360 KiB
360 KiB
Wieloklasowa klasyfikacja tekstu
Celem projektu było stworzenie modelu, który klasyfikuje wypowiedzi zgłaszane przez studentów z Indii przygotowujących się do egzaminów JEE Advanced, JEE Mains i NEET do jednej z kilku możliwych klas opisujących przedmiot związany z wypowiedzią.
Import bibliotek
import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
from sklearn.model_selection import train_test_split
import re
import nltk
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('wordnet')
nltk.download('omw-1.4')
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer
from nltk.corpus import stopwords
stopwords.words('english')
import string
string.punctuation
from nltk.stem.porter import PorterStemmer
import optuna
from keras.optimizers import Adam
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau, TensorBoard, CSVLogger
from keras.models import Sequential
from keras.layers import Embedding, LSTM, Dense, Conv1D, GlobalMaxPooling1D, GRU, GlobalAveragePooling1DPodgląd zbioru danych
df = pd.read_csv("subjects-questions.csv")
df.head()| eng | Subject | |
|---|---|---|
| 0 | An anti-forest measure is\nA. Afforestation\nB... | Biology |
| 1 | Among the following organic acids, the acid pr... | Chemistry |
| 2 | If the area of two similar triangles are equal... | Maths |
| 3 | In recent year, there has been a growing\nconc... | Biology |
| 4 | Which of the following statement\nregarding tr... | Physics |
subject_counts = df['Subject'].value_counts()
plt.figure(figsize=(10, 6))
subject_counts.plot(kind='bar', color='skyblue')
plt.title('Liczba wystąpień poszczególnych przedmiotów')
plt.xlabel('Przedmiot')
plt.ylabel('Liczba wystąpień')
plt.xticks(rotation=0)
plt.grid(axis='y', linestyle='--', linewidth=0.7)
plt.show()Preprocessing danych
df = pd.get_dummies(df, columns=['Subject'])
df.head()| eng | Subject_Biology | Subject_Chemistry | Subject_Maths | Subject_Physics | |
|---|---|---|---|---|---|
| 0 | An anti-forest measure is\nA. Afforestation\nB... | True | False | False | False |
| 1 | Among the following organic acids, the acid pr... | False | True | False | False |
| 2 | If the area of two similar triangles are equal... | False | False | True | False |
| 3 | In recent year, there has been a growing\nconc... | True | False | False | False |
| 4 | Which of the following statement\nregarding tr... | False | False | False | True |
def remove_stopwords(text):
stop_words = set(stopwords.words('english'))
words = word_tokenize(text)
filtered_words = [word for word in words if word.lower() not in stop_words]
return ' '.join(filtered_words)
def lemmatize_text(text):
lemmatizer = WordNetLemmatizer()
words = word_tokenize(text)
lemmatized_words = [lemmatizer.lemmatize(word) for word in words]
return ' '.join(lemmatized_words)
def clean_text(text):
text = re.sub(r'\n', ' ', text)
text = re.sub(r'[^a-zA-Z\s]', '', text)
text = text.lower()
text = ' '.join(text.split())
return text
def stem_text(text):
stemmer = PorterStemmer()
token_words = word_tokenize(text)
stem_sentence = [stemmer.stem(word) for word in token_words]
return " ".join(stem_sentence)df['prepared_text'] = df['eng'].apply(remove_stopwords)
df['prepared_text'] = df['eng'].apply(lemmatize_text)
df['prepared_text'] = df['eng'].apply(clean_text)
df['prepared_text'] = df['eng'].apply(stem_text)
df.head()| eng | Subject_Biology | Subject_Chemistry | Subject_Maths | Subject_Physics | prepared_text | |
|---|---|---|---|---|---|---|
| 0 | An anti-forest measure is\nA. Afforestation\nB... | True | False | False | False | an anti-forest measur is a. afforest b . selec... |
| 1 | Among the following organic acids, the acid pr... | False | True | False | False | among the follow organ acid , the acid present... |
| 2 | If the area of two similar triangles are equal... | False | False | True | False | if the area of two similar triangl are equal ,... |
| 3 | In recent year, there has been a growing\nconc... | True | False | False | False | in recent year , there ha been a grow concern ... |
| 4 | Which of the following statement\nregarding tr... | False | False | False | True | which of the follow statement regard transform... |
df.to_csv("subjects-questions-prepared.csv")X = df['prepared_text']
y = df[['Subject_Biology', 'Subject_Chemistry', 'Subject_Maths', 'Subject_Physics']]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
print(len(X))
print(len(X_train))
print(len(X_test))122519 98015 24504
max_words = df['prepared_text'].str.len().max()
tokenizer = Tokenizer(num_words=max_words, oov_token='<OOV>')
tokenizer.fit_on_texts(X_train)
tokenizer.fit_on_texts(X_test)
train_sequences = tokenizer.texts_to_sequences(X_train)
test_sequences = tokenizer.texts_to_sequences(X_test)
padded_train = pad_sequences(train_sequences, maxlen=5, padding='post', truncating='post')
padded_test = pad_sequences(test_sequences, maxlen=5, padding='post', truncating='post')
Definicja modeli
Model 1
Model składający się z warstw Embedding, LSTM, GlobalAveragePooling1D oraz kilku warstw Dense
model_1 = Sequential()
model_1.add(Embedding(input_dim=10000, output_dim=16))
model_1.add(LSTM(units=64, return_sequences=True))
model_1.add(GlobalAveragePooling1D())
model_1.add(Dense(254, activation='relu'))
model_1.add(Dense(128, activation='relu'))
model_1.add(Dense(4, activation='softmax'))
model_1.compile(optimizer='adam', loss='categorical_crossentropy',
metrics=['accuracy',
tf.keras.metrics.Precision(name='precision'),
tf.keras.metrics.Recall(name='recall'),
tf.keras.metrics.AUC(name='auc'),
tf.keras.metrics.TruePositives(name='tp'),
tf.keras.metrics.FalsePositives(name='fp'),
tf.keras.metrics.TrueNegatives(name='tn'),
tf.keras.metrics.FalseNegatives(name='fn')])
model_1.summary()Model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding_1 (Embedding) (None, None, 16) 160000
lstm_1 (LSTM) (None, None, 64) 20736
global_average_pooling1d_1 (None, 64) 0
(GlobalAveragePooling1D)
dense (Dense) (None, 254) 16510
dense_1 (Dense) (None, 128) 32640
dense_2 (Dense) (None, 4) 516
=================================================================
Total params: 230,402
Trainable params: 230,402
Non-trainable params: 0
_________________________________________________________________
Model 2
Model konwolucyjny z warstwą Embedding i jedną warstwą konwolucyjną 1D
model_2 = Sequential()
model_2.add(Embedding(input_dim=10000, output_dim=16))
model_2.add(Conv1D(filters=128, kernel_size=5, activation='relu'))
model_2.add(GlobalMaxPooling1D())
model_2.add(Dense(4, activation='softmax'))
model_2.compile(optimizer='adam', loss='categorical_crossentropy',
metrics=['accuracy',
tf.keras.metrics.Precision(name='precision'),
tf.keras.metrics.Recall(name='recall'),
tf.keras.metrics.AUC(name='auc'),
tf.keras.metrics.TruePositives(name='tp'),
tf.keras.metrics.FalsePositives(name='fp'),
tf.keras.metrics.TrueNegatives(name='tn'),
tf.keras.metrics.FalseNegatives(name='fn')])
model_2.summary()Model: "sequential_2"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding_2 (Embedding) (None, None, 16) 160000
conv1d (Conv1D) (None, None, 128) 10368
global_max_pooling1d (Globa (None, 128) 0
lMaxPooling1D)
dense_3 (Dense) (None, 4) 516
=================================================================
Total params: 170,884
Trainable params: 170,884
Non-trainable params: 0
_________________________________________________________________
Model 3
Model rekurencyjny z warstwą Embedding i jedną warstwą GRU
model_3 = Sequential()
model_3.add(Embedding(input_dim=10000, output_dim=16))
model_3.add(GRU(units=64))
model_3.add(Dense(units=4, activation='softmax'))
model_3.compile(optimizer='adam', loss='categorical_crossentropy',
metrics=['accuracy',
tf.keras.metrics.Precision(name='precision'),
tf.keras.metrics.Recall(name='recall'),
tf.keras.metrics.AUC(name='auc'),
tf.keras.metrics.TruePositives(name='tp'),
tf.keras.metrics.FalsePositives(name='fp'),
tf.keras.metrics.TrueNegatives(name='tn'),
tf.keras.metrics.FalseNegatives(name='fn')])
model_3.summary()Model: "sequential_4"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding_4 (Embedding) (None, None, 16) 160000
gru_1 (GRU) (None, 64) 15744
dense_5 (Dense) (None, 4) 260
=================================================================
Total params: 176,004
Trainable params: 176,004
Non-trainable params: 0
_________________________________________________________________
Trening i walidacja modeli
def checkpoint(model_name):
return ModelCheckpoint(filepath='best_model_'+ model_name +'.h5', monitor='val_accuracy', save_best_only=True)
early_stopping = EarlyStopping(monitor='val_loss', patience=5)
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=3, min_lr=0.0001)history = model_1.fit(padded_train, y_train,
steps_per_epoch = 30,
epochs = 100,
validation_split=0.2,
verbose = 1,
validation_steps = 50,
callbacks=[checkpoint("1"), early_stopping, reduce_lr],
)Epoch 1/100 30/30 [==============================] - 1s 47ms/step - loss: 0.3951 - accuracy: 0.8490 - precision: 0.8730 - recall: 0.8248 - auc: 0.9727 - tp: 64673.0000 - fp: 9412.0000 - tn: 225824.0000 - fn: 13739.0000 - val_loss: 0.5155 - val_accuracy: 0.8170 - val_precision: 0.8458 - val_recall: 0.7902 - val_auc: 0.9559 - val_tp: 15490.0000 - val_fp: 2823.0000 - val_tn: 55986.0000 - val_fn: 4113.0000 - lr: 2.0000e-04 Epoch 2/100 30/30 [==============================] - 1s 41ms/step - loss: 0.3950 - accuracy: 0.8484 - precision: 0.8742 - recall: 0.8232 - auc: 0.9727 - tp: 64545.0000 - fp: 9290.0000 - tn: 225946.0000 - fn: 13867.0000 - val_loss: 0.5165 - val_accuracy: 0.8174 - val_precision: 0.8442 - val_recall: 0.7933 - val_auc: 0.9558 - val_tp: 15552.0000 - val_fp: 2871.0000 - val_tn: 55938.0000 - val_fn: 4051.0000 - lr: 2.0000e-04 Epoch 3/100 30/30 [==============================] - 1s 40ms/step - loss: 0.3944 - accuracy: 0.8490 - precision: 0.8732 - recall: 0.8252 - auc: 0.9727 - tp: 64706.0000 - fp: 9392.0000 - tn: 225844.0000 - fn: 13706.0000 - val_loss: 0.5172 - val_accuracy: 0.8171 - val_precision: 0.8456 - val_recall: 0.7905 - val_auc: 0.9557 - val_tp: 15496.0000 - val_fp: 2829.0000 - val_tn: 55980.0000 - val_fn: 4107.0000 - lr: 2.0000e-04 Epoch 4/100 30/30 [==============================] - 1s 45ms/step - loss: 0.3938 - accuracy: 0.8494 - precision: 0.8744 - recall: 0.8242 - auc: 0.9728 - tp: 64624.0000 - fp: 9286.0000 - tn: 225950.0000 - fn: 13788.0000 - val_loss: 0.5176 - val_accuracy: 0.8169 - val_precision: 0.8424 - val_recall: 0.7944 - val_auc: 0.9558 - val_tp: 15573.0000 - val_fp: 2914.0000 - val_tn: 55895.0000 - val_fn: 4030.0000 - lr: 2.0000e-04 Epoch 5/100 30/30 [==============================] - 1s 45ms/step - loss: 0.3924 - accuracy: 0.8496 - precision: 0.8735 - recall: 0.8261 - auc: 0.9730 - tp: 64774.0000 - fp: 9377.0000 - tn: 225859.0000 - fn: 13638.0000 - val_loss: 0.5172 - val_accuracy: 0.8171 - val_precision: 0.8435 - val_recall: 0.7935 - val_auc: 0.9557 - val_tp: 15555.0000 - val_fp: 2886.0000 - val_tn: 55923.0000 - val_fn: 4048.0000 - lr: 1.0000e-04 Epoch 6/100 30/30 [==============================] - 1s 44ms/step - loss: 0.3923 - accuracy: 0.8496 - precision: 0.8736 - recall: 0.8257 - auc: 0.9730 - tp: 64747.0000 - fp: 9369.0000 - tn: 225867.0000 - fn: 13665.0000 - val_loss: 0.5176 - val_accuracy: 0.8168 - val_precision: 0.8436 - val_recall: 0.7939 - val_auc: 0.9557 - val_tp: 15563.0000 - val_fp: 2886.0000 - val_tn: 55923.0000 - val_fn: 4040.0000 - lr: 1.0000e-04
score = model_1.evaluate(padded_test, y_test, verbose=0)
print("Loss:", score[0])
print("Accuracy:", score[1])
print("Precision:", score[2])
print("Recall:", score[3])
print("AUC:", score[4])
print("True Positives:", score[5])
print("False Positives:", score[6])
print("True Negatives:", score[7])
print("False Negatives:", score[8])Loss: 0.5143917202949524 Accuracy: 0.8162341117858887 Precision: 0.8397026062011719 Recall: 0.7926868796348572 AUC: 0.9560295343399048 True Positives: 19424.0 False Positives: 3708.0 True Negatives: 69804.0 False Negatives: 5080.0
history = model_2.fit(padded_train, y_train,
steps_per_epoch = 30,
epochs = 100,
validation_split=0.2,
verbose = 1,
validation_steps = 50,
callbacks=[checkpoint("2"), early_stopping, reduce_lr],
)Epoch 1/100 30/30 [==============================] - 1s 40ms/step - loss: 0.3013 - accuracy: 0.8852 - precision: 0.9036 - recall: 0.8683 - auc: 0.9840 - tp: 68085.0000 - fp: 7263.0000 - tn: 227973.0000 - fn: 10327.0000 - val_loss: 0.5586 - val_accuracy: 0.8162 - val_precision: 0.8363 - val_recall: 0.8018 - val_auc: 0.9532 - val_tp: 15717.0000 - val_fp: 3076.0000 - val_tn: 55733.0000 - val_fn: 3886.0000 - lr: 1.0000e-04 Epoch 2/100 30/30 [==============================] - 1s 33ms/step - loss: 0.3011 - accuracy: 0.8855 - precision: 0.9035 - recall: 0.8686 - auc: 0.9840 - tp: 68112.0000 - fp: 7272.0000 - tn: 227964.0000 - fn: 10300.0000 - val_loss: 0.5590 - val_accuracy: 0.8162 - val_precision: 0.8359 - val_recall: 0.8020 - val_auc: 0.9532 - val_tp: 15722.0000 - val_fp: 3087.0000 - val_tn: 55722.0000 - val_fn: 3881.0000 - lr: 1.0000e-04 Epoch 3/100 30/30 [==============================] - 1s 36ms/step - loss: 0.3008 - accuracy: 0.8855 - precision: 0.9037 - recall: 0.8684 - auc: 0.9840 - tp: 68090.0000 - fp: 7254.0000 - tn: 227982.0000 - fn: 10322.0000 - val_loss: 0.5597 - val_accuracy: 0.8164 - val_precision: 0.8361 - val_recall: 0.8016 - val_auc: 0.9531 - val_tp: 15713.0000 - val_fp: 3080.0000 - val_tn: 55729.0000 - val_fn: 3890.0000 - lr: 1.0000e-04 Epoch 4/100 30/30 [==============================] - 1s 34ms/step - loss: 0.3006 - accuracy: 0.8857 - precision: 0.9037 - recall: 0.8688 - auc: 0.9840 - tp: 68127.0000 - fp: 7260.0000 - tn: 227976.0000 - fn: 10285.0000 - val_loss: 0.5602 - val_accuracy: 0.8163 - val_precision: 0.8358 - val_recall: 0.8017 - val_auc: 0.9531 - val_tp: 15715.0000 - val_fp: 3088.0000 - val_tn: 55721.0000 - val_fn: 3888.0000 - lr: 1.0000e-04 Epoch 5/100 30/30 [==============================] - 1s 40ms/step - loss: 0.3003 - accuracy: 0.8856 - precision: 0.9037 - recall: 0.8690 - auc: 0.9841 - tp: 68143.0000 - fp: 7259.0000 - tn: 227977.0000 - fn: 10269.0000 - val_loss: 0.5607 - val_accuracy: 0.8161 - val_precision: 0.8356 - val_recall: 0.8018 - val_auc: 0.9530 - val_tp: 15717.0000 - val_fp: 3092.0000 - val_tn: 55717.0000 - val_fn: 3886.0000 - lr: 1.0000e-04 Epoch 6/100 30/30 [==============================] - 2s 56ms/step - loss: 0.3000 - accuracy: 0.8861 - precision: 0.9037 - recall: 0.8693 - auc: 0.9841 - tp: 68160.0000 - fp: 7267.0000 - tn: 227969.0000 - fn: 10252.0000 - val_loss: 0.5614 - val_accuracy: 0.8161 - val_precision: 0.8354 - val_recall: 0.8016 - val_auc: 0.9530 - val_tp: 15713.0000 - val_fp: 3097.0000 - val_tn: 55712.0000 - val_fn: 3890.0000 - lr: 1.0000e-04
score = model_2.evaluate(padded_test, y_test, verbose=0)
print("Loss:", score[0])
print("Accuracy:", score[1])
print("Precision:", score[2])
print("Recall:", score[3])
print("AUC:", score[4])
print("True Positives:", score[5])
print("False Positives:", score[6])
print("True Negatives:", score[7])
print("False Negatives:", score[8])Loss: 0.5664545297622681 Accuracy: 0.8139079213142395 Precision: 0.8310044407844543 Recall: 0.7984818816184998 AUC: 0.9523454308509827 True Positives: 19566.0 False Positives: 3979.0 True Negatives: 69533.0 False Negatives: 4938.0
history = model_3.fit(padded_train, y_train,
steps_per_epoch = 30,
epochs = 100,
validation_split=0.2,
verbose = 1,
validation_steps = 50,
callbacks=[checkpoint("3"), early_stopping, reduce_lr],
)Epoch 1/100 30/30 [==============================] - 1s 43ms/step - loss: 0.3993 - accuracy: 0.8482 - precision: 0.8744 - recall: 0.8214 - auc: 0.9722 - tp: 64409.0000 - fp: 9252.0000 - tn: 225984.0000 - fn: 14003.0000 - val_loss: 0.4974 - val_accuracy: 0.8173 - val_precision: 0.8476 - val_recall: 0.7934 - val_auc: 0.9581 - val_tp: 15553.0000 - val_fp: 2796.0000 - val_tn: 56013.0000 - val_fn: 4050.0000 - lr: 2.0000e-04 Epoch 2/100 30/30 [==============================] - 1s 39ms/step - loss: 0.3986 - accuracy: 0.8482 - precision: 0.8742 - recall: 0.8219 - auc: 0.9722 - tp: 64443.0000 - fp: 9272.0000 - tn: 225964.0000 - fn: 13969.0000 - val_loss: 0.4981 - val_accuracy: 0.8167 - val_precision: 0.8492 - val_recall: 0.7896 - val_auc: 0.9580 - val_tp: 15479.0000 - val_fp: 2749.0000 - val_tn: 56060.0000 - val_fn: 4124.0000 - lr: 2.0000e-04 Epoch 3/100 30/30 [==============================] - 1s 42ms/step - loss: 0.3982 - accuracy: 0.8486 - precision: 0.8749 - recall: 0.8209 - auc: 0.9723 - tp: 64366.0000 - fp: 9204.0000 - tn: 226032.0000 - fn: 14046.0000 - val_loss: 0.4987 - val_accuracy: 0.8157 - val_precision: 0.8468 - val_recall: 0.7926 - val_auc: 0.9580 - val_tp: 15537.0000 - val_fp: 2811.0000 - val_tn: 55998.0000 - val_fn: 4066.0000 - lr: 2.0000e-04 Epoch 4/100 30/30 [==============================] - 1s 33ms/step - loss: 0.3976 - accuracy: 0.8488 - precision: 0.8760 - recall: 0.8207 - auc: 0.9724 - tp: 64354.0000 - fp: 9111.0000 - tn: 226125.0000 - fn: 14058.0000 - val_loss: 0.4988 - val_accuracy: 0.8161 - val_precision: 0.8468 - val_recall: 0.7929 - val_auc: 0.9580 - val_tp: 15543.0000 - val_fp: 2811.0000 - val_tn: 55998.0000 - val_fn: 4060.0000 - lr: 2.0000e-04 Epoch 5/100 30/30 [==============================] - 1s 39ms/step - loss: 0.3965 - accuracy: 0.8492 - precision: 0.8751 - recall: 0.8225 - auc: 0.9725 - tp: 64493.0000 - fp: 9206.0000 - tn: 226030.0000 - fn: 13919.0000 - val_loss: 0.4986 - val_accuracy: 0.8176 - val_precision: 0.8478 - val_recall: 0.7936 - val_auc: 0.9580 - val_tp: 15556.0000 - val_fp: 2792.0000 - val_tn: 56017.0000 - val_fn: 4047.0000 - lr: 1.0000e-04 Epoch 6/100 30/30 [==============================] - 1s 35ms/step - loss: 0.3963 - accuracy: 0.8493 - precision: 0.8747 - recall: 0.8235 - auc: 0.9726 - tp: 64574.0000 - fp: 9254.0000 - tn: 225982.0000 - fn: 13838.0000 - val_loss: 0.4986 - val_accuracy: 0.8174 - val_precision: 0.8474 - val_recall: 0.7938 - val_auc: 0.9580 - val_tp: 15560.0000 - val_fp: 2802.0000 - val_tn: 56007.0000 - val_fn: 4043.0000 - lr: 1.0000e-04
score = model_3.evaluate(padded_test, y_test, verbose=0)
print("Loss:", score[0])
print("Accuracy:", score[1])
print("Precision:", score[2])
print("Recall:", score[3])
print("AUC:", score[4])
print("True Positives:", score[5])
print("False Positives:", score[6])
print("True Negatives:", score[7])
print("False Negatives:", score[8])Loss: 0.5007005929946899 Accuracy: 0.816642165184021 Precision: 0.8432700037956238 Recall: 0.7930949926376343 AUC: 0.9578227400779724 True Positives: 19434.0 False Positives: 3612.0 True Negatives: 69900.0 False Negatives: 5070.0
Eksperymenty
Trening i waldacja modelu 2 na wszystkich epokach
history = model_2.fit(padded_train, y_train,
steps_per_epoch = 30,
epochs = 100,
validation_split=0.2,
verbose = 1,
validation_steps = 50,
callbacks=[checkpoint("2_all_epochs"), reduce_lr],
)Epoch 1/100 30/30 [==============================] - 1s 42ms/step - loss: 0.2998 - accuracy: 0.8859 - precision: 0.9040 - recall: 0.8694 - auc: 0.9841 - tp: 68169.0000 - fp: 7240.0000 - tn: 227996.0000 - fn: 10243.0000 - val_loss: 0.5619 - val_accuracy: 0.8164 - val_precision: 0.8352 - val_recall: 0.8020 - val_auc: 0.9529 - val_tp: 15722.0000 - val_fp: 3102.0000 - val_tn: 55707.0000 - val_fn: 3881.0000 - lr: 1.0000e-04 Epoch 2/100 30/30 [==============================] - 1s 37ms/step - loss: 0.2996 - accuracy: 0.8859 - precision: 0.9039 - recall: 0.8695 - auc: 0.9841 - tp: 68178.0000 - fp: 7252.0000 - tn: 227984.0000 - fn: 10234.0000 - val_loss: 0.5626 - val_accuracy: 0.8164 - val_precision: 0.8357 - val_recall: 0.8019 - val_auc: 0.9528 - val_tp: 15719.0000 - val_fp: 3091.0000 - val_tn: 55718.0000 - val_fn: 3884.0000 - lr: 1.0000e-04 Epoch 3/100 30/30 [==============================] - 1s 41ms/step - loss: 0.2994 - accuracy: 0.8860 - precision: 0.9041 - recall: 0.8691 - auc: 0.9842 - tp: 68144.0000 - fp: 7225.0000 - tn: 228011.0000 - fn: 10268.0000 - val_loss: 0.5631 - val_accuracy: 0.8162 - val_precision: 0.8349 - val_recall: 0.8017 - val_auc: 0.9528 - val_tp: 15716.0000 - val_fp: 3108.0000 - val_tn: 55701.0000 - val_fn: 3887.0000 - lr: 1.0000e-04 Epoch 4/100 30/30 [==============================] - 1s 39ms/step - loss: 0.2991 - accuracy: 0.8862 - precision: 0.9040 - recall: 0.8694 - auc: 0.9842 - tp: 68169.0000 - fp: 7242.0000 - tn: 227994.0000 - fn: 10243.0000 - val_loss: 0.5638 - val_accuracy: 0.8163 - val_precision: 0.8351 - val_recall: 0.8019 - val_auc: 0.9528 - val_tp: 15720.0000 - val_fp: 3105.0000 - val_tn: 55704.0000 - val_fn: 3883.0000 - lr: 1.0000e-04 Epoch 5/100 30/30 [==============================] - 1s 36ms/step - loss: 0.2989 - accuracy: 0.8863 - precision: 0.9043 - recall: 0.8698 - auc: 0.9842 - tp: 68201.0000 - fp: 7218.0000 - tn: 228018.0000 - fn: 10211.0000 - val_loss: 0.5643 - val_accuracy: 0.8164 - val_precision: 0.8353 - val_recall: 0.8022 - val_auc: 0.9528 - val_tp: 15725.0000 - val_fp: 3100.0000 - val_tn: 55709.0000 - val_fn: 3878.0000 - lr: 1.0000e-04 Epoch 6/100 30/30 [==============================] - 1s 38ms/step - loss: 0.2986 - accuracy: 0.8863 - precision: 0.9042 - recall: 0.8699 - auc: 0.9842 - tp: 68207.0000 - fp: 7226.0000 - tn: 228010.0000 - fn: 10205.0000 - val_loss: 0.5650 - val_accuracy: 0.8163 - val_precision: 0.8345 - val_recall: 0.8020 - val_auc: 0.9526 - val_tp: 15722.0000 - val_fp: 3117.0000 - val_tn: 55692.0000 - val_fn: 3881.0000 - lr: 1.0000e-04 Epoch 7/100 30/30 [==============================] - 1s 38ms/step - loss: 0.2984 - accuracy: 0.8866 - precision: 0.9044 - recall: 0.8700 - auc: 0.9843 - tp: 68216.0000 - fp: 7214.0000 - tn: 228022.0000 - fn: 10196.0000 - val_loss: 0.5655 - val_accuracy: 0.8160 - val_precision: 0.8350 - val_recall: 0.8019 - val_auc: 0.9526 - val_tp: 15719.0000 - val_fp: 3106.0000 - val_tn: 55703.0000 - val_fn: 3884.0000 - lr: 1.0000e-04 Epoch 8/100 30/30 [==============================] - 1s 46ms/step - loss: 0.2982 - accuracy: 0.8866 - precision: 0.9044 - recall: 0.8701 - auc: 0.9843 - tp: 68230.0000 - fp: 7213.0000 - tn: 228023.0000 - fn: 10182.0000 - val_loss: 0.5660 - val_accuracy: 0.8165 - val_precision: 0.8351 - val_recall: 0.8023 - val_auc: 0.9526 - val_tp: 15727.0000 - val_fp: 3105.0000 - val_tn: 55704.0000 - val_fn: 3876.0000 - lr: 1.0000e-04 Epoch 9/100 30/30 [==============================] - 1s 43ms/step - loss: 0.2980 - accuracy: 0.8865 - precision: 0.9044 - recall: 0.8701 - auc: 0.9843 - tp: 68229.0000 - fp: 7216.0000 - tn: 228020.0000 - fn: 10183.0000 - val_loss: 0.5667 - val_accuracy: 0.8160 - val_precision: 0.8344 - val_recall: 0.8021 - val_auc: 0.9525 - val_tp: 15724.0000 - val_fp: 3120.0000 - val_tn: 55689.0000 - val_fn: 3879.0000 - lr: 1.0000e-04 Epoch 10/100 30/30 [==============================] - 1s 42ms/step - loss: 0.2977 - accuracy: 0.8864 - precision: 0.9043 - recall: 0.8700 - auc: 0.9843 - tp: 68217.0000 - fp: 7222.0000 - tn: 228014.0000 - fn: 10195.0000 - val_loss: 0.5673 - val_accuracy: 0.8157 - val_precision: 0.8344 - val_recall: 0.8022 - val_auc: 0.9525 - val_tp: 15725.0000 - val_fp: 3122.0000 - val_tn: 55687.0000 - val_fn: 3878.0000 - lr: 1.0000e-04 Epoch 11/100 30/30 [==============================] - 1s 40ms/step - loss: 0.2975 - accuracy: 0.8870 - precision: 0.9046 - recall: 0.8702 - auc: 0.9843 - tp: 68236.0000 - fp: 7199.0000 - tn: 228037.0000 - fn: 10176.0000 - val_loss: 0.5678 - val_accuracy: 0.8162 - val_precision: 0.8349 - val_recall: 0.8020 - val_auc: 0.9525 - val_tp: 15722.0000 - val_fp: 3109.0000 - val_tn: 55700.0000 - val_fn: 3881.0000 - lr: 1.0000e-04 Epoch 12/100 30/30 [==============================] - 1s 49ms/step - loss: 0.2973 - accuracy: 0.8869 - precision: 0.9047 - recall: 0.8703 - auc: 0.9844 - tp: 68241.0000 - fp: 7188.0000 - tn: 228048.0000 - fn: 10171.0000 - val_loss: 0.5684 - val_accuracy: 0.8160 - val_precision: 0.8347 - val_recall: 0.8022 - val_auc: 0.9524 - val_tp: 15725.0000 - val_fp: 3115.0000 - val_tn: 55694.0000 - val_fn: 3878.0000 - lr: 1.0000e-04 Epoch 13/100 30/30 [==============================] - 2s 55ms/step - loss: 0.2971 - accuracy: 0.8869 - precision: 0.9047 - recall: 0.8707 - auc: 0.9844 - tp: 68270.0000 - fp: 7194.0000 - tn: 228042.0000 - fn: 10142.0000 - val_loss: 0.5690 - val_accuracy: 0.8160 - val_precision: 0.8348 - val_recall: 0.8026 - val_auc: 0.9524 - val_tp: 15734.0000 - val_fp: 3114.0000 - val_tn: 55695.0000 - val_fn: 3869.0000 - lr: 1.0000e-04 Epoch 14/100 30/30 [==============================] - 1s 40ms/step - loss: 0.2968 - accuracy: 0.8870 - precision: 0.9047 - recall: 0.8704 - auc: 0.9844 - tp: 68252.0000 - fp: 7189.0000 - tn: 228047.0000 - fn: 10160.0000 - val_loss: 0.5696 - val_accuracy: 0.8163 - val_precision: 0.8345 - val_recall: 0.8020 - val_auc: 0.9523 - val_tp: 15722.0000 - val_fp: 3117.0000 - val_tn: 55692.0000 - val_fn: 3881.0000 - lr: 1.0000e-04 Epoch 15/100 30/30 [==============================] - 1s 41ms/step - loss: 0.2966 - accuracy: 0.8870 - precision: 0.9046 - recall: 0.8706 - auc: 0.9844 - tp: 68265.0000 - fp: 7203.0000 - tn: 228033.0000 - fn: 10147.0000 - val_loss: 0.5702 - val_accuracy: 0.8160 - val_precision: 0.8346 - val_recall: 0.8017 - val_auc: 0.9523 - val_tp: 15716.0000 - val_fp: 3114.0000 - val_tn: 55695.0000 - val_fn: 3887.0000 - lr: 1.0000e-04 Epoch 16/100 30/30 [==============================] - 1s 39ms/step - loss: 0.2964 - accuracy: 0.8873 - precision: 0.9050 - recall: 0.8707 - auc: 0.9844 - tp: 68275.0000 - fp: 7165.0000 - tn: 228071.0000 - fn: 10137.0000 - val_loss: 0.5708 - val_accuracy: 0.8160 - val_precision: 0.8347 - val_recall: 0.8021 - val_auc: 0.9522 - val_tp: 15723.0000 - val_fp: 3113.0000 - val_tn: 55696.0000 - val_fn: 3880.0000 - lr: 1.0000e-04 Epoch 17/100 30/30 [==============================] - 1s 37ms/step - loss: 0.2962 - accuracy: 0.8871 - precision: 0.9046 - recall: 0.8706 - auc: 0.9845 - tp: 68265.0000 - fp: 7196.0000 - tn: 228040.0000 - fn: 10147.0000 - val_loss: 0.5713 - val_accuracy: 0.8160 - val_precision: 0.8343 - val_recall: 0.8022 - val_auc: 0.9522 - val_tp: 15725.0000 - val_fp: 3123.0000 - val_tn: 55686.0000 - val_fn: 3878.0000 - lr: 1.0000e-04 Epoch 18/100 30/30 [==============================] - 1s 35ms/step - loss: 0.2960 - accuracy: 0.8871 - precision: 0.9048 - recall: 0.8710 - auc: 0.9845 - tp: 68294.0000 - fp: 7186.0000 - tn: 228050.0000 - fn: 10118.0000 - val_loss: 0.5719 - val_accuracy: 0.8160 - val_precision: 0.8342 - val_recall: 0.8019 - val_auc: 0.9522 - val_tp: 15720.0000 - val_fp: 3124.0000 - val_tn: 55685.0000 - val_fn: 3883.0000 - lr: 1.0000e-04 Epoch 19/100 30/30 [==============================] - 1s 36ms/step - loss: 0.2958 - accuracy: 0.8871 - precision: 0.9048 - recall: 0.8708 - auc: 0.9845 - tp: 68284.0000 - fp: 7183.0000 - tn: 228053.0000 - fn: 10128.0000 - val_loss: 0.5724 - val_accuracy: 0.8157 - val_precision: 0.8341 - val_recall: 0.8022 - val_auc: 0.9521 - val_tp: 15725.0000 - val_fp: 3128.0000 - val_tn: 55681.0000 - val_fn: 3878.0000 - lr: 1.0000e-04 Epoch 20/100 30/30 [==============================] - 1s 35ms/step - loss: 0.2955 - accuracy: 0.8873 - precision: 0.9049 - recall: 0.8709 - auc: 0.9845 - tp: 68291.0000 - fp: 7174.0000 - tn: 228062.0000 - fn: 10121.0000 - val_loss: 0.5731 - val_accuracy: 0.8160 - val_precision: 0.8342 - val_recall: 0.8021 - val_auc: 0.9521 - val_tp: 15724.0000 - val_fp: 3125.0000 - val_tn: 55684.0000 - val_fn: 3879.0000 - lr: 1.0000e-04 Epoch 21/100 30/30 [==============================] - 1s 35ms/step - loss: 0.2954 - accuracy: 0.8875 - precision: 0.9049 - recall: 0.8711 - auc: 0.9846 - tp: 68306.0000 - fp: 7178.0000 - tn: 228058.0000 - fn: 10106.0000 - val_loss: 0.5736 - val_accuracy: 0.8156 - val_precision: 0.8338 - val_recall: 0.8018 - val_auc: 0.9520 - val_tp: 15717.0000 - val_fp: 3132.0000 - val_tn: 55677.0000 - val_fn: 3886.0000 - lr: 1.0000e-04 Epoch 22/100 30/30 [==============================] - 1s 37ms/step - loss: 0.2952 - accuracy: 0.8873 - precision: 0.9050 - recall: 0.8712 - auc: 0.9846 - tp: 68314.0000 - fp: 7171.0000 - tn: 228065.0000 - fn: 10098.0000 - val_loss: 0.5743 - val_accuracy: 0.8153 - val_precision: 0.8338 - val_recall: 0.8018 - val_auc: 0.9520 - val_tp: 15717.0000 - val_fp: 3133.0000 - val_tn: 55676.0000 - val_fn: 3886.0000 - lr: 1.0000e-04 Epoch 23/100 30/30 [==============================] - 1s 39ms/step - loss: 0.2950 - accuracy: 0.8876 - precision: 0.9051 - recall: 0.8711 - auc: 0.9846 - tp: 68302.0000 - fp: 7163.0000 - tn: 228073.0000 - fn: 10110.0000 - val_loss: 0.5748 - val_accuracy: 0.8155 - val_precision: 0.8336 - val_recall: 0.8019 - val_auc: 0.9520 - val_tp: 15719.0000 - val_fp: 3138.0000 - val_tn: 55671.0000 - val_fn: 3884.0000 - lr: 1.0000e-04 Epoch 24/100 30/30 [==============================] - 1s 35ms/step - loss: 0.2947 - accuracy: 0.8876 - precision: 0.9052 - recall: 0.8714 - auc: 0.9846 - tp: 68331.0000 - fp: 7159.0000 - tn: 228077.0000 - fn: 10081.0000 - val_loss: 0.5754 - val_accuracy: 0.8152 - val_precision: 0.8331 - val_recall: 0.8019 - val_auc: 0.9519 - val_tp: 15720.0000 - val_fp: 3150.0000 - val_tn: 55659.0000 - val_fn: 3883.0000 - lr: 1.0000e-04 Epoch 25/100 30/30 [==============================] - 1s 37ms/step - loss: 0.2945 - accuracy: 0.8875 - precision: 0.9050 - recall: 0.8714 - auc: 0.9846 - tp: 68329.0000 - fp: 7170.0000 - tn: 228066.0000 - fn: 10083.0000 - val_loss: 0.5759 - val_accuracy: 0.8155 - val_precision: 0.8333 - val_recall: 0.8020 - val_auc: 0.9518 - val_tp: 15722.0000 - val_fp: 3145.0000 - val_tn: 55664.0000 - val_fn: 3881.0000 - lr: 1.0000e-04 Epoch 26/100 30/30 [==============================] - 1s 35ms/step - loss: 0.2943 - accuracy: 0.8876 - precision: 0.9051 - recall: 0.8715 - auc: 0.9847 - tp: 68333.0000 - fp: 7165.0000 - tn: 228071.0000 - fn: 10079.0000 - val_loss: 0.5766 - val_accuracy: 0.8154 - val_precision: 0.8335 - val_recall: 0.8019 - val_auc: 0.9518 - val_tp: 15719.0000 - val_fp: 3141.0000 - val_tn: 55668.0000 - val_fn: 3884.0000 - lr: 1.0000e-04 Epoch 27/100 30/30 [==============================] - 1s 35ms/step - loss: 0.2941 - accuracy: 0.8875 - precision: 0.9053 - recall: 0.8714 - auc: 0.9847 - tp: 68332.0000 - fp: 7146.0000 - tn: 228090.0000 - fn: 10080.0000 - val_loss: 0.5771 - val_accuracy: 0.8151 - val_precision: 0.8334 - val_recall: 0.8017 - val_auc: 0.9517 - val_tp: 15716.0000 - val_fp: 3142.0000 - val_tn: 55667.0000 - val_fn: 3887.0000 - lr: 1.0000e-04 Epoch 28/100 30/30 [==============================] - 1s 39ms/step - loss: 0.2939 - accuracy: 0.8877 - precision: 0.9052 - recall: 0.8716 - auc: 0.9847 - tp: 68345.0000 - fp: 7159.0000 - tn: 228077.0000 - fn: 10067.0000 - val_loss: 0.5777 - val_accuracy: 0.8152 - val_precision: 0.8334 - val_recall: 0.8019 - val_auc: 0.9517 - val_tp: 15720.0000 - val_fp: 3142.0000 - val_tn: 55667.0000 - val_fn: 3883.0000 - lr: 1.0000e-04 Epoch 29/100 30/30 [==============================] - 1s 34ms/step - loss: 0.2937 - accuracy: 0.8876 - precision: 0.9051 - recall: 0.8717 - auc: 0.9847 - tp: 68350.0000 - fp: 7164.0000 - tn: 228072.0000 - fn: 10062.0000 - val_loss: 0.5782 - val_accuracy: 0.8156 - val_precision: 0.8334 - val_recall: 0.8019 - val_auc: 0.9517 - val_tp: 15720.0000 - val_fp: 3142.0000 - val_tn: 55667.0000 - val_fn: 3883.0000 - lr: 1.0000e-04 Epoch 30/100 30/30 [==============================] - 1s 34ms/step - loss: 0.2935 - accuracy: 0.8877 - precision: 0.9055 - recall: 0.8717 - auc: 0.9847 - tp: 68348.0000 - fp: 7132.0000 - tn: 228104.0000 - fn: 10064.0000 - val_loss: 0.5788 - val_accuracy: 0.8153 - val_precision: 0.8336 - val_recall: 0.8015 - val_auc: 0.9516 - val_tp: 15712.0000 - val_fp: 3136.0000 - val_tn: 55673.0000 - val_fn: 3891.0000 - lr: 1.0000e-04 Epoch 31/100 30/30 [==============================] - 1s 36ms/step - loss: 0.2933 - accuracy: 0.8881 - precision: 0.9054 - recall: 0.8720 - auc: 0.9847 - tp: 68374.0000 - fp: 7142.0000 - tn: 228094.0000 - fn: 10038.0000 - val_loss: 0.5796 - val_accuracy: 0.8147 - val_precision: 0.8333 - val_recall: 0.8019 - val_auc: 0.9515 - val_tp: 15719.0000 - val_fp: 3145.0000 - val_tn: 55664.0000 - val_fn: 3884.0000 - lr: 1.0000e-04 Epoch 32/100 30/30 [==============================] - 1s 37ms/step - loss: 0.2932 - accuracy: 0.8880 - precision: 0.9054 - recall: 0.8718 - auc: 0.9848 - tp: 68363.0000 - fp: 7147.0000 - tn: 228089.0000 - fn: 10049.0000 - val_loss: 0.5800 - val_accuracy: 0.8153 - val_precision: 0.8333 - val_recall: 0.8020 - val_auc: 0.9514 - val_tp: 15721.0000 - val_fp: 3144.0000 - val_tn: 55665.0000 - val_fn: 3882.0000 - lr: 1.0000e-04 Epoch 33/100 30/30 [==============================] - 1s 35ms/step - loss: 0.2929 - accuracy: 0.8882 - precision: 0.9055 - recall: 0.8719 - auc: 0.9848 - tp: 68366.0000 - fp: 7132.0000 - tn: 228104.0000 - fn: 10046.0000 - val_loss: 0.5806 - val_accuracy: 0.8153 - val_precision: 0.8329 - val_recall: 0.8014 - val_auc: 0.9515 - val_tp: 15710.0000 - val_fp: 3151.0000 - val_tn: 55658.0000 - val_fn: 3893.0000 - lr: 1.0000e-04 Epoch 34/100 30/30 [==============================] - 1s 37ms/step - loss: 0.2927 - accuracy: 0.8882 - precision: 0.9054 - recall: 0.8721 - auc: 0.9848 - tp: 68384.0000 - fp: 7146.0000 - tn: 228090.0000 - fn: 10028.0000 - val_loss: 0.5811 - val_accuracy: 0.8152 - val_precision: 0.8331 - val_recall: 0.8020 - val_auc: 0.9514 - val_tp: 15721.0000 - val_fp: 3150.0000 - val_tn: 55659.0000 - val_fn: 3882.0000 - lr: 1.0000e-04 Epoch 35/100 30/30 [==============================] - 1s 34ms/step - loss: 0.2926 - accuracy: 0.8880 - precision: 0.9054 - recall: 0.8721 - auc: 0.9848 - tp: 68383.0000 - fp: 7143.0000 - tn: 228093.0000 - fn: 10029.0000 - val_loss: 0.5817 - val_accuracy: 0.8151 - val_precision: 0.8329 - val_recall: 0.8015 - val_auc: 0.9514 - val_tp: 15712.0000 - val_fp: 3152.0000 - val_tn: 55657.0000 - val_fn: 3891.0000 - lr: 1.0000e-04 Epoch 36/100 30/30 [==============================] - 1s 36ms/step - loss: 0.2923 - accuracy: 0.8883 - precision: 0.9057 - recall: 0.8723 - auc: 0.9848 - tp: 68401.0000 - fp: 7118.0000 - tn: 228118.0000 - fn: 10011.0000 - val_loss: 0.5823 - val_accuracy: 0.8152 - val_precision: 0.8334 - val_recall: 0.8014 - val_auc: 0.9513 - val_tp: 15710.0000 - val_fp: 3140.0000 - val_tn: 55669.0000 - val_fn: 3893.0000 - lr: 1.0000e-04 Epoch 37/100 30/30 [==============================] - 1s 36ms/step - loss: 0.2922 - accuracy: 0.8883 - precision: 0.9056 - recall: 0.8720 - auc: 0.9849 - tp: 68378.0000 - fp: 7128.0000 - tn: 228108.0000 - fn: 10034.0000 - val_loss: 0.5829 - val_accuracy: 0.8153 - val_precision: 0.8330 - val_recall: 0.8018 - val_auc: 0.9513 - val_tp: 15717.0000 - val_fp: 3152.0000 - val_tn: 55657.0000 - val_fn: 3886.0000 - lr: 1.0000e-04 Epoch 38/100 30/30 [==============================] - 1s 36ms/step - loss: 0.2920 - accuracy: 0.8883 - precision: 0.9056 - recall: 0.8725 - auc: 0.9849 - tp: 68415.0000 - fp: 7133.0000 - tn: 228103.0000 - fn: 9997.0000 - val_loss: 0.5835 - val_accuracy: 0.8152 - val_precision: 0.8329 - val_recall: 0.8011 - val_auc: 0.9513 - val_tp: 15704.0000 - val_fp: 3150.0000 - val_tn: 55659.0000 - val_fn: 3899.0000 - lr: 1.0000e-04 Epoch 39/100 30/30 [==============================] - 1s 39ms/step - loss: 0.2918 - accuracy: 0.8886 - precision: 0.9056 - recall: 0.8724 - auc: 0.9849 - tp: 68409.0000 - fp: 7133.0000 - tn: 228103.0000 - fn: 10003.0000 - val_loss: 0.5840 - val_accuracy: 0.8153 - val_precision: 0.8334 - val_recall: 0.8016 - val_auc: 0.9512 - val_tp: 15714.0000 - val_fp: 3141.0000 - val_tn: 55668.0000 - val_fn: 3889.0000 - lr: 1.0000e-04 Epoch 40/100 30/30 [==============================] - 1s 42ms/step - loss: 0.2916 - accuracy: 0.8883 - precision: 0.9053 - recall: 0.8725 - auc: 0.9849 - tp: 68412.0000 - fp: 7159.0000 - tn: 228077.0000 - fn: 10000.0000 - val_loss: 0.5846 - val_accuracy: 0.8149 - val_precision: 0.8332 - val_recall: 0.8014 - val_auc: 0.9511 - val_tp: 15710.0000 - val_fp: 3145.0000 - val_tn: 55664.0000 - val_fn: 3893.0000 - lr: 1.0000e-04 Epoch 41/100 30/30 [==============================] - 1s 36ms/step - loss: 0.2915 - accuracy: 0.8885 - precision: 0.9053 - recall: 0.8725 - auc: 0.9849 - tp: 68414.0000 - fp: 7156.0000 - tn: 228080.0000 - fn: 9998.0000 - val_loss: 0.5852 - val_accuracy: 0.8146 - val_precision: 0.8326 - 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val_loss: 0.6080 - val_accuracy: 0.8134 - val_precision: 0.8307 - val_recall: 0.8000 - val_auc: 0.9492 - val_tp: 15682.0000 - val_fp: 3195.0000 - val_tn: 55614.0000 - val_fn: 3921.0000 - lr: 1.0000e-04 Epoch 83/100 30/30 [==============================] - 1s 38ms/step - loss: 0.2847 - accuracy: 0.8910 - precision: 0.9071 - recall: 0.8756 - auc: 0.9856 - tp: 68654.0000 - fp: 7030.0000 - tn: 228206.0000 - fn: 9758.0000 - val_loss: 0.6085 - val_accuracy: 0.8132 - val_precision: 0.8299 - val_recall: 0.8003 - val_auc: 0.9492 - val_tp: 15689.0000 - val_fp: 3216.0000 - val_tn: 55593.0000 - val_fn: 3914.0000 - lr: 1.0000e-04 Epoch 84/100 30/30 [==============================] - 1s 40ms/step - loss: 0.2845 - accuracy: 0.8911 - precision: 0.9074 - recall: 0.8756 - auc: 0.9856 - tp: 68660.0000 - fp: 7010.0000 - tn: 228226.0000 - fn: 9752.0000 - val_loss: 0.6090 - val_accuracy: 0.8131 - val_precision: 0.8304 - val_recall: 0.8003 - val_auc: 0.9491 - val_tp: 15688.0000 - val_fp: 3203.0000 - val_tn: 55606.0000 - val_fn: 3915.0000 - lr: 1.0000e-04 Epoch 85/100 30/30 [==============================] - 1s 38ms/step - loss: 0.2844 - accuracy: 0.8912 - precision: 0.9073 - recall: 0.8758 - auc: 0.9856 - tp: 68677.0000 - fp: 7016.0000 - tn: 228220.0000 - fn: 9735.0000 - val_loss: 0.6096 - val_accuracy: 0.8132 - val_precision: 0.8305 - val_recall: 0.8003 - val_auc: 0.9491 - val_tp: 15689.0000 - val_fp: 3202.0000 - val_tn: 55607.0000 - val_fn: 3914.0000 - lr: 1.0000e-04 Epoch 86/100 30/30 [==============================] - 1s 37ms/step - loss: 0.2842 - accuracy: 0.8913 - precision: 0.9074 - recall: 0.8757 - auc: 0.9856 - tp: 68668.0000 - fp: 7006.0000 - tn: 228230.0000 - fn: 9744.0000 - val_loss: 0.6101 - val_accuracy: 0.8132 - val_precision: 0.8307 - val_recall: 0.8000 - val_auc: 0.9491 - val_tp: 15682.0000 - val_fp: 3196.0000 - val_tn: 55613.0000 - val_fn: 3921.0000 - lr: 1.0000e-04 Epoch 87/100 30/30 [==============================] - 1s 36ms/step - loss: 0.2841 - accuracy: 0.8913 - precision: 0.9073 - recall: 0.8758 - auc: 0.9856 - tp: 68677.0000 - fp: 7017.0000 - tn: 228219.0000 - fn: 9735.0000 - val_loss: 0.6107 - val_accuracy: 0.8131 - val_precision: 0.8296 - val_recall: 0.7999 - val_auc: 0.9490 - val_tp: 15681.0000 - val_fp: 3221.0000 - val_tn: 55588.0000 - val_fn: 3922.0000 - lr: 1.0000e-04 Epoch 88/100 30/30 [==============================] - 1s 32ms/step - loss: 0.2840 - accuracy: 0.8913 - precision: 0.9076 - recall: 0.8757 - auc: 0.9857 - tp: 68662.0000 - fp: 6992.0000 - tn: 228244.0000 - fn: 9750.0000 - val_loss: 0.6112 - val_accuracy: 0.8132 - val_precision: 0.8304 - val_recall: 0.7999 - val_auc: 0.9490 - val_tp: 15681.0000 - val_fp: 3203.0000 - val_tn: 55606.0000 - val_fn: 3922.0000 - lr: 1.0000e-04 Epoch 89/100 30/30 [==============================] - 1s 33ms/step - loss: 0.2839 - accuracy: 0.8915 - precision: 0.9074 - recall: 0.8760 - auc: 0.9857 - tp: 68692.0000 - fp: 7012.0000 - tn: 228224.0000 - fn: 9720.0000 - val_loss: 0.6119 - val_accuracy: 0.8130 - val_precision: 0.8299 - val_recall: 0.7998 - val_auc: 0.9490 - val_tp: 15679.0000 - val_fp: 3214.0000 - val_tn: 55595.0000 - val_fn: 3924.0000 - lr: 1.0000e-04 Epoch 90/100 30/30 [==============================] - 1s 32ms/step - loss: 0.2837 - accuracy: 0.8914 - precision: 0.9075 - recall: 0.8761 - auc: 0.9857 - tp: 68696.0000 - fp: 6998.0000 - tn: 228238.0000 - fn: 9716.0000 - val_loss: 0.6123 - val_accuracy: 0.8131 - val_precision: 0.8298 - val_recall: 0.8003 - val_auc: 0.9489 - val_tp: 15689.0000 - val_fp: 3217.0000 - val_tn: 55592.0000 - val_fn: 3914.0000 - lr: 1.0000e-04 Epoch 91/100 30/30 [==============================] - 1s 34ms/step - loss: 0.2836 - accuracy: 0.8916 - precision: 0.9076 - recall: 0.8761 - auc: 0.9857 - tp: 68697.0000 - fp: 6994.0000 - tn: 228242.0000 - fn: 9715.0000 - val_loss: 0.6129 - val_accuracy: 0.8133 - val_precision: 0.8301 - val_recall: 0.8003 - val_auc: 0.9489 - val_tp: 15688.0000 - val_fp: 3212.0000 - val_tn: 55597.0000 - val_fn: 3915.0000 - lr: 1.0000e-04 Epoch 92/100 30/30 [==============================] - 1s 34ms/step - loss: 0.2834 - accuracy: 0.8916 - precision: 0.9078 - recall: 0.8760 - auc: 0.9857 - tp: 68690.0000 - fp: 6980.0000 - tn: 228256.0000 - fn: 9722.0000 - val_loss: 0.6134 - val_accuracy: 0.8130 - val_precision: 0.8301 - val_recall: 0.8004 - val_auc: 0.9488 - val_tp: 15691.0000 - val_fp: 3211.0000 - val_tn: 55598.0000 - val_fn: 3912.0000 - lr: 1.0000e-04 Epoch 93/100 30/30 [==============================] - 1s 35ms/step - loss: 0.2833 - accuracy: 0.8915 - precision: 0.9073 - recall: 0.8761 - auc: 0.9857 - tp: 68698.0000 - fp: 7021.0000 - tn: 228215.0000 - fn: 9714.0000 - val_loss: 0.6141 - val_accuracy: 0.8129 - val_precision: 0.8300 - val_recall: 0.8002 - val_auc: 0.9488 - val_tp: 15687.0000 - val_fp: 3214.0000 - val_tn: 55595.0000 - val_fn: 3916.0000 - lr: 1.0000e-04 Epoch 94/100 30/30 [==============================] - 1s 35ms/step - loss: 0.2832 - accuracy: 0.8919 - precision: 0.9074 - recall: 0.8765 - auc: 0.9857 - tp: 68730.0000 - fp: 7017.0000 - tn: 228219.0000 - fn: 9682.0000 - val_loss: 0.6144 - val_accuracy: 0.8132 - val_precision: 0.8301 - val_recall: 0.8005 - val_auc: 0.9488 - val_tp: 15692.0000 - val_fp: 3212.0000 - val_tn: 55597.0000 - val_fn: 3911.0000 - lr: 1.0000e-04 Epoch 95/100 30/30 [==============================] - 1s 38ms/step - loss: 0.2830 - accuracy: 0.8919 - precision: 0.9076 - recall: 0.8766 - auc: 0.9857 - tp: 68739.0000 - fp: 7000.0000 - tn: 228236.0000 - fn: 9673.0000 - val_loss: 0.6150 - val_accuracy: 0.8132 - val_precision: 0.8301 - val_recall: 0.8008 - val_auc: 0.9487 - val_tp: 15698.0000 - val_fp: 3212.0000 - val_tn: 55597.0000 - val_fn: 3905.0000 - lr: 1.0000e-04 Epoch 96/100 30/30 [==============================] - 1s 41ms/step - loss: 0.2829 - accuracy: 0.8917 - precision: 0.9075 - recall: 0.8763 - auc: 0.9857 - tp: 68714.0000 - fp: 7001.0000 - tn: 228235.0000 - fn: 9698.0000 - val_loss: 0.6155 - val_accuracy: 0.8130 - val_precision: 0.8302 - val_recall: 0.8005 - val_auc: 0.9487 - val_tp: 15692.0000 - val_fp: 3209.0000 - val_tn: 55600.0000 - val_fn: 3911.0000 - lr: 1.0000e-04 Epoch 97/100 30/30 [==============================] - 1s 37ms/step - loss: 0.2828 - accuracy: 0.8916 - precision: 0.9074 - recall: 0.8764 - auc: 0.9858 - tp: 68723.0000 - fp: 7014.0000 - tn: 228222.0000 - fn: 9689.0000 - val_loss: 0.6161 - val_accuracy: 0.8132 - val_precision: 0.8299 - val_recall: 0.8007 - val_auc: 0.9487 - val_tp: 15697.0000 - val_fp: 3217.0000 - val_tn: 55592.0000 - val_fn: 3906.0000 - lr: 1.0000e-04 Epoch 98/100 30/30 [==============================] - 1s 33ms/step - loss: 0.2827 - accuracy: 0.8918 - precision: 0.9075 - recall: 0.8768 - auc: 0.9858 - tp: 68749.0000 - fp: 7008.0000 - tn: 228228.0000 - fn: 9663.0000 - val_loss: 0.6167 - val_accuracy: 0.8132 - val_precision: 0.8300 - val_recall: 0.8005 - val_auc: 0.9486 - val_tp: 15693.0000 - val_fp: 3215.0000 - val_tn: 55594.0000 - val_fn: 3910.0000 - lr: 1.0000e-04 Epoch 99/100 30/30 [==============================] - 1s 36ms/step - loss: 0.2825 - accuracy: 0.8918 - precision: 0.9077 - recall: 0.8764 - auc: 0.9858 - tp: 68721.0000 - fp: 6984.0000 - tn: 228252.0000 - fn: 9691.0000 - val_loss: 0.6173 - val_accuracy: 0.8129 - val_precision: 0.8301 - val_recall: 0.8001 - val_auc: 0.9485 - val_tp: 15685.0000 - val_fp: 3211.0000 - val_tn: 55598.0000 - val_fn: 3918.0000 - lr: 1.0000e-04 Epoch 100/100 30/30 [==============================] - 1s 39ms/step - loss: 0.2824 - accuracy: 0.8920 - precision: 0.9076 - recall: 0.8767 - auc: 0.9858 - tp: 68743.0000 - fp: 6996.0000 - tn: 228240.0000 - fn: 9669.0000 - val_loss: 0.6178 - val_accuracy: 0.8134 - val_precision: 0.8305 - val_recall: 0.8003 - val_auc: 0.9485 - val_tp: 15689.0000 - val_fp: 3203.0000 - val_tn: 55606.0000 - val_fn: 3914.0000 - lr: 1.0000e-04
score = model_2.evaluate(padded_test, y_test, verbose=0)
print("Loss:", score[0])
print("Accuracy:", score[1])
print("Precision:", score[2])
print("Recall:", score[3])
print("AUC:", score[4])
print("True Positives:", score[5])
print("False Positives:", score[6])
print("True Negatives:", score[7])
print("False Negatives:", score[8])Loss: 0.6283017992973328 Accuracy: 0.8093372583389282 Precision: 0.8246207237243652 Recall: 0.7963189482688904 AUC: 0.9471717476844788 True Positives: 19513.0 False Positives: 4150.0 True Negatives: 69362.0 False Negatives: 4991.0
Fine tune modelu 2
def objective(trial):
num_filters = trial.suggest_int('num_filters', 32, 256)
kernel_size = trial.suggest_int('kernel_size', 3, 5)
learning_rate = trial.suggest_loguniform('learning_rate', 1e-4, 1e-2)
model_2_fine_tune = Sequential([
Embedding(input_dim=10000, output_dim=16),
Conv1D(filters=num_filters, kernel_size=kernel_size, activation='relu'),
GlobalMaxPooling1D(),
Dense(4, activation='softmax')
])
optimizer = Adam(learning_rate=learning_rate)
model_2_fine_tune.compile(optimizer=optimizer, loss='categorical_crossentropy',
metrics=['accuracy',
tf.keras.metrics.Precision(name='precision'),
tf.keras.metrics.Recall(name='recall'),
tf.keras.metrics.AUC(name='auc'),
tf.keras.metrics.TruePositives(name='tp'),
tf.keras.metrics.FalsePositives(name='fp'),
tf.keras.metrics.TrueNegatives(name='tn'),
tf.keras.metrics.FalseNegatives(name='fn')])
model_2_fine_tune.fit(padded_train, y_train,
steps_per_epoch = 30,
epochs = 100,
validation_split=0.2,
verbose = 1,
validation_steps = 50,
callbacks=[checkpoint("2_fine_tune"), reduce_lr],
)
score = model_2_fine_tune.evaluate(padded_test, y_test, verbose=0)
print("Loss:", score[0])
print("Accuracy:", score[1])
print("Precision:", score[2])
print("Recall:", score[3])
print("AUC:", score[4])
print("True Positives:", score[5])
print("False Positives:", score[6])
print("True Negatives:", score[7])
print("False Negatives:", score[8])
return score[1]
study = optuna.create_study(direction='maximize')
study.optimize(objective, n_trials=5)[I 2024-06-08 13:54:59,853] A new study created in memory with name: no-name-2054c194-0d55-4969-91e6-f0f12af54a54
C:\Users\Michał\AppData\Local\Temp\ipykernel_33252\265862631.py:5: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
learning_rate = trial.suggest_loguniform('learning_rate', 1e-4, 1e-2)
Epoch 1/100 30/30 [==============================] - 3s 50ms/step - loss: 1.3636 - accuracy: 0.4293 - precision: 0.0000e+00 - recall: 0.0000e+00 - auc: 0.6770 - tp: 0.0000e+00 - fp: 0.0000e+00 - tn: 235236.0000 - fn: 78412.0000 - val_loss: 1.3366 - val_accuracy: 0.5192 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.7490 - val_tp: 0.0000e+00 - val_fp: 0.0000e+00 - val_tn: 58809.0000 - val_fn: 19603.0000 - lr: 5.8385e-04 Epoch 2/100 30/30 [==============================] - 1s 22ms/step - loss: 1.2935 - accuracy: 0.5733 - precision: 0.0000e+00 - recall: 0.0000e+00 - auc: 0.7779 - tp: 0.0000e+00 - fp: 0.0000e+00 - tn: 235236.0000 - fn: 78412.0000 - val_loss: 1.2388 - val_accuracy: 0.6064 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.8067 - val_tp: 0.0000e+00 - val_fp: 0.0000e+00 - val_tn: 58809.0000 - val_fn: 19603.0000 - lr: 5.8385e-04 Epoch 3/100 30/30 [==============================] - 1s 21ms/step - loss: 1.1618 - accuracy: 0.6429 - precision: 0.8046 - recall: 0.0279 - auc: 0.8282 - tp: 2190.0000 - fp: 532.0000 - tn: 234704.0000 - fn: 76222.0000 - val_loss: 1.0777 - val_accuracy: 0.6686 - val_precision: 0.8556 - val_recall: 0.1137 - val_auc: 0.8483 - val_tp: 2228.0000 - val_fp: 376.0000 - val_tn: 58433.0000 - val_fn: 17375.0000 - lr: 5.8385e-04 Epoch 4/100 30/30 [==============================] - 1s 21ms/step - loss: 0.9811 - accuracy: 0.6952 - precision: 0.8830 - recall: 0.2839 - auc: 0.8698 - tp: 22263.0000 - fp: 2950.0000 - tn: 232286.0000 - fn: 56149.0000 - val_loss: 0.8973 - val_accuracy: 0.7031 - val_precision: 0.8776 - val_recall: 0.4413 - val_auc: 0.8845 - val_tp: 8650.0000 - val_fp: 1206.0000 - val_tn: 57603.0000 - val_fn: 10953.0000 - lr: 5.8385e-04 Epoch 5/100 30/30 [==============================] - 1s 21ms/step - loss: 0.8165 - accuracy: 0.7212 - precision: 0.8668 - recall: 0.5415 - auc: 0.9023 - tp: 42458.0000 - fp: 6524.0000 - tn: 228712.0000 - fn: 35954.0000 - val_loss: 0.7649 - val_accuracy: 0.7247 - val_precision: 0.8524 - val_recall: 0.5987 - val_auc: 0.9109 - val_tp: 11737.0000 - val_fp: 2033.0000 - val_tn: 56776.0000 - val_fn: 7866.0000 - lr: 5.8385e-04 Epoch 6/100 30/30 [==============================] - 1s 21ms/step - loss: 0.7070 - accuracy: 0.7398 - precision: 0.8521 - recall: 0.6408 - auc: 0.9237 - tp: 50249.0000 - fp: 8720.0000 - tn: 226516.0000 - fn: 28163.0000 - val_loss: 0.6861 - val_accuracy: 0.7409 - val_precision: 0.8417 - val_recall: 0.6552 - val_auc: 0.9260 - val_tp: 12844.0000 - val_fp: 2416.0000 - val_tn: 56393.0000 - val_fn: 6759.0000 - lr: 5.8385e-04 Epoch 7/100 30/30 [==============================] - 1s 22ms/step - loss: 0.6396 - accuracy: 0.7590 - precision: 0.8509 - recall: 0.6783 - auc: 0.9362 - tp: 53189.0000 - fp: 9320.0000 - tn: 225916.0000 - fn: 25223.0000 - val_loss: 0.6371 - val_accuracy: 0.7571 - val_precision: 0.8406 - val_recall: 0.6789 - val_auc: 0.9348 - val_tp: 13309.0000 - val_fp: 2523.0000 - val_tn: 56286.0000 - val_fn: 6294.0000 - lr: 5.8385e-04 Epoch 8/100 30/30 [==============================] - 1s 30ms/step - loss: 0.5939 - accuracy: 0.7798 - precision: 0.8534 - recall: 0.6967 - auc: 0.9441 - tp: 54633.0000 - fp: 9385.0000 - tn: 225851.0000 - fn: 23779.0000 - val_loss: 0.6025 - val_accuracy: 0.7744 - val_precision: 0.8446 - val_recall: 0.6905 - val_auc: 0.9408 - val_tp: 13535.0000 - val_fp: 2491.0000 - val_tn: 56318.0000 - val_fn: 6068.0000 - lr: 5.8385e-04 Epoch 9/100 30/30 [==============================] - 1s 22ms/step - loss: 0.5587 - accuracy: 0.7991 - precision: 0.8592 - recall: 0.7070 - auc: 0.9499 - tp: 55439.0000 - fp: 9088.0000 - tn: 226148.0000 - fn: 22973.0000 - val_loss: 0.5757 - val_accuracy: 0.7881 - val_precision: 0.8508 - val_recall: 0.6991 - val_auc: 0.9452 - val_tp: 13705.0000 - val_fp: 2404.0000 - val_tn: 56405.0000 - val_fn: 5898.0000 - lr: 5.8385e-04 Epoch 10/100 30/30 [==============================] - 1s 20ms/step - loss: 0.5295 - accuracy: 0.8121 - precision: 0.8657 - recall: 0.7255 - auc: 0.9545 - tp: 56886.0000 - fp: 8828.0000 - tn: 226408.0000 - fn: 21526.0000 - val_loss: 0.5537 - val_accuracy: 0.7978 - val_precision: 0.8530 - val_recall: 0.7263 - val_auc: 0.9488 - val_tp: 14238.0000 - val_fp: 2453.0000 - val_tn: 56356.0000 - val_fn: 5365.0000 - lr: 5.8385e-04 Epoch 11/100 30/30 [==============================] - 1s 21ms/step - loss: 0.5049 - accuracy: 0.8224 - precision: 0.8693 - recall: 0.7534 - auc: 0.9582 - tp: 59073.0000 - fp: 8884.0000 - tn: 226352.0000 - fn: 19339.0000 - val_loss: 0.5360 - val_accuracy: 0.8058 - val_precision: 0.8549 - val_recall: 0.7467 - val_auc: 0.9516 - val_tp: 14637.0000 - val_fp: 2484.0000 - val_tn: 56325.0000 - val_fn: 4966.0000 - lr: 5.8385e-04 Epoch 12/100 30/30 [==============================] - 1s 20ms/step - loss: 0.4840 - accuracy: 0.8288 - precision: 0.8712 - recall: 0.7749 - auc: 0.9612 - tp: 60761.0000 - fp: 8982.0000 - tn: 226254.0000 - fn: 17651.0000 - val_loss: 0.5220 - val_accuracy: 0.8099 - val_precision: 0.8562 - val_recall: 0.7591 - val_auc: 0.9537 - val_tp: 14880.0000 - val_fp: 2500.0000 - val_tn: 56309.0000 - val_fn: 4723.0000 - lr: 5.8385e-04 Epoch 13/100 30/30 [==============================] - 1s 19ms/step - loss: 0.4669 - accuracy: 0.8337 - precision: 0.8727 - recall: 0.7866 - auc: 0.9636 - tp: 61675.0000 - fp: 8995.0000 - tn: 226241.0000 - fn: 16737.0000 - val_loss: 0.5112 - val_accuracy: 0.8128 - val_precision: 0.8535 - val_recall: 0.7701 - val_auc: 0.9554 - val_tp: 15097.0000 - val_fp: 2591.0000 - val_tn: 56218.0000 - val_fn: 4506.0000 - lr: 5.8385e-04 Epoch 14/100 30/30 [==============================] - 1s 20ms/step - loss: 0.4530 - accuracy: 0.8369 - precision: 0.8739 - recall: 0.7957 - auc: 0.9655 - tp: 62391.0000 - fp: 8999.0000 - tn: 226237.0000 - fn: 16021.0000 - val_loss: 0.5031 - val_accuracy: 0.8147 - val_precision: 0.8531 - val_recall: 0.7750 - val_auc: 0.9566 - val_tp: 15192.0000 - val_fp: 2615.0000 - val_tn: 56194.0000 - val_fn: 4411.0000 - lr: 5.8385e-04 Epoch 15/100 30/30 [==============================] - 1s 21ms/step - loss: 0.4415 - accuracy: 0.8405 - precision: 0.8740 - recall: 0.8029 - auc: 0.9670 - tp: 62959.0000 - fp: 9078.0000 - tn: 226158.0000 - fn: 15453.0000 - val_loss: 0.4969 - val_accuracy: 0.8166 - val_precision: 0.8536 - val_recall: 0.7782 - val_auc: 0.9576 - val_tp: 15256.0000 - val_fp: 2617.0000 - val_tn: 56192.0000 - val_fn: 4347.0000 - lr: 5.8385e-04 Epoch 16/100 30/30 [==============================] - 1s 20ms/step - loss: 0.4319 - accuracy: 0.8429 - precision: 0.8755 - recall: 0.8067 - auc: 0.9682 - tp: 63256.0000 - fp: 8994.0000 - tn: 226242.0000 - fn: 15156.0000 - val_loss: 0.4923 - val_accuracy: 0.8172 - val_precision: 0.8547 - val_recall: 0.7778 - val_auc: 0.9582 - val_tp: 15247.0000 - val_fp: 2591.0000 - val_tn: 56218.0000 - val_fn: 4356.0000 - lr: 5.8385e-04 Epoch 17/100 30/30 [==============================] - 1s 20ms/step - loss: 0.4237 - accuracy: 0.8455 - precision: 0.8756 - recall: 0.8129 - auc: 0.9693 - tp: 63740.0000 - fp: 9052.0000 - tn: 226184.0000 - fn: 14672.0000 - val_loss: 0.4889 - val_accuracy: 0.8191 - val_precision: 0.8527 - 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1s 20ms/step - loss: 0.4049 - accuracy: 0.8505 - precision: 0.8781 - recall: 0.8218 - auc: 0.9718 - tp: 64442.0000 - fp: 8947.0000 - tn: 226289.0000 - fn: 13970.0000 - val_loss: 0.4828 - val_accuracy: 0.8225 - val_precision: 0.8527 - val_recall: 0.7927 - val_auc: 0.9598 - val_tp: 15540.0000 - val_fp: 2684.0000 - val_tn: 56125.0000 - val_fn: 4063.0000 - lr: 5.8385e-04 Epoch 21/100 30/30 [==============================] - 1s 25ms/step - loss: 0.3997 - accuracy: 0.8522 - precision: 0.8790 - recall: 0.8252 - auc: 0.9724 - tp: 64703.0000 - fp: 8905.0000 - tn: 226331.0000 - fn: 13709.0000 - val_loss: 0.4812 - val_accuracy: 0.8228 - val_precision: 0.8514 - val_recall: 0.7935 - val_auc: 0.9601 - val_tp: 15555.0000 - val_fp: 2715.0000 - val_tn: 56094.0000 - val_fn: 4048.0000 - lr: 5.8385e-04 Epoch 22/100 30/30 [==============================] - 1s 22ms/step - loss: 0.3949 - accuracy: 0.8539 - precision: 0.8798 - recall: 0.8273 - auc: 0.9730 - tp: 64867.0000 - fp: 8863.0000 - tn: 226373.0000 - fn: 13545.0000 - 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recall: 0.8491 - auc: 0.9791 - tp: 66578.0000 - fp: 8013.0000 - tn: 227223.0000 - fn: 11834.0000 - val_loss: 0.4921 - val_accuracy: 0.8240 - val_precision: 0.8475 - val_recall: 0.8002 - val_auc: 0.9593 - val_tp: 15687.0000 - val_fp: 2823.0000 - val_tn: 55986.0000 - val_fn: 3916.0000 - lr: 1.0000e-04 Epoch 76/100 30/30 [==============================] - 1s 19ms/step - loss: 0.3451 - accuracy: 0.8710 - precision: 0.8925 - recall: 0.8494 - auc: 0.9792 - tp: 66605.0000 - fp: 8024.0000 - tn: 227212.0000 - fn: 11807.0000 - val_loss: 0.4924 - val_accuracy: 0.8241 - val_precision: 0.8472 - val_recall: 0.8002 - val_auc: 0.9592 - val_tp: 15686.0000 - val_fp: 2829.0000 - val_tn: 55980.0000 - val_fn: 3917.0000 - lr: 1.0000e-04 Epoch 77/100 30/30 [==============================] - 1s 20ms/step - loss: 0.3447 - accuracy: 0.8709 - precision: 0.8927 - recall: 0.8497 - auc: 0.9792 - tp: 66627.0000 - fp: 8007.0000 - tn: 227229.0000 - fn: 11785.0000 - val_loss: 0.4928 - val_accuracy: 0.8242 - val_precision: 0.8474 - 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1s 19ms/step - loss: 0.3433 - accuracy: 0.8715 - precision: 0.8932 - recall: 0.8504 - auc: 0.9794 - tp: 66680.0000 - fp: 7977.0000 - tn: 227259.0000 - fn: 11732.0000 - val_loss: 0.4938 - val_accuracy: 0.8241 - val_precision: 0.8474 - val_recall: 0.8011 - val_auc: 0.9591 - val_tp: 15703.0000 - val_fp: 2827.0000 - val_tn: 55982.0000 - val_fn: 3900.0000 - lr: 1.0000e-04 Epoch 81/100 30/30 [==============================] - 1s 19ms/step - loss: 0.3429 - accuracy: 0.8715 - precision: 0.8933 - recall: 0.8507 - auc: 0.9794 - tp: 66709.0000 - fp: 7965.0000 - tn: 227271.0000 - fn: 11703.0000 - val_loss: 0.4941 - val_accuracy: 0.8240 - val_precision: 0.8473 - val_recall: 0.8008 - val_auc: 0.9590 - val_tp: 15698.0000 - val_fp: 2829.0000 - val_tn: 55980.0000 - val_fn: 3905.0000 - lr: 1.0000e-04 Epoch 82/100 30/30 [==============================] - 1s 19ms/step - loss: 0.3425 - accuracy: 0.8718 - precision: 0.8936 - recall: 0.8508 - auc: 0.9795 - tp: 66716.0000 - fp: 7947.0000 - tn: 227289.0000 - fn: 11696.0000 - 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val_fn: 3909.0000 - lr: 1.0000e-04 Epoch 85/100 30/30 [==============================] - 1s 19ms/step - loss: 0.3412 - accuracy: 0.8723 - precision: 0.8932 - recall: 0.8516 - auc: 0.9796 - tp: 66778.0000 - fp: 7981.0000 - tn: 227255.0000 - fn: 11634.0000 - val_loss: 0.4955 - val_accuracy: 0.8241 - val_precision: 0.8466 - val_recall: 0.8007 - val_auc: 0.9588 - val_tp: 15696.0000 - val_fp: 2843.0000 - val_tn: 55966.0000 - val_fn: 3907.0000 - lr: 1.0000e-04 Epoch 86/100 30/30 [==============================] - 1s 19ms/step - loss: 0.3408 - accuracy: 0.8725 - precision: 0.8936 - recall: 0.8517 - auc: 0.9797 - tp: 66785.0000 - fp: 7951.0000 - tn: 227285.0000 - fn: 11627.0000 - val_loss: 0.4959 - val_accuracy: 0.8239 - val_precision: 0.8469 - val_recall: 0.8008 - val_auc: 0.9588 - val_tp: 15699.0000 - val_fp: 2837.0000 - val_tn: 55972.0000 - val_fn: 3904.0000 - lr: 1.0000e-04 Epoch 87/100 30/30 [==============================] - 1s 22ms/step - loss: 0.3403 - accuracy: 0.8728 - precision: 0.8936 - 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1s 20ms/step - loss: 0.3383 - accuracy: 0.8733 - precision: 0.8939 - recall: 0.8529 - auc: 0.9800 - tp: 66878.0000 - fp: 7934.0000 - tn: 227302.0000 - fn: 11534.0000 - val_loss: 0.4980 - val_accuracy: 0.8230 - val_precision: 0.8462 - val_recall: 0.8012 - val_auc: 0.9586 - val_tp: 15705.0000 - val_fp: 2854.0000 - val_tn: 55955.0000 - val_fn: 3898.0000 - lr: 1.0000e-04 Epoch 93/100 30/30 [==============================] - 1s 18ms/step - loss: 0.3379 - accuracy: 0.8735 - precision: 0.8939 - recall: 0.8530 - auc: 0.9800 - tp: 66886.0000 - fp: 7937.0000 - tn: 227299.0000 - fn: 11526.0000 - val_loss: 0.4983 - val_accuracy: 0.8230 - val_precision: 0.8463 - val_recall: 0.8013 - val_auc: 0.9586 - val_tp: 15707.0000 - val_fp: 2852.0000 - val_tn: 55957.0000 - val_fn: 3896.0000 - lr: 1.0000e-04 Epoch 94/100 30/30 [==============================] - 1s 19ms/step - loss: 0.3375 - accuracy: 0.8739 - precision: 0.8945 - recall: 0.8533 - auc: 0.9801 - tp: 66908.0000 - fp: 7895.0000 - tn: 227341.0000 - fn: 11504.0000 - 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val_fn: 3895.0000 - lr: 1.0000e-04 Epoch 97/100 30/30 [==============================] - 1s 21ms/step - loss: 0.3363 - accuracy: 0.8740 - precision: 0.8944 - recall: 0.8539 - auc: 0.9802 - tp: 66956.0000 - fp: 7908.0000 - tn: 227328.0000 - fn: 11456.0000 - val_loss: 0.4998 - val_accuracy: 0.8230 - val_precision: 0.8460 - val_recall: 0.8014 - val_auc: 0.9585 - val_tp: 15710.0000 - val_fp: 2859.0000 - val_tn: 55950.0000 - val_fn: 3893.0000 - lr: 1.0000e-04 Epoch 98/100 30/30 [==============================] - 1s 21ms/step - loss: 0.3359 - accuracy: 0.8744 - precision: 0.8947 - recall: 0.8540 - auc: 0.9802 - tp: 66966.0000 - fp: 7880.0000 - tn: 227356.0000 - fn: 11446.0000 - val_loss: 0.5001 - val_accuracy: 0.8227 - val_precision: 0.8458 - val_recall: 0.8012 - val_auc: 0.9585 - val_tp: 15706.0000 - val_fp: 2863.0000 - val_tn: 55946.0000 - val_fn: 3897.0000 - lr: 1.0000e-04 Epoch 99/100 30/30 [==============================] - 1s 20ms/step - loss: 0.3355 - accuracy: 0.8747 - precision: 0.8948 - recall: 0.8541 - auc: 0.9803 - tp: 66972.0000 - fp: 7874.0000 - tn: 227362.0000 - fn: 11440.0000 - val_loss: 0.5005 - val_accuracy: 0.8228 - val_precision: 0.8456 - val_recall: 0.8012 - val_auc: 0.9584 - val_tp: 15706.0000 - val_fp: 2867.0000 - val_tn: 55942.0000 - val_fn: 3897.0000 - lr: 1.0000e-04 Epoch 100/100 30/30 [==============================] - 1s 20ms/step - loss: 0.3352 - accuracy: 0.8747 - precision: 0.8950 - recall: 0.8542 - auc: 0.9803 - tp: 66979.0000 - fp: 7860.0000 - tn: 227376.0000 - fn: 11433.0000 - val_loss: 0.5009 - val_accuracy: 0.8225 - val_precision: 0.8458 - val_recall: 0.8013 - val_auc: 0.9584 - val_tp: 15707.0000 - val_fp: 2864.0000 - val_tn: 55945.0000 - val_fn: 3896.0000 - lr: 1.0000e-04
[I 2024-06-08 13:56:09,227] Trial 0 finished with value: 0.819294810295105 and parameters: {'num_filters': 37, 'kernel_size': 5, 'learning_rate': 0.0005838498821337493}. Best is trial 0 with value: 0.819294810295105.
Loss: 0.5012595057487488 Accuracy: 0.819294810295105 Precision: 0.840825080871582 Recall: 0.7984818816184998 AUC: 0.9585517644882202 True Positives: 19566.0 False Positives: 3704.0 True Negatives: 69808.0 False Negatives: 4938.0 Epoch 1/100
C:\Users\Michał\AppData\Local\Temp\ipykernel_33252\265862631.py:5: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
learning_rate = trial.suggest_loguniform('learning_rate', 1e-4, 1e-2)
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val_fn: 3990.0000 - lr: 1.0000e-04 Epoch 85/100 30/30 [==============================] - 1s 19ms/step - loss: 0.3736 - accuracy: 0.8599 - precision: 0.8857 - recall: 0.8345 - auc: 0.9758 - tp: 65438.0000 - fp: 8442.0000 - tn: 226794.0000 - fn: 12974.0000 - val_loss: 0.5001 - val_accuracy: 0.8218 - val_precision: 0.8501 - val_recall: 0.7967 - val_auc: 0.9580 - val_tp: 15617.0000 - val_fp: 2753.0000 - val_tn: 56056.0000 - val_fn: 3986.0000 - lr: 1.0000e-04 Epoch 86/100 30/30 [==============================] - 1s 19ms/step - loss: 0.3732 - accuracy: 0.8601 - precision: 0.8859 - recall: 0.8348 - auc: 0.9758 - tp: 65462.0000 - fp: 8432.0000 - tn: 226804.0000 - fn: 12950.0000 - val_loss: 0.5002 - val_accuracy: 0.8221 - val_precision: 0.8504 - val_recall: 0.7966 - val_auc: 0.9580 - val_tp: 15615.0000 - val_fp: 2747.0000 - val_tn: 56062.0000 - val_fn: 3988.0000 - lr: 1.0000e-04 Epoch 87/100 30/30 [==============================] - 1s 20ms/step - loss: 0.3728 - accuracy: 0.8601 - precision: 0.8857 - recall: 0.8350 - auc: 0.9758 - tp: 65473.0000 - fp: 8447.0000 - tn: 226789.0000 - fn: 12939.0000 - val_loss: 0.5004 - val_accuracy: 0.8224 - val_precision: 0.8505 - val_recall: 0.7967 - val_auc: 0.9580 - val_tp: 15617.0000 - val_fp: 2745.0000 - val_tn: 56064.0000 - val_fn: 3986.0000 - lr: 1.0000e-04 Epoch 88/100 30/30 [==============================] - 1s 20ms/step - loss: 0.3724 - accuracy: 0.8601 - precision: 0.8861 - recall: 0.8351 - auc: 0.9759 - tp: 65479.0000 - fp: 8419.0000 - tn: 226817.0000 - fn: 12933.0000 - val_loss: 0.5005 - val_accuracy: 0.8220 - val_precision: 0.8506 - val_recall: 0.7968 - val_auc: 0.9580 - val_tp: 15619.0000 - val_fp: 2743.0000 - val_tn: 56066.0000 - val_fn: 3984.0000 - lr: 1.0000e-04 Epoch 89/100 30/30 [==============================] - 1s 20ms/step - loss: 0.3721 - accuracy: 0.8603 - precision: 0.8862 - recall: 0.8352 - auc: 0.9759 - tp: 65486.0000 - fp: 8409.0000 - tn: 226827.0000 - fn: 12926.0000 - val_loss: 0.5006 - val_accuracy: 0.8220 - val_precision: 0.8506 - 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1s 20ms/step - loss: 0.3709 - accuracy: 0.8606 - precision: 0.8862 - recall: 0.8358 - auc: 0.9761 - tp: 65533.0000 - fp: 8412.0000 - tn: 226824.0000 - fn: 12879.0000 - val_loss: 0.5011 - val_accuracy: 0.8222 - val_precision: 0.8500 - val_recall: 0.7971 - val_auc: 0.9579 - val_tp: 15625.0000 - val_fp: 2757.0000 - val_tn: 56052.0000 - val_fn: 3978.0000 - lr: 1.0000e-04 Epoch 93/100 30/30 [==============================] - 1s 20ms/step - loss: 0.3705 - accuracy: 0.8610 - precision: 0.8867 - recall: 0.8360 - auc: 0.9761 - tp: 65550.0000 - fp: 8373.0000 - tn: 226863.0000 - fn: 12862.0000 - val_loss: 0.5012 - val_accuracy: 0.8220 - val_precision: 0.8499 - val_recall: 0.7972 - val_auc: 0.9579 - val_tp: 15627.0000 - val_fp: 2760.0000 - val_tn: 56049.0000 - val_fn: 3976.0000 - lr: 1.0000e-04 Epoch 94/100 30/30 [==============================] - 1s 24ms/step - loss: 0.3701 - accuracy: 0.8610 - precision: 0.8865 - recall: 0.8360 - auc: 0.9762 - tp: 65554.0000 - fp: 8397.0000 - tn: 226839.0000 - fn: 12858.0000 - val_loss: 0.5013 - val_accuracy: 0.8223 - val_precision: 0.8501 - val_recall: 0.7971 - val_auc: 0.9579 - val_tp: 15625.0000 - val_fp: 2755.0000 - val_tn: 56054.0000 - val_fn: 3978.0000 - lr: 1.0000e-04 Epoch 95/100 30/30 [==============================] - 1s 22ms/step - loss: 0.3697 - accuracy: 0.8613 - precision: 0.8870 - recall: 0.8362 - auc: 0.9762 - tp: 65569.0000 - fp: 8353.0000 - tn: 226883.0000 - fn: 12843.0000 - val_loss: 0.5015 - val_accuracy: 0.8226 - val_precision: 0.8501 - val_recall: 0.7972 - val_auc: 0.9579 - val_tp: 15628.0000 - val_fp: 2756.0000 - val_tn: 56053.0000 - val_fn: 3975.0000 - lr: 1.0000e-04 Epoch 96/100 30/30 [==============================] - 1s 21ms/step - loss: 0.3693 - accuracy: 0.8611 - precision: 0.8864 - recall: 0.8364 - auc: 0.9763 - tp: 65585.0000 - fp: 8404.0000 - tn: 226832.0000 - fn: 12827.0000 - val_loss: 0.5016 - val_accuracy: 0.8223 - val_precision: 0.8497 - val_recall: 0.7971 - val_auc: 0.9579 - val_tp: 15625.0000 - val_fp: 2763.0000 - val_tn: 56046.0000 - val_fn: 3978.0000 - lr: 1.0000e-04 Epoch 97/100 30/30 [==============================] - 1s 22ms/step - loss: 0.3690 - accuracy: 0.8613 - precision: 0.8868 - recall: 0.8367 - auc: 0.9763 - tp: 65604.0000 - fp: 8376.0000 - tn: 226860.0000 - fn: 12808.0000 - val_loss: 0.5018 - val_accuracy: 0.8225 - val_precision: 0.8497 - val_recall: 0.7972 - val_auc: 0.9579 - val_tp: 15628.0000 - val_fp: 2764.0000 - val_tn: 56045.0000 - val_fn: 3975.0000 - lr: 1.0000e-04 Epoch 98/100 30/30 [==============================] - 1s 24ms/step - loss: 0.3686 - accuracy: 0.8616 - precision: 0.8870 - recall: 0.8367 - auc: 0.9764 - tp: 65607.0000 - fp: 8360.0000 - tn: 226876.0000 - fn: 12805.0000 - val_loss: 0.5019 - val_accuracy: 0.8229 - val_precision: 0.8496 - val_recall: 0.7968 - val_auc: 0.9579 - val_tp: 15619.0000 - val_fp: 2766.0000 - val_tn: 56043.0000 - val_fn: 3984.0000 - lr: 1.0000e-04 Epoch 99/100 30/30 [==============================] - 1s 24ms/step - loss: 0.3682 - accuracy: 0.8619 - precision: 0.8873 - recall: 0.8368 - auc: 0.9764 - tp: 65615.0000 - fp: 8333.0000 - tn: 226903.0000 - fn: 12797.0000 - val_loss: 0.5021 - val_accuracy: 0.8229 - val_precision: 0.8499 - val_recall: 0.7972 - val_auc: 0.9579 - val_tp: 15627.0000 - val_fp: 2760.0000 - val_tn: 56049.0000 - val_fn: 3976.0000 - lr: 1.0000e-04 Epoch 100/100 30/30 [==============================] - 1s 21ms/step - loss: 0.3678 - accuracy: 0.8619 - precision: 0.8871 - recall: 0.8368 - auc: 0.9764 - tp: 65617.0000 - fp: 8348.0000 - tn: 226888.0000 - fn: 12795.0000 - val_loss: 0.5023 - val_accuracy: 0.8226 - val_precision: 0.8495 - val_recall: 0.7971 - val_auc: 0.9578 - val_tp: 15625.0000 - val_fp: 2769.0000 - val_tn: 56040.0000 - val_fn: 3978.0000 - lr: 1.0000e-04
[I 2024-06-08 13:57:17,306] Trial 1 finished with value: 0.817417562007904 and parameters: {'num_filters': 42, 'kernel_size': 3, 'learning_rate': 0.0003947618323958337}. Best is trial 0 with value: 0.819294810295105.
Loss: 0.5054721236228943 Accuracy: 0.817417562007904 Precision: 0.8435232043266296 Recall: 0.7921971678733826 AUC: 0.9574187994003296 True Positives: 19412.0 False Positives: 3601.0 True Negatives: 69911.0 False Negatives: 5092.0 Epoch 1/100
C:\Users\Michał\AppData\Local\Temp\ipykernel_33252\265862631.py:5: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
learning_rate = trial.suggest_loguniform('learning_rate', 1e-4, 1e-2)
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tp: 25325.0000 - fp: 4265.0000 - tn: 230971.0000 - fn: 53087.0000 - val_loss: 0.8371 - val_accuracy: 0.6942 - val_precision: 0.8357 - val_recall: 0.5254 - val_auc: 0.8944 - val_tp: 10300.0000 - val_fp: 2025.0000 - val_tn: 56784.0000 - val_fn: 9303.0000 - lr: 7.1779e-04 Epoch 4/100 30/30 [==============================] - 1s 23ms/step - loss: 0.7322 - accuracy: 0.7347 - precision: 0.8367 - recall: 0.6103 - auc: 0.9187 - tp: 47852.0000 - fp: 9341.0000 - tn: 225895.0000 - fn: 30560.0000 - val_loss: 0.6716 - val_accuracy: 0.7512 - val_precision: 0.8323 - val_recall: 0.6526 - val_auc: 0.9285 - val_tp: 12792.0000 - val_fp: 2578.0000 - val_tn: 56231.0000 - val_fn: 6811.0000 - lr: 7.1779e-04 Epoch 5/100 30/30 [==============================] - 1s 20ms/step - loss: 0.6062 - accuracy: 0.7818 - precision: 0.8461 - recall: 0.6929 - auc: 0.9416 - tp: 54333.0000 - fp: 9881.0000 - tn: 225355.0000 - fn: 24079.0000 - val_loss: 0.5958 - val_accuracy: 0.7801 - val_precision: 0.8398 - val_recall: 0.7083 - 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val_fn: 4169.0000 - lr: 7.1779e-04 Epoch 13/100 30/30 [==============================] - 1s 20ms/step - loss: 0.4142 - accuracy: 0.8456 - precision: 0.8746 - recall: 0.8159 - auc: 0.9703 - tp: 63973.0000 - fp: 9170.0000 - tn: 226066.0000 - fn: 14439.0000 - val_loss: 0.4905 - val_accuracy: 0.8201 - val_precision: 0.8543 - val_recall: 0.7877 - val_auc: 0.9586 - val_tp: 15441.0000 - val_fp: 2634.0000 - val_tn: 56175.0000 - val_fn: 4162.0000 - lr: 7.1779e-04 Epoch 14/100 30/30 [==============================] - 1s 20ms/step - loss: 0.4074 - accuracy: 0.8480 - precision: 0.8750 - recall: 0.8208 - auc: 0.9712 - tp: 64364.0000 - fp: 9195.0000 - tn: 226041.0000 - fn: 14048.0000 - val_loss: 0.4892 - val_accuracy: 0.8212 - val_precision: 0.8517 - val_recall: 0.7922 - val_auc: 0.9588 - val_tp: 15530.0000 - val_fp: 2704.0000 - val_tn: 56105.0000 - val_fn: 4073.0000 - lr: 7.1779e-04 Epoch 15/100 30/30 [==============================] - 1s 20ms/step - loss: 0.4013 - accuracy: 0.8499 - precision: 0.8769 - recall: 0.8229 - auc: 0.9720 - tp: 64524.0000 - fp: 9054.0000 - tn: 226182.0000 - fn: 13888.0000 - val_loss: 0.4891 - val_accuracy: 0.8213 - val_precision: 0.8511 - val_recall: 0.7943 - val_auc: 0.9589 - val_tp: 15571.0000 - val_fp: 2724.0000 - val_tn: 56085.0000 - val_fn: 4032.0000 - lr: 7.1779e-04 Epoch 16/100 30/30 [==============================] - 1s 20ms/step - loss: 0.3964 - accuracy: 0.8515 - precision: 0.8789 - recall: 0.8250 - auc: 0.9727 - tp: 64689.0000 - fp: 8915.0000 - tn: 226321.0000 - fn: 13723.0000 - val_loss: 0.4887 - val_accuracy: 0.8214 - val_precision: 0.8515 - val_recall: 0.7947 - val_auc: 0.9590 - val_tp: 15578.0000 - val_fp: 2717.0000 - val_tn: 56092.0000 - val_fn: 4025.0000 - lr: 7.1779e-04 Epoch 17/100 30/30 [==============================] - 1s 19ms/step - loss: 0.3913 - accuracy: 0.8531 - precision: 0.8783 - recall: 0.8280 - auc: 0.9733 - tp: 64928.0000 - fp: 8998.0000 - tn: 226238.0000 - fn: 13484.0000 - val_loss: 0.4893 - val_accuracy: 0.8222 - val_precision: 0.8512 - 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recall: 0.8497 - auc: 0.9793 - tp: 66629.0000 - fp: 8153.0000 - tn: 227083.0000 - fn: 11783.0000 - val_loss: 0.5130 - val_accuracy: 0.8197 - val_precision: 0.8440 - val_recall: 0.7994 - val_auc: 0.9570 - val_tp: 15671.0000 - val_fp: 2897.0000 - val_tn: 55912.0000 - val_fn: 3932.0000 - lr: 1.0000e-04 Epoch 100/100 30/30 [==============================] - 1s 21ms/step - loss: 0.3426 - accuracy: 0.8700 - precision: 0.8912 - recall: 0.8497 - auc: 0.9793 - tp: 66624.0000 - fp: 8131.0000 - tn: 227105.0000 - fn: 11788.0000 - val_loss: 0.5134 - val_accuracy: 0.8198 - val_precision: 0.8441 - val_recall: 0.7997 - val_auc: 0.9570 - val_tp: 15677.0000 - val_fp: 2895.0000 - val_tn: 55914.0000 - val_fn: 3926.0000 - lr: 1.0000e-04
[I 2024-06-08 13:58:26,323] Trial 2 finished with value: 0.816274881362915 and parameters: {'num_filters': 124, 'kernel_size': 4, 'learning_rate': 0.0007177946981171184}. Best is trial 0 with value: 0.819294810295105.
Loss: 0.5146223902702332 Accuracy: 0.816274881362915 Precision: 0.8387221693992615 Recall: 0.7960740923881531 AUC: 0.9571151733398438 True Positives: 19507.0 False Positives: 3751.0 True Negatives: 69761.0 False Negatives: 4997.0 Epoch 1/100
C:\Users\Michał\AppData\Local\Temp\ipykernel_33252\265862631.py:5: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
learning_rate = trial.suggest_loguniform('learning_rate', 1e-4, 1e-2)
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val_fn: 3963.0000 - lr: 1.0000e-04 Epoch 85/100 30/30 [==============================] - 1s 18ms/step - loss: 0.3613 - accuracy: 0.8639 - precision: 0.8876 - recall: 0.8406 - auc: 0.9772 - tp: 65910.0000 - fp: 8346.0000 - tn: 226890.0000 - fn: 12502.0000 - val_loss: 0.5138 - val_accuracy: 0.8226 - val_precision: 0.8486 - val_recall: 0.7977 - val_auc: 0.9568 - val_tp: 15638.0000 - val_fp: 2790.0000 - val_tn: 56019.0000 - val_fn: 3965.0000 - lr: 1.0000e-04 Epoch 86/100 30/30 [==============================] - 1s 20ms/step - loss: 0.3609 - accuracy: 0.8641 - precision: 0.8877 - recall: 0.8407 - auc: 0.9772 - tp: 65924.0000 - fp: 8342.0000 - tn: 226894.0000 - fn: 12488.0000 - val_loss: 0.5141 - val_accuracy: 0.8224 - val_precision: 0.8483 - val_recall: 0.7979 - val_auc: 0.9567 - val_tp: 15642.0000 - val_fp: 2797.0000 - val_tn: 56012.0000 - val_fn: 3961.0000 - lr: 1.0000e-04 Epoch 87/100 30/30 [==============================] - 1s 22ms/step - loss: 0.3605 - accuracy: 0.8642 - precision: 0.8876 - recall: 0.8408 - auc: 0.9773 - tp: 65927.0000 - fp: 8350.0000 - tn: 226886.0000 - fn: 12485.0000 - val_loss: 0.5143 - val_accuracy: 0.8225 - val_precision: 0.8485 - val_recall: 0.7983 - val_auc: 0.9568 - val_tp: 15649.0000 - val_fp: 2795.0000 - val_tn: 56014.0000 - val_fn: 3954.0000 - lr: 1.0000e-04 Epoch 88/100 30/30 [==============================] - 1s 22ms/step - loss: 0.3601 - accuracy: 0.8644 - precision: 0.8877 - recall: 0.8412 - auc: 0.9773 - tp: 65960.0000 - fp: 8346.0000 - tn: 226890.0000 - fn: 12452.0000 - val_loss: 0.5147 - val_accuracy: 0.8225 - val_precision: 0.8480 - val_recall: 0.7983 - val_auc: 0.9567 - val_tp: 15649.0000 - val_fp: 2805.0000 - val_tn: 56004.0000 - val_fn: 3954.0000 - lr: 1.0000e-04 Epoch 89/100 30/30 [==============================] - 1s 21ms/step - loss: 0.3597 - accuracy: 0.8644 - precision: 0.8881 - recall: 0.8412 - auc: 0.9774 - tp: 65958.0000 - fp: 8310.0000 - tn: 226926.0000 - fn: 12454.0000 - val_loss: 0.5150 - val_accuracy: 0.8230 - val_precision: 0.8486 - 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1s 20ms/step - loss: 0.3585 - accuracy: 0.8649 - precision: 0.8884 - recall: 0.8416 - auc: 0.9775 - tp: 65989.0000 - fp: 8288.0000 - tn: 226948.0000 - fn: 12423.0000 - val_loss: 0.5159 - val_accuracy: 0.8226 - val_precision: 0.8483 - val_recall: 0.7982 - val_auc: 0.9566 - val_tp: 15648.0000 - val_fp: 2799.0000 - val_tn: 56010.0000 - val_fn: 3955.0000 - lr: 1.0000e-04 Epoch 93/100 30/30 [==============================] - 1s 21ms/step - loss: 0.3581 - accuracy: 0.8650 - precision: 0.8884 - recall: 0.8419 - auc: 0.9776 - tp: 66014.0000 - fp: 8290.0000 - tn: 226946.0000 - fn: 12398.0000 - val_loss: 0.5160 - val_accuracy: 0.8224 - val_precision: 0.8484 - val_recall: 0.7983 - val_auc: 0.9566 - val_tp: 15650.0000 - val_fp: 2797.0000 - val_tn: 56012.0000 - val_fn: 3953.0000 - lr: 1.0000e-04 Epoch 94/100 30/30 [==============================] - 1s 21ms/step - loss: 0.3577 - accuracy: 0.8653 - precision: 0.8885 - recall: 0.8421 - auc: 0.9776 - tp: 66028.0000 - fp: 8284.0000 - tn: 226952.0000 - fn: 12384.0000 - 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val_fn: 3947.0000 - lr: 1.0000e-04 Epoch 97/100 30/30 [==============================] - 1s 19ms/step - loss: 0.3565 - accuracy: 0.8660 - precision: 0.8890 - recall: 0.8425 - auc: 0.9777 - tp: 66065.0000 - fp: 8247.0000 - tn: 226989.0000 - fn: 12347.0000 - val_loss: 0.5173 - val_accuracy: 0.8230 - val_precision: 0.8481 - val_recall: 0.7987 - val_auc: 0.9565 - val_tp: 15657.0000 - val_fp: 2804.0000 - val_tn: 56005.0000 - val_fn: 3946.0000 - lr: 1.0000e-04 Epoch 98/100 30/30 [==============================] - 1s 21ms/step - loss: 0.3561 - accuracy: 0.8658 - precision: 0.8891 - recall: 0.8426 - auc: 0.9778 - tp: 66069.0000 - fp: 8239.0000 - tn: 226997.0000 - fn: 12343.0000 - val_loss: 0.5176 - val_accuracy: 0.8228 - val_precision: 0.8482 - val_recall: 0.7986 - val_auc: 0.9564 - val_tp: 15654.0000 - val_fp: 2802.0000 - val_tn: 56007.0000 - val_fn: 3949.0000 - lr: 1.0000e-04 Epoch 99/100 30/30 [==============================] - 1s 19ms/step - loss: 0.3558 - accuracy: 0.8659 - precision: 0.8891 - recall: 0.8425 - auc: 0.9778 - tp: 66064.0000 - fp: 8243.0000 - tn: 226993.0000 - fn: 12348.0000 - val_loss: 0.5179 - val_accuracy: 0.8227 - val_precision: 0.8476 - val_recall: 0.7982 - val_auc: 0.9564 - val_tp: 15647.0000 - val_fp: 2814.0000 - val_tn: 55995.0000 - val_fn: 3956.0000 - lr: 1.0000e-04 Epoch 100/100 30/30 [==============================] - 1s 20ms/step - loss: 0.3554 - accuracy: 0.8661 - precision: 0.8890 - recall: 0.8425 - auc: 0.9779 - tp: 66066.0000 - fp: 8250.0000 - tn: 226986.0000 - fn: 12346.0000 - val_loss: 0.5182 - val_accuracy: 0.8227 - val_precision: 0.8479 - val_recall: 0.7988 - val_auc: 0.9564 - val_tp: 15659.0000 - val_fp: 2809.0000 - val_tn: 56000.0000 - val_fn: 3944.0000 - lr: 1.0000e-04
[I 2024-06-08 13:59:32,822] Trial 3 finished with value: 0.8176215887069702 and parameters: {'num_filters': 156, 'kernel_size': 3, 'learning_rate': 0.0006131200612586219}. Best is trial 0 with value: 0.819294810295105.
Loss: 0.517291784286499 Accuracy: 0.8176215887069702 Precision: 0.8432790637016296 Recall: 0.7946865558624268 AUC: 0.9564386010169983 True Positives: 19473.0 False Positives: 3619.0 True Negatives: 69893.0 False Negatives: 5031.0 Epoch 1/100
C:\Users\Michał\AppData\Local\Temp\ipykernel_33252\265862631.py:5: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.
learning_rate = trial.suggest_loguniform('learning_rate', 1e-4, 1e-2)
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tn: 225571.0000 - fn: 19112.0000 - val_loss: 0.5056 - val_accuracy: 0.8136 - val_precision: 0.8510 - val_recall: 0.7759 - val_auc: 0.9560 - val_tp: 15210.0000 - val_fp: 2664.0000 - val_tn: 56145.0000 - val_fn: 4393.0000 - lr: 0.0026 Epoch 4/100 30/30 [==============================] - 1s 21ms/step - loss: 0.4368 - accuracy: 0.8396 - precision: 0.8701 - recall: 0.8068 - auc: 0.9670 - tp: 63264.0000 - fp: 9447.0000 - tn: 225789.0000 - fn: 15148.0000 - val_loss: 0.4854 - val_accuracy: 0.8208 - val_precision: 0.8507 - val_recall: 0.7904 - val_auc: 0.9594 - val_tp: 15495.0000 - val_fp: 2719.0000 - val_tn: 56090.0000 - val_fn: 4108.0000 - lr: 0.0026 Epoch 5/100 30/30 [==============================] - 1s 22ms/step - loss: 0.4057 - accuracy: 0.8494 - precision: 0.8761 - recall: 0.8224 - auc: 0.9713 - tp: 64484.0000 - fp: 9121.0000 - tn: 226115.0000 - fn: 13928.0000 - val_loss: 0.4810 - val_accuracy: 0.8220 - val_precision: 0.8525 - val_recall: 0.7944 - val_auc: 0.9603 - val_tp: 15572.0000 - val_fp: 2695.0000 - val_tn: 56114.0000 - val_fn: 4031.0000 - lr: 0.0026 Epoch 6/100 30/30 [==============================] - 1s 21ms/step - loss: 0.3856 - accuracy: 0.8560 - precision: 0.8806 - recall: 0.8309 - auc: 0.9740 - tp: 65152.0000 - fp: 8838.0000 - tn: 226398.0000 - fn: 13260.0000 - val_loss: 0.4865 - val_accuracy: 0.8211 - val_precision: 0.8538 - val_recall: 0.7912 - val_auc: 0.9596 - val_tp: 15509.0000 - val_fp: 2655.0000 - val_tn: 56154.0000 - val_fn: 4094.0000 - lr: 0.0026 Epoch 7/100 30/30 [==============================] - 1s 20ms/step - loss: 0.3702 - accuracy: 0.8631 - precision: 0.8860 - recall: 0.8391 - auc: 0.9760 - tp: 65796.0000 - fp: 8467.0000 - tn: 226769.0000 - fn: 12616.0000 - val_loss: 0.4915 - val_accuracy: 0.8225 - val_precision: 0.8515 - val_recall: 0.7935 - val_auc: 0.9592 - val_tp: 15555.0000 - val_fp: 2712.0000 - val_tn: 56097.0000 - val_fn: 4048.0000 - lr: 0.0026 Epoch 8/100 30/30 [==============================] - 1s 19ms/step - loss: 0.3573 - accuracy: 0.8664 - precision: 0.8897 - 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1s 29ms/step - loss: 0.3102 - accuracy: 0.8837 - precision: 0.9024 - recall: 0.8660 - auc: 0.9830 - tp: 67906.0000 - fp: 7343.0000 - tn: 227893.0000 - fn: 10506.0000 - val_loss: 0.5332 - val_accuracy: 0.8183 - val_precision: 0.8384 - val_recall: 0.8012 - val_auc: 0.9552 - val_tp: 15705.0000 - val_fp: 3028.0000 - val_tn: 55781.0000 - val_fn: 3898.0000 - lr: 1.0000e-04 Epoch 62/100 30/30 [==============================] - 1s 30ms/step - loss: 0.3099 - accuracy: 0.8835 - precision: 0.9022 - recall: 0.8663 - auc: 0.9830 - tp: 67926.0000 - fp: 7360.0000 - tn: 227876.0000 - fn: 10486.0000 - val_loss: 0.5339 - val_accuracy: 0.8179 - val_precision: 0.8380 - val_recall: 0.8012 - val_auc: 0.9551 - val_tp: 15705.0000 - val_fp: 3037.0000 - val_tn: 55772.0000 - val_fn: 3898.0000 - lr: 1.0000e-04 Epoch 63/100 30/30 [==============================] - 1s 33ms/step - loss: 0.3096 - accuracy: 0.8839 - precision: 0.9023 - recall: 0.8663 - auc: 0.9830 - tp: 67927.0000 - fp: 7357.0000 - tn: 227879.0000 - fn: 10485.0000 - val_loss: 0.5344 - val_accuracy: 0.8180 - val_precision: 0.8381 - val_recall: 0.8011 - val_auc: 0.9551 - val_tp: 15704.0000 - val_fp: 3034.0000 - val_tn: 55775.0000 - val_fn: 3899.0000 - lr: 1.0000e-04 Epoch 64/100 30/30 [==============================] - 1s 30ms/step - loss: 0.3092 - accuracy: 0.8839 - precision: 0.9023 - recall: 0.8666 - auc: 0.9831 - tp: 67948.0000 - fp: 7358.0000 - tn: 227878.0000 - fn: 10464.0000 - val_loss: 0.5351 - val_accuracy: 0.8183 - val_precision: 0.8379 - val_recall: 0.8012 - val_auc: 0.9550 - val_tp: 15705.0000 - val_fp: 3038.0000 - val_tn: 55771.0000 - val_fn: 3898.0000 - lr: 1.0000e-04 Epoch 65/100 30/30 [==============================] - 1s 30ms/step - loss: 0.3090 - accuracy: 0.8840 - precision: 0.9025 - recall: 0.8668 - auc: 0.9831 - tp: 67969.0000 - fp: 7345.0000 - tn: 227891.0000 - fn: 10443.0000 - val_loss: 0.5355 - val_accuracy: 0.8178 - val_precision: 0.8379 - val_recall: 0.8014 - val_auc: 0.9550 - val_tp: 15709.0000 - val_fp: 3039.0000 - val_tn: 55770.0000 - val_fn: 3894.0000 - lr: 1.0000e-04 Epoch 66/100 30/30 [==============================] - 1s 31ms/step - loss: 0.3087 - accuracy: 0.8841 - precision: 0.9024 - recall: 0.8668 - auc: 0.9831 - tp: 67970.0000 - fp: 7348.0000 - tn: 227888.0000 - fn: 10442.0000 - val_loss: 0.5361 - val_accuracy: 0.8182 - val_precision: 0.8381 - val_recall: 0.8016 - val_auc: 0.9549 - val_tp: 15713.0000 - val_fp: 3036.0000 - val_tn: 55773.0000 - val_fn: 3890.0000 - lr: 1.0000e-04 Epoch 67/100 30/30 [==============================] - 1s 35ms/step - loss: 0.3084 - accuracy: 0.8841 - precision: 0.9023 - recall: 0.8671 - auc: 0.9832 - tp: 67992.0000 - fp: 7361.0000 - tn: 227875.0000 - fn: 10420.0000 - val_loss: 0.5367 - val_accuracy: 0.8176 - val_precision: 0.8375 - val_recall: 0.8011 - val_auc: 0.9549 - val_tp: 15703.0000 - val_fp: 3047.0000 - val_tn: 55762.0000 - val_fn: 3900.0000 - lr: 1.0000e-04 Epoch 68/100 30/30 [==============================] - 1s 31ms/step - loss: 0.3081 - accuracy: 0.8843 - precision: 0.9028 - recall: 0.8670 - auc: 0.9832 - tp: 67986.0000 - fp: 7322.0000 - tn: 227914.0000 - fn: 10426.0000 - val_loss: 0.5373 - val_accuracy: 0.8180 - val_precision: 0.8377 - val_recall: 0.8015 - val_auc: 0.9548 - val_tp: 15711.0000 - val_fp: 3044.0000 - val_tn: 55765.0000 - val_fn: 3892.0000 - lr: 1.0000e-04 Epoch 69/100 30/30 [==============================] - 1s 32ms/step - loss: 0.3078 - accuracy: 0.8845 - precision: 0.9026 - recall: 0.8676 - auc: 0.9832 - tp: 68027.0000 - fp: 7342.0000 - tn: 227894.0000 - fn: 10385.0000 - val_loss: 0.5377 - val_accuracy: 0.8179 - val_precision: 0.8371 - val_recall: 0.8014 - val_auc: 0.9548 - val_tp: 15709.0000 - val_fp: 3057.0000 - val_tn: 55752.0000 - val_fn: 3894.0000 - lr: 1.0000e-04 Epoch 70/100 30/30 [==============================] - 1s 32ms/step - loss: 0.3076 - accuracy: 0.8847 - precision: 0.9028 - recall: 0.8674 - auc: 0.9833 - tp: 68013.0000 - fp: 7323.0000 - tn: 227913.0000 - fn: 10399.0000 - val_loss: 0.5383 - val_accuracy: 0.8183 - val_precision: 0.8377 - val_recall: 0.8018 - val_auc: 0.9547 - val_tp: 15717.0000 - val_fp: 3044.0000 - val_tn: 55765.0000 - val_fn: 3886.0000 - lr: 1.0000e-04 Epoch 71/100 30/30 [==============================] - 1s 31ms/step - loss: 0.3073 - accuracy: 0.8845 - precision: 0.9027 - recall: 0.8675 - auc: 0.9833 - tp: 68021.0000 - fp: 7333.0000 - tn: 227903.0000 - fn: 10391.0000 - val_loss: 0.5389 - val_accuracy: 0.8182 - val_precision: 0.8375 - val_recall: 0.8015 - val_auc: 0.9546 - val_tp: 15712.0000 - val_fp: 3049.0000 - val_tn: 55760.0000 - val_fn: 3891.0000 - lr: 1.0000e-04 Epoch 72/100 30/30 [==============================] - 1s 40ms/step - loss: 0.3070 - accuracy: 0.8846 - precision: 0.9030 - recall: 0.8677 - auc: 0.9833 - tp: 68040.0000 - fp: 7312.0000 - tn: 227924.0000 - fn: 10372.0000 - val_loss: 0.5394 - val_accuracy: 0.8183 - val_precision: 0.8373 - val_recall: 0.8015 - val_auc: 0.9546 - val_tp: 15711.0000 - val_fp: 3053.0000 - val_tn: 55756.0000 - val_fn: 3892.0000 - lr: 1.0000e-04 Epoch 73/100 30/30 [==============================] - 1s 33ms/step - loss: 0.3068 - accuracy: 0.8845 - precision: 0.9027 - recall: 0.8677 - auc: 0.9833 - tp: 68037.0000 - fp: 7334.0000 - tn: 227902.0000 - fn: 10375.0000 - val_loss: 0.5400 - val_accuracy: 0.8177 - val_precision: 0.8375 - val_recall: 0.8016 - val_auc: 0.9545 - val_tp: 15714.0000 - val_fp: 3049.0000 - val_tn: 55760.0000 - val_fn: 3889.0000 - lr: 1.0000e-04 Epoch 74/100 30/30 [==============================] - 1s 33ms/step - loss: 0.3065 - accuracy: 0.8848 - precision: 0.9030 - recall: 0.8679 - auc: 0.9834 - tp: 68056.0000 - fp: 7311.0000 - tn: 227925.0000 - fn: 10356.0000 - val_loss: 0.5405 - val_accuracy: 0.8180 - val_precision: 0.8371 - val_recall: 0.8015 - val_auc: 0.9545 - val_tp: 15712.0000 - val_fp: 3057.0000 - val_tn: 55752.0000 - val_fn: 3891.0000 - lr: 1.0000e-04 Epoch 75/100 30/30 [==============================] - 1s 30ms/step - loss: 0.3062 - accuracy: 0.8848 - precision: 0.9029 - recall: 0.8680 - auc: 0.9834 - tp: 68058.0000 - fp: 7315.0000 - tn: 227921.0000 - fn: 10354.0000 - val_loss: 0.5411 - val_accuracy: 0.8183 - val_precision: 0.8375 - val_recall: 0.8020 - val_auc: 0.9544 - val_tp: 15722.0000 - val_fp: 3051.0000 - val_tn: 55758.0000 - val_fn: 3881.0000 - lr: 1.0000e-04 Epoch 76/100 30/30 [==============================] - 1s 30ms/step - loss: 0.3059 - accuracy: 0.8847 - precision: 0.9031 - recall: 0.8684 - auc: 0.9834 - tp: 68095.0000 - fp: 7310.0000 - tn: 227926.0000 - fn: 10317.0000 - val_loss: 0.5417 - val_accuracy: 0.8177 - val_precision: 0.8366 - val_recall: 0.8016 - val_auc: 0.9544 - val_tp: 15714.0000 - val_fp: 3069.0000 - val_tn: 55740.0000 - val_fn: 3889.0000 - lr: 1.0000e-04 Epoch 77/100 30/30 [==============================] - 1s 33ms/step - loss: 0.3057 - accuracy: 0.8849 - precision: 0.9031 - recall: 0.8684 - auc: 0.9835 - tp: 68093.0000 - fp: 7307.0000 - tn: 227929.0000 - fn: 10319.0000 - val_loss: 0.5421 - val_accuracy: 0.8180 - val_precision: 0.8369 - val_recall: 0.8018 - val_auc: 0.9544 - val_tp: 15717.0000 - val_fp: 3063.0000 - val_tn: 55746.0000 - val_fn: 3886.0000 - lr: 1.0000e-04 Epoch 78/100 30/30 [==============================] - 1s 30ms/step - loss: 0.3054 - accuracy: 0.8850 - precision: 0.9034 - recall: 0.8685 - auc: 0.9835 - tp: 68102.0000 - fp: 7284.0000 - tn: 227952.0000 - fn: 10310.0000 - val_loss: 0.5427 - val_accuracy: 0.8179 - val_precision: 0.8369 - val_recall: 0.8016 - val_auc: 0.9543 - val_tp: 15714.0000 - val_fp: 3063.0000 - val_tn: 55746.0000 - val_fn: 3889.0000 - lr: 1.0000e-04 Epoch 79/100 30/30 [==============================] - 1s 31ms/step - loss: 0.3052 - accuracy: 0.8849 - precision: 0.9034 - recall: 0.8687 - auc: 0.9835 - tp: 68116.0000 - fp: 7287.0000 - tn: 227949.0000 - fn: 10296.0000 - val_loss: 0.5431 - val_accuracy: 0.8175 - val_precision: 0.8370 - val_recall: 0.8016 - val_auc: 0.9543 - val_tp: 15713.0000 - val_fp: 3061.0000 - val_tn: 55748.0000 - val_fn: 3890.0000 - lr: 1.0000e-04 Epoch 80/100 30/30 [==============================] - 1s 36ms/step - loss: 0.3049 - accuracy: 0.8852 - precision: 0.9033 - recall: 0.8690 - auc: 0.9835 - tp: 68140.0000 - fp: 7292.0000 - tn: 227944.0000 - fn: 10272.0000 - val_loss: 0.5438 - val_accuracy: 0.8176 - val_precision: 0.8363 - val_recall: 0.8017 - val_auc: 0.9542 - val_tp: 15716.0000 - val_fp: 3076.0000 - val_tn: 55733.0000 - val_fn: 3887.0000 - lr: 1.0000e-04 Epoch 81/100 30/30 [==============================] - 1s 32ms/step - loss: 0.3047 - accuracy: 0.8852 - precision: 0.9038 - recall: 0.8688 - auc: 0.9836 - tp: 68128.0000 - fp: 7254.0000 - tn: 227982.0000 - fn: 10284.0000 - val_loss: 0.5444 - val_accuracy: 0.8181 - val_precision: 0.8366 - val_recall: 0.8017 - val_auc: 0.9542 - val_tp: 15716.0000 - val_fp: 3070.0000 - val_tn: 55739.0000 - val_fn: 3887.0000 - lr: 1.0000e-04 Epoch 82/100 30/30 [==============================] - 1s 32ms/step - loss: 0.3044 - accuracy: 0.8855 - precision: 0.9037 - recall: 0.8692 - auc: 0.9836 - tp: 68158.0000 - fp: 7265.0000 - tn: 227971.0000 - fn: 10254.0000 - val_loss: 0.5449 - val_accuracy: 0.8178 - val_precision: 0.8367 - val_recall: 0.8017 - val_auc: 0.9541 - val_tp: 15715.0000 - val_fp: 3067.0000 - val_tn: 55742.0000 - val_fn: 3888.0000 - lr: 1.0000e-04 Epoch 83/100 30/30 [==============================] - 1s 30ms/step - loss: 0.3042 - accuracy: 0.8854 - precision: 0.9036 - recall: 0.8689 - auc: 0.9836 - tp: 68134.0000 - fp: 7266.0000 - tn: 227970.0000 - fn: 10278.0000 - val_loss: 0.5454 - val_accuracy: 0.8176 - val_precision: 0.8365 - val_recall: 0.8014 - val_auc: 0.9541 - val_tp: 15709.0000 - val_fp: 3071.0000 - val_tn: 55738.0000 - val_fn: 3894.0000 - lr: 1.0000e-04 Epoch 84/100 30/30 [==============================] - 1s 32ms/step - loss: 0.3039 - accuracy: 0.8858 - precision: 0.9038 - recall: 0.8693 - auc: 0.9837 - tp: 68163.0000 - fp: 7257.0000 - tn: 227979.0000 - fn: 10249.0000 - val_loss: 0.5459 - val_accuracy: 0.8178 - val_precision: 0.8363 - val_recall: 0.8017 - val_auc: 0.9540 - val_tp: 15716.0000 - val_fp: 3077.0000 - val_tn: 55732.0000 - val_fn: 3887.0000 - lr: 1.0000e-04 Epoch 85/100 30/30 [==============================] - 1s 31ms/step - loss: 0.3036 - accuracy: 0.8857 - precision: 0.9039 - recall: 0.8692 - auc: 0.9837 - tp: 68156.0000 - fp: 7250.0000 - tn: 227986.0000 - fn: 10256.0000 - val_loss: 0.5464 - val_accuracy: 0.8177 - val_precision: 0.8361 - val_recall: 0.8013 - val_auc: 0.9540 - val_tp: 15708.0000 - val_fp: 3080.0000 - val_tn: 55729.0000 - val_fn: 3895.0000 - lr: 1.0000e-04 Epoch 86/100 30/30 [==============================] - 1s 30ms/step - loss: 0.3034 - accuracy: 0.8857 - precision: 0.9040 - recall: 0.8694 - auc: 0.9837 - tp: 68174.0000 - fp: 7238.0000 - tn: 227998.0000 - fn: 10238.0000 - val_loss: 0.5469 - val_accuracy: 0.8174 - val_precision: 0.8359 - val_recall: 0.8010 - val_auc: 0.9539 - val_tp: 15702.0000 - val_fp: 3082.0000 - val_tn: 55727.0000 - val_fn: 3901.0000 - lr: 1.0000e-04 Epoch 87/100 30/30 [==============================] - 1s 38ms/step - loss: 0.3031 - accuracy: 0.8860 - precision: 0.9043 - recall: 0.8696 - auc: 0.9837 - tp: 68185.0000 - fp: 7213.0000 - tn: 228023.0000 - fn: 10227.0000 - val_loss: 0.5476 - val_accuracy: 0.8178 - val_precision: 0.8363 - val_recall: 0.8015 - val_auc: 0.9538 - val_tp: 15712.0000 - val_fp: 3075.0000 - val_tn: 55734.0000 - val_fn: 3891.0000 - lr: 1.0000e-04 Epoch 88/100 30/30 [==============================] - 1s 32ms/step - loss: 0.3029 - accuracy: 0.8858 - precision: 0.9039 - recall: 0.8698 - auc: 0.9838 - tp: 68204.0000 - fp: 7250.0000 - tn: 227986.0000 - fn: 10208.0000 - val_loss: 0.5481 - val_accuracy: 0.8179 - val_precision: 0.8357 - val_recall: 0.8014 - val_auc: 0.9539 - val_tp: 15709.0000 - val_fp: 3089.0000 - val_tn: 55720.0000 - val_fn: 3894.0000 - lr: 1.0000e-04 Epoch 89/100 30/30 [==============================] - 1s 33ms/step - loss: 0.3027 - accuracy: 0.8859 - precision: 0.9040 - recall: 0.8696 - auc: 0.9838 - tp: 68186.0000 - fp: 7237.0000 - tn: 227999.0000 - fn: 10226.0000 - val_loss: 0.5487 - val_accuracy: 0.8177 - val_precision: 0.8359 - val_recall: 0.8010 - val_auc: 0.9538 - val_tp: 15702.0000 - val_fp: 3083.0000 - val_tn: 55726.0000 - val_fn: 3901.0000 - lr: 1.0000e-04 Epoch 90/100 30/30 [==============================] - 1s 34ms/step - loss: 0.3025 - accuracy: 0.8858 - precision: 0.9040 - recall: 0.8697 - auc: 0.9838 - tp: 68198.0000 - fp: 7241.0000 - tn: 227995.0000 - fn: 10214.0000 - val_loss: 0.5491 - val_accuracy: 0.8176 - val_precision: 0.8360 - val_recall: 0.8011 - val_auc: 0.9537 - val_tp: 15704.0000 - val_fp: 3080.0000 - val_tn: 55729.0000 - val_fn: 3899.0000 - lr: 1.0000e-04 Epoch 91/100 30/30 [==============================] - 1s 30ms/step - loss: 0.3022 - accuracy: 0.8864 - precision: 0.9043 - recall: 0.8700 - auc: 0.9838 - tp: 68220.0000 - fp: 7217.0000 - tn: 228019.0000 - fn: 10192.0000 - val_loss: 0.5496 - val_accuracy: 0.8180 - val_precision: 0.8361 - val_recall: 0.8013 - val_auc: 0.9537 - val_tp: 15708.0000 - val_fp: 3079.0000 - val_tn: 55730.0000 - val_fn: 3895.0000 - lr: 1.0000e-04 Epoch 92/100 30/30 [==============================] - 1s 31ms/step - loss: 0.3020 - accuracy: 0.8863 - precision: 0.9043 - recall: 0.8699 - auc: 0.9838 - tp: 68213.0000 - fp: 7218.0000 - tn: 228018.0000 - fn: 10199.0000 - val_loss: 0.5502 - val_accuracy: 0.8176 - val_precision: 0.8360 - val_recall: 0.8008 - val_auc: 0.9536 - val_tp: 15699.0000 - val_fp: 3079.0000 - val_tn: 55730.0000 - val_fn: 3904.0000 - lr: 1.0000e-04 Epoch 93/100 30/30 [==============================] - 1s 42ms/step - loss: 0.3018 - accuracy: 0.8862 - precision: 0.9044 - recall: 0.8700 - auc: 0.9839 - tp: 68222.0000 - fp: 7213.0000 - tn: 228023.0000 - fn: 10190.0000 - val_loss: 0.5508 - val_accuracy: 0.8179 - val_precision: 0.8355 - val_recall: 0.8011 - val_auc: 0.9536 - val_tp: 15704.0000 - val_fp: 3092.0000 - val_tn: 55717.0000 - val_fn: 3899.0000 - lr: 1.0000e-04 Epoch 94/100 30/30 [==============================] - 1s 31ms/step - loss: 0.3015 - accuracy: 0.8866 - precision: 0.9043 - recall: 0.8702 - auc: 0.9839 - tp: 68237.0000 - fp: 7218.0000 - tn: 228018.0000 - fn: 10175.0000 - val_loss: 0.5513 - val_accuracy: 0.8173 - val_precision: 0.8360 - val_recall: 0.8012 - val_auc: 0.9535 - val_tp: 15705.0000 - val_fp: 3082.0000 - val_tn: 55727.0000 - val_fn: 3898.0000 - lr: 1.0000e-04 Epoch 95/100 30/30 [==============================] - 1s 29ms/step - loss: 0.3013 - accuracy: 0.8866 - precision: 0.9045 - recall: 0.8706 - auc: 0.9839 - tp: 68263.0000 - fp: 7208.0000 - tn: 228028.0000 - fn: 10149.0000 - val_loss: 0.5518 - val_accuracy: 0.8175 - val_precision: 0.8356 - val_recall: 0.8014 - val_auc: 0.9534 - val_tp: 15710.0000 - val_fp: 3090.0000 - val_tn: 55719.0000 - val_fn: 3893.0000 - lr: 1.0000e-04 Epoch 96/100 30/30 [==============================] - 1s 31ms/step - loss: 0.3010 - accuracy: 0.8865 - precision: 0.9045 - recall: 0.8705 - auc: 0.9839 - tp: 68258.0000 - fp: 7206.0000 - tn: 228030.0000 - fn: 10154.0000 - val_loss: 0.5524 - val_accuracy: 0.8176 - val_precision: 0.8355 - val_recall: 0.8011 - val_auc: 0.9534 - val_tp: 15704.0000 - val_fp: 3092.0000 - val_tn: 55717.0000 - val_fn: 3899.0000 - lr: 1.0000e-04 Epoch 97/100 30/30 [==============================] - 1s 32ms/step - loss: 0.3008 - accuracy: 0.8866 - precision: 0.9044 - recall: 0.8702 - auc: 0.9840 - tp: 68237.0000 - fp: 7209.0000 - tn: 228027.0000 - fn: 10175.0000 - val_loss: 0.5529 - val_accuracy: 0.8178 - val_precision: 0.8356 - val_recall: 0.8014 - val_auc: 0.9533 - val_tp: 15709.0000 - val_fp: 3090.0000 - val_tn: 55719.0000 - val_fn: 3894.0000 - lr: 1.0000e-04 Epoch 98/100 30/30 [==============================] - 1s 32ms/step - loss: 0.3006 - accuracy: 0.8870 - precision: 0.9046 - recall: 0.8707 - auc: 0.9840 - tp: 68273.0000 - fp: 7199.0000 - tn: 228037.0000 - fn: 10139.0000 - val_loss: 0.5534 - val_accuracy: 0.8177 - val_precision: 0.8354 - val_recall: 0.8014 - val_auc: 0.9533 - val_tp: 15709.0000 - val_fp: 3095.0000 - val_tn: 55714.0000 - val_fn: 3894.0000 - lr: 1.0000e-04 Epoch 99/100 30/30 [==============================] - 1s 33ms/step - loss: 0.3003 - accuracy: 0.8868 - precision: 0.9046 - recall: 0.8707 - auc: 0.9840 - tp: 68273.0000 - fp: 7201.0000 - tn: 228035.0000 - fn: 10139.0000 - val_loss: 0.5539 - val_accuracy: 0.8176 - val_precision: 0.8353 - val_recall: 0.8011 - val_auc: 0.9533 - val_tp: 15703.0000 - val_fp: 3097.0000 - val_tn: 55712.0000 - val_fn: 3900.0000 - lr: 1.0000e-04 Epoch 100/100 30/30 [==============================] - 1s 33ms/step - loss: 0.3001 - accuracy: 0.8870 - precision: 0.9048 - recall: 0.8707 - auc: 0.9840 - tp: 68275.0000 - fp: 7185.0000 - tn: 228051.0000 - fn: 10137.0000 - val_loss: 0.5545 - val_accuracy: 0.8173 - val_precision: 0.8353 - val_recall: 0.8011 - val_auc: 0.9532 - val_tp: 15703.0000 - val_fp: 3096.0000 - val_tn: 55713.0000 - val_fn: 3900.0000 - lr: 1.0000e-04
[I 2024-06-08 14:01:04,384] Trial 4 finished with value: 0.815499484539032 and parameters: {'num_filters': 86, 'kernel_size': 5, 'learning_rate': 0.0026465008265824665}. Best is trial 0 with value: 0.819294810295105.
Loss: 0.558591902256012 Accuracy: 0.815499484539032 Precision: 0.8317180871963501 Recall: 0.7985227108001709 AUC: 0.95306795835495 True Positives: 19567.0 False Positives: 3959.0 True Negatives: 69553.0 False Negatives: 4937.0
print("Najlepsze wartości hiperparametrów:", study.best_params)
print("Wartość metryki dla najlepszych hiperparametrów:", study.best_value)Najlepsze wartości hiperparametrów: {'num_filters': 37, 'kernel_size': 5, 'learning_rate': 0.0005838498821337493}
Wartość metryki dla najlepszych hiperparametrów: 0.819294810295105