深度学习 两层隐藏层网络实现猫狗分类

• 加载数据集，导入相应的包

• ---

  import numpy as np
  import torch
  from torch import nn
  import h5py
  from torch.nn import init
  
  
  def load_data():
      train_dataset = h5py.File('E:\文档\【吴恩达课后编程作业】第二周作业 - Logistic回归-识别猫的图片资源/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('E:\文档\【吴恩达课后编程作业】第二周作业 - Logistic回归-识别猫的图片资源/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]))