逻辑回归--预测64*64*3图像 二分类

import numpy as np
import matplotlib.pyplot as plt
import h5py
import scipy
from PIL import Image
from scipy import ndimage

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

# sigmoid函数
def sigmoid(z):
    return 1 / (1 + np.exp(-z))
  
# 参数初始化函数
def initialize_with_zeros(dim):
    w = np.zeros((dim, 1))      # 权重
    b = 0                       # 偏置
    assert (w.shape == (dim, 1))
    assert (isinstance(b, float) or isinstance(b, int))
    return w, b
  
# 反向、前向传播函数
def propagate(w, b, X, Y):
    """
    w -- weights, a numpy array of size (num_px * num_px * 3, 1)
    b -- bias, a scalar
    X -- data of size (num_px * num_px * 3, number of examples)
    Y -- true "label" vector (containing 0 if non-cat, 1 if cat) of size (1, number of examples)
    Return:
    cost -- negative log-likelihood cost for logistic regression
    dw --