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;
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
def apply(self, X):
return nnfuns.sigmoid(self.apply_lin(X));
def apply(self, X):
h, t=self.apply_lin(X);
h=nnfuns.sigmoid(h);
t=nnfuns.sigmoid(t);
return h*t+X*(1-t);
def apply(self, X):
h, t = self.apply_lin(X)
h = nnfuns.sigmoid(h)
t = nnfuns.sigmoid(t)
return h * t + X * (1 - t)
