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Removed tensorflow specific code in presets (#59)

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* Add generic layer specification for using in presets

* Modify presets to use the generic scheme
This commit is contained in:
Sina Afrooze
2018-11-06 07:39:29 -08:00
committed by Gal Leibovich
parent 811152126c
commit 93571306c3
19 changed files with 233 additions and 101 deletions
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33
rl_coach/presets/CARLA_CIL.py
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@@ -1,7 +1,6 @@
import os
import numpy as np
import tensorflow as tf
# make sure you have $CARLA_ROOT/PythonClient in your PYTHONPATH
from carla.driving_benchmark.experiment_suites import CoRL2017
from rl_coach.logger import screen
@@ -10,7 +9,7 @@ from rl_coach.agents.cil_agent import CILAgentParameters
from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
from rl_coach.architectures.head_parameters import RegressionHeadParameters
from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
from rl_coach.architectures.tensorflow_components.layers import Conv2d, Dense, BatchnormActivationDropout
from rl_coach.architectures.layers import Conv2d, Dense, BatchnormActivationDropout
from rl_coach.base_parameters import VisualizationParameters
from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps
from rl_coach.environments.carla_environment import CarlaEnvironmentParameters
@@ -46,34 +45,34 @@ agent_params.network_wrappers['main'].input_embedders_parameters = {
'CameraRGB': InputEmbedderParameters(
scheme=[
Conv2d(32, 5, 2),
BatchnormActivationDropout(batchnorm=True, activation_function=tf.tanh),
BatchnormActivationDropout(batchnorm=True, activation_function='tanh'),
Conv2d(32, 3, 1),
BatchnormActivationDropout(batchnorm=True, activation_function=tf.tanh),
BatchnormActivationDropout(batchnorm=True, activation_function='tanh'),
Conv2d(64, 3, 2),
BatchnormActivationDropout(batchnorm=True, activation_function=tf.tanh),
BatchnormActivationDropout(batchnorm=True, activation_function='tanh'),
Conv2d(64, 3, 1),
BatchnormActivationDropout(batchnorm=True, activation_function=tf.tanh),
BatchnormActivationDropout(batchnorm=True, activation_function='tanh'),
Conv2d(128, 3, 2),
BatchnormActivationDropout(batchnorm=True, activation_function=tf.tanh),
BatchnormActivationDropout(batchnorm=True, activation_function='tanh'),
Conv2d(128, 3, 1),
BatchnormActivationDropout(batchnorm=True, activation_function=tf.tanh),
BatchnormActivationDropout(batchnorm=True, activation_function='tanh'),
Conv2d(256, 3, 1),
BatchnormActivationDropout(batchnorm=True, activation_function=tf.tanh),
BatchnormActivationDropout(batchnorm=True, activation_function='tanh'),
Conv2d(256, 3, 1),
BatchnormActivationDropout(batchnorm=True, activation_function=tf.tanh),
BatchnormActivationDropout(batchnorm=True, activation_function='tanh'),
Dense(512),
BatchnormActivationDropout(activation_function=tf.tanh, dropout_rate=0.3),
BatchnormActivationDropout(activation_function='tanh', dropout_rate=0.3),
Dense(512),
BatchnormActivationDropout(activation_function=tf.tanh, dropout_rate=0.3)
BatchnormActivationDropout(activation_function='tanh', dropout_rate=0.3)
],
activation_function='none' # we define the activation function for each layer explicitly
),
'measurements': InputEmbedderParameters(
scheme=[
Dense(128),
BatchnormActivationDropout(activation_function=tf.tanh, dropout_rate=0.5),
BatchnormActivationDropout(activation_function='tanh', dropout_rate=0.5),
Dense(128),
BatchnormActivationDropout(activation_function=tf.tanh, dropout_rate=0.5)
BatchnormActivationDropout(activation_function='tanh', dropout_rate=0.5)
],
activation_function='none' # we define the activation function for each layer explicitly
)
@@ -84,7 +83,7 @@ agent_params.network_wrappers['main'].middleware_parameters = \
FCMiddlewareParameters(
scheme=[
Dense(512),
BatchnormActivationDropout(activation_function=tf.tanh, dropout_rate=0.5)
BatchnormActivationDropout(activation_function='tanh', dropout_rate=0.5)
],
activation_function='none'
)
@@ -94,9 +93,9 @@ agent_params.network_wrappers['main'].heads_parameters = [
RegressionHeadParameters(
scheme=[
Dense(256),
BatchnormActivationDropout(activation_function=tf.tanh, dropout_rate=0.5),
BatchnormActivationDropout(activation_function='tanh', dropout_rate=0.5),
Dense(256),
BatchnormActivationDropout(activation_function=tf.tanh)
BatchnormActivationDropout(activation_function='tanh')
],
num_output_head_copies=4 # follow lane, left, right, straight
)