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layers.py
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layers.py
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"""
Layer functions
"""
import theano
import theano.tensor as tensor
import numpy
# layer normalization
def ln(x, b, s):
_eps = 1e-5
output = (x - x.mean(1)[:,None]) / tensor.sqrt((x.var(1)[:,None] + _eps))
output = s[None, :] * output + b[None,:]
return output
# layers: 'name': ('parameter initializer', 'feedforward')
layers = {'ff': ('param_init_fflayer', 'fflayer'),
'gru': ('param_init_gru', 'gru_layer'),
'lngru': ('param_init_lngru', 'lngru_layer'),
'lstm': ('param_init_lstm', 'lstm_layer'),
'lnlstm': ('param_init_lnlstm', 'lnlstm_layer'),
}
def get_layer(name):
"""
Return param init and feedforward functions for the given layer name
"""
fns = layers[name]
return (eval(fns[0]), eval(fns[1]))
# Feedforward layer
def param_init_fflayer(options, params, prefix='ff', nin=None, nout=None, ortho=True):
"""
Affine transformation + point-wise nonlinearity
"""
if nin == None:
nin = options['dim_proj']
if nout == None:
nout = options['dim_proj']
params[_p(prefix,'W')] = norm_weight(nin, nout, ortho=ortho)
params[_p(prefix,'b')] = numpy.zeros((nout,)).astype('float32')
return params
def fflayer(tparams, state_below, options, prefix='rconv', activ='lambda x: tensor.tanh(x)', **kwargs):
"""
Feedforward pass
"""
return eval(activ)(tensor.dot(state_below, tparams[_p(prefix,'W')])+tparams[_p(prefix,'b')])
# GRU layer
def param_init_gru(options, params, prefix='gru', nin=None, dim=None):
"""
Gated Recurrent Unit (GRU)
"""
if nin == None:
nin = options['dim_proj']
if dim == None:
dim = options['dim_proj']
W = numpy.concatenate([norm_weight(nin,dim),
norm_weight(nin,dim)], axis=1)
params[_p(prefix,'W')] = W
params[_p(prefix,'b')] = numpy.zeros((2 * dim,)).astype('float32')
U = numpy.concatenate([ortho_weight(dim),
ortho_weight(dim)], axis=1)
params[_p(prefix,'U')] = U
Wx = norm_weight(nin, dim)
params[_p(prefix,'Wx')] = Wx
Ux = ortho_weight(dim)
params[_p(prefix,'Ux')] = Ux
params[_p(prefix,'bx')] = numpy.zeros((dim,)).astype('float32')
return params
def gru_layer(tparams, state_below, init_state, options, prefix='gru', mask=None, **kwargs):
"""
Feedforward pass through GRU
"""
nsteps = state_below.shape[0]
if state_below.ndim == 3:
n_samples = state_below.shape[1]
else:
n_samples = 1
dim = tparams[_p(prefix,'Ux')].shape[1]
if init_state == None:
init_state = tensor.alloc(0., n_samples, dim)
if mask == None:
mask = tensor.alloc(1., state_below.shape[0], 1)
def _slice(_x, n, dim):
if _x.ndim == 3:
return _x[:, :, n*dim:(n+1)*dim]
return _x[:, n*dim:(n+1)*dim]
state_below_ = tensor.dot(state_below, tparams[_p(prefix, 'W')]) + tparams[_p(prefix, 'b')]
state_belowx = tensor.dot(state_below, tparams[_p(prefix, 'Wx')]) + tparams[_p(prefix, 'bx')]
U = tparams[_p(prefix, 'U')]
Ux = tparams[_p(prefix, 'Ux')]
def _step_slice(m_, x_, xx_, h_, U, Ux):
preact = tensor.dot(h_, U)
preact += x_
r = tensor.nnet.sigmoid(_slice(preact, 0, dim))
u = tensor.nnet.sigmoid(_slice(preact, 1, dim))
preactx = tensor.dot(h_, Ux)
preactx = preactx * r
preactx = preactx + xx_
h = tensor.tanh(preactx)
h = u * h_ + (1. - u) * h
h = m_[:,None] * h + (1. - m_)[:,None] * h_
return h
seqs = [mask, state_below_, state_belowx]
_step = _step_slice
rval, updates = theano.scan(_step,
sequences=seqs,
outputs_info = [init_state],
non_sequences = [tparams[_p(prefix, 'U')],
tparams[_p(prefix, 'Ux')]],
name=_p(prefix, '_layers'),
n_steps=nsteps,
profile=False,
strict=True)
rval = [rval]
return rval
# LN-GRU layer
def param_init_lngru(options, params, prefix='lngru', nin=None, dim=None):
"""
Gated Recurrent Unit (GRU) with LN
"""
if nin == None:
nin = options['dim_proj']
if dim == None:
dim = options['dim_proj']
W = numpy.concatenate([norm_weight(nin,dim),
norm_weight(nin,dim)], axis=1)
params[_p(prefix,'W')] = W
params[_p(prefix,'b')] = numpy.zeros((2 * dim,)).astype('float32')
U = numpy.concatenate([ortho_weight(dim),
ortho_weight(dim)], axis=1)
params[_p(prefix,'U')] = U
Wx = norm_weight(nin, dim)
params[_p(prefix,'Wx')] = Wx
Ux = ortho_weight(dim)
params[_p(prefix,'Ux')] = Ux
params[_p(prefix,'bx')] = numpy.zeros((dim,)).astype('float32')
# LN parameters
scale_add = 0.0
scale_mul = 1.0
params[_p(prefix,'b1')] = scale_add * numpy.ones((2*dim)).astype('float32')
params[_p(prefix,'b2')] = scale_add * numpy.ones((1*dim)).astype('float32')
params[_p(prefix,'b3')] = scale_add * numpy.ones((2*dim)).astype('float32')
params[_p(prefix,'b4')] = scale_add * numpy.ones((1*dim)).astype('float32')
params[_p(prefix,'s1')] = scale_mul * numpy.ones((2*dim)).astype('float32')
params[_p(prefix,'s2')] = scale_mul * numpy.ones((1*dim)).astype('float32')
params[_p(prefix,'s3')] = scale_mul * numpy.ones((2*dim)).astype('float32')
params[_p(prefix,'s4')] = scale_mul * numpy.ones((1*dim)).astype('float32')
return params
def lngru_layer(tparams, state_below, init_state, options, prefix='lngru', mask=None, one_step=False, **kwargs):
"""
Feedforward pass through GRU with LN
"""
nsteps = state_below.shape[0]
if state_below.ndim == 3:
n_samples = state_below.shape[1]
else:
n_samples = 1
dim = tparams[_p(prefix,'Ux')].shape[1]
if init_state == None:
init_state = tensor.alloc(0., n_samples, dim)
if mask == None:
mask = tensor.alloc(1., state_below.shape[0], 1)
def _slice(_x, n, dim):
if _x.ndim == 3:
return _x[:, :, n*dim:(n+1)*dim]
return _x[:, n*dim:(n+1)*dim]
state_below_ = tensor.dot(state_below, tparams[_p(prefix, 'W')]) + tparams[_p(prefix, 'b')]
state_belowx = tensor.dot(state_below, tparams[_p(prefix, 'Wx')]) + tparams[_p(prefix, 'bx')]
U = tparams[_p(prefix, 'U')]
Ux = tparams[_p(prefix, 'Ux')]
def _step_slice(m_, x_, xx_, h_, U, Ux, b1, b2, b3, b4, s1, s2, s3, s4):
x_ = ln(x_, b1, s1)
xx_ = ln(xx_, b2, s2)
preact = tensor.dot(h_, U)
preact = ln(preact, b3, s3)
preact += x_
r = tensor.nnet.sigmoid(_slice(preact, 0, dim))
u = tensor.nnet.sigmoid(_slice(preact, 1, dim))
preactx = tensor.dot(h_, Ux)
preactx = ln(preactx, b4, s4)
preactx = preactx * r
preactx = preactx + xx_
h = tensor.tanh(preactx)
h = u * h_ + (1. - u) * h
h = m_[:,None] * h + (1. - m_)[:,None] * h_
return h
seqs = [mask, state_below_, state_belowx]
_step = _step_slice
non_seqs = [tparams[_p(prefix, 'U')], tparams[_p(prefix, 'Ux')]]
non_seqs += [tparams[_p(prefix, 'b1')], tparams[_p(prefix, 'b2')], tparams[_p(prefix, 'b3')], tparams[_p(prefix, 'b4')]]
