吴恩达cs229|编程作业第三周（Python）

练习三：多分类和神经网络

目录

1.包含的文件

2.多分类问题

3.神经网络

1.包含的文件

文件名	含义
ex3.py	逻辑回归多分类
ex3_nn.py	神经网络分类
ex3data1.mat	手写数字集
ex3weights.mat	神经网络初始权重
displayData.py	可视化数据
sigmoid.py	Sigmoid函数
lrCostFunction.py	多分类逻辑回归代价函数
oneVsAll.py	训练多分类器
predictOneVsAll.py	使用多分类器进行预测
predict.py	使用神经网络预测

红色部分需要自己填写。

2.多分类问题

• 需要的包以及初始化：

import matplotlib.pyplot as plt
import numpy as np
import scipy.io as scio

import displayData as dd
import lrCostFunction as lCF
import oneVsAll as ova
import predictOneVsAll as pova

plt.ion()

# Setup the parameters you will use for this part of the exercise
input_layer_size = 400  # 20x20 input images of Digits
num_labels = 10         # 10 labels, from 0 to 9
                        # Note that we have mapped "0" to label 10

2.1加载数据并可视化

• 可视化程序displayData.py：

import matplotlib.pyplot as plt
import numpy as np


def display_data(x):
    (m, n) = x.shape

    # Set example_width automatically if not passed in
    example_width = np.round(np.sqrt(n)).astype(int)
    example_height = (n / example_width).astype(int)

    # Compute the number of items to display
    display_rows = np.floor(np.sqrt(m)).astype(int)
    display_cols = np.ceil(m / display_rows).astype(int)

    # Between images padding
    pad = 1

    # Setup blank display
    display_array = - np.ones((pad + display_rows * (example_height + pad),
                              pad + display_rows * (example_height + pad)))

    # Copy each example into a patch on the display array
    curr_ex = 0
    for j in range(display_rows):
        for i in range(display_cols):
            if curr_ex > m:
                break

            # Copy the patch
            # Get the max value of the patch
            max_val = np.max(np.abs(x[curr_ex]))
            display_array[pad + j * (example_height + pad) + np.arange(example_height),
                          pad + i * (example_width + pad) + np.arange(example_width)[:, np.newaxis]] = \
                          x[curr_ex].reshape((example_height, example_width)) / max_val
            curr_ex += 1

        if curr_ex > m:
            break

    # Display image
    plt.figure()
    plt.imshow(display_array, cmap='gray', extent=[-1, 1, -1, 1])
    plt.axis('off')

• 测试代码：

# ===================== Part 1: Loading and Visualizing Data =====================
# We start the exercise by first loading and visualizing the dataset.
# You will be working with a dataset that contains handwritten digits.
#
# Load Training Data
print('Loading and Visualizing Data ...')

data = scio.loadmat('ex3data1.mat')  #读取训练集 包括两部分 输入特征和标签
X = data['X']   #提取输入特征 5000*400的矩阵  5000个训练样本 每个样本特征维度为400 一行代表一个训练样本
y = data['y'].flatten() #提取标签 data['y']是一个5000*1的2维数组 利用flatten()将