├── 01_1_Intro_tensores_leccion.ipynb ├── 01_2_funciones.ipynb ├── 01_3_ejercicio.ipynb ├── 02_red_neuronal.ipynb ├── 03_red_neuronal_secuencial.ipynb ├── 04_entrenamiento.ipynb ├── 05_validacion.ipynb ├── 06_doe_una_variable.ipynb ├── 07_lectura_de_imagenes.ipynb ├── 08_RN_Convolucional.ipynb ├── 08_RN_Convolucional_GPU.ipynb ├── 08_RN_Convolucional_solucion.ipynb ├── 08_respaldo_de_modelos.ipynb ├── 09_Autoencoder.ipynb ├── LICENSE ├── MLP_autoencoder.ipynb ├── README.md ├── archivos ├── function_approx.png ├── lenet.png ├── net.png └── simple_neuron.png ├── fc_model.py ├── helper.py ├── requirements.txt ├── soluciones ├── 02_red_neuronal_solucion.ipynb ├── 04_entrenamiento_solucion.ipynb └── helper.py └── task_tranformer_cactus.ipynb /01_1_Intro_tensores_leccion.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Redes neuronales con Pytorch\n", 8 | "\n", 9 | "@jivg.org\n", 10 | "\n", 11 | "## Introducción\n", 12 | "\n", 13 | "¡Bienvenidos a esta serie de prácticas diseñadas para aprender PyTorch, uno de los frameworks más potentes y flexibles para el desarrollo de proyectos en aprendizaje profundo!\n", 14 | "\n", 15 | "PyTorch ha ganado popularidad por su enfoque intuitivo, su integración con Python y su soporte para la creación de modelos complejos de redes neuronales. Este conjunto de prácticas te guiará desde los conceptos básicos hasta la implementación de redes neuronales avanzadas, brindándote las herramientas necesarias para abordar problemas reales en el mundo del aprendizaje automático.\n", 16 | "\n", 17 | "\n", 18 | "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/irvingvasquez/practicas_pytorch/blob/master/01_1_Intro_tensores_leccion.ipynb)" 19 | ] 20 | }, 21 | { 22 | "cell_type": "markdown", 23 | "metadata": {}, 24 | "source": [ 25 | "## Tensores en Pytorch\n", 26 | "\n", 27 | "Los tensores son una generalización de los vectores y matrices. Cuando hablamos de un tensor de una dimensión nos estamos refierendo a un vector que puede tener $n$ elementos. Podemos tener tensores de varias dimensiones, por ejemplo una matriz 2D sería un tensor 2D y así sucesivamente. Los tensores son la base de muchos paquetes de para programar deep learning, debido a que las redes neuronales son ¡un montón de operaciones matriciales!, además mantienen la conección en el grafo y permiten la implementación del gradiente descendiente. Asi que es indispensable tener una representación adecuada para reducir el tiempo de entrenamiento. \n", 28 | "\n", 29 | "Sin más preámbulo vayamos a la programación." 30 | ] 31 | }, 32 | { 33 | "cell_type": "code", 34 | "execution_count": 1, 35 | "metadata": {}, 36 | "outputs": [ 37 | { 38 | "name": "stdout", 39 | "output_type": "stream", 40 | "text": [ 41 | "1.26.4\n", 42 | "1.12.0\n" 43 | ] 44 | } 45 | ], 46 | "source": [ 47 | "# Primero importemos los paquetes necesarios\n", 48 | "\n", 49 | "%matplotlib inline\n", 50 | "%config InlineBackend.figure_format = 'retina'\n", 51 | "\n", 52 | "import numpy as np\n", 53 | "print(np.__version__)\n", 54 | "import torch\n", 55 | "print(torch.