theanets.feedforward.Regressor¶
-
class
theanets.feedforward.
Regressor
(layers=(), loss='mse', weighted=False, rng=13)¶ A regressor attempts to produce a target output given some inputs.
Notes
Regressor models default to a
MSE
loss. To use a different loss, provide a non-default argument for theloss
keyword argument when constructing your model.Examples
To create a regression model, just create a new class instance. Often you’ll provide the layer configuration at this time:
>>> model = theanets.Regressor([10, 20, 3])
See Creating a Model for more information.
Data
Training data for a regression model takes the form of two two-dimensional arrays. The shapes of both of these arrays are (num-examples, num-variables) – the first axis enumerates data points in a batch, and the second enumerates the relevant variables (input variables for the input array, and output variables for the output array).
For instance, to create a training dataset containing 1000 examples:
>>> inputs = np.random.randn(1000, 10).astype('f') >>> outputs = np.random.randn(1000, 3).astype('f')
Training
Training the model can be as simple as calling the
train()
method, with the inputs and target outputs as data:>>> model.train([inputs, outputs])
See Training a Model for more information.
Use
A regression model can be used to
predict()
the output of some input data points:>>> test = np.random.randn(3, 10).astype('f') >>> print(model.predict(test))
See Using a Model for more information.
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__init__
(layers=(), loss='mse', weighted=False, rng=13)¶
Methods
Attributes
DEFAULT_OUTPUT_ACTIVATION
INPUT_NDIM
inputs
A list of Theano variables for feedforward computations. num_params
Number of parameters in the entire network model. params
A list of the learnable Theano parameters for this network. variables
A list of Theano variables for loss computations. -