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@RafayAK
Last active June 20, 2019 11:47
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Defining a 2 layer neural net
# 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)
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