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Want to execute for cpu4.csv of Donut dataset #18
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hi, dguhanus: Have you solved this problem? I meet the same FailedPreconditionError right now. |
hi:
Sorry,I haven't solved this problem all the time. And then I started doing something else. I'm sorry I can't help you.
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…------------------ Original ------------------
From: backhamburger <[email protected]>
Date: Wed,Dec 2,2020 10:22 PM
To: NetManAIOps/donut <[email protected]>
Cc: qpan7777 <[email protected]>, Comment <[email protected]>
Subject: Re: [NetManAIOps/donut] Want to execute for cpu4.csv of Donut dataset (#18)
hi, dguhanus:
Have you solved this problem? I meet the same FailedPreconditionError right now.
Thank you sincerely.
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Hi
I could not solve the problem.
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hi, dguhanus:
Have you solved this problem? I meet the same FailedPreconditionError
right now.
Thank you sincerely.
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Hi Haowen Xu,
I am trying to run the Donut sample dataset cpu4.csv
I have done these following things for invoking cpu4.csv
df = pd.read_csv("sample_data/cpu4.csv")
timestamp, values, labels = df.timestamp, df.value, df.label
import tensorflow as tf
from donut import Donut
from tensorflow import keras as K
from tfsnippet.modules import Sequential
We build the entire model within the scope of
model_vs
,it should hold exactly all the variables of
model
, includingthe variables created by Keras layers.
with tf.variable_scope('model') as model_vs:
model = Donut(
h_for_p_x=Sequential([
K.layers.Dense(100, kernel_regularizer=K.regularizers.l2(0.001),
activation=tf.nn.relu),
K.layers.Dense(100, kernel_regularizer=K.regularizers.l2(0.001),
activation=tf.nn.relu),
]),
h_for_q_z=Sequential([
K.layers.Dense(100, kernel_regularizer=K.regularizers.l2(0.001),
activation=tf.nn.relu),
K.layers.Dense(100, kernel_regularizer=K.regularizers.l2(0.001),
activation=tf.nn.relu),
]),
x_dims=120,
z_dims=5,
)
'''
Training of Donut model
'''
from donut import DonutTrainer, DonutPredictor
trainer = DonutTrainer(model=model, model_vs=model_vs)
predictor = DonutPredictor(model)
with tf.Session().as_default():
trainer.fit(train_values, train_labels, train_missing, mean, std)
test_score = predictor.get_score(test_values, test_missing)
I am not able to understand how to set input_x and input_y
I am getting this error message:
FailedPreconditionError: Error while reading resource variable model/sequential_1/forward/_1/dense_3/bias from Container: localhost. This could mean that the variable was uninitialized. Not found: Container localhost does not exist. (Could not find resource: localhost/model/sequential_1/forward/_1/dense_3/bias)
The text was updated successfully, but these errors were encountered: