55 lines
1.3 KiB
Python
55 lines
1.3 KiB
Python
import os
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import matplotlib.pyplot as plt
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import numpy as np
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import tensorflow as tf
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from tensorflow import keras
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from tensorflow.keras import layers
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import pandas as pd
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model_name="model"
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train=pd.read_csv('train.csv', header=None, skiprows=1)
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indexNames = train[train[1] ==2].index
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train.drop(indexNames, inplace=True)
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cols=[0,2,3]
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X=train[cols].to_numpy()
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y=train[1].to_numpy()
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X=np.asarray(X).astype('float32')
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model = keras.Sequential(name="winner")
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model.add(keras.Input(shape=(3), name="game_info"))
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model.add(layers.Dense(4, activation="relu", name="layer1"))
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model.add(layers.Dense(8, activation="relu", name="layer2"))
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model.add(layers.Dense(8, activation="relu", name="layer3"))
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model.add(layers.Dense(5, activation="relu", name="layer4"))
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model.add(layers.Dense(1, activation="relu", name="output"))
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model.compile(
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optimizer=keras.optimizers.Adam(),
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loss=keras.losses.MeanSquaredError(),
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)
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history = model.fit(
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X,
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y,
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batch_size=16,
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epochs=15,)
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model.save(model_name)
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test=pd.read_csv('test.csv', header=None, skiprows=1)
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cols=[0,2,3]
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indexNames = test[test[1] ==2].index
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test.drop(indexNames, inplace=True)
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X_test=test[cols].to_numpy()
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y_test=test[1].to_numpy()
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X_test=np.asarray(X_test).astype('float32')
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predictions=model.predict(X_test)
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pd.DataFrame(predictions).to_csv('results.csv', sep='\t', index=False, header=False)
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