用Python构建深度神经网络

学习目标：

使用ReLU等非线性单元提升模型性能，构建深度神经网络，执行便于使用的神经网络类

需要定义的函数：

def initialize_parameters_deep(layer_dims): #layer_dims是包含每层隐藏单元数量的array

np.random.seed(1)

parameters = {}

L = len(layer_dims) 

for l in range(1, L):

parameters['W' + str(l)] = np.random.randn(layer_dims[l], layer_dims[l-1]) * 0.01

parameters['b' + str(l)] = np.zeros((layer_dims[l], 1))

return parameters

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def linear_forward(A, W, b):

Z = np.dot(W, A) + b

cache = (A, W, b) # for backward

return Z, cache

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def linear_activation_forward(A_prev, W, b, activation):

if activation == 'sigmoid':

Z, linear_cache = linear_forward(A_prev, W, b)

A, activation_cache = sigmoid(Z)

elif activation =