theano.py

#!/usr/bin/env python3
 
 
 
try:
    import theano
    import theano.tensor
except ImportError:
    print("Please install theano: pip3 install theano")
    import sys
    sys.exit(1)
 
import numpy
 
 
class State:
    """
    State class for proper handling of parameters and data during function calls. This is a very brief theano example.
    """
 
    def __init__(self, x=None, y=None, params=None, cost=None, updates=None, train_function=None, eval_function=None):
        """
        Constructor of the State class
        """
        # TODO: make serializable with __getstate__(), __setstate__()
        
        self.xx = x
        
        self.yy = y
 
        
        self.paramsparams = params
        
        self.costcost = cost
 
        
        self.updatesupdates = updates
 
        
        self.train_functiontrain_function = train_function
        
        self.eval_functioneval_function = eval_function
 
 
def get_model(number_of_features, number_of_spectators, number_of_events, training_fraction, parameters):
 
    x = theano.tensor.matrix('x')
    y = theano.tensor.vector('y', dtype='float32')
 
    # learning_rate = parameters.get('learning_rate', 0.1)
    learning_rate = 0.1
 
    n_in = number_of_features
    n_out = 1
    rng = numpy.random.RandomState(1234)
    w_values = numpy.asarray(
        rng.uniform(
            low=-numpy.sqrt(6. / (n_in + n_out)),
            high=numpy.sqrt(6. / (n_in + n_out)),
            size=(n_in, n_out)
        ),
        dtype=theano.config.floatX
    )
 
    w_values *= 4
    w = theano.shared(value=w_values, name='W', borrow=True)
 
    b_values = numpy.zeros((n_out,), dtype=theano.config.floatX)
    b = theano.shared(value=b_values, name='b', borrow=True)
 
    activation = theano.tensor.nnet.sigmoid
 
    output = activation(theano.tensor.dot(x, w) + b)
 
    cost = theano.tensor.nnet.binary_crossentropy(output.T, y).mean()
 
    params = [w, b]
 
    grad_params = [theano.tensor.grad(cost, param) for param in params]
 
    updates = [(param, param - learning_rate * gparam) for param, gparam in zip(params, grad_params)]
 
    train_function = theano.function(
        inputs=[x, y],
        outputs=cost,
        updates=updates
    )
 
    eval_function = theano.function(
        inputs=[x],
        outputs=output
    )
 
    return State(x, y, params, cost, updates, train_function, eval_function)
 
 
def feature_importance(state):
    """
    Return a list containing the feature importances
    """
    return []
 
 
def load(obj):
    state = State(eval_function=obj[0])
    return state
 
 
def apply(state, X):
    result = state.eval_function(X)
    return numpy.require(result, dtype=numpy.float32, requirements=['A', 'W', 'C', 'O'])
 
 
def begin_fit(state, Xvalid, Svalid, yvalid, wvalid, nBatches):
    return state
 
 
def partial_fit(state, X, S, y, w, epoch, batch):
    avg_cost = state.train_function(X, y) / len(y)
    print("Epoch:", f'{int(epoch):04}', "Batch:", f'{int(batch):04}', "cost=", f"{avg_cost:.9f}")
    return True
 
 
def end_fit(state):
    # FIXME: this might not work as intended
    # a better method can be found at: http://deeplearning.net/software/theano/tutorial/loading_and_saving.html
    return [state.eval_function]