__version__)" 56 | ] 57 | }, 58 | { 59 | "cell_type": "markdown", 60 | "metadata": {}, 61 | "source": [ 62 | "### Construcción\n", 63 | "\n", 64 | "Comenzemos por explorar el uso de tensores. Algunas funciones básias para construir los tensores son:\n", 65 | "\n", 66 | "**`torch.tensor`**:\n", 67 | " - Esta función se usa para crear un tensor directamente a partir de datos. Ejemplo de uso:\n", 68 | " ```python\n", 69 | " data = [[1, 2], [3, 4]]\n", 70 | " tensor = torch.tensor(data)\n", 71 | " ```\n", 72 | "\n", 73 | " **`torch.ones`**:\n", 74 | " - Crea un tensor lleno de unos con las dimensiones especificadas. Ejemplo de uso:\n", 75 | " ```python\n", 76 | " tensor = torch.ones(2, 4) # Crea un tensor 2x4 lleno de unos\n", 77 | " ```\n", 78 | "\n", 79 | "**`torch.arange`**:\n", 80 | " - Genera un tensor con valores en un rango especificado con un paso determinado. Ejemplo de uso:\n", 81 | " ```python\n", 82 | " tensor = torch.arange(0, 10, 2) # Crea un tensor con valores [0, 2, 4, 6, 8]\n", 83 | " ```\n", 84 | "\n", 85 | "**`torch.randn`**:\n", 86 | " - Genera un tensor con valores aleatorios distribuidos normalmente (media 0, desviación estándar 1). Ejemplo de uso:\n", 87 | " ```python\n", 88 | " tensor = torch.randn(3, 3) # Crea un tensor 3x3 con valores aleatorios de una distribución normal\n", 89 | " ```" 90 | ] 91 | }, 92 | { 93 | "cell_type": "code", 94 | "execution_count": 2, 95 | "metadata": {}, 96 | "outputs": [ 97 | { 98 | "name": "stdout", 99 | "output_type": "stream", 100 | "text": [ 101 | "tensor([[1., 1.],\n", 102 | " [1., 1.],\n", 103 | " [1., 1.],\n", 104 | " [1., 1.]])\n", 105 | "tensor([[ 1.1353, -0.6624, -1.1494],\n", 106 | " [ 0.2382, 0.2050, 0.3718],\n", 107 | " [ 1.5074, -0.1585, 1.0304]])\n" 108 | ] 109 | } 110 | ], 111 | "source": [ 112 | "# Tensor de 4 por 2 elementos relleno de unos\n", 113 | "y = torch.ones(4,2)\n", 114 | "# Tensor de 3 por 3 elementos relleno de valores aleatorios\n", 115 | "r = torch.randn(3,3)\n", 116 | "\n", 117 | "print(y)\n", 118 | "print(r)" 119 | ] 120 | }, 121 | { 122 | "cell_type": "markdown", 123 | "metadata": {}, 124 | "source": [ 125 | "En PyTorch, puedes realizar una amplia variedad de operaciones básicas con tensores. A continuación veremos algunas de las operaciones más comunes:\n", 126 | "\n", 127 | "- Adición. Se usa el operador `+`\n", 128 | "- Sustracción. Se usa el operador `-`\n", 129 | "- Multiplicación elemento por elemento. Operador `*`\n", 130 | "- División elemento por elemento. Operador `/`\n", 131 | "- Exponenciación. Operador `**`\n" 132 | ] 133 | }, 134 | { 135 | "cell_type": "code", 136 | "execution_count": 3, 137 | "metadata": {}, 138 | "outputs": [ 139 | { 140 | "name": "stdout", 141 | "output_type": "stream", 142 | "text": [ 143 | "Suma: tensor([[3., 3.],\n", 144 | " [3., 3.],\n", 145 | " [3., 3.],\n", 146 | " [3., 3.]])\n", 147 | "Resta: tensor([[2., 2.],\n", 148 | " [2., 2.],\n", 149 | " [2., 2.],\n", 150 | " [2., 2.]])\n" 151 | ] 152 | } 153 | ], 154 | "source": [ 155 | "# Operaciónes entre tensores\n", 156 | "# suma de tensores\n", 157 | "z = y + y + y\n", 158 | "print(\"Suma:\", z)\n", 159 | "\n", 160 | "# resta de tensores\n", 161 | "z = z - y\n", 162 | "print(\"Resta:\", z)" 163 | ] 164 | }, 165 | { 166 | "cell_type": "markdown", 167 | "metadata": {}, 168 | "source": [ 169 | "Acceder a los elementos de un tensor en PyTorch es muy similar a cómo se accede a los elementos en una lista o un arreglo de NumPy." 170 | ] 171 | }, 172 | { 173 | "cell_type": "code", 174 | "execution_count": 4, 175 | "metadata": {}, 176 | "outputs": [ 177 | { 178 | "name": "stdout", 179 | "output_type": "stream", 180 | "text": [ 181 | "tensor(2.)\n", 182 | "tensor([2., 2.])\n", 183 | "tensor([2., 2., 2., 2.])\n" 184 | ] 185 | } 186 | ], 187 | "source": [ 188 | "# acceder a un elemento del tensor\n", 189 | "print(z[1][1])\n", 190 | "\n", 191 | "# acceder a la segunda fila\n", 192 | "print(z[1])\n", 193 | "\n", 194 | "# acceder a la segunda columna\n", 195 | "print(z[:,1])" 196 | ] 197 | }, 198 | { 199 | "cell_type": "code", 200 | "execution_count": 5, 201 | "metadata": {}, 202 | "outputs": [ 203 | { 204 | "data": { 205 | "text/plain": [ 206 | "tensor([[2.],\n", 207 | " [2.],\n", 208 | " [2.],\n", 209 | " [2.]])" 210 | ] 211 | }, 212 | "execution_count": 5, 213 | "metadata": {}, 214 | "output_type": "execute_result" 215 | } 216 | ], 217 | "source": [ 218 | "# Observa el siguiete código y trata de entender que hace\n", 219 | "z[:,1:]" 220 | ] 221 | }, 222 | { 223 | "cell_type": "markdown", 224 | "metadata": {}, 225 | "source": [ 226 | "La notación z[:, 1:] en PyTorch se utiliza para seleccionar una porción específica de un tensor. Aquí está el desglose:\n", 227 | "- : indica que se seleccionan todas las filas del tensor.\n", 228 | "- 1: indica que se seleccionan todas las columnas desde la segunda columna en adelante (recordando que los índices en Python son cero-basados).\n", 229 | "Por lo tanto, z[:, 1:] selecciona todas las filas y todas las columnas desde la segunda columna hasta el final" 230 | ] 231 | }, 232 | { 233 | "cell_type": "markdown", 234 | "metadata": {}, 235 | "source": [ 236 | "### Reshape\n", 237 | "\n", 238 | "Una de las cosas más comunes que realizaŕas con los tensores es consultar su 'forma' es decir, la cantidad de elementos que hay en él, para tal función existe *.size()* \n", 239 | "\n", 240 | "Si deseamos cambiar la forma del tensor entonces utilizaremos *.resize_()* Observa que el guión bajo indica que se va a modificar el tensor en sí, si no utilizamos el guión bajo se creará un nuevo tensor." 241 | ] 242 | }, 243 | { 244 | "cell_type": "code", 245 | "execution_count": 6, 246 | "metadata": {}, 247 | "outputs": [ 248 | { 249 | "data": { 250 | "text/plain": [ 251 | "torch.Size([4, 2])" 252 | ] 253 | }, 254 | "execution_count": 6, 255 | "metadata": {}, 256 | "output_type": "execute_result" 257 | } 258 | ], 259 | "source": [ 260 | "z.size()" 261 | ] 262 | }, 263 | { 264 | "cell_type": "code", 265 | "execution_count": 7, 266 | "metadata": {}, 267 | "outputs": [ 268 | { 269 | "data": { 270 | "text/plain": [ 271 | "tensor([[2.0000e+00, 2.0000e+00, 2.0000e+00],\n", 272 | " [2.0000e+00, 2.0000e+00, 2.0000e+00],\n", 273 | " [2.0000e+00, 2.0000e+00, 1.0378e-38]])" 274 | ] 275 | }, 276 | "execution_count": 7, 277 | "metadata": {}, 278 | "output_type": "execute_result" 279 | } 280 | ], 281 | "source": [ 282 | "z.resize_(3,3)" 283 | ] 284 | }, 285 | { 286 | "cell_type": "markdown", 287 | "metadata": {}, 288 | "source": [ 289 | "### Pasando Tensores a Numpy\n", 290 | "\n", 291 | "Antes de hacer el entrenamiento de una red es muy probable que tengamos que hacer un preprocesamiento de los datos. Usualmente, el procesamiento se hace en numpy por facilidad además que tenemos muchísimas herramientas disponibles. Afortunadamente, pytorch provee una funciones para poder convertir entre formatos de forma casi directa. Para convetir de numpy a tensor usamos *torch.from_numpy()* y para retornar de torch a numpy utilizamos el método *.numpy()*" 292 | ] 293 | }, 294 | { 295 | "cell_type": "code", 296 | "execution_count": 8, 297 | "metadata": {}, 298 | "outputs": [ 299 | { 300 | "name": "stdout", 301 | "output_type": "stream", 302 | "text": [ 303 | "[[0.88287308 0.16807635 0.31020138]\n", 304 | " [0.91777182 0.40180744 0.70725716]\n", 305 | " [0.54134548 0.91257211 0.25928722]\n", 306 | " [0.15786873 0.57274718 0.6367663 ]]\n" 307 | ] 308 | } 309 | ], 310 | "source": [ 311 | "# generemos