<jupyter_start><jupyter_text>KNN Results<jupyter_code>Training on 17707 sample, validating on 932 samples
Number of features: 2285
Training accuracy with kNN: 0.9435816343818829
Validation accuracy with kNN: 0.9334763948497854<jupyter_output><empty_output><jupyter_text>Random Forest Results<jupyter_code>Training on 17707 sample, validating on 932 samples
Number of features: 2285
/home/khanhi83/anaconda3/lib/python3.7/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.
  "10 in version 0.20 to 100 in 0.22.", FutureWarning)
Training accuracy with Random Forest: 0.9952561134014797
Validation accuracy with Random Forest: 0.9281115879828327<jupyter_output><empty_output><jupyter_text>ANN Results<jupyter_code>Training on 17707 sample, validating on 932 samples
Number of features: 2285
Training accuracy with Neural Networks: 0.8472
Validation accuracy with Neural Networks: 0.8294
Training loss with Neural Networks: 0.0030
Validation loss with Neural Networks: 4.2113
Number of Training Epochs: 200<jupyter_output><empty_output><jupyter_text>. /home/khanhi83/.local/share/virtualenvs/Rotorcraft-Safety-BOPmdVWD/bin/activate  To activate this project's virtualenv, run pipenv shell. Alternatively, run a command inside the virtualenv with pipenv run.  import structlog  !which python !conda install -c conda-forge structlog !pip install typing_extensions import structlog import osos.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" For mutliple devices (GPUs: 4, 5, 6, 7)os.environ["CUDA_VISIBLE_DEVICES"] = "7"import osimport zipfilefrom typing import List, Tuple, Dictimport numpy as npimport pandas as pdimport requests import structlogimport matplotlib.pyplot as pltfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.feature_extraction.text import TfidfVectorizerfrom sklearn.model_selection import train_test_splitfrom sklearn.neighbors import KNeighborsClassifierfrom tensorflow import keras, one_hotfrom tensorflow.keras import layersfrom tensorflow.keras.callbacks import CSVLoggerfrom tensorflow.keras.layers import ReLUfrom tensorflow.keras.utils import to_categoricalplt.rcParams.update({'figure.figsize': (16.0, 12.0)}) _LOGGER = structlog.get_logger(__file__)HEADER_COLUMN = 12LABEL_COLUMN = 'False Warning'TEXT_COLUMN = 'Text' def download_file(url: str, local_dir: str = '.', local_filename: str = '') -> str:    """    Downloads a file from a provided url to a local directory    :param url: URL to download the file from    :param local_dir: Local directory to download the file to (created if it does not exist)    :param local_filename: What to name the file when saved     (if empty or none, assume the name of the original name of the file)    :return: the name of the file which was saved    """    os.makedirs(f'{local_dir}', exist_ok=True)    local_filename = local_filename if local_filename else url.split('/')[-1]    if os.path.exists(f'{local_dir}/{local_filename}'):         _LOGGER.info(f'{local_dir}/{local_filename} already exists. Skipping download.')        print("{0}/{1} already exists. Skipping download.".format(local_dir, local_filename))    else:         _LOGGER.info(f"Downloading file from {url} to {local_dir}/{local_filename}.")        print("Downloading file from {0} to {1}/{2}.".format(url, local_dir, local_filename))        with requests.get(url, stream=True) as r:            r.raise_for_status()            with open(f'./{local_dir}/{local_filename}', 'wb') as f:                for chunk in r.iter_content(chunk_size=128):                    f.write(chunk)         _LOGGER.info(f"Finished saving file from {url} to {local_dir}/{local_filename}.")        print("Finished saving file from {0} to {1}/{2}.".format(url, local_dir, local_filename))    return f'{local_dir}/{local_filename}'def unzip_file(path_to_zip_file: str, dir_to_extract_to: str) -> str:    """    Unzips a zip file to a provided directory    :param path_to_file: path to zip file    :param dir_to_extract_to: directory to extract zip file    :return: full path to unzipped file (assuming there is only one)    """    with zipfile.ZipFile(path_to_zip_file, 'r') as zip_ref:        zip_ref.extractall(dir_to_extract_to)        return f'{dir_to_extract_to}/{zip_ref.namelist()[0]}'def load_data(path_to_file: str) -> pd.DataFrame:    """    Loads excel data from a supplied path into a Pandas dataframe    :param path_to_file: path to excel file    :return: Pandas dataframe containing contents of excel spreadsheet    """     _LOGGER.info(f"Started loading the excel data from {path_to_file} into a dataframe - this may take a while. "                  f"You may want to grab a coffee.")    print("Started loading the excel data from {0} into a datafram - this may take a while. You may want to grab a coffee.".format(path_to_file))    df = pd.read_excel(path_to_file, engine='openpyxl', header=HEADER_COLUMN)     _LOGGER.info(f"Finished loading the excel data from {path_to_file} into a dataframe.")    print("Finished loading the excel data from {0} into a dataframe.".format(path_to_file))    return dfdef vectorize(df: pd.DataFrame, **kwargs) -> Tuple[np.array, List[str]]:     _LOGGER.info("Converting text to feature matrix")    print("Converting text to feature matrix")    vectorizer = TfidfVectorizer(**kwargs)    sparse_matrix = vectorizer.fit_transform(df[TEXT_COLUMN])    feature_matrix = sparse_matrix.todense()    return feature_matrix, vectorizer.get_feature_names()def extract_and_encode_labels(df: pd.DataFrame) -> Tuple[np.array, Dict[str, int]]:    label_mapping = dict((label, i) for i, label in enumerate(df[LABEL_COLUMN].unique()))    labels = list(df[LABEL_COLUMN].map(label_mapping))    return np.array(labels), label_mapping if __name__ == "__main__":    local_dir = './data'    compute_features = not os.path.exists(f'{local_dir}/feature_data.csv')    model_type = "mlp" "{knn", "mlp", "rf"}      if compute_features:         download the file        path_to_downloaded_zip_file = download_file(            'https://www.fire.tc.faa.gov/zip/MasterModelVersion3DDeliverable.zip',            local_dir)         unzip the file        path_to_file = unzip_file(path_to_downloaded_zip_file, local_dir)         load the file into a Pandas dataframe        df = load_data(path_to_file)         save preprocessed data to save time for future runs        df.to_csv(f'{local_dir}/feature_data.csv')    else:         don't go through the hassle of preprocessing if we already have the preprocessed data saved        df = pd.read_csv(f'{local_dir}/feature_data.csv')    count_of_no_text = len(df[df[TEXT_COLUMN].isnull()])    df = df.dropna(subset=[TEXT_COLUMN])     _LOGGER.info(f"Dropped {count_of_no_text} records because {TEXT_COLUMN} was null or NaN")    print("Dropped {0} records because {1} was null or NaN".format(count_of_no_text, TEXT_COLUMN))    count_of_null_labels = len(df[df[LABEL_COLUMN].isnull()])    df = df.dropna(subset=[LABEL_COLUMN])     _LOGGER.info(f"Dropped {count_of_null_labels} records because {LABEL_COLUMN} was null or NaN")    print("Dropped {0} records because {1} was null or NaN".format(count_of_null_labels, LABEL_COLUMN))     create a sparse feature matrix of size n x m,     where n = number of documents, m = number of words in vocabulary    feature_matrix, feature_names = vectorize(df, min_df=0.001)    labels, label_mapping = extract_and_encode_labels(df)    num_labels = len(label_mapping)    num_features = feature_matrix.shape[1]    X_train, X_test, y_train, y_test = train_test_split(feature_matrix, labels, test_size=0.05, random_state=1)     _LOGGER.info(f"Training on {X_train.shape[0]} samples, validating on {X_test.shape[0]} samples.")    print("Training on {0} sample, validating on {1} samples".format(X_train.shape[0], X_test.shape[0]))     _LOGGER.info(f"Number of features: {num_features}")    print("Number of features: {0}".format(num_features))    if model_type == "mlp":        labels = one_hot(np.array(labels), len(label_mapping))        inputs = keras.Input(shape=(num_features,))        layer_1 = layers.Dense(8192, activation=ReLU())(inputs)        layer_2 = layers.Dense(2048, activation=ReLU())(layer_1)        layer_3 = layers.Dense(512, activation=ReLU())(layer_2)        layer_4 = layers.Dense(128, activation=ReLU())(layer_3)        layer_5 = layers.Dense(32, activation=ReLU())(layer_4)        layer_6 = layers.Dense(8, activation=ReLU())(layer_5)        outputs = layers.Dense(num_labels, activation="softmax")(layer_6)        model = keras.Model(inputs=inputs, outputs=outputs)         _LOGGER.info(model.summary())        print(model.summary())        model.compile(            optimizer=keras.optimizers.Adamax(),   Optimizer            loss=keras.losses.CategoricalCrossentropy(),   Loss function to minimize            metrics=[keras.metrics.Accuracy()]   List of metrics to monitor        )         print("X_Train:", X_train.shape, " y_Train:", y_train.shape)        Changed        y_train = to_categorical(y_train)        y_test = to_categorical(y_test)        Changed        model.fit(X_train, y_train,                  validation_data=(X_test, y_test), shuffle=True, epochs=500, batch_size=128,                  callbacks=[CSVLogger('./results.csv')])        model.save('model')    elif model_type == "rf":        rf = RandomForestClassifier(n_jobs=-1)        rf.fit(X_train, y_train)        training_acc = rf.score(X_train, y_train)        validation_acc = rf.score(X_test, y_test)         _LOGGER.info(f"Training accuracy with Random Forest: {training_acc}")        print("Training accuracy with Random Forest: {0}".format(training_acc))         _LOGGER.info(f"Validation accuracy with Random Forest: {validation_acc}")        print("Validation accuracy with Random Forest: {0}".format(validation_acc))    elif model_type == "knn":        knn = KNeighborsClassifier(n_neighbors=5, n_jobs=-1)        knn.fit(X_train, y_train)        training_acc = knn.score(X_train, y_train)        validation_acc = knn.score(X_test, y_test)         _LOGGER.info(f"Training accuracy with kNN: {training_acc}")        print("Training accuracy with kNN: {0}".format(training_acc))         _LOGGER.info(f"Validation accuracy with kNN: {validation_acc}")        print("Validation accuracy with kNN: {0}".format(validation_acc))<jupyter_code>/home/khanhi83/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3058: DtypeWarning: Columns (2,5,9,12,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,87,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,117,118,119,121,122,123,124,125,126,127,128,130,131,132,145,146,148,215,224) have mixed types. Specify dtype option on import or set low_memory=False.
  interactivity=interactivity, compiler=compiler, result=result)
Dropped 3198 records because Text was null or NaN
Dropped 4438 records because False Warning was null or NaN
Converting text to feature matrix
Training on 17707 sample, validating on 932 samples
Number of features: 2285
Model: "model_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_2 (InputLayer)         [(None, 2285)]            0         
_________________________________________________________________
dense_7 (Dense)              (None, 8192)              18726912  
_________________________________________________________________
dense_8 (Dense)              (None, 2048)              16779264  
_________________________________________________________________
dense_9 (Dense)              (None, 512)               1049088   
_________________________________________________________________
dense_10 (Dense)             (None, 128)               65664     
_________________________________________________________________
dense_11 (Dense)             (None, 32)                4128      
_________________________________________________________________
dense_12 (Dense)             (None, 8)                 264       
_________________________________________________________________
dense_13 (Dense)             (None, 6)                 54        
=================================================================
Total params: 36,625,374
Trainable params: 36,625,374
Non-trainable params: 0
_________________________________________________________________
None
Train on 17707 samples, validate on 932 samples
Epoch 1/200
17707/17707 [==============================] - 23s 1ms/sample - loss: 1.4150 - accuracy: 0.0000e+00 - val_loss: 0.9210 - val_accuracy: 0.0000e+00
Epoch 2/200
17707/17707 [==============================] - 21s 1ms/sample - loss: 0.3722 - accuracy: 0.0143 - val_loss: 0.3149 - val_accuracy: 0.0023
Epoch 3/200
17707/17707 [==============================] - 21s 1ms/sample - loss: 0.1743 - accuracy: 0.0360 - val_loss: 0.3176 - val_accuracy: 0.0304
Epoch 4/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 0.1284 - accuracy: 0.0615 - val_loss: 0.3605 - val_accuracy: 0.0899
Epoch 5/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 0.1000 - accuracy: 0.1414 - val_loss: 0.4587 - val_accuracy: 0.1947
Epoch 6/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 0.0823 - accuracy: 0.2460 - val_loss: 0.5036 - val_accuracy: 0.2786
Epoch 7/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 0.0699 - accuracy: 0.3137 - val_loss: 0.6804 - val_accuracy: 0.3389
Epoch 8/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 0.0560 - accuracy: 0.3642 - val_loss: 0.7671 - val_accuracy: 0.4054
Epoch 9/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 0.0442 - accuracy: 0.4358 - val_loss: 0.9645 - val_accuracy: 0.4515
Epoch 10/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 0.0275 - accuracy: 0.4755 - val_loss: 1.1239 - val_accuracy: 0.4937
Epoch 11/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 0.0159 - accuracy: 0.5119 - val_loss: 1.3274 - val_accuracy: 0.4950
Epoch 12/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 0.0115 - accuracy: 0.5280 - val_loss: 1.4747 - val_accuracy: 0.5299
Epoch 13/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 0.0066 - accuracy: 0.5471 - val_loss: 1.6228 - val_accuracy: 0.5477
Epoch 14/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 0.0040 - accuracy: 0.5597 - val_loss: 1.6750 - val_accuracy: 0.5445
Epoch 15/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 0.0044 - accuracy: 0.5728 - val_loss: 1.9112 - val_accuracy: 0.5696
Epoch 16/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 0.0032 - accuracy: 0.5788 - val_loss: 1.8407 - val_accuracy: 0.5519
Epoch 17/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 0.0029 - accuracy: 0.5792 - val_loss: 1.9215 - val_accuracy: 0.5617
Epoch 18/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 0.0019 - accuracy: 0.5771 - val_loss: 1.8772 - val_accuracy: 0.5588
Epoch 19/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 0.0016 - accuracy: 0.5818 - val_loss: 2.0288 - val_accuracy: 0.5676
Epoch 20/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 0.0015 - accuracy: 0.5926 - val_loss: 2.1321 - val_accuracy: 0.5805
Epoch 21/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 0.0012 - accuracy: 0.5903 - val_loss: 2.1226 - val_accuracy: 0.5765
Epoch 22/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 8.0182e-04 - accuracy: 0.5997 - val_loss: 2.1272 - val_accuracy: 0.5794
Epoch 23/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 5.2876e-04 - accuracy: 0.6025 - val_loss: 2.3240 - val_accuracy: 0.5853
Epoch 24/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 0.0015 - accuracy: 0.6043 - val_loss: 2.2679 - val_accuracy: 0.5950
Epoch 25/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 0.0010 - accuracy: 0.6122 - val_loss: 2.3546 - val_accuracy: 0.5969
Epoch 26/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 0.0017 - accuracy: 0.6151 - val_loss: 2.3396 - val_accuracy: 0.6059
Epoch 27/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 0.0014 - accuracy: 0.6215 - val_loss: 2.3133 - val_accuracy: 0.5975
Epoch 28/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 0.0012 - accuracy: 0.6211 - val_loss: 2.5287 - val_accuracy: 0.6109
Epoch 29/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 6.3126e-04 - accuracy: 0.6211 - val_loss: 2.3866 - val_accuracy: 0.6014
Epoch 30/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 3.9634e-04 - accuracy: 0.6271 - val_loss: 2.4712 - val_accuracy: 0.6132
Epoch 31/200
17707/17707 [==============================] - 19s 1ms/sample - loss: 4.5758e-04 - accuracy: 0.6306 - val_loss: 2.4926 - val_accuracy: 0.6093
Epoch 32/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 7.4534e-04 - accuracy: 0.6300 - val_loss: 2.5364 - val_accuracy: 0.6109
Epoch 33/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 5.5584e-04 - accuracy: 0.6338 - val_loss: 2.5000 - val_accuracy: 0.6137
Epoch 34/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 0.0017 - accuracy: 0.6241 - val_loss: 2.8836 - val_accuracy: 0.6440
Epoch 35/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 0.0035 - accuracy: 0.6295 - val_loss: 2.5241 - val_accuracy: 0.6148
Epoch 36/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 7.9955e-04 - accuracy: 0.6252 - val_loss: 2.5342 - val_accuracy: 0.6046
Epoch 37/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 3.0255e-04 - accuracy: 0.6235 - val_loss: 2.4824 - val_accuracy: 0.6075
Epoch 38/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 2.1674e-04 - accuracy: 0.6268 - val_loss: 2.5254 - val_accuracy: 0.6096
Epoch 39/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 1.8860e-04 - accuracy: 0.6290 - val_loss: 2.5533 - val_accuracy: 0.6103
Epoch 40/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 1.7277e-04 - accuracy: 0.6305 - val_loss: 2.6051 - val_accuracy: 0.6141
Epoch 41/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 1.5573e-04 - accuracy: 0.6326 - val_loss: 2.6354 - val_accuracy: 0.6159
Epoch 42/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 1.4272e-04 - accuracy: 0.6345 - val_loss: 2.6866 - val_accuracy: 0.6200
Epoch 43/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 1.3079e-04 - accuracy: 0.6368 - val_loss: 2.7114 - val_accuracy: 0.6187
Epoch 44/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 1.2389e-04 - accuracy: 0.6416 - val_loss: 2.7729 - val_accuracy: 0.6254
Epoch 45/200
17707/17707 [==============================] - 19s 1ms/sample - loss: 1.1437e-04 - accuracy: 0.6433 - val_loss: 2.8304 - val_accuracy: 0.6270
Epoch 46/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 1.4042e-04 - accuracy: 0.6533 - val_loss: 2.7734 - val_accuracy: 0.6153
Epoch 47/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 4.8073e-04 - accuracy: 0.6546 - val_loss: 3.0387 - val_accuracy: 0.6495
Epoch 48/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 4.9365e-04 - accuracy: 0.6580 - val_loss: 3.0219 - val_accuracy: 0.6427
Epoch 49/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 0.0023 - accuracy: 0.6498 - val_loss: 3.0022 - val_accuracy: 0.6509
Epoch 50/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 0.0017 - accuracy: 0.6505 - val_loss: 2.8578 - val_accuracy: 0.6186
Epoch 51/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 1.9768e-04 - accuracy: 0.6414 - val_loss: 2.8186 - val_accuracy: 0.6164
Epoch 52/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 3.0440e-05 - accuracy: 0.6401 - val_loss: 2.8520 - val_accuracy: 0.6209
Epoch 53/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 2.4216e-05 - accuracy: 0.6428 - val_loss: 2.8699 - val_accuracy: 0.6220
Epoch 54/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 2.0284e-05 - accuracy: 0.6453 - val_loss: 2.8923 - val_accuracy: 0.6237
Epoch 55/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 1.7129e-05 - accuracy: 0.6470 - val_loss: 2.9173 - val_accuracy: 0.6259
Epoch 56/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 1.4437e-05 - accuracy: 0.6493 - val_loss: 2.9416 - val_accuracy: 0.6270
Epoch 57/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 1.2049e-05 - accuracy: 0.6530 - val_loss: 2.9664 - val_accuracy: 0.6296
Epoch 58/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 1.0006e-05 - accuracy: 0.6544 - val_loss: 3.0012 - val_accuracy: 0.6339
Epoch 59/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 8.0733e-06 - accuracy: 0.6577 - val_loss: 3.0306 - val_accuracy: 0.6361
Epoch 60/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 6.3958e-06 - accuracy: 0.6627 - val_loss: 3.0657 - val_accuracy: 0.6397
Epoch 61/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 5.1267e-06 - accuracy: 0.6665 - val_loss: 3.1096 - val_accuracy: 0.6434
Epoch 62/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 4.3071e-06 - accuracy: 0.6717 - val_loss: 3.1489 - val_accuracy: 0.6486
Epoch 63/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 5.0519e-06 - accuracy: 0.6747 - val_loss: 3.1842 - val_accuracy: 0.6527
Epoch 64/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 1.3886e-05 - accuracy: 0.6886 - val_loss: 3.2555 - val_accuracy: 0.6617
Epoch 65/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 0.0026 - accuracy: 0.6769 - val_loss: 3.0588 - val_accuracy: 0.6477
Epoch 66/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 7.6556e-04 - accuracy: 0.6598 - val_loss: 3.0826 - val_accuracy: 0.6465
Epoch 67/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 2.7393e-04 - accuracy: 0.6490 - val_loss: 2.9321 - val_accuracy: 0.6311
Epoch 68/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 1.8850e-05 - accuracy: 0.6543 - val_loss: 2.9670 - val_accuracy: 0.6348
Epoch 69/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 9.6568e-06 - accuracy: 0.6557 - val_loss: 2.9885 - val_accuracy: 0.6372
Epoch 70/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 7.1857e-06 - accuracy: 0.6603 - val_loss: 3.0026 - val_accuracy: 0.6391
Epoch 71/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 5.9654e-06 - accuracy: 0.6622 - val_loss: 3.0144 - val_accuracy: 0.6398
Epoch 72/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 5.0051e-06 - accuracy: 0.6635 - val_loss: 3.0308 - val_accuracy: 0.6427
Epoch 73/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 4.2040e-06 - accuracy: 0.6667 - val_loss: 3.0433 - val_accuracy: 0.6440
Epoch 74/200
17707/17707 [==============================] - 19s 1ms/sample - loss: 3.6052e-06 - accuracy: 0.6686 - val_loss: 3.0571 - val_accuracy: 0.6452
Epoch 75/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 3.0645e-06 - accuracy: 0.6709 - val_loss: 3.0771 - val_accuracy: 0.6475
Epoch 76/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 2.5762e-06 - accuracy: 0.6733 - val_loss: 3.0965 - val_accuracy: 0.6491
Epoch 77/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 2.1639e-06 - accuracy: 0.6759 - val_loss: 3.1221 - val_accuracy: 0.6540
Epoch 78/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 1.7895e-06 - accuracy: 0.6803 - val_loss: 3.1421 - val_accuracy: 0.6559
Epoch 79/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 1.4851e-06 - accuracy: 0.6823 - val_loss: 3.1696 - val_accuracy: 0.6592
Epoch 80/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 1.2142e-06 - accuracy: 0.6877 - val_loss: 3.1955 - val_accuracy: 0.6624
Epoch 81/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 9.9492e-07 - accuracy: 0.6903 - val_loss: 3.2292 - val_accuracy: 0.6677
Epoch 82/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 8.0725e-07 - accuracy: 0.6928 - val_loss: 3.2590 - val_accuracy: 0.6727
Epoch 83/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 6.3853e-07 - accuracy: 0.7008 - val_loss: 3.2905 - val_accuracy: 0.6753
Epoch 84/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 5.1452e-07 - accuracy: 0.7026 - val_loss: 3.3332 - val_accuracy: 0.6812
Epoch 85/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 4.1269e-07 - accuracy: 0.7081 - val_loss: 3.3570 - val_accuracy: 0.6849
Epoch 86/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 3.3819e-07 - accuracy: 0.7145 - val_loss: 3.4000 - val_accuracy: 0.6897
Epoch 87/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 2.8479e-07 - accuracy: 0.7184 - val_loss: 3.4330 - val_accuracy: 0.6940
Epoch 88/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 6.6479e-05 - accuracy: 0.7220 - val_loss: 3.4322 - val_accuracy: 0.6922
Epoch 89/200
17707/17707 [==============================] - 19s 1ms/sample - loss: 5.3481e-04 - accuracy: 0.7163 - val_loss: 3.3538 - val_accuracy: 0.6786
Epoch 90/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 5.7037e-04 - accuracy: 0.7150 - val_loss: 3.3997 - val_accuracy: 0.6890
Epoch 91/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 0.0039 - accuracy: 0.7198 - val_loss: 3.2467 - val_accuracy: 0.6888
Epoch 92/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 9.3487e-04 - accuracy: 0.7072 - val_loss: 3.1163 - val_accuracy: 0.6781
Epoch 93/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 2.4818e-06 - accuracy: 0.7032 - val_loss: 3.1275 - val_accuracy: 0.6810
Epoch 94/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 1.5704e-06 - accuracy: 0.7042 - val_loss: 3.1413 - val_accuracy: 0.6815
Epoch 95/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 1.2467e-06 - accuracy: 0.7048 - val_loss: 3.1527 - val_accuracy: 0.6819
Epoch 96/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 1.0487e-06 - accuracy: 0.7052 - val_loss: 3.1643 - val_accuracy: 0.6822
Epoch 97/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 9.0087e-07 - accuracy: 0.7059 - val_loss: 3.1746 - val_accuracy: 0.6828
Epoch 98/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 7.9197e-07 - accuracy: 0.7066 - val_loss: 3.1861 - val_accuracy: 0.6835
Epoch 99/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 7.1044e-07 - accuracy: 0.7072 - val_loss: 3.1972 - val_accuracy: 0.6837
Epoch 100/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 6.4034e-07 - accuracy: 0.7077 - val_loss: 3.2089 - val_accuracy: 0.6840
Epoch 101/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 5.8114e-07 - accuracy: 0.7083 - val_loss: 3.2216 - val_accuracy: 0.6849
Epoch 102/200
17707/17707 [==============================] - 19s 1ms/sample - loss: 5.2810e-07 - accuracy: 0.7092 - val_loss: 3.2348 - val_accuracy: 0.6860
Epoch 103/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 4.7881e-07 - accuracy: 0.7101 - val_loss: 3.2486 - val_accuracy: 0.6874
Epoch 104/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 4.3473e-07 - accuracy: 0.7115 - val_loss: 3.2612 - val_accuracy: 0.6876
Epoch 105/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 3.9043e-07 - accuracy: 0.7129 - val_loss: 3.2775 - val_accuracy: 0.6883
Epoch 106/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 3.5096e-07 - accuracy: 0.7136 - val_loss: 3.2960 - val_accuracy: 0.6892
Epoch 107/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 3.1304e-07 - accuracy: 0.7154 - val_loss: 3.3091 - val_accuracy: 0.6899
Epoch 108/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 2.8164e-07 - accuracy: 0.7168 - val_loss: 3.3277 - val_accuracy: 0.6917
Epoch 109/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 2.4663e-07 - accuracy: 0.7180 - val_loss: 3.3451 - val_accuracy: 0.6930
Epoch 110/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 2.1381e-07 - accuracy: 0.7210 - val_loss: 3.3692 - val_accuracy: 0.6949
Epoch 111/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 2.0528e-07 - accuracy: 0.7225 - val_loss: 3.3853 - val_accuracy: 0.6978
Epoch 112/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 3.8632e-07 - accuracy: 0.7253 - val_loss: 3.4080 - val_accuracy: 0.6990
Epoch 113/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 3.0249e-06 - accuracy: 0.7338 - val_loss: 3.4579 - val_accuracy: 0.7094
Epoch 114/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 2.2313e-04 - accuracy: 0.7333 - val_loss: 3.5653 - val_accuracy: 0.7123
Epoch 115/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 8.3918e-04 - accuracy: 0.7269 - val_loss: 3.3964 - val_accuracy: 0.7055
Epoch 116/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 0.0026 - accuracy: 0.7191 - val_loss: 3.1772 - val_accuracy: 0.6806
Epoch 117/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 0.0011 - accuracy: 0.7093 - val_loss: 3.2756 - val_accuracy: 0.6863
Epoch 118/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 6.1081e-07 - accuracy: 0.7100 - val_loss: 3.2729 - val_accuracy: 0.6860
Epoch 119/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 5.3656e-07 - accuracy: 0.7099 - val_loss: 3.2735 - val_accuracy: 0.6858
Epoch 120/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 4.8155e-07 - accuracy: 0.7097 - val_loss: 3.2759 - val_accuracy: 0.6858
Epoch 121/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 4.3504e-07 - accuracy: 0.7098 - val_loss: 3.2788 - val_accuracy: 0.6856
Epoch 122/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 3.9244e-07 - accuracy: 0.7100 - val_loss: 3.2830 - val_accuracy: 0.6856
Epoch 123/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 3.5581e-07 - accuracy: 0.7102 - val_loss: 3.2871 - val_accuracy: 0.6856
Epoch 124/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 3.2177e-07 - accuracy: 0.7104 - val_loss: 3.2929 - val_accuracy: 0.6856
Epoch 125/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 2.9022e-07 - accuracy: 0.7108 - val_loss: 3.2981 - val_accuracy: 0.6860
Epoch 126/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 2.6197e-07 - accuracy: 0.7113 - val_loss: 3.3057 - val_accuracy: 0.6860
Epoch 127/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 2.3825e-07 - accuracy: 0.7117 - val_loss: 3.3139 - val_accuracy: 0.6862
Epoch 128/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 2.1435e-07 - accuracy: 0.7122 - val_loss: 3.3202 - val_accuracy: 0.6867
Epoch 129/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 1.9494e-07 - accuracy: 0.7128 - val_loss: 3.3296 - val_accuracy: 0.6878
Epoch 130/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 1.7767e-07 - accuracy: 0.7136 - val_loss: 3.3374 - val_accuracy: 0.6874
Epoch 131/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 1.6490e-07 - accuracy: 0.7148 - val_loss: 3.3452 - val_accuracy: 0.6885
Epoch 132/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 1.4464e-07 - accuracy: 0.7153 - val_loss: 3.3543 - val_accuracy: 0.6892
Epoch 133/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 1.3184e-07 - accuracy: 0.7165 - val_loss: 3.3641 - val_accuracy: 0.6906
Epoch 134/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 1.1750e-07 - accuracy: 0.7174 - val_loss: 3.3769 - val_accuracy: 0.6917
Epoch 135/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 1.0396e-07 - accuracy: 0.7183 - val_loss: 3.3866 - val_accuracy: 0.6919
Epoch 136/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 9.0787e-08 - accuracy: 0.7202 - val_loss: 3.3981 - val_accuracy: 0.6940
Epoch 137/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 8.0094e-08 - accuracy: 0.7213 - val_loss: 3.4094 - val_accuracy: 0.6949
Epoch 138/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 6.9433e-08 - accuracy: 0.7235 - val_loss: 3.4242 - val_accuracy: 0.6969
Epoch 139/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 5.9506e-08 - accuracy: 0.7252 - val_loss: 3.4375 - val_accuracy: 0.6981
Epoch 140/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 5.1342e-08 - accuracy: 0.7273 - val_loss: 3.4475 - val_accuracy: 0.6994
Epoch 141/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 4.2477e-08 - accuracy: 0.7294 - val_loss: 3.4640 - val_accuracy: 0.7010
Epoch 142/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 3.6143e-08 - accuracy: 0.7311 - val_loss: 3.4777 - val_accuracy: 0.7039
Epoch 143/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 2.9910e-08 - accuracy: 0.7338 - val_loss: 3.4938 - val_accuracy: 0.7042
Epoch 144/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 2.4929e-08 - accuracy: 0.7348 - val_loss: 3.5050 - val_accuracy: 0.7062
Epoch 145/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 2.0728e-08 - accuracy: 0.7371 - val_loss: 3.5214 - val_accuracy: 0.7080
Epoch 146/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 1.7032e-08 - accuracy: 0.7397 - val_loss: 3.5310 - val_accuracy: 0.7085
Epoch 147/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 1.4541e-08 - accuracy: 0.7406 - val_loss: 3.5369 - val_accuracy: 0.7087
Epoch 148/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 1.2354e-08 - accuracy: 0.7424 - val_loss: 3.5390 - val_accuracy: 0.7112
Epoch 149/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 1.0341e-08 - accuracy: 0.7440 - val_loss: 3.5404 - val_accuracy: 0.7116
Epoch 150/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 8.9472e-09 - accuracy: 0.7448 - val_loss: 3.5398 - val_accuracy: 0.7130
Epoch 151/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 7.6546e-09 - accuracy: 0.7462 - val_loss: 3.5351 - val_accuracy: 0.7133
Epoch 152/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 6.7458e-09 - accuracy: 0.7472 - val_loss: 3.5270 - val_accuracy: 0.7141
Epoch 153/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 5.9850e-09 - accuracy: 0.7482 - val_loss: 3.5134 - val_accuracy: 0.7149
Epoch 154/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 5.5609e-09 - accuracy: 0.7479 - val_loss: 3.5023 - val_accuracy: 0.7160
Epoch 155/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 4.8607e-09 - accuracy: 0.7490 - val_loss: 3.4928 - val_accuracy: 0.7166
Epoch 156/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 4.6588e-09 - accuracy: 0.7492 - val_loss: 3.4851 - val_accuracy: 0.7176
Epoch 157/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 4.0865e-09 - accuracy: 0.7493 - val_loss: 3.4662 - val_accuracy: 0.7173
Epoch 158/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 4.0932e-09 - accuracy: 0.7501 - val_loss: 3.4540 - val_accuracy: 0.7185
Epoch 159/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 3.9047e-09 - accuracy: 0.7507 - val_loss: 3.4220 - val_accuracy: 0.7166
Epoch 160/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 3.5075e-09 - accuracy: 0.7503 - val_loss: 3.4023 - val_accuracy: 0.7175
Epoch 161/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 3.2382e-09 - accuracy: 0.7497 - val_loss: 3.3552 - val_accuracy: 0.7164
Epoch 162/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 2.9824e-09 - accuracy: 0.7508 - val_loss: 3.3444 - val_accuracy: 0.7171
Epoch 163/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 2.7737e-09 - accuracy: 0.7507 - val_loss: 3.3085 - val_accuracy: 0.7167
Epoch 164/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 2.8208e-09 - accuracy: 0.7500 - val_loss: 3.2948 - val_accuracy: 0.7175
Epoch 165/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 2.6727e-09 - accuracy: 0.7515 - val_loss: 3.2698 - val_accuracy: 0.7166
Epoch 166/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 2.4102e-09 - accuracy: 0.7526 - val_loss: 3.2838 - val_accuracy: 0.7198
Epoch 167/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 2.2149e-09 - accuracy: 0.7544 - val_loss: 3.2732 - val_accuracy: 0.7223
Epoch 168/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 2.1476e-09 - accuracy: 0.7575 - val_loss: 3.2563 - val_accuracy: 0.7242
Epoch 169/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 1.8716e-09 - accuracy: 0.7597 - val_loss: 3.2661 - val_accuracy: 0.7269
Epoch 170/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 1.6831e-09 - accuracy: 0.7618 - val_loss: 3.2464 - val_accuracy: 0.7242
Epoch 171/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 1.6965e-09 - accuracy: 0.7648 - val_loss: 3.2787 - val_accuracy: 0.7325
Epoch 172/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 1.3465e-09 - accuracy: 0.7703 - val_loss: 3.2839 - val_accuracy: 0.7328
Epoch 173/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 1.3263e-09 - accuracy: 0.7712 - val_loss: 3.2700 - val_accuracy: 0.7355
Epoch 174/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 1.3869e-09 - accuracy: 0.7743 - val_loss: 3.2856 - val_accuracy: 0.7387
Epoch 175/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 1.2186e-09 - accuracy: 0.7765 - val_loss: 3.2790 - val_accuracy: 0.7393
Epoch 176/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 1.2387e-09 - accuracy: 0.7778 - val_loss: 3.2878 - val_accuracy: 0.7414
Epoch 177/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 1.0974e-09 - accuracy: 0.7812 - val_loss: 3.3076 - val_accuracy: 0.7452
Epoch 178/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 8.7520e-10 - accuracy: 0.7846 - val_loss: 3.3238 - val_accuracy: 0.7443
Epoch 179/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 9.7619e-10 - accuracy: 0.7844 - val_loss: 3.3235 - val_accuracy: 0.7459
Epoch 180/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 1.0166e-09 - accuracy: 0.7869 - val_loss: 3.3406 - val_accuracy: 0.7500
