7-1 线性逻辑回归.py

# -*- coding: utf-8 -*-
"""
Created on Fri May 19 11:57:46 2017
@author: 代码医生 qq群：40016981，公众号：xiangyuejiqiren
@blog：http://blog.youkuaiyun.com/lijin6249
"""

import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
from sklearn.utils import shuffle


# 模拟数据点
def generate(sample_size, mean, cov, diff, regression):
    num_classes = 2  # len(diff)
    samples_per_class = int(sample_size / 2)

    X0 = np.random.multivariate_normal(mean, cov, samples_per_class)
    Y0 = np.zeros(samples_per_class)

    for ci, d in enumerate(diff):
        X1 = np.random.multivariate_normal(mean + d, cov, samples_per_class)
        Y1 = (ci + 1) * np.ones(samples_per_class)

        X0 = np.concatenate((X0, X1))
        Y0 = np.concatenate((Y0, Y1))

    if regression == False:  # one-hot  0 into the vector "1 0
        class_ind = [Y == class_number for class_number in range(num_classes)]
        Y = np.asarray(np.hstack(class_ind), dtype=np.float32)
    X, Y = shuffle(X0, Y0)

    return X, Y


input_dim = 2
np.random.seed(10)
num_classes = 2
mean = np.random.randn(num_classes)
cov = np.eye(num_classes)
X, Y = generate(1000, mean, cov, [3.0], True)
colors = ['r' if l == 0 else 'b' for l in Y[:]]
plt.scatter(X[:, 0], X[:, 1], c=colors)
plt.xlabel("Scaled age (in yrs)")
plt.ylabel("Tumor size (in cm)")
plt.show()
lab_dim = 1
# tf Graph Input
input_features = tf.placeholder(tf.float32, [None, input_dim])
input_labels = tf.placeholder(tf.float32, [None, lab_dim])
# Set model weights
W = tf.Variable(tf.random_normal([input_dim, lab_dim]), name="weight")
b = tf.Variable(tf.zeros([lab_dim]), name="bias")

output = tf.nn.sigmoid(tf.matmul(input_features, W) + b)
cross_entropy = -(input_labels * tf.log(output) + (1 - input_labels) * tf.log(1 - output))
ser = tf.square(input_labels - output)
loss = tf.reduce_mean(cross_entropy)
err = tf.reduce_mean(ser)
optimizer = tf.train.AdamOptimizer(0.04)  # 尽量用这个--收敛快，会动态调节梯度
train = optimizer.minimize(loss)  # let the optimizer train

maxEpochs = 50
minibatchSize = 25

# 启动session
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())

    for epoch in range(maxEpochs):
        sumerr = 0
        for i in range(np.int32(len(Y) / minibatchSize)):
            x1 = X[i * minibatchSize:(i + 1) * minibatchSize, :]
            y1 = np.reshape(Y[i * minibatchSize:(i + 1) * minibatchSize], [-1, 1])
            tf.reshape(y1, [-1, 1])
            _, lossval, outputval, errval = sess.run([train, loss, output, err],
                                                     feed_dict={input_features: x1, input_labels: y1})
            sumerr = sumerr + errval

        print("Epoch:", '%04d' % (epoch + 1), "cost=", "{:.9f}".format(lossval), "err=",
              sumerr / np.int32(len(Y) / minibatchSize))

    # 图形显示
    train_X, train_Y = generate(100, mean, cov, [3.0], True)
    colors = ['r' if l == 0 else 'b' for l in train_Y[:]]
    plt.scatter(train_X[:, 0], train_X[:, 1], c=colors)
    # plt.scatter(train_X[:, 0], train_X[:, 1], c=train_Y)
    # plt.colorbar()

    #    x1w1+x2*w2+b=0
    #    x2=-x1* w1/w2-b/w2
    x = np.linspace(-1, 8, 200)
    y = -x * (sess.run(W)[0] / sess.run(W)[1]) - sess.run(b) / sess.run(W)[1]
    plt.plot(x, y, label='Fitted line')
    plt.legend()
    plt.show()