def __init__(self, network_class, **overrides):
'''Set up an experiment by parsing arguments and building a network.
The only input this constructor needs is the Python class of the network
to build. Other configuration---for example, creating the appropriate
trainer class---typically takes place by parsing command-line argument
values, or by a call to train(...).
Any keyword arguments provided to the constructor will be used to
override values passed on the command line. (Typically this is used to
provide experiment-specific default values for command line arguments
that have no global defaults, e.g., network architecture.)
'''
self.args, self.kwargs = climate.parse_args(**overrides)
if 'activation' in self.kwargs:
warnings.warn(
'please use --hidden-activation instead of --activation',
DeprecationWarning)
self.kwargs['hidden_activation'] = self.kwargs.pop('activation')
if self.kwargs.get('help_activation'):
print(HELP_ACTIVATION)
sys.exit(0)
if self.kwargs.get('help_optimize'):
print(HELP_OPTIMIZE)
sys.exit(0)
assert network_class is not feedforward.Network, \
'use a concrete theanets.Network subclass ' \
'like theanets.{Autoencoder,Regressor,...}'
self.network = network_class(**self.kwargs)
def __init__(self, network_class, **overrides):
'''Set up an experiment by parsing arguments and building a network.
The only input this constructor needs is the Python class of the network
to build. Other configuration---for example, creating the appropriate
trainer class---typically takes place by parsing command-line argument
values, or by a call to train(...).
Any keyword arguments provided to the constructor will be used to
override values passed on the command line. (Typically this is used to
provide experiment-specific default values for command line arguments
that have no global defaults, e.g., network architecture.)
'''
self.args, self.kwargs = climate.parse_args(**overrides)
if 'activation' in self.kwargs:
warnings.warn(
'please use --hidden-activation instead of --activation',
DeprecationWarning)
self.kwargs['hidden_activation'] = self.kwargs.pop('activation')
if self.kwargs.get('help_activation'):
print(HELP_ACTIVATION)
sys.exit(0)
if self.kwargs.get('help_optimize'):
print(HELP_OPTIMIZE)
sys.exit(0)
assert network_class is not feedforward.Network, \
'use a concrete theanets.Network subclass ' \
'like theanets.{Autoencoder,Regressor,...}'
self.network = network_class(**self.kwargs)
def __init__(self, network_class, **overrides):
'''Set up an experiment -- build a network and a trainer.
The only input this constructor needs is the Python class of the network
to build. Other configuration---for example, creating the appropriate
trainer class---typically takes place by parsing command-line argument
values.
Datasets also need to be added to the experiment, either :
- manually, by calling add_dataset(...), or
- at runtime, by providing data to the run(train_data, valid_data)
method.
Datasets are typically provided as numpy arrays, but they can also be
provided as callables, as described in the dataset module.
Any keyword arguments provided to the constructor will be used to
override values passed on the command line. (Typically this is used to
provide experiment-specific default values for command line arguments
that have no global defaults, e.g., network architecture.)
'''
self.trainers = []
self.datasets = {}
self.args, self.kwargs = climate.parse_args(**overrides)
if 'activation' in self.kwargs:
warnings.warn(
'please use --hidden-activation instead of --activation',
DeprecationWarning)
activation = self.kwargs.pop('activation')
if not self.kwargs.get('hidden_activation'):
self.kwargs['hidden_activation'] = activation
if self.kwargs.get('help_activation'):
print(HELP_ACTIVATION)
sys.exit(0)
if self.kwargs.get('help_optimize'):
print(HELP_OPTIMIZE)
sys.exit(0)
kw = {}
kw.update(self.kwargs)
self.network = self._build_network(network_class, **kw)
kw = {}
kw.update(self.kwargs)
self._build_trainers(**kw)
def __init__(self, network_class, **overrides):
'''Set up an experiment -- build a network and a trainer.
The only input this constructor needs is the Python class of the network
to build. Other configuration---for example, creating the appropriate
trainer class---typically takes place by parsing command-line argument
values.
Datasets also need to be added to the experiment, either :
- manually, by calling add_dataset(...), or
- at runtime, by providing data to the run(train_data, valid_data)
method.
Datasets are typically provided as numpy arrays, but they can also be
provided as callables, as described in the dataset module.
Any keyword arguments provided to the constructor will be used to
override values passed on the command line. (Typically this is used to
provide experiment-specific default values for command line arguments
that have no global defaults, e.g., network architecture.)
'''
self.trainers = []
self.datasets = {}
self.args, self.kwargs = climate.parse_args(**overrides)
if 'activation' in self.kwargs:
warnings.warn(
'please use --hidden-activation instead of --activation',
DeprecationWarning)
activation = self.kwargs.pop('activation')
if not self.kwargs.get('hidden_activation'):
self.kwargs['hidden_activation'] = activation
if self.kwargs.get('help_activation'):
print(HELP_ACTIVATION)
sys.exit(0)
if self.kwargs.get('help_optimize'):
print(HELP_OPTIMIZE)
sys.exit(0)
kw = {}
kw.update(self.kwargs)
self.network = self._build_network(network_class, **kw)
kw = {}
kw.update(self.kwargs)
self._build_trainers(**kw)
import os, sys
import transform
import util
from transform import transformFFT
import numpy as np
import re
from scipy.signal import blackmanharris as blackmanharris
import climate
if __name__ == "__main__":
if len(sys.argv) > -1:
climate.add_arg('--db', help="the dataset path")
climate.add_arg('--feature_path',
help="the path where to save the features")
kwargs = climate.parse_args()
db = None
if kwargs.__getattribute__('db'):
db = kwargs.__getattribute__('db')
# else:
# db='/home/marius/Documents/Database/iKala/'
if kwargs.__getattribute__('feature_path'):
feature_path = kwargs.__getattribute__('feature_path')
else:
feature_path = os.path.join(db, 'transforms', 't1')
assert os.path.isdir(
db
), "Please input the directory for the iKala dataset with --db path_to_iKala"
tt = None
for f in os.listdir(os.path.join(db, "Wavfile")):
