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tensorflow_first.py
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tensorflow_first.py
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#!/usr/bin/python
#coding=utf-8
''' tf first'''
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
def main():
# 生成原始数据
train_X = np.linspace(-1, 1, 100)
train_Y = 2*train_X + np.random.randn(*train_X.shape)*0.3
# 重置图
tf.reset_default_graph()
# 创建模型
# 占位符
X = tf.placeholder("float")
Y = tf.placeholder("float")
# 模型参数
W = tf.Variable(tf.random_normal([1]), name="weight")
b = tf.Variable(tf.zeros([1]), name="bias")
# 前向结构
z = tf.multiply(X, W) + b
# 反向优化
cost = tf.reduce_mean(tf.square(Y - z))
learning_rate = 0.01
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
# 训练模型
# 初始化所有变量
init = tf.global_variables_initializer()
# 定义参数
training_epochs = 20
display_step = 2
# 保存训练模型
saver = tf.train.Saver()
save_dir = "train_model/"
# 启动session
with tf.Session() as sess:
sess.run(init)
plotdata = {"batchsize":[], "loss":[]}
# 向模型输入数据
for epoch in range(training_epochs):
for (x, y) in zip(train_X, train_Y):
sess.run(optimizer, feed_dict={X: x, Y: y})
# 显示训练中的详细信息
if epoch % display_step == 0:
loss = sess.run(cost, feed_dict={X:train_X, Y:train_Y})
print("Epoch:", epoch+1, "cost=", loss, "W=", sess.run(W), "b=", sess.run(b))
if not (loss == "NA"):
plotdata["batchsize"].append(epoch)
plotdata["loss"].append(loss)
print("Finished!")
saver.save(sess, save_dir + "linermodel.cpkt")
print("cost=", sess.run(cost, feed_dict={X:train_X, Y:train_Y}), "W=", sess.run(W), "b=", sess.run(b))
# 图形显示
plt.figure(1)
plt.subplot(211)
plt.plot(train_X, train_Y, 'ro', label='Original data')
plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
plt.legend()
plotdata["avgloss"] = moving_average(plotdata["loss"])
plt.subplot(212)
plt.plot(plotdata["batchsize"], plotdata["avgloss"], 'b--')
plt.xlabel("Minibatch number")
plt.ylabel("Loss")
plt.title("Minibatch run vs. Training loss")
plt.show()
print("x = 0.2, z = ", sess.run(z, feed_dict={X: 0.2}))
def moving_average(a, w=10):
if len(a) < w:
return a[:]
return [val if idx < w else sum(a[(idx-w):idx]) /w for idx, val in enumerate(a)]
if __name__ == '__main__':
plotdata = {
"batchsize": [],
"loss": []
}
main()