得到一個錯誤的TensorFlow

我一個YouTube TensorFlow教程下面一起，我已經遇到了一個錯誤：得到一個錯誤的TensorFlow

這是我的代碼：

import tensorflow as tf 

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

n_nodes_hl1 = 500 
n_nodes_hl2 = 500 
n_nodes_hl3 = 500 

n_classes = 10 
batch_size = 100 

# height x width 
x = tf.placeholder("float") 
y = tf.placeholder("float") 

def neural_network_model(data): 
    hidden_1_layer = {"weights":tf.Variable(tf.random_normal([784,n_nodes_hl1])),"biases":tf.Variable(tf.random_normal([n_nodes_hl1]))} 

    hidden_2_layer = {"weights":tf.Variable(tf.random_normal([n_nodes_hl1,n_nodes_hl2])),"biases":tf.Variable(tf.random_normal([n_nodes_hl2]))} 

    hidden_3_layer = {"weights":tf.Variable(tf.random_normal([n_nodes_hl2,n_nodes_hl3])),"biases":tf.Variable(tf.random_normal([n_nodes_hl3]))} 

    output_layer = {"weights":tf.Variable(tf.random_normal([n_nodes_hl3,n_classes])),"biases":tf.Variable(tf.random_normal([n_classes]))} 

    # (input_data * weights + biases 
    l1 = tf.add(tf.matmul(data,hidden_1_layer["weights"]), hidden_1_layer["biases"]) 
    l1 = tf.nn.relu(l1) 

    l2 = tf.add(tf.matmul(l1,hidden_2_layer["weights"]), hidden_2_layer["biases"]) 
    l2 = tf.nn.relu(l2) 

    l3 = tf.add(tf.matmul(l2,hidden_3_layer["weights"]), hidden_3_layer["biases"]) 
    l3 = tf.nn.relu(l3) 

    output = tf.matmul(l3,output_layer["weights"]) + output_layer["biases"] 

    return output 

def train_neural_network(x): 
    prediction = neural_network_model(x) 
    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=prediction,logits=y)) 
    optimizer = tf.train.AdamOptimizer().minimize(cost) 

    # cycles of 
    hm_epochs = 10 

    with tf.Session() as sess: 
     sess.run(tf.initialize_all_variables()) 

     for epoch in range(hm_epoch): 
      epoch_loss = 0 
      for i in range(int(mnist.train.num.examples/batch_size)): 
       epoch_x, epoch_y = mnist.train.next_batch(batch_size) 
       i, c = sess.run([optimizer,cost],feed_dict = {x:epoch_x,y:epoch_y}) 
       epoch_loss += c 
      print("Epoch", epoch, "completed out of", hm_epochs, "loss:" , epoch_loss) 
     correct = tf.equal(tf.argmax(prediction,1), tf.argmax(y1)) 

     accuracy = tf.reduce_mean(tf,cast(correct, "float")) 
     print("Accuracy", accuracy.eval({x:mnist.test.images, y:mnist.test.labels})) 



train_neural_network(x)

和，這是錯誤：

Traceback (most recent call last): 
    File "/home/markus/Documents/NN-Tutorial-04.py", line 65, in <module> 
    train_neural_network(x) 
    File "/home/markus/Documents/NN-Tutorial-04.py", line 43, in train_neural_network 
    optimizer = tf.train.AdamOptimizer().minimize(cost) 
    File "/home/markus/.local/lib/python3.5/site-packages/tensorflow/python/training/optimizer.py", line 322, 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 'Variable:0' shape=(784, 500) dtype=float32_ref>", "<tf.Variable 'Variable_1:0' shape=(500,) dtype=float32_ref>", "<tf.Variable 'Variable_2:0' shape=(500, 500) dtype=float32_ref>", "<tf.Variable 'Variable_3:0' shape=(500,) dtype=float32_ref>", "<tf.Variable 'Variable_4:0' shape=(500, 500) dtype=float32_ref>", "<tf.Variable 'Variable_5:0' shape=(500,) dtype=float32_ref>", "<tf.Variable 'Variable_6:0' shape=(500, 10) dtype=float32_ref>", "<tf.Variable 'Variable_7:0' shape=(10,) dtype=float32_ref>"] and loss Tensor("Mean:0", dtype=float32).

任何幫助將不勝感激，這是我所關注的視頻（https://www.youtube.com/watch?v=PwAGxqrXSCs）。

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來源

2017-09-17 Markus

您在調用tf.nn.softmax_cross_entropy_with_logits()一直困惑標籤和logits。這種方式tensorflow可能會嘗試爲標籤分配漸變（我不認爲這應該是可能的，因爲這些是佔位符），並且圖中的變量不會得到任何漸變。這是不正確的。取而代之的

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
     labels=prediction,logits=y) 
)

你應該寫

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
     labels=y,logits=prediction) 
)

此外，也有一些語法錯誤，你也覆蓋i變量（這是外循環的計數器），在內部循環與返回的優化器。現在你不使用i，但如果你願意的話，它會導致很難診斷錯誤。只需將返回的變量重命名爲_（python約定用於未使用的返回值）：

_, c = sess.run([optimizer,cost],feed_dict = {x:epoch_x,y:epoch_y})

來源

2017-09-17 06:55:53 Drop

得到一個錯誤的TensorFlow

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