def lstm(dh, dc, sv, x):
# projected contribution from input(s), hidden, and bias
proj3 = b + times(x, W) + times(dh, H) + times(sv, Hsv)
it_proj = slice(proj3, stack_axis, 0 * stacked_dim, 1 * stacked_dim)
ft_proj = slice(proj3, stack_axis, 1 * stacked_dim, 2 * stacked_dim)
ot_proj = slice(proj3, stack_axis, 2 * stacked_dim, 3 * stacked_dim)
it = sigmoid(it_proj) # input gate(t)
ft = sigmoid(ft_proj) # forget-me-not gate(t)
ot = sigmoid(ot_proj) # output gate(t)
# the following is reading gate
proj3rg = sigmoid(
times(x, Wrg) + times(dh, Hrg) + times(sv, Hsvrg) + brg)
v = proj3rg * sv
cx_t = tanh(times(x, Wcx) + times(dh, Hcx))
# need to do stablization ??
# update memory cell
c = it * cx_t + ft * dc + tanh(times(v, Wfc))
h = ot * tanh(c)
return (h, c, v)
def gru_cell(shape, init=glorot_uniform(), name=''): # (x, (h,c))
""" GRU cell function
"""
shape = _as_tuple(shape)
if len(shape) != 1:
raise ValueError("gru_cell: shape must be vectors (rank-1 tensors)")
# determine stacking dimensions
cell_shape_stacked = shape * 2 # patched dims with stack_axis duplicated 2 times
# parameters
Wz = Parameter(cell_shape_stacked, init=init, name='Wz')
Wr = Parameter(cell_shape_stacked, init=init, name='Wr')
Wh = Parameter(cell_shape_stacked, init=init, name='Wh')
Uz = Parameter(_INFERRED + shape, init=init, name='Uz')
Ur = Parameter(_INFERRED + shape, init=init, name='Ur')
Uh = Parameter(_INFERRED + shape, init=init, name='Uh')
def create_s_placeholder():
# we pass the known dimensions here, which makes dimension inference easier
return Placeholder(shape=shape, name='S') # (h, c)
# parameters to model function
x = Placeholder(name='gru_block_arg')
prev_status = create_s_placeholder()
# formula of model function
Sn_1 = prev_status
z = sigmoid(times(x, Uz, name='x*Uz') + times(Sn_1, Wz, name='Sprev*Wz'),
name='z')
r = sigmoid(times(x, Ur, name='x*Ur') + times(Sn_1, Wr, name='Sprev*Wr'),
name='r')
h = tanh(times(x, Uh, name='x*Uh') +
times(element_times(Sn_1, r, name='Sprev*r'), Wh),
name='h')
s = plus(element_times((1 - z), h, name='(1-z)*h'),
element_times(z, Sn_1, name='z*SPrev'),
name=name)
apply_x_s = combine([s])
apply_x_s.create_placeholder = create_s_placeholder
return apply_x_s
def LSTM(shape, cell_shape=None, use_peepholes=use_peepholes_default_or_False,
init=init_default_or_glorot_uniform, init_bias=init_bias_default_or_0,
enable_self_stabilization=enable_self_stabilization_default_or_False): # (x, (h, c))
use_peepholes = use_peepholes if _is_given(use_peepholes) else _current_default_options.use_peepholes
enable_self_stabilization = enable_self_stabilization if _is_given(enable_self_stabilization) else _current_default_options.enable_self_stabilization
has_projection = cell_shape is not None
has_aux = False
if has_aux:
UntestedBranchError("LSTM, has_aux option")
shape = _as_tuple(shape)
cell_shape = _as_tuple(cell_shape) if cell_shape is not None else shape
if len(shape) != 1 or len(cell_shape) != 1:
raise ValueError("LSTM: shape and cell_shape must be vectors (rank-1 tensors)")
# otherwise we'd need to fix slicing and Param initializers
stack_axis = -1 # stacking along the fastest-changing one, to match BS
# determine stacking dimensions
cell_shape_list = list(cell_shape)
stacked_dim = cell_shape_list[0]
