{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "-WXqA_23DfeC"
},
"source": [
"Modelo de clasificación de videos con redes recurrentes\n",
"======================================================="
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"PRECAUCIÓN 😱: El tema presentado en esta sección está clasificado como avanzado. El entendimiento de este contenido es totalmente opcional.
"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "j3576kEYCwpp"
},
"source": [
"## Introducción"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "o-aoxMmlCwpp"
},
"source": [
"La clasificación de videos es la tarea por la cual un modelo de aprendizaje automático asigna una o varias etiquetas a todo un video dependiendo del contenido del mismo. Esta tarea nos permite reconocer acciones o estados que son transmitidos en un video. Para ejemplificar esta tarea, construiremos una red recurrente basada en LSTM donde los valores de entrada de la misma corresponderán a vectores que se aprendieron con una red de convolución."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> **Importante:** Es posible que no pueda ejecutar este notebook en Google Colab. No tendrá suficiente memoria en las tarjetas de GPU que suelen disponibilizarce. Si dispone de un equipo con GPU compatible con CUDA, ejecutelo allí."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TghjzJl0wKId"
},
"source": [
"### Preparación del ambiente"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Ez9d-jEjDfeV"
},
"source": [
"Intalamos las librerias necesarias"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "mrs7T2yDDfeW"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[31mERROR: responsibleai 0.10.0 has requirement dice-ml<0.8,>=0.7.1, but you'll have dice-ml 0.6.1 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: raiwidgets 0.10.0 has requirement jinja2==2.11.3, but you'll have jinja2 2.11.2 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: azureml-responsibleai 1.34.0 has requirement responsibleai==0.9.4, but you'll have responsibleai 0.10.0 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: autokeras 1.0.16 has requirement tensorflow<=2.5.0,>=2.3.0, but you'll have tensorflow 2.1.0 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: tensorflow-metadata 1.2.0 has requirement absl-py<0.13,>=0.9, but you'll have absl-py 0.13.0 which is incompatible.\u001b[0m\n"
]
}
],
"source": [
"!wget https://raw.githubusercontent.com/santiagxf/M72109/master/docs/vision/tasks/sequences/code/lstm_cnn_class.txt \\\n",
" --quiet --no-clobber\n",
"!pip install -r lstm_cnn_class.txt --quiet"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZJ1mR6CBCwpt"
},
"source": [
"Para ejemplificar esta técnica utilizaremos un conjunto de datos muy popular llamado UCF-101. UCF-101 es un conjunto de datos con videos reales extraidos de YouTube clasificados en 101 categorías dendiendo de la acción que muestran. Este conjunto de datos tiene originalmente 13320 videos de las 101 categorias disponibles. Estos videos, adicionalmente estan agrupados en subgrupos donde los videos muestran propiedades similares como por ejemplo fondos similares, angulo de la cámara, etc.\n",
"\n",
"Puede obtener más información sobre este conjunto de datos y sus derechos de autor en: [UCF101 - Action Recognition Data Set](https://www.crcv.ucf.edu/data/UCF101.php)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "yIxm9UX6HkYj"
},
"source": [
"Para simplificar el uso de este conjunto de datos, utilizaremos una versión reducida del mismo con solo 3 categorias:\n",
"\n",
"- Tocando la guitarra\n",
"- Tocando el violin\n",
"- Tocando el chelo\n",
"\n",
"```\n",
"Dataset/\n",
"├─ PlayingGuitar/\n",
"│ ├─ video1.avi\n",
"│ ├─ video2.avi\n",
"│ └─ ...\n",
"└─ PlayingCello/\n",
"│ ├─ video1.avi\n",
"│ ├─ video2.avi\n",
"│ └─ ...\n",
"└─ PlayingViolin/\n",
" ├─ video1.avi\n",
" ├─ video2.avi\n",
" └─ ...\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Descargamos el conjunto de datos:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "E8DkMctIHo5M"
},
"outputs": [],
"source": [
"!wget https://santiagxf.blob.core.windows.net/public/datasets/UCF3.zip \\\n",
" --quiet --no-clobber\n",
"!mkdir -p /tmp/videos\n",
"!unzip -qq UCF3.zip -d /tmp/videos"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "c2ydVcyxCwpx"
},
"source": [
"Antes de comenzar necesitaremos verificar que tenemos el runtime correcto en nuestro ambiente. Esta tarea se beneficiará mucho de una GPU."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ABQ6LdkQDfeZ",
"outputId": "0bc4b540-ef1e-4ba3-ce82-468eae6271a9"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"GPUs disponibles: 4\n"
]
}
],
"source": [
"import tensorflow as tf\n",
"print(\"GPUs disponibles: \", len(tf.config.experimental.list_physical_devices('GPU')))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Trabajando con video"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Generando frames de un video"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Como se mencionó, un video puede ser defragmentado en una secuencia de cuadros que se transicionan en el tiempo. Sin embargo, descomponer un video de tal forma puede dar lugar a estructuras de datos másivas. Considere un video de 30 segundos, a 24 cuadros tendriamos secuencias de 720 pasos. En general deberemos utilizar alguna estrategia de sampling para seleccionar los cuadros.\n",
"\n",
"El siguiente ejemplo toma como entrada un directorio donde se encuentran videos, para generar otro directorio donde cada video es una carpeta. En tal carpeta se encuentran todos los cuadros de tal video. Los cuadros son seleccionado equitativamente en el tiempo para obtener la cantidad de secuencias necesarias.\n",
"\n",
"```\n",
"Dataset/\n",
"├─ vide1/\n",
"│ ├─ video1_frame_00.jpg\n",
"│ ├─ video1_frame_01.jpg\n",
"│ ├─ video1_frame_02.jpg\n",
"│ ├─ video1_frame_03.jpg\n",
"│ └─ ...\n",
"└─ video2/\n",
" ├─ video2_frame_00.jpg\n",
" ├─ video2_frame_01.jpg\n",
" ├─ video2_frame_02.jpg\n",
" ├─ video2_frame_03.jpg\n",
" └─ ...\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "hRpVtzaaPVpk"
},
"outputs": [],
"source": [
"import cv2\n",
"import os\n",
"from typing import Optional\n",
"from tqdm import tqdm\n",
"import pathlib\n",
"\n",
"def extract_frames(videos_dir: str, out_dir: str, sample_each_seconds: Optional[int], max_sequence_lenght: Optional[int]):\n",
" for file_ in tqdm(pathlib.PosixPath(videos_dir).glob(\"*/*.avi\")):\n",
" count = 0\n",
" basename = file_.name.split('.')[0]\n",
" label = file_.parent.name\n",
" targetpath = os.path.join(out_dir, label, basename)\n",
" if os.path.isdir(targetpath):\n",
" continue\n",
" \n",
" os.makedirs(targetpath, exist_ok=True)\n",
" vidcap = cv2.VideoCapture(str(file_))\n",
"\n",
" if (sample_each_seconds):\n",
" sample_every_frame = 1000 * sample_each_seconds\n",
" else:\n",
" num_frames = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
" sample_every_frame = max(1, num_frames // max_sequence_lenght)\n",
"\n",
" success, image = vidcap.read()\n",
" while success:\n",
" cv2.imwrite(os.path.join(targetpath, \"%s_frame_%d.jpg\" % (basename, count)), image)\n",
" count += 1\n",
" vidcap.set(cv2.CAP_PROP_POS_MSEC,(count*sample_every_frame))\n",
" success,image = vidcap.read()\n",
" \n",
" vidcap.release()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ejecutamos este procedimiento en el directorio donde se encuentra nuestro conjnto de datos:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"SEQUENCE_LENGTH=10\n",
"IMG_SIZE = 224\n",
"CHANNELS = 3"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"VIDEOS_PATH = '/tmp/videos'\n",
"FRAMES_PATH = '/tmp/frames'"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "tKreFOwEwZto",
