def __init__(self, input, n_in, n_out, prefix='hidden', W=None, b=None,
activation=T.tanh):
self.input = input
if W is None:
W_values = numpy.asarray(
np.random.uniform(
low=-numpy.sqrt(6. / (n_in + n_out)),
high=numpy.sqrt(6. / (n_in + n_out)),
size=(n_in, n_out)
),
dtype=theano.config.floatX
)
if activation == theano.tensor.nnet.sigmoid:
W_values *= 4
W = theano.shared(value=W_values, name=_p(prefix, 'W'), borrow=True)
if b is None:
b_values = numpy.zeros((n_out,), dtype=theano.config.floatX)
b = theano.shared(value=b_values, name=_p(prefix, 'b'), borrow=True)
self.W = W
self.b = b
self.pre_activation = T.dot(input, self.W) + self.b
if activation is None:
self.output = self.pre_activation
elif activation == T.nnet.softmax:
shape= self.pre_activation.shape
tmp = self.pre_activation.reshape((T.prod(shape[:-1]), shape[-1]))
self.output = T.nnet.softmax(tmp).reshape(shape)
else:
self.output = activation(self.pre_activation)
self.params = {_p(prefix, 'W'):W, _p(prefix, 'b'):b}
def __init__(self,
input,
n_in,
n_out,
prefix='hidden',
W=None,
b=None,
activation=T.tanh):
self.input = input
if W is None:
W_values = numpy.asarray(np.random.uniform(
low=-numpy.sqrt(6. / (n_in + n_out)),
high=numpy.sqrt(6. / (n_in + n_out)),
size=(n_in, n_out)),
dtype=theano.config.floatX)
if activation == theano.tensor.nnet.sigmoid:
W_values *= 4
W = theano.shared(value=W_values,
name=_p(prefix, 'W'),
borrow=True)
if b is None:
b_values = numpy.zeros((n_out, ), dtype=theano.config.floatX)
b = theano.shared(value=b_values,
name=_p(prefix, 'b'),
borrow=True)
self.W = W
self.b = b
self.pre_activation = T.dot(input, self.W) + self.b
if activation is None:
self.output = self.pre_activation
elif activation == T.nnet.softmax:
shape = self.pre_activation.shape
tmp = self.pre_activation.reshape((T.prod(shape[:-1]), shape[-1]))
self.output = T.nnet.softmax(tmp).reshape(shape)
else:
self.output = activation(self.pre_activation)
self.params = {_p(prefix, 'W'): W, _p(prefix, 'b'): b}
def __init__(self, x, x_mask, n_samples_const, n_features, n_hidden_units, prefix = 'lstm', forget=True):
# shape check
n_samples, n_steps = x_mask.shape
self.x = x.reshape((n_samples, n_features, n_steps)).dimshuffle(2, 0, 1)
self.x_mask = x_mask.T
# init the parameters
self.params = dict()
num_activations = 3 + forget
W = self.stacked_ortho_wts(n_features, n_hidden_units, num_activations)
self.params[_p(prefix, 'W')] = shared(W)
U = self.stacked_ortho_wts(n_hidden_units, n_hidden_units, num_activations)
self.params[_p(prefix, 'U')] = shared(U)
b = np.zeros(num_activations * n_hidden_units)
self.params[_p(prefix, 'b')] = shared(b)
out0 = shared(np.zeros((n_samples_const, n_hidden_units)).astype(theano.config.floatX))
cell0 = shared(np.zeros((n_samples_const, n_hidden_units)).astype(theano.config.floatX))
def _step(m_, x_, out_tmp, cell_tmp):
preact = T.dot(out_tmp, self.params[_p(prefix, 'U')]) + x_
inn_gate = T.nnet.sigmoid(preact[:, :n_hidden_units])
out_gate = T.nnet.sigmoid(preact[:, n_hidden_units:2*n_hidden_units])
fgt_gate = T.nnet.sigmoid(
preact[:, 2*n_hidden_units:3*n_hidden_units]) if forget else 1 - inn_gate
# pre activation, tanh
cell_val = T.tanh(preact[:, -n_hidden_units:])
cell_val = fgt_gate * cell_tmp + inn_gate * cell_val
cell_val = m_[:, None] * cell_val + (1. - m_)[:, None] * cell_tmp
# after activation, linear
out = out_gate * cell_val
out = m_[:, None] * out + (1. - m_)[:, None] * out_tmp
return out, cell_val
self.param_w = [self.params[_p(prefix, 'W')]]
