# MNIST LogReg¶

class deepobs.tensorflow.testproblems.mnist_logreg.mnist_logreg(batch_size, weight_decay=None)[source]

DeepOBS test problem class for multinomial logistic regression on MNIST.

No regularization is used and the weights and biases are initialized to 0.0.

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.
dataset

The DeepOBS data set class for MNIST.

train_init_op

A tensorflow operation initializing the test problem for the training phase.

train_eval_init_op

A tensorflow operation initializing the test problem for evaluating on training data.

test_init_op

A tensorflow operation initializing the test problem for evaluating on test data.

losses

A tf.Tensor of shape (batch_size, ) containing the per-example loss values.

regularizer

A scalar tf.Tensor containing a regularization term. Will always be 0.0 since no regularizer is used.

accuracy

A scalar tf.Tensor containing the mini-batch mean accuracy.

set_up()[source]

Sets up the logistic regression test problem on MNIST.