2D Data Set¶
-
class
deepobs.tensorflow.datasets.two_d.
two_d
(batch_size, train_size=10000, noise_level=1.0)[source]¶ DeepOBS data set class to create two dimensional stochastic testproblems.
This toy data set consists of a fixed number (train_size
) of iid draws from two scalar zero-mean normal distributions with standard deviation specified by thenoise_level
.Parameters: - batch_size (int) -- The mini-batch size to use. Note that, if
batch_size
is not a divider of the dataset size (1000
for train and test) the remainder is dropped in each epoch (after shuffling). - train_size (int) -- Size of the training data set. This will also be used as
the train_eval and test set size. Defaults to
1000
. - noise_level (float) -- Standard deviation of the data points around the mean.
The data points are drawn from a Gaussian distribution. Defaults to
1.0
.
-
batch
¶ A tuple
(x, y)
of tensors with random x and y that can be used to create a noisy two dimensional testproblem. Executing these tensors raises atf.errors.OutOfRangeError
after one epoch.
-
train_init_op
¶ A tensorflow operation initializing the dataset for the training phase.
-
train_eval_init_op
¶ A tensorflow operation initializing the testproblem for evaluating on training data.
-
test_init_op
¶ A tensorflow operation initializing the testproblem for evaluating on test data.
-
phase
¶ A string-value tf.Variable that is set to "train", "train_eval" or "test", depending on the current phase. This can be used by testproblems to adapt their behavior to this phase.
- batch_size (int) -- The mini-batch size to use. Note that, if