mltemplate.py

from dense import Dense
from tanh import Tanh
from losses import mse, mse_prime
import numpy as np

def test_outputs(inputs, network):
    for i in inputs:
        inp=np.reshape(i, (len(i), 1))
        for layer in network:
            inp = layer.forward(inp)
        print(f"input: {"".join(str (j) for j in i[:2])} + {"".join(str(j) for j in i[2:])}\noutput: {inp.flatten()}")



inputs=[[0,0,0,0],[0,0,0,1],[0,0,1,0],[0,0,1,1],
        [0,1,0,0],[0,1,0,1],[0,1,1,0],[0,1,1,1],
        [1,0,0,0],[1,0,0,1],[1,0,1,0],[1,0,1,1],
        [1,1,0,0],[1,1,0,1],[1,1,1,0],[1,1,1,1]]
outputs = [[0,0,0],[0,0,1],[0,1,0],[0,1,1],
           [0,0,1],[0,1,0],[0,1,1],[1,0,0],
           [0,1,0],[0,1,1],[1,0,0],[1,0,1],
           [0,1,1],[1,0,0],[1,0,1],[1,1,0]]
# NOTE: X is possible inputs
# Must be matched to Y
# Parameter 2 must be in the form (a, b, c)
# a is the number of corresponing outputs
# b is the number of data points per input
# c = 1
X = np.reshape(inputs,(len(inputs), len(inputs[0]), 1))

# NOTE: Y is DESIRED outputs
# Must be matched to X
# Parameter 2 must be in the form (a, b, c)
# a is the number of corresponing outputs
# b, c = 1
Y = np.reshape(outputs,(len(outputs), len(outputs[0]), 1))

# NOTE: Dense layer always goes first and must have even number of layers
# for layer 1, the first number must be the number of inputs of data
# for all other dense layers, the first number must be the same as the second number of the last layer
# for the last dense layer, the second number should be 1
network = [
    Dense(len(inputs[0]),5),
    Tanh(),
    Dense(5,4),
    Tanh(),
    Dense(4,len(outputs[0])),
    Tanh()
]

epochs = 10000
learning_rate = 0.1

for e in range(epochs):
    error = 0
    for x,y in zip(X,Y):
        output = x
        for layer in network:
            output = layer.forward(output)
        error += mse(y,output)

        grad = mse_prime(y,output)
        for layer in reversed(network):
            grad = layer.backward(grad, learning_rate)
    error /= len(X)
    print('%d/%d, error=%f' % (e+1,epochs,error))

# NOTE: Testing the model
# Define the new input
# must be the shape of the values of X
# param 2 must be in the form (a, b)
# where a is the number of values and b = 1
test_outputs(inputs,network)