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NetworkLstmLayer.py
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NetworkLstmLayer.py
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import numpy
import json
from theano import tensor as T
import theano
from NetworkHiddenLayer import HiddenLayer, _NoOpLayer
from ActivationFunctions import strtoact
class RecurrentLayer(HiddenLayer):
recurrent = True
layer_class = "recurrent"
def __init__(self, reverse=False, truncation=-1, compile=True, projection=0, sampling=1, **kwargs):
kwargs.setdefault("activation", "tanh")
super(RecurrentLayer, self).__init__(**kwargs)
self.set_attr('reverse', reverse)
self.set_attr('truncation', truncation)
self.set_attr('sampling', sampling)
self.set_attr('projection', projection)
n_in = sum([s.attrs['n_out'] for s in self.sources])
n_out = self.attrs['n_out']
self.act = self.create_bias(n_out)
if projection:
n_re_in = projection
else:
n_re_in = n_out
self.W_re = self.add_param(self.create_random_normal_weights(n=n_re_in, m=n_out, scale=n_in,
name="W_re_%s" % self.name))
if projection:
self.W_proj = self.add_param(self.create_forward_weights(n_out, projection, name='W_proj_%s' % self.name))
else:
self.W_proj = None
#for s, W in zip(self.sources, self.W_in):
# W.set_value(self.create_random_normal_weights(n=s.attrs['n_out'], m=n_out, scale=n_in,
# name=W.name).get_value())
self.o = theano.shared(value = numpy.ones((n_out,), dtype='int8'), borrow=True)
if compile: self.compile()
def compile(self):
def step(x_t, i_t, h_p):
h_pp = T.dot(h_p, self.W_re) if self.W_proj else h_p
i = T.outer(i_t, self.o)
z = T.dot(h_pp, self.W_re) + self.b
for i in range(len(self.sources)):
z += T.dot(self.mass * self.masks[i] * x_t[i], self.W_in[i])
#z = (T.dot(x_t, self.mass * self.mask * self.W_in) + self.b) * T.nnet.sigmoid(T.dot(h_p, self.W_re))
h_t = (z if self.activation is None else self.activation(z))
return h_t * i
self.output, _ = theano.scan(step,
name="scan_%s" % self.name,
go_backwards=self.attrs['reverse'],
truncate_gradient=self.attrs['truncation'],
sequences = [T.stack(self.sources), self.index],
outputs_info = [T.alloc(self.act, self.sources[0].output.shape[1], self.attrs['n_out'])])
self.output = self.output[::-(2 * self.attrs['reverse'] - 1)]
def create_recurrent_weights(self, n, m):
nin = n + m + m + m
return self.create_random_normal_weights(n, m, nin), self.create_random_normal_weights(m, m, nin)
class LstmLayer(RecurrentLayer):
layer_class = "lstm"
def __init__(self, n_out, sharpgates='none', **kwargs):
kwargs.setdefault("activation", "sigmoid")
kwargs.setdefault("compile", False)
projection = kwargs.get("projection", None)
n_re = projection if projection is not None else n_out
W_in_m = n_out * 3 + n_re # output dim of W_in and dim of bias. see step()
kwargs["n_out"] = W_in_m
super(LstmLayer, self).__init__(**kwargs)
self.set_attr('n_out', n_out)
if not isinstance(self.activation, (list, tuple)):
self.activation = [T.tanh, T.nnet.sigmoid, T.nnet.sigmoid, T.nnet.sigmoid, T.tanh]
else:
assert len(self.activation) == 5, "lstm activations have to be specified as 5 tuple (input, ingate, forgetgate, outgate, output)"
self.set_attr('sharpgates', sharpgates)
CI, GI, GF, GO, CO = self.activation
n_in = sum([s.attrs['n_out'] for s in self.sources])
self.b.set_value(numpy.zeros((n_out * 3 + n_re,), dtype=theano.config.floatX))
if projection:
W_proj = self.create_random_uniform_weights(n_out, n_re, n_in + n_out + n_re, name="W_proj_%s" % self.name)
self.W_proj.set_value(W_proj.get_value())
W_re = self.create_random_uniform_weights(n=n_re, m=W_in_m, p=n_in + n_re + W_in_m,
name="W_re_%s" % self.name)
self.W_re.set_value(W_re.get_value())
assert len(self.sources) == len(self.W_in)
for s, W in zip(self.sources, self.W_in):
W.set_value(self.create_random_uniform_weights(n=s.attrs['n_out'], m=W_in_m,
p=s.attrs['n_out'] + n_out + W_in_m,
name=W.name).get_value(borrow=True, return_internal_type=True), borrow=True)
self.o.set_value(numpy.ones((n_out,), dtype='int8')) #TODO what is this good for?
