def __init__(self,
batch_size,
layerable_cnn_list,
lstm_model,
seq_len=10,
learning_rate=1e-05,
computable_loss=None,
opt_params=None,
verificatable_result=None,
pre_learned_path_list=None,
verbose_mode=False):
logger = getLogger("pydbm")
handler = StreamHandler()
if verbose_mode is True:
handler.setLevel(DEBUG)
logger.setLevel(DEBUG)
else:
handler.setLevel(ERROR)
logger.setLevel(ERROR)
logger.addHandler(handler)
self.__logger = getLogger("pyqlearning")
handler = StreamHandler()
if verbose_mode is True:
self.__logger.setLevel(DEBUG)
else:
self.__logger.setLevel(ERROR)
self.__logger.addHandler(handler)
if isinstance(lstm_model, LSTMModel) is False:
raise TypeError()
if computable_loss is None:
computable_loss = MeanSquaredError()
if verificatable_result is None:
verificatable_result = VerificateFunctionApproximation()
if opt_params is None:
opt_params = Adam()
opt_params.weight_limit = 0.5
opt_params.dropout_rate = 0.0
cnn = ConvolutionalNeuralNetwork(
# The `list` of `ConvolutionLayer`.
layerable_cnn_list=layerable_cnn_list,
# The number of epochs in mini-batch training.
epochs=200,
# The batch size.
batch_size=batch_size,
# Learning rate.
learning_rate=learning_rate,
# Loss function.
computable_loss=computable_loss,
# Optimizer.
opt_params=opt_params,
# Verification.
verificatable_result=verificatable_result,
# Others.
learning_attenuate_rate=0.1,
attenuate_epoch=50)
self.__cnn = cnn
self.__lstm_model = lstm_model
self.__seq_len = seq_len
self.__batch_size = batch_size
self.__computable_loss = computable_loss
self.__learning_rate = learning_rate
self.__verbose_mode = verbose_mode
self.__q_shape = None
self.__q_logs_list = []
def __init__(
self,
batch_size,
layerable_cnn_list,
learning_rate=1e-05,
computable_loss=None,
opt_params=None,
verificatable_result=None,
pre_learned_path_list=None,
fc_w_arr=None,
fc_activation_function=None,
cnn=None,
verbose_mode=False
):
'''
Init.
Args:
batch_size: Batch size in mini-batch.
layerable_cnn_list: `list` of `LayerableCNN`.
learning_rate: Learning rate.
computable_loss: is-a `ComputableLoss`.
opt_params: is-a `OptParams`.
verificatable_result: is-a `VerificateFunctionApproximation`.
pre_learned_path_list: `list` of file path that stored pre-learned parameters.
This parameters will be refered only when `cnn` is `None`.
fc_w_arr: `np.ndarray` of weight matrix in output layer.
fc_activation_function: is-a `ActivatingFunctionInterface`.
cnn: is-a `ConvolutionalNeuralNetwork` as a model in this class.
If not `None`, `self.__cnn` will be overrided by this `cnn`.
If `None`, this class initialize `ConvolutionalNeuralNetwork`
by default hyper parameters.
verbose_mode: Verbose mode or not.
'''
logger = getLogger("pydbm")
handler = StreamHandler()
if verbose_mode is True:
handler.setLevel(DEBUG)
logger.setLevel(DEBUG)
else:
handler.setLevel(ERROR)
logger.setLevel(ERROR)
logger.addHandler(handler)
self.__logger = getLogger("pyqlearning")
handler = StreamHandler()
if verbose_mode is True:
self.__logger.setLevel(DEBUG)
else:
self.__logger.setLevel(ERROR)
self.__logger.addHandler(handler)
if computable_loss is None:
computable_loss = MeanSquaredError()
if verificatable_result is None:
verificatable_result = VerificateFunctionApproximation()
if opt_params is None:
opt_params = Adam()
opt_params.weight_limit = 0.5
opt_params.dropout_rate = 0.0
if cnn is None:
cnn = ConvolutionalNeuralNetwork(
# The `list` of `ConvolutionLayer`.
layerable_cnn_list=layerable_cnn_list,
# The number of epochs in mini-batch training.
epochs=200,
# The batch size.
batch_size=batch_size,
# Learning rate.
learning_rate=learning_rate,
# Loss function.
computable_loss=computable_loss,
# Optimizer.
opt_params=opt_params,
# Verification.
verificatable_result=verificatable_result,
# Pre-learned parameters.
pre_learned_path_list=pre_learned_path_list,
# Others.
learning_attenuate_rate=0.1,
attenuate_epoch=50
)
self.__cnn = cnn
self.__batch_size = batch_size
self.__computable_loss = computable_loss
self.__learning_rate = learning_rate
self.__verbose_mode = verbose_mode
self.__fc_w_arr = fc_w_arr
self.__fc_activation_function = fc_activation_function
self.__q_shape = None
self.__loss_list = []
def __init__(self,
deconvolution_model,
batch_size,
layerable_cnn_list,
learning_rate=1e-05,
learning_attenuate_rate=0.1,
attenuate_epoch=50,
computable_loss=None,
opt_params=None,
verificatable_result=None,
cnn=None,
condition_noise_sampler=None):
'''
Init.
Args:
deconvolution_model: is-a `DeconvolutionModel`.
batch_size: Batch size in mini-batch.
layerable_cnn_list: `list` of `LayerableCNN`.
cnn_output_graph: is-a `CNNOutputGraph`.
learning_rate: Learning rate.
learning_attenuate_rate: Attenuate the `learning_rate` by a factor of this value every `attenuate_epoch`.
attenuate_epoch: Attenuate the `learning_rate` by a factor of `learning_attenuate_rate` every `attenuate_epoch`.
Additionally, in relation to regularization,
this class constrains weight matrixes every `attenuate_epoch`.
computable_loss: is-a `ComputableLoss`.
This parameters will be refered only when `cnn` is `None`.
opt_params: is-a `OptParams`.
This parameters will be refered only when `cnn` is `None`.
verificatable_result: is-a `VerificateFunctionApproximation`.
This parameters will be refered only when `cnn` is `None`.
cnn: is-a `ConvolutionalNeuralNetwork` as a model in this class.
If not `None`, `self.__cnn` will be overrided by this `cnn`.
If `None`, this class initialize `ConvolutionalNeuralNetwork`
by default hyper parameters.
condition_noise_sampler: is-a `NoiseSampler` to add noise to outputs from `Conditioner`.
'''
if isinstance(deconvolution_model, DeconvolutionModel) is False:
raise TypeError()
self.__deconvolution_model = deconvolution_model
if cnn is None:
for layerable_cnn in layerable_cnn_list:
if isinstance(layerable_cnn, LayerableCNN) is False:
raise TypeError()
self.__layerable_cnn_list = layerable_cnn_list
self.__learning_rate = learning_rate
self.__opt_params = opt_params
if cnn is None:
if computable_loss is None:
computable_loss = MeanSquaredError()
if isinstance(computable_loss, ComputableLoss) is False:
raise TypeError()
if verificatable_result is None:
verificatable_result = VerificateFunctionApproximation()
if isinstance(verificatable_result, VerificatableResult) is False:
raise TypeError()
if opt_params is None:
opt_params = Adam()
opt_params.weight_limit = 1e+10
opt_params.dropout_rate = 0.0
if isinstance(opt_params, OptParams) is False:
raise TypeError()
cnn = ConvolutionalNeuralNetwork(
layerable_cnn_list=layerable_cnn_list,
computable_loss=computable_loss,
opt_params=opt_params,
verificatable_result=verificatable_result,
epochs=100,
batch_size=batch_size,
learning_rate=learning_rate,
learning_attenuate_rate=learning_attenuate_rate,
test_size_rate=0.3,
tol=1e-15,
tld=100.0,
save_flag=False,
pre_learned_path_list=None)
self.__cnn = cnn
self.__condition_noise_sampler = condition_noise_sampler
self.__batch_size = batch_size
self.__computable_loss = computable_loss
self.__learning_rate = learning_rate
self.__attenuate_epoch = attenuate_epoch
self.__learning_attenuate_rate = learning_attenuate_rate
self.__q_shape = None
self.__loss_list = []
self.__epoch_counter = 0
logger = getLogger("pygan")
self.__logger = logger
def __init__(self,
batch_size,
layerable_cnn_list,
cnn_output_graph,
learning_rate=1e-05,
computable_loss=None,
opt_params=None,
verificatable_result=None,
cnn=None,
feature_matching_layer=0):
'''
Init.
Args:
batch_size: Batch size in mini-batch.
layerable_cnn_list: `list` of `LayerableCNN`.
cnn_output_graph: is-a `CNNOutputGraph`.
learning_rate: Learning rate.
computable_loss: is-a `ComputableLoss`.
This parameters will be refered only when `cnn` is `None`.
opt_params: is-a `OptParams`.
This parameters will be refered only when `cnn` is `None`.
verificatable_result: is-a `VerificateFunctionApproximation`.
This parameters will be refered only when `cnn` is `None`.
cnn: is-a `ConvolutionalNeuralNetwork` as a model in this class.
If not `None`, `self.__cnn` will be overrided by this `cnn`.
If `None`, this class initialize `ConvolutionalNeuralNetwork`
by default hyper parameters.
feature_matching_layer: Key of layer number for feature matching forward/backward.
'''
for layerable_cnn in layerable_cnn_list:
if isinstance(layerable_cnn, LayerableCNN) is False:
raise TypeError()
self.__layerable_cnn_list = layerable_cnn_list
self.__learning_rate = learning_rate
self.__opt_params = opt_params
if cnn is None:
if computable_loss is None:
computable_loss = MeanSquaredError()
if isinstance(computable_loss, ComputableLoss) is False:
raise TypeError()
if verificatable_result is None:
verificatable_result = VerificateFunctionApproximation()
if isinstance(verificatable_result, VerificatableResult) is False:
raise TypeError()
if opt_params is None:
opt_params = Adam()
opt_params.weight_limit = 1e+10
opt_params.dropout_rate = 0.0
if isinstance(opt_params, OptParams) is False:
raise TypeError()
cnn = ConvolutionalNeuralNetwork(
layerable_cnn_list=layerable_cnn_list,
computable_loss=computable_loss,
opt_params=opt_params,
verificatable_result=verificatable_result,
epochs=100,
batch_size=batch_size,
learning_rate=learning_rate,
learning_attenuate_rate=0.1,
test_size_rate=0.3,
tol=1e-15,
tld=100.0,
save_flag=False,
pre_learned_path_list=None)
cnn.setup_output_layer(cnn_output_graph)
self.__cnn = cnn
self.__batch_size = batch_size
self.__computable_loss = computable_loss
self.__learning_rate = learning_rate
self.__q_shape = None
self.__loss_list = []
self.__feature_matching_layer = feature_matching_layer
self.__epoch_counter = 0
logger = getLogger("pygan")
self.__logger = logger
def __init__(self,
batch_size,
layerable_cnn_list,
cnn_output_graph,
learning_rate=1e-05,
learning_attenuate_rate=0.1,
attenuate_epoch=50,
computable_loss=None,
opt_params=None,
verificatable_result=None,
pre_learned_path_list=None,
pre_learned_output_path=None,
cnn=None,
verbose_mode=False):
'''
Init.
Args:
batch_size: Batch size in mini-batch.
layerable_cnn_list: `list` of `LayerableCNN`.
cnn_output_graph: Computation graph which is-a `CNNOutputGraph` to compute parameters in output layer.
learning_rate: Learning rate.
learning_attenuate_rate: Attenuate the `learning_rate` by a factor of this value every `attenuate_epoch`.
attenuate_epoch: Attenuate the `learning_rate` by a factor of `learning_attenuate_rate` every `attenuate_epoch`.
computable_loss: is-a `ComputableLoss`.
opt_params: is-a `OptParams`.
verificatable_result: is-a `VerificateFunctionApproximation`.
pre_learned_path_list: `list` of file path that stored pre-learned parameters.
This parameters will be refered only when `cnn` is `None`.
pre_learned_output_path: File path that stores pre-learned parameters.
cnn: is-a `ConvolutionalNeuralNetwork` as a model in this class.
If not `None`, `self.__cnn` will be overrided by this `cnn`.
If `None`, this class initialize `ConvolutionalNeuralNetwork`
by default hyper parameters.
verbose_mode: Verbose mode or not.
