General Settings

A dictionary specifying the developement set. For details on datasets, see Datasets
E.g. gpu or cpu. Although RETURNN will automatically detect and use a GPU if available, a specific device can be enforced by setting this parameter.
extern_data (former num_outputs)

Defines the source/target dimensions of the data. Both can be integers. extern_data can also be a dict if your dataset has other data streams. The standard source data is called “data” by default, and the standard target data is called “classes” by default. You can also specify whether your data is dense or sparse (i.e. it is just the index), which is specified by the number of dimensions, i.e. 2 (time-dim + feature-dim) or 1 (just time-dim). When using no explicit definition, it is assumed that the data contains a time axis.

Example: extern_data = {"data": [100, 2], "classes": [5000, 1]}. This defines an input dimension of 100, and the input is dense (2), and an output dimension of 5000, and the output provided by the dataset is sparse (1).

For a more explicit definition of the shapes, you can provide a dict instead of a list or tuple. This dict may contain information to create “Data” objects. For extern_data, only dim and shape are required. Example: 'feature_data': {'dim': 80, 'shape': (None, 80)} This defines 80 dimensional features with a time axis of arbitrary length. Example: 'speaker_classes': {'dim': 1172, 'shape': (), 'sparse': True} This defines a sparse input for e.g. speaker classes that do not have a time axis.

In general, all input parameters to can be provided

path to the log, or list of paths for multiple logs.
If set to True, for each batch the number of sequences and maximal sequence length is displayed
An integer or list of integer. Common values are 3 or 4. Starting with 5, you will get an output per mini-batch. If a list is proved for logs, log_verbosity can be specified for each log.
Defines the model file where RETURNN will save all model params after an epoch of training. For each epoch, it will suffix the filename by the epoch number. If load_from is not set, the model will also be loaded from this path.
This is a nested dict which defines the network topology. It consists of layer-names as strings, mapped on dicts, which defines the layers. The layer dict consists of keys as strings and the value type depends on the key. The layer dict should contain the key class which defines the class or type of the layer, such as linear for a feed-forward layer, rec for a recurrent layer (including LSTM) or softmax for the output layer (doesn’t need to have the softmax activation). Usually it also contains the key n_out which defines the feature-dimension of the output of this layer, and the key from which defines the inputs to this layer, which is a list of other layers. For details sett Layers / Modules.
Input feature dimension of the network, related to the ‘data’ tag. Deprecated for the TensorFlow backend, see extern_data
Output feature dimension of the network, related to the ‘classes’ tag. Deprecated for the TensorFlow backend, see extern_data
The task to run. Common cases are train, forward or search.
If set to True, will display the current GPU memory usage when using the tensorflow backend.

Defines the folder where the tensorflow/tensorboard logs are writting. Per default, the logs are written next to the models. .. note:

has to be set specifically when loading a model from a folder without write permission
A dictionary specifying the training dataset. For details on datasets, see Datasets
If you set this to True, TensorFlow will be used.