Created
March 11, 2019 14:48
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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) # Step 1: Apply mask D2 to shut down the same neurons as during the forward propagation | |
dA2 = dA2/keep_prob # Step 2: Scale the value of neurons that haven't been shut down | |
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) # Step 1: Apply mask D1 to shut down the same neurons as during the forward propagation | |
dA1 = dA1/keep_prob # Step 2: Scale the value of neurons that haven't been shut down | |
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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