def __init__(
self,
input,
input_shape,
id,
rng=None,
input_params=None,
dropout_rate=0,
verbose=2,
):
if verbose >= 3:
print "... setting up the dropout layer, just in case."
if rng is None:
rng = numpy.random
super(dropout_batch_norm_layer_1d,
self).__init__(input=input,
input_shape=input_shape,
id=id,
rng=rng,
borrow=borrow,
input_params=input_params,
verbose=verbose)
if not dropout_rate == 0:
self.output = _dropout(rng=rng,
params=self.output,
dropout_rate=dropout_rate)
if verbose >= 3:
print "... Dropped out"
self.dropout_rate = dropout_rate
def __init__(self,
input,
num_neurons,
input_shape,
id,
dropout_rate=0.5,
rng=None,
input_params=None,
borrow=True,
activation='relu',
batch_norm=True,
verbose=2):
if verbose >= 3:
print "... set up the dropout dot product layer"
if rng is None:
rng = numpy.random
super(dropout_dot_product_layer,
self).__init__(input=input,
num_neurons=num_neurons,
input_shape=input_shape,
id=id,
rng=rng,
input_params=input_params,
borrow=borrow,
activation=activation,
batch_norm=batch_norm,
verbose=verbose)
if not dropout_rate == 0:
self.output = _dropout(rng=rng,
params=self.output,
dropout_rate=dropout_rate)
self.dropout_rate = dropout_rate
if verbose >= 3:
print "... Dropped out"
def __init__(self,
input,
input_shape,
id,
rng=None,
dropout_rate=0.5,
angle=None,
borrow=True,
verbose=2):
if verbose >= 3:
print("... set up the dropout rotate layer")
if rng is None:
rng = numpy.random
super(dropout_rotate_layer, self).__init__(input=input,
input_shape=input_shape,
id=id,
borrow=borrow,
verbose=verbose)
if not dropout_rate == 0:
self.output = _dropout(rng=rng,
params=self.output,
dropout_rate=dropout_rate)
self.dropout_rate = dropout_rate
if verbose >= 3:
print("... Dropped out")
def __init__(self,
x,
input_shape,
id=-1,
error='rmse',
rng=None,
dropout_rate=0.5,
verbose=2):
if verbose >= 3:
print "... set up the dropout merge layer"
if rng is None:
rng = numpy.random
super(dropout_merge_layer, self).__init__(x=x,
input_shape=input_shape,
error=error,
id=id,
verbose=verbose)
if not dropout_rate == 0:
self.output = _dropout(rng=rng,
params=self.output,
dropout_rate=dropout_rate)
if verbose >= 3:
print "... Dropped out"
def __init__(
self,
input,
nkerns,
input_shape,
id,
output_shape,
dropout_rate=0.5,
filter_shape=(3, 3),
poolsize=(1, 1),
pooltype='max',
batch_norm=True,
border_mode='valid',
stride=(1, 1),
rng=None,
borrow=True,
activation='relu',
input_params=None,
verbose=2,
):
if verbose >= 3:
print "... setting up the dropout layer, just in case."
if rng is None:
rng = numpy.random
super(dropout_deconv_layer_2d,
self).__init__(input=input,
nkerns=nkerns,
input_shape=input_shape,
id=id,
output_shape=output_shape,
filter_shape=filter_shape,
poolsize=poolsize,
pooltype=pooltype,
batch_norm=batch_norm,
border_mode=border_mode,
stride=stride,
rng=rng,
borrow=borrow,
activation=activation,
input_params=input_params,
verbose=verbose)
if not dropout_rate == 0:
self.output = _dropout(rng=rng,
params=self.output,
dropout_rate=dropout_rate)
if verbose >= 3:
print "... Dropped out"
self.dropout_rate = dropout_rate
def __init__( self,
input,
nkerns,
input_shape,
id,
dropout_rate = 0.5,
filter_shape = (3,3),
poolsize = (2,2),
pooltype = 'max',
batch_norm = True,
border_mode = 'valid',
stride = (1,1),
rng = None,
borrow = True,
activation = 'relu',
input_params = None,
verbose = 2,
):
if verbose >=3:
print "... setting up the dropout layer, just in case."
if rng is None:
rng = numpy.random
super(dropout_conv_pool_layer_2d, self).__init__(
input = input,
nkerns = nkerns,
input_shape = input_shape,
id = id,
filter_shape = filter_shape,
poolsize = poolsize,
pooltype = pooltype,
batch_norm = batch_norm,
border_mode = border_mode,
stride = stride,
rng = rng,
borrow = borrow,
activation = activation,
input_params = input_params,
verbose = verbose
)
if not dropout_rate == 0:
self.output = _dropout(rng = rng,
params = self.output,
dropout_rate = dropout_rate)
if verbose >=3:
print "... Dropped out"
self.dropout_rate = dropout_rate
def __init__ (self,
input,
num_neurons,
input_shape,
id,
dropout_rate = 0.5,
rng = None,
input_params = None,
borrow = True,
activation = 'relu',
batch_norm = True,
verbose = 2 ):
if verbose >= 3:
print "... set up the dropout dot product layer"
if rng is None:
rng = numpy.random
super(dropout_dot_product_layer, self).__init__ (
input = input,
num_neurons = num_neurons,
input_shape = input_shape,
id = id,
rng = rng,
input_params = input_params,
borrow = borrow,
activation = activation,
batch_norm = batch_norm,
verbose = verbose
)
if not dropout_rate == 0:
self.output = _dropout(rng = rng,
params = self.output,
dropout_rate = dropout_rate)
self.dropout_rate = dropout_rate
if verbose >=3:
print "... Dropped out"
