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# Copyright 2018 The TensorFlow Authors All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Evaluation utility functions.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
from collections import namedtuple
logging = tf.logging
import gin.tf
@gin.configurable
def evaluate_checkpoint_repeatedly(checkpoint_dir,
evaluate_checkpoint_fn,
eval_interval_secs=600,
max_number_of_evaluations=None,
checkpoint_timeout=None,
timeout_fn=None):
"""Evaluates a checkpointed model at a set interval."""
if max_number_of_evaluations is not None and max_number_of_evaluations <= 0:
raise ValueError(
'`max_number_of_evaluations` must be either None or a positive number.')
number_of_evaluations = 0
for checkpoint_path in tf.contrib.training.checkpoints_iterator(
checkpoint_dir,
min_interval_secs=eval_interval_secs,
timeout=checkpoint_timeout,
timeout_fn=timeout_fn):
retries = 3
for _ in range(retries):
try:
should_stop = evaluate_checkpoint_fn(checkpoint_path)
break
except tf.errors.DataLossError as e:
logging.warn(
'Encountered a DataLossError while evaluating a checkpoint. This '
'can happen when reading a checkpoint before it is fully written. '
'Retrying...'
)
time.sleep(2.0)
def compute_model_loss(sess, model_rollout_fn, states, actions):
"""Computes model loss."""
preds, losses = [], []
preds.append(states[0])
losses.append(0)
for state, action in zip(states[1:], actions[1:]):
pred = model_rollout_fn(sess, preds[-1], action)
loss = np.sqrt(np.sum((state - pred) ** 2))
preds.append(pred)
losses.append(loss)
return preds, losses
def compute_average_reward(sess, env_base, step_fn, gamma, num_steps,
num_episodes):
"""Computes the discounted reward for a given number of steps.
Args:
sess: The tensorflow session.
env_base: A python environment.
step_fn: A function that takes in `sess` and returns a list of
[state, action, reward, discount, transition_type] values.
gamma: discounting factor to apply to the reward.
num_steps: number of steps to compute the reward over.
num_episodes: number of episodes to average the reward over.
Returns:
average_reward: a scalar of discounted reward.
last_reward: last reward received.
"""
average_reward = 0
average_last_reward = 0
average_meta_reward = 0
average_last_meta_reward = 0
average_success = 0.
states, actions = None, None
for i in range(num_episodes):
env_base.end_episode()
env_base.begin_episode()
(reward, last_reward, meta_reward, last_meta_reward,
states, actions) = compute_reward(
sess, step_fn, gamma, num_steps)
s_reward = last_meta_reward # Navigation
success = (s_reward > -5.0) # When using diff=False
logging.info('Episode = %d, reward = %s, meta_reward = %f, '
'last_reward = %s, last meta_reward = %f, success = %s',
i, reward, meta_reward, last_reward, last_meta_reward,
success)
average_reward += reward
average_last_reward += last_reward
average_meta_reward += meta_reward
average_last_meta_reward += last_meta_reward
average_success += success
average_reward /= num_episodes
average_last_reward /= num_episodes
average_meta_reward /= num_episodes
average_last_meta_reward /= num_episodes
average_success /= num_episodes
return (average_reward, average_last_reward,
average_meta_reward, average_last_meta_reward,
average_success,
states, actions)
def compute_reward(sess, step_fn, gamma, num_steps):
"""Computes the discounted reward for a given number of steps.
Args:
sess: The tensorflow session.
step_fn: A function that takes in `sess` and returns a list of
[state, action, reward, discount, transition_type] values.
gamma: discounting factor to apply to the reward.
num_steps: number of steps to compute the reward over.
Returns:
reward: cumulative discounted reward.
last_reward: reward received at final step.
"""
total_reward = 0
total_meta_reward = 0
gamma_step = 1
states = []
actions = []
for _ in range(num_steps):
state, action, transition_type, reward, meta_reward, discount, _, _ = step_fn(sess)
total_reward += reward * gamma_step * discount
total_meta_reward += meta_reward * gamma_step * discount
gamma_step *= gamma
states.append(state)
actions.append(action)
return (total_reward, reward, total_meta_reward, meta_reward,
states, actions)
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