Ошибка несовместимости форм в байесовской нейронной сети

Я новичок в машинном обучении. У меня есть проект о байесовской нейронной сети для прогнозирования футбольного результата. Затем я следую инструкциям по этой ссылке. Затем я делаю такой код:

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))

Но когда я отлаживаю его, возникает такая ошибка:

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)]]

в этой строке:

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

В чем смысл ошибки? И как это решить?