def _compile(self):
self.all_trainable_parameters_symbol = layers.all_trainable_parameters(
self._get_output_layer())
self.all_save_parameters_symbol = layers.all_parameters(
self._get_output_layer())
# can switch to gen_updates_regular_momentum
self.updates_symbol = layers.gen_updates_regular_momentum(
self._get_cost_symbol(),
self.all_trainable_parameters_symbol,
learning_rate=self.learning_rate_symbol,
momentum=0.9,
weight_decay=1e-5)
self.train_func = theano.function(
[self._get_input_symbol()],
self._get_cost_symbol(),
updates=self.updates_symbol)
self.eval_func = theano.function(
[self._get_input_symbol()],
self._get_cost_symbol())
self.prediction_func = theano.function(
[self._get_input_symbol()],
self._get_output_symbol())
def _compile(self):
output = self._get_output_layer()
all_trainable_parameters = layers.all_trainable_parameters(output)
self.all_trainable_parameters_symbol = list(set(
all_trainable_parameters))
all_save_parameters = layers.all_parameters(output)
self.all_save_parameters_symbol = list(set(all_save_parameters))
self.updates_symbol = layers.gen_updates_regular_momentum(
self._get_cost_symbol(),
self.all_trainable_parameters_symbol,
learning_rate=self.learning_rate_symbol,
momentum=0.9,
weight_decay=1e-5)
self.train_func = theano.function(
[self._get_input_symbol(), self._get_y_symbol()],
[self._get_cost_symbol(), self._get_accuracy_symbol()],
updates=self.updates_symbol)
self.eval_func = theano.function(
[self._get_input_symbol(), self._get_y_symbol()],
self._get_accuracy_symbol())
self.prediction_func = theano.function(
[self._get_input_symbol()],
self._get_output_symbol())
def _compile(self):
output = self._get_output_layer()
all_trainable_parameters = layers.all_trainable_parameters(output)
self.all_trainable_parameters_symbol = list(
set(all_trainable_parameters))
all_save_parameters = layers.all_parameters(output)
self.all_save_parameters_symbol = list(set(all_save_parameters))
self.updates_symbol = layers.gen_updates_regular_momentum(
self._get_cost_symbol(),
self.all_trainable_parameters_symbol,
learning_rate=self.learning_rate_symbol,
momentum=0.9,
weight_decay=1e-5)
self.train_func = theano.function(
[self._get_input_symbol(),
self._get_y_symbol()],
[self._get_cost_symbol(),
self._get_accuracy_symbol()],
updates=self.updates_symbol)
self.eval_func = theano.function(
[self._get_input_symbol(),
self._get_y_symbol()], self._get_accuracy_symbol())
self.prediction_func = theano.function([self._get_input_symbol()],
self._get_output_symbol())
def _compile(self):
self.all_trainable_parameters_symbol = layers.all_trainable_parameters(
self._get_output_layer())
self.all_save_parameters_symbol = layers.all_parameters(
self._get_output_layer())
self.updates_symbol = layers.gen_updates_regular_momentum(
self._get_cost_symbol(),
self.all_trainable_parameters_symbol,
learning_rate=self.learning_rate_symbol,
momentum=0.9,
weight_decay=1e-5)
self.train_func = theano.function(
[self._get_input_symbol(),
self.action_symbol,
self.y_symbol],
self._get_cost_symbol(),
updates=self.updates_symbol)
self.eval_func = theano.function(
[self._get_input_symbol(),
self.action_symbol,
self.y_symbol],
self._get_cost_symbol()
)
self.prediction_func = theano.function(
[self._get_input_symbol()],
self._get_output_symbol()
)
# New funcs for ReinforcementModel
self.action_func = theano.function(
[self._get_input_symbol()],
self._get_action_symbol()
)
self.max_q_func = theano.function(
[self._get_input_symbol()],
self._get_max_q_symbol()
)
self.y_func = theano.function(
[self._get_input_symbol(),
self.r_symbol,
self.gamma_symbol],
self._get_y_symbol()
)
self.value_func = theano.function(
[self._get_input_symbol(),
self.action_symbol],
self._get_value_symbol()
)
def _compile(self):
self.all_trainable_parameters_symbol = layers.all_trainable_parameters(
self._get_output_layer())
self.all_save_parameters_symbol = layers.all_parameters(
self._get_output_layer())
self.updates_symbol = layers.gen_updates_regular_momentum(
self._get_cost_symbol(),
self.all_trainable_parameters_symbol,
learning_rate=self.learning_rate_symbol,
momentum=0.9,
weight_decay=1e-5)
self.train_func = theano.function(
[self._get_input_symbol(), self.action_symbol, self.y_symbol],
self._get_cost_symbol(),
updates=self.updates_symbol)
self.eval_func = theano.function(
[self._get_input_symbol(), self.action_symbol, self.y_symbol],
self._get_cost_symbol())
self.prediction_func = theano.function([self._get_input_symbol()],
self._get_output_symbol())
# New funcs for ReinforcementModel
self.action_func = theano.function([self._get_input_symbol()],
self._get_action_symbol())
self.max_q_func = theano.function([self._get_input_symbol()],
self._get_max_q_symbol())
self.y_func = theano.function(
[self._get_input_symbol(), self.r_symbol, self.gamma_symbol],
self._get_y_symbol())
self.value_func = theano.function(
[self._get_input_symbol(), self.action_symbol],
self._get_value_symbol())
def _compile(self):
self.all_trainable_parameters_symbol = layers.all_trainable_parameters(
self._get_output_layer())
self.all_save_parameters_symbol = layers.all_parameters(
self._get_output_layer())
# can switch to gen_updates_regular_momentum
self.updates_symbol = layers.gen_updates_regular_momentum(
self._get_cost_symbol(),
self.all_trainable_parameters_symbol,
learning_rate=self.learning_rate_symbol,
momentum=0.9,
weight_decay=1e-5)
self.train_func = theano.function([self._get_input_symbol()],
self._get_cost_symbol(),
updates=self.updates_symbol)
self.eval_func = theano.function([self._get_input_symbol()],
self._get_cost_symbol())
self.prediction_func = theano.function([self._get_input_symbol()],
self._get_output_symbol())
