class LSTMSchema(RecurrentSchema):
units = fields.Int()
activation = StrOrFct(allow_none=True,
validate=validate.OneOf(ACTIVATION_VALUES))
use_bias = fields.Bool(default=True, missing=True)
recurrent_activation = StrOrFct(allow_none=True,
validate=validate.OneOf(ACTIVATION_VALUES))
kernel_initializer = fields.Nested(InitializerSchema,
default=None,
missing=None)
recurrent_initializer = fields.Nested(InitializerSchema,
default=None,
missing=None)
bias_initializer = fields.Nested(InitializerSchema,
default=None,
missing=None)
unit_forget_bias = fields.Bool(default=True, missing=True)
kernel_regularizer = fields.Nested(RegularizerSchema,
default=None,
missing=None)
recurrent_regularizer = fields.Nested(RegularizerSchema,
default=None,
missing=None)
bias_regularizer = fields.Nested(RegularizerSchema,
default=None,
missing=None)
activity_regularizer = fields.Nested(RegularizerSchema,
default=None,
missing=None)
kernel_constraint = fields.Nested(ConstraintSchema,
default=None,
missing=None)
recurrent_constraint = fields.Nested(ConstraintSchema,
default=None,
missing=None)
bias_constraint = fields.Nested(ConstraintSchema,
default=None,
missing=None)
dropout = fields.Float(default=0., missing=0.)
recurrent_dropout = fields.Float(default=0., missing=0.)
class Meta:
ordered = True
@post_load
def make(self, data):
return LSTMConfig(**data)
@post_dump
def unmake(self, data):
return LSTMConfig.remove_reduced_attrs(data)
class Conv2DSchema(BaseLayerSchema):
filters = fields.Int()
kernel_size = ObjectOrListObject(fields.Int, min=2, max=2)
strides = ObjectOrListObject(fields.Int, min=2, max=2, default=(1, 1), missing=(1, 1))
padding = fields.Str(default='valid', missing='valid',
validate=validate.OneOf(['same', 'valid']))
data_format = fields.Str(default=None, missing=None,
validate=validate.OneOf('channels_first', 'channels_last'))
dilation_rate = ObjectOrListObject(fields.Int, min=2, max=2, default=(1, 1), missing=(1, 1))
activation = StrOrFct(allow_none=True, validate=validate.OneOf(ACTIVATION_VALUES))
use_bias = fields.Bool(default=True, missing=True)
kernel_initializer = fields.Nested(InitializerSchema, allow_none=True)
bias_initializer = fields.Nested(InitializerSchema, allow_none=True)
kernel_regularizer = fields.Nested(RegularizerSchema, allow_none=True)
bias_regularizer = fields.Nested(RegularizerSchema, allow_none=True)
activity_regularizer = fields.Nested(RegularizerSchema, allow_none=True)
kernel_constraint = fields.Nested(ConstraintSchema, allow_none=True)
bias_constraint = fields.Nested(ConstraintSchema, allow_none=True)
class Meta:
ordered = True
@post_load
def make_load(self, data):
return Conv2DConfig(**data)
class LSTMSchema(RecurrentSchema):
units = fields.Int()
activation = StrOrFct(allow_none=True,
validate=validate.OneOf(ACTIVATION_VALUES))
use_bias = fields.Bool(default=True, missing=True)
recurrent_activation = StrOrFct(allow_none=True,
validate=validate.OneOf(ACTIVATION_VALUES))
kernel_initializer = fields.Nested(InitializerSchema,
default=None,
missing=None)
recurrent_initializer = fields.Nested(InitializerSchema,
default=None,
missing=None)
bias_initializer = fields.Nested(InitializerSchema,
default=None,
missing=None)
unit_forget_bias = fields.Bool(default=True, missing=True)
kernel_regularizer = fields.Nested(RegularizerSchema,
default=None,
missing=None)
recurrent_regularizer = fields.Nested(RegularizerSchema,
default=None,
missing=None)
bias_regularizer = fields.Nested(RegularizerSchema,
default=None,
missing=None)
activity_regularizer = fields.Nested(RegularizerSchema,
default=None,
missing=None)
kernel_constraint = fields.Nested(ConstraintSchema,
default=None,
missing=None)
recurrent_constraint = fields.Nested(ConstraintSchema,
default=None,
missing=None)
bias_constraint = fields.Nested(ConstraintSchema,
default=None,
missing=None)
dropout = fields.Float(default=0., missing=0.)
recurrent_dropout = fields.Float(default=0., missing=0.)
