第一个完整的神经网络样例程序
程序代码:
import os
import tensorflow as tf
from numpy.random import RandomState
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
#定义神经网络的参数,输入和输出节点
batch_size = 8
w1 = tf.Variable(tf.random_normal([2, 3], stddev=1, seed=1))
w2 = tf.Variable(tf.random_normal([3, 1], stddev=1, seed=1))
x = tf.placeholder(tf.float32, shape=(None, 2), name="x-input")
y_ = tf.placeholder(tf.float32, shape=(None, 1), name='y-input')
#定义前向传播过程,损失函数及反向传播算法
a = tf.matmul(x, w1)
y = tf.matmul(a, w2)
cross_entropy = -tf.reduce_mean(y_ * tf.log(tf.clip_by_value(y, 1e-10, 1.0)))
train_step = tf.train.AdamOptimizer(0.001).minimize(cross_entropy)
#生成模拟数据集
rdm = RandomState(1)
X = rdm.rand(128,2)
Y = [[int(x1+x2 < 1)] for (x1, x2) in X]
#创建一个会话来运行TensorFlow程序
with tf.Session() as sess:
init_op = tf.global_variables_initializer()
sess.run(init_op)
# 输出目前(未经训练)的参数取值。
print("w1:", sess.run(w1))
print("w2:", sess.run(w2))
print("\n")
# 训练模型。
STEPS = 5000
for i in range(STEPS):
start = (i*batch_size) % 128
end = (i*batch_size) % 128 + batch_size
sess.run(train_step, feed_dict={x: X[start:end], y_: Y[start:end]})
if i % 1000 == 0:
total_cross_entropy = sess.run(cross_entropy, feed_dict={x: X, y_: Y})
print("After %d training step(s), cross entropy on all data is %g" % (i, total_cross_entropy))
# 输出训练后的参数取值。
print ("\n")
print ("w1:", sess.run(w1))
print ("w2:", sess.run(w2))
运行结果: