):
m = X.shape[1]
dy = cache['y'] - Y
dW2 = (1 / m) * np.dot(dy, np.transpose(cache['A1']))
db2... db1 = (1 / m) * np.sum(dZ1, axis=1, keepdims=True)
return {"dW1": dW1, "db1": db1, "dW2": dW2, "db2...": db2}
def backPropagation(X, Y, params,cache)中的parama和cache是什么?...']
return {'W1': W1, 'W2': W2, 'b1': b1, 'b2': b2}
循环是关键
需要多次迭代才能找到回归最低成本的参数。...现在开始循环!