def __init__(self, input, n_in):
self.W = gen_param(name='W', shape=(n_in, ))
self.b = gen_param(name='b', shape=(1, ))
self.output = T.dot(input, self.W) + self.b
self.params = [self.W, self.b]
self.input = input
def __init__(self,
input,
rng=None,
theano_rng=None,
n_visible=None,
n_hidden=None,
W=None,
hbias=None,
vbias=None,
dropout=None,
v_activation=T.nnet.sigmoid,
h_activation=T.nnet.sigmoid,
optimizer=SGD,
is_train=0):
if rng is None:
rng = np.random.RandomState(888)
if theano_rng is None:
theano_rng = RandomStreams(rng.randint(2**30))
if W is None:
W = gen_param(name='W', shape=(n_visible, n_hidden), rng=rng)
if hbias is None:
hbias = gen_param(name='hbias', shape=(n_hidden, ))
if vbias is None:
vbias = gen_param(name='vbias', shape=(n_visible, ))
self.W = W
self.hbias = hbias
self.vbias = vbias
self.dropout = dropout
self.is_train = is_train
self.input = input
self.theano_rng = theano_rng
self.n_visible = n_visible
self.n_hidden = n_hidden
self.params = [self.W, self.hbias, self.vbias]
# model updates
self.opt = optimizer(self.params)
self.v_activation = v_activation
self.h_activation = h_activation
def __init__(self, input, n_in, n_out, W=None, b=None):
if W is None:
W = gen_param(name='W', shape=(n_in, n_out))
if b is None:
b = gen_param(name='b', shape=(n_out, ))
self.W = W
self.b = b
self.p_y_given_x = T.nnet.softmax(T.dot(input, self.W) + self.b)
self.y_pred = T.argmax(self.p_y_given_x, axis=1)
self.params = [self.W, self.b]
self.input = input
def __init__(self, n_out, rng):
self._gamma = theano.shared(
value=np.asarray(
rng.uniform(
low=1.,
high=1.,
size=(n_out,)
),
dtype=theano.config.floatX
),
name='gamma',
borrow=True
)
self._beta = gen_param(name='beta', shape=(n_out,))
self.params = [self._gamma, self._beta]
def __init__(self, input, rng, theano_rng, n_in, n_out, W=None, b=None,
activation=None, dropout=None, dropconnect=None,
is_train=0):
self.params = []
if W is None:
W = gen_param(name='W', shape=(n_in, n_out), rng=rng)
if b is None:
b = gen_param(name='b', shape=(n_out,))
self.W = W
self.b = b
if (dropconnect is None) or (dropconnect == 0.):
lin_output = T.dot(input, self.W) + self.b
# bn = BN(n_out, rng)
# self.params.extend(bn.params)
# output = activation(bn.batch_norm(lin_output))
output = activation(lin_output)
self.consider_constant = None
else:
output = theano.ifelse.ifelse(
condition=T.eq(is_train, 1),
then_branch=activation(T.dot(input, T.switch(
theano_rng.binomial(
size=(n_in, n_out),
p=(1.-dropconnect),
dtype=theano.config.floatX
),
self.W, 0.
)) + self.b),
else_branch=activation(
T.dot(input, (1.-dropconnect)*self.W) + self.b)
# else_branch=activation(
# T.mean(normal_sample, axis=0) + self.b)
)
self.consider_constant = None
if (dropout is not None) and (dropout > 0.):
output = theano.ifelse.ifelse(
condition=T.eq(is_train, 1),
then_branch=T.switch(
theano_rng.binomial(
size=(n_out,),
p=(1.-dropout),
dtype=theano.config.floatX
),
output, 0.
),
else_branch=output*(1.-dropout)
)
self.output = output
# self.params = [self.W, self.b]
self.params.extend([self.W, self.b])
self.input = input
self.n_in = n_in
self.n_out = n_out
self.is_train = is_train