为什么我的TensorFlow神经网络对XOR只有大约0.5的准确度？

Question

我用TensorFlow为异或输入编写了一个神经网络。我用了一个隐藏层与2个单位和softmax分类。输入的格式为<；1，x_1，x_2，zero，one>；，其中

• 1是偏见
• 对于所有组合{00，01，10，11}，x_1和x_2要么在0和1之间。选择在0或1左右正态分布
• 0：如果输出为零，则为1
• 一：如果输出为1，则为1

精度始终在0.5左右。出什么问题了？神经网络的架构是错误的，还是代码有问题？在

import tensorflow as tf
import numpy as np
from random import randint

DEBUG=True

def init_weights(shape):
    return tf.Variable(tf.random_normal(shape, stddev=0.01))


def model(X, weight_hidden, weight_output):
    # [1,3] x [3,n_hiddent_units] = [1,n_hiddent_units]
    hiddern_units_output = tf.nn.sigmoid(tf.matmul(X, weight_hidden))

    # [1,n_hiddent_units] x [n_hiddent_units, 2] = [1,2]
    return hiddern_units_output
    #return tf.matmul(hiddern_units_output, weight_output)


def getHiddenLayerOutput(X, weight_hidden):
    hiddern_units_output = tf.nn.sigmoid(tf.matmul(X, weight_hidden))
    return hiddern_units_output

total_inputs = 100
zeros = tf.zeros([total_inputs,1])
ones = tf.ones([total_inputs,1])
around_zeros = tf.random_normal([total_inputs,1], mean=0, stddev=0.01)
around_ones = tf.random_normal([total_inputs,1], mean=1, stddev=0.01)

batch_size = 10
n_hiddent_units = 2
X = tf.placeholder("float", [None, 3])
Y = tf.placeholder("float", [None, 2])

weight_hidden = init_weights([3, n_hiddent_units])
weight_output = init_weights([n_hiddent_units, 2])

hiddern_units_output = getHiddenLayerOutput(X, weight_hidden)
py_x = model(X, weight_hidden, weight_output)

#cost = tf.square(Y - py_x)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=py_x, labels=Y))
train_op = tf.train.GradientDescentOptimizer(0.05).minimize(cost)

with tf.Session() as sess:
    tf.global_variables_initializer().run()

    trX_0_0 = sess.run(tf.concat([ones, around_zeros, around_zeros, ones, zeros], axis=1))
    trX_0_1 = sess.run(tf.concat([ones, around_zeros, around_ones, zeros, ones], axis=1))
    trX_1_0 = sess.run(tf.concat([ones, around_ones, around_zeros, zeros, ones], axis=1))
    trX_1_1 = sess.run(tf.concat([ones, around_ones, around_ones, ones, zeros], axis=1))
    trX = sess.run(tf.concat([trX_0_0, trX_0_1, trX_1_0, trX_1_1], axis=0))
    trX = sess.run(tf.random_shuffle(trX))
    print(trX)

    for i in range(10):
        for start, end in zip(range(0, len(trX), batch_size), range(batch_size, len(trX) + 1, batch_size)):
            trY = tf.identity(trX[start:end,3:5])
            trY = sess.run(tf.reshape(trY,[batch_size, 2]))
            sess.run(train_op, feed_dict={ X: trX[start:end,0:3], Y: trY })

        start_index = randint(0, (total_inputs*4)-batch_size)
        y_0 = sess.run(py_x, feed_dict={X: trX[start_index:start_index+batch_size,0:3]})
        print("iteration :",i, " accuracy :", np.mean(np.absolute(trX[start_index:start_index+batch_size,3:5]-y_0)),"\n")

查看“注释”部分中的更新代码

Answer 1

问题在于随机分配的权重。Here是经过一系列跟踪和错误后得到的修改版本。在