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ykro/implementaci-n-de-una-red-neuronal-convolucional-con-tensorflow.ipynb

Created June 2, 2024 01:02
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Implementación de una Red Neuronal Convolucional con TensorFlow.ipynb
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implementaci-n-de-una-red-neuronal-convolucional-con-tensorflow.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"gpuType": "T4",
"authorship_tag": "ABX9TyPLokRc6A42ZIHXd8tfVxKx",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/ykro/f25c10bf966ba3452d0498670c9b1ac0/implementaci-n-de-una-red-neuronal-convolucional-con-tensorflow.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [],
"metadata": {
"id": "0jAh2Ft6ICcv"
}
},
{
"cell_type": "markdown",
"source": [
"# 1. Importar de Bibliotecas:\n",
"\n",
"tensorflow: para construir y entrenar modelos de deep learning.\n",
"datasets, layers, models de Keras (dentro de TensorFlow): para trabajar con datos, definir capas del modelo, y construir el modelo secuencial.\n",
"matplotlib.pyplot: para visualizar datos e imágenes."
],
"metadata": {
"id": "m44Slb_hH_xH"
}
},
{
"cell_type": "code",
"source": [
"# Importar las bibliotecas necesarias\n",
"import tensorflow as tf\n",
"from tensorflow.keras import datasets, layers, models\n",
"import matplotlib.pyplot as plt"
],
"metadata": {
"id": "i-KRBRsWG3wq"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# 2. Cargar y Preprocesar Datos:\n",
"\n",
"Cargamos el conjunto de datos MNIST, que contiene imágenes de dígitos escritos a mano.\n",
"Redimensionamos las imágenes para que tengan un canal adicional (necesario para las redes convolucionales).\n",
"Normalizamos las imágenes para que los valores de los píxeles estén entre 0 y 1, lo que ayuda a mejorar el rendimiento del modelo."
],
"metadata": {
"id": "kHsdp9_pIFct"
}
},
{
"cell_type": "code",
"source": [
"# Cargar y preprocesar el conjunto de datos MNIST\n",
"(train_images, train_labels), (test_images, test_labels) = datasets.mnist.load_data()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Fxs37GkUG4M9",
"outputId": "92e271a7-cb19-4a6c-9e01-2321ce6aeaa4"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
"11490434/11490434 [==============================] - 0s 0us/step\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Redimensionar las imágenes para que tengan un canal adicional (para trabajar con redes convolucionales)\n",
"train_images = train_images.reshape((60000, 28, 28, 1))\n",
"test_images = test_images.reshape((10000, 28, 28, 1))\n",
"\n",
"# Normalizar las imágenes a valores entre 0 y 1 dividiendo por 255.0\n",
"train_images, test_images = train_images / 255.0, test_images / 255.0"
],
"metadata": {
"id": "YT5W_j9TG9Lb"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# 3. Visualización Inicial:\n",
"\n",
"Visualizamos algunas imágenes del conjunto de datos para familiarizarnos con su estructura y etiquetas."
],
"metadata": {
"id": "_3rXe9zaIRpZ"
}
},
{
"cell_type": "code",
"source": [
"# Visualizar algunas imágenes del conjunto de datos para entender su estructura\n",
"class_names = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']\n",
"plt.figure(figsize=(10,10))\n",
"for i in range(25):\n",
" plt.subplot(5,5,i+1) # Crear una cuadrícula de 5x5 de subplots\n",
" plt.xticks([]) # Eliminar las marcas en el eje x\n",
" plt.yticks([]) # Eliminar las marcas en el eje y\n",
" plt.grid(False) # Desactivar la cuadrícula\n",
" plt.imshow(train_images[i].reshape(28,28), cmap=plt.cm.binary) # Mostrar la imagen en escala de grises\n",
" plt.xlabel(class_names[train_labels[i]]) # Etiquetar la imagen con su categoría\n",
"plt.show()\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 826
},
"id": "8HgQfdmCHCou",
"outputId": "e5a0bc06-92cf-45c7-9553-7838b5d3210a"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1000x1000 with 25 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"# 4. Construcción del Modelo CNN:\n",
"\n",
"Definimos la arquitectura de la CNN utilizando capas convolucionales (Conv2D), de pooling (MaxPooling2D), de aplanamiento (Flatten), y densas (Dense).\n",
"Las capas convolucionales extraen características de las imágenes, las capas de pooling reducen la dimensionalidad, y las capas densas realizan la clasificación final."