"outputId": "a7fb6fce-3d56-4743-fe21-3fc67bb0170e"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"424it [00:35, 11.90it/s]\n"
]
}
],
"source": [
"extract_frames(VIDEOS_PATH, FRAMES_PATH, sample_each_seconds=1, max_sequence_lenght=SEQUENCE_LENGTH)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Labels:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"labels = [folder.name for folder in pathlib.PosixPath(FRAMES_PATH).glob('*/')]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3\n"
]
}
],
"source": [
"NUM_LABELS = len(labels)\n",
"print(NUM_LABELS)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Construyendo un modelo basado en CNN y LSTM"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Utilizaremos `TensorFlow` para construir un modelo que pueda extraer los predictores desde las imágenes de forma independiente para luego unir todos estos predictores en una secuencia que sera utilizada como entrada para una red recurrente."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-vp215uiDfer"
},
"source": [
"Nuestra red CNN estará basada en una CNNs típica. En este caso la misma constará de:\n",
" - 2 capas de CNN\n",
" - 1 capas de Pooling\n",
" - 1 capa de regularización\n",
" \n",
"Esta unidad básica la repetiremos 4 veces sobre cada imagen de la secuencia. Para realizar esta operación utilizaremos la capa `TimeDistributed` que permite realizar una misma operación de forma distribuida sobre todos los elementos de una secuencia."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"id": "a6UPyxr9Iy_2"
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"import tensorflow.keras as keras"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"id": "3l3IDPVNI3lM"
},
"outputs": [],
"source": [
"def build_cnn():\n",
" model = keras.models.Sequential([\n",
" keras.layers.InputLayer(input_shape=(IMG_SIZE, IMG_SIZE, CHANNELS)),\n",
" keras.layers.Conv2D(32, (3, 3), padding='same', activation='relu'),\n",
" keras.layers.Conv2D(32, (3, 3), padding='same', activation='relu'),\n",
" keras.layers.MaxPooling2D((2, 2)),\n",
" \n",
" keras.layers.Conv2D(64, (3, 3), padding='same', activation='relu'),\n",
" keras.layers.Conv2D(64, (3, 3), padding='same', activation='relu'),\n",
" keras.layers.MaxPooling2D((2, 2)),\n",
" \n",
" keras.layers.Conv2D(128, (3, 3), padding='same', activation='relu'),\n",
" keras.layers.Conv2D(128, (3, 3), padding='same', activation='relu'),\n",
" keras.layers.MaxPooling2D((2, 2)),\n",
" \n",
" keras.layers.Conv2D(128, (4, 4), padding='same', activation='relu'),\n",
" keras.layers.Conv2D(128, (3, 3), padding='same', activation='relu'),\n",
" keras.layers.MaxPooling2D((2, 2)),\n",
" \n",
" keras.layers.Flatten(),\n",
" ])\n",
" \n",
" return model\n",
"\n",
"def build_lstm(feature_extractor):\n",
" model = keras.models.Sequential([\n",
" keras.layers.InputLayer(input_shape=(SEQUENCE_LENGTH, IMG_SIZE, IMG_SIZE, CHANNELS)),\n",
" keras.layers.TimeDistributed(feature_extractor),\n",
" keras.layers.Masking(mask_value=0.),\n",
" keras.layers.LSTM(128, dropout=0.5, recurrent_dropout=0.5),\n",
" keras.layers.Dense(32, activation='relu'),\n",
" keras.layers.Dropout(0.3),\n",
" keras.layers.Dense(NUM_LABELS, activation='softmax')\n",
" ])\n",
"\n",
" model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(), metrics=['accuracy'])\n",
" return model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Contruimos el modelo:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"sequential_2\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"conv2d_8 (Conv2D) (None, 224, 224, 32) 896 \n",
"_________________________________________________________________\n",
"conv2d_9 (Conv2D) (None, 224, 224, 32) 9248 \n",
"_________________________________________________________________\n",
"max_pooling2d_4 (MaxPooling2 (None, 112, 112, 32) 0 \n",
"_________________________________________________________________\n",
"conv2d_10 (Conv2D) (None, 112, 112, 64) 18496 \n",
