|
|
|
from transformers import PreTrainedModel |
|
from .MyConfig import MnistConfig |
|
from torch import nn |
|
import torch.nn.functional as F |
|
|
|
class MnistModel(PreTrainedModel): |
|
config_class = MnistConfig |
|
def __init__(self, config): |
|
super().__init__(config) |
|
|
|
self.conv1 = nn.Conv2d(1, config.conv1, kernel_size=5) |
|
self.conv2 = nn.Conv2d(config.conv1, config.conv2, kernel_size=5) |
|
self.conv2_drop = nn.Dropout2d() |
|
self.fc1 = nn.Linear(320, 50) |
|
self.fc2 = nn.Linear(50, 10) |
|
self.softmax = nn.Softmax(dim=-1) |
|
def forward(self, x,labels=None): |
|
x = F.relu(F.max_pool2d(self.conv1(x), 2)) |
|
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2)) |
|
x = x.view(-1, 320) |
|
x = F.relu(self.fc1(x)) |
|
x = F.dropout(x, training=self.training) |
|
x = self.fc2(x) |
|
output = self.softmax(x) |
|
if labels != None : |
|
print("continue training script here") |
|
|
|
return output |
|
|
