def __init__(self,
n_tasks,
n_features,
logdir=None,
layer_sizes=[1000],
weight_init_stddevs=[.02],
bias_init_consts=[1.],
penalty=0.0,
penalty_type="l2",
dropouts=[0.5],
learning_rate=0.002,
momentum=.8,
optimizer="adam",
batch_size=50,
fit_transformers=[],
n_evals=1,
verbose=True,
seed=None,
**kwargs):
"""Initialize TensorflowMultiTaskFitTransformRegressor
Parameters
----------
n_tasks: int
Number of tasks
n_features: list or int
Number of features.
logdir: str
Location to save data
layer_sizes: list
List of layer sizes.
weight_init_stddevs: list
List of standard deviations for weights (sampled from zero-mean
gaussians). One for each layer.
bias_init_consts: list
List of bias initializations. One for each layer.
penalty: float
Amount of penalty (l2 or l1 applied)
penalty_type: str
Either "l2" or "l1"
dropouts: list
List of dropout amounts. One for each layer.
learning_rate: float
Learning rate for model.
momentum: float
Momentum. Only applied if optimizer=="momentum"
optimizer: str
Type of optimizer applied.
batch_size: int
Size of minibatches for training.
fit_transformers: list
List of dc.trans.FitTransformer objects
n_evals: int
Number of evalations per example at predict time
verbose: True
Perform logging.
seed: int
If not none, is used as random seed for tensorflow.
"""
self.fit_transformers = fit_transformers
self.n_evals = n_evals
# Run fit transformers on dummy dataset to determine n_features after transformation
if isinstance(n_features, list):
X_b = np.ones([batch_size] + n_features)
elif isinstance(n_features, int):
X_b = np.ones([batch_size, n_features])
else:
raise ValueError("n_features should be list or int")
for transformer in self.fit_transformers:
X_b = transformer.X_transform(X_b)
n_features = X_b.shape[1]
print("n_features after fit_transform: %d" % int(n_features))
TensorflowGraphModel.__init__(self,
n_tasks,
n_features,
logdir=logdir,
layer_sizes=layer_sizes,
weight_init_stddevs=weight_init_stddevs,
bias_init_consts=bias_init_consts,
penalty=penalty,
penalty_type=penalty_type,
dropouts=dropouts,
learning_rate=learning_rate,
momentum=momentum,
optimizer=optimizer,
batch_size=batch_size,
pad_batches=False,
verbose=verbose,
seed=seed,
**kwargs)
def __init__(self,
n_tasks,
K=10,
logdir=None,
n_classes=2,
penalty=0.0,
penalty_type="l2",
learning_rate=0.001,
momentum=.8,
optimizer="adam",
batch_size=50,
verbose=True,
seed=None,
**kwargs):
"""Initialize TensorflowMultiTaskIRVClassifier
Parameters
----------
n_tasks: int
Number of tasks
K: int
Number of nearest neighbours used in classification
logdir: str
Location to save data
n_classes: int
number of different labels
penalty: float
Amount of penalty (l2 or l1 applied)
penalty_type: str
Either "l2" or "l1"
learning_rate: float
Learning rate for model.
momentum: float
Momentum. Only applied if optimizer=="momentum"
optimizer: str
Type of optimizer applied.
batch_size: int
Size of minibatches for training.
verbose: True
Perform logging.
seed: int
If not none, is used as random seed for tensorflow.
"""
self.n_tasks = n_tasks
self.K = K
self.n_features = 2 * self.K * self.n_tasks
print("n_features after fit_transform: %d" % int(self.n_features))
TensorflowGraphModel.__init__(self,
n_tasks,
self.n_features,
logdir=logdir,
layer_sizes=None,
weight_init_stddevs=None,
bias_init_consts=None,
penalty=penalty,
penalty_type=penalty_type,
dropouts=None,
n_classes=n_classes,
learning_rate=learning_rate,
momentum=momentum,
optimizer=optimizer,
batch_size=batch_size,
pad_batches=False,
verbose=verbose,
seed=seed,
**kwargs)
def __init__(self,
n_tasks,
n_features,
logdir=None,
layer_sizes=[1000],
weight_init_stddevs=[.02],
bias_init_consts=[1.],
penalty=0.0,
penalty_type="l2",
dropouts=[0.5],
learning_rate=0.002,
momentum=.8,
optimizer="adam",
batch_size=50,
fit_transformers=[],
n_evals=1,
verbose=True,
seed=None,
**kwargs):
"""Initialize TensorflowMultiTaskFitTransformRegressor
Parameters
----------
n_tasks: int
Number of tasks
n_features: list or int
Number of features.
