Linear Regression Example

目標：簡單處理二維平面中線性擬合一堆數據

效果：

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
rng = np.random

learning_rate = 0.1
training_epochs = 1000
display_step = 50

train_X = np.asarray([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 = np.asarray([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 = train_X.shape[0]
print(n_samples)


# tf Graph Input
X = tf.placeholder("float")
Y = tf.placeholder("float")

# Set model weights
W = tf.Variable(rng.randn(), name="weight")
b = tf.Variable(rng.randn(), name="bias")

pred = tf.add(tf.multiply(X, W), b)

cost = tf.reduce_sum(tf.square(pred-Y))/(2*n_samples)
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for epoch in range(training_epochs) :
        # zip() 函數用於將可迭代的對象做爲參數，將對象中對應的元素打包成一個個元組，而後返回由這些元組組成的列表。
        for (x,y) in zip(train_X,train_Y):
            sess.run(optimizer,feed_dict={X:x,Y:y})

        #Display logs per epoch step
        if (epoch+1) % display_step == 0:
            c = sess.run(cost, feed_dict={X: train_X, Y:train_Y})
            print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c), \
                "W=", sess.run(W), "b=", sess.run(b))

    # print("Optimization Finished!")
    # training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y})
    # print("Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n')

    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()
    plt.show()
複製代碼

代碼二：eager API

from __future__ import absolute_import, division, print_function

import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
import tensorflow.contrib.eager as tfe

tf.enable_eager_execution()

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)
print(n_samples)

# Parameters
learning_rate = 0.01
display_step = 100
num_steps = 1000

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)


# SGD 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()
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