def _setup_functions(self):
self._activation_func = nnprocessors.build_activation(self.activation)
self._softmax_func = nnprocessors.build_activation('softmax')
top_rep, self.output_func = self._recursive_func()
self.monitors.append(("top_rep<0.1", 100 * (abs(top_rep) < 0.1).mean()))
self.monitors.append(("top_rep<0.9", 100 * (abs(top_rep) < 0.9).mean()))
self.monitors.append(("top_rep:mean", abs(top_rep).mean()))
def _setup_functions(self):
self._activation_func = nnprocessors.build_activation(self.activation)
self._softmax_func = nnprocessors.build_activation('softmax')
self.hidden_func, self.output_func = self._recurrent_func()
self.monitors.append(
("h<0.1", 100 * (abs(self.hidden_func[-1]) < 0.1).mean()))
self.monitors.append(
("h<0.9", 100 * (abs(self.hidden_func[-1]) < 0.9).mean()))
def _setup_functions(self):
self._activation_func = nnprocessors.build_activation(self.activation)
self._softmax_func = nnprocessors.build_activation('softmax')
top_rep, self.output_func = self._recursive_func()
self.monitors.append(
("top_rep<0.1", 100 * (abs(top_rep) < 0.1).mean()))
self.monitors.append(
("top_rep<0.9", 100 * (abs(top_rep) < 0.9).mean()))
self.monitors.append(("top_rep:mean", abs(top_rep).mean()))
def _setup_functions(self):
self._assistive_params = []
self._activation_func = nnprocessors.build_activation(self.activation)
self._softmax_func = nnprocessors.build_activation('softmax')
top_rep, self.output_func = self._recursive_func()
# self.predict_func, self.predict_updates = self._encode_func()
self.monitors.append(("top_rep<0.1", 100 * (abs(top_rep) < 0.1).mean()))
self.monitors.append(("top_rep<0.9", 100 * (abs(top_rep) < 0.9).mean()))
self.monitors.append(("top_rep:mean", abs(top_rep).mean()))
def _setup_functions(self):
self._assistive_params = []
self._activation_func = nnprocessors.build_activation(self.activation)
self._softmax_func = nnprocessors.build_activation('softmax')
top_rep, self.output_func = self._recursive_func()
# self.predict_func, self.predict_updates = self._encode_func()
self.monitors.append(("top_rep<0.1", 100 * (abs(top_rep) < 0.1).mean()))
self.monitors.append(("top_rep<0.9", 100 * (abs(top_rep) < 0.9).mean()))
self.monitors.append(("top_rep:mean", abs(top_rep).mean()))
def _setup_functions(self):
self._assistive_params = []
self._activation_func = nnprocessors.build_activation(self.activation)
self._softmax_func = nnprocessors.build_activation('softmax')
self.hidden_func, self.output_func, recurrent_updates = self._recurrent_func()
self.predict_func, self.predict_updates = self._predict_func()
self.monitors.append(("hh<0.1", 100 * (abs(self.hidden_func[-1]) < 0.1).mean()))
self.monitors.append(("hh<0.9", 100 * (abs(self.hidden_func[-1]) < 0.9).mean()))
self.updates.extend(recurrent_updates.items())
if self.update_h0:
self.updates.append((self.h0, ifelse(T.eq(self._vars.k[-1], 0), self.init_h, self.hidden_func[-1])))
self.params.extend(self._assistive_params)
def _setup_functions(self):
self._tanh = nnprocessors.build_activation('tanh')
self._sigmoid = nnprocessors.build_activation('sigmoid')
self._softmax = nnprocessors.build_activation('softmax')
[self.output_func, self.hidden_func, self.memory_func], recurrent_updates = self._recurrent_func()
self.predict_func, self.predict_updates = self._predict_func()
self.monitors.append(("last_h<0.1", 100 * (abs(self.hidden_func[-1]) < 0.1).mean()))
self.monitors.append(("last_h<0.9", 100 * (abs(self.hidden_func[-1]) < 0.9).mean()))
self.monitors.append(("c<0.1", 100 * (abs(self.memory_func[-1]) < 0.1).mean()))
self.monitors.append(("c<0.9", 100 * (abs(self.memory_func[-1]) < 0.9).mean()))
self.updates.extend(recurrent_updates)
if self.update_h0:
self.updates.append((self.h0, ifelse(T.eq(self._vars.k[-1], 0), self.init_h, self.hidden_func[-1])))
self.updates.append((self.c0, ifelse(T.eq(self._vars.k[-1], 0), self.init_c, self.memory_func[-1])))
def _setup_functions(self):
if self.shared_bias:
self._vars.update_if_not_existing(self.shared_bias, self.B)
bias = self.B if not self.shared_bias else self._vars.get(
self.shared_bias)
if self.disable_bias:
bias = 0
self._activation_func = nnprocessors.build_activation(self.activation)
self.preact_func = T.dot(self.x, self.W) + bias
self.output_func = nnprocessors.add_noise(
self._activation_func(self.preact_func), self.noise, self.dropouts)
def _setup_functions(self):
if self.shared_bias:
self._vars.update_if_not_existing(self.shared_bias, self.B)
bias = self.B if not self.shared_bias else self._vars.get(self.shared_bias)
if self.disable_bias:
bias = 0
self._activation_func = nnprocessors.build_activation(self.activation)
self.preact_func = T.dot(self.x, self.W) + bias
self.output_func = nnprocessors.add_noise(
self._activation_func(self.preact_func),
self.noise,
self.dropouts)
def _setup_functions(self):
self._assistive_params = []
self._activation_func = nnprocessors.build_activation(self.activation)
self.output_func = self._output_func()
def _setup_functions(self):
self._assistive_params = []
self._activation_func = nnprocessors.build_activation(self.activation)
self.output_func = self._output_func()
def _setup_functions(self):
self._activation_func = nnprocessors.build_activation(self.activation)
self._softmax_func = nnprocessors.build_activation('softmax')
self.hidden_func, self.output_func = self._recurrent_func()
self.monitors.append(("h<0.1", 100 * (abs(self.hidden_func[-1]) < 0.1).mean()))
self.monitors.append(("h<0.9", 100 * (abs(self.hidden_func[-1]) < 0.9).mean()))