💰
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@ -1,5 +1,5 @@
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FROM ubuntu:22.04
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RUN apt update && apt install -y vim make python3 python3-pip python-is-python3 gcc g++ golang wget unzip git
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RUN pip install pandas matplotlib scikit-learn
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RUN pip install pandas matplotlib scikit-learn tensorflow
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CMD "bash"
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@ -8,7 +8,7 @@ node {
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checkout([$class: 'GitSCM', branches: [[name: 'ztm']], extensions: [], userRemoteConfigs: [[url: 'https://git.wmi.amu.edu.pl/s452639/ium_452639']]])
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sh 'cd src; ./prepare-ztm-data.sh'
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archiveArtifacts artifacts: 'src/stop_times.normalized.tsv,src/stop_times.train.tsv,src/stop_times.test.tsv,src/stop_times.valid.tsv',
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archiveArtifacts artifacts: 'src/stop_times.normalized.tsv,src/stop_times.train.tsv,src/stop_times.test.tsv,src/stop_times.valid.tsv,src/stop_times.categories.tsv',
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followSymlinks: false
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}
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}
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2
run.sh
2
run.sh
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set -xe
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docker build -t ium .
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docker run -it ium
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docker run -v .:/ium/ -it ium
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2
src/.gitignore
vendored
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2
src/.gitignore
vendored
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model.keras
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pics
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@ -4,7 +4,7 @@ from sklearn.model_selection import train_test_split
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data = pd.read_csv('./stop_times.normalized.tsv', sep='\t')
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train, test = train_test_split(data, test_size=0.5)
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train, test = train_test_split(data, test_size=0.8)
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valid, test = train_test_split(test, test_size=0.5)
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train.to_csv('stop_times.train.tsv', sep='\t')
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15
src/tf_test.py
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15
src/tf_test.py
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from tf_train import *
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import numpy as np
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def test():
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global model, le
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test_x, test_y, _ = load_data('./stop_times.test.tsv', le)
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test_x = tf.convert_to_tensor(test_x, dtype=tf.float32)
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test_y = tf.convert_to_tensor(test_y)
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model = tf.keras.models.load_model('model.keras')
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pd.DataFrame(model.predict(test_x), columns=le.classes_).to_csv('stop_times.predictions.tsv', sep='\t')
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if __name__ == "__main__":
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test()
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@ -22,20 +22,22 @@ def load_data(path: str, le: LabelEncoder):
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num_classes = len(le.classes_)
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model = tf.keras.Sequential([
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def train():
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global le
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model = tf.keras.Sequential([
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tf.keras.layers.Input(shape=(2,)),
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tf.keras.layers.Dense(4 * num_classes, activation='relu'),
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tf.keras.layers.Dense(4 * num_classes, activation='relu'),
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tf.keras.layers.Dense(4 * num_classes, activation='relu'),
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tf.keras.layers.Dense(num_classes, activation='softmax')
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])
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])
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model.compile(optimizer='adam',
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model.compile(optimizer='adam',
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loss=tf.keras.losses.SparseCategoricalCrossentropy(),
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metrics=['accuracy'])
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def train():
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global model, le
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train_x, train_y, _ = load_data('./stop_times.train.tsv', le)
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train_x = tf.convert_to_tensor(train_x, dtype=tf.float32)
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train_y = tf.convert_to_tensor(train_y)
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@ -50,22 +52,7 @@ def train():
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with open('history', 'w') as f:
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print(repr(history), file=f)
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model.save_weights('model.ckpt')
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model.save('model.keras')
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def test():
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global model, le
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test_x, test_y, _ = load_data('./stop_times.test.tsv', le)
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test_x = tf.convert_to_tensor(test_x, dtype=tf.float32)
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test_y = tf.convert_to_tensor(test_y)
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model.load_weights('model.ckpt')
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model.evaluate(test_x, test_y)
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SUBCOMMANDS = {
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"test": test,
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"train": train,
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}
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import sys
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assert len(sys.argv) == 2
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assert sys.argv[1] in SUBCOMMANDS.keys()
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SUBCOMMANDS[sys.argv[1]]()
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if __name__ == "__main__":
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train()
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