],
"metadata": {
"id": "YM2wVN4UIWEc"
}
},
{
"cell_type": "code",
"source": [
"# Construir la arquitectura del modelo CNN\n",
"model = models.Sequential()\n",
"# Primera capa convolucional con 32 filtros, tamaño de kernel 3x3, y función de activación ReLU\n",
"model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))\n",
"# Primera capa de max pooling con tamaño de ventana 2x2\n",
"model.add(layers.MaxPooling2D((2, 2)))\n",
"# Segunda capa convolucional con 64 filtros, tamaño de kernel 3x3, y función de activación ReLU\n",
"model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
"# Segunda capa de max pooling con tamaño de ventana 2x2\n",
"model.add(layers.MaxPooling2D((2, 2)))\n",
"# Tercera capa convolucional con 64 filtros, tamaño de kernel 3x3, y función de activación ReLU\n",
"model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
"\n",
"# Aplanar la salida de la última capa convolucional para conectar con la capa densa\n",
"model.add(layers.Flatten())\n",
"# Capa densa con 64 unidades y función de activación ReLU\n",
"model.add(layers.Dense(64, activation='relu'))\n",
"# Capa de salida con 10 unidades (una por cada clase) y función de activación softmax para obtener probabilidades\n",
"model.add(layers.Dense(10, activation='softmax'))"
],
"metadata": {
"id": "kScQzXK0HFIp"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"print(model.summary())"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "LQqbvj30Huo5",
"outputId": "23bef328-e020-4f4f-d62e-1cc0c8c3535d"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Model: \"sequential\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" conv2d (Conv2D) (None, 26, 26, 32) 320 \n",
" \n",
" max_pooling2d (MaxPooling2 (None, 13, 13, 32) 0 \n",
" D) \n",
" \n",
" conv2d_1 (Conv2D) (None, 11, 11, 64) 18496 \n",
" \n",
" max_pooling2d_1 (MaxPoolin (None, 5, 5, 64) 0 \n",
" g2D) \n",
" \n",
" conv2d_2 (Conv2D) (None, 3, 3, 64) 36928 \n",
" \n",
" flatten (Flatten) (None, 576) 0 \n",
" \n",
" dense (Dense) (None, 64) 36928 \n",
" \n",
" dense_1 (Dense) (None, 10) 650 \n",
" \n",
"=================================================================\n",
"Total params: 93322 (364.54 KB)\n",
"Trainable params: 93322 (364.54 KB)\n",
"Non-trainable params: 0 (0.00 Byte)\n",
"_________________________________________________________________\n",
"None\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# 5. Compilar el Modelo:\n",
"\n",
"Configuramos el modelo con el optimizador Adam y la función de pérdida sparse_categorical_crossentropy. Las métricas de evaluación incluyen la precisión (accuracy)."
],
"metadata": {
"id": "JBO4GHq1Ialq"
}
},
{
"cell_type": "code",
"source": [
"# Compilar el modelo especificando el optimizador, la función de pérdida y las métricas de evaluación\n",
"model.compile(optimizer='adam',\n",
" loss='sparse_categorical_crossentropy',\n",
" metrics=['accuracy'])"
],
"metadata": {
"id": "rKv4OKG0HFUx"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"6. Entrenamiento del Modelo:\n",
"\n",
"Entrenamos el modelo utilizando el conjunto de datos de entrenamiento. Validamos el rendimiento con el conjunto de datos de prueba durante cada época de entrenamiento."
],
"metadata": {
"id": "W8F-PBKLIhSj"
}
},
{
"cell_type": "code",
"source": [
"# Entrenar el modelo con el conjunto de datos de entrenamiento y validar con el conjunto de datos de prueba\n",
"history = model.fit(train_images, train_labels, epochs=5,\n",
" validation_data=(test_images, test_labels))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "WR-1tHkQHF8-",
"outputId": "d6407e66-2e1a-47a2-b1e5-a8c3985c17b6"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/5\n",
"1875/1875 [==============================] - 21s 8ms/step - loss: 0.1397 - accuracy: 0.9569 - val_loss: 0.0431 - val_accuracy: 0.9859\n",
"Epoch 2/5\n",
"1875/1875 [==============================] - 10s 5ms/step - loss: 0.0455 - accuracy: 0.9857 - val_loss: 0.0328 - val_accuracy: 0.9891\n",
"Epoch 3/5\n",
"1875/1875 [==============================] - 11s 6ms/step - loss: 0.0313 - accuracy: 0.9897 - val_loss: 0.0353 - val_accuracy: 0.9882\n",
"Epoch 4/5\n",
"1875/1875 [==============================] - 10s 5ms/step - loss: 0.0267 - accuracy: 0.9912 - val_loss: 0.0288 - val_accuracy: 0.9916\n",
"Epoch 5/5\n",
"1875/1875 [==============================] - 10s 5ms/step - loss: 0.0192 - accuracy: 0.9938 - val_loss: 0.0297 - val_accuracy: 0.9919\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# 7. Evaluación del Modelo:\n",
"\n",
"Evaluamos la precisión del modelo en el conjunto de datos de prueba y mostramos el resultado."
