Tensorflow：ValueError：無法爲Tensor'佔位符：0'提供形狀（423，）的值，其形狀爲'（？，423）'

Question

我是ML的新手，並通過此工具學習TF tutorial -

在下面的代碼中，我可以計算曆元丟失但不準確。

import tensorflow as tf 
from wordsnlp import create_feature_sets_and_labels 
import numpy as np 
train_x,train_y,test_x,test_y = create_feature_sets_and_labels('pos.txt','neg.txt') 
n_nodes_hl1 = 500 


n_classes = 2 

batch_size = 100 

x = tf.placeholder('float',[None,len(train_x[0])]) 
y = tf.placeholder('float') 

#(input_data*weights) + biases 
def neural_network_model(data): 
    hidden_1_layer = {'weights': tf.Variable(tf.random_normal([len(train_x[0]),n_nodes_hl1])), 
         'biases': tf.Variable(tf.random_normal([n_nodes_hl1]))} 

    return output 

def neural_network_model(data): 
hidden_1_layer = {'weights': tf.Variable(tf.random_normal([len(train_x[0]),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]))} 

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']) 
l1 = tf.nn.relu(l2) 

l3= tf.add(tf.matmul(l2, hidden_2_layer['weights']) , hidden_2_layer['biases']) 
l1 = 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(logits=prediction,labels=y)) 
    optimizer = tf.train.AdamOptimizer().minimize(cost) 

    hm_epochs = 10 

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

     for epoch in range(hm_epochs): 
      epoch_loss=0 
      i=0 
      while i < len(train_x): 
       start = i 
       end = i + batch_size 
       batch_x = np.array(train_x[start:end]) 
       batch_y = np.array(train_y[start:end]) 

       _,c = sess.run([optimizer,cost] , feed_dict = {x: batch_x , y : batch_y}) 
       epoch_loss+= c 
       i+= batch_size 
      print("Epoch",epoch , 'completed out of ' ,hm_epochs, ' loss: ', epoch_loss) 



     correct = tf.equal(tf.argmax(prediction,1), tf.argmax(y,1)) 
     accuracy = tf.reduce_mean(tf.cast(correct, 'float')) 
     print('Accuracy: ', accuracy.eval({x:test_x , y: test_y})) 



train_neural_network(x) 

這我得到這個代碼（我簡化的）而我計算精度誤差：

ValueError: Cannot feed value of shape (423,) for Tensor 'Placeholder:0', which has shape '(?, 423)'

能否請你指出問題出是什麼？ 在此先感謝。

Answer 1

首先，您的代碼不完整，請檢查neural_network_model函數。

反正下面的代碼有效。現在，我剛剛使用了一個網絡層，您可以在neural_network_model函數中添加更多圖層。確保n_classes和output的大小在neural_network_model函數中是相同的。

現在運行下面的代碼，然後更新neural_network_model函數。

import tensorflow as tf 
import numpy as np 
import random 
import nltk 
from nltk.tokenize import word_tokenize 
import numpy as np 
import random 
import pickle 
from collections import Counter 
from nltk.stem import WordNetLemmatizer 

lemmatizer = WordNetLemmatizer() 
hm_lines = 100000 

def create_lexicon(pos,neg): 
    lexicon = [] 
    with open(pos,'r') as f: 
     contents = f.readlines() 
     for l in contents[:hm_lines]: 
      all_words = word_tokenize(l) 
      lexicon += list(all_words) 

    with open(neg,'r') as f: 
     contents = f.readlines() 
     for l in contents[:hm_lines]: 
      all_words = word_tokenize(l) 
      lexicon += list(all_words) 

    lexicon = [lemmatizer.lemmatize(i) for i in lexicon] 
    w_counts = Counter(lexicon) 
    l2 = [] 
    for w in w_counts: 
     #print(w_counts[w]) 
     if 1000 > w_counts[w] > 50: 
      l2.append(w) 
    print(len(l2)) 
    return l2 


def sample_handling(sample,lexicon,classification): 
    featureset = [] 
    with open(sample,'r') as f: 
     contents = f.readlines() 
     for l in contents[:hm_lines]: 
      current_words = word_tokenize(l.lower()) 
      current_words = [lemmatizer.lemmatize(i) for i in current_words] 
      features = np.zeros(len(lexicon)) 
      for word in current_words: 
       if word.lower() in lexicon: 
        index_value = lexicon.index(word.lower()) 
        features[index_value] += 1 
      features = list(features) 
      featureset.append([features,classification]) 
    return featureset 

def create_feature_sets_and_labels(pos,neg,test_size = 0.1): 
    lexicon = create_lexicon(pos,neg) 
    features = [] 
    features += sample_handling('pos.txt',lexicon,[1,0]) 
    features += sample_handling('neg.txt',lexicon,[0,1]) 
    random.shuffle(features) 
    features = np.array(features) 

    testing_size = int(test_size*len(features)) 

    train_x = list(features[:,0][:-testing_size]) 
    train_y = list(features[:,1][:-testing_size]) 
    test_x = list(features[:,0][-testing_size:]) 
    test_y = list(features[:,1][-testing_size:]) 

    return train_x,train_y,test_x,test_y 

train_x,train_y,test_x,test_y = create_feature_sets_and_labels('pos.txt','neg.txt') 
n_nodes_hl1 = 2 


n_classes = 2 

batch_size = 100 

x = tf.placeholder('float',[None,len(train_x[0])]) 
y = tf.placeholder('float') 

