無法獲得tensorflow中張量的更新值

我使用下面的代碼進行簡單邏輯迴歸。我能夠獲得b的更新值：培訓前後b.eval()的值不同。但是，W.eval()的值保持不變。我想知道我犯了什麼錯誤？謝謝！無法獲得tensorflow中張量的更新值

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}))

來源

2017-08-14 vki

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

我沒有初始化全零。我使用隨機正常初始化。此外，該模型在訓練後具有較高的預測性能，因此W不能爲零矩陣。 – vki

當我們打印numpy的陣列僅起始和最後一個值將得到的印刷，並且在MNIST的情況下的權重的那些索引沒有更新爲在圖像中的對應像素作爲所有數字被寫入的中心部分保持恆定數組或圖像不沿邊界區域。從一個輸入樣本到另一個輸入樣本變化的實際像素是中心像素，因此只有那些相應的權重元素纔會更新。之前比較重和訓練就可以使用numpy.array_equal後（W1，W2）或者，您可以通過打印整個numpy的數組：進口numpy的 numpy.set_printoptions（閾值=「男」）或者，你可以比較逐個元素，並只打印那些相差一定閾值的數組的值

來源

2017-08-19 12:50:11

無法獲得tensorflow中張量的更新值

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