Defining a 2 layer neural net

defining_layers.py

# define training constants
learning_rate = 1
number_of_epochs = 5000

np.random.seed(48) # set seed value so that the results are reproduceable
                  # (weights will now be initailzaed to the same pseudo-random numbers, each time)


# Our network architecture has the shape: 
#                   (input)--> [Linear->Sigmoid] -> [Linear->Sigmoid] -->(output)  

#------ LAYER-1 ----- define hidden layer that takes in training data 
Z1 = LinearLayer(input_shape=X_train.shape, n_out=3, ini_type='xavier')
A1 = SigmoidLayer(Z1.Z.shape)

#------ LAYER-2 ----- define output layer that take is values from hidden layer
Z2= LinearLayer(input_shape=A1.A.shape, n_out=1, ini_type='xavier')
A2= SigmoidLayer(Z2.Z.shape)