model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
#print("Traning Model...")
model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, callbacks=[checkpoint], validation_data=(X_test, y_test))  # starts training
print("Testing Model")
output = model.predict(X_final, batch_size = batch_size)
for pred_i in output:
    pred_i[pred_i >=0.5] = 1
    pred_i[pred_i < 0.5] = 0
print "F1: " + str(f1_score(y_final, output, average='micro'))
print "Accuracy: " + str(accuracy_score(y_final, output))

mscores = model.evaluate(X_final, y_final, batch_size = batch_size)
print mscores

Creating Model...
Testing Model
F1: 0.7157894736842105
Accuracy: 0.3530864197530864
405/405 [==============================] - 8s 20ms/step
['0.15227678694106914', '0.9422222640779283']