I am really new to the machine Learnining Community and I was doing a Pytorch/Captum Tutorial. To be specific this one: https://captum.ai/tutorials/Titanic_Basic_Interpret
Now I was trying to do the same just with another Dataset:
https://www.kaggle.com/hb20007/gender-classification
I have changed the gender values to 0 for male and 1 for female
My Code looks like this:
dataset_path = "assets/Transformed Data Set - Sheet1.csv"
gender_data = pd.read_csv(dataset_path)
gender_data = pd.concat([gender_data,
pd.get_dummies(gender_data['Favorite Color'], prefix="color"),
pd.get_dummies(gender_data['Favorite Music Genre'],prefix="music"),
pd.get_dummies(gender_data['Favorite Beverage'],prefix="beverage"),
pd.get_dummies(gender_data['Favorite Soft Drink'],prefix="drink")], axis=1)
gender_data = gender_data.drop(['Favorite Color','Favorite Music Genre','Favorite Beverage','Favorite Soft Drink'], axis=1)
# Set random seed for reproducibility.
np.random.seed(131254)
# Convert features and labels to numpy arrays.
labels = gender_data["Gender"].to_numpy()
gender_data = gender_data.drop(['Gender'], axis=1)
feature_names = list(gender_data.columns)
data = gender_data.to_numpy()
# Separate training and test sets using
train_indices = np.random.choice(len(labels), int(0.7*len(labels)), replace=False)
test_indices = list(set(range(len(labels))) - set(train_indices))
train_features = data[train_indices]
train_labels = labels[train_indices]
test_features = data[test_indices]
test_labels = labels[test_indices]
import torch
import torch.nn as nn
torch.manual_seed(1) # Set seed for reproducibility.
class GenderSimpleNNModel(nn.Module):
def __init__(self):
super().__init__()
self.linear1 = nn.Linear(12, 12)
self.sigmoid1 = nn.Sigmoid()
self.linear2 = nn.Linear(12, 8)
self.sigmoid2 = nn.Sigmoid()
self.linear3 = nn.Linear(8, 2)
self.softmax = nn.Softmax(dim=1)
def forward(self, x):
lin1_out = self.linear1(x)
sigmoid_out1 = self.sigmoid1(lin1_out)
sigmoid_out2 = self.sigmoid2(self.linear2(sigmoid_out1))
return self.softmax(self.linear3(sigmoid_out2))
net = GenderSimpleNNModel()
USE_PRETRAINED_MODEL = False
if USE_PRETRAINED_MODEL:
net.load_state_dict(torch.load('models/gender_model.pt'))
print("Model Loaded!")
else:
criterion = nn.CrossEntropyLoss()
num_epochs = 200
optimizer = torch.optim.Adam(net.parameters(), lr=0.1)
input_tensor = torch.from_numpy(train_features).type(torch.FloatTensor)
label_tensor = torch.from_numpy(train_labels)
for epoch in range(num_epochs):
output = net(input_tensor)
loss = criterion(output, label_tensor)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if epoch % 20 == 0:
print ('Epoch {}/{} => Loss: {:.2f}'.format(epoch+1, num_epochs, loss.item()))
torch.save(net.state_dict(), 'models/gender_model.pt')
Now I have done it like in the Tutorial, but I get an error, to be specific I get this error:
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-7-c565b5b7e2f2> in <module>
13 label_tensor = torch.from_numpy(train_labels)
14 for epoch in range(num_epochs):
---> 15 output = net(input_tensor)
16 loss = criterion(output, label_tensor)
17 optimizer.zero_grad()
~/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
539 result = self._slow_forward(*input, **kwargs)
540 else:
--> 541 result = self.forward(*input, **kwargs)
542 for hook in self._forward_hooks.values():
543 hook_result = hook(self, input, result)
<ipython-input-6-dbf1be8585cd> in forward(self, x)
13
14 def forward(self, x):
---> 15 lin1_out = self.linear1(x)
16 sigmoid_out1 = self.sigmoid1(lin1_out)
17 sigmoid_out2 = self.sigmoid2(self.linear2(sigmoid_out1))
~/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
539 result = self._slow_forward(*input, **kwargs)
540 else:
--> 541 result = self.forward(*input, **kwargs)
542 for hook in self._forward_hooks.values():
543 hook_result = hook(self, input, result)
~/anaconda3/lib/python3.7/site-packages/torch/nn/modules/linear.py in forward(self, input)
85
86 def forward(self, input):
---> 87 return F.linear(input, self.weight, self.bias)
88
89 def extra_repr(self):
~/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py in linear(input, weight, bias)
1368 if input.dim() == 2 and bias is not None:
1369 # fused op is marginally faster
-> 1370 ret = torch.addmm(bias, input, weight.t())
1371 else:
1372 output = input.matmul(weight.t())
RuntimeError: size mismatch, m1: [46 x 20], m2: [12 x 12] at /opt/conda/conda-bld/pytorch_1570710822989/work/aten/src/TH/generic/THTensorMath.cpp:197
I am desperate. I don’t know where to put what, so that the sizes will match and I won’t get that error anymore. Please Help me :S
