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我是机器学习的新手。我有一个关于贝叶斯神经网络的项目来预测足球结果。然后我按照此链接的说明进行操作。然后我编写这样的代码：

import sys
from math import floor

import edward as ed
import numpy as np
import pandas as pd
import tensorflow as tf
from edward.models import Normal, Categorical
from fancyimpute import KNN
from tqdm import tqdm
from sklearn.preprocessing import OneHotEncoder

data = pd.read_csv('features_dummies_with_label.csv', sep=',')


def impute_missing_values_by_KNN():
    home_data = data[[col for col in data.columns if 'hp' in col]]
    away_data = data[[col for col in data.columns if 'ap' in col]]
    label_data = data[[col for col in data.columns if 'label' in col]]

    home_filled = pd.DataFrame(KNN(3).complete(home_data))
    home_filled.columns = home_data.columns
    home_filled.index = home_data.index

    away_filled = pd.DataFrame(KNN(3).complete(away_data))
    away_filled.columns = away_data.columns
    away_filled.index = away_data.index

    data_frame_out = pd.concat([home_filled, away_filled, label_data], axis=1)

    return data_frame_out


dataset = impute_missing_values_by_KNN()

dataset = pd.DataFrame(data=dataset)

data_x = dataset.loc[:, dataset.columns != 'label'].as_matrix().astype(np.float32)
data_y_ = dataset.loc[:, 'label'].as_matrix().astype(np.float32)

enc = OneHotEncoder(sparse=False)
integer_encoded = np.array(data_y_).reshape(-1)
integer_encoded = integer_encoded.reshape(len(integer_encoded), 1)
onehot_encoded = enc.fit_transform(integer_encoded)

data_y = onehot_encoded

train_size = 0.9

train_cnt = floor(data_x.shape[0] * train_size)

N = int(train_cnt)

train_x, test_x = data_x[0:N], data_x[N:]
train_y, test_y = data_y[0:N], data_y[N:]

in_size = train_x.shape[1]
out_size = train_y.shape[1]

EPOCH_SUM = 5
BATCH_SIZE = 10

train_y2 = np.argmax(train_y, axis=1)
test_y2 = np.argmax(test_y, axis=1)

n_nodes_hl1 = 500

x_ = tf.placeholder(tf.float32, [None, in_size])
y_ = tf.placeholder(tf.float32)

# def neural_network_model(data):
w_h1 = Normal(loc=tf.zeros([in_size, out_size]), scale=tf.ones([in_size, out_size]))

b_h1 = Normal(loc=tf.zeros([out_size]), scale=tf.ones([out_size]))

y_pre = Normal(tf.matmul(x_, w_h1) + b_h1, scale=1.0)

qw_h1 = Normal(loc=tf.Variable(tf.random_normal([in_size, out_size])),
               scale=tf.Variable(tf.random_normal([in_size, out_size])))

qb_h1 = Normal(loc=tf.Variable(tf.random_normal([out_size])), scale=tf.Variable(tf.random_normal([out_size])))

y = Normal(tf.matmul(x_, qw_h1) + qb_h1, scale=1.0)

inference = ed.KLqp({w_h1: qw_h1, b_h1: qb_h1}, data={y_pre: y_})
inference.initialize()

sess = tf.Session()
sess.run(tf.global_variables_initializer())

with sess:
    samples_num = 100
    for epoch in tqdm(range(EPOCH_SUM), file=sys.stdout):
        perm = np.random.permutation(N)
        for i in range(0, N, BATCH_SIZE):
            batch_x = train_x[perm[i:i + BATCH_SIZE]]
            batch_y = train_y2[perm[i:i + BATCH_SIZE]]
            inference.update(feed_dict={x_: batch_x, y_: batch_y})
        y_samples = y.sample(samples_num).eval(feed_dict={x_: train_x})
        acc = (np.round(y_samples.sum(axis=0) / samples_num) == train_y2).mean()
        y_samples = y.sample(samples_num).eval(feed_dict={x_: test_x})
        tets_acc = (np.round(y_samples.sum(axis=0) / samples_num) == test_y2).mean()
        if (epoch + 1) % 1 == 0:
            tqdm.write('epoch:\t{}\taccuracy:\t{}\tvaridation accuracy:\t{}'.format(epoch + 1, acc, tets_acc))

但是，当我调试它时，会出现这样的错误：

InvalidArgumentError (see above for traceback): Incompatible shapes: [10] vs. [10,3]
     [[Node: inference/sample/Normal_2/log_prob/standardize/sub = Sub[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_Placeholder_1_0_1, inference/sample/Normal_2/loc)]]

在这一行：

inference.update(feed_dict={x_: batch_x, y_: batch_y})

错误是什么意思？以及如何解决？

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1 回答 1

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如果没有看到回溯，就很难调试错误。但是，我假设您传递给 inference.update 的张量或数组与您在声明中定义的形状相同。因此，我会检查例如：batch_x、train_x（每次迭代）、w_h1、qw_h1、...的形状。打印出这些数组/张量或使用 tfdbg 调试并比较它们。

请不要将此答案视为最终答案，而是将其视为评论。但是，由于我的分数 < 50 分，我无法发表评论。但是，我想做出贡献，因为我认为我的帖子可以为解决方案做出贡献。

于 2018-02-05T14:31:47.110 回答