驗證函數中張量的值

Question

我正在爲MNIST輸入創建深度學習完全連接的NN。我有一個函數（它佔位符輸入）

# Create model                             
def multilayer_perceptron(x, activation_fn, weights, biases, dbg=False): 
    layerDatas = OrderedDict() 
    # get each layer data                          
    prev = x 
    for i in range(len(weights)-1): 
     weight = weights.items()[i][1] 
     bias = biases.items()[i][1] 
     var = 'layer_' + str(i+1) 
     layerData = tf.add(tf.matmul(prev, weight), bias) 
     layerData = activation_fn(layerData) 
     prev = layerData 
     layerDatas[var] = layerData 

    # output layer with linear function, using the last layer output value              
    val = tf.matmul(prev, weights['out']) 
    out_layer = tf.matmul(prev, weights['out']) + biases['out'] 
    print x.eval() # debug the data 
    return out_layer 

它在權重和偏差中需要多個層。我打電話跟

sess = tf.InteractiveSession() # start a session                   

print 'Data', n_input, n_classes 
print 'Train', train_set_x.shape, train_set_y.shape 

(weights, biases) = createWeightsBiases(layers, n_input, n_classes, dbg) 

# tf Graph input                           
x = tf.placeholder("float", [None, n_input]) 
y = tf.placeholder("float", [None, n_classes]) 

# Construct model                           
pred = multilayer_perceptron(x, activation_fn, weights, biases, dbg) 
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) 
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) 

# Initializing the variables                        
init = tf.initialize_all_variables() 

done_looping = False 

display_step = 1 

# Add ops to save and restore all the variables.                   
saver = tf.train.Saver() 

# Launch the graph                           
sess.run(init) 

# Training cycle 
epochs = 1000                          
for epoch in range(epochs): 
    avg_cost = 0. 
    total_batch = int(len(train_set_x)/batch_size) 
    print 'Batch', total_batch, batch_size 
    # Loop over all batches                        
    for i in range(total_batch): 
     batch_x = train_set_x[i * batch_size: (i + 1) * batch_size] 
     batch_y = train_set_y[i * batch_size: (i + 1) * batch_size]                       
     # Run optimization op (backprop) and cost op (to get loss value)             
     _, c = sess.run([optimizer, cost], feed_dict={x: batch_x, 
                  y: batch_y}) 
     # Compute average loss                        
     avg_cost += c/total_batch 
    # Display logs per epoch step                       
    if epoch % display_step == 0: 
     print "Epoch:", '%04d' % (epoch+1), "cost=", \ 
      "{:.9f}".format(avg_cost) 
print "Optimization Finished!" 

# Test model                            
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) 

# Calculate accuracy                          
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) 
print "Accuracy:", accuracy.eval({x: valid_set_x, y: valid_set_y}) 

主程序當我嘗試打印張在我multilayer_perceptron功能，我得到

tensorflow.python.framework.errors.InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder' with dtype float 
    [[Node: Placeholder = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]] 

崩潰我希望在解決這個得到幫助。

Answer 1

您無法評估佔位符。相反，您可以爲圖表提供適當的佔位符值，然後才能提取內容（這是您輸入圖表的值）。

因此，從multilayer_perceptron函數中刪除print x.eval() # debug the data行。

要檢查佔位符的價值，你必須餵它並提取你剛給它的值（注意：它是無用的）。 如果你真的想這樣做，這就是如何：

placeholder_value = sess.run(x, feed_dict={x: [1,2,3,4]}) 
print placeholder_value 

它會打印出值[1,2,3,4]