DeepOBS test problem class for a three convolutional and three dense layered neural network on SVHN.
The network consists of
- thre conv layers with ReLUs, each followed by max-pooling
- two fully-connected layers with
256units and ReLU activation
- 10-unit output layer with softmax
- cross-entropy loss
- L2 regularization on the weights (but not the biases) with a default factor of 0.002
The weight matrices are initialized using Xavier initialization and the biases are initialized to
- batch_size (int) -- Batch size to use.
- weight_decay (float) -- Weight decay factor. Weight decay (L2-regularization)
is used on the weights but not the biases. Defaults to
The DeepOBS data set class for SVHN.
A tensorflow operation initializing the test problem for the training phase.
A tensorflow operation initializing the test problem for evaluating on training data.
A tensorflow operation initializing the test problem for evaluating on test data.
A tf.Tensor of shape (batch_size, ) containing the per-example loss values.
A scalar tf.Tensor containing a regularization term.
A scalar tf.Tensor containing the mini-batch mean accuracy.
Set up the vanilla CNN test problem on SVHN.