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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/policy.py | Policy.get_current_step | def get_current_step(self):
"""
Gets current model step.
:return: current model step.
"""
step = self.sess.run(self.model.global_step)
return step | python | def get_current_step(self):
"""
Gets current model step.
:return: current model step.
"""
step = self.sess.run(self.model.global_step)
return step | [
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/policy.py | Policy.save_model | def save_model(self, steps):
"""
Saves the model
:param steps: The number of steps the model was trained for
:return:
"""
with self.graph.as_default():
last_checkpoint = self.model_path + '/model-' + str(steps) + '.cptk'
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"""
Saves the model
:param steps: The number of steps the model was trained for
:return:
"""
with self.graph.as_default():
last_checkpoint = self.model_path + '/model-' + str(steps) + '.cptk'
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/policy.py | Policy.export_model | def export_model(self):
"""
Exports latest saved model to .nn format for Unity embedding.
"""
with self.graph.as_default():
target_nodes = ','.join(self._process_graph())
ckpt = tf.train.get_checkpoint_state(self.model_path)
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"""
Exports latest saved model to .nn format for Unity embedding.
"""
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target_nodes = ','.join(self._process_graph())
ckpt = tf.train.get_checkpoint_state(self.model_path)
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"""
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"""
Gets the list of the output nodes present in the graph for inference
:return: list of node names
"""
all_nodes = [x.name for x in self.graph.as_graph_def().node]
nodes = [x for x in all_nodes if x in self.possible_output_nodes]
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/buffer.py | Buffer.reset_local_buffers | def reset_local_buffers(self):
"""
Resets all the local local_buffers
"""
agent_ids = list(self.keys())
for k in agent_ids:
self[k].reset_agent() | python | def reset_local_buffers(self):
"""
Resets all the local local_buffers
"""
agent_ids = list(self.keys())
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/buffer.py | Buffer.append_update_buffer | def append_update_buffer(self, agent_id, key_list=None, batch_size=None, training_length=None):
"""
Appends the buffer of an agent to the update buffer.
:param agent_id: The id of the agent which data will be appended
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Appends the buffer of an agent to the update buffer.
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/buffer.py | Buffer.append_all_agent_batch_to_update_buffer | def append_all_agent_batch_to_update_buffer(self, key_list=None, batch_size=None, training_length=None):
"""
Appends the buffer of all agents to the update buffer.
:param key_list: The fields that must be added. If None: all fields will be appended.
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Appends the buffer of all agents to the update buffer.
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/learn.py | run_training | def run_training(sub_id: int, run_seed: int, run_options, process_queue):
"""
Launches training session.
:param process_queue: Queue used to send signal back to main.
:param sub_id: Unique id for training session.
:param run_seed: Random seed used for training.
:param run_options: Command line a... | python | def run_training(sub_id: int, run_seed: int, run_options, process_queue):
"""
Launches training session.
:param process_queue: Queue used to send signal back to main.
:param sub_id: Unique id for training session.
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/trainer.py | Trainer.get_action | def get_action(self, curr_info: BrainInfo) -> ActionInfo:
"""
Get an action using this trainer's current policy.
:param curr_info: Current BrainInfo.
:return: The ActionInfo given by the policy given the BrainInfo.
"""
self.trainer_metrics.start_experience_collection_time... | python | def get_action(self, curr_info: BrainInfo) -> ActionInfo:
"""
Get an action using this trainer's current policy.
:param curr_info: Current BrainInfo.
:return: The ActionInfo given by the policy given the BrainInfo.
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/trainer.py | Trainer.write_summary | def write_summary(self, global_step, delta_train_start, lesson_num=0):
"""
Saves training statistics to Tensorboard.
:param delta_train_start: Time elapsed since training started.
:param lesson_num: Current lesson number in curriculum.
:param global_step: The number of steps the... | python | def write_summary(self, global_step, delta_train_start, lesson_num=0):
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Saves training statistics to Tensorboard.
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/trainer.py | Trainer.write_tensorboard_text | def write_tensorboard_text(self, key, input_dict):
"""
Saves text to Tensorboard.
Note: Only works on tensorflow r1.2 or above.
:param key: The name of the text.
:param input_dict: A dictionary that will be displayed in a table on Tensorboard.
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Saves text to Tensorboard.