if sharpgates == 'global':
self.sharpness = self.create_random_uniform_weights(3, n_out)
elif sharpgates == 'shared':
if not hasattr(LstmLayer, 'sharpgates'):
LstmLayer.sharpgates = self.create_bias(3)
self.add_param(LstmLayer.sharpgates, 'gate_scaling')
self.sharpness = LstmLayer.sharpgates
elif sharpgates == 'single':
if not hasattr(LstmLayer, 'sharpgates'):
LstmLayer.sharpgates = self.create_bias(1)
self.add_param(LstmLayer.sharpgates, 'gate_scaling')
self.sharpness = LstmLayer.sharpgates
else:
self.sharpness = theano.shared(value = numpy.zeros((3,), dtype=theano.config.floatX), borrow=True, name = 'lambda')
self.sharpness.set_value(numpy.ones(self.sharpness.get_value().shape, dtype = theano.config.floatX))
if sharpgates != 'none' and sharpgates != "shared" and sharpgates != "single":
self.sharpness = self.add_param(self.sharpness, 'gate_scaling')
#set default value if not set
if not 'optimization' in self.attrs:
self.attrs['optimization'] = 'speed'
assert self.attrs['optimization'] in ['memory', 'speed']
if self.attrs['optimization'] == 'speed':
z = self.b
for x_t, m, W in zip(self.sources, self.masks, self.W_in):
if x_t.attrs['sparse']:
z += W[T.cast(x_t.output[:,:,0], 'int32')]
elif m is None:
z += T.tensordot(x_t.output, W, [[2],[2]])
#z += T.dot(x_t.output, W)
else:
z += T.dot(self.mass * m * x_t.output, W)
else:
z = 0 if not self.sources else T.concatenate([x.output for x in self.sources], axis = -1)
def step(z, i_t, s_p, h_p):
if self.attrs['optimization'] == 'memory':
offset = 0
y = 0
for x_t, m, W in zip(self.sources, self.masks, self.W_in):
xin = z[:,:,offset:x_t.output.shape[2]]
offset += x_t.output.shape[2]
if x_t.attrs['sparse']:
y += W[T.cast(xin[:,:,0], 'int32')]
elif m is None:
y += T.dot(xin, W)
else:
y += T.dot(self.mass * m * xin, W)
z = y + self.b
z += T.dot(h_p, self.W_re)
i = T.outer(i_t, T.alloc(numpy.cast['int8'](1), n_out))
j = i if not self.W_proj else T.outer(i_t, T.alloc(numpy.cast['int8'](1), n_re))
ingate = GI(z[:,n_out: 2 * n_out])
forgetgate = GF(z[:,2 * n_out:3 * n_out])
outgate = GO(z[:,3 * n_out:])
input = CI(z[:,:n_out])
s_i = input * ingate + s_p * forgetgate
s_t = s_i if not self.W_proj else T.dot(s_i, self.W_proj)
h_t = CO(s_t) * outgate
return theano.gradient.grad_clip(s_i * i, -50, 50), h_t * j
def osstep(*args):
x_ts = args[:self.num_sources]
i_t = args[self.num_sources]
s_p = args[self.num_sources + 1]
h_p = args[self.num_sources + 2]
if any(self.masks):
masks = args[self.num_sources + 3:]
else:
masks = [None] * len(self.W_in)
i = T.outer(i_t, T.alloc(numpy.cast['int8'](1), n_out))
j = i if not self.W_proj else T.outer(i_t, T.alloc(numpy.cast['int8'](1), n_re))
z = T.dot(h_p, self.W_re) + self.b
assert len(x_ts) == len(masks) == len(self.W_in)
for x_t, m, W in zip(x_ts, masks, self.W_in):
if m is None:
z += T.dot(x_t, W)
else:
z += T.dot(self.mass * m * x_t, W)
if sharpgates != 'none':
ingate = GI(self.sharpness[0] * z[:, n_out:2 * n_out])
forgetgate = GF(self.sharpness[1] * z[:, 2 * n_out:3 * n_out])
outgate = GO(self.sharpness[2] * z[:, 3 * n_out:])
else:
ingate = GI(z[:, n_out:2 * n_out])
forgetgate = GF(z[:, 2 * n_out:3 * n_out])
outgate = GO(z[:, 3 * n_out:])
input = CI(z[:, :n_out])
s_i = input * ingate + s_p * forgetgate
s_t = s_i if not self.W_proj else T.dot(s_i, self.W_proj)
h_t = CO(s_t) * outgate
return s_i * i, h_t * j
[state, act], _ = theano.scan(step,
name = "scan_%s"%self.name,
truncate_gradient = self.attrs['truncation'],
go_backwards = self.attrs['reverse'],
sequences = [ s.output[::self.attrs['sampling']] for s in self.sources ] + [self.index[::self.attrs['sampling']]],
non_sequences = self.masks if any(self.masks) else [],
outputs_info = [ T.alloc(numpy.cast[theano.config.floatX](0), self.sources[0].output.shape[1] / self.attrs['sampling'], n_out),
T.alloc(numpy.cast[theano.config.floatX](0), self.sources[0].output.shape[1] / self.attrs['sampling'], n_re), ])
if self.attrs['sampling'] > 1:
act = T.repeat(act, self.attrs['sampling'], axis = 1)[::self.sources[0].output.shape[1]]
self.output = act[::-(2 * self.attrs['reverse'] - 1)]
#faster but needs much more memory
class OptimizedLstmLayer(RecurrentLayer):
layer_class = "lstm_opt"
def __init__(self, n_out, sharpgates='none', encoder = None, n_dec = 0, **kwargs):
kwargs.setdefault("activation", "sigmoid")
kwargs["compile"] = False
kwargs["n_out"] = n_out * 4
super(OptimizedLstmLayer, self).__init__(**kwargs)
self.set_attr('n_out', n_out)
if n_dec: self.set_attr('n_dec', n_dec)
if encoder:
self.set_attr('encoder', ",".join([e.name for e in encoder]))
projection = kwargs.get("projection", None)
if not isinstance(self.activation, (list, tuple)):
self.activation = [T.tanh, T.nnet.sigmoid, T.nnet.sigmoid, T.nnet.sigmoid, T.tanh]
else:
assert len(self.activation) == 5, "lstm activations have to be specified as 5 tuple (input, ingate, forgetgate, outgate, output)"
