def eval_at_state(self):
model_input = ModelInput()
model_input.state = self.preprocess_frame(
self.episode.state_for_agent())
model_input.hidden = self.hidden
model_output = self.model.forward(model_input)
return model_output
def eval_at_state(self):
model_input = ModelInput()
model_input.state = self.preprocess_frame(
self.episode.state_for_agent())
model_input.hidden = self.hidden
model_input.additional_state_info = gpuify(
torch.Tensor(self.info[1]).unsqueeze(0), self.gpu_id)
model_output = self.model.forward(model_input)
return model_output
def eval_at_state(self):
model_input = ModelInput()
model_input.state = self.preprocess_frame(
self.episode.state_for_agent())
model_input.hidden = self.hidden
## add 'augState' as additional_state_info list to 'model_input' container
model_input.additional_state_info = self.augState
print(
'eval_at_state agent.py ..printing model_input.additional_state_info tensor..',
model_input.additional_state_info.data[0])
model_output = self.model.forward(model_input)
return model_output
def eval_at_state(self):
episode_actions_taken = set(
[a['action'] for a in self.episode.actions_taken])
extra_state = [
1. if 'Seen' + t in episode_actions_taken else 0.
for t in self.episode.targets
]
extra_state = torch.tensor(extra_state).unsqueeze(0)
if self.gpu_id >= 0:
with torch.cuda.device(self.gpu_id):
extra_state = extra_state.cuda()
model_input = ModelInput()
model_input.state = self.preprocess_frame(
self.episode.state_for_agent())
model_input.extra_state = extra_state
model_input.hidden = self.hidden
model_output = self.model.forward(model_input)
return model_output
def instantiate_model(mode, config, data_set):
"""
Instantiates the model with the given configuration and data.
:param mode: Mode enum specifying train/test/validation.
:param config:
:param data_set: a tf.data.dataset
:return: model, summary operation
"""
name = Mode.to_string(mode)
initializer = tf.random_uniform_initializer(-config.init_scale,
config.init_scale)
with tf.name_scope(name=name):
model_input = ModelInput(config=config, data=data_set)
with tf.variable_scope("Model",
reuse=tf.AUTO_REUSE,
initializer=initializer):
model = SeqModel(is_training=(mode == Mode.train),
config=config,
dataset=model_input)
summary = tf.summary.scalar(name + " Loss", model.normalized_cost)
if mode == Mode.train:
summ_lr = tf.summary.scalar("Learning Rate",
model.config.learning_rate)
summary = tf.summary.merge([summary, summ_lr])
summ_layers = tf.summary.scalar("Layers", model.config.num_layers)
summ_bs = tf.summary.scalar("Batch Size", model.config.batch_size)
summ_grad_norm = tf.summary.scalar("Gradient Norm",
model.config.max_grad_norm)
summ_keep_prob = tf.summary.scalar("Dropout Keep Probability",
model.config.keep_prob)
summ_hidden_size = tf.summary.scalar("State Size",
model.config.hidden_size)
summary = tf.summary.merge([
summary, summ_layers, summ_bs, summ_grad_norm, summ_keep_prob,
summ_hidden_size
])
return model, summary
def main():
model = Model()
#
# model.setup(155, base_stats_by_attr,
# {'AP': 10, 'MP': 5, 'Range': 5, 'Agility': 134, 'Critical Hits': 45},
# {}, set(), objective_values_2, options)
# model.run()
# print model.get_result_string()
model.setup(
ModelInput(73, base_stats_by_attr_case2, {
'AP': 10,
'MP': 4,
'Range': 2,
'Summon': 1
}, {}, set(['Dofusteuse', 'Vulbis Dofus']), {
key: 2 * value
for (key, value) in objective_values_3.iteritems()
}, options_caso2))
model.run(1)
print model.get_result_string()