# theanets.feedforward.Classifier¶

class theanets.feedforward.Classifier(layers, loss='xe', weighted=False, rng=13)

A classifier computes a distribution over labels, given an input.

Notes

Classifier models default to a cross-entropy loss. To use a different loss, provide a non-default argument for the loss keyword argument when constructing your model.

Examples

To create a classification model, just create a new class instance. Often you’ll provide the layer configuration at this time:

>>> model = theanets.Classifier([10, (20, 'tanh'), 50])


Data

Training data for a classification model takes the form of a two-dimensional array of input data and a one-dimensional vector of target labels. The input array has a shape (num-examples, num-variables): the first axis enumerates data points in a batch, and the second enumerates the input variables in the model.

The second array provides the target class labels for the inputs. Its shape is (num-examples, ), and each integer value in the array gives the class label for the corresponding input example.

For instance, to create a training dataset containing 1000 examples:

>>> inputs = np.random.randn(1000, 10).astype('f')
>>> outputs = np.random.randint(50, size=1000).astype('i')


Training

Training the model can be as simple as calling the train() method, giving the inputs and target outputs as a dataset:

>>> model.train([inputs, outputs])


Use

A classification 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))


This method returns a vector containing the most likely class for each input example.

To retrieve the probabilities of the classes for each example, use predict_proba():

>>> model.predict_proba(test).shape
(3, 50)


__init__(layers, loss='xe', weighted=False, rng=13)

Methods

 __init__(layers[, loss, weighted, rng]) add_layer([layer, is_output]) Add a layer to our network graph. add_loss([loss]) Add a loss function to the model. build_graph([regularizers]) Connect the layers in this network to form a computation graph. classify(x) feed_forward(x, **kwargs) Compute a forward pass of all layers from the given input. find(which, param) Get a parameter from a layer in the network. itertrain(train[, valid, algo, subalgo, ...]) Train our network, one batch at a time. load(filename) Load a saved network from disk. loss(**kwargs) Return a variable representing the regularized loss for this network. monitors(**kwargs) Return expressions that should be computed to monitor training. predict(x) Compute a greedy classification for the given set of data. predict_logit(x) Compute the logit values that underlie the softmax output. predict_proba(x) Compute class posterior probabilities for the given set of data. save(filename) Save the state of this network to a pickle file on disk. score(x, y[, w]) Compute the mean accuracy on a set of labeled data. set_loss(*args, **kwargs) Clear the current loss functions from the network and add a new one. train(*args, **kwargs) Train the network until the trainer converges. updates(**kwargs) Return expressions to run as updates during network training.

Attributes

 DEFAULT_OUTPUT_ACTIVATION INPUT_NDIM OUTPUT_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.
monitors(**kwargs)

Return expressions that should be computed to monitor training.

Returns: monitors : list of (name, expression) pairs A list of named monitor expressions to compute for this network.
predict(x)

Compute a greedy classification for the given set of data.

Parameters: x : ndarray (num-examples, num-variables) An array containing examples to classify. Examples are given as the rows in this array. k : ndarray (num-examples, ) A vector of class index values, one per row of input data.
predict_logit(x)

Compute the logit values that underlie the softmax output.

Parameters: x : ndarray (num-examples, num-variables) An array containing examples to classify. Examples are given as the rows in this array. l : ndarray (num-examples, num-classes) An array of posterior class logit values, one row of logit values per row of input data.
predict_proba(x)

Compute class posterior probabilities for the given set of data.

Parameters: x : ndarray (num-examples, num-variables) An array containing examples to predict. Examples are given as the rows in this array. p : ndarray (num-examples, num-classes) An array of class posterior probability values, one per row of input data.
score(x, y, w=None)

Compute the mean accuracy on a set of labeled data.

Parameters: x : ndarray (num-examples, num-variables) An array containing examples to classify. Examples are given as the rows in this array. y : ndarray (num-examples, ) A vector of integer class labels, one for each row of input data. w : ndarray (num-examples, ) A vector of weights, one for each row of input data. score : float The (possibly weighted) mean accuracy of the model on the data.