logdir: str
Location to save data
layer_sizes: list
List of layer sizes.
weight_init_stddevs: list
List of standard deviations for weights (sampled from zero-mean
gaussians). One for each layer.
bias_init_consts: list
List of bias initializations. One for each layer.
penalty: float
Amount of penalty (l2 or l1 applied)
penalty_type: str
Either "l2" or "l1"
dropouts: list
List of dropout amounts. One for each layer.
learning_rate: float
Learning rate for model.
momentum: float
Momentum. Only applied if optimizer=="momentum"
optimizer: str
Type of optimizer applied.
batch_size: int
Size of minibatches for training.
fit_transformers: list
List of dc.trans.FitTransformer objects
n_evals: int
Number of evalations per example at predict time
verbose: True
Perform logging.
seed: int
If not none, is used as random seed for tensorflow.
"""
self.fit_transformers = fit_transformers
self.n_evals = n_evals
# Run fit transformers on dummy dataset to determine n_features after transformation
if isinstance(n_features, list):
X_b = np.ones([batch_size] + n_features)
elif isinstance(n_features, int):
X_b = np.ones([batch_size, n_features])
else:
raise ValueError("n_features should be list or int")
for transformer in self.fit_transformers:
X_b = transformer.X_transform(X_b)
n_features = X_b.shape[1]
print("n_features after fit_transform: %d" % int(n_features))
TensorflowGraphModel.__init__(
self,
n_tasks,
n_features,
logdir=logdir,
layer_sizes=layer_sizes,
weight_init_stddevs=weight_init_stddevs,
bias_init_consts=bias_init_consts,
penalty=penalty,
penalty_type=penalty_type,
dropouts=dropouts,
learning_rate=learning_rate,
momentum=momentum,
optimizer=optimizer,
batch_size=batch_size,
pad_batches=False,
verbose=verbose,
seed=seed,
**kwargs)
def __init__(self,
n_tasks,
K=10,
logdir=None,
n_classes=2,
penalty=0.0,
penalty_type="l2",
learning_rate=0.001,
momentum=.8,
optimizer="adam",
batch_size=50,
verbose=True,
seed=None,
**kwargs):
"""Initialize TensorflowMultiTaskIRVClassifier
Parameters
----------
n_tasks: int
Number of tasks
K: int
Number of nearest neighbours used in classification
logdir: str
Location to save data
n_classes: int
number of different labels
penalty: float
Amount of penalty (l2 or l1 applied)
penalty_type: str
Either "l2" or "l1"
learning_rate: float
Learning rate for model.
momentum: float
Momentum. Only applied if optimizer=="momentum"
optimizer: str
Type of optimizer applied.
batch_size: int
Size of minibatches for training.
verbose: True
Perform logging.
seed: int
If not none, is used as random seed for tensorflow.
"""
self.n_tasks = n_tasks
self.K = K
self.n_features = 2 * self.K * self.n_tasks
print("n_features after fit_transform: %d" % int(self.n_features))
TensorflowGraphModel.__init__(
self,
n_tasks,
self.n_features,
logdir=logdir,
layer_sizes=None,
weight_init_stddevs=None,
bias_init_consts=None,
penalty=penalty,
penalty_type=penalty_type,
dropouts=None,
n_classes=n_classes,
learning_rate=learning_rate,
momentum=momentum,
optimizer=optimizer,
batch_size=batch_size,
pad_batches=False,
verbose=verbose,
seed=seed,
**kwargs)