|
|
| import os |
| import torch |
| from torch import nn, optim |
| from torch.nn import functional as F |
| from torchvision import datasets, transforms |
| from torchvision.transforms.functional import to_pil_image |
|
|
| import PIL |
| import matplotlib.pyplot as plt |
| import numpy as np |
| import pandas as pd |
|
|
| from sklearn.metrics import accuracy_score |
|
|
|
|
|
|
| arquivo = 'lenet-modular2024-06-26.pt' |
|
|
| class Lenet5Modular(nn.Module): |
| def __init__(self): |
| super(Lenet5Modular,self).__init__() |
|
|
| self.conv1 = nn.Sequential( |
| nn.Conv2d(in_channels=3,out_channels=6,kernel_size=(5,5),stride=(1,1)), |
| nn.Tanh(), |
| nn.AvgPool2d(kernel_size=(2,2),stride=(2,2)) |
| ) |
|
|
| self.conv2 = nn.Sequential( |
| nn.Conv2d(in_channels=6,out_channels=16,kernel_size=(5,5),stride=(1,1)), |
| nn.Tanh(), |
| nn.AvgPool2d(kernel_size=(2,2),stride=(2,2)) |
| ) |
|
|
| self.conv3 = nn.Sequential( |
| nn.Conv2d(in_channels=16,out_channels=120,kernel_size=(5,5),stride=(1,1)), |
| nn.Tanh() |
| ) |
|
|
| self.flat = nn.Flatten() |
|
|
| self.linear1 = nn.Sequential( |
| nn.Linear(120,84), |
| nn.Tanh() |
| ) |
|
|
| self.linear2 = nn.Linear(84,10) |
|
|
| def forward(self,x): |
| x = self.conv1(x) |
| x = self.conv2(x) |
| x = self.conv3(x) |
| x = self.flat(x) |
| x = self.linear1(x) |
| x = self.linear2(x) |
|
|
| return x |
|
|
|
|
| modelo = Lenet5Modular() |
|
|
| |
|
|
| modelo.cpu() |
|
|
| a = torch.rand(1, 3, 32, 32) |
|
|
| b = modelo(a) |
|
|
| b.size() |
|
|
|
|
| |
| def create_transformer(): |
| return transforms.Compose([ |
| transforms.Resize((32, 32)), |
| transforms.RandomHorizontalFlip(), |
| transforms.RandomRotation(10), |
| transforms.RandomAffine(0, shear=10, scale=(0.8,1.2)), |
| transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2), |
| transforms.ToTensor() |
| ]) |
