I have been following along with 04. PyTorch Custom Datasets - Zero to Mastery Learn PyTorch for Deep Learning
In the tutorial, the in_features = hidden_units1616. But this gave me a matrix multiplication error.
I am wondering how I can set the in_features automatically. Right now, I get a matrix multiplication error, and whichever value fixes the issue I put in for the in_features.
Thanks so much for the help!
import torch
from torch import nn
class TinyVGG(nn.Module):
"""
Model architecture copying TinyVGG from:
https://poloclub.github.io/cnn-explainer/
"""
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.ReLU(),
nn.Conv2d(in_channels=hidden_units,
out_channels=hidden_units,
kernel_size=3,
stride=1,
padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2,
stride=2) # default stride value is same as kernel_size
)
self.conv_block_2 = nn.Sequential(
nn.Conv2d(hidden_units, hidden_units, kernel_size=3, padding=1),
nn.ReLU(),
nn.Conv2d(hidden_units, hidden_units, kernel_size=3, padding=1),
nn.ReLU(),
nn.MaxPool2d(2)
)
self.classifier = nn.Sequential(
nn.Flatten(),
# 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= 31360, # modified from: hidden_units*16*16,
out_features=output_shape)
)
def forward(self, x: torch.Tensor):
x = self.conv_block_1(x)
# print(x.shape)
x = self.conv_block_2(x)
# print(x.shape)
x = self.classifier(x)
# print(x.shape)
return x
# return self.classifier(self.conv_block_2(self.conv_block_1(x))) # <- leverage the benefits of operator fusion
torch.manual_seed(42)
model_0 = TinyVGG(input_shape=128, # number of color channels (3 for RGB) - modified
hidden_units=10,
output_shape=len(train_data_custom.classes)).to(device)
model_0