@staticmethod
def schema_config():
return LSTMConfig
class ActivationSchema(BaseLayerSchema):
activation = StrOrFct(allow_none=True,
validate=validate.OneOf(ACTIVATION_VALUES))
class Meta:
ordered = True
@post_load
def make_load(self, data):
return ActivationConfig(**data)
class LocallyConnected1DSchema(BaseLayerSchema):
filters = fields.Int()
kernel_size = ObjectOrListObject(fields.Int, min=1, max=1)
strides = ObjectOrListObject(fields.Int,
min=1,
max=1,
default=1,
missing=1)
padding = fields.Str(default='valid',
missing='valid',
validate=validate.OneOf(['same', 'valid']))
data_format = fields.Str(default=None,
missing=None,
validate=validate.OneOf('channels_first',
'channels_last'))
activation = StrOrFct(allow_none=True,
validate=validate.OneOf(ACTIVATION_VALUES))
use_bias = fields.Bool(default=True, missing=True)
kernel_initializer = fields.Nested(InitializerSchema,
default=None,
missing=None)
bias_initializer = fields.Nested(InitializerSchema,
default=None,
missing=None)
kernel_regularizer = fields.Nested(RegularizerSchema,
default=None,
missing=None)
bias_regularizer = fields.Nested(RegularizerSchema,
default=None,
missing=None)
activity_regularizer = fields.Nested(RegularizerSchema,
default=None,
missing=None)
kernel_constraint = fields.Nested(RegularizerSchema,
default=None,
missing=None)
bias_constraint = fields.Nested(RegularizerSchema,
default=None,
missing=None)
class Meta:
ordered = True
@post_load
def make(self, data):
return LocallyConnected1DConfig(**data)
@post_dump
def unmake(self, data):
return LocallyConnected1DConfig.remove_reduced_attrs(data)
class ActivationSchema(BaseLayerSchema):
activation = StrOrFct(allow_none=True,
validate=validate.OneOf(ACTIVATION_VALUES))
class Meta:
ordered = True
@post_load
def make(self, data):
return ActivationConfig(**data)
@post_dump
def unmake(self, data):
return ActivationConfig.remove_reduced_attrs(data)
class DenseSchema(BaseLayerSchema):
units = fields.Int()
activation = StrOrFct(allow_none=True, validate=validate.OneOf(ACTIVATION_VALUES))
use_bias = fields.Bool(allow_none=True)
kernel_initializer = fields.Nested(InitializerSchema, allow_none=True)
bias_initializer = fields.Nested(InitializerSchema, allow_none=True)
kernel_regularizer = fields.Nested(RegularizerSchema, allow_none=True)
bias_regularizer = fields.Nested(RegularizerSchema, allow_none=True)
activity_regularizer = fields.Nested(RegularizerSchema, allow_none=True)
kernel_constraint = fields.Nested(ConstraintSchema, allow_none=True)
bias_constraint = fields.Nested(ConstraintSchema, allow_none=True)
@staticmethod
def schema_config():
return DenseConfig
class SimpleRNNSchema(RecurrentSchema):
units = fields.Int()
activation = StrOrFct(allow_none=True,
validate=validate.OneOf(ACTIVATION_VALUES))
use_bias = fields.Bool(default=True, missing=True)
kernel_initializer = fields.Nested(InitializerSchema,
default=None,
missing=None)
recurrent_initializer = fields.Nested(InitializerSchema,
default=None,
missing=None)
bias_initializer = fields.Nested(InitializerSchema,
default=None,
missing=None)
kernel_regularizer = fields.Nested(RegularizerSchema,
default=None,
missing=None)
recurrent_regularizer = fields.Nested(RegularizerSchema,
default=None,
missing=None)
bias_regularizer = fields.Nested(RegularizerSchema,
default=None,
missing=None)
activity_regularizer = fields.Nested(RegularizerSchema,
default=None,
missing=None)
kernel_constraint = fields.Nested(ConstraintSchema,
default=None,
missing=None)
recurrent_constraint = fields.Nested(ConstraintSchema,
default=None,
missing=None)
bias_constraint = fields.Nested(ConstraintSchema,
default=None,
missing=None)
dropout = fields.Float(default=0., missing=0.)
recurrent_dropout = fields.Float(default=0., missing=0.)
class Meta:
ordered = True
@post_load
def make_load(self, data):
return SimpleRNNConfig(**data)
class DenseSchema(BaseLayerSchema):
units = fields.Int()
activation = StrOrFct(allow_none=True,
validate=validate.OneOf(ACTIVATION_VALUES))
use_bias = fields.Bool(allow_none=True)
kernel_initializer = fields.Nested(InitializerSchema, allow_none=True)
bias_initializer = fields.Nested(InitializerSchema, allow_none=True)
kernel_regularizer = fields.Nested(RegularizerSchema, allow_none=True)
bias_regularizer = fields.Nested(RegularizerSchema, allow_none=True)
activity_regularizer = fields.Nested(RegularizerSchema, allow_none=True)
kernel_constraint = fields.Nested(ConstraintSchema, allow_none=True)
bias_constraint = fields.Nested(ConstraintSchema, allow_none=True)
class Meta:
ordered = True
@post_load
def make_load(self, data):
return DenseConfig(**data)
class LocallyConnected2DSchema(BaseLayerSchema):
filters = fields.Int()
kernel_size = ObjectOrListObject(fields.Int, min=2, max=2)
strides = ObjectOrListObject(fields.Int,
min=2,
max=2,
default=(1, 1),
missing=(1, 1))
padding = fields.Str(default='valid',
missing='valid',
validate=validate.OneOf(['same', 'valid']))
data_format = fields.Str(default=None,
missing=None,
validate=validate.OneOf('channels_first',
'channels_last'))
activation = StrOrFct(allow_none=True,
validate=validate.OneOf(ACTIVATION_VALUES))
use_bias = fields.Bool(default=True, missing=True)
kernel_initializer = fields.Nested(InitializerSchema,
default=None,
missing=None)
bias_initializer = fields.Nested(InitializerSchema,
default=None,
missing=None)
kernel_regularizer = fields.Nested(RegularizerSchema,
default=None,
missing=None)
bias_regularizer = fields.Nested(RegularizerSchema,
default=None,
missing=None)
activity_regularizer = fields.Nested(RegularizerSchema,
default=None,
missing=None)
kernel_constraint = fields.Nested(RegularizerSchema,
default=None,
missing=None)
bias_constraint = fields.Nested(RegularizerSchema,
default=None,
missing=None)
@staticmethod
def schema_config():
return LocallyConnected2DConfig
class ActivationSchema(BaseLayerSchema):
activation = StrOrFct(allow_none=True, validate=validate.OneOf(ACTIVATION_VALUES))
@staticmethod
def schema_config():
return ActivationConfig