This commit is contained in:
Jakub Pogodziński 2021-05-15 18:18:35 +02:00
parent 718dbe45c7
commit fa2f7b4d1b
11 changed files with 60351 additions and 60335 deletions

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@ -4,6 +4,9 @@ RUN apt update && apt install -y python3 python3-pip
RUN pip3 install kaggle
RUN pip3 install pandas
RUN pip3 install tensorflow
RUN pip3 install numpy
RUN pip3 install matplotlib

230
IUM.ipynb

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5
Jenkinsfile vendored
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@ -2,11 +2,6 @@ pipeline{
agent any
properties([parameters([text(defaultValue: '50', description: 'Number of lines to cutoff', name: 'CUTOFF')])])
stages{
stage('Stage 1'){
steps{
echo 'Hello World!'
}
}
stage('checkout: Check out from version control'){
steps{
git url: 'https://github.com/jfrogdev/project-examples.git'

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@ -0,0 +1,29 @@
pipeline{
agent any
properties([parameters([
buildSelector(
defaultSelector: lastSuccessful(),
description: 'Which build to use for copying artifacts',
name: 'BUILD_SELECTOR')
])])
stages{
stage('checkout: Check out from version control'){
steps{
git credentialsId: 'b4ba8ec9-8fc6-4f68-bf24-695634cec73e', url: 'https://git.wmi.amu.edu.pl/s437622/ium_s437622.git'
}
}
stage('copy artifacts'){
copyArtifacts filter: 'dev.csv, train.csv, test.csv', fingerprintArtifacts: true, projectName: 's437622-create-dataset', selector: buildParameter('BUILD_SELECTOR')
}
stage('sh: Shell Script'){
steps{
./stats.sh
}
}
stage('Archive artifacts'){
steps{
archiveArtifacts 'stats.txt'
}
}
}
}

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@ -1 +1,5 @@
test3
15.05
ML - uczenie działa
przewiduje same zera (czyli nie działa)
wynik jest zapisywany do pliku results.csv
do Dockera dodane są biblioteki potrzebne do ML

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chess.csv

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dev.csv

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test.csv

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train.csv

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51
zad5.py
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@ -1,7 +1,54 @@
import os
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import pandas as pd
print("TensorFlow version: {}".format(tf.__version__))
print("Eager execution: {}".format(tf.executing_eagerly()))
model_name="model"
train=pd.read_csv('train.csv', header=None, skiprows=1)
indexNames = train[train[1] ==2].index
train.drop(indexNames, inplace=True)
cols=[0,2,3]
X=train[cols].to_numpy()
y=train[1].to_numpy()
X=np.asarray(X).astype('float32')
model = keras.Sequential(name="winner")
model.add(keras.Input(shape=(3), name="game_info"))
model.add(layers.Dense(4, activation="relu", name="layer1"))
model.add(layers.Dense(8, activation="relu", name="layer2"))
model.add(layers.Dense(8, activation="relu", name="layer3"))
model.add(layers.Dense(5, activation="relu", name="layer4"))
model.add(layers.Dense(1, activation="relu", name="output"))
model.compile(
optimizer=keras.optimizers.RMSprop(),
loss=keras.losses.MeanSquaredError(),
)
history = model.fit(
X,
y,
batch_size=16,
epochs=15,)
model.save(model_name)
test=pd.read_csv('test.csv', header=None, skiprows=1)
cols=[0,2,3]
indexNames = test[test[1] ==2].index
test.drop(indexNames, inplace=True)
X_test=test[cols].to_numpy()
y_test=test[1].to_numpy()
X_test=np.asarray(X_test).astype('float32')
predictions=model.predict(X_test)
pd.DataFrame(predictions).to_csv('results.csv', sep='\t', index=False, header=False)