"_________________________________________________________________\n",
"conv2d_11 (Conv2D) (None, 112, 112, 64) 36928 \n",
"_________________________________________________________________\n",
"max_pooling2d_5 (MaxPooling2 (None, 56, 56, 64) 0 \n",
"_________________________________________________________________\n",
"conv2d_12 (Conv2D) (None, 56, 56, 128) 73856 \n",
"_________________________________________________________________\n",
"conv2d_13 (Conv2D) (None, 56, 56, 128) 147584 \n",
"_________________________________________________________________\n",
"max_pooling2d_6 (MaxPooling2 (None, 28, 28, 128) 0 \n",
"_________________________________________________________________\n",
"conv2d_14 (Conv2D) (None, 28, 28, 128) 262272 \n",
"_________________________________________________________________\n",
"conv2d_15 (Conv2D) (None, 28, 28, 128) 147584 \n",
"_________________________________________________________________\n",
"max_pooling2d_7 (MaxPooling2 (None, 14, 14, 128) 0 \n",
"_________________________________________________________________\n",
"flatten_1 (Flatten) (None, 25088) 0 \n",
"=================================================================\n",
"Total params: 696,864\n",
"Trainable params: 696,864\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"cnn_model = build_cnn()\n",
"cnn_model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 272
},
"id": "PJrD3ZI343OB",
"outputId": "40eeb80f-1262-4275-e8bb-c976f3df0990"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
"Model: \"sequential_3\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"time_distributed_1 (TimeDist (None, 10, 25088) 696864 \n",
"_________________________________________________________________\n",
"masking_1 (Masking) (None, 10, 25088) 0 \n",
"_________________________________________________________________\n",
"lstm_1 (LSTM) (None, 128) 12911104 \n",
"_________________________________________________________________\n",
"dense_2 (Dense) (None, 32) 4128 \n",
"_________________________________________________________________\n",
"dropout_1 (Dropout) (None, 32) 0 \n",
"_________________________________________________________________\n",
"dense_3 (Dense) (None, 3) 99 \n",
"=================================================================\n",
"Total params: 13,612,195\n",
"Trainable params: 13,612,195\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"model = build_lstm(cnn_model)\n",
"model.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Generando un conjunto de datos que funcione con el modelo"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generaremos una función que tome un directorio donde se encuentran las imágenes y retorne tensores con los valores de los pixeles junto con su respectiva etiqueta. Recuerde como estan almacenados nuestros datos:\n",
"\n",
"```\n",
"Dataset/\n",
"├─ clase1/\n",
"│ ├─ vide1/\n",
"│ │ ├─ video1_frame_00.jpg\n",
"│ │ ├─ video1_frame_01.jpg\n",
"│ │ ├─ video1_frame_02.jpg\n",
"│ │ ├─ video1_frame_03.jpg\n",
"│ │ └─ ...\n",
"│ └─ video2/\n",
"│ ├─ video2_frame_00.jpg\n",
"│ ├─ video2_frame_01.jpg\n",
"│ ├─ video2_frame_02.jpg\n",
"│ ├─ video2_frame_03.jpg\n",
"│ └─ ...\n",
"├─ clase2/\n",
"│ ├─ vide1/\n",
"│ │ ├─ video1_frame_00.jpg\n",
"│ │ ├─ video1_frame_01.jpg\n",
"│ │ ├─ video1_frame_02.jpg\n",
"│ │ ├─ video1_frame_03.jpg\n",
"│ │ └─ ...\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Etiquetas"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Veamos cuales son las etiquetas disponibles:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"id": "oBn_FjWUTDbB"
},
"outputs": [
{
"data": {
"text/plain": [
"LabelEncoder()"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pathlib\n",
"from sklearn import preprocessing\n",
"\n",
"label_encoder = preprocessing.LabelEncoder()\n",