'''
logger = getLogger("pydbm")
handler = StreamHandler()
if verbose_mode is True:
handler.setLevel(DEBUG)
logger.setLevel(DEBUG)
else:
handler.setLevel(ERROR)
logger.setLevel(ERROR)
logger.addHandler(handler)
self.__logger = getLogger("pyqlearning")
handler = StreamHandler()
if verbose_mode is True:
self.__logger.setLevel(DEBUG)
else:
self.__logger.setLevel(ERROR)
self.__logger.addHandler(handler)
if computable_loss is None:
computable_loss = MeanSquaredError()
if verificatable_result is None:
verificatable_result = VerificateFunctionApproximation()
if opt_params is None:
opt_params = Adam()
opt_params.weight_limit = 1e+10
opt_params.dropout_rate = 0.0
if cnn is None:
cnn = ConvolutionalNeuralNetwork(
# The `list` of `ConvolutionLayer`.
layerable_cnn_list=layerable_cnn_list,
# The number of epochs in mini-batch training.
epochs=200,
# The batch size.
batch_size=batch_size,
# Learning rate.
learning_rate=learning_rate,
# Loss function.
computable_loss=computable_loss,
# Optimizer.
opt_params=opt_params,
# Verification.
verificatable_result=verificatable_result,
# Pre-learned parameters.
pre_learned_path_list=pre_learned_path_list,
# Others.
learning_attenuate_rate=learning_attenuate_rate,
attenuate_epoch=attenuate_epoch)
cnn.setup_output_layer(cnn_output_graph, pre_learned_output_path)
self.__cnn = cnn
self.__batch_size = batch_size
self.__computable_loss = computable_loss
self.__learning_rate = learning_rate
self.__learning_attenuate_rate = learning_attenuate_rate
self.__attenuate_epoch = attenuate_epoch
self.__verbose_mode = verbose_mode
self.__loss_list = []
self.__epoch_counter = 0
def __init__(self,
batch_size,
layerable_cnn_list,
cnn_output_graph,
learning_rate=1e-05,
computable_loss=None,
opt_params=None,
verificatable_result=None,
cnn=None,
verbose_mode=False):
'''
Init.
Args:
batch_size: Batch size in mini-batch.
layerable_cnn_list: `list` of `LayerableCNN`.
cnn_output_graph: is-a `CNNOutputGraph`.
learning_rate: Learning rate.
computable_loss: is-a `ComputableLoss`.
This parameters will be refered only when `cnn` is `None`.
opt_params: is-a `OptParams`.
This parameters will be refered only when `cnn` is `None`.
verificatable_result: is-a `VerificateFunctionApproximation`.
This parameters will be refered only when `cnn` is `None`.
cnn: is-a `ConvolutionalNeuralNetwork` as a model in this class.
If not `None`, `self.__cnn` will be overrided by this `cnn`.
If `None`, this class initialize `ConvolutionalNeuralNetwork`
by default hyper parameters.
verbose_mode: Verbose mode or not.
'''
for layerable_cnn in layerable_cnn_list:
if isinstance(layerable_cnn, LayerableCNN) is False:
raise TypeError()
logger = getLogger("pydbm")
handler = StreamHandler()
if verbose_mode is True:
handler.setLevel(DEBUG)
logger.setLevel(DEBUG)
else:
handler.setLevel(ERROR)
logger.setLevel(ERROR)
logger.addHandler(handler)
self.__layerable_cnn_list = layerable_cnn_list
self.__learning_rate = learning_rate
self.__opt_params = opt_params
self.__logger = logger
if cnn is None:
if computable_loss is None:
computable_loss = MeanSquaredError()
if isinstance(computable_loss, ComputableLoss) is False:
raise TypeError()
if verificatable_result is None:
verificatable_result = VerificateFunctionApproximation()
if isinstance(verificatable_result, VerificatableResult) is False:
raise TypeError()
if opt_params is None:
opt_params = Adam()
opt_params.weight_limit = 0.5
opt_params.dropout_rate = 0.0
if isinstance(opt_params, OptParams) is False:
raise TypeError()
cnn = ConvolutionalNeuralNetwork(
layerable_cnn_list=layerable_cnn_list,
computable_loss=computable_loss,
opt_params=opt_params,
verificatable_result=verificatable_result,
epochs=100,
batch_size=batch_size,
learning_rate=learning_rate,
learning_attenuate_rate=0.1,
test_size_rate=0.3,
tol=1e-15,
tld=100.0,
save_flag=False,
pre_learned_path_list=None)
cnn.setup_output_layer(cnn_output_graph)
self.__cnn = cnn
self.__batch_size = batch_size
self.__computable_loss = computable_loss
self.__learning_rate = learning_rate
self.__verbose_mode = verbose_mode
self.__q_shape = None
self.__loss_list = []