"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def jitter(arr):\n",
" return arr + np.random.uniform(low=-0.35, high=0.35, size=len(arr))\n",
"\n",
"plt.figure(figsize=(16, 36))\n",
"for i in range (1, X.shape[1]):\n",
" plt.subplot(X.shape[1]//4, 4, i)\n",
" plt.scatter(jitter(y), jitter(X.iloc[:, i]), c=X[\"IsDog\"], edgecolors=\"black\")\n",
" plt.xlabel('Outcome type', fontsize=14)\n",
" plt.ylabel(str(X.columns[i]), fontsize=14)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Разобьём выборку на тренировочную и тестовую. "
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(21383, 37)\n",
"(5346, 37)\n"
]
}
],
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify = y, random_state = 42)\n",
"\n",
"print(X_train.shape)\n",
"print(X_test.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Константный прогноз\n",
"\n",
"Построим константный прогноз, чтобы было с чем сравнивать и прогноз по какой-нибудь модели. "
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0.40289015, 0.0655661 , 0.17906748, 0.35247627])"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.metrics import log_loss\n",
"\n",
"probas = np.array(pd.Series(y_train).value_counts(normalize=True).sort_index().tolist())\n",
"probas"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.2202552496487795"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"log_loss(y_test, np.tile(probas, X_test.shape[0]).reshape(X_test.shape[0], 4))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Логистическая регрессия"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"E:\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
"\n",
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
"Please also refer to the documentation for alternative solver options:\n",
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
" n_iter_i = _check_optimize_result(\n"
]
},
{
"data": {
"text/plain": [
"1.069059090175452"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"\n",
"logreg = LogisticRegression()\n",
"logreg.fit(X_train, y_train)\n",
"log_loss(y_test, logreg.predict_proba(X_test))"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[0.18708564, 0.06705396, 0.04653656, 0.69932384],\n",
" [0.71548491, 0.02923366, 0.10843243, 0.14684899],\n",
" [0.52376425, 0.03809907, 0.18068785, 0.25744883],\n",
" ...,\n",
" [0.77510103, 0.01024946, 0.17602131, 0.0386282 ],\n",
" [0.22847272, 0.05774225, 0.03826619, 0.67551884],\n",
" [0.66540766, 0.0301212 , 0.12082815, 0.18364299]])"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"logreg.predict_proba(X_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Случайный лес "
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"0.7624441876025413"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.ensemble import RandomForestClassifier\n",
"\n",
"rf = RandomForestClassifier(n_estimators=1000, n_jobs=3)\n",
"rf.fit(X_train, y_train)\n",
"log_loss(y_test, rf.predict_proba(X_test))"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[0.846, 0.011, 0.015, 0.128],\n",
" [0.339, 0.067, 0.27 , 0.324],\n",
" [0.499, 0.197, 0.08 , 0.224],\n",
" ...,\n",
" [0.254, 0.006, 0.247, 0.493],\n",
" [0.804, 0.028, 0.017, 0.151],\n",
" [0.842, 0.004, 0.114, 0.04 ]])"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rf.predict_proba(X_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Неплохой результат. Попробуем улучшить его с помошью нейросеток. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Собираем свою нейросеть"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"import keras\n",
"from keras.models import Sequential # Последовательность слоёв\n",
"import keras.layers as L # Разные слои\n",
"import keras.optimizers as opt # Оптимизаторы\n",
"from tensorflow.keras.utils import to_categorical # Для преобразования y в категориальный формат "
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"def get_new_model( ):\n",
" # Задаём модель и называем её ...\n",
" model = Sequential(name = 'Archibald')\n",
"\n",
" # Добавляем в нашу модель первый слой из 25 нейронов\n",
" # На вход в эти 25 нейронов будут индти все наши регрессоры\n",
" model.add(L.Dense(25, input_dim = X_train.shape[1], kernel_initializer='random_normal'))\n",
"\n",
" # Добавляем функцию активации на первый слой \n",
" model.add(L.Activation('sigmoid'))\n",
"\n",
" # Добавляем ещё один слой из 25 нейронов\n",
" model.add(L.Dense(25, kernel_initializer='random_normal'))\n",
" model.add(L.Activation('sigmoid'))\n",
"\n",
" # На выходе мы должны получить вероятности того, что объект относится к разным классам \n",
" # Сделать такое преобразование позволяет softmax как функция активации\n",
" # На выход будет идти 4 вероятности по числу классов\n",
" model.add(L.Dense(4, activation='softmax', kernel_initializer = 'random_normal'))\n",
"\n",
