DeepOBS test problem class for a two convolutional and two dense layered neural network on MNIST.
The network has been adapted from the TensorFlow tutorial and consists of
- two conv layers with ReLUs, each followed by max-pooling
- one fully-connected layers with ReLUs
- 10-unit output layer with softmax
- cross-entropy loss
- No regularization
The weight matrices are initialized with truncated normal (standard deviation of
0.05) and the biases are initialized to
- batch_size (int) -- Batch size to use.
- weight_decay (float) -- No weight decay (L2-regularization) is used in this
test problem. Defaults to
Noneand any input here is ignored.
The DeepOBS data set class for MNIST.
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. Will always be
0.0since no regularizer is used.
A scalar tf.Tensor containing the mini-batch mean accuracy.
Sets up the vanilla CNN test problem on MNIST.