import numpy as np
import pandas as pd
import sklearn.datasets as skd
import matplotlib.pyplot as plt

X = skd.load_breast_cancer()['data']
Y = skd.load_breast_cancer()['target']

X = ((X - np.mean(X))/np.std(X)).T
Y = Y.reshape(-1,1).T

layer_dims = [X.shape[0]]
W = {}
b = {}
activations = []
A = {"A0":X}
Z = {}
cost = [] 

def add_layer(layer_dim,activation):
    if not(activation in "sigmoid relu"):
        print("Activation Error")
    else:
        l = len(layer_dims)
        layer_dims.append(layer_dim)
        W[f"W{l}"] = np.random.randn(layer_dim,layer_dims[l-1]) *0.01
        b[f"b{l}"] = 0
        activations.append(activation)