un array en numpy\n", 312 | "a = np.random.rand(4,3)\n", 313 | "print(a)\n" 314 | ] 315 | }, 316 | { 317 | "cell_type": "code", 318 | "execution_count": 9, 319 | "metadata": {}, 320 | "outputs": [ 321 | { 322 | "name": "stdout", 323 | "output_type": "stream", 324 | "text": [ 325 | "tensor([[0.8829, 0.1681, 0.3102],\n", 326 | " [0.9178, 0.4018, 0.7073],\n", 327 | " [0.5413, 0.9126, 0.2593],\n", 328 | " [0.1579, 0.5727, 0.6368]], dtype=torch.float64)\n" 329 | ] 330 | } 331 | ], 332 | "source": [ 333 | "# convertir a pytorch\n", 334 | "A = torch.from_numpy(a)\n", 335 | "print(A)" 336 | ] 337 | }, 338 | { 339 | "cell_type": "code", 340 | "execution_count": 10, 341 | "metadata": {}, 342 | "outputs": [ 343 | { 344 | "name": "stdout", 345 | "output_type": "stream", 346 | "text": [ 347 | "[[0.88287308 0.16807635 0.31020138]\n", 348 | " [0.91777182 0.40180744 0.70725716]\n", 349 | " [0.54134548 0.91257211 0.25928722]\n", 350 | " [0.15786873 0.57274718 0.6367663 ]]\n" 351 | ] 352 | } 353 | ], 354 | "source": [ 355 | "# convertir a numpy\n", 356 | "b = A.numpy()\n", 357 | "print(b)" 358 | ] 359 | }, 360 | { 361 | "cell_type": "markdown", 362 | "metadata": {}, 363 | "source": [ 364 | "Advertencia! Ten cuidado por que resulta que las variables convertidas de pytorch a numpy comparte memoria asi que si modificas uno se modificará el otro!" 365 | ] 366 | }, 367 | { 368 | "cell_type": "code", 369 | "execution_count": 11, 370 | "metadata": {}, 371 | "outputs": [ 372 | { 373 | "name": "stdout", 374 | "output_type": "stream", 375 | "text": [ 376 | "tensor([[0.8829, 0.1681, 0.3102],\n", 377 | " [0.9178, 0.4018, 0.7073],\n", 378 | " [0.5413, 0.9126, 0.2593],\n", 379 | " [0.1579, 0.5727, 0.6368]], dtype=torch.float64)\n" 380 | ] 381 | } 382 | ], 383 | "source": [ 384 | "print(A)" 385 | ] 386 | }, 387 | { 388 | "cell_type": "code", 389 | "execution_count": 12, 390 | "metadata": {}, 391 | "outputs": [ 392 | { 393 | "name": "stdout", 394 | "output_type": "stream", 395 | "text": [ 396 | "tensor([[1.7657, 0.3362, 0.6204],\n", 397 | " [1.8355, 0.8036, 1.4145],\n", 398 | " [1.0827, 1.8251, 0.5186],\n", 399 | " [0.3157, 1.1455, 1.2735]], dtype=torch.float64)\n" 400 | ] 401 | } 402 | ], 403 | "source": [ 404 | "# multipliquemos el tensor\n", 405 | "A.mul_(2)\n", 406 | "print(A)" 407 | ] 408 | }, 409 | { 410 | "cell_type": "markdown", 411 | "metadata": {}, 412 | "source": [ 413 | "ahora observemos el array" 414 | ] 415 | }, 416 | { 417 | "cell_type": "code", 418 | "execution_count": 13, 419 | "metadata": {}, 420 | "outputs": [ 421 | { 422 | "data": { 423 | "text/plain": [ 424 | "array([[1.76574615, 0.33615271, 0.62040276],\n", 425 | " [1.83554364, 0.80361487, 1.41451433],\n", 426 | " [1.08269096, 1.82514422, 0.51857444],\n", 427 | " [0.31573746, 1.14549436, 1.2735326 ]])" 428 | ] 429 | }, 430 | "execution_count": 13, 431 | "metadata": {}, 432 | "output_type": "execute_result" 433 | } 434 | ], 435 | "source": [ 436 | "a" 437 | ] 438 | }, 439 | { 440 | "cell_type": "markdown", 441 | "metadata": {}, 442 | "source": [ 443 | "### Funciones básicas\n", 444 | "\n", 445 | "PyTorch proporciona una serie de funciones básicas para realizar operaciones aritméticas y de manipulación de tensores.\n", 446 | "\n", 447 | "- **`torch.add`**:\n", 448 | " - Suma dos tensores elemento por elemento. Uso:\n", 449 | " ```python\n", 450 | " a = torch.tensor([1, 2, 3])\n", 451 | " b = torch.tensor([4, 5, 6])\n", 452 | " c = torch.add(a, b) # c = [5, 7, 9]\n", 453 | " ```\n", 454 | "- **`torch.sub`**:\n", 455 | " - Resta dos tensores elemento por elemento.\n", 456 | " ```python\n", 457 | " c = torch.sub(a, b) # c = [-3, -3, -3]\n", 458 | " ```\n", 459 | "- **`torch.dot`:**\n", 460 | " - Realiza el producto punto de dos vectores.\n", 461 | " ```python\n", 462 | " c = torch.dot(a, b) \n", 463 | " ```\n" 464 | ] 465 | }, 466 | { 467 | "cell_type": "code", 468 | "execution_count": 15, 469 | "metadata": {}, 470 | "outputs": [ 471 | { 472 | "name": "stdout", 473 | "output_type": "stream", 474 | "text": [ 475 | "tensor([2., 3., 4.], dtype=torch.float64)\n", 476 | "tensor([0.1000, 0.1000, 0.2000], dtype=torch.float64)\n", 477 | "tensor([1.], dtype=torch.float64)\n" 478 | ] 479 | } 480 | ], 481 | "source": [ 482 | "# Implementa un perceptrón\n", 483 | "x_np = np.array([2.0, 3.0, 4.0])\n", 484 | "w_np = np.array([0.1, 0.1, 0.2])\n", 485 | "b_np = np.array([1.0])\n", 486 | "\n", 487 | "# Convierte los vectores a tensores de pytorch\n", 488 | "X = torch.from_numpy(x_np)\n", 489 | "print(X)\n", 490 | "W = torch.from_numpy(w_np)\n", 491 | "print(W)\n", 492 | "B = torch.from_numpy(b_np)\n", 493 | "print(B)" 494 | ] 495 | }, 496 | { 497 | "cell_type": "markdown", 498 | "metadata": {}, 499 | "source": [ 500 | "## Implementando el perceptrón\n", 501 | "\n", 502 | "\n", 503 | "Imagen tomada de [Udacity]\n", 504 | "\n", 505 | "Podemos ver la salida del perceptrón como una secuencia de operaciones, primero se calcula $h$ a partir del producto punto, entre las entradas $X$ y los pesos $W$, sumado a el sesgo $b$, es decir\n", 506 | "\n", 507 | "$$h = MX + b$$\n", 508 | "\n", 509 | "despúes se 'pasa' $h$ por una función de activación que transforma el valor,\n", 510 | "\n", 511 | "$$y = f(h)$$" 512 | ] 513 | }, 514 | { 515 | "cell_type": "code", 516 | "execution_count": 16, 517 | "metadata": {}, 518 | "outputs": [ 519 | { 520 | "name": "stdout", 521 | "output_type": "stream", 522 | "text": [ 523 | "tensor([2.3000], dtype=torch.float64)\n" 524 | ] 525 | } 526 | ], 527 | "source": [ 528 | "# realiza las operaciones necesarias y calcula la combinación lineal h\n", 529 | "# algunas funciones útiles son torch.add() y torch.dot()\n", 530 | "# y = W*X + B\n", 531 | "H = torch.add(torch.dot(W,X),B)\n", 532 | "print(H)\n" 533 | ] 534 | }, 535 | { 536 | "cell_type": "code", 537 | "execution_count": 18, 538 | "metadata": {}, 539 | "outputs": [ 540 | { 541 | "name": "stdout", 542 | "output_type": "stream", 543 | "text": [ 544 | "tensor([0.9801], dtype=torch.float64)\n" 545 | ] 546 | } 547 | ], 548 | "source": [ 549 | "# Pasa h por la función de activación segmoide torch.sigmoid()\n", 550 | "Y = torch.tanh(H)\n", 551 | "print(Y)" 552 | ] 553 | }, 554 | { 555 | "cell_type": "code", 556 | "execution_count": 19, 557 | "metadata": {}, 558 | "outputs": [ 559 | { 560 | "name": "stdout", 561 | "output_type": "stream", 562 | "text": [ 563 | "[0.9800964]\n" 564 | ] 565 | } 566 | ], 567 | "source": [ 568 | "# Finalmente regresamos el tensor a un array de numpy\n", 569 | "y_np = Y.numpy()\n", 570 | "print(y_np)" 571 | ] 572 | }, 573 | { 574 | "cell_type": "code", 575 | "execution_count": null, 576 | "metadata": {}, 577 | "outputs": [], 578 | "source": [] 579 | } 580 | ], 581 | "metadata": { 582 | "kernelspec": { 583 | "display_name": "pytorch_1_12", 584 | "language": "python", 585 | "name": "python3" 586 | }, 587 | "language_info": { 588 | "codemirror_mode": { 589 | "name": "ipython", 590 | "version": 3 591 | }, 592 | "file_extension": ".py", 593 | "mimetype": "text/x-python", 594 | "name": "python", 595 | "nbconvert_exporter": "python", 596 | "pygments_lexer": "ipython3", 597 | "version": "3.10.4" 598 | }, 599 | "widgets": { 600 | "state": {}, 601 | "version": "1.1.2" 602 | } 603 | }, 604 | "nbformat": 4, 605 | "nbformat_minor": 2 606 | } 607 | -------------------------------------------------------------------------------- /01_2_funciones.