正文
Python实现单神经元分类图片的训练
提示：扫一扫查出行【扫一扫了解最新限行尾号】
复制提示
1、加载包和数据
- numpy is the fundamental package for scientific computing with Python.
- h5py is a common package to interact with a dataset that is stored on an H5 file.
- matplotlib is a famous library to plot graphs in Python.
- PIL and scipy are used here to test your model with your own picture at the end
__author__ = 'Qian Chenglong' import numpy as np
import matplotlib.pyplot as plt
import h5py
import scipy
from scipy import ndimage
from lr_utils import load_dataset #load data
train_set_x_orig, train_set_y, test_set_x_orig, test_set_y, classes = load_dataset()
2、处理数据
1）看一下数据的形状
#get she shape of thed data
print(train_set_x_orig.shape)
print(train_set_y.shape)
print(test_set_x_orig.shape)
print(test_set_y.shape)
print(classes.shape)
2）获取训练数据个数和测试数据个数，图片是64*64*3的格式
#the number of data,train number is 209 test number is50
m_train = train_set_x_orig.shape[0]
m_test = test_set_x_orig.shape[0] #the picture's row * col =64*64 channel=3
num_px = train_set_x_orig.shape[1]
3）重构数据形状
A trick when you want to flatten a matrix X of shape (a,b,c,d) to a matrix X_flatten of shape (b∗c∗d, a) is to use:
X_flatten = X.reshape(X.shape[0], -1).T # X.T is the transpose of X
train_set_x_flatten = train_set_x_orig.reshape(train_set_x_orig.shape[0], -1).T
test_set_x_flatten = test_set_x_orig.reshape(test_set_x_orig.shape[0], -1).T
4）标准化（归一化）处理数据，因为图片的数据都是0~255的所以我们直接/255
# standardize our dataset.
train_set_x = train_set_x_flatten/255.
test_set_x = test_set_x_flatten/255.
5）定义要使用的函数
sigmoid 函数在这里被称为激活函数。
