
start = 0
end = batch_size
learning_rate = 0.000001
num_classes = 4
time_steps = 4
embedding = 2
step = 1
_units = 500
num_of_filters = 1000
train_set_x = [[[1,2],[3,4],[5,6],[7,8]],[[1,2],[3,4],[5,6],[7,8]]]
train_set_y = [0,1]
X = tf.placeholder(tf.float32, [None,time_steps,embedding])
Y = tf.placeholder(tf.int32, [None])
x = tf.expand_dims(X,3)
filter_shape = [1, embedding, 1, num_of_filters]
conv_weights = tf.get_variable("conv_weights1" , filter_shape, tf.float32, tf.contrib.layers.xavier_initializer())
conv_biases = tf.Variable(tf.constant(0.1, shape=[num_of_filters]))
conv = tf.nn.conv2d(x, conv_weights, strides=[1,1,1,1], padding = "VALID")
normalize = conv + conv_biases
tf_normalize = tflearn.layers.normalization.batch_normalization(normalize)
relu = tf.nn.elu(tf_normalize)
pooling = tf.reduce_max(relu, reduction_indices = 3, keep_dims = True)
outputs_fed_lstm = pooling
filter_shape2 = [1, 1, 1, num_of_filters]
conv_weights2 = tf.get_variable("conv_weights2" , filter_shape2, tf.float32, tf.contrib.layers.xavier_initializer())
conv_biases2 = tf.Variable(tf.constant(0.1, shape=[num_of_filters]))
conv2 = tf.nn.conv2d(outputs_fed_lstm, conv_weights2, strides=[1,1,1,1], padding = "VALID")
normalize2 = conv2 + conv_biases2
tf_normalize2 = tflearn.layers.normalization.batch_normalization(normalize2)
relu2 = tf.nn.elu(tf_normalize2)
pooling2 = tf.reduce_max(relu2, reduction_indices = 3, keep_dims = True)
outputs_fed_lstm2 = pooling2
x = tf.squeeze(outputs_fed_lstm2, [2])
x = tf.transpose(x, [1, 0, 2])
x = tf.reshape(x, [-1, 1])
x = tf.split(0, time_steps, x)
lstm = tf.nn.rnn_cell.LSTMCell(num_units = _units)
# multi_lstm = tf.nn.rnn_cell.MultiRNNCell([lstm] * lstm_layers, state_is_tuple = True)
outputs , state = tf.nn.rnn(lstm,x, dtype = tf.float32)
weights = tf.Variable(tf.random_normal([_units,num_classes]))
biases = tf.Variable(tf.random_normal([num_classes]))
logits = tf.matmul(outputs[-1], weights) + biases
c_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits,Y)
loss = tf.reduce_mean(c_loss)
global_step = tf.Variable(0, name="global_step", trainable=False)
# decayed_learning_rate = tf.train.exponential_decay(learning_rate,0,10000,0.9)
optimizer= tf.train.AdamOptimizer(learning_rate)
minimize_loss = optimizer.minimize(loss, global_step=global_step)
grads_and_vars = optimizer.compute_gradients(loss,[conv_weights2])
correct_predict = tf.nn.in_top_k(logits, Y, 1)
accuracy = tf.reduce_mean(tf.cast(correct_predict, tf.float32))
init = tf.initialize_all_variables()
with tf.Session() as sess:
sess.run(init)
for i in range(1):
for j in range(1):
x = train_set_x
y = train_set_y
sess.run(minimize_loss,feed_dict={X : x, Y : y})
step += 1
gr_print = sess.run([grad for grad, _ in grads_and_vars], feed_dict={X : x, Y : y})
print (gr_print)
cost = sess.run(loss,feed_dict = {X: x,Y: y})
accu = sess.run(accuracy,feed_dict = {X: x, Y: y})
print ("Loss after one Epoch(Training) = " + "{:.6f}".format(cost) + ", Training Accuracy= " + "{:.5f}".format(accu))
And here is the output 这是输出
[array([[[[ 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 5.21326828, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ]]]], dtype=float32)]
1 个解决方案
解决方案1
8 已采纳 2016-08-18 12:02:49
What you compute is kind of weird. 您所计算的有点奇怪。 Let's examine the shapes in your model: 让我们检查一下模型中的形状：
- input
x:[batch_size, 4, 2, 1]输入x：[batch_size, 4, 2, 1] - 1st convolution
conv:[batch_size, 4, 1, 1000]1st卷积conv：[batch_size, 4, 1, 1000] - 1st max pool
pooling:[batch_size, 4, 1, 1]第一个最大池pooling：[batch_size, 4, 1, 1] - 2nd convolution
conv2:[batch_size, 4, 1, 1000]第二次卷积conv2：[batch_size, 4, 1, 1000]conv2[batch_size, 4, 1, 1000] - 2nd max pool
polling2:[batch_size, 4, 1, 1]第二个最大池polling2：[batch_size, 4, 1, 1]polling2[batch_size, 4, 1, 1] - input to LSTM:
[4, batch_size, 1]输入LSTM： [4, batch_size, 1] - output of LSTM:
[batch_size, 500]LSTM的输出： [batch_size, 500]
From what I understand, you try to apply two 1D convolutions and then an LSTM. 据我了解，您尝试应用两个1D卷积，然后再应用LSTM。 However, the first convolution is on the 3rd dimension of size embedding=2 . 但是，第一个卷积在大小embedding=2的第三个维度上。
After that, you apply a max pooling on all the 1000-sized embedding . 之后，您将对所有1000大小的嵌入应用最大池。 You should maybe apply the max pooling to the 2nd dimension of size 4: 您可能应该将最大池应用于大小为4的第二维：
pooling = tf.nn.max_pool(conv, [1, 2, 1, 1], [1, 2, 1, 1], "VALID")
# pooling has shape [batch_size, 2, 1, 1000]
Concerning your gradient issue, it comes from the two max pooling. 关于梯度问题，它来自两个最大池。 Only 1 of the 1000 inputs is passed through, so the gradients for 999 of the inputs is 0. 1000个输入中只有1个通过，因此999个输入的渐变为0。
This is why your first conv weights have only 2 non-zero gradients , and the second conv weights have only 1 non-zero gradients . 这就是为什么您的第一个conv权重只有2个非零梯度 ，而第二个conv权重只有1个非零梯度 。
All in all, the real issue is your architecture here, you should maybe rewrite it down on a piece of paper first. 总而言之，真正的问题是这里的体系结构，您应该首先将其重写为一张纸。
声明:本站的技术帖子网页，遵循CC BY-SA 4.0协议，如果您需要转载，请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.
相关问题
Tensorflow：LSTM和GradientDescentOptimizer的梯度为零 - Tensorflow: gradients are zero for LSTM and GradientDescentOptimizer
重复卷积张量流的零梯度？ - Zero gradients for repetitve convolutions tensorflow?
tensorflow_probability：反向传播正态分布样本的 log_prob 时，梯度始终为零 - tensorflow_probability: Gradients always zero when backpropagating the log_prob of a sample of a normal distribution
Tensorflow 预测始终为零 - Tensorflow Prediction always zero
经过训练的TensorFlow模型始终输出零 - Trained TensorFlow model always outputs zero
Tensorflow无法评估梯度 - Tensorflow fails to evaluate gradients
计算张量流梯度 - Calculating tensorflow gradients
Tensorflow 1.14没有提供渐变 - Tensorflow 1.14 no gradients provided
张量流中几个梯度的计算 - computation of several gradients in tensorflow
Tensorflow 中的变量没有梯度 - No gradients for variables in Tensorflow
相关标签