import?numpy?as?np
import?matplotlib.pyplot?as?plt
import?h5py
import?sklearn
import?sklearn.datasets

def?sigmoid（x）:
????“““
????Compute?the?sigmoid?of?x

????Arguments:
????x?--?A?scalar?or?numpy?array?of?any?size.

????Return:
????s?--?sigmoid（x）
????“““
????s?=?1/（1+np.exp（-x））
????return?s

def?relu（x）:
????“““
????Compute?the?relu?of?x

????Arguments:
????x?--?A?scalar?or?numpy?array?of?any?size.

????Return:
????s?--?relu（x）
????“““
????s?=?np.maximum（0x）
????
????return?s

def?forward_propagation（X?parameters）:
????“““
????Implements?the?forward?propagation?（and?computes?the?loss）?presented?in?Figure?2.
????
????Arguments:
????X?--?input?dataset?of?shape?（input?size?number?of?examples）
????Y?--?true?“label“?vector?（containing?0?if?cat?1?if?non-cat）
????parameters?--?python?di