def __init__(self, in_dim, out_dim, act_fun=None):
"""Long-short Term Memory
Parameters
----------
in_dim : int
input dimension
out_dim : int
hidden dimension
act_fun : function
activation function for input
"""
self.in_dim = in_dim
self.out_dim = out_dim
self.in_layer = IdentityLayer(in_dim=self.in_dim,
out_dim=self.out_dim * 4,
layer_name="LSTM in layer")
if act_fun is None:
self.act_fun = tanh
else:
self.act_fun = act_fun
self.init_weights()
def __init__(self,
in_dim,
out_dim,
act_fun=None):
"""Long-short Term Memory
Parameters
----------
in_dim : int
input dimension
out_dim : int
hidden dimension
act_fun : function
activation function for input
"""
self.in_dim=in_dim;
self.out_dim=out_dim;
self.in_layer=IdentityLayer(in_dim=self.in_dim,
out_dim=self.out_dim*4,
layer_name="LSTM in layer");
if act_fun is None:
self.act_fun=tanh;
else:
self.act_fun=act_fun;
self.init_weights();
class LSTM(object):
"""LSTM implementation"""
def __init__(self, in_dim, out_dim, act_fun=None):
"""Long-short Term Memory
Parameters
----------
in_dim : int
input dimension
out_dim : int
hidden dimension
act_fun : function
activation function for input
"""
self.in_dim = in_dim
self.out_dim = out_dim
self.in_layer = IdentityLayer(in_dim=self.in_dim,
out_dim=self.out_dim * 4,
layer_name="LSTM in layer")
if act_fun is None:
self.act_fun = tanh
else:
self.act_fun = act_fun
self.init_weights()
def init_weights(self):
self.W_state = util.init_weights("W_state",
self.out_dim * 4,
self.in_dim,
weight_type="sigmoid")
self.W_cell_to_in = util.shared_floatx_nans((self.out_dim, ),
name="W cell to in")
self.W_cell_to_forget = util.shared_floatx_nans(
(self.out_dim, ), name="W cell to forget")
self.W_cell_to_out = util.shared_floatx_nans((self.out_dim, ),
name="W cell to out")
self.init_state = util.shared_floatx_zeros((self.out_dim, ),
name="initial states")
self.init_cell = util.shared_floatx_zeros((self.out_dim, ),
name="initial cell")
def apply(self, X, states, cells, mask=None):
"""Apply LSTM activation
Parameters
----------
X : 2D tensor
input samples in (batch_size, input_dim)
states : 2D tensor
features in (batch_size, feature_dim)
cells : 2D tensor
cell states in (batch_size, cell_dim)
Returns
-------
states : 2D tensor
next state of network
cells : 2D tensor
next cell activation
"""
def slice_st(x, no):
return x[:, no * self.dim:(no + 1) * self.dim]
activation = T.dot(states, self.W_state) + self.in_layer.apply(X)
in_gate = sigmoid(slice_st(activation, 0) + cells * self.W_cell_to_in)
forget_gate = sigmoid(
slice_st(activation, 1) + cells * self.W_cell_to_forget)
next_cells = (forget_gate * cells +
in_gate * self.act_fun(slice_st(activation, 2)))
out_gate = sigmoid(
slice_st(activation, 3) + next_cells * self.W_cell_to_out)
next_states = out_gate * self.act_fun(next_cells)
if mask:
next_states = (mask[:, None] * next_states +
(1 - mask[:, None] * states))
next_cells = (mask[:, None] * next_cells +
(1 - mask[:, None] * cells))
return next_states, next_cells
@property
def params(self):
return (self.in_layer.params, self.W_state, self.W_cell_to_in,
self.W_cell_to_forget, self.W_cell_to_out, self.init_state,
self.init_cell)
class LSTM(object):
"""LSTM implementation"""
def __init__(self,
in_dim,
out_dim,
act_fun=None):
"""Long-short Term Memory
Parameters
----------
in_dim : int
input dimension
out_dim : int
hidden dimension
act_fun : function
activation function for input
"""
self.in_dim=in_dim;
self.out_dim=out_dim;
self.in_layer=IdentityLayer(in_dim=self.in_dim,
out_dim=self.out_dim*4,
layer_name="LSTM in layer");
if act_fun is None:
self.act_fun=tanh;
else:
self.act_fun=act_fun;
self.init_weights();
def init_weights(self):
self.W_state=util.init_weights("W_state", self.out_dim*4, self.in_dim, weight_type="sigmoid");
self.W_cell_to_in=util.shared_floatx_nans((self.out_dim,), name="W cell to in");
self.W_cell_to_forget=util.shared_floatx_nans((self.out_dim,), name="W cell to forget");
self.W_cell_to_out=util.shared_floatx_nans((self.out_dim,), name="W cell to out");
self.init_state=util.shared_floatx_zeros((self.out_dim, ), name="initial states");
self.init_cell=util.shared_floatx_zeros((self.out_dim, ), name="initial cell");
def apply(self, X, states, cells, mask=None):
"""Apply LSTM activation
Parameters
----------
X : 2D tensor
input samples in (batch_size, input_dim)
states : 2D tensor
features in (batch_size, feature_dim)
cells : 2D tensor
cell states in (batch_size, cell_dim)
Returns
-------
states : 2D tensor
next state of network
cells : 2D tensor
next cell activation
"""
def slice_st(x, no):
return x[:, no*self.dim:(no+1)*self.dim];
activation=T.dot(states, self.W_state)+self.in_layer.apply(X);
in_gate=sigmoid(slice_st(activation, 0)+cells*self.W_cell_to_in);
forget_gate=sigmoid(slice_st(activation, 1)+cells*self.W_cell_to_forget);
next_cells=(forget_gate*cells+in_gate*self.act_fun(slice_st(activation, 2)));
out_gate=sigmoid(slice_st(activation,3)+next_cells*self.W_cell_to_out)
next_states=out_gate*self.act_fun(next_cells);
if mask:
next_states=(mask[:, None]*next_states+(1-mask[:,None]*states));
next_cells=(mask[:, None]*next_cells+(1-mask[:,None]*cells));
return next_states, next_cells;
@property
def params(self):
return (self.in_layer.params, self.W_state, self.W_cell_to_in,
self.W_cell_to_forget, self.W_cell_to_out, self.init_state, self.init_cell);
