Antrenați și evaluați un model

Deep Learning avansat cu Keras

Zach Deane-Mayer

Data Scientist

Date baschet

Obiectiv: Prezicerea rezultatelor turneului

Date disponibile: clasamentele echipelor de la organizatorii turneului

import pandas as pd
games_tourney = pd.read_csv('datasets/games_tourney.csv')
games_tourney.head()

Out[1]:
   season  team_1  team_2  home  seed_diff  score_diff  score_1  score_2  won
0    1985     288      73     0         -3          -9       41       50    0
1    1985    5929      73     0          4           6       61       55    1
2    1985    9884      73     0          5          -4       59       63    0
3    1985      73     288     0          3           9       50       41    1
4    1985    3920     410     0          1          -9       54       63    0
Deep Learning avansat cu Keras

Date baschet

Intrare: Diferența de clasament

import pandas as pd
games_tourney = pd.read_csv('datasets/games_tourney.csv')
games_tourney.head()

Deep Learning avansat cu Keras

Date baschet

Ieșire: Diferența de scor

import pandas as pd
games_tourney = pd.read_csv('datasets/games_tourney.csv')
games_tourney.head()

Deep Learning avansat cu Keras

Date baschet

Intrare:

  • Diferența de clasament - un număr: -15 la +15
  • Clasamentul variază între 1–16
  • Diferența maximă este 16-1 = +15
  • Diferența minimă este 1-16 = -15

Ieșire:

  • Diferența de scor - un număr: -50 la +50
Deep Learning avansat cu Keras

Date baschet

  • Diferență clasament: 15
    • Echipa 1: 16
    • Echipa 2: 1
  • Diferență clasament: -15
    • Echipa 1: 1
    • Echipa 2: 16

Deep Learning avansat cu Keras

Date baschet

  • Diferență scor: -9
    • Echipa 1: 41
    • Echipa 2: 50
  • Diferență scor: 6
    • Echipa 1: 61
    • Echipa 2: 55

Deep Learning avansat cu Keras

Date baschet

import pandas as pd
games_tourney = pd.read_csv('datasets/games_tourney_samp.csv')
games_tourney.head()

Out[1]:
   season  team_1  team_2  home  seed_diff  score_diff  score_1  score_2  won
0    2017     320    6323     0         13          18      100       82    1
1    2017    6323     320     0        -13         -18       82      100    0
Deep Learning avansat cu Keras

Construiți modelul

from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Dense
input_tensor = Input(shape=(1,))
output_tensor = Dense(1)(input_tensor)
model = Model(input_tensor, output_tensor)
model.compile(optimizer='adam', loss='mae')

Deep Learning avansat cu Keras

Antrenați modelul

from pandas import read_csv
games = read_csv('datasets/games_tourney.csv')
model.fit(games['seed_diff'],
          games['score_diff'],
          batch_size=64,
          validation_split=.20,
          verbose=True)

Deep Learning avansat cu Keras

Evaluați modelul

model.evaluate(games_test['seed_diff'],
               games_test['score_diff'])

1000/1000 [==============================] - 0s 26us/step
Out[1]: 9.145421981811523
Deep Learning avansat cu Keras

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Deep Learning avansat cu Keras

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