self.set_attr('sharpgates', sharpgates)
CI, GI, GF, GO, CO = self.activation # T.tanh, T.nnet.sigmoid, T.nnet.sigmoid, T.nnet.sigmoid, T.tanh
n_in = sum([s.attrs['n_out'] for s in self.sources])
#self.state = self.create_bias(n_out, 'state')
#self.act = self.create_bias(n_re, 'act')
if self.depth > 1:
value = numpy.zeros((self.depth, n_out * 4), dtype = theano.config.floatX)
value[:,2 * n_out:3 * n_out] = 1
else:
value = numpy.zeros((n_out * 4, ), dtype = theano.config.floatX)
value[2 * n_out:3 * n_out] = 0
self.b.set_value(value)
n_re = n_out
if projection:
n_re = projection
W_proj = self.create_random_uniform_weights(n_out, projection, projection + n_out, name="W_proj_%s" % self.name)
self.W_proj.set_value(W_proj.get_value())
#self.set_attr('n_out', projection)
W_re = self.create_random_uniform_weights(n_re, n_out * 4, n_in + n_out * 4,
name="W_re_%s" % self.name)
self.W_re.set_value(W_re.get_value())
for s, W in zip(self.sources, self.W_in):
W.set_value(self.create_random_uniform_weights(s.attrs['n_out'], n_out * 4,
s.attrs['n_out'] + n_out + n_out * 4,
name="W_in_%s_%s" % (s.name, self.name)).get_value(), borrow = True)
if sharpgates == 'global':
self.sharpness = self.create_random_uniform_weights(3, n_out)
elif sharpgates == 'shared':
if not hasattr(LstmLayer, 'sharpgates'):
LstmLayer.sharpgates = self.create_bias(3)
self.add_param(LstmLayer.sharpgates, 'gate_scaling')
self.sharpness = LstmLayer.sharpgates
elif sharpgates == 'single':
if not hasattr(LstmLayer, 'sharpgates'):
LstmLayer.sharpgates = self.create_bias(1)
self.add_param(LstmLayer.sharpgates, 'gate_scaling')
self.sharpness = LstmLayer.sharpgates
else:
self.sharpness = theano.shared(value = numpy.zeros((3,), dtype=theano.config.floatX), borrow=True, name='lambda')
self.sharpness.set_value(numpy.ones(self.sharpness.get_value().shape, dtype = theano.config.floatX))
if sharpgates != 'none' and sharpgates != "shared" and sharpgates != "single":
self.add_param(self.sharpness, 'gate_scaling')
z = self.b
for x_t, m, W in zip(self.sources, self.masks, self.W_in):
if x_t.attrs['sparse']:
z += W[T.cast(x_t.output[:,:,0], 'int32')]
elif m is None:
#z += T.tensordot(source.output, W, [[2],[0]])
#z += T.dot(x_t.output.dimshuffle(0,1,'x',2), W)
z += self.dot(x_t.output, W) #, [[2],[0]]) #.reshape((x_t.output.shape[0], x_t.output.shape[1], self.depth, 4 * n_out), ndim = 4) # tbd4m
#z += T.tensordot(x_t.output, W, [[0], [0]])
else:
z += self.dot(self.mass * m * x_t.output, W)
#self.set_attr('n_out', self.attrs['n_out'] * 4)
#self.output = T.sum(z, axis=2) #.reshape((x_t.output.shape[0], x_t.output.shape[1], 4 * n_out), ndim = 3)
#self.output = self.sources[0].output
#return
def index_step(z_batch, i_t, s_batch, h_batch): # why is this slower :(
q_t = i_t #T.switch(T.any(i_t), i_t, T.ones_like(i_t))
j_t = (q_t > 0).nonzero()
s_p = s_batch[j_t]
h_p = h_batch[j_t]
z = z_batch[j_t]
z += T.dot(h_p, self.W_re)
ingate = GI(z[:,n_out: 2 * n_out])
forgetgate = GF(z[:,2 * n_out:3 * n_out])
outgate = GO(z[:,3 * n_out:])
input = CI(z[:,:n_out])
s_i = input * ingate + s_p * forgetgate
s_t = s_i if not self.W_proj else T.dot(s_i, self.W_proj)
h_t = CO(s_t) * outgate
s_out = T.set_subtensor(s_batch[j_t], s_i)
h_out = T.set_subtensor(h_batch[j_t], h_t)
return theano.gradient.grad_clip(s_out, -50, 50), h_out
def step(z, i_t, s_p, h_p, W_re):
h_r = h_p if self.depth == 1 else self.make_consensus(h_p, axis = 1) # bdm -> bm
if self.attrs['projection']:
idx = T.argmax(GO(self.dot(h_r, self.W_proj)), -1)
h_x = self.dot(GO(self.dot(h_r, self.W_proj)), W_re)
#h_x = W_re[idx,:]
else:
h_x = self.dot(h_r, W_re) if self.depth == 1 else self.make_consensus(self.dot(h_r, W_re), axis = 1)
#T.max(GO(T.dot(T.sum(h_p, axis = -1), self.W_proj))) #T.max(GO(T.tensordot(h_p, self.W_proj, [[2], [2]])), axis = -1)
z += h_x
if len(self.W_in) == 0:
z += self.b
if self.depth > 1:
i = T.outer(i_t, T.alloc(numpy.cast['float32'](1), n_out)).dimshuffle(0, 'x', 1).repeat(self.depth, axis=1)
ingate = GI(z[:,:,n_out: 2 * n_out]) # bdm
forgetgate = GF(z[:,:,2 * n_out:3 * n_out]) # bdm
outgate = GO(z[:,:,3 * n_out:]) # bdm
input = CI(z[:,:,:n_out]) # bdm
else:
i = T.outer(i_t, T.alloc(numpy.cast['float32'](1), n_out))
#ingate = GI(z[:,n_out: 2 * n_out])
#forgetgate = GF(z[:,2 * n_out:3 * n_out])
#outgate = GO(z[:,3 * n_out:])
#input = CI(z[:,:n_out]) # bdm
ingate = GI(z[:,: n_out])
forgetgate = GF(z[:,1 * n_out:2 * n_out])
outgate = GO(z[:,2 * n_out:3 * n_out])
input = CI(z[:,3 * n_out:]) # bdm
#s_i = input * ingate + s_p * forgetgate
s_t = (input * ingate + s_p * forgetgate) # bdm #if not self.W_proj else T.dot(s_i, self.W_proj)
#h_t = T.max(CO(s_t) * outgate, axis = -1, keepdims = False) #T.max(CO(s_t) * outgate, axis=-1, keepdims=True) #T.max(CO(s_t) * outgate, axis = -1, keepdims = True)