self.param_b = [self.params[_p(prefix, 'b')]]
self.x_prime = T.dot(self.x, self.params[_p(prefix, 'W')]) + self.params[_p(prefix, 'b')][None, None, :]
rval, updates = theano.scan(_step,
sequences=[self.x_mask, self.x_prime],
outputs_info = [out0, cell0],
name=_p(prefix, '_layers'))
self.output = rval[0]
self.n_out = n_hidden_units
def _step(m_, x_, out_tmp, cell_tmp):
preact = T.dot(out_tmp, self.params[_p(prefix, 'U')]) + x_
inn_gate = T.nnet.sigmoid(preact[:, :n_hidden_units])
out_gate = T.nnet.sigmoid(preact[:, n_hidden_units:2*n_hidden_units])
fgt_gate = T.nnet.sigmoid(
preact[:, 2*n_hidden_units:3*n_hidden_units]) if forget else 1 - inn_gate
# pre activation, tanh
cell_val = T.tanh(preact[:, -n_hidden_units:])
cell_val = fgt_gate * cell_tmp + inn_gate * cell_val
cell_val = m_[:, None] * cell_val + (1. - m_)[:, None] * cell_tmp
# after activation, linear
out = out_gate * cell_val
out = m_[:, None] * out + (1. - m_)[:, None] * out_tmp
return out, cell_val
def _step(m_, x_, out_tmp, cell_tmp):
preact = T.dot(out_tmp, self.params[_p(prefix, 'U')]) + x_
inn_gate = T.nnet.sigmoid(preact[:, :n_hidden_units])
out_gate = T.nnet.sigmoid(preact[:, n_hidden_units:2 *
n_hidden_units])
fgt_gate = T.nnet.sigmoid(
preact[:, 2 * n_hidden_units:3 *
n_hidden_units]) if forget else 1 - inn_gate
# pre activation, tanh
cell_val = T.tanh(preact[:, -n_hidden_units:])
cell_val = fgt_gate * cell_tmp + inn_gate * cell_val
cell_val = m_[:, None] * cell_val + (1. - m_)[:, None] * cell_tmp
# after activation, linear
out = out_gate * cell_val
out = m_[:, None] * out + (1. - m_)[:, None] * out_tmp
return out, cell_val
def __init__(self,
x,
x_mask,
n_samples_const,
n_features,
n_hidden_units,
prefix='lstm',
forget=True):
# shape check
n_samples, n_steps = x_mask.shape
self.x = x.reshape(
(n_samples, n_features, n_steps)).dimshuffle(2, 0, 1)
self.x_mask = x_mask.T
# init the parameters
self.params = dict()
num_activations = 3 + forget
W = self.stacked_ortho_wts(n_features, n_hidden_units, num_activations)
self.params[_p(prefix, 'W')] = shared(W)
U = self.stacked_ortho_wts(n_hidden_units, n_hidden_units,
num_activations)
self.params[_p(prefix, 'U')] = shared(U)
b = np.zeros(num_activations * n_hidden_units)
self.params[_p(prefix, 'b')] = shared(b)
out0 = shared(
np.zeros((n_samples_const,
n_hidden_units)).astype(theano.config.floatX))
cell0 = shared(
np.zeros((n_samples_const,
n_hidden_units)).astype(theano.config.floatX))
def _step(m_, x_, out_tmp, cell_tmp):
preact = T.dot(out_tmp, self.params[_p(prefix, 'U')]) + x_
inn_gate = T.nnet.sigmoid(preact[:, :n_hidden_units])
out_gate = T.nnet.sigmoid(preact[:, n_hidden_units:2 *
n_hidden_units])
fgt_gate = T.nnet.sigmoid(
preact[:, 2 * n_hidden_units:3 *
n_hidden_units]) if forget else 1 - inn_gate
# pre activation, tanh
cell_val = T.tanh(preact[:, -n_hidden_units:])
cell_val = fgt_gate * cell_tmp + inn_gate * cell_val
cell_val = m_[:, None] * cell_val + (1. - m_)[:, None] * cell_tmp
# after activation, linear
out = out_gate * cell_val
out = m_[:, None] * out + (1. - m_)[:, None] * out_tmp
return out, cell_val
self.param_w = [self.params[_p(prefix, 'W')]]
self.param_b = [self.params[_p(prefix, 'b')]]
self.x_prime = T.dot(self.x, self.params[_p(
prefix, 'W')]) + self.params[_p(prefix, 'b')][None, None, :]
rval, updates = theano.scan(_step,
sequences=[self.x_mask, self.x_prime],
outputs_info=[out0, cell0],
name=_p(prefix, '_layers'))
self.output = rval[0]
self.n_out = n_hidden_units
