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refactor hyperband implementation

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This commit is contained in:
René Knaebel 2017-09-29 22:59:57 +02:00
parent 090c89a127
commit 605447440f
4 changed files with 81 additions and 47 deletions
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42
hyperband.py
View File

@ -3,13 +3,15 @@
# https://arxiv.org/pdf/1603.06560.pdf
import logging
import random
from math import log, ceil
from math import ceil, log
from random import random as rng
from time import time, ctime
from time import ctime, time
import numpy as np
from keras.callbacks import EarlyStopping
import models
from main import create_model
logger = logging.getLogger('logger')
@ -22,10 +24,10 @@ def sample_params(param_distribution: dict):
class Hyperband:
def __init__(self, param_distribution, X, y):
def __init__(self, param_distribution, X, y, max_iter=81):
self.get_params = lambda: sample_params(param_distribution)
self.max_iter = 81 # maximum iterations per configuration
self.max_iter = max_iter # maximum iterations per configuration
self.eta = 3 # defines configuration downsampling rate (default = 3)
self.logeta = lambda x: log(x) / log(self.eta)
@ -42,19 +44,31 @@ class Hyperband:
def try_params(self, n_iterations, params):
n_iterations = int(round(n_iterations))
embedding, model = models.get_models_by_params(params)
embedding, model, new_model = models.get_models_by_params(params)
model = create_model(model, params["output"])
new_model = create_model(new_model, params["output"])
if params["type"] in ("inter", "staggered"):
model = new_model
callbacks = [EarlyStopping(monitor='val_loss',
patience=5,
verbose=False)]
model.compile(optimizer='adam',
loss='categorical_crossentropy',
loss='binary_crossentropy',
metrics=['accuracy'])
history = model.fit(self.X,
self.y,
batch_size=params["batch_size"],
epochs=n_iterations,
callbacks=callbacks,
shuffle=True,
validation_split=0.2)
validation_split=0.4)
return {"loss": history.history['loss'][-1]}
return {"loss": history.history['val_loss'][-1], "early_stop": True}
# can be called multiple times
def run(self, skip_last=0, dry_run=False):
@ -68,7 +82,7 @@ class Hyperband:
r = self.max_iter * self.eta ** (-s)
# n random configurations
T = [self.get_params() for _ in range(n)]
random_configs = [self.get_params() for _ in range(n)]
for i in range((s + 1) - int(skip_last)): # changed from s + 1
@ -79,16 +93,16 @@ class Hyperband:
n_iterations = r * self.eta ** (i)
logger.info("\n*** {} configurations x {:.1f} iterations each".format(
n_configs, n_iterations))
n_configs, n_iterations))
val_losses = []
early_stops = []
for t in T:
for t in random_configs:
self.counter += 1
logger.info("\n{} | {} | lowest loss so far: {:.4f} (run {})\n".format(
self.counter, ctime(), self.best_loss, self.best_counter))
self.counter, ctime(), self.best_loss, self.best_counter))
start_time = time()
@ -125,7 +139,7 @@ class Hyperband:
# select a number of best configurations for the next loop
# filter out early stops, if any
indices = np.argsort(val_losses)
T = [T[i] for i in indices if not early_stops[i]]
T = T[0:int(n_configs / self.eta)]
random_configs = [random_configs[i] for i in indices if not early_stops[i]]
random_configs = random_configs[0:int(n_configs / self.eta)]
return self.results

33
main.py
View File

@ -1,4 +1,3 @@
import json
import logging
import os
@ -100,33 +99,39 @@ def main_paul_best():
def main_hyperband():
params = {
# static params
"type": ["paul"],
"type": [args.model_type],
"depth": [args.model_depth],
"output": [args.model_output],
"batch_size": [args.batch_size],
"window_size": [10],
"domain_length": [40],
"flow_features": [3],
"input_length": [40],
# model params
"embedding_size": [8, 16, 32, 64, 128, 256],
"filter_embedding": [8, 16, 32, 64, 128, 256],
"embedding_size": [2 ** x for x in range(3, 7)],
"filter_embedding": [2 ** x for x in range(1, 10)],
"kernel_embedding": [1, 3, 5, 7, 9],
"hidden_embedding": [8, 16, 32, 64, 128, 256],
"dense_embedding": [2 ** x for x in range(4, 10)],
"dropout": [0.5],
"domain_features": [8, 16, 32, 64, 128, 256],
"filter_main": [8, 16, 32, 64, 128, 256],
"kernels_main": [1, 3, 5, 7, 9],
"dense_main": [8, 16, 32, 64, 128, 256],
"filter_main": [2 ** x for x in range(1, 10)],
"kernel_main": [1, 3, 5, 7, 9],
"dense_main": [2 ** x for x in range(1, 12)],
}
logger.info("create training dataset")
domain_tr, flow_tr, name_tr, client_tr, server_tr = dataset.load_or_generate_h5data(args.train_h5data,
args.train_data,
args.domain_length, args.window)
domain_tr, flow_tr, name_tr, client_tr, server_windows_tr = dataset.load_or_generate_h5data(args.train_h5data,
args.train_data,
args.domain_length,
args.window)
server_tr = np.max(server_windows_tr, axis=1)
if args.model_type in ("inter", "staggered"):
server_tr = np.expand_dims(server_windows_tr, 2)
hp = hyperband.Hyperband(params,
[domain_tr, flow_tr],
[client_tr, server_tr])
results = hp.run()
json.dump(results, open("hyperband.json"))
joblib.dump(results, "hyperband.joblib")
def main_train(param=None):

5
models/__init__.py
View File

@ -17,7 +17,6 @@ def get_models_by_params(params: dict):
dropout = params.get("dropout")
# mainly prediction model
flow_features = params.get("flow_features")
domain_features = params.get("domain_features")
window_size = params.get("window_size")
domain_length = params.get("domain_length")
filter_main = params.get("filter_main")
@ -36,10 +35,10 @@ def get_models_by_params(params: dict):
embedding_model = networks.get_embedding(embedding_size, input_length, filter_embedding, kernel_embedding,
hidden_embedding, 0.5)
old_model = networks.get_model(0.25, flow_features, domain_features, window_size, domain_length,
old_model = networks.get_model(0.25, flow_features, hidden_embedding, window_size, domain_length,
filter_main, kernel_main, dense_dim, embedding_model, model_output)
new_model = networks.get_new_model(0.25, flow_features, domain_features, window_size, domain_length,
new_model = networks.get_new_model(0.25, flow_features, hidden_embedding, window_size, domain_length,
filter_main, kernel_main, dense_dim, embedding_model, model_output)
return embedding_model, old_model, new_model

16
rerun_models.sh Normal file
View File

@ -0,0 +1,16 @@
#!/usr/bin/env bash
SRC=$1
DEST=$2
DATADIR=$3
INIT=$4
EPOCHS=$5
BS=128
for i in `ls -d $SRC*/`
do
echo "retrain model in ${i}"
name=$(basename $i)
python3 main.py --mode retrain --model_src ${i} --model_dest ${DEST}/${name} --init_epoch $INIT --epochs $EPOCHS --batch $BS --train ${DATADIR}
done