"label_encoder.fit(labels)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Consultemos sus valores:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['PlayingCello', 'PlayingGuitar', 'PlayingViolin'], dtype='= sequence_length:\n",
" break\n",
" padded_sequence[idx] = parse_image(img, channels, img_size)\n",
"\n",
" return padded_sequence\n",
"\n",
"def generate_sequences():\n",
" for video_folder in pathlib.PosixPath(FRAMES_PATH).glob('*/*/'):\n",
" label = video_folder.parent.name\n",
" yield (parse_video(video_folder, SEQUENCE_LENGTH, CHANNELS, IMG_SIZE), label_encoder.transform([str(label)])[0])\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Construimos un objeto `tf.data.Dataset`:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"id": "IRf8GJhYraop"
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"\n",
"dataset = tf.data.Dataset.from_generator(generate_sequences, \n",
" output_signature=(\n",
" tf.TensorSpec(shape=(SEQUENCE_LENGTH, IMG_SIZE, IMG_SIZE, CHANNELS), dtype=tf.float32), \n",
" tf.TensorSpec(shape=(), dtype=tf.int16))\n",
" ).shuffle(400).batch(16)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "COmfsQ7I_DVh"
},
"source": [
"Probemos el generador para revisar si funciona correctamente:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "oC1eFI-49yTe",
"outputId": "1df39c3b-4ef4-4f73-8855-24a1d90d2dd9"
},
"outputs": [
{
"data": {
"text/plain": [
"TensorShape([16, 10, 224, 224, 3])"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(dataset.take(1))[0][0].shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Entrenando el modelo"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "7vRlk_Jj5M_e",
"outputId": "f79669d7-0d0a-4798-8ded-19d2bd3e0082"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/10\n",
"27/27 [==============================] - 75s 2s/step - loss: 1.1001 - accuracy: 0.3561\n",
"Epoch 2/10\n",
"27/27 [==============================] - 48s 1s/step - loss: 1.0946 - accuracy: 0.3514\n",
"Epoch 3/10\n",
"27/27 [==============================] - 48s 1s/step - loss: 0.8938 - accuracy: 0.5448\n",
"Epoch 4/10\n",
"27/27 [==============================] - 48s 1s/step - loss: 0.7407 - accuracy: 0.6651\n",
"Epoch 5/10\n",
"27/27 [==============================] - 48s 1s/step - loss: 0.7253 - accuracy: 0.6533\n",
"Epoch 6/10\n",
"27/27 [==============================] - 48s 1s/step - loss: 0.9794 - accuracy: 0.4717\n",
"Epoch 7/10\n",
"27/27 [==============================] - 48s 1s/step - loss: 0.6776 - accuracy: 0.6698\n",
"Epoch 8/10\n",
"27/27 [==============================] - 48s 1s/step - loss: 0.5862 - accuracy: 0.7052\n",
"Epoch 9/10\n",
"27/27 [==============================] - 48s 1s/step - loss: 0.5810 - accuracy: 0.7264\n",
"Epoch 10/10\n",
"27/27 [==============================] - 48s 1s/step - loss: 0.6770 - accuracy: 0.7217\n"
]
}
],
"source": [
"history = model.fit(dataset, epochs=10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Preguntas:\n",
" \n",
" - ¿Que le parecen estos resulados?\n",
" - ¿Como podría aplicar transferencia de aprendizaje en este ejemplo?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Utilizando una capa más potente como extractor de predictores"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Podemos combinar esta técnica con transferencia de aprendizaje. Para realizar esto podemos utilizar TensorFlow Hub.\n",
"\n",
"> Nota: esta forma de implementación resulta ineficiente computacionalmente, aunque sencilla de interpretar."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"EXTRACTOR_SIZE = 1280"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"import tensorflow_hub as tfhub\n",
"\n",
"def build_cnn_tfhub():\n",
" extractor = tfhub.KerasLayer(\"https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\",\n",
" input_shape=(IMG_SIZE, IMG_SIZE, CHANNELS),\n",
" output_shape=(EXTRACTOR_SIZE),\n",
" trainable=False)\n",
" \n",
