model.add(Dense(20, activation='relu', input_dim=len(elo_data.columns) - 1))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='rmsprop',
loss='binary_crossentropy',
metrics=['accuracy'])
model.fit(X_train_elo,
Y_train_elo,
epochs=10,
batch_size=50,
validation_split=0.2,
verbose=1)
model.test_on_batch(X_test_elo, Y_test_elo, sample_weight=None)
model.evaluate(X_test_elo, Y_test_elo, verbose=1)
pred = model.predict_classes(X_test_elo, verbose=1)
plot_model(model, to_file='model.png', show_shapes=True)
SVG(model_to_dot(model).create(prog='dot', format='svg'))
print(confusion_matrix(Y_test_elo, pred))
print classification_report(Y_test_elo, pred)
print(accuracy_score(Y_test_elo, pred))
fpr_elo, tpr_elo, thresholds_elo = roc_curve(Y_test_elo, pred)
auc = auc(fpr_elo, tpr_elo)
plt.figure(1)
model.add(Dense(100, activation='relu', input_dim=X_data.shape[1]))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='sgd',
loss='binary_crossentropy',
metrics=['accuracy'])
model.fit(X_train_Perf_based,
Y_train_Perf_based,
epochs=100,
batch_size=20,
validation_split=0.3,
verbose=1)
model.test_on_batch(X_test_Perf_based, Y_test_Perf_based, sample_weight=None)
pred_Perf_based = model.predict_classes(X_test_Perf_based, verbose=1)
print(confusion_matrix(Y_test_Perf_based, pred_Perf_based))
print classification_report(Y_test_Perf_based, pred_Perf_based)
print(accuracy_score(Y_test_Perf_based, pred_Perf_based))
fpr_Perf_based, tpr_Perf_based, thresholds_Perf_based = roc_curve(
Y_test_Perf_based, pred_Perf_based)
auc_keras = auc(fpr_Perf_based, tpr_Perf_based)
plt.figure(1)
plt.plot([0, 1], [0, 1], 'k--')
plt.plot(fpr_Perf_based,
tpr_Perf_based,
