return [0, 1, 0, 0]
else:
return [1, 0, 0, 0]
def binary_encode(x: int) -> List[int]:
"""
10 digit binary encoding of x
"""
return [x >> i & 1 for i in range(10)]
inputs = np.array([binary_encode(x) for x in range(101, 1024)])
targets = np.array([fizz_buzz_encode(x) for x in range(101, 1024)])
net = NeuralNet([
Linear(input_size=10, output_size=50),
Tanh(),
Linear(input_size=50, output_size=4)
])
train(net, inputs, targets, num_epochs=5000, optimizer=SGD(lr=0.001))
for x in range(1, 101):
predicted = net.forward(binary_encode(x))
predicted_idx = np.argmax(predicted)
actual_idx = np.argmax(fizz_buzz_encode(x))
labels = [str(x), "fizz", "buzz", "fizzbuzz"]
print(x, labels[predicted_idx], labels[actual_idx])
# net = NeuralNet([
# Linear(input_size=30, output_size=16),
# reLu(),
# Linear(input_size=16, output_size=24),
# reLu(),
# Linear(input_size=24, output_size=20),
# reLu(),
# Linear(input_size=20, output_size=24),
# reLu(),
# Linear(input_size=24, output_size=1),
# Sigmoid(),
# Linear(input_size=1, output_size=1)
# ])
net = NeuralNet([
Linear(input_size=30, output_size=24),
Tanh(),
Linear(input_size=24, output_size=30),
Tanh(),
Linear(input_size=30, output_size=35),
Tanh(),
Linear(input_size=35, output_size=1),
Sigmoid()
])
n_epochs = 200
loss_list = train(net,
inputs,
targets,
optimizer=Adam(lr=1e-2, gamma1=0.3, gamma2=0.4),
iterator=BatchIterator(128),
num_epochs=n_epochs)