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Uso de funciones torch.nn\n", 8 | "\n", 9 | "El módulo torch.nn en PyTorch contiene un amplio conjunto de clases y funciones que facilitan la construcción y el entrenamiento de redes neuronales. A continuación usaremos varias funciones como práctica.\n", 10 | "\n", 11 | "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/irvingvasquez/practicas_pytorch/blob/master/01_2_funciones.ipynb)" 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": 2, 17 | "metadata": {}, 18 | "outputs": [ 19 | { 20 | "name": "stdout", 21 | "output_type": "stream", 22 | "text": [ 23 | "2.3.1+cpu\n" 24 | ] 25 | } 26 | ], 27 | "source": [ 28 | "import torch\n", 29 | "print(torch.__version__)" 30 | ] 31 | }, 32 | { 33 | "cell_type": "markdown", 34 | "metadata": {}, 35 | "source": [ 36 | "### Implementación de ReLU\n", 37 | "\n", 38 | "La función ReLU (Rectified Linear Unit) es una de las funciones de activación más populares en las redes neuronales, especialmente en las redes neuronales profundas. Su popularidad se debe a su simplicidad y efectividad en resolver algunos problemas comunes en el entrenamiento de redes neuronales, como el problema del desvanecimiento del gradiente.\n", 39 | "\n", 40 | "$$z = max(0, x)$$\n", 41 | "\n", 42 | "Si bien la función ReLU ya la podemos encontrar implementada en PyTorch,\n", 43 | "\n", 44 | "```python\n", 45 | "torch.nn.functional.relu(tensor)\n", 46 | "```\n", 47 | "\n", 48 | "La idea de este ejercicio es familiarizarnos con las funciones de PyTorch y su funcionamiento. Por ejemplo, podemos hacer uso de las funciones:\n", 49 | "\n", 50 | "```python\n", 51 | "torch.maximum()\n", 52 | "torch.zeros()\n", 53 | "torch.exp()\n", 54 | "```" 55 | ] 56 | }, 57 | { 58 | "cell_type": "code", 59 | "execution_count": 5, 60 | "metadata": {}, 61 | "outputs": [], 62 | "source": [ 63 | "# Implementa la función de activaciòn ReLU\n", 64 | "def ReLU(tensor):\n", 65 | " return torch.maximum(torch.zeros(tensor.shape), tensor)" 66 | ] 67 | }, 68 | { 69 | "cell_type": "code", 70 | "execution_count": 6, 71 | "metadata": {}, 72 | "outputs": [ 73 | { 74 | "data": { 75 | "text/plain": [ 76 | "tensor([[1.0000, 2.0000, 0.0000],\n", 77 | " [2.5000, 0.0000, 6.0000]])" 78 | ] 79 | }, 80 | "execution_count": 6, 81 | "metadata": {}, 82 | "output_type": "execute_result" 83 | } 84 | ], 85 | "source": [ 86 | "# Verificar el funcionamiento de la función ReLU\n", 87 | "a = torch.tensor([[1, 2, -3], [2.5, -0.2, 6]])\n", 88 | "ReLU(a)" 89 | ] 90 | } 91 | ], 92 | "metadata": { 93 | "kernelspec": { 94 | "display_name": "pt2x", 95 | "language": "python", 96 | "name": "python3" 97 | }, 98 | "language_info": { 99 | "codemirror_mode": { 100 | "name": "ipython", 101 | "version": 3 102 | }, 103 | "file_extension": ".py", 104 | "mimetype": "text/x-python", 105 | "name": "python", 106 | "nbconvert_exporter": "python", 107 | "pygments_lexer": "ipython3", 108 | "version": "3.12.4" 109 | } 110 | }, 111 | "nbformat": 4, 112 | "nbformat_minor": 2 113 | } 114 | -------------------------------------------------------------------------------- /01_3_ejercicio.