IndexError：数组索引过多

Question

我最近开始使用tensorflow，这就像我的第二段代码一样，在设计该神经网络时我陷入了困境。 我无法增加批处理大小，并且这个问题已经存在了一段时间。

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，他们在其中使用了以下代码。 希望您能够将此代码与我的代码进行比较。 如果有任何更正，请帮助我

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}))

如您所见，我的代码与MNIST的代码非常相似，但我无法返回此代码段中的特定元组。

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

提前致谢。 如果您认为该问题是重复性的，我想解释一下我找不到其他相关问题。

Answer 1

我不太了解您对数据执行的重塑，也不了解其原始格式，但是在这里y = dataset.iloc[:, 13].values似乎y是一维数组，而在这里tuple(y_train[:,currentprev:current]就像2D矩阵一样访问它，错误提示您正在使用太多（2）索引来索引1D数组。