def compute_result(model, dataset, data):
"""Compute output value of data samples by using the trained model
Args:
model: A NeuralNet model
dataset: A Dataset object returned by ``create_dataset``
data: list of pedestrian tuples returned by ``decomp_body``
Returns:
A tuple (train, valid, test) where each is three matrices
(image_tensor4D, output_matrix, target_matrix)
"""
result = cachem.load('result')
if result is None:
import theano
import theano.tensor as T
from reid.models.layers import CompLayer
from reid.models.neural_net import NeuralNet
model = NeuralNet([model, CompLayer()])
x = T.matrix()
f = theano.function(inputs=[x], outputs=model.get_output(x))
def compute_output(X):
outputs = [f(X[i:i+1, :]).ravel() for i in xrange(X.shape[0])]
return numpy.asarray(outputs)
images = numpy.asarray([p[0] for p in data])
train = (images[dataset.train_ind],
compute_output(dataset.train_x.get_value(borrow=True)),
dataset.train_y.get_value(borrow=True))
valid = (images[dataset.valid_ind],
compute_output(dataset.valid_x.get_value(borrow=True)),
dataset.valid_y.get_value(borrow=True))
test = (images[dataset.test_ind],
compute_output(dataset.test_x.get_value(borrow=True)),
dataset.test_y.get_value(borrow=True))
result = (train, valid, test)
return result
multitask = DecompLayer([(1,), (3,)])
model = NeuralNet([decomp, columns, comp, multitask])
# Build up the target value adapter
adapter = DecompLayer([(1,), (3,)])
# Build up evaluator
evaluator = Evaluator(model, [mse, mse], [mse, mse], adapter)
# Compute the expression by using the model
x = T.matrix('x')
y = T.matrix('y')
output = model.get_output(x)
cost, updates = evaluator.get_cost_updates(x, y, 1.0)
error = evaluator.get_error(x, y)
f = theano.function(inputs=[x, y],
outputs=[cost, error])
cost, error = f(X, Y)
print "The target matrix is ", Y
print "The cost is ", cost
print "The error is ", error
cost, error = f(X, Z)
print "The target matrix is ", Z
print "The cost is ", cost
print "The error is ", error
comp = CompLayer()
multitask = DecompLayer([(1, ), (3, )])
model = NeuralNet([decomp, columns, comp, multitask])
# Build up the target value adapter
adapter = DecompLayer([(1, ), (3, )])
# Build up evaluator
evaluator = Evaluator(model, [mse, mse], [mse, mse], adapter)
# Compute the expression by using the model
x = T.matrix('x')
y = T.matrix('y')
output = model.get_output(x)
cost, updates = evaluator.get_cost_updates(x, y, 1.0)
error = evaluator.get_error(x, y)
f = theano.function(inputs=[x, y], outputs=[cost, error])
cost, error = f(X, Y)
print "The target matrix is ", Y
print "The cost is ", cost
print "The error is ", error
cost, error = f(X, Z)
print "The target matrix is ", Z
print "The cost is ", cost
print "The error is ", error
