41 lines
1.3 KiB
Python
41 lines
1.3 KiB
Python
import tensorflow as tf
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import numpy as np
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import matplotlib.pyplot as plt
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# 准备初始数据
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x_data = np.float32(np.random.rand(100)) # 生成 100 个随机数，作为输入数据
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y_data = x_data * 2 + 1.6 # 根据线性方程 y = 2x + 1.6 生成输出数据
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# 初始化权重（W）和偏置（b）变量
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weights = tf.Variable(tf.zeros([1]), name='weights') # 初始化权重为 0
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biases = tf.Variable(tf.zeros([1]), name='biases') # 初始化偏置为 0
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# 定义模型
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def linear_model(x):
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return weights * x + biases
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# 定义损失函数
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def loss_fn(y_true, y_pred):
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return tf.reduce_mean(tf.square(y_true - y_pred))
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# 定义优化器
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optimizer = tf.optimizers.SGD(0.5) # 梯度下降优化器，学习率为 0.5
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# 训练过程
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def train_step(x, y):
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with tf.GradientTape() as tape:
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y_pred = linear_model(x)
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loss = loss_fn(y, y_pred)
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gradients = tape.gradient(loss, [weights, biases])
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optimizer.apply_gradients(zip(gradients, [weights, biases]))
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# 训练模型
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for step in range(201):
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train_step(x_data, y_data)
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if step % 20 == 0:
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print(f"Step {step}: weights = {weights.numpy()}, biases = {biases.numpy()}")
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# 绘图（可选）
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plt.scatter(x_data, y_data, c='r')
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plt.plot(x_data, linear_model(x_data), c='g')
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plt.show() |