I made a function so you don’t have to count padding, max pooling, strides, etc when flattening a convolutional layer.

```
def flatten(w, k=3, s=1, p=0, z=True):
"""
Returns the right size of the flattened tensor after
convolutional transformation
:param w: width of image
:param k: kernel size
:param s: stride
:param p: padding
:param m: max pooling(bool)
:return: proper shape and params: use x * x * previous_out_channels
Example:
with three convolutional layers:
r = flatten(*flatten(*flatten(w=100, k=3, s=1, p=0, m=True)))[0]
self.fc1 = nn.Linear(r*r*128, 1024)
"""
return int((np.floor((w - k + 2 * p) / s) + 1) / 2 if m else 1), k, s, p, m
```

Here’s an example to show that it works:

```
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader
x = np.random.rand(1_00, 3, 100, 100)
y = np.random.randint(0, 2, 1_00)
if torch.cuda.is_available():
x = torch.from_numpy(x.astype('float32')).cuda()
y = torch.from_numpy(y.astype('float32')).cuda()
def flatten(w, k=3, s=1, p=0, m=True):
return int((np.floor((w - k + 2 * p) / s) + 1) / 2 if m else 1), k, s, p, m
class ConvNet(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 32, 3)
self.conv2 = nn.Conv2d(32, 64, 3)
self.conv3 = nn.Conv2d(64, 128, 3)
r = flatten(*flatten(*flatten(w=100, k=3, s=1, p=0, m=True)))[0]
# picture width is 100, kernel is 3, stride is 1, padding is 0,
self.fc1 = nn.Linear(r*r*128, 1024) # 128 is wrong here
self.fc2 = nn.Linear(1024, 1)
def forward(self, x):
x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
x = F.max_pool2d(F.relu(self.conv2(x)), (2, 2))
x = F.max_pool2d(F.relu(self.conv3(x)), (2, 2))
x = x.view(x.size(0), -1)
x = F.relu(self.fc1(x))
x = torch.sigmoid(self.fc2(x))
return x
net = ConvNet()
net.cuda()
optimizer = optim.Adam(net.parameters(), lr=0.03)
loss_function = nn.BCELoss()
class Train:
def __init__(self):
self.len = x.shape[0]
self.x_train = x
self.y_train = y
def __getitem__(self, index):
return x[index], y[index].unsqueeze(0)
def __len__(self):
return self.len
train = Train()
train_loader = DataLoader(dataset=train, batch_size=64, shuffle=True)
epochs = 1
train_losses = list()
for e in range(epochs):
running_loss = 0
for images, labels in train_loader:
optimizer.zero_grad()
log_ps = net(images)
loss = loss_function(log_ps, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
print('It\'s working.')
```