def evaluate(self, X_test, Y_test, Y_test_classes):
if not self.model:
raise Exception("Load or fit new model first")
score, acc = self.model.evaluate(X_test, Y_test, batch_size=3)
print("Test accuracy:", acc)
evaluator = Evaluator()
predictions_encoded = self.model.predict(X_test)
predictions = self.lb.inverse_transform(
[np.argmax(pred) for pred in predictions_encoded])
evaluator.accuracy(Y_test_classes, predictions)
# evaluator.classification_report(Y_test_classes, predictions)
evaluator.confusion_matrix(Y_test_classes, predictions)
DNN_UNITS = 256
OUTPUT_CLASSES = len(get_classes(data))
DROPOUT_RATE = 0.2
NB_EPOCHS = 7
custom_model = CustomIMapModel(
vocabulary_size=VOCAB_LENGTH,
embedding_dimensions=EMB_DIM,
cnn_filters=CNN_FILTERS,
dnn_units=DNN_UNITS,
model_output_classes=OUTPUT_CLASSES,
dropout_rate=DROPOUT_RATE,
)
custom_model.compile(
loss="sparse_categorical_crossentropy",
optimizer="adam",
metrics=["sparse_categorical_accuracy"],
)
custom_model.fit(train_data, epochs=NB_EPOCHS)
results_predicted = custom_model.predict(test_data)
evaluator = Evaluator()
predictions = lb.inverse_transform(
[np.argmax(pred) for pred in results_predicted])
test_classes = lb.inverse_transform(y_test)
evaluator.accuracy(test_classes, predictions)
evaluator.classification_report(test_classes, predictions)
evaluator.confusion_matrix(test_classes, predictions)