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This commit is contained in:
Karolina Oparczyk 2021-05-17 20:04:15 +02:00
parent ed16ad4d5c
commit 3d254d98a6
3 changed files with 426 additions and 11 deletions
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2
JenkinsfileNeural
View File

@ -12,7 +12,7 @@ node {
}
stage('Clone repo') {
docker.image("karopa/ium:05").inside {
docker.image("karopa/ium:06").inside {
stage('Test') {
checkout([$class: 'GitSCM', branches: [[name: '*/master']], doGenerateSubmoduleConfigurations: false, extensions: [], submoduleCfg: [], userRemoteConfigs: [[url: 'https://git.wmi.amu.edu.pl/s434765/ium_434765']]])
copyArtifacts fingerprintArtifacts: true, projectName: 's434765-create-dataset', selector: buildParameter("BUILD_SELECTOR")

29
neural_network.py
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@ -1,6 +1,7 @@
import pandas as pd
import numpy as np
from keras import optimizers
from sklearn.metrics import mean_squared_error
from tensorflow import keras
@ -28,18 +29,20 @@ print(max_val_like)
model = keras.Sequential([
keras.layers.Dense(32,input_dim = X.shape[1], activation='relu'),
keras.layers.Dense(64, activation='relu'),
keras.layers.Dense(1,activation='relu'),
keras.layers.Dense(512,input_dim = X.shape[1], activation='relu'),
keras.layers.Dense(256, activation='relu'),
keras.layers.Dense(256, activation='relu'),
keras.layers.Dense(128, activation='relu'),
keras.layers.Dense(1,activation='linear'),
])
model.compile(loss='mean_absolute_error', optimizer="Adam", metrics=['mean_absolute_error'])
model.fit(X, y, epochs=15, validation_split = 0.3)
model.fit(X, y, epochs=30, validation_split = 0.3)
data = pd.read_csv("data_dev", sep=',', error_bad_lines=False).dropna()
X_test = data.loc[:,data.columns == "2805317"].astype(int)
y_test = data.loc[:,data.columns == "198909"].astype(int)
X_test = data.loc[:,data.columns == "440265"].astype(int)
y_test = data.loc[:,data.columns == "21629"].astype(int)
min_val_sub = np.min(X_test)
max_val_sub = np.max(X_test)
@ -49,13 +52,19 @@ print(max_val_sub)
min_val_like = np.min(y_test)
max_val_like = np.max(y_test)
y_test = (y_test - min_val_like) / (max_val_like - min_val_like)
print(min_val_like)
print(max_val_like)
prediction = model.predict(X_test)
prediction_denormalized = []
for pred in prediction:
denorm = pred[0] * (max_val_like[0] - min_val_like[0]) + min_val_like[0]
prediction_denormalized.append(denorm)
f = open("predictions.txt", "w")
for (pred, test) in zip(prediction, y_test.values):
f.write("predicted: %s expected: %s\n" % (str(pred), str(test)))
for (pred, test) in zip(prediction_denormalized, y_test.values):
f.write("predicted: %s expected: %s\n" % (str(pred), str(test[0])))
error = mean_squared_error(y_test, prediction_denormalized)
print(error)

406
predictions.txt Normal file
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@ -0,0 +1,406 @@
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predicted: -1452.4859614851885 expected: 4173
predicted: -1881.536280438304 expected: 1281
predicted: -1719.1801632007118 expected: 637
predicted: 75101.28287255019 expected: 110060
predicted: -2390.4036697989795 expected: 1930