" # В качестве оптимизации будем использовать Adam\n",
" optimizer = opt.Adam(learning_rate=1e-3)\n",
"\n",
" # Собираем модельa\n",
" model.compile(loss = 'categorical_crossentropy', metrics=[\"accuracy\"], optimizer=optimizer)\n",
" return model\n",
"\n",
"model = get_new_model( )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Итак, только что мы собрали свою первую нейросеть с двумя скрытыми слоями. Посмотрим сколько параметров нам предстоит оценить. "
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"Model: \"Archibald\" \n",
"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
"┃ Layer (type) ┃ Output Shape ┃ Param # ┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
"│ dense_3 (Dense ) │ (None , 25 ) │ 950 │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ activation_2 (Activation ) │ (None , 25 ) │ 0 │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ dense_4 (Dense ) │ (None , 25 ) │ 650 │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ activation_3 (Activation ) │ (None , 25 ) │ 0 │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ dense_5 (Dense ) │ (None , 4 ) │ 104 │\n",
"└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
" \n"
],
"text/plain": [
"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
"┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
"│ dense_3 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m) │ \u001b[38;5;34m950\u001b[0m │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ activation_2 (\u001b[38;5;33mActivation\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ dense_4 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m) │ \u001b[38;5;34m650\u001b[0m │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ activation_3 (\u001b[38;5;33mActivation\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ dense_5 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m) │ \u001b[38;5;34m104\u001b[0m │\n",
"└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
" Total params: 1,704 (6.66 KB)\n",
" Trainable params: 1,704 (6.66 KB)\n",
" Non-trainable params: 0 (0.00 B)\n",
""
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"start = 1\n",
"plt.plot(hist.history['loss'][start:])\n",
"plt.plot(hist.history['val_loss'][start:])\n",
"plt.legend(['Train loss', 'Validation loss'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Модель сходится довольно плохо. Функция потерь на валидации со временем начинает скакать. "
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m168/168\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step\n"
]
},
{
"data": {
"text/plain": [
"array([[0.4583996 , 0.05052387, 0.08544645, 0.4056301 ],\n",
" [0.4583996 , 0.05052387, 0.08544645, 0.4056301 ],\n",
" [0.4583996 , 0.05052387, 0.08544645, 0.4056301 ],\n",
" ...,\n",
" [0.4583996 , 0.05052386, 0.08544645, 0.4056301 ],\n",
" [0.4583996 , 0.05052386, 0.08544645, 0.4056301 ],\n",
" [0.4583996 , 0.05052386, 0.08544645, 0.4056301 ]], dtype=float32)"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Можно предсказать вероятности для тестовой выборки\n",
"model.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m168/168\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step\n"
]
},
{
"data": {
"text/plain": [
"array([0, 0, 0, ..., 0, 0, 0], dtype=int64)"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# А можно классы \n",
"# model.predict_classes(X_test)\n",
"np.argmax(model.predict(X_test), axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m168/168\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step\n"
]
},
{
"data": {
"text/plain": [
"1.156659624791302"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Можно найти logloss \n",
"log_loss(y_test,model.predict(X_test))"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
]
}
],
"source": [
"# Можно сохранить модель\n",
"model.save(\"weights.h5\")"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m168/168\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.4304 - loss: 1.1623\n"
]
},
{
"data": {
"text/plain": [
"[1.1566599607467651, 0.42611297965049744]"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Можно посмотреть на качество модели по всем указанным при коммпиляции метрикам. \n",
"score = model.evaluate(X_test, to_categorical(y_test))\n",
"score "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Вспомним про такую замечательную штуку как нормальзация и посмотрим насколько она улучшит скорость обучения и качество модели. Заодно увеличим количество эпох. Ударим двойным кулаком по плохой сходимости. "
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"E:\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\core\\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
" super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 5ms/step - accuracy: 0.4392 - loss: 1.1990 - val_accuracy: 0.6030 - val_loss: 0.9716\n",
