深度学习编程练习-用python实现的识别猫神经网络

 python实现识别猫神经网络

用pycharm进行编程实现的，具体细节可参考：

https://blog.youkuaiyun.com/qq_34290470/article/details/99849514

百度云pycharm项目源码：https://pan.baidu.com/s/12q_Er1vJpeo-O8h_KQYgCQ

完整python代码：

import numpy as np
import matplotlib.pyplot as plt
import h5py


def load_dataset():
    train_dataset = h5py.File('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('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

index=25
train_set_x_orig , train_set_y , test_set_x_orig , test_set_y , classes = load_dataset() # 加载数据集
plt.imshow(train_set_x_orig[index]) # 查看训练集中的图片
plt.show()

# 打印出当前的训练标签值
# train_set_y是二维数组，使用np.squeeze的目的是压缩维度,即去掉shape中的1
# classe[0]='non-cat',clas