def __init__(
self,
hidden_neurons=None,
hidden_activation=nn.ReLU(False),
output_activation=nn.Sigmoid(),
loss=nn.MSELoss(),
optimizer='adam',
epochs=100,
batch_size=32,
dropout_rate=0.2,
l2_regularizer=0.001,
validation_size=0.1,
preprocessing=True,
verbose=1,
random_state=None,
threshold=1,
):
super(Lstm_VAE, self).__init__()
self.hidden_neurons = hidden_neurons
self.hidden_activation = hidden_activation
self.output_activation = output_activation
self.loss = loss
self.optimizer = optimizer
self.epochs = epochs
self.batch_size = batch_size
self.dropout_rate = dropout_rate
self.l2_regularizer = l2_regularizer
self.validation_size = validation_size
self.preprocessing = preprocessing
self.verbose = verbose
self.random_state = random_state
self.threshold = threshold
# default values
if self.hidden_neurons is None:
self.hidden_neurons = [32, 16, 16, 32]
# Verify the network design is valid
if not self.hidden_neurons == self.hidden_neurons[::-1]:
print(self.hidden_neurons)
raise ValueError("Hidden units should be symmetric")
self.hidden_neurons_ = self.hidden_neurons
check_parameter(dropout_rate,
0,
1,
param_name='dropout_rate',
include_left=True)
def __init__(self, hidden_neurons=None,
hidden_activation=nn.Tanh(), output_activation=nn.Softmax(dim=1),
gmm_k=4 , lambda_energy = 0.1 , lambda_cov_diag = 0.005 , optimizer='adam',
epochs=100, batch_size=32,dropout_rate=0.5,
l2_regularizer=0.001, validation_size=0.1, preprocessing=True,
verbose=1, random_state=None, threshold=1.0):
super(Dagmm, self).__init__()
self.gmm_k = gmm_k
self.lambda_energy = lambda_energy
self.lambda_cov_diag = lambda_cov_diag
self.hidden_neurons = hidden_neurons
self.hidden_activation = hidden_activation
self.output_activation = output_activation
self.optimizer = optimizer
self.dropout_rate = dropout_rate
self.epochs = epochs
self.batch_size = batch_size
self.l2_regularizer = l2_regularizer
self.validation_size = validation_size
self.preprocessing = preprocessing
self.verbose = verbose
self.random_state = random_state
self.threshold = threshold
# default values
if self.hidden_neurons is None:
self.hidden_neurons = [64, 32, 10, 1, 1, 10, 32, 64]
# Verify the network design is valid
if not self.hidden_neurons == self.hidden_neurons[::-1]:
print(self.hidden_neurons)
raise ValueError("Hidden units should be symmetric")
self.hidden_neurons_ = self.hidden_neurons
check_parameter(dropout_rate, 0, 1, param_name='dropout_rate',
include_left=True)
def test_check_parameter_range(self):
# verify parameter type correction
with assert_raises(TypeError):
check_parameter('f', 0, 100)
with assert_raises(TypeError):
check_parameter(1, 'f', 100)
with assert_raises(TypeError):
check_parameter(1, 0, 'f')
with assert_raises(TypeError):
check_parameter(argmaxn(value_list=[1, 2, 3], n=1), 0, 100)
# if low and high are both unset
with assert_raises(ValueError):
check_parameter(50)
# if low <= high
with assert_raises(ValueError):
check_parameter(50, 100, 99)
with assert_raises(ValueError):
check_parameter(50, 100, 100)
# check one side
with assert_raises(ValueError):
check_parameter(50, low=100)
with assert_raises(ValueError):
check_parameter(50, high=0)
assert_equal(True, check_parameter(50, low=10))
assert_equal(True, check_parameter(50, high=100))
# if check fails
with assert_raises(ValueError):
check_parameter(-1, 0, 100)
with assert_raises(ValueError):
check_parameter(101, 0, 100)
with assert_raises(ValueError):
check_parameter(0.5, 0.2, 0.3)
# if check passes
assert_equal(True, check_parameter(50, 0, 100))
assert_equal(True, check_parameter(0.5, 0.1, 0.8))
# if includes left or right bounds
with assert_raises(ValueError):
check_parameter(100, 0, 100, include_left=False,
include_right=False)
assert_equal(True, check_parameter(0, 0, 100, include_left=True,
include_right=False))
assert_equal(True, check_parameter(0, 0, 100, include_left=True,
include_right=True))
assert_equal(True, check_parameter(100, 0, 100, include_left=False,
include_right=True))
assert_equal(True, check_parameter(100, 0, 100, include_left=True,
include_right=True))
def test_check_parameter_range(self):
# verify parameter type correction
with assert_raises(TypeError):
check_parameter('f', 0, 100)
with assert_raises(TypeError):
check_parameter(1, 'f', 100)
with assert_raises(TypeError):
check_parameter(1, 0, 'f')
with assert_raises(TypeError):
check_parameter(argmaxn(value_list=[1, 2, 3], n=1), 0, 100)
# if low and high are both unset
with assert_raises(ValueError):
check_parameter(50)
# if low <= high
with assert_raises(ValueError):
check_parameter(50, 100, 99)
with assert_raises(ValueError):
check_parameter(50, 100, 100)
# check one side
with assert_raises(ValueError):
check_parameter(50, low=100)
with assert_raises(ValueError):
check_parameter(50, high=0)
assert_equal(True, check_parameter(50, low=10))
assert_equal(True, check_parameter(50, high=100))
# if check fails
with assert_raises(ValueError):
check_parameter(-1, 0, 100)
with assert_raises(ValueError):
check_parameter(101, 0, 100)
with assert_raises(ValueError):
check_parameter(0.5, 0.2, 0.3)
# if check passes
assert_equal(True, check_parameter(50, 0, 100))
assert_equal(True, check_parameter(0.5, 0.1, 0.8))
# if includes left or right bounds
with assert_raises(ValueError):
check_parameter(100, 0, 100, include_left=False,
include_right=False)
assert_equal(True, check_parameter(0, 0, 100, include_left=True,
include_right=False))
assert_equal(True, check_parameter(0, 0, 100, include_left=True,
include_right=True))
assert_equal(True, check_parameter(100, 0, 100, include_left=False,
include_right=True))
assert_equal(True, check_parameter(100, 0, 100, include_left=True,
include_right=True))