Question

我是tensorflow的初学者，我从网上尝试了一些玩具示例：

import io
import tensorflow as tf
import math

def _runModel(data):
        hidden1 = tf.nn.relu(tf.matmul(data, weights) + biases) 
        print(weights.eval())
        hidden2 = tf.nn.relu(tf.matmul(hidden1, weights) + biases)  
        print(weights.eval())
        logits = tf.matmul(hidden2, weights) + biases   
        print(weights.eval())
        return logits

sess = tf.InteractiveSession()
weights = tf.Variable( tf.truncated_normal([5,5], stddev=1.0 / math.sqrt(float(5))), name='weights')
biases = tf.Variable(tf.zeros([5]), name='biases')
data_placeholder = tf.placeholder(tf.float32, shape= [1,5]) 
labels_placeholder = tf.placeholder(tf.int32, shape=[1])
sess.run(tf.global_variables_initializer())
data_placeholder = [[0.0, 1.0, 2.0, 3.0, 4.0]]
labels_placeholder = [1]

inference=_runModel(data_placeholder)
logits = inference.eval()
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=tf.to_int64(labels_placeholder), logits=logits, name='xentropy')
loss = tf.reduce_mean(cross_entropy, name='xentropy_mean')

optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(loss)

_, c = sess.run([optimizer, loss])

但是当我运行它时，出现以下错误：

Traceback (most recent call last):   File "test_tensorflow.py", line 43, in <module>
    optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(loss)   File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/optimizer.py", line 421, in minimize
    ([str(v) for _, v in grads_and_vars], loss)) ValueError: No gradients provided for any variable, check your graph for ops that do not support gradients, between variables ["<tf.Variable 'weights:0' shape=(5, 5) dtype=float32_ref>", "<tf.Variable 'biases:0' shape=(5,) dtype=float32_ref>"] and loss Tensor("xentropy_mean:0", shape=(), dtype=float32).

如果您有什么想法要帮助我...

Answer 1

谢谢，我关注了您的评论和这个stackoverflow（Tensorflow - No gradients provided for any variable），现在它可以工作了：

import io
import tensorflow as tf
import math


def _runModel(data):
        hidden1 = tf.nn.relu(tf.matmul(data, weights) + biases)
        hidden2 = tf.nn.relu(tf.matmul(hidden1, weights) + biases)
        logits = tf.matmul(hidden2, weights) + biases
        return logits

weights = tf.Variable( tf.truncated_normal([5,5], stddev=1.0 / math.sqrt(float(5))), name='weights')

biases = tf.Variable(tf.zeros([5]), name='biases')

data_placeholder = tf.placeholder(tf.float32, shape= [1,5])

labels_placeholder = tf.placeholder(tf.int32, shape=[1])

logits = _runModel(data_placeholder)

cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=tf.to_int64(labels_placeholder), logits=logits, name='xentropy')

loss = tf.reduce_mean(cross_entropy, name='xentropy_mean')


optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(loss)



sess = tf.InteractiveSession()

with sess.as_default():

    sess.run(tf.global_variables_initializer())

    feed_dict = {
        data_placeholder: [[0.0, 1.0, 2.0, 3.0, 4.0]],
        labels_placeholder: [1],
    }

    _, c = sess.run([optimizer, loss], feed_dict=feed_dict)

    print(c)

Answer 2

您的logits应该是张量

将呼叫转移到eval()。

此调用进行前向传递，并返回一个数组，该数组的值具有网络的随机初始化的权重。此时，您不在张量流领域，并且不再可以进行反向传播。

ValueError：没有为任何变量提供渐变，请检查图形是否支持gradien

2 个答案: