Linear Regression with Eager API

最新推荐文章于 2019-09-02 21:27:58 发布

gz153016

最新推荐文章于 2019-09-02 21:27:58 发布

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分类专栏： TensorFlow学习笔记

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本文详细介绍使用TensorFlow实现线性回归的过程，包括数据准备、模型定义、损失函数计算、优化器选择及训练过程。通过具体实例展示了如何利用梯度下降法调整权重和偏置，以最小化预测值与真实值之间的差距。

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from __future__ import absolute_import, division, print_function
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf

# Set Eeger API

tf.enable_eager_execution()
tfe = tf.contrib.eager

# Training Data
train_X = [3.3, 4.4, 5.5, 6.71, 6.93, 4.168, 9.779, 6.182, 7.59, 2.167,
           7.042, 10.791, 5.313, 7.997, 5.654, 9.27, 3.1]
train_Y = [1.7, 2.76, 2.09, 3.19, 1.694, 1.573, 3.366, 2.596, 2.53, 1.221,
           2.827, 3.465, 1.65, 2.904, 2.42, 2.94, 1.3]

n_samples = len(train_X)

# Parameters
learning_rate = 0.01
display_step = 100
num_steps = 1000

# Weight and Bias
W = tfe.Variable(np.random.randn())
b = tfe.Variable(np.random.randn())
# Linear regression(Wx + b)
def linear_regression(inputs):
    return inputs * W + b
# Mean square error
def mean_square_fn(model_fn, inputs, labels):
    return tf.reduce_sum(tf.pow(model_fn(inputs) - labels, 2)) / (2 * n_samples)

# SDG Optimizer
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)
# Compute gradients
grad = tfe.implicit_gradients(mean_square_fn)

# Initial cost, before optimizing
print("Initial cost={:.9f}".format(mean_square_fn(linear_regression, train_X, train_Y)),
      "W=", W.numpy(), "b=", b.numpy())



# Training
for step in range(num_steps):
    optimizer.apply_gradients(grad(linear_regression, train_X, train_Y))
    if (step + 1) % display_step == 0 or step == 0:
        print("Epoch:", '%04d' % (step + 1), "cost=",
              "{:.9f}".format(mean_square_fn(linear_regression, train_X, train_Y)),
              "W=", W.numpy(), "b=", b.numpy())


# Graphic display
plt.plot(train_X, train_Y, 'ro', label='Original data')
plt.plot(train_X, np.array(W * train_X + b), label='Fitted line')
plt.legend()
plt.show()

if __name__ == "__main__":
    pass