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## Ejercicios\n", 8 | "\n", 9 | "Resuelve los siguientes ejercicios.\n", 10 | "Total de puntos: 3\n", 11 | "\n", 12 | "### SotfMax\n", 13 | "\n", 14 | "Implementa la función SoftMax (1 punto)\n", 15 | "\n", 16 | "$\n", 17 | "\\text{softmax}(z_{i}) = \\frac{\\exp(z_i)}{\\sum_j \\exp(z_j)}\n", 18 | "$\n" 19 | ] 20 | }, 21 | { 22 | "cell_type": "code", 23 | "execution_count": null, 24 | "metadata": {}, 25 | "outputs": [], 26 | "source": [ 27 | "def SoftMax():\n", 28 | " return None" 29 | ] 30 | }, 31 | { 32 | "cell_type": "code", 33 | "execution_count": null, 34 | "metadata": {}, 35 | "outputs": [], 36 | "source": [ 37 | "# Verificar el funcionamiento de la función SoftMax\n", 38 | "a = torch.tensor([0.6, 5.2, 9.2])\n", 39 | "SoftMax(a)" 40 | ] 41 | }, 42 | { 43 | "cell_type": "markdown", 44 | "metadata": {}, 45 | "source": [ 46 | "### Implementa la combinación lineal\n", 47 | "\n", 48 | "Deberás implementar (1 punto)\n", 49 | "\n", 50 | "$$\n", 51 | "Y = WX + B\n", 52 | "$$" 53 | ] 54 | }, 55 | { 56 | "cell_type": "code", 57 | "execution_count": null, 58 | "metadata": {}, 59 | "outputs": [], 60 | "source": [ 61 | "# Implementa la combinación lineal de las entradas\n", 62 | "def combinacion(X, W, B):\n", 63 | " return None" 64 | ] 65 | }, 66 | { 67 | "cell_type": "code", 68 | "execution_count": null, 69 | "metadata": {}, 70 | "outputs": [], 71 | "source": [ 72 | "# Verificar el funcionamiento de la función combinacion\n", 73 | "W = torch.tensor([[1.2, 0.3, 0.1], [.01, 2.1, 0.7]])\n", 74 | "B = torch.tensor([2.1, 0.89])\n", 75 | "\n", 76 | "# Genera un vector aleatorio en numpy y pasalo a tensor. Verifica que el tamaño sea correcto.\n", 77 | "# 1 punto\n", 78 | "X = None\n", 79 | "\n", 80 | "Y = combinacion(X, W, B)\n", 81 | "S = SoftMax(Y)\n", 82 | "\n", 83 | "if(torch.sum(S) == 1): \n", 84 | " print(\"La función es correcta\")\n", 85 | "else:\n", 86 | " print(\"La función es incorrecta\")" 87 | ] 88 | } 89 | ], 90 | "metadata": { 91 | "language_info": { 92 | "name": "python" 93 | } 94 | }, 95 | "nbformat": 4, 96 | "nbformat_minor": 2 97 | } 98 | -------------------------------------------------------------------------------- /02_red_neuronal.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Red neuronal multicapa en PyTorch\n", 8 | "\n", 9 | "En este notebook veremos como implementar una red neuronal usando PyTorch. En particular, implementaremos un perceptrón multicapa (MLP por sus siglas en inglés) para la classificación de dígitos del conjunto de datos MNIST.\n", 10 | "Total de puntos: 2\n", 11 | "\n", 12 | "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/irvingvasquez/practicas_pytorch/blob/master/02_red_neuronal.ipynb)\n", 13 | "\n", 14 | "@juan1rving" 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": null, 20 | "metadata": {}, 21 | "outputs": [], 22 | "source": [ 23 | "# Primero llamamos a los paquetes necesarios\n", 24 | "%matplotlib inline\n", 25 | "%config InlineBackend.figure_format = 'retina'\n", 26 | "\n", 27 | "import helper\n", 28 | "import matplotlib.pyplot as plt\n", 29 | "\n", 30 | "import numpy as np\n", 31 | "import torch\n", 32 | "from torchvision import datasets, transforms" 33 | ] 34 | }, 35 | { 36 | "cell_type": "markdown", 37 | "metadata": {}, 38 | "source": [ 39 | "## Conjunto de datos\n", 40 | "\n", 41 | "Para la práctica necesitaremos un conjunto de datos (dataset). Afortunadamente el paquete **torchvision** provee diversos conjuntos de datos de ejemplo. En este ejercicio, utilizaremos MNIST, el cual