commit cf4d8b8e457e9636ac264cd700a9aeaa642a0113 parent a6bfcc4f3fcba60a559a19c22e0e7ffcf4bf7a49 Author: MTRNord <mtrnord1@gmail.com> Date: Sat, 1 Oct 2022 02:37:39 +0200 Move to a transfer training approach to not get issues due to the dataset. (hyperparameter search disabled as training it needs ~200-400GB which I currently dont have free) Diffstat:
297 files changed, 584 insertions(+), 978 deletions(-)
diff --git a/Cargo.lock b/Cargo.lock @@ -290,12 +290,11 @@ dependencies = [ [[package]] name = "crossbeam-utils" -version = "0.8.11" +version = "0.8.12" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "51887d4adc7b564537b15adcfb307936f8075dfcd5f00dde9a9f1d29383682bc" +checksum = "edbafec5fa1f196ca66527c1b12c2ec4745ca14b50f1ad8f9f6f720b55d11fac" dependencies = [ "cfg-if", - "once_cell", ] [[package]] diff --git a/Dockerfile b/Dockerfile @@ -8,6 +8,6 @@ RUN cargo build --release ENV MODEL_PATH /app/models/matrix_spam # Copy the model files to the image -COPY ./models/spam_keras_1664303305.1441052 /app/models/matrix_spam +COPY ./models/spam_keras_1664583738.3207538 /app/models/matrix_spam CMD ["./target/release/model_server"] \ No newline at end of file diff --git a/README.md b/README.md @@ -18,6 +18,11 @@ Newlines will be stripped anyway and URLs tend to break the model result. To train the model, run `python3 model_v2.py`. This will train the model and save it to `./model/`. Please make sure you installed tensorflow. +#### Notes about the data + +Please ensure to remove all urls, html tags and new lines. Also make sure to strip duplicate whitespace. +All of these reduce accuracy easily. + ### Running the server To run the server, run `cargo run --release`. This will start the server on port `3000`. diff --git a/crates/model_server/Cargo.toml b/crates/model_server/Cargo.toml @@ -14,7 +14,8 @@ linkify = "0.9.0" once_cell = "1.15.0" serde = {version = "1.0", features = ["derive"]} serde_json = "1.0" -tensorflow = {version = "0.19.1", features = ["tensorflow_gpu"]} +#tensorflow = {version = "0.19.1", features = ["tensorflow_gpu"]} +tensorflow = {version = "0.19.1"} tokio = {version = "1.0", features = ["full"]} tracing = "0.1" tracing-subscriber = {version = "0.3", features = ["env-filter"]} diff --git a/crates/model_server/src/main.rs b/crates/model_server/src/main.rs @@ -53,12 +53,9 @@ async fn test(Json(payload): Json<TestData>) -> impl IntoResponse { let graph = GRAPH.get().unwrap(); let session = &bundle.session; let meta = bundle.meta_graph_def(); - debug!("Signatures: {:#?}", meta.signatures()); let signature = meta .get_signature(tensorflow::DEFAULT_SERVING_SIGNATURE_DEF_KEY) .unwrap(); - debug!("Inputs: {:#?}", signature.inputs()); - debug!("Outputs: {:#?}", signature.outputs()); let input_info = signature.get_input("input_1").unwrap(); let output_info = signature.get_output("output_1").unwrap(); @@ -131,8 +128,9 @@ async fn submit_for_review( // Sanitize // We remove newlines, html tags and links - let sanitized = payload.input_data.replace(['\r', '\n'], " "); - let mut sanitized = strip::strip_tags(&sanitized); + let sanitized = strip::strip_tags(&payload.input_data); + let sanitized = sanitized.replace(['\r', '\n'], " "); + let mut sanitized = trim_whitespace(&sanitized); let mut finder = LinkFinder::new(); let cloned_sanitized = sanitized.clone(); finder.url_must_have_scheme(false); @@ -156,6 +154,17 @@ async fn submit_for_review( StatusCode::OK } +fn trim_whitespace(s: &str) -> String { + let mut new_str = s.trim().to_owned(); + let mut prev = ' '; // The initial value doesn't really matter + new_str.retain(|ch| { + let result = ch != ' ' || prev != ' '; + prev = ch; + result + }); + new_str +} + #[derive(Deserialize, Serialize)] #[cfg_attr(test, derive(schemars::JsonSchema))] struct TestData { diff --git a/dataset_analysis.ipynb b/dataset_analysis.ipynb @@ -0,0 +1,145 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook is mainly to debug the dataset and to see how the data is distributed. It is also used to generate the dataset statistics." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np # numerical computing \n", + "import pandas as pd # data analysis, working with DataFrames\n", + "import seaborn as sns\n", + "import csv\n", + "\n", + "df = pd.read_csv(\"./input/MatrixData\", sep='\\t', quoting=csv.QUOTE_NONE, encoding='utf-8')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<AxesSubplot: xlabel='label', ylabel='count'>" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.countplot(x=df['label']) # countplot for label" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<AxesSubplot: ylabel='count'>" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.countplot(x=[len(df.loc[i]['message']) for i in range(len(df))])" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "we have 88584 words in our Dataframe\n", + "the average word count in every scentence is 15\n" + ] + }, + { + "data": { + "text/plain": [ + "([20, 6, 28, 11, 13], 88584, 15)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "text_words_lengths = [len(df.loc[i]['message'].split()) for i in range(0, len(df))]\n", + "total_length = np.sum(text_words_lengths)\n", + "text_words_mean = int(np.mean(text_words_lengths))\n", + "print('we have ' + str(total_length) + ' words in our Dataframe')\n", + "print('the average word count in every scentence is ' + str(text_words_mean))\n", + "text_words_lengths[:5], total_length, text_words_mean" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.6 64-bit", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/input/MatrixData b/input/MatrixData @@ -5598,4 +5598,21 @@ ham O thanks, i'll take a look ham Hello all you Element experts, I'm setting up a homelab with Synapse and Element-web. 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datetime.now().strftime("%Y%m%d-%H%M%S") + "_hypermodel" -hypermodel_tensorboard_callback = keras.callbacks.TensorBoard( - log_dir=hypermodel_logdir) +hypermodel_logdir = ( + "logs/scalars/" + datetime.now().strftime("%Y%m%d-%H%M%S") + "_hypermodel" +) +hypermodel_tensorboard_callback = keras.callbacks.TensorBoard(log_dir=hypermodel_logdir) -hypertuner_logdir = "hypertuner_logs/scalars/" + \ - datetime.now().strftime("%Y%m%d-%H%M%S") -hypertuner_tensorboard_callback = keras.callbacks.TensorBoard( - log_dir=hypertuner_logdir) +hypertuner_logdir = "hypertuner_logs/scalars/" + datetime.now().strftime( + "%Y%m%d-%H%M%S" +) +hypertuner_tensorboard_callback = keras.callbacks.TensorBoard(log_dir=hypertuner_logdir) # Define the checkpoint directory to store the checkpoints. -checkpoint_dir = './training_checkpoints' +checkpoint_dir = "./training_checkpoints" # Define the name of the checkpoint files. checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt_{epoch}") @@ -44,263 +43,396 @@ progress_bar = tf.keras.callbacks.ProgbarLogger() class SpamDectionModel(tf.keras.Model): - def __init__(self, vectorize_layer, vocab_size, embedding_dim, max_length, hp_units, hp_dropout, hp_l2): + def __init__(self, vectorize_layer, hp_embedding_dim, hp_dense1, hp_dropout, hp_l2): super(SpamDectionModel, self).__init__() - #self.input_layer = tf.keras.Input(shape=(1,), dtype=tf.string) - self.vectorize_layer = vectorize_layer - self.embedding = tf.keras.layers.Embedding( - vocab_size + 1, embedding_dim, input_length=max_length, name="text_input") - self.glob_average_pooling_1d = tf.keras.layers.GlobalAveragePooling1D() - self.dropout = tf.keras.layers.Dropout(hp_dropout,) - self.dense1 = tf.keras.layers.Dense(units=hp_units, activation='relu', - kernel_regularizer=tf.keras.regularizers.l2(hp_l2)) - # tf.keras.layers.Dense(6, activation='relu', - # kernel_regularizer=tf.keras.regularizers.l2(0.0001)), + # self.vectorize_layer = vectorize_layer + # self.embedding = tf.keras.layers.Embedding( + # input_dim=len(vectorize_layer.get_vocabulary()) + 1, + # output_dim=hp_embedding_dim, + # name="text_input", + # embeddings_initializer="uniform", + # # mask_zero=True, + # ) + self.dropout = tf.keras.layers.Dropout( + hp_dropout, + ) + self.dense1 = tf.keras.layers.Dense( + hp_dense1, + activation="relu", + # kernel_regularizer=tf.keras.regularizers.l2(hp_l2), + ) self.dense2 = tf.keras.layers.Dense( - 1, activation='sigmoid', name="score_output") + 1, activation="sigmoid", name="score_output" + ) + # self.glob_average_pooling_1d = tf.keras.layers.GlobalAveragePooling1D() + self.use_layer = hub.KerasLayer( + "https://tfhub.dev/google/universal-sentence-encoder/4", + trainable=False, + input_shape=[], + dtype=tf.string, + name="USE", + ) @tf.function def call(self, x, training=False): - #x = self.input_layer(x) - x = self.vectorize_layer(x) - x = self.embedding(x) - x = self.glob_average_pooling_1d(x) + # x = self.vectorize_layer(x) + # x = self.embedding(x) + # x = self.glob_average_pooling_1d(x) + x = self.use_layer(x) if training: x = self.dropout(x, training=training) x = self.dense1(x) - if training: - x = self.dropout(x, training=training) + # if training: + # x = self.dropout(x, training=training) return self.dense2(x) class SpamDectionHyperModel(kt.HyperModel): - def __init__(self, vectorize_layer, vocab_size, embedding_dim, max_length,): + def __init__(self, vectorize_layer): super(SpamDectionHyperModel, self).__init__() self.vectorize_layer = vectorize_layer - self.vocab_size = vocab_size - self.embedding_dim = embedding_dim - self.max_length = max_length def build(self, hp): # Tune the number of units in the first Dense layer # Choose an optimal value between 6-512 - hp_units = hp.Int('units', min_value=6, max_value=512, step=12) - hp_dropout = hp.Float('dropout', min_value=.1, max_value=.9, step=.01) - hp_l2 = hp.Float('l2', min_value=0.0001, max_value=0.001, step=0.0001) - model = SpamDectionModel(self.vectorize_layer, - self.vocab_size, self.embedding_dim, self.max_length, hp_units, hp_dropout, hp_l2) + hp_dense1 = hp.Int("dense1", min_value=6, max_value=512, step=12) + hp_embedding_dim = hp.Int( + "embedding_dim", min_value=300, max_value=512, step=16 + ) + hp_dropout = hp.Float("dropout", min_value=0.1, max_value=0.9, step=0.1) + hp_l2 = hp.Float("l2", min_value=0.0001, max_value=0.001, step=0.0001) + model = SpamDectionModel( + self.vectorize_layer, + hp_embedding_dim + 1, + hp_dense1, + hp_dropout, + hp_l2, + ) # Adam was best so far # tf.keras.optimizers.Nadam() has similar results to Adam but a bit worse. second best - hp_learning_rate = hp.Choice('learning_rate', values=[ - 1e-2, 1e-3, 1e-4, 1e-5, ]) - opt = tf.keras.optimizers.Adam(learning_rate=hp_learning_rate) - # opt = tf.keras.optimizers.Nadam() - model.compile(loss=tf.keras.losses.BinaryCrossentropy(), - optimizer=opt, metrics=['accuracy']) - # print(model.summary()) + hp_learning_rate = hp.Choice( + "learning_rate", + values=[ + 1e-2, + 1e-3, + 1e-4, + 1e-5, + ], + ) + model.compile( + loss=tf.keras.losses.BinaryCrossentropy(), + optimizer=tf.keras.optimizers.Adam(learning_rate=hp_learning_rate), + metrics=["accuracy"], + ) return model def remove_stopwords(input_text): - ''' + """ Function to remove English stopwords from a Pandas Series. Parameters: input_text : text to clean Output: cleaned Pandas Series - ''' - stopwords_list = stopwords.words('english') + """ + stopwords_list = stopwords.words("english") # Some words which might indicate a certain sentiment are kept via a whitelist whitelist = ["n't", "not", "no"] words = input_text.split() - clean_words = [word for word in words if ( - word not in stopwords_list or word in whitelist) and len(word) > 1] + clean_words = [ + word + for word in words + if (word not in stopwords_list or word in whitelist) and len(word) > 1 + ] return " ".join(clean_words) -@tf.function -def change_labels(x): return 1 if x == "spam" else 0 +def change_labels(x): + return 1 if x == "spam" else 0 def tokenize_data(data): - # tokenizer = Tokenizer(num_words=vocab_size, oov_token=oov_tok) - - # tokenizer.fit_on_texts(training_sentences) - - # sequences = tokenizer.texts_to_sequences(training_sentences) - # padded = pad_sequences(sequences, maxlen=max_length, padding=padding_type, - # truncating=trunc_type) - - # testing_sequences = tokenizer.texts_to_sequences(testing_sentences) - # testing_padded = pad_sequences(testing_sequences, maxlen=max_length, - # padding=padding_type, truncating=trunc_type) - vectorize_layer = tf.keras.layers.TextVectorization( max_tokens=vocab_size, - output_mode='int', - output_sequence_length=max_length) + output_mode="int", + standardize="lower_and_strip_punctuation", + ) # Now that the vocab layer has been created, call `adapt` on the text-only # dataset to create the vocabulary. You don't have to batch, but for large # datasets this means we're not keeping spare copies of the dataset. vectorize_layer.adapt(data) - # Create the model that uses the vectorize text layer - # model = tf.keras.models.Sequential() - # Start by creating an explicit input layer. It needs to have a shape of - # (1,) (because we need to guarantee that there is exactly one string - # input per batch), and the dtype needs to be 'string'. - # model.add(tf.keras.Input(shape=(1,), dtype=tf.string)) + # vocab = np.array(vectorize_layer.get_vocabulary()) + # print(f"First 20 of Vocab: {vocab[:20]}") - # The first layer in our model is the vectorization layer. After this - # layer, we have a tensor of shape (batch_size, max_len) containing vocab - # indices. - # model.add(vectorize_layer) - - # Now, the model can map strings to integers, and you can add an embedding - # layer to map these integers to learned embeddings. - # padded = model.predict(training_sentences) - # testing_padded = model.predict(testing_sentences) - - return vectorize_layer # , tokenizer + return vectorize_layer def load_data(): - data = pd.read_csv('./input/MatrixData', sep='\t') + data = pd.read_csv( + "./input/MatrixData", sep="\t", quoting=csv.QUOTE_NONE, encoding="utf-8" + ) # Remove unknown data.dropna(inplace=True) - data['label'] = data['label'].apply(change_labels) + data["label"] = data["label"].apply(change_labels) # Remove stopwords - data['message'] = data['message'].apply(remove_stopwords) + data["message"] = data["message"].apply(remove_stopwords) # Shuffle data data = data.sample(frac=1).reset_index(drop=True) # Split data into messages and label sets - sentences = data['message'].tolist() - labels = data['label'].tolist() + sentences = data["message"].tolist() + labels = data["label"].tolist() + + # Separate out the sentences and labels into training and test sets + # training_size = int(len(sentences) * 0.8) + training_size = int(len(sentences) * 0.7) + training_sentences = sentences[0:training_size] + testing_sentences = sentences[training_size:] + training_labels = labels[0:training_size] + testing_labels = labels[training_size:] # Make labels into numpy arrays for use with the network later - labels_final = np.array(labels) - sentences_final = np.array(sentences) + testing_labels_final = np.array(testing_labels) + training_labels_final = np.array(training_labels) + training_sentences_final = np.array(training_sentences) + testing_sentences_final = np.array(testing_sentences) vectorize_layer = tokenize_data(sentences) - return vectorize_layer, sentences_final, labels_final - - -def train_hyperparamters(data, labels_final, tuner): - stop_early = tf.keras.callbacks.EarlyStopping( - monitor='val_loss', patience=5) - tuner.search(data, labels_final, epochs=5, verbose=1, validation_split=0.3, - callbacks=[hypertuner_tensorboard_callback, stop_early, progress_bar]) + return ( + vectorize_layer, + training_sentences_final, + testing_sentences_final, + training_labels_final, + testing_labels_final, + ) + + +def train_hyperparamters( + training_sentences_final, + testing_sentences_final, + training_labels_final, + testing_labels_final, + tuner, +): + stop_early = tf.keras.callbacks.EarlyStopping(monitor="val_loss", patience=3) + tuner.search( + training_sentences_final, + training_labels_final, + epochs=5, + verbose=1, + validation_data=(testing_sentences_final, testing_labels_final), + callbacks=[hypertuner_tensorboard_callback, stop_early, progress_bar], + ) # Get the optimal hyperparameters best_hps = tuner.get_best_hyperparameters(num_trials=1)[0] - print(f""" + print( + f""" The hyperparameter search is complete. The optimal number of units in the first densely-connected - layer is {best_hps.get('units')} and the optimal learning rate for the optimizer is {best_hps.get('learning_rate')}. + layer is {best_hps.get('dense1')} and the optimal learning rate for the optimizer is {best_hps.get('learning_rate')}. The optimal dropout rate is {best_hps.get('dropout')} and the optimal l2 rate is {best_hps.get('l2')}. - """) + The optimal embedding_dim is {best_hps.get('embedding_dim')}. + """ + ) return best_hps -def train_model(data, labels_final, best_hps, tuner): +def train_model( + vectorize_layer, + training_sentences_final, + testing_sentences_final, + training_labels_final, + testing_labels_final, + # best_hps, + # tuner, +): + model = SpamDectionModel( + vectorize_layer, + 0, + 64, + 0.2, + 0, + ) + model.compile( + loss=tf.keras.losses.BinaryCrossentropy(), + optimizer=tf.keras.optimizers.Adam(), + metrics=["accuracy"], + ) num_epochs = 200 - model = tuner.hypermodel.build(best_hps) - history = model.fit(data, - labels_final, - epochs=num_epochs, - verbose=1, validation_split=0.3, - callbacks=[tensorboard_callback, - progress_bar],) - val_acc_per_epoch = history.history['val_accuracy'] - best_epoch = val_acc_per_epoch.index(max(val_acc_per_epoch)) + 5 - print('Best epoch: %d' % (best_epoch,)) - print("Average train loss: ", np.average(history.history['loss'])) - print("Average test loss: ", np.average(history.history['val_loss'])) - - hypermodel = tuner.hypermodel.build(best_hps) - hypermodel_history = hypermodel.fit(data, labels_final, verbose=1, - epochs=best_epoch, validation_split=0.3, - callbacks=[hypermodel_tensorboard_callback, - tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_prefix, - save_weights_only=True), progress_bar - # es_callback - ] - ) - - print("Average train loss(hypermodel_history): ", - np.average(hypermodel_history.history['loss'])) - print("Average test loss(hypermodel_history): ", - np.average(hypermodel_history.history['val_loss'])) - - return hypermodel + # model = tuner.hypermodel.build(best_hps) + history = model.fit( + training_sentences_final, + training_labels_final, + epochs=num_epochs, + verbose=1, + validation_data=(testing_sentences_final, testing_labels_final), + callbacks=[tensorboard_callback, progress_bar], + ) + val_acc_per_epoch = history.history["val_accuracy"] + best_epoch = val_acc_per_epoch.index(max(val_acc_per_epoch)) + 1 + print("Best epoch: %d" % (best_epoch,)) + print("Average train loss: ", np.average(history.history["loss"])) + print("Average test loss: ", np.average(history.history["val_loss"])) + + model = SpamDectionModel( + vectorize_layer, + 0, + 64, + 0.2, + 0, + ) + model.compile( + loss=tf.keras.losses.BinaryCrossentropy(), + optimizer=tf.keras.optimizers.Adam(), + metrics=["accuracy"], + ) + # hypermodel = tuner.hypermodel.build(best_hps) + hypermodel_history = model.fit( + training_sentences_final, + training_labels_final, + verbose=1, + epochs=best_epoch, + validation_data=(testing_sentences_final, testing_labels_final), + callbacks=[ + hypermodel_tensorboard_callback, + # tf.keras.callbacks.ModelCheckpoint( + # filepath=checkpoint_prefix, save_weights_only=True + # ), + progress_bar + # es_callback + ], + ) + + print( + "Average train loss(hypermodel_history): ", + np.average(hypermodel_history.history["loss"]), + ) + print( + "Average test loss(hypermodel_history): ", + np.average(hypermodel_history.history["val_loss"]), + ) + + return model def test_model(vectorize_layer, model): # Use the model to predict whether a message is spam - text_messages = ['Greg, can you call me back once you get this?', - 'Congrats on your new iPhone! Click here to claim your prize...', - 'Really like that new photo of you', - 'Did you hear the news today? Terrible what has happened...', - 'Attend this free COVID webinar today: Book your session now...', - 'Are you coming to the party tonight?', - 'Your parcel has gone missing', - 'Do not forget to bring friends!', - 'You have won a million dollars! Fill out your bank details here...', - 'Looking forward to seeing you again', - 'oh wow https://github.com/MGCodesandStats/tensorflow-nlp/blob/master/spam%20detection%20tensorflow%20v2.ipynb works really good on spam detection. Guess I go with that as the base model then lol :D', - 'ayo', - 'Almost all my spam is coming to my non-gmail address actually', - 'Oh neat I think I found the sizing sweetspot for my data :D', - 'would never click on buttons in gmail :D always expecting there to be a bug in gmail that allows js to grab your google credentials :D XSS via email lol. I am too scared for touching spam in gmail', - 'back to cacophony ', - 'Room version 11 when', - 'skip 11 and go straight to 12', - '100 events should clear out any events that might be causing a request to fail lol'] + text_messages = [ + "Greg, can you call me back once you get this?", + "Congrats on your new iPhone! Click here to claim your prize...", + "Really like that new photo of you", + "Did you hear the news today? Terrible what has happened...", + "Attend this free COVID webinar today: Book your session now...", + "Are you coming to the party tonight?", + "Your parcel has gone missing", + "Do not forget to bring friends!", + "You have won a million dollars! Fill out your bank details here...", + "Looking forward to seeing you again", + "oh wow https://github.com/MGCodesandStats/tensorflow-nlp/blob/master/spam%20detection%20tensorflow%20v2.ipynb works really good on spam detection. Guess I go with that as the base model then lol :D", + "ayo", + "Almost all my spam is coming to my non-gmail address actually", + "Oh neat I think I found the sizing sweetspot for my data :D", + "would never click on buttons in gmail :D always expecting there to be a bug in gmail that allows js to grab your google credentials :D XSS via email lol. I am too scared for touching spam in gmail", + "back to cacophony ", + "Room version 11 when", + "skip 11 and go straight to 12", + "100 events should clear out any events that might be causing a request to fail lol", + ] + + spam_no_spam = [ + False, + True, + False, + False, + True, + False, + False, + False, + True, + False, + False, + False, + False, + False, + False, + False, + False, + False, + False, + ] # print(text_messages) # Create the sequences - padding_type = 'post' - # sample_sequences = tokenizer.texts_to_sequences(text_messages) - # fakes_padded = pad_sequences( - # sample_sequences, padding=padding_type, maxlen=max_length) - classes = model.predict(text_messages) + classes = model.predict(np.array(text_messages)) # The closer the class is to 1, the more likely that the message is spam for x in range(len(text_messages)): - print(f"Message: \"{text_messages[x]}\"") + print(f'Message: "{text_messages[x]}"') print(f"Likeliness of spam in percentage: {classes[x][0]:.5f}") - print('\n') + spam = classes[x][0] >= 0.8 + if spam: + print("Vote by AI: Spam") + else: + print("Vote by AI: Not Spam") + if spam_no_spam[x] != spam: + print("Model failed to predict correctly") + else: + print("Model predicted correctly") + print("\n") def main(): + tf.get_logger().setLevel("ERROR") print("TensorFlow version:", tf.__version__) print("[Step 1/6] Loading data") - vectorize_layer, data, labels_final = load_data() - model = SpamDectionHyperModel( - vectorize_layer, vocab_size, embedding_dim, max_length) - # print("[Step 2/6] Plotting model") - # tf.keras.utils.plot_model(model, rankdir="LR", show_shapes=True) - tuner = kt.Hyperband(model, hyperband_iterations=2, - objective='val_accuracy', - max_epochs=200, - directory='hyper_tuning', - project_name='spam-keras') - print("[Step 3/6] Tuning hypervalues") - best_hps = train_hyperparamters(data, labels_final, tuner) + ( + vectorize_layer, + training_sentences_final, + testing_sentences_final, + training_labels_final, + testing_labels_final, + ) = load_data() + + # model = SpamDectionHyperModel(vectorize_layer) + # tuner = kt.Hyperband( + # model, + # objective="val_accuracy", + # max_epochs=100, + # directory="hyper_tuning", + # project_name="spam-keras", + # ) + # print("[Step 3/6] Tuning hypervalues") + # best_hps = train_hyperparamters( + # training_sentences_final, + # testing_sentences_final, + # training_labels_final, + # testing_labels_final, + # tuner, + # ) + print("[Step 4/6] Training model") - model = train_model(data, labels_final, best_hps, tuner) + model = train_model( + vectorize_layer, + training_sentences_final, + testing_sentences_final, + training_labels_final, + testing_labels_final, + # best_hps, + # tuner, + ) print("[Step 5/6] Saving model") export_path = f"./models/spam_keras_{time.time()}" - print('Exporting trained model to', export_path) + print("Exporting trained model to", export_path) model.save(export_path) diff --git 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Didnt fully review it. Has some unsafe due to using simd" + [[audits.axum-auth]] who = "MTRNord <mtrnord1@gmail.com>" criteria = "safe-to-deploy" version = "0.3.0" notes = "Only extracts headers and does basic splitting. No unsafe code and seems sane" +[[audits.linkify]] +who = "MTRNord <mtrnord1@gmail.com>" +criteria = "safe-to-deploy" +version = "0.9.0" +notes = "Seems to be doing what it claims, has benches and tests that make sense." + [[audits.openssl-sys]] who = "MTRNord <mtrnord1@gmail.com>" criteria = "safe-to-deploy" @@ -19,3 +31,9 @@ criteria = "safe-to-deploy" delta = "1.21.1 -> 1.21.2" notes = "Removes a dependecy and inlines it. Code seems simple enough and has safety explained. Looks sane." +[[audits.voca_rs]] +who = "MTRNord <mtrnord1@gmail.com>" +criteria = "safe-to-deploy" +version = "1.14.0" +notes = "Does what it claims, no unsafe code, seems to have no tests" +