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please fill in all the question marks and DO NOT ALTER / DELETE EXISTING CODE OR ADD NEW LINES: # Define the activation function. sigma

please fill in all the question marks and DO NOT ALTER/DELETE EXISTING CODE OR ADD NEW LINES: # Define the activation function.
sigma = np.tanh
# Let's use a random initial weight and bias.
W = np.array([[-0.94529712,-0.2667356,-0.91219181],
[2.05529992,1.21797092,0.22914497]])
b = np.array([0.61273249,1.6422662])
# define our feed forward function
def a1(a0) :
# Notice the next line is almost the same as previously,
# except we are using matrix multiplication rather than scalar multiplication
z =???
# Everything else is the same though,
return sigma(z)
# Next, if a training example is,
x = np.array([0.7,0.6,0.2])
y = np.array([0.9,0.6])
# Then the cost function is,
d =??? # Vector difference between observed and expected activation
C =??? # Absolute value squared of the difference.
# First define our sigma function.
sigma = np.tanh
# Next define the feed-forward equation.
def a1(w1, b1, a0) :
z =???
return sigma(z)
# This function returns the derivative of the cost function with
# respect to the weight.
def dCdw (w1, b1, x, y) :
dCda =??? # Derivative of cost with activation
dadz =??? # derivative of activation with weighted sum z
J =???
dzdw =???# derivative of weighted sum z with weight
J =???
return J # Return the chain rule product.
# This function returns the derivative of the cost function with
# respect to the bias.
# It is very similar to the previous function.
# You should complete this function.
def dCdb (w1, b1, x, y) :
dCda =???
dadz =???
# Change the next line to give the derivative of
# the weighted sum, z, with respect to the bias, b.
dzdb =???
return dCda * dadz * dzdb
dCdb (W, b, x, y)
array([[-2.19184549e+00],
[1.42277240e-03]])
dCdw (W, b, x, y)
array([[-1.53429185e+00,-1.31510730e+00,-4.38369099e-01],
[9.95940681e-04,8.53663441e-04,2.84554480e-04]])

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