],
"metadata": {
"id": "7hwfhUDEIppk"
}
},
{
"cell_type": "code",
"source": [
"# Evaluar el modelo utilizando el conjunto de datos de prueba y mostrar la precisión\n",
"test_loss, test_acc = model.evaluate(test_images, test_labels)\n",
"print(f\"Precisión en el conjunto de prueba: {test_acc}\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "RxnzLs4jHFy2",
"outputId": "1558ab7e-9a69-4249-b6f2-c52a6bb33a57"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"313/313 [==============================] - 9s 13ms/step - loss: 2.3074 - accuracy: 0.1085\n",
"Precisión en el conjunto de prueba: 0.10849999636411667\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# 8. Visualización de Resultados:\n",
"\n",
"Graficamos la precisión del entrenamiento y la validación a lo largo de las épocas para visualizar el rendimiento del modelo."
],
"metadata": {
"id": "vQt-vDofItFm"
}
},
{
"cell_type": "code",
"source": [
"# Visualizar los resultados del entrenamiento (precisión) a lo largo de las épocas\n",
"plt.plot(history.history['accuracy'], label='Precisión en entrenamiento')\n",
"plt.plot(history.history['val_accuracy'], label = 'Precisión en validación')\n",
"plt.xlabel('Época')\n",
"plt.ylabel('Precisión')\n",
"plt.ylim([0.5, 1]) # Ajustar el rango del eje y\n",
"plt.legend(loc='lower right')\n",
"plt.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 457
},
"id": "BCdhyHU4HZuL",
"outputId": "ba85bdf2-dabb-4d54-c44b-b7e2fff87657"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"# Realizar predicciones con el modelo entrenado sobre el conjunto de datos de prueba\n",
"predictions = model.predict(test_images)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "bKOIGlqgHcZY",
"outputId": "382d81fb-24bf-4ee9-a5ed-452d579144f5"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"313/313 [==============================] - 1s 2ms/step\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Función para visualizar las predicciones\n",
"def plot_image(i, predictions_array, true_label, img):\n",
" predictions_array, true_label, img = predictions_array[i], true_label[i], img[i]\n",
" plt.grid(False)\n",
" plt.xticks([])\n",
" plt.yticks([])\n",
" plt.imshow(img.reshape(28,28), cmap=plt.cm.binary)\n",
"\n",
" predicted_label = tf.argmax(predictions_array)\n",
" if predicted_label == true_label:\n",
" color = 'blue'\n",
" else:\n",
" color = 'red'\n",
"\n",
" plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n",
" 100*tf.reduce_max(predictions_array),\n",
" class_names[true_label]),\n",
" color=color)\n",
"\n",
"# Función para visualizar la distribución de probabilidades de la predicción\n",
"def plot_value_array(i, predictions_array, true_label):\n",
" predictions_array, true_label = predictions_array[i], true_label[i]\n",
" plt.grid(False)\n",
" plt.xticks(range(10))\n",
" plt.yticks([])\n",
" thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n",
" plt.ylim([0, 1])\n",
" predicted_label = tf.argmax(predictions_array)\n",
"\n",
" thisplot[predicted_label].set_color('red')\n",
" thisplot[true_label].set_color('blue')"
],
"metadata": {
"id": "UUElSwr0HgA3"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Visualizar las predicciones para las primeras 15 imágenes del conjunto de prueba\n",
"num_rows = 5\n",
"num_cols = 3\n",
"num_images = num_rows*num_cols\n",
"plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
"for i in range(num_images):\n",
" plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
" plot_image(i, predictions, test_labels, test_images)\n",
" plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
" plot_value_array(i, predictions, test_labels)\n",
"plt.tight_layout()\n",
"plt.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "LF4VHAUKHi-k",
"outputId": "319457f7-d45b-4b59-e8c1-750a296a497c"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1200x1000 with 30 Axes>"
],
"image/png": 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},
"metadata": {}
}
]
}
]
}
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