01-NN

import tensorflow as tf
import numpy as np

# 使用np来创造两个样本[0,1],[2,3]
x = np.arange(4, dtype=np.float32).reshape(2,2)

# 使用np来创造两个label[0,1]
y_ = np.array([0,1], dtype=np.float32).reshape(2,1)

# 对神经元的权重w和偏差b进行初始化
w1 = tf.Variable(tf.random_normal([2,3], stddev=1, seed=1))
w2 = tf.Variable(tf.random_normal([3,1], stddev=1, seed=1))
b1 = tf.Variable(tf.zeros([3]))
b2 = tf.Variable(tf.zeros([1]))

# 计算wx+b运算之后的激活值
a = tf.nn.relu(tf.matmul(x,w1) + b1)
y = tf.matmul(a, w2) + b2

# 采用交叉熵作为损失函数
cost = tf.nn.sigmoid_cross_entropy_with_logits(logits=y, labels=y_, name=None)
# 采用地图下降法作为优化器
train_op = tf.train.GradientDescentOptimizer(0.01).minimize(cost)
# 执行会话
with tf.Session() as sess:
    init = tf.initialize_all_variables()
    # init = tf.global_variables_initialize()
    sess.run(init)
    # 这里定义了100次循环
    for i in range(100):
        sess.run(train_op)

    print(sess.run(w1))
    print(sess.run(w2))