问题描述
我正在尝试使用 tune 程序包进行超参数调整雷的.
I am trying to do a hyper parameter tuning with the tune package of Ray.
下面显示的是我的代码:
Shown below is my code:
# 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.另请注意，示例算法由ray给出的在我的机器上工作正常.
When I try to run this, I am getting an eror. Shown below is the stack trace. This is my first time using Tune so I am not sure what I'm doing wrong here. Also note that the example algorithm given by ray works fine on my machine.
在处理上述异常期间，发生了另一个异常:
During handling of the above exception, another exception occurred:
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，name) _pickle.PicklingError:无法腌制.WrappedFunc'>:不是 发现为ray.tune.trainable.wrap_function..WrappedFunc
Traceback (most recent call last): File "/home/suleka/anaconda3/lib/python3.6/site-packages/ray/cloudpickle/cloudpickle.py", line 639, in save_global return Pickler.save_global(self, obj, name=name) File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 922, in save_global (obj, module_name, name)) _pickle.PicklingError: Can't pickle .WrappedFunc'>: it's not found as ray.tune.trainable.wrap_function..WrappedFunc
在处理上述异常期间，发生了另一个异常:
During handling of the above exception, another exception occurred:
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"， save_dynamic_class中的第495行 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"， save_dynamic_class中的第495行 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
Traceback (most recent call last): File "/home/suleka/Documents/sales_prediction/auto_LSTM_withoutZero.py", line 322, in register_trainable('train_mnist', train) File "/home/suleka/anaconda3/lib/python3.6/site-packages/ray/tune/registry.py", line 38, in register_trainable _global_registry.register(TRAINABLE_CLASS, name, trainable) File "/home/suleka/anaconda3/lib/python3.6/site-packages/ray/tune/registry.py", line 77, in register self._to_flush[(category, key)] = pickle.dumps(value) File "/home/suleka/anaconda3/lib/python3.6/site-packages/ray/cloudpickle/cloudpickle.py", line 881, in dumps cp.dump(obj) File "/home/suleka/anaconda3/lib/python3.6/site-packages/ray/cloudpickle/cloudpickle.py", line 268, in dump return Pickler.dump(self, obj) File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 409, in dump self.save(obj) File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/suleka/anaconda3/lib/python3.6/site-packages/ray/cloudpickle/cloudpickle.py", line 648, in save_global return self.save_dynamic_class(obj) File "/home/suleka/anaconda3/lib/python3.6/site-packages/ray/cloudpickle/cloudpickle.py", line 495, in save_dynamic_class save(clsdict) File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 847, in _batch_setitems save(v) File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/suleka/anaconda3/lib/python3.6/site-packages/ray/cloudpickle/cloudpickle.py", line 410, in save_function self.save_function_tuple(obj) File "/home/suleka/anaconda3/lib/python3.6/site-packages/ray/cloudpickle/cloudpickle.py", line 553, in save_function_tuple save(state) File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 847, in _batch_setitems save(v) File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 781, in save_list self._batch_appends(obj) File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 808, in _batch_appends save(tmp[0]) File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/suleka/anaconda3/lib/python3.6/site-packages/ray/cloudpickle/cloudpickle.py", line 405, in save_function self.save_function_tuple(obj) File "/home/suleka/anaconda3/lib/python3.6/site-packages/ray/cloudpickle/cloudpickle.py", line 553, in save_function_tuple save(state) File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 847, in _batch_setitems save(v) File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 847, in _batch_setitems save(v) File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/suleka/anaconda3/lib/python3.6/site-packages/ray/cloudpickle/cloudpickle.py", line 405, in save_function self.save_function_tuple(obj) File "/home/suleka/anaconda3/lib/python3.6/site-packages/ray/cloudpickle/cloudpickle.py", line 553, in save_function_tuple save(state) File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 847, in _batch_setitems save(v) File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 847, in _batch_setitems save(v) File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/suleka/anaconda3/lib/python3.6/site-packages/ray/cloudpickle/cloudpickle.py", line 405, in save_function self.save_function_tuple(obj) File "/home/suleka/anaconda3/lib/python3.6/site-packages/ray/cloudpickle/cloudpickle.py", line 553, in save_function_tuple save(state) File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 847, in _batch_setitems save(v) File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 847, in _batch_setitems save(v) File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 521, in save self.save_reduce(obj=obj, *rv) File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 605, in save_reduce save(cls) File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/suleka/anaconda3/lib/python3.6/site-packages/ray/cloudpickle/cloudpickle.py", line 636, in save_global return self.save_dynamic_class(obj) File "/home/suleka/anaconda3/lib/python3.6/site-packages/ray/cloudpickle/cloudpickle.py", line 495, in save_dynamic_class save(clsdict) File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 847, in _batch_setitems save(v) File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 521, in save self.save_reduce(obj=obj, *rv) File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 634, in save_reduce save(state) File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 847, in _batch_setitems save(v) File "/home/suleka/anaconda3/lib/python3.6/pickle.py", line 496, in save rv = reduce(self.proto) TypeError: can't pickle _thread.RLock objects
推荐答案
您将必须在def train(config, reporter=None)函数中调用rnn_config = RNNconfig().最重要的是，tf.Graph()需要在train中初始化，因为它不容易被酸洗.
You will have to call rnn_config = RNNconfig() in def train(config, reporter=None) function. Most importantly, the tf.Graph() needs to be initialized within train because it is not easily pickleable.
请注意，您的其余代码也可能需要相应地进行调整.
Note that the rest of your code may also need to be adjusted accordingly.
这篇关于为python运行ray packge调优时无法腌制_thread.RLock对象(超级参数调整)的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！