"Epoch 2/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6060 - loss: 0.9585 - val_accuracy: 0.6329 - val_loss: 0.8914\n",
"Epoch 3/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6292 - loss: 0.8995 - val_accuracy: 0.6444 - val_loss: 0.8761\n",
"Epoch 4/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - accuracy: 0.6339 - loss: 0.8899 - val_accuracy: 0.6423 - val_loss: 0.8685\n",
"Epoch 5/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6374 - loss: 0.8815 - val_accuracy: 0.6486 - val_loss: 0.8650\n",
"Epoch 6/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6478 - loss: 0.8665 - val_accuracy: 0.6481 - val_loss: 0.8594\n",
"Epoch 7/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6434 - loss: 0.8687 - val_accuracy: 0.6451 - val_loss: 0.8560\n",
"Epoch 8/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6497 - loss: 0.8529 - val_accuracy: 0.6472 - val_loss: 0.8505\n",
"Epoch 9/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6469 - loss: 0.8611 - val_accuracy: 0.6467 - val_loss: 0.8496\n",
"Epoch 10/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6474 - loss: 0.8536 - val_accuracy: 0.6502 - val_loss: 0.8431\n",
"Epoch 11/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6519 - loss: 0.8483 - val_accuracy: 0.6509 - val_loss: 0.8433\n",
"Epoch 12/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6508 - loss: 0.8401 - val_accuracy: 0.6523 - val_loss: 0.8368\n",
"Epoch 13/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6446 - loss: 0.8568 - val_accuracy: 0.6537 - val_loss: 0.8359\n",
"Epoch 14/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6544 - loss: 0.8312 - val_accuracy: 0.6533 - val_loss: 0.8326\n",
"Epoch 15/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.6538 - loss: 0.8311 - val_accuracy: 0.6537 - val_loss: 0.8314\n",
"Epoch 16/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6555 - loss: 0.8342 - val_accuracy: 0.6512 - val_loss: 0.8375\n",
"Epoch 17/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.6540 - loss: 0.8339 - val_accuracy: 0.6579 - val_loss: 0.8284\n",
"Epoch 18/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6583 - loss: 0.8288 - val_accuracy: 0.6547 - val_loss: 0.8271\n",
"Epoch 19/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6583 - loss: 0.8218 - val_accuracy: 0.6586 - val_loss: 0.8246\n",
"Epoch 20/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6595 - loss: 0.8294 - val_accuracy: 0.6591 - val_loss: 0.8241\n",
"Epoch 21/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6609 - loss: 0.8176 - val_accuracy: 0.6584 - val_loss: 0.8216\n",
"Epoch 22/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6604 - loss: 0.8183 - val_accuracy: 0.6600 - val_loss: 0.8234\n",
"Epoch 23/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6590 - loss: 0.8137 - val_accuracy: 0.6593 - val_loss: 0.8195\n",
"Epoch 24/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6580 - loss: 0.8161 - val_accuracy: 0.6554 - val_loss: 0.8197\n",
"Epoch 25/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6543 - loss: 0.8212 - val_accuracy: 0.6617 - val_loss: 0.8202\n",
"Epoch 26/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6606 - loss: 0.8141 - val_accuracy: 0.6568 - val_loss: 0.8215\n",
"Epoch 27/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6613 - loss: 0.8045 - val_accuracy: 0.6605 - val_loss: 0.8162\n",
"Epoch 28/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6591 - loss: 0.8157 - val_accuracy: 0.6619 - val_loss: 0.8148\n",
"Epoch 29/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6677 - loss: 0.7953 - val_accuracy: 0.6621 - val_loss: 0.8128\n",
"Epoch 30/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.6692 - loss: 0.7951 - val_accuracy: 0.6624 - val_loss: 0.8140\n",
"Epoch 31/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6625 - loss: 0.8007 - val_accuracy: 0.6626 - val_loss: 0.8123\n",
"Epoch 32/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6590 - loss: 0.8105 - val_accuracy: 0.6628 - val_loss: 0.8087\n",
"Epoch 33/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6606 - loss: 0.8042 - val_accuracy: 0.6617 - val_loss: 0.8103\n",
"Epoch 34/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6647 - loss: 0.7991 - val_accuracy: 0.6652 - val_loss: 0.8081\n",
"Epoch 35/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6685 - loss: 0.7987 - val_accuracy: 0.6652 - val_loss: 0.8076\n",
"Epoch 36/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6617 - loss: 0.8024 - val_accuracy: 0.6624 - val_loss: 0.8063\n",
"Epoch 37/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6726 - loss: 0.7810 - val_accuracy: 0.6638 - val_loss: 0.8077\n",
"Epoch 38/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6697 - loss: 0.7909 - val_accuracy: 0.6624 - val_loss: 0.8062\n",
"Epoch 39/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6642 - loss: 0.7985 - val_accuracy: 0.6661 - val_loss: 0.8052\n",
"Epoch 40/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6673 - loss: 0.7857 - val_accuracy: 0.6589 - val_loss: 0.8062\n",
"Epoch 41/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6673 - loss: 0.7879 - val_accuracy: 0.6678 - val_loss: 0.8083\n",
"Epoch 42/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6652 - loss: 0.7878 - val_accuracy: 0.6654 - val_loss: 0.8049\n",
"Epoch 43/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6717 - loss: 0.7911 - val_accuracy: 0.6687 - val_loss: 0.8065\n",
"Epoch 44/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6708 - loss: 0.7774 - val_accuracy: 0.6706 - val_loss: 0.8033\n",
"Epoch 45/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6734 - loss: 0.7816 - val_accuracy: 0.6687 - val_loss: 0.8041\n",
"Epoch 46/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6734 - loss: 0.7800 - val_accuracy: 0.6661 - val_loss: 0.8059\n",
"Epoch 47/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6760 - loss: 0.7827 - val_accuracy: 0.6645 - val_loss: 0.8051\n",
"Epoch 48/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6670 - loss: 0.7842 - val_accuracy: 0.6680 - val_loss: 0.8016\n",
"Epoch 49/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6725 - loss: 0.7828 - val_accuracy: 0.6685 - val_loss: 0.8026\n",
"Epoch 50/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6750 - loss: 0.7799 - val_accuracy: 0.6654 - val_loss: 0.8036\n"
]
}
],
"source": [
"from sklearn.preprocessing import StandardScaler\n",
"ss = StandardScaler()\n",
"\n",
"model = get_new_model( ) # Сбросим старые результаты обучения \n",
"hist = model.fit(ss.fit_transform(X_train), to_categorical(y_train), validation_split=0.2, epochs=50, verbose=1)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"start = 0\n",
"plt.plot(hist.history['loss'][start:])\n",
"plt.plot(hist.history['val_loss'][start:])\n",
"plt.legend(['Train loss', 'Validation loss'])"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m168/168\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.6581 - loss: 0.8298\n"
]
},
{
"data": {
"text/plain": [
"[0.8258541822433472, 0.6677889823913574]"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"score = model.evaluate(ss.transform(X_test), to_categorical(y_test))\n",
"score "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Модель стала сходиться лучше, но мы упёрлись в новую проблему. Поначалу качество модели на валидацим возростало. Спустя несколько эпох, качество натрэйне полжило расти, а на валидации стало падоть. Как вы помните, всё это является признаками переобучения. Попробуем дать переобучению бой. \n",
"\n",
"## 3. Бои с переобучением\n",
"\n",
"Существует несколько способов по регуляризации нейронных сетей. \n",
"\n",
"* EarlyStopping\n",
"* Dropout\n",
"* $l_1$ и $l_2$ регуляризация \n",
"\n",
"\n",
"Попробуем всего по немногу. \n",
"\n",
"### 3.1 Early stopping\n",
"\n",
"Одни сети сходятся за 5 эпох, другие не сходятся и за тысячу. Тренировать сетку в течение 1000 эпох, если она сходится за 5 - это не очень хорошая идея, так как качество на валидации начнает падать и модель переобучается. Давайте попробуем останавливать обучение модели, как только оценка качества модели на валидации падает в течение нескольких эпох. Например, пяти. "
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"E:\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\core\\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
" super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 6ms/step - accuracy: 0.4701 - loss: 1.2032 - val_accuracy: 0.6074 - val_loss: 0.9769\n",
"Epoch 2/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6073 - loss: 0.9677 - val_accuracy: 0.6395 - val_loss: 0.8899\n",
"Epoch 3/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6360 - loss: 0.8935 - val_accuracy: 0.6416 - val_loss: 0.8744\n",
"Epoch 4/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6429 - loss: 0.8777 - val_accuracy: 0.6434 - val_loss: 0.8679\n",
"Epoch 5/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6354 - loss: 0.8824 - val_accuracy: 0.6451 - val_loss: 0.8634\n",
"Epoch 6/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6438 - loss: 0.8719 - val_accuracy: 0.6430 - val_loss: 0.8583\n",
"Epoch 7/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 5ms/step - accuracy: 0.6447 - loss: 0.8622 - val_accuracy: 0.6444 - val_loss: 0.8543\n",
"Epoch 8/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6410 - loss: 0.8676 - val_accuracy: 0.6446 - val_loss: 0.8501\n",
"Epoch 9/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6458 - loss: 0.8493 - val_accuracy: 0.6460 - val_loss: 0.8480\n",
"Epoch 10/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 5ms/step - accuracy: 0.6487 - loss: 0.8479 - val_accuracy: 0.6439 - val_loss: 0.8452\n",
"Epoch 11/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6469 - loss: 0.8465 - val_accuracy: 0.6500 - val_loss: 0.8406\n",
"Epoch 12/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6427 - loss: 0.8522 - val_accuracy: 0.6514 - val_loss: 0.8366\n",
"Epoch 13/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6508 - loss: 0.8422 - val_accuracy: 0.6533 - val_loss: 0.8361\n",
"Epoch 14/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6489 - loss: 0.8441 - val_accuracy: 0.6505 - val_loss: 0.8324\n",
"Epoch 15/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6585 - loss: 0.8293 - val_accuracy: 0.6556 - val_loss: 0.8304\n",
"Epoch 16/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6579 - loss: 0.8285 - val_accuracy: 0.6561 - val_loss: 0.8285\n",
"Epoch 17/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6619 - loss: 0.8213 - val_accuracy: 0.6544 - val_loss: 0.8261\n",
"Epoch 18/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6574 - loss: 0.8232 - val_accuracy: 0.6488 - val_loss: 0.8271\n",
"Epoch 19/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6590 - loss: 0.8261 - val_accuracy: 0.6556 - val_loss: 0.8231\n",
"Epoch 20/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6562 - loss: 0.8200 - val_accuracy: 0.6568 - val_loss: 0.8203\n",
"Epoch 21/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.6628 - loss: 0.8113 - val_accuracy: 0.6565 - val_loss: 0.8195\n",
"Epoch 22/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6589 - loss: 0.8214 - val_accuracy: 0.6591 - val_loss: 0.8189\n",
"Epoch 23/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6584 - loss: 0.8158 - val_accuracy: 0.6565 - val_loss: 0.8162\n",
"Epoch 24/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.6615 - loss: 0.8172 - val_accuracy: 0.6582 - val_loss: 0.8170\n",
"Epoch 25/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 5ms/step - accuracy: 0.6590 - loss: 0.8162 - val_accuracy: 0.6596 - val_loss: 0.8147\n",
"Epoch 26/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6610 - loss: 0.8066 - val_accuracy: 0.6570 - val_loss: 0.8140\n",
"Epoch 27/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6626 - loss: 0.8117 - val_accuracy: 0.6621 - val_loss: 0.8101\n",
"Epoch 28/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6674 - loss: 0.7956 - val_accuracy: 0.6586 - val_loss: 0.8091\n",
"Epoch 29/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6621 - loss: 0.8067 - val_accuracy: 0.6577 - val_loss: 0.8095\n",
"Epoch 30/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6729 - loss: 0.7902 - val_accuracy: 0.6591 - val_loss: 0.8091\n",
"Epoch 31/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6638 - loss: 0.8029 - val_accuracy: 0.6610 - val_loss: 0.8070\n",
"Epoch 32/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6647 - loss: 0.8063 - val_accuracy: 0.6631 - val_loss: 0.8083\n",
"Epoch 33/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6653 - loss: 0.7868 - val_accuracy: 0.6624 - val_loss: 0.8057\n",
"Epoch 34/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.6780 - loss: 0.7811 - val_accuracy: 0.6643 - val_loss: 0.8034\n",
"Epoch 35/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.6765 - loss: 0.7826 - val_accuracy: 0.6664 - val_loss: 0.8040\n",
"Epoch 36/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6694 - loss: 0.7932 - val_accuracy: 0.6645 - val_loss: 0.8031\n",
"Epoch 37/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6732 - loss: 0.7917 - val_accuracy: 0.6621 - val_loss: 0.8072\n",
"Epoch 38/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6706 - loss: 0.7917 - val_accuracy: 0.6675 - val_loss: 0.8000\n",
"Epoch 39/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6708 - loss: 0.7970 - val_accuracy: 0.6680 - val_loss: 0.7992\n",
"Epoch 40/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6683 - loss: 0.7870 - val_accuracy: 0.6664 - val_loss: 0.7987\n",
"Epoch 41/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.6730 - loss: 0.7886 - val_accuracy: 0.6701 - val_loss: 0.7989\n",
"Epoch 42/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6734 - loss: 0.7818 - val_accuracy: 0.6685 - val_loss: 0.7981\n",
"Epoch 43/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.6745 - loss: 0.7879 - val_accuracy: 0.6661 - val_loss: 0.8002\n",
"Epoch 44/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6784 - loss: 0.7873 - val_accuracy: 0.6643 - val_loss: 0.7981\n",
"Epoch 45/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.6756 - loss: 0.7793 - val_accuracy: 0.6680 - val_loss: 0.7999\n",
"Epoch 46/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.6769 - loss: 0.7724 - val_accuracy: 0.6675 - val_loss: 0.8009\n",
