小编Cir*_*lar的帖子

我正在尝试在TensorFlow(Python 3版本)中实现一个简单的全连接前馈神经网络.网络有2个输入和1个输出,我正在尝试训练它输出两个输入的XOR.我的代码如下:

import numpy as np
import tensorflow as tf

sess = tf.InteractiveSession()

inputs = tf.placeholder(tf.float32, shape = [None, 2])
desired_outputs = tf.placeholder(tf.float32, shape = [None, 1])

weights_1 = tf.Variable(tf.zeros([2, 3]))
biases_1 = tf.Variable(tf.zeros([1, 3]))
layer_1_outputs = tf.nn.sigmoid(tf.matmul(inputs, weights_1) + biases_1)

weights_2 = tf.Variable(tf.zeros([3, 1]))
biases_2 = tf.Variable(tf.zeros([1, 1]))
layer_2_outputs = tf.nn.sigmoid(tf.matmul(layer_1_outputs, weights_2) + biases_2)

error_function = -tf.reduce_sum(desired_outputs * tf.log(layer_2_outputs))
train_step = tf.train.GradientDescentOptimizer(0.05).minimize(error_function)

sess.run(tf.initialize_all_variables())

training_inputs = [[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]]
training_outputs = [[0.0], [1.0], [1.0], [0.0]] …