Python theano.tensor.bvector() Examples
The following are 5
code examples of theano.tensor.bvector().
You can vote up the ones you like or vote down the ones you don't like,
and go to the original project or source file by following the links above each example.
You may also want to check out all available functions/classes of the module
theano.tensor
, or try the search function
.
.
Example #1
| Source File: test_pfunc.py From D-VAE with MIT License | 5 votes |
|
def test_param_allow_downcast_int(self):
a = tensor.wvector('a') # int16
b = tensor.bvector('b') # int8
c = tensor.bscalar('c') # int8
f = pfunc([In(a, allow_downcast=True),
In(b, allow_downcast=False),
In(c, allow_downcast=None)],
(a + b + c))
# Both values are in range. Since they're not ndarrays (but lists),
# they will be converted, and their value checked.
assert numpy.all(f([3], [6], 1) == 10)
# Values are in range, but a dtype too large has explicitly been given
# For performance reasons, no check of the data is explicitly performed
# (It might be OK to change this in the future.)
self.assertRaises(TypeError, f,
[3], numpy.array([6], dtype='int16'), 1)
# Value too big for a, silently ignored
assert numpy.all(f([2 ** 20], numpy.ones(1, dtype='int8'), 1) == 2)
# Value too big for b, raises TypeError
self.assertRaises(TypeError, f, [3], [312], 1)
# Value too big for c, raises TypeError
self.assertRaises(TypeError, f, [3], [6], 806)
Example #2
| Source File: test_pfunc.py From D-VAE with MIT License | 5 votes |
|
def test_allow_input_downcast_int(self):
a = tensor.wvector('a') # int16
b = tensor.bvector('b') # int8
c = tensor.bscalar('c') # int8
f = pfunc([a, b, c], (a + b + c), allow_input_downcast=True)
# Value too big for a, b, or c, silently ignored
assert f([2 ** 20], [1], 0) == 1
assert f([3], [312], 0) == 59
assert f([3], [1], 806) == 42
g = pfunc([a, b, c], (a + b + c), allow_input_downcast=False)
# All values are in range. Since they're not ndarrays (but lists
# or scalars), they will be converted, and their value checked.
assert numpy.all(g([3], [6], 0) == 9)
# Values are in range, but a dtype too large has explicitly been given
# For performance reasons, no check of the data is explicitly performed
# (It might be OK to change this in the future.)
self.assertRaises(TypeError, g,
[3], numpy.array([6], dtype='int16'), 0)
# Value too big for b, raises TypeError
self.assertRaises(TypeError, g, [3], [312], 0)
h = pfunc([a, b, c], (a + b + c)) # Default: allow_input_downcast=None
# Everything here should behave like with False
assert numpy.all(h([3], [6], 0) == 9)
self.assertRaises(TypeError, h,
[3], numpy.array([6], dtype='int16'), 0)
self.assertRaises(TypeError, h, [3], [312], 0)
Example #3
| Source File: test_pfunc.py From attention-lvcsr with MIT License | 5 votes |
|
def test_param_allow_downcast_int(self):
a = tensor.wvector('a') # int16
b = tensor.bvector('b') # int8
c = tensor.bscalar('c') # int8
f = pfunc([In(a, allow_downcast=True),
In(b, allow_downcast=False),
In(c, allow_downcast=None)],
(a + b + c))
# Both values are in range. Since they're not ndarrays (but lists),
# they will be converted, and their value checked.
assert numpy.all(f([3], [6], 1) == 10)