"Epoch 47/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6808 - loss: 0.7681 - val_accuracy: 0.6675 - val_loss: 0.7979\n",
"Epoch 48/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.6751 - loss: 0.7809 - val_accuracy: 0.6668 - val_loss: 0.7965\n",
"Epoch 49/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.6814 - loss: 0.7718 - val_accuracy: 0.6671 - val_loss: 0.7957\n",
"Epoch 50/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.6766 - loss: 0.7819 - val_accuracy: 0.6678 - val_loss: 0.7953\n",
"Epoch 51/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.6761 - loss: 0.7773 - val_accuracy: 0.6645 - val_loss: 0.7993\n",
"Epoch 52/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.6765 - loss: 0.7782 - val_accuracy: 0.6666 - val_loss: 0.7961\n",
"Epoch 53/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.6716 - loss: 0.7799 - val_accuracy: 0.6696 - val_loss: 0.7964\n",
"Epoch 54/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.6802 - loss: 0.7693 - val_accuracy: 0.6664 - val_loss: 0.7958\n",
"Epoch 55/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.6782 - loss: 0.7741 - val_accuracy: 0.6682 - val_loss: 0.7949\n",
"Epoch 56/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.6776 - loss: 0.7757 - val_accuracy: 0.6673 - val_loss: 0.7949\n",
"Epoch 57/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.6852 - loss: 0.7670 - val_accuracy: 0.6661 - val_loss: 0.7947\n",
"Epoch 58/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.6797 - loss: 0.7713 - val_accuracy: 0.6664 - val_loss: 0.7941\n",
"Epoch 59/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.6850 - loss: 0.7586 - val_accuracy: 0.6659 - val_loss: 0.7946\n",
"Epoch 60/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.6794 - loss: 0.7709 - val_accuracy: 0.6666 - val_loss: 0.7942\n",
"Epoch 61/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.6775 - loss: 0.7677 - val_accuracy: 0.6638 - val_loss: 0.7935\n",
"Epoch 62/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.6756 - loss: 0.7715 - val_accuracy: 0.6647 - val_loss: 0.7957\n",
"Epoch 63/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.6815 - loss: 0.7685 - val_accuracy: 0.6635 - val_loss: 0.7961\n",
"Epoch 64/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.6805 - loss: 0.7648 - val_accuracy: 0.6657 - val_loss: 0.7951\n",
"Epoch 65/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.6802 - loss: 0.7712 - val_accuracy: 0.6645 - val_loss: 0.7961\n",
"Epoch 66/200\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.6836 - loss: 0.7547 - val_accuracy: 0.6673 - val_loss: 0.7959\n"
]
}
],
"source": [
"# Подгружаем раннюю остановку\n",
"from keras.callbacks import EarlyStopping\n",
"\n",
"# Прикажем модели останавливать обучение, когда качество на валидации не будет улучшаться в течение пяти эпох\n",
"early_stopping_monitor = EarlyStopping(patience = 5)\n",
"\n",
"model = get_new_model( ) # Сбросим старые веса \n",
"hist = model.fit(ss.fit_transform(X_train), to_categorical(y_train), validation_split=0.2, epochs=200, \n",
" verbose=1, callbacks = [early_stopping_monitor])"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"start = 0\n",
"plt.plot(hist.history['loss'][start:])\n",
"plt.plot(hist.history['val_loss'][start:])\n",
"plt.legend(['Train loss', 'Validation loss'])"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m168/168\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.6536 - loss: 0.8178\n"
]
},
{
"data": {
"text/plain": [
"[0.8122668266296387, 0.6657313704490662]"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"score = model.evaluate(ss.transform(X_test), to_categorical(y_test))\n",
"score "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.2 Dropout \n",
"\n",
"Попробуем новые приколы, для которых нам придётся изменять архитектуру нашей модели. Добавим в модель дропаут. "
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"from keras.layers import Dropout\n",
"\n",
"model_dropout = Sequential( )\n",
"\n",
"# Первый слой \n",
"model_dropout.add(L.Dense(25, input_dim = X_train.shape[1], kernel_initializer='random_normal'))\n",
"model_dropout.add(Dropout(0.5)) # слой дропаута\n",
"model_dropout.add(L.Activation('sigmoid'))\n",
"\n",
"# Второй слой \n",
"model_dropout.add(L.Dense(25, kernel_initializer='random_normal'))\n",
"model_dropout.add(Dropout(0.5))\n",
"model_dropout.add(L.Activation('sigmoid'))\n",
"\n",
"\n",
"# Выходной слой \n",
"model_dropout.add(L.Dense(4, activation='softmax', kernel_initializer = 'random_normal'))\n",
"\n",
"# Собираем модель \n",
"model_dropout.compile(loss = 'categorical_crossentropy', metrics=[\"accuracy\"], optimizer=opt.Adam(learning_rate=1e-3))"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 6ms/step - accuracy: 0.4465 - loss: 1.2297 - val_accuracy: 0.6095 - val_loss: 1.0069\n",
"Epoch 2/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6085 - loss: 1.0383 - val_accuracy: 0.6142 - val_loss: 0.9733\n",
"Epoch 3/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6055 - loss: 1.0029 - val_accuracy: 0.6180 - val_loss: 0.9496\n",
"Epoch 4/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6126 - loss: 0.9620 - val_accuracy: 0.6355 - val_loss: 0.9165\n",
"Epoch 5/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6206 - loss: 0.9393 - val_accuracy: 0.6411 - val_loss: 0.9214\n",
"Epoch 6/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - accuracy: 0.6196 - loss: 0.9325 - val_accuracy: 0.6409 - val_loss: 0.9213\n",
"Epoch 7/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6223 - loss: 0.9287 - val_accuracy: 0.6430 - val_loss: 0.9230\n",
"Epoch 8/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6216 - loss: 0.9297 - val_accuracy: 0.6376 - val_loss: 0.9148\n",
"Epoch 9/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6199 - loss: 0.9243 - val_accuracy: 0.6388 - val_loss: 0.9118\n",
"Epoch 10/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6356 - loss: 0.9092 - val_accuracy: 0.6348 - val_loss: 0.9405\n",
"Epoch 11/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6287 - loss: 0.9167 - val_accuracy: 0.6355 - val_loss: 0.9302\n",
"Epoch 12/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6305 - loss: 0.9125 - val_accuracy: 0.6399 - val_loss: 0.9243\n",
"Epoch 13/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6275 - loss: 0.9106 - val_accuracy: 0.6348 - val_loss: 0.9275\n",
"Epoch 14/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6272 - loss: 0.9046 - val_accuracy: 0.6390 - val_loss: 0.9231\n",
"Epoch 15/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6243 - loss: 0.9158 - val_accuracy: 0.6360 - val_loss: 0.9284\n",
"Epoch 16/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6280 - loss: 0.9042 - val_accuracy: 0.6364 - val_loss: 0.9331\n",
"Epoch 17/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6274 - loss: 0.9038 - val_accuracy: 0.6332 - val_loss: 0.9352\n",
"Epoch 18/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6255 - loss: 0.9065 - val_accuracy: 0.6357 - val_loss: 0.9318\n",
"Epoch 19/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6289 - loss: 0.9014 - val_accuracy: 0.6350 - val_loss: 0.9322\n",
"Epoch 20/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6328 - loss: 0.9038 - val_accuracy: 0.6332 - val_loss: 0.9440\n",
"Epoch 21/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6300 - loss: 0.9129 - val_accuracy: 0.6334 - val_loss: 0.9476\n",
"Epoch 22/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6317 - loss: 0.9042 - val_accuracy: 0.6346 - val_loss: 0.9348\n",
"Epoch 23/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6312 - loss: 0.9077 - val_accuracy: 0.6348 - val_loss: 0.9499\n",
"Epoch 24/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6300 - loss: 0.8978 - val_accuracy: 0.6369 - val_loss: 0.9491\n",
"Epoch 25/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6272 - loss: 0.9044 - val_accuracy: 0.6350 - val_loss: 0.9493\n",
"Epoch 26/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6251 - loss: 0.9184 - val_accuracy: 0.6360 - val_loss: 0.9390\n",
"Epoch 27/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6274 - loss: 0.8983 - val_accuracy: 0.6355 - val_loss: 0.9360\n",
"Epoch 28/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6257 - loss: 0.9050 - val_accuracy: 0.6353 - val_loss: 0.9394\n",
"Epoch 29/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6302 - loss: 0.9013 - val_accuracy: 0.6357 - val_loss: 0.9398\n",
"Epoch 30/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6265 - loss: 0.9027 - val_accuracy: 0.6334 - val_loss: 0.9415\n",
"Epoch 31/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6272 - loss: 0.9103 - val_accuracy: 0.6383 - val_loss: 0.9394\n",
"Epoch 32/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6340 - loss: 0.8905 - val_accuracy: 0.6381 - val_loss: 0.9488\n",
"Epoch 33/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6260 - loss: 0.9039 - val_accuracy: 0.6360 - val_loss: 0.9467\n",
"Epoch 34/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6269 - loss: 0.9079 - val_accuracy: 0.6362 - val_loss: 0.9368\n",
"Epoch 35/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6298 - loss: 0.8979 - val_accuracy: 0.6364 - val_loss: 0.9465\n",
"Epoch 36/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6307 - loss: 0.8978 - val_accuracy: 0.6388 - val_loss: 0.9422\n",
"Epoch 37/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6283 - loss: 0.8951 - val_accuracy: 0.6385 - val_loss: 0.9395\n",
"Epoch 38/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6225 - loss: 0.9078 - val_accuracy: 0.6371 - val_loss: 0.9409\n",
"Epoch 39/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6282 - loss: 0.8977 - val_accuracy: 0.6369 - val_loss: 0.9345\n",
