Esempi in Python per plot_and_save_history

Esempio n. 1

def main():

    random_state = 42
    np.random.seed(random_state)

    data_dir_path = './data'
    model_dir_path = './models'
    report_dir_path = './reports'

    # url_data = load_url_data(data_dir_path)

    text_model = np.load("/home/samuel/Desktop/keras-malicious-url-detector/data/train_augmented_encoded_text_model.npy"
                         , allow_pickle=True).item()

    batch_size = 64
    epochs = 30

    classifier = BidirectionalLstmEmbedPredictor()

    history = classifier.fit(text_model=text_model,
                             model_dir_path=model_dir_path,
                             url_data=None, batch_size=batch_size, epochs=epochs)

    plot_and_save_history(history, BidirectionalLstmEmbedPredictor.model_name,
                          report_dir_path + '/' + BidirectionalLstmEmbedPredictor.model_name + '-history.png')

Esempio n. 2

File: cnn_lstm_train.py Progetto: MTKat/phishing-model-generalisation

def main():

    random_state = 42
    np.random.seed(random_state)

    data_dir_path = './data'
    model_dir_path = './models'
    report_dir_path = './reports'

    url_data = load_url_data(data_dir_path)

    text_model = extract_text_model(url_data['text'])

    batch_size = 64
    epochs = 50

    classifier = CnnLstmPredictor()

    history = classifier.fit(text_model=text_model,
                             model_dir_path=model_dir_path,
                             url_data=url_data,
                             batch_size=batch_size,
                             epochs=epochs)

    plot_and_save_history(
        history, CnnLstmPredictor.model_name,
        report_dir_path + '/' + CnnLstmPredictor.model_name + '-history.png')