h_t = CO(s_t) * outgate
return s_t, h_t
#return theano.gradient.grad_clip(s_t, -50, 50), h_t
#return theano.gradient.grad_clip(s_t * i + s_p * (1-i), -50, 50), h_t * i + h_p * (1-i)
self.out_dec = self.index.shape[0]
if encoder and 'n_dec' in encoder[0].attrs:
self.out_dec = encoder[0].out_dec
for s in range(self.attrs['sampling']):
index = self.index[s::self.attrs['sampling']]
sequences = z #T.unbroadcast(z, 3)
if encoder:
n_dec = self.out_dec
if 'n_dec' in self.attrs:
n_dec = self.attrs['n_dec']
#index = T.alloc(numpy.cast[numpy.int8](1), n_dec, encoder.output.shape[1]) #index[:n_dec] #T.alloc(numpy.cast[numpy.int8](1), n_dec, encoder.output.shape[1])
index = T.alloc(numpy.cast[numpy.int8](1), n_dec, encoder.index.shape[1])
outputs_info = [ T.concatenate([e.state[-1] for e in encoder], axis = -1), T.concatenate([e.act[-1] for e in encoder], axis = -1) ]
if len(self.W_in) == 0:
if self.depth == 1:
sequences = T.alloc(numpy.cast[theano.config.floatX](0), n_dec, encoder[0].output.shape[1], n_out * 4)
else:
sequences = T.alloc(numpy.cast[theano.config.floatX](0), n_dec, encoder[0].output.shape[1], self.depth, n_out * 4)
else:
if self.depth > 1:
outputs_info = [ T.alloc(numpy.cast[theano.config.floatX](0), self.sources[0].output.shape[1], self.depth, n_out),
T.alloc(numpy.cast[theano.config.floatX](0), self.sources[0].output.shape[1], self.depth, n_out) ]
else:
outputs_info = [ T.alloc(numpy.cast[theano.config.floatX](0), self.index.shape[1], n_out),
T.alloc(numpy.cast[theano.config.floatX](0), self.index.shape[1], n_out) ]
[state, act], _ = theano.scan(step,
#strict = True,
name = "scan_%s"%self.name,
truncate_gradient = self.attrs['truncation'],
go_backwards = self.attrs['reverse'],
sequences = [ sequences[s::self.attrs['sampling']], T.cast(index, theano.config.floatX) ],
outputs_info = outputs_info,
non_sequences = [self.W_re])
if self.attrs['sampling'] > 1: # time batch dim
if s == 0:
totact = T.repeat(act, self.attrs['sampling'], axis = 0)[:self.sources[0].output.shape[0]]
else:
totact = T.set_subtensor(totact[s::self.attrs['sampling']], act)
else:
totact = act
self.state = state #[::-(2 * self.attrs['reverse'] - 1)]
self.act = totact #[::-(2 * self.attrs['reverse'] - 1)] # tbdm
self.make_output(self.act[::-(2 * self.attrs['reverse'] - 1)]) # if not self.attrs['projection'] else GO(self.dot(self.act, self.W_proj)))
#self.output = T.sum(self.act, axis=2)
#self.output = self.sources[0].output
def get_branching(self):
return sum([W.get_value().shape[0] for W in self.W_in]) + 1 + self.attrs['n_out']
def get_energy(self):
energy = abs(self.b) / (4 * self.attrs['n_out'])
for W in self.W_in:
energy += T.sum(abs(W), axis = 0)
energy += T.sum(abs(self.W_re), axis = 0)
return energy
def make_constraints(self):
if self.attrs['varreg'] > 0.0:
# input: W_in, W_re, b
energy = self.get_energy()
#self.constraints = self.attrs['varreg'] * (2.0 * T.sqrt(T.var(energy)) - 6.0)**2
self.constraints = self.attrs['varreg'] * (T.mean(energy) - T.sqrt(6.)) #T.mean((energy - 6.0)**2) # * T.var(energy) #(T.sqrt(T.var(energy)) - T.sqrt(6.0))**2
return super(OptimizedLstmLayer, self).make_constraints()
class SimpleLstmLayer(RecurrentLayer):
layer_class = "lstm_simple"
def __init__(self, n_out, sharpgates='none', encoder = None, n_dec = 0, **kwargs):
kwargs.setdefault("activation", "sigmoid")
kwargs["compile"] = False
kwargs["n_out"] = n_out * 4
super(SimpleLstmLayer, self).__init__(**kwargs)
self.set_attr('n_out', n_out)
value = numpy.zeros((n_out * 4, ), dtype = theano.config.floatX)
self.b.set_value(value)
n_re = n_out
n_in = sum([s.attrs['n_out'] for s in self.sources])
assert len(self.sources) == 1
W_re = self.create_random_uniform_weights(n_re, n_out * 4, n_in + n_out * 4,
name="W_re_%s" % self.name)
self.W_re.set_value(W_re.get_value())
for s, W in zip(self.sources, self.W_in):
W.set_value(self.create_random_uniform_weights(s.attrs['n_out'], n_out * 4,
s.attrs['n_out'] + n_out + n_out * 4,
name="W_in_%s_%s" % (s.name, self.name)).get_value(), borrow = True)
initial_state = T.alloc(numpy.cast[theano.config.floatX](0), self.sources[0].output.shape[1], n_out)
X = self.sources[0].output[::-(2 * self.attrs['reverse'] - 1)]
W = self.W_in[0]
def _step(x_t, c_tm1, y_tm1):
z_t = T.dot(x_t, W) + T.dot(y_tm1, self.W_re) + self.b
partition = z_t.shape[1] / 4
ingate = T.nnet.sigmoid(z_t[:,:partition])
forgetgate = T.nnet.sigmoid(z_t[:,partition:2*partition])
outgate = T.nnet.sigmoid(z_t[:,2*partition:3*partition])
input = T.tanh(z_t[:,3*partition:4*partition])
c_t = forgetgate * c_tm1 + ingate * input
y_t = outgate * T.tanh(c_t)
return c_t, y_t
[self.state, self.act], _ = theano.scan(_step, sequences=[X],
outputs_info=[initial_state,
initial_state])
self.make_output(self.act[::-(2 * self.attrs['reverse'] - 1)])