"Epoch 40/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6286 - loss: 0.8951 - val_accuracy: 0.6388 - val_loss: 0.9417\n",
"Epoch 41/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6307 - loss: 0.8957 - val_accuracy: 0.6404 - val_loss: 0.9396\n",
"Epoch 42/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6221 - loss: 0.9073 - val_accuracy: 0.6371 - val_loss: 0.9375\n",
"Epoch 43/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6280 - loss: 0.9021 - val_accuracy: 0.6357 - val_loss: 0.9612\n",
"Epoch 44/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6309 - loss: 0.8953 - val_accuracy: 0.6395 - val_loss: 0.9433\n",
"Epoch 45/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.6364 - loss: 0.8863 - val_accuracy: 0.6411 - val_loss: 0.9461\n",
"Epoch 46/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6298 - loss: 0.8995 - val_accuracy: 0.6388 - val_loss: 0.9366\n",
"Epoch 47/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6318 - loss: 0.9006 - val_accuracy: 0.6402 - val_loss: 0.9373\n",
"Epoch 48/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6326 - loss: 0.8944 - val_accuracy: 0.6411 - val_loss: 0.9410\n",
"Epoch 49/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6209 - loss: 0.9083 - val_accuracy: 0.6395 - val_loss: 0.9522\n",
"Epoch 50/50\n",
"\u001b[1m535/535\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6262 - loss: 0.9023 - val_accuracy: 0.6413 - val_loss: 0.9439\n"
]
}
],
"source": [
"# Прикажем модели останавливать обучение, когда качество на валидации не будет улучшаться в течение двух эпох\n",
"hist = model_dropout.fit(ss.fit_transform(X_train), to_categorical(y_train), \n",
" validation_split=0.2, epochs=50, verbose=1)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"start = 0\n",
"plt.plot(hist.history['loss'][start:])\n",
"plt.plot(hist.history['val_loss'][start:])\n",
"plt.legend(['Train loss', 'Validation loss'])"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m168/168\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.6371 - loss: 0.9739\n"
]
},
{
"data": {
"text/plain": [
"[0.9786399602890015, 0.6404788494110107]"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"score = model_dropout.evaluate(ss.transform(X_test), to_categorical(y_test))\n",
"score "
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The Zen of Python, by Tim Peters\n",
"\n",
"Beautiful is better than ugly.\n",
"Explicit is better than implicit.\n",
"Simple is better than complex.\n",
"Complex is better than complicated.\n",
"Flat is better than nested.\n",
"Sparse is better than dense.\n",
"Readability counts.\n",
"Special cases aren't special enough to break the rules.\n",
"Although practicality beats purity.\n",
"Errors should never pass silently.\n",
"Unless explicitly silenced.\n",
"In the face of ambiguity, refuse the temptation to guess.\n",
"There should be one-- and preferably only one --obvious way to do it.\n",
"Although that way may not be obvious at first unless you're Dutch.\n",
"Now is better than never.\n",
"Although never is often better than *right* now.\n",
"If the implementation is hard to explain, it's a bad idea.\n",
"If the implementation is easy to explain, it may be a good idea.\n",
"Namespaces are one honking great idea -- let's do more of those!\n"
]
}
],
"source": [
"import this"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Различные воспитательные трюки \n",
"\n",
"1. Начните с маленькой сети. Не забывайте прикидывать сколько наблдюдений $n$ тратится на оценку каждого из $k$ параметров. Если величина $\\frac{n}{k}$ очень маленькая, то не может идти речи об адекватных оценках параметров! \n",
"2. Всегда оставляйте часть выборки под валидацию на каждой эпохе.\n",
"3. Усложняйте модель, пока качество на валидации не начнёт падать.\n",
"4. Не забывайте проскалировать ваши наблюдения для лучшей сходимости. \n",
"5. Можно попробовать ещё целую серию различных **трюков**: \n",
"\n",
"\n",
"* __Архитектура нейросети__\n",
" * Больше/меньше нейронов\n",
" * Больше/меньше слоёв \n",
" * Другие функции активации (tanh, relu, leaky relu, etc) \n",
" * Регуляризация (dropout, l1,l2) \n",
"\n",
"\n",
"* __Более качественная оптимизация__ \n",
" * Можно попробовать выбрать другой метод оптимизации \n",
" * Можно попробовать менять скорость обучения, моментум и др.\n",
"\n",
"\n",
"* __Попробовать собрать больше данных__ \n",
"* __Для случая картинок объёмы данных можно увеличить искусственно с помощью подхода, который называется Data augmemntation, но об этом позже__\n",
"\n",
"И это далеко не полный список. Обратите внимание, что делать grid_search для больших сеток это довольно времязатратное занятие... "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.4"
}
},
"nbformat": 4,
"nbformat_minor": 4
}