2D Beale¶
-
class
deepobs.tensorflow.testproblems.two_d_beale.
two_d_beale
(batch_size, weight_decay=None)[source]¶ DeepOBS test problem class for a stochastic version of thetwo-dimensional Beale function as the loss function.
Using the deterministic Beale function and adding stochastic noise of the form
\(u \cdot x + v \cdot y\)
where
x
andy
are normally distributed with mean0.0
and standard deviation1.0
we get a loss function of the form\(((1.5 - u + u \cdot v)^2 + (2.25 - u + u \cdot v ^ 2) ^ 2 + (2.625 -\ u + u \cdot v ^ 3) ^ 2) + u \cdot x + v \cdot y\).
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 the two_d stochastic test problem.
-
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.