Tensorflow：ValueError：无法为Tensor 'Placeholder:0'提供形状值（423，），其形状为'(?, 423)'

1 回答

• 1

  首先，您的代码不完整，请检查 neural_network_model 函数 .

  无论如何，以下代码有效 . 目前，我刚刚使用了一个网络层，您可以在 neural_network_model 函数中添加更多图层 . 确保 n_classes 和 neural_network_model 函数中 output 的大小相同 .

  现在运行以下代码，然后更新 neural_network_model 函数 .

  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)
  

  注意：代码在其他级别有缺陷，但这不是这个问题的重点，我从place you pointed获取了缺失的函数

  Edit 2:

  我想我不应该用你的愚蠢错误来鼓励你，这是我最后一次修理的事情 . 你又一次搞砸了同样的功能 . 在将代码发布到堆栈溢出之前，您必须首先完成代码，这样您才能确定是在问您遇到的正确问题，而不是一个愚蠢的错误 .

  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)
  

  回复于 2024-05-03T21:02:33+08:00