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/meta_curriculum.py | MetaCurriculum.lesson_nums | def lesson_nums(self):
"""A dict from brain name to the brain's curriculum's lesson number."""
lesson_nums = {}
for brain_name, curriculum in self.brains_to_curriculums.items():
lesson_nums[brain_name] = curriculum.lesson_num
return lesson_nums | python | def lesson_nums(self):
"""A dict from brain name to the brain's curriculum's lesson number."""
lesson_nums = {}
for brain_name, curriculum in self.brains_to_curriculums.items():
lesson_nums[brain_name] = curriculum.lesson_num
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/meta_curriculum.py | MetaCurriculum.increment_lessons | def increment_lessons(self, measure_vals, reward_buff_sizes=None):
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MetaCurriculum. Note that calling this method does not guarantee the
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/meta_curriculum.py | MetaCurriculum.set_all_curriculums_to_lesson_num | def set_all_curriculums_to_lesson_num(self, lesson_num):
"""Sets all the curriculums in this meta curriculum to a specified
lesson number.
Args:
lesson_num (int): The lesson number which all the curriculums will
be set to.
"""
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Args:
lesson_num (int): The lesson number which all the curriculums will
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Returns:
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Returns:
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Unity-Technologies/ml-agents | ml-agents-envs/mlagents/envs/environment.py | UnityEnvironment.reset | def reset(self, config=None, train_mode=True, custom_reset_parameters=None) -> AllBrainInfo:
"""
Sends a signal to reset the unity environment.
:return: AllBrainInfo : A data structure corresponding to the initial reset state of the environment.
"""
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... | python | def reset(self, config=None, train_mode=True, custom_reset_parameters=None) -> AllBrainInfo:
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Sends a signal to reset the unity environment.
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Provides the environment with an action, moves the environment dynamics forward accordingly,
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:return: flattened list.
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Unity-Technologies/ml-agents | ml-agents-envs/mlagents/envs/environment.py | UnityEnvironment._get_state | def _get_state(self, output: UnityRLOutput) -> (AllBrainInfo, bool):
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Collects experience information from all external brains in environment at current step.
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/trainer_metrics.py | TrainerMetrics.end_experience_collection_timer | def end_experience_collection_timer(self):
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"""
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... | python | def end_experience_collection_timer(self):
"""
Inform Metrics class that experience collection is done.
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/trainer_metrics.py | TrainerMetrics.add_delta_step | def add_delta_step(self, delta: float):
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Inform Metrics class about time to step in environment.
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"""
Inform Metrics class about time to step in environment.
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Inform Metrics class that policy update has started.
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/trainer_metrics.py | TrainerMetrics.end_policy_update | def end_policy_update(self):
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/trainer_metrics.py | TrainerMetrics.write_training_metrics | def write_training_metrics(self):
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Write Training Metrics to CSV
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/ppo/models.py | PPOModel.create_inverse_model | def create_inverse_model(self, encoded_state, encoded_next_state):
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/ppo/models.py | PPOModel.create_forward_model | def create_forward_model(self, encoded_state, encoded_next_state):
"""
Creates forward model TensorFlow ops for Curiosity module.
Predicts encoded future state based on encoded current state and given action.
:param encoded_state: Tensor corresponding to encoded current state.
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/ppo/policy.py | PPOPolicy.evaluate | def evaluate(self, brain_info):
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:param brain_info: BrainInfo object containing inputs.
:return: Outputs from network as defined by self.inference_dict.
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feed_dict = {self.model.batch_size: len(brain_info.vector_o... | python | def evaluate(self, brain_info):
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Evaluates policy for the agent experiences provided.
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Updates model using buffer.
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/ppo/policy.py | PPOPolicy.get_intrinsic_rewards | def get_intrinsic_rewards(self, curr_info, next_info):
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Generates intrinsic reward used for Curiosity-based training.
:BrainInfo curr_info: Current BrainInfo.
:BrainInfo next_info: Next BrainInfo.
:return: Intrinsic rewards for all agents.
"""
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/ppo/policy.py | PPOPolicy.get_value_estimate | def get_value_estimate(self, brain_info, idx):
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Generates value estimates for bootstrapping.
:param brain_info: BrainInfo to be used for bootstrapping.
:param idx: Index in BrainInfo of agent.
