IndexError：数组索引太多

Question

我最近开始使用tensorflow，这就像我的第二段代码，我在设计这个神经网络时被卡住了。我无法增加批量，这个问题一直存在。

import numpy as np 
import pandas as pd 
import tensorflow as tf 
import math 

#importing the data and preprocessing it 

dataset = pd.read_csv('C:\\Users\\Geeks_Sid\\Documents\\Deep-Learning-A-Z\Deep Learning A-Z\\Volume 1 - Supervised Deep Learning\\Part 1 - Artificial Neural Networks (ANN)\\Section 4 - Building an ANN\\Artificial_Neural_Networks\\Churn_Modelling.csv') 
X = dataset.iloc[:, 3:13].values 
y = dataset.iloc[:, 13].values 

from sklearn.preprocessing import LabelEncoder, OneHotEncoder 
labelencoder_X_1 = LabelEncoder() 
X[:, 1] = labelencoder_X_1.fit_transform(X[:, 1]) 
labelencoder_X_2 = LabelEncoder() 
X[:, 2] = labelencoder_X_2.fit_transform(X[:, 2]) 
onehotencoder = OneHotEncoder(categorical_features = [1]) 
X = onehotencoder.fit_transform(X).toarray() 

#creating a train test split 
from sklearn.model_selection import train_test_split 
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0) 


# Feature Scaling 
from sklearn.preprocessing import StandardScaler 
sc = StandardScaler() 
X_train = sc.fit_transform(X_train) 
X_test = sc.transform(X_test) 


# Creating layers for Neural network 

n_nodes_hl1 = 1000 
n_nodes_hl2 = 1000 
n_nodes_hl3 = 1000 
n_classes = 1 
batch_size = 50 
x = tf.placeholder('float', [None, 11]) 
y = tf.placeholder('float') 

def neural_network_model(data): 
    hidden_1_layer = {'weights':tf.Variable(tf.random_normal([11, 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'] 
    print("I was in neural netowrk m") 
    return output 

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

    hm_epochs = 10 
    config = tf.ConfigProto() 
    config.gpu_options.allow_growth = True 
    with tf.Session(config=config) as sess: 
     # OLD: 
     for epoch in range(hm_epochs): 
      epoch_loss = 0 
      current = 0 
      for _ in range(80): 
       currentprev = current 
       current += 100 
       epoch_x, epoch_y = tuple(X_train[:,currentprev:current]) ,tuple(y_train[:,currentprev:current]) 
       _, 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(y, 1)) 

     accuracy = tf.reduce_mean(tf.cast(correct, 'float')) 
     print('Accuracy:',accuracy.eval({x:X_test, y:y_test})) 

     #sess.run(tf.initialize_all_variables()) 
     # NEW: 
     sess.run(tf.global_variables_initializer()) 

     correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1)) 



train_neural_network(x) 

我正在处理看起来像这样的错误。

train_neural_network(x) 
I was in neural netowrk m 
Traceback (most recent call last): 

    File "<ipython-input-8-7c7cbdae9b34>", line 1, in <module> 
    train_neural_network(x) 

    File "<ipython-input-7-b7e263fe7976>", line 20, in train_neural_network 
    epoch_x, epoch_y = tuple(X_train[:,currentprev:current]) ,tuple(y_train[:,currentprev:current]) 

IndexError: too many indices for array 

我正在尝试复制张量流MNIST数据集分类的代码，他们在这里使用下面这段代码。我希望你能够将这些代码与我的代码进行比较。如果有任何修改，请帮我

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

    hm_epochs = 10 
    config = tf.ConfigProto() 
    config.gpu_options.allow_growth = True 
    with tf.Session(config=config) as sess: 
     # OLD: 
     #sess.run(tf.initialize_all_variables()) 
     # NEW: 
     sess.run(tf.global_variables_initializer()) 

     for epoch in range(hm_epochs): 
      epoch_loss = 0 
      for _ in range(int(mnist.train.num_examples/batch_size)): 
       epoch_x, epoch_y = mnist.train.next_batch(batch_size) 
       _, 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(y, 1)) 

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

正如你看到的，我的代码是非常相似的一个MNIST但我不能够返回一个特定的元组是在这段代码。

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

在此先感谢。如果你觉得这个问题是重复的，我想解释一下，我找不到其他相关的东西。

Answer 1

我不很了解你正在执行对数据的重塑，也不是它的原始格式，但这里y = dataset.iloc[:, 13].values似乎y是一维数组，而这里tuple(y_train[:,currentprev:current]你像一个二维矩阵访问它并且错误告诉你，你正在使用太多（2）索引来索引一维数组。 “