Classificatiemodellen

Introductie tot Deep Learning in Python

Dan Becker

Data Scientist and contributor to Keras and TensorFlow libraries

Classificatie

Verliesfunctie 'categorical_crossentropy'
Vergelijkbaar met log loss: lager is beter
Voeg metrics = ['accuracy'] toe bij compile voor duidelijke diagnostiek
Outputlaag: aparte node per uitkomst, met 'softmax'-activatie

Snelle blik op de data

Omzetten naar categorisch

Classificatie

from tensorflow.keras.utils import to_categorical

data = pd.read_csv('basketball_shot_log.csv')
predictors = data.drop(['shot_result'], axis=1).values
target = to_categorical(data['shot_result'])

model = Sequential()
model.add(Dense(100, activation='relu', input_shape = (n_cols,)))
model.add(Dense(100, activation='relu'))
model.add(Dense(100, activation='relu'))
model.add(Dense(2, activation='softmax'))
model.compile(optimizer='adam', loss='categorical_crossentropy',
              metrics=['accuracy'])
model.fit(predictors, target)

Classificatie

Epoch 1/10
128069/128069 [==============================] - 4s - loss: 0.7706 - acc: 0.5759
Epoch 2/10
128069/128069 [==============================] - 5s - loss: 0.6656 - acc: 0.6003
Epoch 3/10
128069/128069 [==============================] - 6s - loss: 0.6611 - acc: 0.6094
Epoch 4/10
128069/128069 [==============================] - 7s - loss: 0.6584 - acc: 0.6106
Epoch 5/10
128069/128069 [==============================] - 7s - loss: 0.6561 - acc: 0.6150
Epoch 6/10
128069/128069 [==============================] - 9s - loss: 0.6553 - acc: 0.6158
Epoch 7/10
128069/128069 [==============================] - 9s - loss: 0.6543 - acc: 0.6162
Epoch 8/10
128069/128069 [==============================] - 9s - loss: 0.6538 - acc: 0.6158
Epoch 9/10
128069/128069 [==============================] - 10s - loss: 0.6535 - acc: 0.6157
Epoch 10/10
128069/128069 [==============================] - 10s - loss: 0.6531 - acc: 0.6166

Laten we oefenen!

Introductie tot Deep Learning in Python