无法获得tensorflow中张量的更新值

我使用下面的代码进行简单逻辑回归。我能够获得b的更新值：培训前后b.eval()的值不同。但是，W.eval()的值保持不变。我想知道我犯了什么错误？谢谢！无法获得tensorflow中张量的更新值

from __future__ import print_function 

import tensorflow as tf 

# Import MNIST data 
from tensorflow.examples.tutorials.mnist import input_data 
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) 

# Parameters 
learning_rate = 0.01 
training_epochs = 20 
batch_size = 100 
display_step = 1 

# tf Graph Input 
x = tf.placeholder(tf.float32, [None, 784]) # mnist data image of shape 28*28=784 
y = tf.placeholder(tf.float32, [None, 10]) # 0-9 digits recognition => 10 classes 

# Set model weights 
W = tf.Variable(tf.random_normal([784, 10])) 
b = tf.Variable(tf.zeros([10])) 

# Construct model 
pred = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax 

# Minimize error using cross entropy 
cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1)) 
# Gradient Descent 
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) 

# Initializing the variables 
init = tf.global_variables_initializer() 

# Launch the graph 
with tf.Session() as sess: 
    sess.run(init) 

    print('W is:') 
    print(W.eval()) 
    print('b is:') 
    print(b.eval()) 
    # Training cycle 
    for epoch in range(training_epochs): 
     avg_cost = 0. 
     total_batch = int(mnist.train.num_examples/batch_size) 
     # Loop over all batches 
     for i in range(total_batch): 
      batch_xs, batch_ys = mnist.train.next_batch(batch_size) 
      # Run optimization op (backprop) and cost op (to get loss value) 
      _, c = sess.run([optimizer, cost], feed_dict={x: batch_xs, 
               y: batch_ys}) 
      # Compute average loss 
      avg_cost += c/total_batch 
     # Display logs per epoch step 
     if (epoch+1) % display_step == 0: 
      print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost)) 

    print("Optimization Finished!") 

    print('W is:') 
    print(W.eval()) 
    print('b is:') 
    print(b.eval()) 
    # Test model 
    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) 
    # Calculate accuracy 
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) 
    print("Accuracy:", accuracy.eval({x: mnist.test.images, y:  mnist.test.labels}))

来源

2017-08-14 vki

请参见[this]（https://stackoverflow.com/a/35962343/2861681） – vmg

我没有初始化全零。我使用随机正常初始化。此外，该模型在训练后具有较高的预测性能，因此W不能为零矩阵。 – vki

当我们打印numpy的阵列仅起始和最后一个值将得到的印刷，并且在MNIST的情况下的权重的那些索引没有更新为在图像中的对应像素作为所有数字被写入的中心部分保持恒定数组或图像不沿边界区域。从一个输入样本到另一个输入样本变化的实际像素是中心像素，因此只有那些相应的权重元素才会更新。之前比较重和训练就可以使用numpy.array_equal后（W1，W2）或者，您可以通过打印整个numpy的数组：进口numpy的 numpy.set_printoptions（阈值=“男”）或者，你可以比较逐个元素，并只打印那些相差一定阈值的数组的值

来源

2017-08-19 12:50:11

无法获得tensorflow中张量的更新值

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