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import pyflashlight | |
import pyflashlight.nn as nn | |
import pyflashlight.optim as optim | |
import random | |
import math | |
random.seed(1) | |
class MyModel(nn.Module): | |
def __init__(self): |
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from abc import ABC | |
from norch.tensor import Tensor | |
class Optimizer(ABC): | |
""" | |
Abstract class for optimizers | |
""" | |
def __init__(self, parameters): | |
if isinstance(parameters, Tensor): |
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from .module import Module | |
import math | |
class Sigmoid(Module): | |
def __init__(self): | |
super().__init__() | |
def forward(self, x): | |
return 1.0 / (1.0 + (math.e) ** (-x)) |
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from .module import Module | |
class MSELoss(Module): | |
def __init__(self): | |
pass | |
def forward(self, predictions, labels): | |
assert labels.shape == predictions.shape, \ | |
"Labels and predictions shape does not match: {} and {}".format(labels.shape, predictions.shape) | |
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from ..module import Module | |
from ..parameter import Parameter | |
class Linear(Module): | |
def __init__(self, input_dim, output_dim): | |
super().__init__() | |
self.input_dim = input_dim | |
self.output_dim = output_dim | |
self.weight = Parameter(shape=[self.output_dim, self.input_dim]) | |
self.bias = Parameter(shape=[self.output_dim, 1]) |
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from .parameter import Parameter | |
from collections import OrderedDict | |
from abc import ABC | |
import inspect | |
class Module(ABC): | |
""" | |
Abstract class for modules | |
""" |
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def generate_random_list(shape): | |
""" | |
Generate a list with random numbers and shape 'shape' | |
[4, 2] --> [[rand1, rand2], [rand3, rand4], [rand5, rand6], [rand7, rand8]] | |
""" | |
if len(shape) == 0: | |
return [] | |
else: | |
inner_shape = shape[1:] | |
if len(inner_shape) == 0: |
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from norch.tensor import Tensor | |
from norch.utils import utils | |
import random | |
class Parameter(Tensor): | |
""" | |
A parameter is a trainable tensor. | |
""" | |
def __init__(self, shape): | |
data = utils.generate_random_list(shape=shape) |
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def zero_grad(self): | |
self.grad = None | |
def detach(self): | |
self.grad = None | |
self.grad_fn = None |
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def backward(self, gradient=None): | |
if not self.requires_grad: | |
return | |
if gradient is None: | |
if self.shape == [1]: | |
gradient = Tensor([1]) # dx/dx = 1 case | |
else: | |
raise RuntimeError("Gradient argument must be specified for non-scalar tensors.") |
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