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https://github.com/newnius/YAO-optimizer.git
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update
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parent
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7
serve.py
7
serve.py
@ -20,6 +20,7 @@ from math import sqrt
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import numpy
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import random
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import traceback
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from keras.models import load_model
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PORT_NUMBER = 8080
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lock = Lock()
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@ -125,7 +126,8 @@ def train_models(job):
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train_trimmed = train_scaled[t1:, :]
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model = fit_lstm(train_trimmed, batch_size, 30, 4)
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models[job]['model'] = model
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model.saver.save('./data/checkpoint-' + job)
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models[job]['scaler'] = scaler
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models[job]['batch_size'] = batch_size
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@ -142,6 +144,7 @@ def predict(job, seq):
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'seq': seq,
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'value': 0,
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}
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model = load_model('./data/checkpoint-' + job)
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file = './data/' + job + '.' + str(random.randint(1000, 9999)) + '.csv'
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df = pd.read_csv('./data/' + job + '.csv', usecols=['seq', 'value'])
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@ -176,7 +179,7 @@ def predict(job, seq):
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# forecast the entire training dataset to build up state for forecasting
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test_reshaped = test_scaled[:, 0:-1]
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test_reshaped = test_reshaped.reshape(len(test_reshaped), 1, lag)
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output = models[job]['model'].predict(test_reshaped, batch_size=batch_size)
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output = model.predict(test_reshaped, batch_size=batch_size)
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predictions = list()
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for i in range(len(output)):
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yhat = output[i, 0]
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