def make_lstm_step(n_cells, W_re,
W_out_proj=None, W_re_proj=None,
W_peep_i=None, W_peep_f=None, W_peep_o=None,
grad_clip=None, CI=None, CO=None, G=None):
# W_re: recurrent matrix. (n_out,n_cells*4)
# W_out_proj: (n_cells,n_out) or None
# W_re_proj: (n_out,n_proj) or None
# W_peep_*: (n_cells,) or None
if not CI: CI = T.tanh
if not CO: CO = T.tanh
if not G: G = T.nnet.sigmoid
def lstm_step(z_t, i_t, s_p, h_p):
# z_t: current input. (batch,n_cells*4)
# i_t: 0 or 1 (via index). (batch,)
# s_p: previous cell state. (batch,n_cells)
# h_p: previous hidden out. (batch,n_out)
i_t_bc = i_t.dimshuffle(0, 'x')
if W_re_proj:
h_p = T.dot(h_p, W_re_proj)
z_t += T.dot(h_p, W_re)
z_t *= i_t_bc
input = CI(z_t[:,:n_cells])
if W_peep_i or W_peep_f or W_peep_o:
ingate = z_t[:,n_cells:2 * n_cells]
forgetgate = z_t[:,2 * n_cells:3 * n_cells]
if W_peep_i: ingate += s_p * W_peep_i.dimshuffle('x', 0) * i_t_bc
if W_peep_f: forgetgate += s_p * W_peep_f.dimshuffle('x', 0) * i_t_bc
s_t = input * G(ingate) + s_p * G(forgetgate)
outgate = z_t[:,3 * n_cells:]
if W_peep_o: outgate += s_t * W_peep_o.dimshuffle('x', 0) * i_t_bc
h_t = CO(s_t) * G(outgate)
else: # no peepholes. simplified and faster
gates = G(z_t[:,n_cells:])
ingate = gates[:,:n_cells]
forgetgate = gates[:,n_cells:2 * n_cells]
outgate = gates[:,2 * n_cells:]
s_t = input * ingate + s_p * forgetgate
h_t = CO(s_t) * outgate
if W_out_proj:
h_t = T.dot(h_t, W_out_proj)
s_t *= i_t_bc
h_t *= i_t_bc
if grad_clip:
s_t = theano.gradient.grad_clip(s_t, -grad_clip, grad_clip)
h_t = theano.gradient.grad_clip(h_t, -grad_clip, grad_clip)
return s_t, h_t
return lstm_step
def lstm(z, i, W_re, W_out_proj=None, W_re_proj=None, W_peep_i=None, W_peep_f=None, W_peep_o=None,
CI=None, CO=None, G=None,
grad_clip=None, direction=1):
# z: (n_time,n_batch,n_cells*4)
# i: (n_time,n_batch)
# W_re: (n_out,n_cells*4)
# W_out_proj: (n_cells,n_out) or None
# W_re_proj: (n_out,n_proj) or None
# W_peep_*: (n_cells,) or None
n_batch = z.shape[1]
assert W_re.ndim == 2
n_cells = W_re.shape[1] // 4
n_out = W_re.shape[0] # normally the same as n_cells, but with W_proj, can be different
if W_re_proj:
n_out = W_re_proj.shape[0]
i = T.cast(i, dtype="float32") # so that it can run on gpu
if grad_clip:
grad_clip = numpy.float32(grad_clip)
lstm_step = make_lstm_step(
n_cells=n_cells, W_re=W_re,
W_out_proj=W_out_proj, W_re_proj=W_re_proj, W_peep_i=W_peep_i, W_peep_f=W_peep_f, W_peep_o=W_peep_o,
CI=CI, CO=CO, G=G,
grad_clip=grad_clip)
s_initial = T.zeros((n_batch, n_cells), dtype="float32")
h_initial = T.zeros((n_batch, n_out), dtype="float32")
go_backwards = {1:False, -1:True}[direction]
(s, h), _ = theano.scan(lstm_step,
sequences=[z, i], go_backwards=go_backwards,
outputs_info=[s_initial, h_initial])
h = h[::direction]
return h
class Lstm2Layer(HiddenLayer):
recurrent = True
layer_class = "lstm2"
def __init__(self, n_out, n_cells=None, n_proj=None, peepholes=False, direction=1, activation=None, grad_clip=None, truncation=None, **kwargs):
if not n_cells: n_cells = n_out
# It's a hidden layer, thus this will create the feed forward layer for the LSTM for the input.
super(Lstm2Layer, self).__init__(n_out=n_cells * 4, **kwargs)
self.set_attr('n_out', n_out)
self.set_attr('n_cells', n_cells)
if n_proj: self.set_attr('n_proj', n_proj)
self.set_attr('peepholes', peepholes)
self.set_attr('direction', direction)
if grad_clip: self.set_attr('grad_clip', grad_clip)
if activation: self.set_attr('activation', activation)
n_re_in = n_out
if n_proj:
# Applied before recurrent matrix.
self.W_re_proj = self.add_param(self.create_recurrent_weights(n=n_out, m=n_proj, name="W_re_proj_%s" % self.name))
n_re_in = n_proj
else:
self.W_re_proj = None
self.W_re = self.add_param(self.create_recurrent_weights(n=n_re_in, m=n_cells * 4, name="W_re_%s" % self.name))
if n_out != n_cells:
# Applied before output.
self.W_out_proj = self.add_param(self.create_forward_weights(n_cells, n_out, name='W_proj_%s' % self.name))
else:
self.W_out_proj = None
if peepholes:
self.W_peepholes = [
self.add_param(self.create_random_uniform_weights2(n_cells, name="W_peep_%s_%s" % (g, self.name)))
for g in "ifo"]
else:
self.W_peepholes = [None] * 3
CI, CO, G = [T.tanh, T.tanh, T.nnet.sigmoid]
if activation:
act_f = strtoact(activation)
if isinstance(act_f, list):
if len(act_f) == 2:
CI, CO = act_f
elif len(act_f) == 3:
CI, CO, G = act_f
else:
assert False, "invalid number of activation funcs: %r" % act_f
else:
CI = CO = act_f
z = self.get_linear_forward_output()
h = lstm(z=z, i=self.index, W_re=self.W_re,
W_out_proj=self.W_out_proj, W_re_proj=self.W_re_proj,
W_peep_i=self.W_peepholes[0], W_peep_f=self.W_peepholes[1], W_peep_o=self.W_peepholes[2],
CI=CI, CO=CO, G=G,
grad_clip=grad_clip, direction=direction)
self.make_output(h)
class Lstm3Layer(HiddenLayer):
"""
Like lstm2 but even simpler.
