Spaces:
Sleeping
Sleeping
File size: 2,842 Bytes
05101d2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 |
import torch
import torchvision
from torch import nn
class TinyCNN(nn.Module):
def __init__(self, input_shape: int, hidden_units: int, output_shape: int) -> None:
super().__init__()
self.conv_block_1 = nn.Sequential(
nn.Conv2d(in_channels=input_shape,
out_channels=hidden_units,
kernel_size=3, # how big is the square that's going over the image?
stride=1, # default
padding=1), # options = "valid" (no padding) or "same" (output has same shape as input) or int for specific number
nn.BatchNorm2d(hidden_units),
nn.ReLU(),
# nn.Conv2d(in_channels=hidden_units,
# out_channels=128,
# kernel_size=3,
# stride=1,
# padding=0),
# nn.BatchNorm2d(128),
# nn.ReLU(),
nn.MaxPool2d(kernel_size=2,
stride=2), # default stride value is same as kernel_size
nn.Dropout(p=0.25)
)
self.conv_block_2 = nn.Sequential(
nn.Conv2d(hidden_units, 128, kernel_size=3, padding=1),
# nn.ReLU(),
# nn.Conv2d(128, 128, kernel_size=3, padding=0),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Dropout(p=0.25)
)
self.conv_block_3 = nn.Sequential(
nn.Conv2d(128, 512, kernel_size=3, padding=1),
# nn.ReLU(),
# nn.Conv2d(128, 512, kernel_size=3, padding=0),
nn.BatchNorm2d(512),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Dropout(p=0.25)
)
self.conv_block_4 = nn.Sequential(
nn.Conv2d(512, 512, kernel_size=3, padding=1),
# nn.ReLU(),
# nn.Conv2d(512, 512, kernel_size=3, padding=2),
nn.BatchNorm2d(512),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Dropout(p=0.25)
)
self.fc_1 = nn.Sequential(
nn.Flatten(),
nn.Linear(in_features=256*392, out_features = 256),
nn.BatchNorm1d(256),
nn.ReLU(),
nn.Dropout(p=0.25)
)
self.fc_2 = nn.Sequential(
# Where did this in_features shape come from?
# It's because each layer of our network compresses and changes the shape of our inputs data.
nn.Linear(in_features=256,
out_features=512),
nn.BatchNorm1d(512),
nn.ReLU(),
nn.Dropout(p=0.25)
)
self.classifier = nn.Sequential(
nn.Linear(in_features=512,
out_features=output_shape)
)
def forward(self, x):
x = self.conv_block_1(x)
x = self.conv_block_2(x)
x = self.conv_block_3(x)
x = self.conv_block_4(x)
x = self.fc_1(x)
x = self.fc_2(x)
x = self.classifier(x)
return x
|