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December 1, 2022 18:33
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Kaggle_MNIST.ipynb
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| { | |
| "nbformat": 4, | |
| "nbformat_minor": 0, | |
| "metadata": { | |
| "colab": { | |
| "private_outputs": true, | |
| "provenance": [], | |
| "include_colab_link": true | |
| }, | |
| "kernelspec": { | |
| "name": "python3", | |
| "display_name": "Python 3" | |
| }, | |
| "language_info": { | |
| "name": "python" | |
| } | |
| }, | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/eguar11011/a1bebce02904acc3cf36cbad317ac62f/kaggle_mnist.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "xUfKuq6xVQ8n" | |
| }, | |
| "source": [ | |
| "Descarga de los datos" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "hCA9eQT1J4ti" | |
| }, | |
| "source": [ | |
| "import os\n", | |
| "import pandas as pd\n", | |
| "import numpy as np\n", | |
| "#import shutil" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "Mmz74XTYK_Ee" | |
| }, | |
| "source": [ | |
| "os.environ['KAGGLE_USERNAME']= \"eduardschipatecua\"\n", | |
| "os.environ['KAGGLE_KEY']= \"47e27b1aaeb355ea4e23cd8fd7ce0108\"" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "dw5YIwe_LBkP" | |
| }, | |
| "source": [ | |
| "!kaggle competitions download -c digit-recognizer" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "ex7T5kBfVWB-" | |
| }, | |
| "source": [ | |
| "Tratamiento de los datos" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "aXkcp881LRY-" | |
| }, | |
| "source": [ | |
| "!unzip -q digit-recognizer.zip\n" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "aGQkF6HZLVPM" | |
| }, | |
| "source": [ | |
| "from keras import models\n", | |
| "from keras import layers\n", | |
| "from tensorflow.keras.utils import to_categorical" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "viPpDzySMLNm" | |
| }, | |
| "source": [ | |
| "table_train = pd.read_csv(\"train.csv\")\n", | |
| "table_test = pd.read_csv(\"test.csv\")" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "y9lHJMBpNr96" | |
| }, | |
| "source": [ | |
| "table_train.head()" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "0eQP6fPEN6co" | |
| }, | |
| "source": [ | |
| "table_test.head()" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "2ZjBQFtfOAlb" | |
| }, | |
| "source": [ | |
| "#Separamos las etiquetas y los pixeles y pasamos los datos a un arreglo de numpy\n", | |
| "train_labels = table_train['label'].to_numpy()\n", | |
| "train_images =table_train.drop(['label'], axis=1).to_numpy()\n", | |
| "#Convertimos test en un arreglo de numpy\n", | |
| "table_test = table_test.to_numpy()\n" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "ghQkcl4QO4zv" | |
| }, | |
| "source": [ | |
| "train_images" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "gMrIHmrsSXtZ" | |
| }, | |
| "source": [ | |
| "#Vizualización de los números con los pixeles " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "nG21CgHhSPK-" | |
| }, | |
| "source": [ | |
| "import matplotlib.pyplot as plt" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "kJljfW9fTfj7" | |
| }, | |
| "source": [ | |
| "#dígitos de train_images con los pixeles\n", | |
| "for i in range(4):\n", | |
| " image = train_images[i].reshape(28,28) # 784 = 28 x 28\n", | |
| " plt.imshow(image, cmap=plt.cm.binary)\n", | |
| " plt.show()" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "ML6fzHU0Se_y" | |
| }, | |
| "source": [ | |
| "#Primeros 4 números del test\n", | |
| "for i in range(9):\n", | |
| " print(i)\n", | |
| " image = table_test[i].reshape(28,28) # 784 = 28 x 28\n", | |
| " plt.imshow(image, cmap=plt.cm.binary)\n", | |
| " plt.show()" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "yBIF2n_kVj8D" | |
| }, | |
| "source": [ | |
| "Tratamiento de los datos " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "I65A4visT3xu" | |
| }, | |
| "source": [ | |
| "#Pixeles con valores de 0 a 1\n", | |
| "train_images = train_images.astype('float32') / 255\n", | |
| "table_test = table_test.astype('float32') / 255\n", | |
| "#Vector de enteros convertidos en una matriz de clase binaria \n", | |
| "train_labels = to_categorical(train_labels)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "JLdlwcNSYhdc" | |
| }, | |
| "source": [ | |
| "len(train_images)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "Fj-qWaSyWff3" | |
| }, | |
| "source": [ | |
| "Modelo secuencial " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "OIQnhs09Wj0B" | |
| }, | |
| "source": [ | |
| "#Creando la red\n", | |
| "network = models.Sequential()\n", | |
| "network.add(layers.Dense(512, activation= 'relu', input_shape=(28 * 28,)))\n", | |
| "network.add(layers.Dense(10, activation= 'softmax'))\n", | |
| "\n", | |
| "network.compile(optimizer ='rmsprop',\n", | |
| " loss='categorical_crossentropy',\n", | |
| " metrics =['accuracy'])" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "jTdVVvY-XZTB" | |
| }, | |
| "source": [ | |
| "#entrenando al modelo\n", | |
| "network.fit(train_images, train_labels, epochs=5, batch_size=128)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "Qw4RRtToXvf3" | |
| }, | |
| "source": [ | |
| "#Realizando las predicciones y empaquetandolas para la entrega\n", | |
| "predictions = network.predict(table_test)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "WLb8-6Vkkqvr" | |
| }, | |
| "source": [ | |
| "#Encontrando la mayor probabilidad y tomando su indice, tal que esa será la etiqueta \n", | |
| "predictions = [list(x).index(max(x)) for x in predictions]" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "ChcIPQ-tlLCU" | |
| }, | |
| "source": [ | |
| "len(predictions)\n" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "Jie-styBYzI1" | |
| }, | |
| "source": [ | |
| " # csv de entrega\n", | |
| "output = pd.DataFrame({'Label': predictions})\n", | |
| "output = output.rename_axis('ImageId').reset_index()\n", | |
| "\n", | |
| "output['ImageId'] = range(1, len(predictions)+1)\n", | |
| "\n", | |
| "output.to_csv('submission.csv', index=False)\n", | |
| "\n" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "meRR9Dy9edLM" | |
| }, | |
| "source": [ | |
| "output.dtypes" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "PZO44p0b3BKx" | |
| }, | |
| "source": [ | |
| "output.shape" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "o-2zaQTw5KOP" | |
| }, | |
| "source": [ | |
| "output.head(10)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "IckrHjsieyBX" | |
| }, | |
| "source": [ | |
| "! kaggle competitions submit -c digit-recognizer -f submission.csv -m \"MNIST\"" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "El cual tuvimos una precisión del 0.97314. El cual se puede ver en la imagen de abajo que es el puntaje de la compentencia." | |
| ], | |
| "metadata": { | |
| "id": "DEi1hkXe9FVG" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "" | |
| ], | |
| "metadata": { | |
| "id": "hh7DzHxU8z4O" | |
| } | |
| } | |
| ] | |
| } |
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