tensorflow深度学习浅显的例子

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

plotdata = {"batchsize": [], "loss": []}


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)]


train_X = np.linspace(-1, 1, 100)
train_Y = 2 * train_X + np.random.randn(*train_X.shape) * 0.3  # y=2x,但是加入了噪声
# 显示模拟数据点
plt.plot(train_X, train_Y, 'ro', label='Original data')
plt.legend()
plt.show()

# 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)

# 初始化所有变量
init = tf.global_variables_initializer()
# 定义参数
training_epochs = 200
display_step = 2
# 启动
with tf.Session() as sess:
    sess.run(init)
    plotdata = {"batchsize": [], "loss": []}  # 存放批次值和损失值
    # 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!")
    print("cost=", sess.run(cost, feed_dict={X: train_X, Y: train_Y}), "W=", sess.run(W), "b=", sess.run(b))

    plt.plot(train_X, train_Y, "ro", label='Original data')
    plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fittedline')
    plt.legend()
    plt.show()

    plotdata["avgloss"] = moving_average(plotdata["loss"])
    plt.figure(1)
    plt.subplot(211)
    plt.plot(plotdata["batchsize"], plotdata["avgloss"], 'b--')
    plt.xlabel('Minibatch number')
    plt.ylabel('Loss')
    plt.title('Minibatch run vs. Training loss')
    plt.show()