non_seqs += [tparams[_p(prefix, 's1')], tparams[_p(prefix, 's2')], tparams[_p(prefix, 's3')], tparams[_p(prefix, 's4')]]
if one_step:
rval = _step(*(seqs+[init_state, tparams[_p(prefix, 'U')], tparams[_p(prefix, 'Ux')]]))
else:
rval, updates = theano.scan(_step,
sequences=seqs,
outputs_info = [init_state],
non_sequences = non_seqs,
name=_p(prefix, '_layers'),
n_steps=nsteps,
profile=False,
strict=True)
rval = [rval]
return rval
# LSTM layer init
def param_init_lstm(options,
params,
prefix='lstm',
nin=None,
dim=None):
if nin is None:
nin = options['dim_proj']
if dim is None:
dim = options['dim_proj']
W = numpy.concatenate([norm_weight(nin,dim),
norm_weight(nin,dim),
norm_weight(nin,dim),
norm_weight(nin,dim)],
axis=1)
params[prfx(prefix,'W')] = W
U = numpy.concatenate([ortho_weight(dim),
ortho_weight(dim),
ortho_weight(dim),
ortho_weight(dim)],
axis=1)
params[prfx(prefix,'U')] = U
params[prfx(prefix,'b')] = numpy.zeros((4 * dim,)).astype('float32')
return params
# LSTM layer
def lstm_layer(tparams, state_below,
options,
prefix='lstm',
mask=None, one_step=False,
init_state=None,
init_memory=None,
nsteps=None,
**kwargs):
if nsteps is None:
nsteps = state_below.shape[0]
if state_below.ndim == 3:
n_samples = state_below.shape[1]
else:
n_samples = 1
param = lambda name: tparams[prfx(prefix, name)]
dim = param('U').shape[0]
if mask is None:
mask = tensor.alloc(1., state_below.shape[0], 1)
# initial/previous state
if init_state is None:
if not options['learn_h0']:
init_state = tensor.alloc(0., n_samples, dim)
else:
init_state0 = sharedX(numpy.zeros((options['dim'])),
name=prfx(prefix, "h0"))
init_state = tensor.alloc(init_state0, n_samples, dim)
tparams[prfx(prefix, 'h0')] = init_state0
U = param('U')
b = param('b')
W = param('W')
non_seqs = [U, b, W]
# initial/previous memory
if init_memory is None:
init_memory = tensor.alloc(0., n_samples, dim)
def _slice(_x, n, dim):
if _x.ndim == 3:
return _x[:, :, n*dim:(n+1)*dim]
return _x[:, n*dim:(n+1)*dim]
def _step(mask, sbelow, sbefore, cell_before, *args):
preact = dot(sbefore, param('U'))
preact += sbelow
preact += param('b')
i = Sigmoid(_slice(preact, 0, dim))
f = Sigmoid(_slice(preact, 1, dim))
o = Sigmoid(_slice(preact, 2, dim))
c = Tanh(_slice(preact, 3, dim))
c = f * cell_before + i * c
c = mask * c + (1. - mask) * cell_before
h = o * tensor.tanh(c)
h = mask * h + (1. - mask) * sbefore
return h, c
lstm_state_below = dot(state_below, param('W')) + param('b')
if state_below.ndim == 3:
lstm_state_below = lstm_state_below.reshape((state_below.shape[0],
state_below.shape[1],
-1))
if one_step:
mask = mask.dimshuffle(0, 'x')
h, c = _step(mask, lstm_state_below, init_state, init_memory)
rval = [h, c]
else:
if mask.ndim == 3 and mask.ndim == state_below.ndim:
mask = mask.reshape((mask.shape[0], \
mask.shape[1]*mask.shape[2])).dimshuffle(0, 1, 'x')
elif mask.ndim == 2:
mask = mask.dimshuffle(0, 1, 'x')
rval, updates = theano.scan(_step,
sequences=[mask, lstm_state_below],
outputs_info = [init_state,
init_memory],
name=prfx(prefix, '_layers'),
non_sequences=non_seqs,
strict=True,
n_steps=nsteps)
return rval
# LN-LSTM init
def param_init_lnlstm(options,
params,
prefix='lnlstm',
nin=None,
dim=None):
if nin is None:
nin = options['dim_proj']
if dim is None:
dim = options['dim_proj']
W = numpy.concatenate([norm_weight(nin,dim),
norm_weight(nin,dim),
norm_weight(nin,dim),
norm_weight(nin,dim)],
axis=1)
params[prfx(prefix,'W')] = W
U = numpy.concatenate([ortho_weight(dim),
ortho_weight(dim),
ortho_weight(dim),
ortho_weight(dim)],
axis=1)
params[prfx(prefix,'U')] = U
params[prfx(prefix,'b')] = numpy.zeros((4 * dim,)).astype('float32')
# lateral parameters
scale_add = 0.0
scale_mul = 1.0
params[prfx(prefix,'b1')] = scale_add * numpy.ones((4*dim)).astype('float32')
params[prfx(prefix,'b2')] = scale_add * numpy.ones((4*dim)).astype('float32')
params[prfx(prefix,'b3')] = scale_add * numpy.ones((1*dim)).astype('float32')
params[prfx(prefix,'s1')] = scale_mul * numpy.ones((4*dim)).astype('float32')
params[prfx(prefix,'s2')] = scale_mul * numpy.ones((4*dim)).astype('float32')
params[prfx(prefix,'s3')] = scale_mul * numpy.ones((1*dim)).astype('float32')
return params
# LN-LSTM layer
def lnlstm_layer(tparams, state_below,
options,
prefix='lnlstm',
mask=None, one_step=False,
init_state=None,
init_memory=None,
nsteps=None,
**kwargs):
if nsteps is None:
nsteps = state_below.shape[0]
if state_below.ndim == 3:
n_samples = state_below.shape[1]
else:
n_samples = 1
param = lambda name: tparams[prfx(prefix, name)]
dim = param('U').shape[0]
if mask is None:
mask = tensor.alloc(1., state_below.shape[0], 1)
# initial/previous state
if init_state is None:
if not options['learn_h0']:
init_state = tensor.alloc(0., n_samples, dim)
else:
init_state0 = sharedX(numpy.zeros((options['dim'])),
name=prfx(prefix, "h0"))
init_state = tensor.alloc(init_state0, n_samples, dim)
tparams[prfx(prefix, 'h0')] = init_state0
U = param('U')
b = param('b')
W = param('W')
non_seqs = [U, b, W]
non_seqs.extend(list(map(param, "b1 b2 b3 s1 s2 s3".split())))
# initial/previous memory
if init_memory is None:
init_memory = tensor.alloc(0., n_samples, dim)
def _slice(_x, n, dim):
if _x.ndim == 3:
return _x[:, :, n*dim:(n+1)*dim]
return _x[:, n*dim:(n+1)*dim]
def _step(mask, sbelow, sbefore, cell_before, *args):
sbelow_ = ln(sbelow, param('b1'), param('s1'))
sbefore_ = ln(dot(sbefore, param('U')), param('b2'), param('s2'))
preact = sbefore_ + sbelow_ + param('b')
i = Sigmoid(_slice(preact, 0, dim))
f = Sigmoid(_slice(preact, 1, dim))
o = Sigmoid(_slice(preact, 2, dim))
c = Tanh(_slice(preact, 3, dim))
c = f * cell_before + i * c
c = mask * c + (1. - mask) * cell_before
c_ = ln(c, param('b3'), param('s3'))
h = o * tensor.tanh(c_)
h = mask * h + (1. - mask) * sbefore
return h, c
lstm_state_below = dot(state_below, param('W')) + param('b')
if state_below.ndim == 3:
lstm_state_below = lstm_state_below.reshape((state_below.shape[0],
state_below.shape[1],
-1))
if one_step:
mask = mask.dimshuffle(0, 'x')
h, c = _step(mask, lstm_state_below, init_state, init_memory)
rval = [h, c]
else:
if mask.ndim == 3 and mask.ndim == state_below.ndim:
mask = mask.reshape((mask.shape[0], \
mask.shape[1]*mask.shape[2])).dimshuffle(0, 1, 'x')
elif mask.ndim == 2:
mask = mask.dimshuffle(0, 1, 'x')
rval, updates = theano.scan(_step,
sequences=[mask, lstm_state_below],
outputs_info = [init_state,
init_memory],
name=prfx(prefix, '_layers'),
non_sequences=non_seqs,
strict=True,
n_steps=nsteps)
return rval