"""
recurrent = True
layer_class = "lstm3"
def __init__(self, n_out, direction=1, grad_clip=None, **kwargs):
n_cells = n_out
n_re_in = n_out
# It's a hidden layer, thus this will create the feed forward layer for the LSTM for the input.
super(Lstm3Layer, self).__init__(n_out=n_cells * 4, **kwargs)
self.set_attr('n_out', n_out)
self.set_attr('n_cells', n_cells)
self.set_attr('direction', direction)
if grad_clip:
self.set_attr('grad_clip', grad_clip)
grad_clip = numpy.float32(grad_clip)
CI, CO, G = [T.tanh, T.tanh, T.nnet.sigmoid]
z = self.get_linear_forward_output()
self.W_re = self.add_param(self.create_recurrent_weights(n=n_re_in, m=n_cells * 4, name="W_re_%s" % self.name))
def lstm_step(z_t, i_t, s_p, h_p):
# z_t: current input. (batch,n_cells*4)
# i_t: 0 or 1 (via index). (batch,)
# s_p: previous cell state. (batch,n_cells)
# h_p: previous hidden out. (batch,n_out)
i_t_bc = i_t.dimshuffle(0, 'x')
z_t += T.dot(h_p, self.W_re)
z_t *= i_t_bc
input = CI(z_t[:, :n_cells])
gates = G(z_t[:, n_cells:])
ingate = gates[:, :n_cells]
forgetgate = gates[:, n_cells:2 * n_cells]
outgate = gates[:, 2 * n_cells:]
s_t = input * ingate + s_p * forgetgate
h_t = CO(s_t) * outgate
s_t *= i_t_bc
h_t *= i_t_bc
if grad_clip:
s_t = theano.gradient.grad_clip(s_t, -grad_clip, grad_clip)
h_t = theano.gradient.grad_clip(h_t, -grad_clip, grad_clip)
return s_t, h_t
i = T.cast(self.index, dtype="float32") # so that it can run on gpu
assert z.ndim == 3 # (time,batch,dim)
n_batch = z.shape[1]
s_initial = T.zeros((n_batch, n_cells), dtype="float32")
h_initial = T.zeros((n_batch, n_out), dtype="float32")
go_backwards = {1: False, -1: True}[direction]
(s, h), _ = theano.scan(lstm_step,
sequences=[z, i], go_backwards=go_backwards,
outputs_info=[s_initial, h_initial])
h = h[::direction]
self.make_output(h)
class LayerNormLstmLayer(HiddenLayer):
"""
Layer Normalization, https://arxiv.org/abs/1607.06450
"""
recurrent = True
layer_class = "ln_lstm"
def __init__(self, n_out, direction=1, grad_clip=None, **kwargs):
n_cells = n_out
n_re_in = n_out
# It's a hidden layer, thus this will create the feed forward layer for the LSTM for the input.
super(LayerNormLstmLayer, self).__init__(n_out=n_cells * 4, **kwargs)
self.set_attr('n_out', n_out)
self.set_attr('n_cells', n_cells)
self.set_attr('direction', direction)
if grad_clip:
self.set_attr('grad_clip', grad_clip)
grad_clip = numpy.float32(grad_clip)
C, G = [T.tanh, T.nnet.sigmoid]
from TheanoUtil import layer_normalization
def _sliced_layer_norm(z, scale, idx):
dimstart = n_cells * idx
dimend = dimstart + n_cells
zdims = (slice(None),) * (z.ndim - 1) + (slice(dimstart, dimend),)
return layer_normalization(z[zdims], bias=None, scale=scale[dimstart:dimend])
def sliced_layer_norm(z, scale):
slices = [_sliced_layer_norm(z, scale=scale, idx=i) for i in range(4)]
return T.concatenate(slices, axis=z.ndim - 1)
z = self.get_linear_forward_output(with_bias=False)
self.ln_zi_scale = self.add_param(self.create_bias(n=n_cells * 4, name="ln_zi_scale_%s" % self.name, init_eval_str="zeros() + 1"))
z = sliced_layer_norm(z, scale=self.ln_zi_scale)
z += self.b
self.W_re = self.add_param(self.create_recurrent_weights(n=n_re_in, m=n_cells * 4, name="W_re_%s" % self.name))
self.ln_zr_scale = self.add_param(self.create_bias(n=n_cells * 4, name="ln_zr_scale_%s" % self.name, init_eval_str="zeros() + 1"))
self.ln_s_bias = self.add_param(self.create_bias(n=n_cells, name="ln_s_bias_%s" % self.name, init_eval_str="zeros()"))
self.ln_s_scale = self.add_param(self.create_bias(n=n_cells, name="ln_s_scale_%s" % self.name, init_eval_str="zeros() + 1"))
def lstm_step(z_t, i_t, s_p, h_p):
# z_t: current input. (batch,n_cells*4)
# i_t: 0 or 1 (via index). (batch,)
# s_p: previous cell state. (batch,n_cells)
# h_p: previous hidden out. (batch,n_out)
i_t_bc = i_t.dimshuffle(0, 'x')
z_t += sliced_layer_norm(T.dot(h_p, self.W_re), scale=self.ln_zr_scale)
z_t *= i_t_bc
input = C(z_t[:, :n_cells])
gates = G(z_t[:, n_cells:])
igate = gates[:, :n_cells]
fgate = gates[:, n_cells:2 * n_cells]
ogate = gates[:, 2 * n_cells:]
s_t = input * igate + s_p * fgate
s_t_ = layer_normalization(s_t, bias=self.ln_s_bias, scale=self.ln_s_scale)
h_t = C(s_t_) * ogate
s_t *= i_t_bc
h_t *= i_t_bc
if grad_clip:
s_t = theano.gradient.grad_clip(s_t, -grad_clip, grad_clip)
h_t = theano.gradient.grad_clip(h_t, -grad_clip, grad_clip)
return s_t, h_t
i = T.cast(self.index, dtype="float32") # so that it can run on gpu
assert z.ndim == 3 # (time,batch,dim)
n_batch = z.shape[1]
s_initial = T.zeros((n_batch, n_cells), dtype="float32")
h_initial = T.zeros((n_batch, n_out), dtype="float32")
go_backwards = {1: False, -1: True}[direction]
(s, h), _ = theano.scan(lstm_step,
sequences=[z, i], go_backwards=go_backwards,
outputs_info=[s_initial, h_initial])
h = h[::direction]
self.make_output(h)
class NativeLstmLayer(HiddenLayer):
recurrent = True
layer_class = "native_lstm"
def __init__(self, n_out, direction=1, truncation=None, **kwargs):
n_cells = n_out
# It's a hidden layer, thus this will create the feed forward layer for the LSTM for the input.
super(NativeLstmLayer, self).__init__(n_out=n_cells * 4, **kwargs)
self.set_attr('n_out', n_out)
self.set_attr('direction', direction)
n_re_in = n_out
self.W_re = self.add_param(self.create_recurrent_weights(n=n_re_in, m=n_cells * 4, name="W_re_%s" % self.name))
z = self.get_linear_forward_output()
assert z.ndim == 3
from NativeOp import LstmGenericBase
lstm_op = LstmGenericBase().make_op()
op_out = lstm_op(*LstmGenericBase.map_layer_inputs_to_op(z[::direction], self.W_re, self.index[::direction]))
from TheanoUtil import make_var_tuple
out = LstmGenericBase.map_layer_output_from_op(*make_var_tuple(op_out))
self.make_output(out[::direction])
class GenericLstmLayer(_NoOpLayer):
"""
LSTM implementation which allows a custom input+recurrent function (n_in + n_out -> n_cells * 4)
and a custom output function (n_cells -> n_out) which is identity by default.
You specify it as a sub layer.
"""
recurrent = True
layer_class = "generic_lstm"
def __init__(self, n_out, sublayer, out_sublayer=None, n_cells=None,
activation=None,
direction=1, grad_clip=None, truncation=None, **kwargs):
super(GenericLstmLayer, self).__init__(**kwargs)
self.set_attr('n_out', n_out)
if n_cells:
self.set_attr('n_cells', n_cells)
else:
n_cells = n_out
self.set_attr('direction', direction)
if grad_clip:
self.set_attr('grad_clip', grad_clip)
grad_clip = numpy.float32(grad_clip)
if isinstance(sublayer, (str, unicode)):
sublayer = json.loads(sublayer)
if isinstance(out_sublayer, (str, unicode)):
out_sublayer = json.loads(out_sublayer)
assert isinstance(sublayer, dict)
self.set_attr('sublayer', sublayer.copy())
if out_sublayer:
assert isinstance(out_sublayer, dict)
self.set_attr('out_sublayer', out_sublayer.copy())
if activation:
self.set_attr('activation', activation)
from NetworkHiddenLayer import concat_sources
x, n_in = concat_sources(self.sources, masks=self.masks, mass=self.mass, unsparse=True) # (n_time,n_batch,n_in)
n_time = x.shape[0]
n_batch = x.shape[1]
from NetworkBaseLayer import SourceLayer
from NetworkLayer import get_layer_class
def make_sublayer(x_in, x_re, index, name):
layer_opts = sublayer.copy()
cl = layer_opts.pop("class")
layer_class = get_layer_class(cl)
s1_layer = SourceLayer(name="%s_source_in" % name, n_out=n_in, x_out=x_in, index=index)
s2_layer = SourceLayer(name="%s_source_re" % name, n_out=n_out, x_out=x_re, index=index)
layer = layer_class(sources=[s1_layer, s2_layer], index=index, name=name, n_out=n_cells * 4,
network=self.network, **layer_opts)
self.sublayer = layer
return layer.output
self.sublayer = None
def make_out_sublayer(h, index, name):
if not out_sublayer: return h
layer_opts = out_sublayer.copy()
cl = layer_opts.pop("class")
layer_class = get_layer_class(cl)
s_layer = SourceLayer(name="%s_source_h" % name, n_out=n_cells, x_out=h, index=index)
layer = layer_class(sources=[s_layer], index=index, name=name, n_out=n_out,
network=self.network, **layer_opts)
self.out_sublayer = layer
return layer.output
self.out_sublayer = None
CI, CO, GF = [T.tanh, T.tanh, T.nnet.sigmoid]
if activation:
act_f = strtoact(activation)
if isinstance(act_f, list):
if len(act_f) == 2:
CI, CO = act_f
elif len(act_f) == 3:
CI, CO, GF = act_f
else:
assert False, "invalid number of activation funcs: %r" % act_f
else:
CI = CO = act_f
def lstm_step(x_t, i_t, s_p, h_p):
# x_t: current input. (dummy,batch,n_in)
# i_t: 0 or 1 (via index). (dummy,batch,)
# s_p: previous cell state. (batch,n_cells)
# h_p: previous out. (dummy,batch,n_out)
z_t = make_sublayer(x_in=x_t, x_re=h_p, index=i_t, name="%s_sublayer" % self.name)
z_t = z_t[0] # remove dummy dimension. (batch,n_cells*4)
gates = GF(z_t[:, :3 * n_cells])
u = CI(z_t[:, 3 * n_cells:])
igate = gates[:, :n_cells]
fgate = gates[:, n_cells:2 * n_cells]
ogate = gates[:, 2 * n_cells:]
s_t = u * igate + s_p * fgate
h_t = s_t
h_t = h_t.reshape((1, n_batch, n_cells)) # dummy,batch,n_cells
h_t = T.patternbroadcast(h_t, (False, False, False)) # might a be Theano bug
h_t = make_out_sublayer(h_t, index=i_t, name="%s_out_sublayer" % self.name)
h_t = CO(h_t) * ogate.dimshuffle('x', 0, 1) # dummy,batch,n_out
s_t *= i_t[0].dimshuffle(0, 'x') # batch,n_cells
h_t *= i_t.dimshuffle(0, 1, 'x') # dummy,batch,n_out
if grad_clip:
s_t = theano.gradient.grad_clip(s_t, -grad_clip, grad_clip)
h_t = theano.gradient.grad_clip(h_t, -grad_clip, grad_clip)
return s_t, h_t
# i: (n_time,n_batch)
i = T.cast(self.index, dtype="float32") # so that it can run on gpu
# Add extra dummy dimension. Used for sublayer.
x = x.reshape((n_time, 1, n_batch, n_in))
x = T.patternbroadcast(x, (False, False, False, False)) # might be a Theano bug
i = i.reshape((n_time, 1, n_batch))
i = T.patternbroadcast(i, (False, False, False)) # might be a Theano bug
s_initial = T.zeros((n_batch, n_cells), dtype="float32")
h_initial = T.zeros((1, n_batch, n_out), dtype="float32")
h_initial = T.patternbroadcast(h_initial, (False, False, False))
go_backwards = {1:False, -1:True}[direction]
(s, h), _ = theano.scan(lstm_step,
sequences=[x, i], go_backwards=go_backwards,
non_sequences=[],
outputs_info=[s_initial, h_initial])
h = h[:, 0] # remove dummy dimension
self.act = [h, s]
h = h[::direction]
self.make_output(h)
self.params.update({"sublayer." + name: param for (name, param) in self.sublayer.params.items()})
if self.out_sublayer:
self.params.update({"out_sublayer." + name: param for (name, param) in self.out_sublayer.params.items()})
class AssociativeLstmLayer(HiddenLayer):
"""
Associative Long Short-Term Memory
http://arxiv.org/abs/1602.03032
"""
recurrent = True
layer_class = "associative_lstm"
def __init__(self, n_out, n_copies, activation="tanh", direction=1, grad_clip=None, **kwargs):
