- 关于week2中的一些不熟悉的代码
from lr_utils import load_dataset
## Loading the data (cat/non-cat)
train_set_x_orig, train_set_y, test_set_x_orig, test_set_y, classes = load_dataset()
lr_utils是一个自己写的.py文件,从lr_utils包中导入了load_dataset方法,该方法可以用来导入数据集。
lr_utils.py
文件如下:
import numpy as np
import h5py
def load_dataset():
train_dataset = h5py.File('datasets/train_catvnoncat.h5', "r")
train_set_x_orig = np.array(train_dataset["train_set_x"][:]) # your train set features
train_set_y_orig = np.array(train_dataset["train_set_y"][:]) # your train set labels
test_dataset = h5py.File('datasets/test_catvnoncat.h5', "r")
test_set_x_orig = np.array(test_dataset["test_set_x"][:]) # your test set features
test_set_y_orig = np.array(test_dataset["test_set_y"][:]) # your test set labels
classes = np.array(test_dataset["list_classes"][:]) # the list of classes
train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))
test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))
return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes
该文件中的load_dataset 函数调用h5py中的file函数得到train_dataset和test_dataset对象,再得到train_set_x_orig、train_set_y_orig等。
Each line of your train_set_x_orig and test_set_x_orig is an array representing an image.
print(train_set_x_orig.shape)
print(test_set_x_orig.shape)