#(input_data*weights) + biases 
def neural_network_model(data): 
    hidden_1_layer = {'weights': tf.Variable(tf.random_normal([len(train_x[0]),n_nodes_hl1])), 
         'biases': tf.Variable(tf.random_normal([n_nodes_hl1]))} 
    output = tf.matmul(data,hidden_1_layer['weights']) + hidden_1_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(logits=prediction,labels=y)) 
    optimizer = tf.train.AdamOptimizer().minimize(cost) 

    hm_epochs = 1 

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

     for epoch in range(hm_epochs): 
      epoch_loss=0 
      i=0 
      while i < len(train_x): 
       start = i 
       end = i + batch_size 
       batch_x = np.array(train_x[start:end]) 
       batch_y = np.array(train_y[start:end]) 

       _,c = sess.run([optimizer,cost] , feed_dict = {x: batch_x , y : batch_y}) 
       epoch_loss+= c 
       i+= batch_size 
      print("Epoch",epoch , 'completed out of ' ,hm_epochs, ' loss: ', epoch_loss) 



     correct = tf.equal(tf.argmax(prediction,1), tf.argmax(y,1)) 
     accuracy = tf.reduce_mean(tf.cast(correct, 'float')) 
     print('Accuracy: ', accuracy.eval({x:test_x , y: test_y})) 

train_neural_network(x) 

注：該代碼具有其他級別的缺陷，但不是這個問題的時候，我把丟失的功能從place you pointed

編輯2：

我猜我不應該用你愚蠢的錯誤來鼓勵你，這是我最後一次修復事情。你再次搞砸了相同的功能。您必須先將代碼完整地發佈到堆棧溢出之前，以確保您所詢問的是您遇到的正確問題，而不是一個側面愚蠢的錯誤。

import tensorflow as tf 
import numpy as np 
import random 
import nltk 
from nltk.tokenize import word_tokenize 
import numpy as np 
import random 
import pickle 
from collections import Counter 
from nltk.stem import WordNetLemmatizer 

lemmatizer = WordNetLemmatizer() 
hm_lines = 100000 

def create_lexicon(pos,neg): 
    lexicon = [] 
    with open(pos,'r') as f: 
     contents = f.readlines() 
     for l in contents[:hm_lines]: 
      all_words = word_tokenize(l) 
      lexicon += list(all_words) 

    with open(neg,'r') as f: 
     contents = f.readlines() 
     for l in contents[:hm_lines]: 
      all_words = word_tokenize(l) 
      lexicon += list(all_words) 

    lexicon = [lemmatizer.lemmatize(i) for i in lexicon] 
    w_counts = Counter(lexicon) 
    l2 = [] 
    for w in w_counts: 
     #print(w_counts[w]) 
     if 1000 > w_counts[w] > 50: 
      l2.append(w) 
    print(len(l2)) 
    return l2 


def sample_handling(sample,lexicon,classification): 
    featureset = [] 
    with open(sample,'r') as f: 
     contents = f.readlines() 
     for l in contents[:hm_lines]: 
      current_words = word_tokenize(l.lower()) 
      current_words = [lemmatizer.lemmatize(i) for i in current_words] 
      features = np.zeros(len(lexicon)) 
      for word in current_words: 
       if word.lower() in lexicon: 
        index_value = lexicon.index(word.lower()) 
        features[index_value] += 1 
      features = list(features) 
      featureset.append([features,classification]) 
    return featureset 

def create_feature_sets_and_labels(pos,neg,test_size = 0.1): 
    lexicon = create_lexicon(pos,neg) 
    features = [] 
    features += sample_handling('pos.txt',lexicon,[1,0]) 
    features += sample_handling('neg.txt',lexicon,[0,1]) 
    random.shuffle(features) 
    features = np.array(features) 

    testing_size = int(test_size*len(features)) 

    train_x = list(features[:,0][:-testing_size]) 
    train_y = list(features[:,1][:-testing_size]) 
    test_x = list(features[:,0][-testing_size:]) 
    test_y = list(features[:,1][-testing_size:]) 

    return train_x,train_y,test_x,test_y 

train_x,train_y,test_x,test_y = create_feature_sets_and_labels('pos.txt','neg.txt') 
n_nodes_hl1 = 2 


n_classes = 2 

batch_size = 100 

x = tf.placeholder('float',[None,len(train_x[0])]) 
y = tf.placeholder('float') 

import tensorflow as tf 

import numpy as np 
train_x,train_y,test_x,test_y = create_feature_sets_and_labels('pos.txt','neg.txt') 
n_nodes_hl1 = 4 
n_nodes_hl2 = 3 
n_nodes_hl3 = 2 

n_classes = 2 

batch_size = 100 

x = tf.placeholder('float',[None,len(train_x[0])]) 
y = tf.placeholder('float') 

def neural_network_model(data): 
    hidden_1_layer = {'weights': tf.Variable(tf.random_normal([len(train_x[0]),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]))} 

    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(logits=prediction,labels=y)) 
    optimizer = tf.train.AdamOptimizer().minimize(cost) 

    hm_epochs = 10 

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

     for epoch in range(hm_epochs): 
      epoch_loss=0 
      i=0 
      while i < len(train_x): 
       start = i 
       end = i + batch_size 
       batch_x = np.array(train_x[start:end]) 
       batch_y = np.array(train_y[start:end]) 

       _,c = sess.run([optimizer,cost] , feed_dict = {x: batch_x , y : batch_y}) 
       epoch_loss+= c 
       i+= batch_size 
      print("Epoch",epoch , 'completed out of ' ,hm_epochs, ' loss: ', epoch_loss) 



     correct = tf.equal(tf.argmax(prediction,1), tf.argmax(y,1)) 
     accuracy = tf.reduce_mean(tf.cast(correct, 'float')) 
     print('Accuracy: ', accuracy.eval({x:test_x , y: test_y})) 



train_neural_network(x)