Created
October 24, 2018 15:22
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def L_model_backward(AL, Y, caches): | |
grads = {} | |
L = len(caches) # the number of layers | |
m = AL.shape[1] | |
Y = Y.reshape(AL.shape) # after this line, Y is the same shape as AL | |
#Initizalizing backward propagation | |
dAL = - (np.divide(Y, AL) - np.divide(1 - Y, 1 - AL)) | |
# Lth layer (SIGMOID -> LINEAR) gradients. | |
#Inputs: "dAL, current_cache". Outputs: "grads["dAL-1"], grads["dWL"], grads["dbL"] | |
current_cache = caches[L-1] | |
grads["dA" + str(L-1)], grads["dW" + str(L)], grads["db" + str(L)] = linear_activation_backward(dAL, current_cache, activation = "sigmoid") | |
# Loop from l=L-2 to l=0 | |
for l in reversed(range(L-1)): | |
# lth layer: (RELU -> LINEAR) gradients. | |
# Inputs: "grads["dA" + str(l + 1)], current_cache". Outputs: "grads["dA" + str(l)] , grads["dW" + str(l + 1)] , grads["db" + str(l + 1)] | |
current_cache = caches[l] | |
dA_prev_temp, dW_temp, db_temp = linear_activation_backward(grads["dA" + str(l+1)], current_cache, activation = "relu") | |
grads["dA" + str(l)] = dA_prev_temp | |
grads["dW" + str(l + 1)] = dW_temp | |
grads["db" + str(l + 1)] = db_temp | |
return grads |
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