cell_shape_list[stack_axis] = stacked_dim*4
cell_shape_stacked = tuple(cell_shape_list) # patched dims with stack_axis duplicated 4 times
# parameters
b = Parameter( cell_shape_stacked, init=init_bias, name='b') # a bias
W = Parameter(_INFERRED + cell_shape_stacked, init=init, name='W') # input
A = Parameter(_INFERRED + cell_shape_stacked, init=init, name='A') if has_aux else None # aux input (optional)
H = Parameter(shape + cell_shape_stacked, init=init, name='H') # hidden-to-hidden
Ci = Parameter( cell_shape, init=init, name='Ci') if use_peepholes else None # cell-to-hiddden {note: applied elementwise}
Cf = Parameter( cell_shape, init=init, name='Cf') if use_peepholes else None # cell-to-hiddden {note: applied elementwise}
Co = Parameter( cell_shape, init=init, name='Co') if use_peepholes else None # cell-to-hiddden {note: applied elementwise}
Wmr = Parameter(cell_shape + shape, init=init) if has_projection else None # final projection
Sdh = Stabilizer() if enable_self_stabilization else identity
Sdc = Stabilizer() if enable_self_stabilization else identity
Sct = Stabilizer() if enable_self_stabilization else identity
Sht = Stabilizer() if enable_self_stabilization else identity
def create_hc_placeholder():
# we pass the known dimensions here, which makes dimension inference easier
return (Placeholder(shape=shape, name='hPh'), Placeholder(shape=cell_shape, name='cPh')) # (h, c)
# parameters to model function
x = Placeholder(name='lstm_block_arg')
prev_state = create_hc_placeholder()
# formula of model function
dh, dc = prev_state
dhs = Sdh(dh) # previous values, stabilized
dcs = Sdc(dc)
# note: input does not get a stabilizer here, user is meant to do that outside
# projected contribution from input(s), hidden, and bias
proj4 = b + times(x, W) + times(dhs, H) + times(aux, A) if has_aux else \
b + times(x, W) + times(dhs, H)
it_proj = slice (proj4, stack_axis, 0*stacked_dim, 1*stacked_dim) # split along stack_axis
bit_proj = slice (proj4, stack_axis, 1*stacked_dim, 2*stacked_dim)
ft_proj = slice (proj4, stack_axis, 2*stacked_dim, 3*stacked_dim)
ot_proj = slice (proj4, stack_axis, 3*stacked_dim, 4*stacked_dim)
# add peephole connection if requested
def peep(x, c, C):
return x + C * c if use_peepholes else x
it = sigmoid (peep (it_proj, dcs, Ci)) # input gate(t)
bit = it * tanh (bit_proj) # applied to tanh of input network
ft = sigmoid (peep (ft_proj, dcs, Cf)) # forget-me-not gate(t)
bft = ft * dc # applied to cell(t-1)
ct = bft + bit # c(t) is sum of both
ot = sigmoid (peep (ot_proj, Sct(ct), Co)) # output gate(t)
ht = ot * tanh (ct) # applied to tanh(cell(t))
c = ct # cell value
h = times(Sht(ht), Wmr) if has_projection else \
ht
_name_node(h, 'h')
if _trace_layers:
_log_node(h) # this looks right
_name_node(c, 'c')
# TODO: figure out how to do scoping, and also rename all the apply... to expression
apply_x_h_c = combine ([h, c])
# return to caller a helper function to create placeholders for recurrence
# Note that this function will only exist in the object returned here, but not any cloned version of it.
apply_x_h_c.create_placeholder = create_hc_placeholder
#return Block(apply_x_h_c, 'LSTM') # BUGBUG: fails with "RuntimeError: A Function instance with more than one output cannot be implicitly converted to a Variable"
return apply_x_h_c