ium_s437622/zad5.py
2021-05-15 18:18:35 +02:00

55 lines
1.3 KiB
Python

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
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)