Python 简单前馈神经网络-索引器：索引59超出大小为47的轴0的界限

我正在尝试用tensorflow学习机器学习。我没有使用现有的iris数据，而是使用自己收集的赛马数据

# Implementation of a simple MLP network with one hidden layer. Tested on the iris data set.
# Requires: numpy, sklearn>=0.18.1, tensorflow>=1.0

# NOTE: In order to make the code simple, we rewrite x * W_1 + b_1 = x' * W_1'
# where x' = [x | 1] and W_1' is the matrix W_1 appended with a new row with elements b_1's.
# Similarly, for h * W_2 + b_2
import tensorflow as tf
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
import pandas as pd

RANDOM_SEED = 42
tf.set_random_seed(RANDOM_SEED)

def init_weights(shape):
    """ Weight initialization """
    weights = tf.random_normal(shape, stddev=0.1)
    return tf.Variable(weights)

def forwardprop(X, w_1, w_2):
    """
    Forward-propagation.
    IMPORTANT: yhat is not softmax since TensorFlow's softmax_cross_entropy_with_logits() does that internally.
    """
    h    = tf.nn.sigmoid(tf.matmul(X, w_1))  # The \sigma function
    yhat = tf.matmul(h, w_2)  # The \varphi function
    return yhat

def get_iris_data():
    df = pd.read_csv('export.csv')
    df = df[['track_id','distance','participant','horse_id','horse_money','horse_is_home','age','trainer_id','start_position','galloped','disqualified','jockey_id','jockey_is_home','shoes_back','shoes_front','finish_position']]
    df.fillna(52, inplace=True)

    data = np.array(df.drop(['finish_position'],1))
    target = np.array(df['finish_position']).astype(int)

    # Prepend the column of 1s for bias
    N, M  = data.shape
    all_X = np.ones((N, M + 1))
    all_X[:, 1:] = data

    # Convert into one-hot vectors
    num_labels = len(np.unique(target))
    all_Y = np.eye(num_labels)[target]  # One liner trick!
    return train_test_split(all_X, all_Y, test_size=0.33, random_state=RANDOM_SEED)

def main():
    train_X, test_X, train_y, test_y = get_iris_data()

    # Layer's sizes
    x_size = train_X.shape[1]   # Number of input nodes: 4 features and 1 bias
    h_size = 256                # Number of hidden nodes
    y_size = train_y.shape[1]   # Number of outcomes (3 iris flowers)

    # Symbols
    X = tf.placeholder("float", shape=[None, x_size])
    y = tf.placeholder("float", shape=[None, y_size])

    # Weight initializations
    w_1 = init_weights((x_size, h_size))
    w_2 = init_weights((h_size, y_size))

    # Forward propagation
    yhat    = forwardprop(X, w_1, w_2)
    predict = tf.argmax(yhat, axis=1)

    # Backward propagation
    cost    = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=yhat))
    updates = tf.train.GradientDescentOptimizer(0.01).minimize(cost)

    # Run SGD
    sess = tf.Session()
    init = tf.global_variables_initializer()
    sess.run(init)

    for epoch in range(100):
        # Train with each example
        for i in range(len(train_X)):
            sess.run(updates, feed_dict={X: train_X[i: i + 1], y: train_y[i: i + 1]})

        train_accuracy = np.mean(np.argmax(train_y, axis=1) ==
                                 sess.run(predict, feed_dict={X: train_X, y: train_y}))
        test_accuracy  = np.mean(np.argmax(test_y, axis=1) ==
                                 sess.run(predict, feed_dict={X: test_X, y: test_y}))

        print("Epoch = %d, train accuracy = %.2f%%, test accuracy = %.2f%%"
              % (epoch + 1, 100. * train_accuracy, 100. * test_accuracy))

    sess.close()

if __name__ == '__main__':
    main()

当我尝试在python中运行此操作时，我遇到了一个错误：

Traceback (most recent call last):
File "test.py", line 94, in <module>
  main()
File "test.py", line 50, in main
  train_X, test_X, train_y, test_y = get_iris_data()
File "test.py", line 46, in get_iris_data
  all_Y = np.eye(num_labels)[target]  # One liner trick!
IndexError: index 59 is out of bounds for axis 0 with size 47

回溯（最近一次呼叫最后一次）：
文件“test.py”，第94行，在
main（）
文件“test.py”，第50行，在main中
训练X，测试X，训练y，测试y=获取iris\u数据（）
get_iris_数据中第46行的文件“test.py”
all_Y=np.eye（num_labels）[target]#一行技巧！
索引器：索引59超出大小为47的轴0的界限

有人能帮我做错事吗？或者给我一个提示，提前谢谢

这是我导出的数据，有50000行。

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您正试图抓住列表中只有47个元素的第59个元素。调试以找出.num_labels是唯一目标数的原因。是否所有目标都在[0，num_-1]范围内？看起来有59个目标，但只有47个不同的目标。您可以分享您的目标吗？我上传了一个导出数据示例，其中有50000行而不是我的600000行，我遇到了相同的问题，但改为“IndexError:index 44超出了大小为18的轴0的界限”。