" return keras.layers.Lambda(lambda x: extractor(x))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Instanciamos el extractor de predictores:"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"feature_extractor = build_cnn_tfhub()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lo insertamos en nuestra red:"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"def build_lstm(feature_extractor):\n",
" model = keras.models.Sequential([\n",
" keras.layers.InputLayer(input_shape=(SEQUENCE_LENGTH, IMG_SIZE, IMG_SIZE, CHANNELS)),\n",
" keras.layers.TimeDistributed(feature_extractor),\n",
" keras.layers.Masking(mask_value=0.),\n",
" keras.layers.LSTM(512, dropout=0.5, recurrent_dropout=0.5),\n",
" keras.layers.Dense(128, activation='relu'),\n",
" keras.layers.Dropout(0.3),\n",
" keras.layers.Dense(3, activation='softmax')\n",
" ])\n",
"\n",
" model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(), metrics=['accuracy'])\n",
" return model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nota: Si `TimeDistributed` no funciona para usted, puede cambiarlo por el siguiente codigo:\n",
"\n",
"```\n",
"keras.layers.Lambda(\n",
" lambda x: tf.reshape(feature_extractor(tf.reshape(x, [-1, IMG_SIZE, IMG_SIZE,CHANNELS])),\n",
" [-1, SEQUENCE_LENGTH, EXTRACTOR_SIZE]),\n",
"),\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Contruimos el modelo"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:Layer lstm_3 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:tensorflow:Layer lstm_3 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"sequential_5\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"time_distributed_3 (TimeDist (None, 10, 25088) 696864 \n",
"_________________________________________________________________\n",
"masking_3 (Masking) (None, 10, 25088) 0 \n",
"_________________________________________________________________\n",
"lstm_3 (LSTM) (None, 512) 52430848 \n",
"_________________________________________________________________\n",
"dense_6 (Dense) (None, 128) 65664 \n",
"_________________________________________________________________\n",
"dropout_3 (Dropout) (None, 128) 0 \n",
"_________________________________________________________________\n",
"dense_7 (Dense) (None, 3) 387 \n",
"=================================================================\n",
"Total params: 53,193,763\n",
"Trainable params: 53,193,763\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"model = build_lstm(cnn_model)\n",
"model.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Entrenamos el modelo"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
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"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/10\n",
"27/27 [==============================] - 56s 1s/step - loss: 0.8045 - accuracy: 0.5802\n",
"Epoch 2/10\n",
"27/27 [==============================] - 53s 2s/step - loss: 0.5732 - accuracy: 0.7146\n",
"Epoch 3/10\n",
"27/27 [==============================] - 53s 2s/step - loss: 0.4512 - accuracy: 0.7948\n",
"Epoch 4/10\n",
"27/27 [==============================] - 53s 2s/step - loss: 0.3997 - accuracy: 0.8396\n",
"Epoch 5/10\n",
"27/27 [==============================] - 53s 2s/step - loss: 0.3705 - accuracy: 0.8349\n",
"Epoch 6/10\n",
"27/27 [==============================] - 53s 2s/step - loss: 0.2939 - accuracy: 0.8939\n",
"Epoch 7/10\n",
"27/27 [==============================] - 53s 2s/step - loss: 0.2220 - accuracy: 0.9175\n",
"Epoch 8/10\n",
"27/27 [==============================] - 53s 2s/step - loss: 0.1632 - accuracy: 0.9575\n",
"Epoch 9/10\n",
"27/27 [==============================] - 53s 2s/step - loss: 0.2006 - accuracy: 0.9387\n",
"Epoch 10/10\n",
"27/27 [==============================] - 53s 2s/step - loss: 0.5872 - accuracy: 0.7783\n"
]
}
],
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"history = model.fit(dataset, epochs=10)"
]
}
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