n_cells = n_out
assert n_cells % 2 == 0 # complex numbers, split real/imag
n_complex_cells = n_cells / 2
# {input,forget,out}-gate have n_complex_cells dim.
# {input,output}-key for holographic memory have n_cells dim.
# update u (earlier called net-input) has n_cells dim.
n_z = n_complex_cells * 3 + n_cells * 2 + n_cells
# It's a hidden layer, thus this will create the feed forward layer for the LSTM for the input.
super(AssociativeLstmLayer, self).__init__(n_out=n_z, **kwargs)
self.set_attr('n_out', n_out)
self.set_attr('n_copies', n_copies)
self.set_attr('direction', direction)
self.set_attr('activation', activation)
if grad_clip:
self.set_attr('grad_clip', grad_clip)
grad_clip = numpy.float32(grad_clip)
self.W_re = self.add_param(self.create_random_uniform_weights(n=n_out, m=n_z, name="W_re_%s" % self.name))
static_rng = numpy.random.RandomState(1234)
def make_permut():
p = numpy.zeros((n_copies, n_cells), dtype="int32")
for i in range(n_copies):
p[i, :n_complex_cells] = static_rng.permutation(n_complex_cells)
# Same permutation for imaginary part.
p[i, n_complex_cells:] = p[i, :n_complex_cells] + n_complex_cells
return T.constant(p)
P = make_permut() # (n_copies,n_cells) -> list of indices
# Some defaults.
CI, CO, RI, RO = [T.tanh] * 4 # original complex_bound, but tanh works better?
G = T.nnet.sigmoid
actf = strtoact(activation)
if isinstance(actf, list):
if len(actf) == 4:
CI, CO, RI, RO = actf
elif len(actf) == 5:
CI, CO, RI, RO, G = actf
else:
assert False, "invalid number of activation functions: %s, %s, %s" % (len(actf), activation, actf)
else:
CI, CO, RI, RO = [actf] * 4 # Not for the gates.
def lstm_step(z_t, i_t, s_p, h_p, W_re):
# z_t: current input. (batch,n_z)
# i_t: 0 or 1 (via index). (batch,)
# s_p: previous cell state. (batch,n_copies,n_cells)
# h_p: previous hidden out. (batch,n_out)
# W_re: recurrent matrix. (n_out,n_z)
i_t_bc = i_t.dimshuffle(0, 'x')
z_t += T.dot(h_p, W_re)
z_t *= i_t_bc
gates = G(z_t[:, 0:n_complex_cells * 3])
meminkey = RI(z_t[:, 3 * n_complex_cells:3 * n_complex_cells + n_cells]) # (batch,n_cells)
memoutkey = RO(z_t[:, 3 * n_complex_cells + n_cells:3 * n_complex_cells + 2 * n_cells]) # (batch,n_cells)
u = CI(z_t[:, 3 * n_complex_cells + 2 * n_cells:])
ingate2 = T.tile(gates[:, 0:n_complex_cells], (1, 2))
forgetgate2 = T.tile(gates[:, n_complex_cells:2 * n_complex_cells], (1, 2))
outgate2 = T.tile(gates[:, 2 * n_complex_cells:], (1, 2))
meminkeyP = meminkey[:, P] # (batch,n_copies,n_cells)
memoutkeyP = memoutkey[:, P] # (batch,n_copies,n_cells)
u_gated = u * ingate2 # (batch,n_cells)
u_gated_bc = u_gated.dimshuffle(0, 'x', 1) # (batch,n_copies,n_cells)
forgetgate2_bc = forgetgate2.dimshuffle(0, 'x', 1) # (batch,n_copies,n_cells)
from TheanoUtil import complex_elemwise_mult
s_t = complex_elemwise_mult(meminkeyP, u_gated_bc) + s_p * forgetgate2_bc # (batch,n_copies,n_cells)
readout_avg = T.mean(complex_elemwise_mult(memoutkeyP, s_t), axis=1) # (batch,n_cells)
h_t = CO(readout_avg) * outgate2
s_t *= i_t.dimshuffle(0, 'x', 'x')
h_t *= i_t_bc
if grad_clip:
s_t = theano.gradient.grad_clip(s_t, -grad_clip, grad_clip)
h_t = theano.gradient.grad_clip(h_t, -grad_clip, grad_clip)
return s_t, h_t
z = self.get_linear_forward_output() # (n_time,n_batch,n_z)
n_batch = z.shape[1]
assert self.W_re.ndim == 2
# i: (n_time,n_batch)
i = T.cast(self.index, dtype="float32") # so that it can run on gpu
s_initial = T.zeros((n_batch, n_copies, n_cells), dtype="float32")
h_initial = T.zeros((n_batch, n_out), dtype="float32")
go_backwards = {1:False, -1:True}[direction]
(s, h), _ = theano.scan(lstm_step,
sequences=[z, i], go_backwards=go_backwards,
non_sequences=[self.W_re],
outputs_info=[s_initial, h_initial])
self.act = [h, s]
h = h[::direction]
self.make_output(h)
class LstmHalfGatesLayer(HiddenLayer):
recurrent = True
layer_class = "lstm_half_gates"
def __init__(self, n_out, direction=1, activation='tanh', grad_clip=None, **kwargs):
n_cells = n_out
assert n_cells % 2 == 0 # complex numbers, split real/imag
n_complex_cells = n_cells / 2
# {input,forget,out}-gate have n_complex_cells dim.
# update u (earlier called net-input) has n_cells dim.
n_z = n_complex_cells * 3 + n_cells
# It's a hidden layer, thus this will create the feed forward layer for the LSTM for the input.
super(LstmHalfGatesLayer, self).__init__(n_out=n_z, **kwargs)
self.set_attr('n_out', n_out)
self.set_attr('direction', direction)
self.set_attr('activation', activation)
if grad_clip:
self.set_attr('grad_clip', grad_clip)
grad_clip = numpy.float32(grad_clip)
self.W_re = self.add_param(self.create_random_uniform_weights(n=n_out, m=n_z, name="W_re_%s" % self.name))
from TheanoUtil import complex_elemwise_mult, complex_bound