Deepnet subcommand

The bigmler deepnet subcommand generates all the resources needed to buid a deepnet model and use it to predict. The deepnet model is a supervised learning method for solving both regression and classification problems. It uses deep neural networks, a composition of layers of different functions that when applied to the input data generate the prediction.

The simplest call to build a deepnet is:

bigmler deepnet --train data/iris.csv

uploads the data in the data/iris.csv file and generates the corresponding source, dataset and deepnet objects in BigML. You can use any of the generated objects to produce new deepnets. For instance, you could set a subgroup of the fields of the generated dataset to produce a different deepnet model by using

bigmler deepnet --dataset dataset/53b1f71437203f5ac30004ed \
                --deepnet-fields="-sepal length"

that would exclude the field sepal length from the deepnet model creation input fields. You can also change some parameters in the deepnet model, like the number_of_hidden_layers, max_iterations or default_numeric_value. Please check the Deepnets section of the API documentation for a detailed description of the available arguments.

bigmler deepnet --dataset dataset/53b1f71437203f5ac30004ed \
                --number-of-hidden-layers 3
                --max-iterations 10 --default-numeric-value mean

with this code, the deepnet is built using 3 hidden layers, approximations will stop after 10 iterations and the missing numerics will be filled with the mean of the rest of values in the field.

Similarly to the models and datasets, the generated deepnets can be shared using the --shared option, e.g.

bigmler deepnet --source source/53b1f71437203f5ac30004e0 \
                --shared

will generate a secret link for both the created dataset and deepnet, that can be used to share the resource selectively.

The deepnet can be used to assign a prediction to each new input data set. The command

bigmler deepnet \
        --deepnet deepnet/5331f71435203f5ac30005c0 \
        --test data/test_iris.csv

would produce a file predictions.csv with the predictions associated to each input. When the command is executed, the deepnet information is downloaded to your local computer and the deepnet predictions are computed locally, with no more latencies involved. Just in case you prefer to use BigML to compute the predictions remotely, you can do so too

bigmler deepnet
        --deepnet deepnet/53b1f71435203f5ac30005c0 \
        --test data/my_test.csv --remote

would create a remote source and dataset from the test file data, generate a batch prediction also remotely and finally download the result to your computer. If you prefer the result not to be dowloaded but to be stored as a new dataset remotely, add --no-csv and to-dataset to the command line. This can be specially helpful when dealing with a high number of scores or when adding to the final result the original dataset fields with --prediction-info full, that may result in a large CSV to be created as output. Other output configurations can be set by using the --batch-prediction-attributes option pointing to a JSON file that contains the desired attributes, like:

{"probabilities": true,
 "all_fields": true}

Deepnet Subcommand Options

--deepnet DEEPNET	BigML deepnet Id
--deepnets PATH	Path to a file containing deepnet/ids. One deepnet per line (e.g., deepnet/4f824203ce80051)
--no-deepnet	No deepnet will be generated
--deepnet-fields DEEPNET_FIELDS	Comma-separated list of fields that will be used in the deepnet construction
--batch-normalization	Specifies whether to normalize the outputs of a network before being passed to the activation function or not.
--default-numeric-value DFT	It accepts any of the following strings to substitute missing numeric values across all the numeric fields in the dataset (options: mean, median minimum, maximum, zero).
--dropout-rate RATE	A number between 0 and 1 specifying the rate at which to drop weights during training to control overfitting.
--hidden-layers LAYERS	A JSON file that contains a list of maps describing the number and type of layers in the network (other than the output layer, which is determined by the type of learning problem).
--learn-residuals	Specifies whether alternate layers should learn a representation of the residuals for a given layer rather than the layer itself or not.
--learning-rate RATE	A number between 0 and 1 specifying the learning rate.
--max-iterations ITERATIONS	A number between 100 and 100000 for the maximum number of gradient steps to take during the optimization.
--max-training-time TIME	The maximum wall-clock training time, in seconds, for which to train the network.
--number-of-hidden-layers #LAYERS	The number of hidden layers to use in the network. If the number is greater than the length of the list of hidden_layers, the list is cycled until the desired number is reached. If the number is smaller than the length of the list of hidden_layers, the list is shortened.
--number-of-model-candidates #CAND	An integer specifying the number of models to try during the model search.
--search	During the deepnet creation, BigML trains and evaluates over all possible network configurations, returning the best networks found for the problem. The final deepnet returned by the search is a compromise between the top n networks found in the search. Since this option builds several networks, it may be significantly slower than the suggest_structure technique.
--missing-numerics	Whether to create an additional binary predictor each numeric field which denotes a missing value. If false, these predictors are not created, and rows containing missing numeric values are dropped.
--tree-embedding	Specify whether to learn a tree-based representation of the data as engineered features along with the raw features, essentially by learning trees over slices of the input space and a small amount of the training data. The theory is that these engineered features will linearize obvious non-linear dependencies before training begins, and so make learning proceed more quickly.
--image-augmentations	A comma-separated list of augmentation strategies to use for image data. The available keys are: flip_horizontal, flip_vertical, brightness
--include-extracted-features	Controls the use of features extracted from images. Use all to include all generated features, none to include none, or a comma-separated list of especific field ids, corresponding to extracted fields to add to the default set.
--no-missing-numerics	Avoids the default behaviour, which creates a new coefficient for missings in numeric fields. Missing rows are discarded.
--deepnet-attributes PATH	Path to a JSON file containing attributes (any of the updatable attributes described in the developers section ) to be used in the deepnet creation call
--deepnet-file PATH	Path to a JSON file containing the deepnet regression info