定义训练样本X和Y、定义输入x，输出y，定义权重w和偏置b，定义线性回归输出out，定义损失函数为均方差损失函数loss，并且使用Adam算法的Optimizer

deep_feedforward_networks_01.py

import tensorflow as tf
import numpy as np

X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
Y = np.array([[0], [1], [1], [0]])

x = tf.placeholder(tf.float32, [None, 2])
y = tf.placeholder(tf.float32, [None, 1])

w = tf.Variable(tf.random_normal([2, 1]))
b = tf.Variable(tf.random_normal([1]))

out = tf.matmul(x, w) + b
loss = tf.reduce_mean(tf.square(out - y))

train = tf.train.AdamOptimizer(0.01).minimize(loss)