40 lines
1.4 KiB
Python
40 lines
1.4 KiB
Python
#=======================================================================
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'''
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# 实现线性回归模型: 拟合线性函数
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# 所谓线性回归模型就是y = W * x + b的形式的表达式拟合的模型。
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'''
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#=======================================================================
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#import tensorflow as tf
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import tensorflow.compat.v1 as tf
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import matplotlib.pyplot as plt
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import numpy as np
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tf.compat.v1.disable_eager_execution()
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# 初始数据准备
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x_data = np.float32(np.random.rand(100)) # np.random.rand 默认数据类型为float64
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y_data = x_data * 2 + 1.6
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# 权重、偏置这些不断更新的值用tf变量存储
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weights = tf.Variable(tf.zeros([1])) # tf.zeros 默认数据类型为float32
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biases = tf.Variable(tf.zeros([1]))
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# 定义线性模型
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y = weights * x_data + biases
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# 损失函数。tf.reduce_mean()是取均值。square是平方。
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loss = tf.reduce_mean(tf.square(y - y_data))
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# 用梯度优化方法最小化损失函数;
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# 训练用到的优化算法里面的数值参数：学习速率,优化速率
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# loss为优化指标
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#optimizer = tf.train.GradientDescentOptimizer(0.5)
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optimizer = tf.compat.v1.train.GradientDescentOptimizer(0.5)
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train = optimizer.minimize(loss)
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with tf.Session() as sess:
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sess.run(tf.global_variables_initializer())
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for step in range(201):
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sess.run(train)
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if step % 20 == 0:
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print (step, sess.run(weights), sess.run(biases)) |