:return: Value estimate.
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feed_dict = {self.model.batch_size: 1, ... | python | def get_value_estimate(self, brain_info, idx):
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Generates value estimates for bootstrapping.
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/ppo/policy.py | PPOPolicy.update_reward | def update_reward(self, new_reward):
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/bc/trainer.py | BCTrainer.add_experiences | def add_experiences(self, curr_info: AllBrainInfo, next_info: AllBrainInfo,
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Adds experiences to each agent's experience history.
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/bc/trainer.py | BCTrainer.process_experiences | def process_experiences(self, current_info: AllBrainInfo, next_info: AllBrainInfo):
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/bc/trainer.py | BCTrainer.end_episode | def end_episode(self):
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Get only called when the academy resets.
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... | python | def end_episode(self):
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A signal that the Episode has ended. The buffer must be reset.
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/bc/trainer.py | BCTrainer.update_policy | def update_policy(self):
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"""
Updates the policy.
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/models.py | LearningModel.create_global_steps | def create_global_steps():
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"""Creates TF ops to track and increment global training step."""
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/models.py | LearningModel.create_visual_input | def create_visual_input(camera_parameters, name):
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/models.py | LearningModel.create_vector_input | def create_vector_input(self, name='vector_observation'):
"""
Creates ops for vector observation input.
:param name: Name of the placeholder op.
:param vec_obs_size: Size of stacked vector observation.
:return:
"""
self.vector_in = tf.placeholder(shape=[None, self... | python | def create_vector_input(self, name='vector_observation'):
"""
Creates ops for vector observation input.
:param name: Name of the placeholder op.
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/models.py | LearningModel.create_vector_observation_encoder | def create_vector_observation_encoder(observation_input, h_size, activation, num_layers, scope,
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Builds a set of hidden state encoders.
:param reuse: Whether to re-use the weights within the same scope.
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/models.py | LearningModel.create_visual_observation_encoder | def create_visual_observation_encoder(self, image_input, h_size, activation, num_layers, scope,
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/models.py | LearningModel.create_discrete_action_masking_layer | def create_discrete_action_masking_layer(all_logits, action_masks, action_size):
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Creates a masking layer for the discrete actions
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/models.py | LearningModel.create_observation_streams | def create_observation_streams(self, num_streams, h_size, num_layers):
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:param num_streams: Number of streams to create.
:param h_size: Size of hidden linear layers in stream.
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/models.py | LearningModel.create_recurrent_encoder | def create_recurrent_encoder(input_state, memory_in, sequence_length, name='lstm'):
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:param sequence_length: Length of sequence to unroll.
:param input_state: The input tensor to the LSTM cell.
:param memory_i... | python | def create_recurrent_encoder(input_state, memory_in, sequence_length, name='lstm'):
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/models.py | LearningModel.create_cc_actor_critic | def create_cc_actor_critic(self, h_size, num_layers):
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Creates Continuous control actor-critic model.
:param h_size: Size of hidden linear layers.
:param num_layers: Number of hidden linear layers.
"""
hidden_streams = self.create_observation_streams(2, h_size, num_lay... | python | def create_cc_actor_critic(self, h_size, num_layers):
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Creates Continuous control actor-critic model.
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:param num_layers: Number of hidden linear layers.
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/models.py | LearningModel.create_dc_actor_critic | def create_dc_actor_critic(self, h_size, num_layers):
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:param h_size: Size of hidden linear layers.
:param num_layers: Number of hidden linear layers.
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/bc/online_trainer.py | OnlineBCTrainer.add_experiences | def add_experiences(self, curr_info: AllBrainInfo, next_info: AllBrainInfo,
take_action_outputs):
"""
Adds experiences to each agent's experience history.
:param curr_info: Current AllBrainInfo (Dictionary of all current brains and corresponding BrainInfo).
