我正在尝试使用Ray的tune软件包进行超参数调整。
下面显示的是我的代码：
# Disable linter warnings to maintain consistency with tutorial.
# pylint: disable=invalid-name
# pylint: disable=g-bad-import-order
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import matplotlib as mplt
mplt.use('agg') # Must be before importing matplotlib.pyplot or pylab!
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error
from math import sqrt
import csv
import argparse
import sys
import tempfile
import pandas as pd
import time
import ray
from ray.tune import grid_search, run_experiments, register_trainable
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np
import tensorflow as tf
import ray
from ray import tune
class RNNconfig():
num_steps = 14
lstm_size = 32
batch_size = 8
init_learning_rate = 0.01
learning_rate_decay = 0.99
init_epoch = 5 # 5
max_epoch = 60 # 100 or 50
hidden1_nodes = 30
hidden2_nodes = 15
hidden1_activation = tf.nn.relu
hidden2_activation = tf.nn.relu
lstm_activation = tf.nn.relu
status_reporter = None
FLAGS = None
input_size = 1
num_layers = 1
fileName = 'store2_1.csv'
graph = tf.Graph()
column_min_max = [[0,11000], [1,7]]
columns = ['Sales', 'DayOfWeek','SchoolHoliday', 'Promo']
features = len(columns)
rnn_config = RNNconfig()
def segmentation(data):
seq = [price for tup in data[rnn_config.columns].values for price in tup]
seq = np.array(seq)
# split into items of features
seq = [np.array(seq[i * rnn_config.features: (i + 1) * rnn_config.features])
for i in range(len(seq) // rnn_config.features)]
# split into groups of num_steps
X = np.array([seq[i: i + rnn_config.num_steps] for i in range(len(seq) - rnn_config.num_steps)])
y = np.array([seq[i + rnn_config.num_steps] for i in range(len(seq) - rnn_config.num_steps)])
# get only sales value
y = [[y[i][0]] for i in range(len(y))]
y = np.asarray(y)
return X, y
def scale(data):
for i in range (len(rnn_config.column_min_max)):
data[rnn_config.columns[i]] = (data[rnn_config.columns[i]] - rnn_config.column_min_max[i][0]) / ((rnn_config.column_min_max[i][1]) - (rnn_config.column_min_max[i][0]))
return data
def rescle(test_pred):
prediction = [(pred * (rnn_config.column_min_max[0][1] - rnn_config.column_min_max[0][0])) + rnn_config.column_min_max[0][0] for pred in test_pred]
return prediction
def pre_process():
store_data = pd.read_csv(rnn_config.fileName)
store_data = store_data.drop(store_data[(store_data.Open == 0) & (store_data.Sales == 0)].index)
#
# store_data = store_data.drop(store_data[(store_data.Open != 0) & (store_data.Sales == 0)].index)
# ---for segmenting original data --------------------------------
original_data = store_data.copy()
## train_size = int(len(store_data) * (1.0 - rnn_config.test_ratio))
validation_len = len(store_data[(store_data.Month == 6) & (store_data.Year == 2015)].index)
test_len = len(store_data[(store_data.Month == 7) & (store_data.Year == 2015)].index)
train_size = int(len(store_data) - (validation_len+test_len))
train_data = store_data[:train_size]
validation_data = store_data[(train_size-rnn_config.num_steps): validation_len+train_size]
test_data = store_data[((validation_len+train_size) - rnn_config.num_steps): ]
original_val_data = validation_data.copy()
original_test_data = test_data.copy()
# -------------- processing train data---------------------------------------
scaled_train_data = scale(train_data)
train_X, train_y = segmentation(scaled_train_data)
# -------------- processing validation data---------------------------------------
scaled_validation_data = scale(validation_data)
val_X, val_y = segmentation(scaled_validation_data)
# -------------- processing test data---------------------------------------
scaled_test_data = scale(test_data)
test_X, test_y = segmentation(scaled_test_data)
# ----segmenting original validation data-----------------------------------------------
nonescaled_val_X, nonescaled_val_y = segmentation(original_val_data)
# ----segmenting original test data-----------------------------------------------
nonescaled_test_X, nonescaled_test_y = segmentation(original_test_data)
return train_X, train_y, test_X, test_y, val_X, val_y, nonescaled_test_y,nonescaled_val_y
def generate_batches(train_X, train_y, batch_size):
num_batches = int(len(train_X)) // batch_size
if batch_size * num_batches < len(train_X):
num_batches += 1
batch_indices = range(num_batches)
for j in batch_indices:
batch_X = train_X[j * batch_size: (j + 1) * batch_size]
batch_y = train_y[j * batch_size: (j + 1) * batch_size]
assert set(map(len, batch_X)) == {rnn_config.num_steps}
yield batch_X, batch_y
def mean_absolute_percentage_error(y_true, y_pred):
y_true, y_pred = np.array(y_true), np.array(y_pred)
itemindex = np.where(y_true == 0)
y_true = np.delete(y_true, itemindex)
y_pred = np.delete(y_pred, itemindex)
return np.mean(np.abs((y_true - y_pred) / y_true)) * 100
def RMSPE(y_true, y_pred):
y_true, y_pred = np.array(y_true), np.array(y_pred)
return np.sqrt(np.mean(np.square(((y_true - y_pred) / y_pred)), axis=0))
def deepnn(inputs):
cell = tf.contrib.rnn.LSTMCell(rnn_config.lstm_size, state_is_tuple=True, activation= rnn_config.lstm_activation)
val1, _ = tf.nn.dynamic_rnn(cell, inputs, dtype=tf.float32)
val = tf.transpose(val1, [1, 0, 2])
last = tf.gather(val, int(val.get_shape()[0]) - 1, name="last_lstm_output")
# hidden layer
hidden1 = tf.layers.dense(last, units=rnn_config.hidden1_nodes, activation=rnn_config.hidden2_activation)
hidden2 = tf.layers.dense(hidden1, units=rnn_config.hidden2_nodes, activation=rnn_config.hidden1_activation)
weight = tf.Variable(tf.truncated_normal([rnn_config.hidden2_nodes, rnn_config.input_size]))
bias = tf.Variable(tf.constant(0.1, shape=[rnn_config.input_size]))
prediction = tf.matmul(hidden2, weight) + bias
return prediction
def main():
train_X, train_y, test_X, test_y, val_X, val_y, nonescaled_test_y, nonescaled_val_y = pre_process()
# Create the model
inputs = tf.placeholder(tf.float32, [None, rnn_config.num_steps, rnn_config.features], name="inputs")
targets = tf.placeholder(tf.float32, [None, rnn_config.input_size], name="targets")
learning_rate = tf.placeholder(tf.float32, None, name="learning_rate")
# Build the graph for the deep net
prediction = deepnn(inputs)
with tf.name_scope('loss'):
model_loss = tf.losses.mean_squared_error(targets, prediction)
with tf.name_scope('adam_optimizer'):
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(model_loss)
# train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
graph_location = "checkpoints_sales/sales_pred.ckpt"
# graph_location = tempfile.mkdtemp()
print('Saving graph to: %s' % graph_location)
train_writer = tf.summary.FileWriter(graph_location)
train_writer.add_graph(tf.get_default_graph())
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
learning_rates_to_use = [
rnn_config.init_learning_rate * (
rnn_config.learning_rate_decay ** max(float(i + 1 -rnn_config.init_epoch), 0.0)
) for i in range(rnn_config.max_epoch)]
for epoch_step in range(rnn_config.max_epoch):
current_lr = learning_rates_to_use[epoch_step]
i = 0
for batch_X, batch_y in generate_batches(train_X, train_y, rnn_config.batch_size):
train_data_feed = {
inputs: batch_X,
targets: batch_y,
learning_rate: current_lr,
}
train_loss, _ = sess.run([model_loss, optimizer], train_data_feed)
if i % 10 == 0:
val_data_feed = {
inputs: val_X,
targets: val_y,
learning_rate: 0.0,
}
val_prediction = prediction.eval(feed_dict=val_data_feed)
meanSquaredError = mean_squared_error(val_y, val_prediction)
val_rootMeanSquaredError = sqrt(meanSquaredError)
print('epoch %d, step %d, training accuracy %g' % (i, epoch_step, val_rootMeanSquaredError))
if rnn_config.status_reporter:
rnn_config.status_reporter(timesteps_total= epoch_step, mean_accuracy= val_rootMeanSquaredError)
i += 1
test_data_feed = {
inputs: test_X,
targets: test_y,
learning_rate: 0.0,
}
test_prediction = prediction.eval(feed_dict=val_data_feed)
meanSquaredError = mean_squared_error(val_y, test_prediction)
test_rootMeanSquaredError = sqrt(meanSquaredError)
print('training accuracy %g' % (test_rootMeanSquaredError))
# !!! Entrypoint for ray.tune !!!
def train(config, reporter=None):
rnn_config.status_reporter = reporter
rnn_config.num_steps= getattr(config["num_steps"])
rnn_config.lstm_size = getattr(config["lstm_size"])
rnn_config.hidden1_nodes = getattr(config["hidden1_nodes"])
rnn_config.hidden2_nodes = getattr(config["hidden2_nodees"])
rnn_config.lstm_activation = getattr(tf.nn, config["lstm_activation"])
rnn_config.init_learning_rate = getattr(config["learning_rate"])
rnn_config.hidden1_activation = getattr(tf.nn, config['hidden1_activation'])
rnn_config.hidden2_activation = getattr(tf.nn, config['hidden2_activation'])
rnn_config.learning_rate_decay = getattr(config["learning_rate_decay"])
rnn_config.max_epoch = getattr(config["max_epoch"])
rnn_config.init_epoch = getattr(config["init_epoch"])
rnn_config.batch_size = getattr(config["batch_size"])
parser = argparse.ArgumentParser()
parser.add_argument(
'--data_dir',
type=str,
default='/tmp/tensorflow/mnist/input_data',
help='Directory for storing input data')
rnn_config.FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
# !!! Example of using the ray.tune Python API !!!
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--smoke-test', action='store_true', help='Finish quickly for testing')
args, _ = parser.parse_known_args()
register_trainable('train_mnist', train)
mnist_spec = {
'run': 'train_mnist',
'stop': {
'mean_accuracy': 0.99,
},
'config': {
"num_steps": tune.grid_search([1, 2, 3,4,5,6,7,8,9,10,11,12,13,14,15]),
"lstm_size": tune.grid_search([8,16,32,64,128]),
"hidden1_nodes" : tune.grid_search([4,8,16,32,64]),
"hidden2_nodees" : tune.grid_search([2,4,8,16,32]),
"lstm_activation" : tune.grid_search(['relu', 'elu', 'tanh']),
"learning_rate" : tune.grid_search([0.01,0.1,0.5,0.05]),
"hidden1_activation" : tune.grid_search(['relu', 'elu', 'tanh']),
"hidden2_activation" : tune.grid_search(['relu', 'elu', 'tanh']),
"learning_rate_decay" : tune.grid_search([0.99,0.8,0.7]),
"max_epoch" : tune.grid_search([60,50,100,120,200]),
"init_epoch" : tune.grid_search([5,10,15,20]),
"batch_size" : tune.grid_search([5,8,16,32,64])
},
}
if args.smoke_test:
mnist_spec['stop']['training_iteration'] = 2
ray.init()
run_experiments({'tune_mnist_test': mnist_spec})
当我尝试运行此命令时，我遇到了错误。下面显示的是堆栈跟踪。这是我第一次使用Tune，因此我不确定自己在做什么错。另外请注意，由ray给出的example algorithm在我的机器上可以正常工作。
/home/suleka/anaconda3/lib/python3.6/site-packages/h5py/ init .py：36： FutureWarning：将issubdtype的第二个参数转换为 已弃用
上获取本地属性'wrap_function..WrappedFunc'float至np.floating。将来会被治疗 为np.float64 == np.dtype(float).type。从._conv导入 register_converters作为_register_converters警告：不更新 未安装setproctitle以来的工作程序名称。用安装pip install setproctitle（或ray [debug]）启用监视 工人流程。追溯（最近一次通话）：文件 “ /home/suleka/anaconda3/lib/python3.6/pickle.py”，第918行，在 save_global obj2，parent = _getattribute（模块，名称）文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，行266，在 _getattribute .format（name，obj））AttributeError：无法在在处理上述异常期间，发生了另一个异常：
回溯（最近通话最近）：文件 “ /home/suleka/anaconda3/lib/python3.6/site-packages/ray/cloudpickle/cloudpickle.py”， 第639行，在save_global中 返回Pickler.save_global（self，obj，name = name）文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，第922行，在 save_global （obj，module_name，名称） _pickle.PicklingError：无法腌制.WrappedFunc'>：不是 发现为ray.tune.trainable.wrap_function..WrappedFunc
在处理上述异常期间，发生了另一个异常：
回溯（最近通话最近）：文件 “ /home/suleka/Documents/sales_prediction/auto_LSTM_withoutZero.py”， 322行，在 register_trainable（'train_mnist'，train）文件“ /home/suleka/anaconda3/lib/python3.6/site-packages/ray/tune/registry.py”， 第38行，在register_trainable中 _global_registry.register（TRAINABLE_CLASS，名称，可训练）文件“ /home/suleka/anaconda3/lib/python3.6/site-packages/ray/tune/registry.py”， 第77行，在寄存器中 self._to_flush [（category，key）] = pickle.dumps（value）文件“ /home/suleka/anaconda3/lib/python3.6/site-packages/ray/cloudpickle/cloudpickle.py”， 881号线，转储 cp.dump（obj）文件“ /home/suleka/anaconda3/lib/python3.6/site-packages/ray/cloudpickle/cloudpickle.py”， 268行，转储中 在转储中返回Pickler.dump（self，obj）文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，第409行 self.save（obj）文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，行476，保存 f（self，obj）＃使用显式self文件“ /home/suleka/anaconda3/lib/python3.6/site-packages/ray/cloudpickle/cloudpickle.py”调用未绑定方法， 第648行，在save_global中 返回self.save_dynamic_class（obj）文件“ /home/suleka/anaconda3/lib/python3.6/site-packages/ray/cloudpickle/cloudpickle.py”， 第495行，在save_dynamic_class中 保存（clsdict）文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，行476，保存 f（self，obj）＃使用显式self文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，在第821行中调用未绑定方法 save_dict self._batch_setitems（obj.items（））文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，行847，在 _batch_setitems 保存（v）保存中的文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，第476行 f（self，obj）＃使用显式self文件“ /home/suleka/anaconda3/lib/python3.6/site-packages/ray/cloudpickle/cloudpickle.py”调用未绑定方法， save_function中的第410行 self.save_function_tuple（obj）文件“ /home/suleka/anaconda3/lib/python3.6/site-packages/ray/cloudpickle/cloudpickle.py”， 第553行，在save_function_tuple中 保存（状态）文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，行476，保存 f（self，obj）＃使用显式self文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，在第821行中调用未绑定方法 save_dict self._batch_setitems（obj.items（））文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，行847，在 _batch_setitems 保存（v）保存中的文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，第476行 f（self，obj）＃使用显式self文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，在第781行中调用未绑定方法 save_list self._batch_appends（obj）文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，第808行，在 _batch_appends 保存（tmp [0]）文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，行476，保存 f（self，obj）＃使用显式self文件“ /home/suleka/anaconda3/lib/python3.6/site-packages/ray/cloudpickle/cloudpickle.py”调用未绑定方法， 第405行，在save_function中 self.save_function_tuple（obj）文件“ /home/suleka/anaconda3/lib/python3.6/site-packages/ray/cloudpickle/cloudpickle.py”， 第553行，在save_function_tuple中 保存（状态）文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，行476，保存 f（self，obj）＃使用显式self文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，在第821行中调用未绑定方法 save_dict self._batch_setitems（obj.items（））文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，行847，在 _batch_setitems 保存（v）保存中的文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，第476行 f（self，obj）＃使用显式self文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，在第821行中调用未绑定方法 save_dict self._batch_setitems（obj.items（））文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，行847，在 _batch_setitems 保存（v）保存中的文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，第476行 f（self，obj）＃使用显式self文件“ /home/suleka/anaconda3/lib/python3.6/site-packages/ray/cloudpickle/cloudpickle.py”调用未绑定方法， 第405行，在save_function中 self.save_function_tuple（obj）文件“ /home/suleka/anaconda3/lib/python3.6/site-packages/ray/cloudpickle/cloudpickle.py”， 第553行，在save_function_tuple中 保存（状态）文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，行476，保存 f（self，obj）＃使用显式self文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，在第821行中调用未绑定方法 save_dict self._batch_setitems（obj.items（））文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，行847，在 _batch_setitems 保存（v）保存中的文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，第476行 f（self，obj）＃使用显式self文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，在第821行中调用未绑定方法 save_dict self._batch_setitems（obj.items（））文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，行847，在 _batch_setitems 保存（v）保存中的文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，第476行 f（self，obj）＃使用显式self文件“ /home/suleka/anaconda3/lib/python3.6/site-packages/ray/cloudpickle/cloudpickle.py”调用未绑定方法， 第405行，在save_function中 self.save_function_tuple（obj）文件“ /home/suleka/anaconda3/lib/python3.6/site-packages/ray/cloudpickle/cloudpickle.py”， 第553行，在save_function_tuple中 保存（状态）文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，行476，保存 f（self，obj）＃使用显式self文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，在第821行中调用未绑定方法 save_dict self._batch_setitems（obj.items（））文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，行847，在 _batch_setitems 保存（v）保存中的文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，第476行 f（self，obj）＃使用显式self文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，在第821行中调用未绑定方法 save_dict self._batch_setitems（obj.items（））文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，行847，在 _batch_setitems 保存（v）保存中的文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，第521行 self.save_reduce（obj = obj，* rv）文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，第605行，在 save_reduce 保存（cls）文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，行476，保存 f（self，obj）＃使用显式self文件“ /home/suleka/anaconda3/lib/python3.6/site-packages/ray/cloudpickle/cloudpickle.py”调用未绑定方法， 第636行，在save_global中 返回self.save_dynamic_class（obj）文件“ /home/suleka/anaconda3/lib/python3.6/site-packages/ray/cloudpickle/cloudpickle.py”， 第495行，在save_dynamic_class中 保存（clsdict）文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，行476，保存 f（self，obj）＃使用显式self文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，在第821行中调用未绑定方法 save_dict self._batch_setitems（obj.items（））文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，行847，在 _batch_setitems 保存（v）保存中的文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，第521行 self.save_reduce（obj = obj，* rv）文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，第634行，在 save_reduce 保存（状态）文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，行476，保存 f（self，obj）＃使用显式self文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，在第821行中调用未绑定方法 save_dict self._batch_setitems（obj.items（））文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，行847，在 _batch_setitems 保存（v）保存中的文件“ /home/suleka/anaconda3/lib/python3.6/pickle.py”，第496行 rv = reduce（self.proto）TypeError：无法腌制_thread.RLock对象
答案 0 :(得分：1)
您将必须在rnn_config = RNNconfig()函数中调用def train(config, reporter=None)。最重要的是，tf.Graph()必须在train中初始化，因为它不容易被酸洗。
请注意，您的其余代码也可能需要相应地进行调整。