def test3_loss_nll(self, mock_activate, mock_mean):
mock_activate.return_value = (self.input_ndarray, self.input_shape)
mock_mean.return_value = 1
self.output_layer = cl(input=self.input_tensor,
id=self.classifier_layer_name,
num_classes=self.classes,
input_shape=self.input_shape,
rng=self.rng,
borrow=self.borrow,
input_params=self.input_params_all,
verbose=self.verbose)
self.assertEqual(
self.output_layer.loss(theano.tensor.ivector('s'), 'nll'), -1)
def test6_loss_hinge(self, mock_activate, mock_max, mock_mean):
mock_mean.return_value = 1
mock_activate.return_value = (self.input_ndarray, self.input_shape)
mock_max.return_value = self.output_dropout_ndarray
self.output_layer = cl(input=self.input_tensor,
id=self.classifier_layer_name,
num_classes=self.classes,
input_shape=self.input_shape,
rng=self.rng,
borrow=self.borrow,
input_params=self.input_params_all,
verbose=self.verbose)
self.assertEqual(self.output_layer.loss(self.input_ndarray, 'hinge'),
0)
def test2_output_params(self, mock_activate):
mock_activate.return_value = (self.input_ndarray, self.input_shape)
self.output_layer = cl(input=self.input_tensor,
id=self.classifier_layer_name,
num_classes=self.classes,
input_shape=self.input_shape,
rng=self.rng,
borrow=self.borrow,
input_params=self.input_params_all,
verbose=self.verbose)
self.assertEqual(self.output_layer.id, self.classifier_layer_name)
self.assertEqual(self.output_layer.output_shape, self.output_shape)
self.assertTrue(
numpy.allclose(self.output_layer.output, self.input_ndarray))
self.assertTrue(
numpy.allclose(self.output_layer.inference, self.input_ndarray))
def test11_get_params(self, mock_activate, mock_max, mock_mean):
self.val = T.dot(self.input_tensor, self.input_tensor)
mock_mean.return_value = 1
mock_activate.return_value = (self.input_ndarray, self.input_shape)
mock_max.return_value = self.output_dropout_ndarray
self.output_layer = cl(input=self.input_tensor,
id=self.classifier_layer_name,
num_classes=self.classes,
input_shape=self.input_shape,
rng=self.rng,
borrow=self.borrow,
input_params=self.input_params_all,
verbose=self.verbose)
self.output_layer.params = [MockParam(), MockParam()]
out = self.output_layer.get_params(borrow=True, verbose=self.verbose)
self.assertTrue(numpy.allclose(out, [1, 1]))
def test10_loss_exception(self, mock_activate, mock_max, mock_mean):
mock_mean.return_value = 1
mock_activate.return_value = (self.input_ndarray, self.input_shape)
mock_max.return_value = self.output_dropout_ndarray
self.output_layer = cl(input=self.input_tensor,
id=self.classifier_layer_name,
num_classes=self.classes,
input_shape=self.input_shape,
rng=self.rng,
borrow=self.borrow,
input_params=self.input_params_all,
verbose=self.verbose)
try:
self.output_layer.loss(self.input_ndarray, 'hinge1')
self.assertEqual(True, False)
except Exception, e:
self.assertEqual(True, True)
def test7_error(self, mock_activate, mock_max, mock_mean, mock_sum):
mock_sum.return_value = 1
mock_mean.return_value = 1
mock_activate.return_value = (self.input_ndarray, self.input_shape)
mock_max.return_value = self.output_dropout_ndarray
self.output_layer = cl(input=self.input_tensor,
id=self.classifier_layer_name,
num_classes=self.classes,
input_shape=self.input_shape,
rng=self.rng,
borrow=self.borrow,
input_params=self.input_params_all,
verbose=self.verbose)
self.output_layer.predictions = theano.tensor.ivector('s')
self.assertEqual(
self.output_layer.errors(self.output_layer.predictions), 1)
def test9_error_exception(self, mock_activate, mock_max, mock_mean,
mock_sum):
mock_sum.return_value = 1
mock_mean.return_value = 1
mock_activate.return_value = (self.input_ndarray, self.input_shape)
mock_max.return_value = self.output_dropout_ndarray
self.output_layer = cl(input=self.input_tensor,
id=self.classifier_layer_name,
num_classes=self.classes,
input_shape=self.input_shape,
rng=self.rng,
borrow=self.borrow,
input_params=self.input_params_all,
verbose=self.verbose)
self.output_layer.predictions = theano.tensor.scalar('s')
try:
self.output_layer.errors(theano.tensor.scalar('s'))
self.assertEqual(True, False)
except NotImplementedError, e:
self.assertEqual(True, True)