:param... | python | def add_experiences(self, curr_info: AllBrainInfo, next_info: AllBrainInfo,
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/bc/online_trainer.py | OnlineBCTrainer.process_experiences | def process_experiences(self, current_info: AllBrainInfo, next_info: AllBrainInfo):
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/tensorflow_to_barracuda.py | flatten | def flatten(items,enter=lambda x:isinstance(x, list)):
# http://stackoverflow.com/a/40857703
# https://github.com/ctmakro/canton/blob/master/canton/misc.py
"""Yield items from any nested iterable; see REF."""
for x in items:
if enter(x):
yield from flatten(x)
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... | python | def flatten(items,enter=lambda x:isinstance(x, list)):
# http://stackoverflow.com/a/40857703
# https://github.com/ctmakro/canton/blob/master/canton/misc.py
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/tensorflow_to_barracuda.py | replace_strings_in_list | def replace_strings_in_list(array_of_strigs, replace_with_strings):
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potentially_nested_list = [replace_with_strings.get(s) or s for s in array_of_strigs]
return list(flatten(potentially_nested_list)) | python | def replace_strings_in_list(array_of_strigs, replace_with_strings):
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potentially_nested_list = [replace_with_strings.get(s) or s for s in array_of_strigs]
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/tensorflow_to_barracuda.py | remove_duplicates_from_list | def remove_duplicates_from_list(array):
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/tensorflow_to_barracuda.py | pool_to_HW | def pool_to_HW(shape, data_frmt):
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if len(shape) != 4:
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""" Convert from NHWC|NCHW => HW
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/demo_loader.py | demo_to_buffer | def demo_to_buffer(file_path, sequence_length):
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:param file_path: Location of demonstration file (.demo).
:param sequence_length: Length of trajectories to fill buffer.
:return:
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/trainer_controller.py | TrainerController._save_model | def _save_model(self, steps=0):
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Saves current model to checkpoint folder.
:param steps: Current number of steps in training process.
:param saver: Tensorflow saver for session.
"""
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"""
Saves current model to checkpoint folder.
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/trainer_controller.py | TrainerController._write_training_metrics | def _write_training_metrics(self):
"""
Write all CSV metrics
:return:
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Write all CSV metrics
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/trainer_controller.py | TrainerController._export_graph | def _export_graph(self):
"""
Exports latest saved models to .nn format for Unity embedding.
"""
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Exports latest saved models to .nn format for Unity embedding.
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/trainer_controller.py | TrainerController.initialize_trainers | def initialize_trainers(self, trainer_config: Dict[str, Dict[str, str]]):
"""
Initialization of the trainers
:param trainer_config: The configurations of the trainers
"""
trainer_parameters_dict = {}
for brain_name in self.external_brains:
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/trainer_controller.py | TrainerController._reset_env | def _reset_env(self, env: BaseUnityEnvironment):
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Returns:
A Data structure corresponding to the initial reset state of the
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"""
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A Data structure corresponding to the initial reset state of the
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Unity-Technologies/ml-agents | ml-agents-envs/mlagents/envs/socket_communicator.py | SocketCommunicator.close | def close(self):
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Sends a shutdown signal to the unity environment, and closes the socket connection.
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/barracuda.py | fuse_batchnorm_weights | def fuse_batchnorm_weights(gamma, beta, mean, var, epsilon):
# https://github.com/Tencent/ncnn/blob/master/src/layer/batchnorm.cpp
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...
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/barracuda.py | rnn | def rnn(name, input, state, kernel, bias, new_state, number_of_gates = 2):
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/ppo/trainer.py | PPOTrainer.increment_step_and_update_last_reward | def increment_step_and_update_last_reward(self):
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/ppo/trainer.py | PPOTrainer.construct_curr_info | def construct_curr_info(self, next_info: BrainInfo) -> BrainInfo:
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Unity-Technologies/ml-agents | ml-agents/mlagents/trainers/ppo/trainer.py | PPOTrainer.add_experiences | def add_experiences(self, curr_all_info: AllBrainInfo, next_all_info: AllBrainInfo, take_action_outputs):
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Returns: observation (object/list): the initial observation of the
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Unity-Technologies/ml-agents | ml-agents-envs/mlagents/envs/brain.py | BrainInfo.from_agent_proto | def from_agent_proto(agent_info_list, brain_params):
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apache/incubator-superset | superset/views/dashboard.py | Dashboard.new | def new(self):
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TaggedObject.object_type == object_type,
Tag... | [
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apache/incubator-superset | superset/utils/import_datasource.py | import_datasource | def import_datasource(
session,
i_datasource,
lookup_database,
lookup_datasource,
import_time):
"""Imports the datasource from the object to the database.