Epoch 181/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 8.4154e-10 - accuracy: 0.7915 - val_loss: 3.3636 - val_accuracy: 0.7518
Epoch 182/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 8.0115e-10 - accuracy: 0.7948 - val_loss: 3.3937 - val_accuracy: 0.7545
Epoch 183/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 8.2134e-10 - accuracy: 0.7971 - val_loss: 3.4130 - val_accuracy: 0.7577
Epoch 184/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 7.2709e-10 - accuracy: 0.7997 - val_loss: 3.4264 - val_accuracy: 0.7591
Epoch 185/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 6.5977e-10 - accuracy: 0.8027 - val_loss: 3.4582 - val_accuracy: 0.7639
Epoch 186/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 7.6748e-10 - accuracy: 0.8040 - val_loss: 3.5013 - val_accuracy: 0.7648
Epoch 187/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 7.2036e-10 - accuracy: 0.8064 - val_loss: 3.5158 - val_accuracy: 0.7713
Epoch 188/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 6.4630e-10 - accuracy: 0.8088 - val_loss: 3.5548 - val_accuracy: 0.7659
Epoch 189/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 7.2036e-10 - accuracy: 0.8102 - val_loss: 3.5711 - val_accuracy: 0.7695
Epoch 190/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 5.3185e-10 - accuracy: 0.8158 - val_loss: 3.6277 - val_accuracy: 0.7740
Epoch 191/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 5.3185e-10 - accuracy: 0.8175 - val_loss: 3.6788 - val_accuracy: 0.7761
Epoch 192/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 5.7898e-10 - accuracy: 0.8196 - val_loss: 3.7305 - val_accuracy: 0.7817
Epoch 193/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 5.5878e-10 - accuracy: 0.8231 - val_loss: 3.7664 - val_accuracy: 0.7800
Epoch 194/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 6.3957e-10 - accuracy: 0.8246 - val_loss: 3.8238 - val_accuracy: 0.7856
Epoch 195/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 5.3185e-10 - accuracy: 0.8274 - val_loss: 3.8742 - val_accuracy: 0.7917
Epoch 196/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 3.3662e-10 - accuracy: 0.8320 - val_loss: 3.9159 - val_accuracy: 0.7904
Epoch 197/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 4.3087e-10 - accuracy: 0.8328 - val_loss: 3.9620 - val_accuracy: 0.7897
Epoch 198/200
17707/17707 [==============================] - 20s 1ms/sample - loss: 3.8719e-04 - accuracy: 0.8373 - val_loss: 4.3451 - val_accuracy: 0.8171
Epoch 199/200
17707/17707 [==============================] - 19s 1ms/sample - loss: 8.1781e-04 - accuracy: 0.8515 - val_loss: 4.3174 - val_accuracy: 0.8205
Epoch 200/200
17707/17707 [==============================] - 19s 1ms/sample - loss: 0.0030 - accuracy: 0.8472 - val_loss: 4.2113 - val_accuracy: 0.8294
WARNING:tensorflow:From /home/khanhi83/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1781: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
INFO:tensorflow:Assets written to: model/assets
/home/khanhi83/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3058: DtypeWarning: Columns (2,5,9,12,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,87,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,117,118,119,121,122,123,124,125,126,127,128,130,131,132,145,146,148,215,224) have mixed types. Specify dtype option on import or set low_memory=False.
  interactivity=interactivity, compiler=compiler, result=result)
Dropped 3198 records because Text was null or NaN
Dropped 4438 records because False Warning was null or NaN
Converting text to feature matrix
Training on 17707 sample, validating on 932 samples
Number of features: 2285
/home/khanhi83/anaconda3/lib/python3.7/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.
  "10 in version 0.20 to 100 in 0.22.", FutureWarning)
Training accuracy with Random Forest: 0.9952561134014797
Validation accuracy with Random Forest: 0.9281115879828327
/home/khanhi83/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3058: DtypeWarning: Columns (2,5,9,12,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,87,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,117,118,119,121,122,123,124,125,126,127,128,130,131,132,145,146,148,215,224) have mixed types. Specify dtype option on import or set low_memory=False.
  interactivity=interactivity, compiler=compiler, result=result)
Dropped 3198 records because Text was null or NaN
Dropped 4438 records because False Warning was null or NaN
Converting text to feature matrix
Training on 17707 sample, validating on 932 samples
Number of features: 2285
Training accuracy with kNN: 0.9435816343818829
Validation accuracy with kNN: 0.9334763948497854<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.24991358451434498, 'fraction_numerical': 0.20286035257518148, 'mean_word_length': 4.143222222222223, 'pattern_counts': {'":': 4, '<': 17, '<?xml version=': 0, '>': 22, 'https://': 1, 'lorem ipsum': 0, 'www.': 1, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 12, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '272129', 'license': 'MIT', 'n_tokens_mistral': 22896, 'n_tokens_neox': 16349, 'n_words': 3767, 'path': '.ipynb_checkpoints/Rotorcraft_Safety-checkpoint.ipynb', 'repo_name': 'hikmatkhan/Rotorcraft-Safety', 'size': '46288'}