def reset_keras(model):
"""Reset Keras Session.
https://forums.fast.ai/t/how-could-i-release-gpu-memory-of-keras/2023/18
https://github.com/keras-team/keras/issues/12625
"""
sess = backend.get_session()
backend.clear_session()
sess.close()
try:
del model
except:
pass
gc.collect()
backend.set_session(Session(config=ConfigProto()))
def GenerateModelV1(tf_saved_model_dir, tftrt_saved_model_dir):
"""Generate and convert a model using TFv1 API."""
def SimpleModel():
"""Define model with a TF graph."""
def GraphFn():
input1 = array_ops.placeholder(dtype=dtypes.float32,
shape=[None, 1, 1],
name="input1")
input2 = array_ops.placeholder(dtype=dtypes.float32,
shape=[None, 1, 1],
name="input2")
var = variables.Variable([[[1.0]]],
dtype=dtypes.float32,
name="v1")
out = GetGraph(input1, input2, var)
return g, var, input1, input2, out
g = ops.Graph()
with g.as_default():
return GraphFn()
g, var, input1, input2, out = SimpleModel()
signature_def = signature_def_utils.build_signature_def(
inputs={
"input1": utils.build_tensor_info(input1),
"input2": utils.build_tensor_info(input2)
},
outputs={"output": utils.build_tensor_info(out)},
method_name=signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY)
saved_model_builder = builder.SavedModelBuilder(tf_saved_model_dir)
with Session(graph=g) as sess:
sess.run(var.initializer)
saved_model_builder.add_meta_graph_and_variables(
sess, [tag_constants.SERVING],
signature_def_map={
signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
signature_def
})
saved_model_builder.save()
# Convert TF model to TensorRT
converter = trt_convert.TrtGraphConverter(
input_saved_model_dir=tf_saved_model_dir, is_dynamic_op=True)
converter.convert()
converter.save(tftrt_saved_model_dir)
def run():
# 创建一个变量, 初始化为标量 0.
state = Variable(0, name="counter")
# 创建一个 op, 其作用是使 state 增加 1
one = constant(1)
new_value = add(state, one)
update = assign(state, new_value)
# 启动图后, 变量必须先经过`初始化` (init) op 初始化,
# 首先必须增加一个`初始化` op 到图中.
init_op = initialize_all_variables()
# 启动图, 运行 op
with Session() as sess:
# 运行 'init' op
sess.run(init_op)
# 打印 'state' 的初始值
print(sess.run(state))
# 运行 op, 更新 'state', 并打印 'state'
for _ in range(3):
sess.run(update)
print(sess.run(state))
import random
import numpy as np
from statistics import median, mean
from collections import Counter
from tensorflow.core.protobuf.config_pb2 import ConfigProto, GPUOptions
from tensorflow.python import Session
from tensorflow.python.keras import Sequential
from tensorflow.python.keras.backend import set_session
from tensorflow.python.keras.layers import Dense, Dropout
from tensorflow.python.keras.optimizer_v2.adam import Adam
config = ConfigProto(gpu_options=GPUOptions(
per_process_gpu_memory_fraction=0.8))
config.gpu_options.allow_growth = True
session = Session(config=config)
set_session(session)
LR = 1e-3
env = gym.make("CartPole-v0")
env.reset()
goal_steps = 500
score_requirement = 50
initial_games = 10000
def initial_population():
# [OBS, MOVES]
training_data = []
# all scores:
scores = []
def config_tensorflow():
config = ConfigProto(gpu_options=GPUOptions(
per_process_gpu_memory_fraction=0.8))
config.gpu_options.allow_growth = True
session = Session(config=config)
set_session(session)