Metrics and columns and datasource will be overrided if exists.
This function can be used to import/export das... | python | def import_datasource(
session,
i_datasource,
lookup_database,
lookup_datasource,
import_time):
"""Imports the datasource from the object to the database.
Metrics and columns and datasource will be overrided if exists.
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apache/incubator-superset | superset/migrations/env.py | run_migrations_online | def run_migrations_online():
"""Run migrations in 'online' mode.
In this scenario we need to create an Engine
and associate a connection with the context.
"""
# this callback is used to prevent an auto-migration from being generated
# when there are no changes to the schema
# reference: h... | python | def run_migrations_online():
"""Run migrations in 'online' mode.
In this scenario we need to create an Engine
and associate a connection with the context.
"""
# this callback is used to prevent an auto-migration from being generated
# when there are no changes to the schema
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apache/incubator-superset | superset/viz.py | BaseViz.get_df | def get_df(self, query_obj=None):
"""Returns a pandas dataframe based on the query object"""
if not query_obj:
query_obj = self.query_obj()
if not query_obj:
return None
self.error_msg = ''
timestamp_format = None
if self.datasource.type == 'tabl... | python | def get_df(self, query_obj=None):
"""Returns a pandas dataframe based on the query object"""
if not query_obj:
query_obj = self.query_obj()
if not query_obj:
return None
self.error_msg = ''
timestamp_format = None
if self.datasource.type == 'tabl... | [
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apache/incubator-superset | superset/viz.py | BaseViz.query_obj | def query_obj(self):
"""Building a query object"""
form_data = self.form_data
self.process_query_filters()
gb = form_data.get('groupby') or []
metrics = self.all_metrics or []
columns = form_data.get('columns') or []
groupby = []
for o in gb + columns:
... | python | def query_obj(self):
"""Building a query object"""
form_data = self.form_data
self.process_query_filters()
gb = form_data.get('groupby') or []
metrics = self.all_metrics or []
columns = form_data.get('columns') or []
groupby = []
for o in gb + columns:
... | [
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apache/incubator-superset | superset/viz.py | BaseViz.cache_key | def cache_key(self, query_obj, **extra):
"""
The cache key is made out of the key/values in `query_obj`, plus any
other key/values in `extra`.
We remove datetime bounds that are hard values, and replace them with
the use-provided inputs to bounds, which may be time-relative (as ... | python | def cache_key(self, query_obj, **extra):
"""
The cache key is made out of the key/values in `query_obj`, plus any
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apache/incubator-superset | superset/viz.py | BaseViz.data | def data(self):
"""This is the data object serialized to the js layer"""
content = {
'form_data': self.form_data,
'token': self.token,
'viz_name': self.viz_type,
'filter_select_enabled': self.datasource.filter_select_enabled,
}
return conte... | python | def data(self):
"""This is the data object serialized to the js layer"""
content = {
'form_data': self.form_data,
'token': self.token,
'viz_name': self.viz_type,
'filter_select_enabled': self.datasource.filter_select_enabled,
}
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apache/incubator-superset | superset/viz.py | HistogramViz.query_obj | def query_obj(self):
"""Returns the query object for this visualization"""
d = super().query_obj()
d['row_limit'] = self.form_data.get(
'row_limit', int(config.get('VIZ_ROW_LIMIT')))
numeric_columns = self.form_data.get('all_columns_x')
if numeric_columns is None:
... | python | def query_obj(self):
"""Returns the query object for this visualization"""
d = super().query_obj()
d['row_limit'] = self.form_data.get(
'row_limit', int(config.get('VIZ_ROW_LIMIT')))
numeric_columns = self.form_data.get('all_columns_x')
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apache/incubator-superset | superset/viz.py | HistogramViz.get_data | def get_data(self, df):
"""Returns the chart data"""
chart_data = []
if len(self.groupby) > 0:
groups = df.groupby(self.groupby)
else:
groups = [((), df)]
for keys, data in groups:
chart_data.extend([{
'key': self.labelify(keys,... | python | def get_data(self, df):
"""Returns the chart data"""
chart_data = []
if len(self.groupby) > 0:
groups = df.groupby(self.groupby)
else:
groups = [((), df)]
for keys, data in groups:
chart_data.extend([{
'key': self.labelify(keys,... | [
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