# Values are in range, but a dtype too large has explicitly been given
# For performance reasons, no check of the data is explicitly performed
# (It might be OK to change this in the future.)
self.assertRaises(TypeError, f,
[3], numpy.array([6], dtype='int16'), 1)
# Value too big for a, silently ignored
assert numpy.all(f([2 ** 20], numpy.ones(1, dtype='int8'), 1) == 2)
# Value too big for b, raises TypeError
self.assertRaises(TypeError, f, [3], [312], 1)
# Value too big for c, raises TypeError
self.assertRaises(TypeError, f, [3], [6], 806)
Example #4
| Source File: test_pfunc.py From attention-lvcsr with MIT License | 5 votes |
|
def test_allow_input_downcast_int(self):
a = tensor.wvector('a') # int16
b = tensor.bvector('b') # int8
c = tensor.bscalar('c') # int8
f = pfunc([a, b, c], (a + b + c), allow_input_downcast=True)
# Value too big for a, b, or c, silently ignored
assert f([2 ** 20], [1], 0) == 1
assert f([3], [312], 0) == 59
assert f([3], [1], 806) == 42
g = pfunc([a, b, c], (a + b + c), allow_input_downcast=False)
# All values are in range. Since they're not ndarrays (but lists
# or scalars), they will be converted, and their value checked.
assert numpy.all(g([3], [6], 0) == 9)
# Values are in range, but a dtype too large has explicitly been given
# For performance reasons, no check of the data is explicitly performed
# (It might be OK to change this in the future.)
self.assertRaises(TypeError, g,
[3], numpy.array([6], dtype='int16'), 0)
# Value too big for b, raises TypeError
self.assertRaises(TypeError, g, [3], [312], 0)
h = pfunc([a, b, c], (a + b + c)) # Default: allow_input_downcast=None
# Everything here should behave like with False
assert numpy.all(h([3], [6], 0) == 9)
self.assertRaises(TypeError, h,
[3], numpy.array([6], dtype='int16'), 0)
self.assertRaises(TypeError, h, [3], [312], 0)
Example #5
| Source File: conv_net.py From kaggle-seizure-prediction with MIT License | 4 votes |
|
def __init__(self, param_dict):
self.param_dict = param_dict
self.training_batch_size = param_dict['training_batch_size']
nkerns = param_dict['nkerns']
recept_width = param_dict['recept_width']
pool_width = param_dict['pool_width']
stride = param_dict['stride']
dropout_prob = param_dict['dropout_prob']
weight_decay = param_dict['l2_reg']
activation = param_dict['activation']
weights_variance = param_dict['weights_variance']
n_channels = param_dict['n_channels']
n_timesteps = param_dict['n_timesteps']
n_fbins = param_dict['n_fbins']
global_pooling = param_dict['global_pooling']
rng = np.random.RandomState(23455)
self.training_mode = T.iscalar('training_mode')
self.x = T.tensor4('x')
self.y = T.bvector('y')
self.batch_size = theano.shared(self.training_batch_size)
self.input = self.x.reshape((self.batch_size, 1, n_channels * n_fbins, n_timesteps))
self.feature_extractor = FeatureExtractor(rng, self.input, nkerns, recept_width, pool_width, stride,
self.training_mode,
dropout_prob[0],
activation, weights_variance, n_channels, n_timesteps, n_fbins,
global_pooling)
self.classifier = SoftmaxLayer(rng=rng, input=self.feature_extractor.output, n_in=nkerns[-1],
training_mode=self.training_mode, dropout_prob=dropout_prob[-1])
self.weights = self.feature_extractor.weights + self.classifier.weights
# ---------------------- BACKPROP
self.cost = self.classifier.cross_entropy_cost(self.y)
self.cost = self.classifier.cross_entropy_cost(self.y)
L2_sqr = sum((weight ** 2).sum() for weight in self.weights[::2])
self.grads = T.grad(self.cost + weight_decay * L2_sqr, self.weights)
self.updates = self.adadelta_updates(self.grads, self.weights)
# self.updates = self.nesterov_momentum(self.grads, self.weights)
# --------------------- FUNCTIONS
self.train_model = theano.function([self.x, self.y, Param(self.training_mode, default=1)],
outputs=self.cost,
updates=self.updates)
self.validate_model = theano.function([self.x, self.y, Param(self.training_mode, default=0)],
self.cost)
self.test_model = theano.function([self.x, Param(self.training_mode, default=0)],
self.classifier.p_y_given_x[:, 1])
