NN.py 文件源码

def backPropagate(Z1, Z2, y, W2, b2):
    ## YOUR CODE HERE ##
    E2 = 0
    E1 = 0
    Eb1 = 0

    # E2 is the error in output layer. To find it we should exract estimated value from actual output.
    # We should find 5 error because there are 5 node in output layer.
    E2 = Z2 - y

    ## E1 is the error in the hidden layer. To find it we should use the error that we found in output layer and the weights between
    ## output and hidden layer
    ## We should find 30 error because there are 30 node in hidden layer.
    E1 = np.dot(W2, np.transpose(E2))

    ## Eb1 is the error bias for hidden layer. To find it we should use the error that we found in output layer and the weights between
    ## output and bias layer
    ## We should find 1 error because there are 1 bias node in hidden layer.
    Eb1 = np.dot(b2, np.transpose(E2))
    ####################
    return E2, E1, Eb1

# calculate the gradients for weights between units and the bias weights