# 深度学习笔记5：正则化与dropout

def forward_propagation_with_dropout(X, parameters, keep_prob = 0.5):
np.random.seed(1) # retrieve parameters
W1 = parameters["W1"]
b1 = parameters["b1"]
W2 = parameters["W2"]
b2 = parameters["b2"]
W3 = parameters["W3"]
b3 = parameters["b3"] # LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SIGMOID
Z1 = np.dot(W1, X) + b1
A1 = relu(Z1)

D1 = np.random.rand(A1.shape[0], A1.shape[1])
D1 = D1 < keep_prob
A1 = np.multiply(D1, A1)
A1 = A1 / keep_prob

A2 = relu(Z2)
Z2 = np.dot(W2, A1) + b2

D2 = np.random.rand(A2.shape[0], A2.shape[1])
D2 = D2 < keep_prob
A2 = np.multiply(D2, A2)
A2 = A2 / keep_prob
A3 = sigmoid(Z3)
Z3 = np.dot(W3, A2) + b3

cache = (Z1, D1, A1, W1, b1, Z2, D2, A2, W2, b2, Z3, A3, W3, b3)
return A3, cache

def backward_propagation_with_dropout(X, Y, cache, keep_prob):

m = X.shape[1]
(Z1, D1, A1, W1, b1, Z2, D2, A2, W2, b2, Z3, A3, W3, b3) = cache

dZ3 = A3 - Y
dW3 = 1./m * np.dot(dZ3, A2.T)
db3 = 1./m * np.sum(dZ3, axis=1, keepdims = True)
dA2 = np.dot(W3.T, dZ3)

dA2 = np.multiply(dA2, D2)
dA2 = dA2 / keep_prob

dZ2 = np.multiply(dA2, np.int64(A2 > 0))
dW2 = 1./m * np.dot(dZ2, A1.T)
db2 = 1./m * np.sum(dZ2, axis=1, keepdims = True)

dA1 = np.dot(W2.T, dZ2)

dA1 = np.multiply(dA1, D1)
dA1 = dA1 / keep_prob

dZ1 = np.multiply(dA1, np.int64(A1 > 0))
dW1 = 1./m * np.dot(dZ1, X.T)
db1 = 1./m * np.sum(dZ1, axis=1, keepdims = True)

gradients = {"dZ3": dZ3, "dW3": dW3, "db3": db3,"dA2": dA2, "dZ2": dZ2, "dW2": dW2, "db2": db2, "dA1": dA1,
"dZ1": dZ1, "dW1": dW1, "db1": db1}
return gradients

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