Get the validation/train loss from Skorch fit - python-3.x

Question

Is there a way to get the train/validation loss from a Skorch fit in e.g a list (if you want to do some plotting, statistics)?

Answer 1

You can use the history (which allows slicing over time) to get this information. For example:
train_loss = net.history[:, 'train_loss']
which returns the train_loss for each recorded epoch.
Here is an example based on the following.
import numpy as np
import torch
import torch.nn.functional as F
from matplotlib import pyplot as plt
from sklearn.datasets import make_classification
from skorch import NeuralNetClassifier
from torch import nn
torch.manual_seed(0)
class ClassifierModule(nn.Module):
def __init__(
self,
num_units=10,
nonlin=F.relu,
dropout=0.5,
):
super(ClassifierModule, self).__init__()
self.num_units = num_units
self.nonlin = nonlin
self.dropout = dropout
self.dense0 = nn.Linear(20, num_units)
self.nonlin = nonlin
self.dropout = nn.Dropout(dropout)
self.dense1 = nn.Linear(num_units, 10)
self.output = nn.Linear(10, 2)
def forward(self, X, **kwargs):
X = self.nonlin(self.dense0(X))
X = self.dropout(X)
X = F.relu(self.dense1(X))
X = F.softmax(self.output(X), dim=-1)
return X
net = NeuralNetClassifier(
ClassifierModule,
max_epochs=20,
lr=0.1,
# device='cuda', # uncomment this to train with CUDA
)
X, y = make_classification(1000, 20, n_informative=10, random_state=0)
X, y = X.astype(np.float32), y.astype(np.int64)
net.fit(X, y)
train_loss = net.history[:, 'train_loss']
valid_loss = net.history[:, 'valid_loss']
plt.plot(train_loss, 'o-', label='training')
plt.plot(valid_loss, 'o-', label='validation')
plt.legend()
plt.show()
And results: