From 89c12cd0e4783f0f5b3cdbce719259922ba9e1f2 Mon Sep 17 00:00:00 2001 From: Newnius Date: Sat, 2 May 2020 19:07:01 +0800 Subject: [PATCH] update --- .idea/workspace.xml | 78 +++++++++++++++++++++++---------------------- serve.py | 10 ++++-- 2 files changed, 47 insertions(+), 41 deletions(-) diff --git a/.idea/workspace.xml b/.idea/workspace.xml index 04f9975..c5105eb 100644 --- a/.idea/workspace.xml +++ b/.idea/workspace.xml @@ -1,7 +1,9 @@ - + + + @@ -227,7 +229,7 @@ - + @@ -268,12 +270,12 @@ - @@ -377,16 +379,6 @@ - - - - - - - - - - @@ -407,25 +399,35 @@ - + - - + + - + + + + - + - - + + - - - - + + + + + + + + + + + diff --git a/serve.py b/serve.py index 0dc930c..42a6336 100644 --- a/serve.py +++ b/serve.py @@ -21,6 +21,7 @@ import numpy import random import traceback from keras.models import load_model +from sklearn.externals import joblib PORT_NUMBER = 8080 lock = Lock() @@ -127,8 +128,9 @@ def train_models(job): model = fit_lstm(train_trimmed, batch_size, 30, 4) model.save('./data/checkpoint-' + job) + scaler_filename = './data/checkpoint-' + job + "-scaler.save" + joblib.dump(scaler, scaler_filename) - models[job]['scaler'] = scaler models[job]['batch_size'] = batch_size models[job]['lock'].release() @@ -145,6 +147,8 @@ def predict(job, seq): 'value': 0, } model = load_model('./data/checkpoint-' + job) + scaler_filename = './data/checkpoint-' + job + "-scaler.save" + scaler = joblib.load(scaler_filename) file = './data/' + job + '.' + str(random.randint(1000, 9999)) + '.csv' df = pd.read_csv('./data/' + job + '.csv', usecols=['seq', 'value']) @@ -174,7 +178,7 @@ def predict(job, seq): print(test) test = test.reshape(test.shape[0], test.shape[1]) - test_scaled = models[job]['scaler'].transform(test) + test_scaled = scaler.transform(test) # forecast the entire training dataset to build up state for forecasting test_reshaped = test_scaled[:, 0:-1] @@ -185,7 +189,7 @@ def predict(job, seq): yhat = output[i, 0] X = test_scaled[i, 0:-1] # invert scaling - yhat = invert_scale(models[job]['scaler'], X, yhat) + yhat = invert_scale(scaler, X, yhat) # invert differencing yhat = inverse_difference(raw_values, yhat, len(test_scaled) + 1 - i) # store forecast