Torch-Yottaxx 0.1.3

a simple autograd library

Simple Torch

implement by numpy

autograd

tensor

Implement: Basic computing between tensors,also,recording depedency between tensors and grad of tensors.

function

Implement: Activation function.There is only tanh currently.

parameter

Implement: Quick create random tensors with requires_grad=True.

module

Implement: Recording of all parameters

optim

Implement: Optimizing for module.

tests

Test for autograd function

example

fizz_buzz

import numpy as np
from typing import List
from autograd import Tensor, Parameter, Module
from autograd.optim import SGD
from autograd.function import tanh

"""
print the numbers 1 to 100,
except  
    if the number is divisible by 3 print "fizz"
    if the number is divisible by 5 print "fizz"
    if the number is divisible by 15 print "fizz_buzz"

"""


def binary_encode(x: int) -> List[int]:
    return [x >> i & 1 for i in range(10)]


def fizz_buzz_encode(x: int) -> List[int]:
    if x % 15 == 0:
        return [0, 0, 0, 1]
    elif x % 5 == 0:
        return [0, 0, 1, 0]
    elif x % 3 == 0:
        return [0, 1, 0, 0]
    else:
        return [1, 0, 0, 0]


x_train = Tensor([binary_encode(x) for x in range(101, 1024)])
y_train = Tensor([fizz_buzz_encode(x) for x in range(101, 1024)])


class FizzBuzzModule(Module):
    def __init__(self, num_hidden: int = 50) -> None:
        self.w1 = Parameter(10, num_hidden)
        self.b1 = Parameter(num_hidden)

        self.w2 = Parameter(num_hidden, 4)
        self.b2 = Parameter(4)

    def predict(self, in_puts: Tensor):
        # inputs (batch_size,10)
        x1 = inputs @ self.w1 + self.b1  # (batch_size,num_hidden)
        x2 = tanh(x1)
        x3 = x2 @ self.w2 + self.b2  # (batch_size,4)
        return x3


optimizer = SGD(lr=0.001)
batch_size = 32
module = FizzBuzzModule()

starts = np.arange(0, x_train.shape[0], batch_size)
for epoch in range(10000):
    epoch_loss = 0.0

    np.random.shuffle(starts)
    for start in starts:
        end = start + batch_size

        module.zero_grad()
        inputs = x_train[start:end]

        predicted = module.predict(inputs)
        actual = y_train[start:end]
        errors = predicted - actual
        loss = (errors * errors).sum()

        loss.backward()
        epoch_loss += loss.data

        optimizer.step(module)
    print(epoch, epoch_loss)

num_correct = 0
for x in range(1, 101):
    inputs = Tensor([binary_encode(x)])
    predicted = module.predict(inputs)[0]
    predicted_idx = np.argmax(predicted.data)
    actual_idx = np.argmax(fizz_buzz_encode(x))
    labels = [str(x), "fizz", "buzz", "fizz_buzz"]

    if predicted_idx == actual_idx:
        num_correct += 1
    print(x, labels[predicted_idx], labels[actual_idx])

print(num_correct,"/100")