MNIST MLP¶
-
class
deepobs.pytorch.testproblems.mnist_mlp.
mnist_mlp
(batch_size, weight_decay=None)[source]¶ DeepOBS test problem class for a multi-layer perceptron neural network on Fashion-MNIST.
The network is build as follows:
- Four fully-connected layers with
1000
,500
,100
and10
units per layer. - The first three layers use ReLU activation, and the last one a softmax activation.
- The biases are initialized to
0.0
and the weight matrices with truncated normal (standard deviation of3e-2
) - The model uses a cross entropy loss.
- No regularization is used.
Parameters: - batch_size (int) -- Batch size to use.
- weight_decay (float) --
- No weight decay (L2-regularization) is used in this
- test problem. Defaults to
None
and any input here is ignored.
-
data
¶ The DeepOBS data set class for Fashion-MNIST.
-
loss_function
¶ The loss function for this testproblem is torch.nn.CrossEntropyLoss()
-
net
¶ The DeepOBS subclass of torch.nn.Module that is trained for this tesproblem (net_mlp).
- Four fully-connected layers with