pekaalto

## keraslstm.py
"""
Investigate keras-lstm inputs, outputs and weights.
Needs tensorflow 2.0

Note: The explanation of weights matches the CPU-implementation of LSTM-layer.
In GPU-implementation the weights are organized slightly differently
"""

import numpy as np
import tensorflow as tf

## embeddings_and_convs.py
"""
Pekka Aalto 2017

This snippet tries to explain by example what deepmind means
in https://arxiv.org/abs/1708.04782
about embedding on channel axis being equivalent to
one-hot-encoding followed by 1x1 conv.

They write:

## sc2vecenv.py
"""
Almost direct copy from https://github.com/openai/baselines/blob/master/baselines/common/vec_env/subproc_vec_env.py
"""

from multiprocessing import Process, Pipe
from pysc2.env import sc2_env, available_actions_printer


def worker(remote, env_fn_wrapper):
    """
	"""
	Investigate keras-lstm inputs, outputs and weights.
	Needs tensorflow 2.0

	Note: The explanation of weights matches the CPU-implementation of LSTM-layer.
	In GPU-implementation the weights are organized slightly differently
	"""

	import numpy as np
	import tensorflow as tf
	"""
	Pekka Aalto 2017

	This snippet tries to explain by example what deepmind means
	in https://arxiv.org/abs/1708.04782
	about embedding on channel axis being equivalent to
	one-hot-encoding followed by 1x1 conv.

	They write:
	"""
	Almost direct copy from https://github.com/openai/baselines/blob/master/baselines/common/vec_env/subproc_vec_env.py
	"""

	from multiprocessing import Process, Pipe
	from pysc2.env import sc2_env, available_actions_printer


	def worker(remote, env_fn_wrapper):
	"""