from tensorflow.keras.optimizers import Adam
from sklearn.metrics import (classification_report, confusion_matrix,
mean_squared_error, r2_score)
input_shape = (X_train.shape[1],)
m = Sequential([
Dense(units = 5, activation = 'elu', input_shape = input_shape),
Dropout(rate = 0.2),
BatchNormalization(),
Dense(units = 10, activation = 'elu'),
Dense(units = 4, activation = 'softmax')
])
m.summary()
earlystopping = EarlyStopping(monitor = 'val_loss', patience = 20)
opt = Adam(learning_rate = 0.005)
m.compile(optimizer = opt, loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])
history = m.fit(X_train, y_train_label_encoded, batch_size = 50, epochs = 500, validation_split = 0.2, callbacks = [earlystopping])
plt.plot(history.history['loss'], label='training_loss')
plt.plot(history.history['val_loss'], label='validation_loss')
plt.legend()
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
# Training
if useTF:
if not (usePCA or useICA or useIsomap or useLLE):
X_train21 = X_train21.to_numpy()
y_train21CPY = y_train21
y_train21 = y_train21.to_numpy()
LE1 = preprocessing.LabelEncoder()
LE1.fit(y_train21)
OneHot1 = OneHotEncoder()
y_train21 = OneHot1.fit_transform(y_train21.reshape(-1, 1)).toarray()
model21.fit(X_train21, y_train21, validation_split=0.1, epochs=nEpochs)
if (iterationNumber == 1):
print(model21.summary())
else:
model21.fit(X_train21, y_train21)
if (iterationNumber == 1):
print(model21)
# Game 2: Training
X_train22 = trainGame2.loc[:, trainGame2.columns != "playerID"]
y_train22 = trainGame2["playerID"]
scaler22 = preprocessing.StandardScaler().fit(X_train22)
X_train22 = pd.DataFrame(scaler22.transform(X_train22.values),
columns=X_train22.columns,
index=X_train22.index)
# Models
tree = DecisionTreeClassifier(criterion='entropy', random_state=0)
