Redoing a tutorial on Captum I have recreated its neural network, TitanicSimpleNNNModel, a simple architecture whereby the last layer performs a softmax operation and has 2 units, corresponding to the outputs of survived (1) or not survived (0). However the problem is that when I try to train it I get a dimension error on the matrices that the model tries to multiply: RuntimeError: mat1 and mat2 shapes cannot be multiplied (916x2 and 12x8). The first one corresponds in the training characteristics (train_features), which seems to be a cipher tensor, but I don't know which one the second one refers to. Indeed it seems that both are in an *input, and I was not able to find out which ones are in it.
Here you have my google colab notebook, but here is how I get the data, the model and the training:
# Initial imports
import numpy as np
import torch
from captum.attr import IntegratedGradients
from captum.attr import LayerConductance
from captum.attr import NeuronConductance
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
from scipy import stats
import pandas as pd
# DATOS
# Download dataset from: https://biostat.app.vumc.org/wiki/pub/Main/DataSets/titanic3.csv
# Update path to dataset here.
! wget https://biostat.app.vumc.org/wiki/pub/Main/DataSets/titanic3.csv
dataset_path = "titanic3.csv"
# Read dataset from csv file.
titanic_data = pd.read_csv(dataset_path)
titanic_data = pd.concat([titanic_data,
pd.get_dummies(titanic_data['sex']),
pd.get_dummies(titanic_data['embarked'],prefix="embark"),
pd.get_dummies(titanic_data['pclass'],prefix="class")], axis=1)
titanic_data["age"] = titanic_data["age"].fillna(titanic_data["age"].mean())
titanic_data["fare"] = titanic_data["fare"].fillna(titanic_data["fare"].mean())
titanic_data = titanic_data.drop(['name','ticket','cabin','boat','body','home.dest','sex','embarked','pclass'], axis=1)
# Set random seed for reproducibility.
np.random.seed(131254)
# Convert features and labels to numpy arrays.
labels = titanic_data["survived"].to_numpy()
titanic_data = titanic_data.drop(['survived'], axis=1)
feature_names = list(titanic_data.columns)
data = titanic_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]
# MODEL
import torch
import torch.nn as nn
torch.manual_seed(1) # Set seed for reproducibility.
class TitanicSimpleNNModel(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 = TitanicSimpleNNModel()
# ENTRENAMIENTO
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/titanic_model.pt')
So I send input_tensor in the init of TitanicSimpleNNNModel(nn.Module). But it is making a call with super to nn.Module which is a torch.nn. No idea what this thing is for.
Here is the log:
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-39-ab215714ae42> in <module>()
10 label_tensor = torch.from_numpy(train_labels)
11 for epoch in range(num_epochs):
---> 12 output = net(input_tensor)
13 loss = criterion(output, label_tensor)
14 optimizer.zero_grad()
3 frames
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1128 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1129 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1130 return forward_call(*input, **kwargs)
1131 # Do not call functions when jit is used
1132 full_backward_hooks, non_full_backward_hooks = [], []
<ipython-input-34-9fde1a60e114> in forward(self, x)
16 sigmoid_out1 = self.sigmoid1(lin1_out)
17 sigmoid_out2 = self.sigmoid2(self.linear2(sigmoid_out1))
---> 18 return self.softmax(self.linear3(sigmoid_out2))
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1128 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1129 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1130 return forward_call(*input, **kwargs)
1131 # Do not call functions when jit is used
1132 full_backward_hooks, non_full_backward_hooks = [], []
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/linear.py in forward(self, input)
112
113 def forward(self, input: Tensor) -> Tensor:
--> 114 return F.linear(input, self.weight, self.bias)
115
116 def extra_repr(self) -> str:
RuntimeError: mat1 and mat2 shapes cannot be multiplied (916x2 and 12x8)
My first thought is that the data may not be up to date. But that would also surprise me.