contiene ejemplos de letras escritas a mano. El siguiente código lee el conjunto de datos y lo separa en un conjunto de entrenamiendo y uno de prueba. \n", 42 | "\n", 43 | "\n", 44 | "En PyTorch, las clases `Dataset` y `DataLoader` son fundamentales para gestionar y procesar datos de manera eficiente en aplicaciones de aprendizaje profundo. Estas clases simplifican el manejo de datos, asegurando que los datos se presenten en lotes, se mezclen aleatoriamente y se procesen de manera escalable, incluso para grandes conjuntos de datos.\n", 45 | "\n", 46 | "La clase `Dataset` es una abstracción que define cómo se accede y manipula un conjunto de datos. Sirve como base para crear datasets personalizados o usar datasets predefinidos de PyTorch (como los de `torchvision`).\n", 47 | "\n", 48 | "El `DataLoader` se encarga de cargar los datos desde un `Dataset`, gestionando el muestreo, el manejo por lotes y el paralelismo. Es especialmente útil para trabajar con grandes conjuntos de datos o para preparar datos en lotes para entrenamiento.\n", 49 | "Parámetros clave de `DataLoader`:\n", 50 | "1. **`dataset`**: El objeto `Dataset` que contiene los datos.\n", 51 | "2. **`batch_size`**: Cantidad de muestras por lote. Si no se especifica, se usa `batch_size=1`.\n", 52 | "3. **`shuffle`**: Si es `True`, mezcla los datos aleatoriamente.\n", 53 | "4. **`num_workers`**: Número de procesos secundarios para cargar datos en paralelo.\n", 54 | "5. **`drop_last`**: Si es `True`, descarta el último lote si no contiene suficientes datos.\n", 55 | "\n" 56 | ] 57 | }, 58 | { 59 | "cell_type": "code", 60 | "execution_count": null, 61 | "metadata": {}, 62 | "outputs": [], 63 | "source": [ 64 | "# Generaramos una transformación para convertir las matrices a tensores y normalizar el conjunto de datos\n", 65 | "transform = transforms.Compose([transforms.ToTensor(),\n", 66 | " transforms.Normalize([0.5],[0.5]) \n", 67 | " ])\n", 68 | "# Descargamos el conjunto de datos de entrenamiento\n", 69 | "trainset = datasets.MNIST('MNIST_data/', download=True, train=True, transform=transform)\n", 70 | "# Cargamos el conjunto\n", 71 | "batch_size=64\n", 72 | "trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True)\n", 73 | "\n", 74 | "# Descargamos y cargamos el conjunto de prueba\n", 75 | "testset = datasets.MNIST('MNIST_data/', download=True, train=False, transform=transform)\n", 76 | "testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=True)" 77 | ] 78 | }, 79 | { 80 | "cell_type": "code", 81 | "execution_count": null, 82 | "metadata": {}, 83 | "outputs": [], 84 | "source": [ 85 | "# Ordenamos los datos para tener parejas de imágenes con su respectiva clase\n", 86 | "# Los datos se encuentran en trainloader asi que generamos un iterador para extraerlos uno por uno\n", 87 | "dataiter = iter(trainloader)\n", 88 | "\n", 89 | "# Obtenemos un lote de ejemplos y sus respectivas etiquetas\n", 90 | "images, labels = next(dataiter)" 91 | ] 92 | }, 93 | { 94 | "cell_type": "markdown", 95 | "metadata": {}, 96 | "source": [ 97 | "Es recomendable verificar que estamos cargando bien el conjunto de datos. Asi que a continuación imprimeremos uno." 98 | ] 99 | }, 100 | { 101 | "cell_type": "code", 102 | "execution_count": null, 103 | "metadata": {}, 104 | "outputs": [], 105 | "source": [ 106 | "plt.imshow(images[1].numpy().squeeze(), cmap='Greys_r');\n", 107 | "images[1].size()" 108 | ] 109 | }, 110 | { 111 | "cell_type": "markdown", 112 | "metadata": {}, 113 | "source": [ 114 | "## Implementación de la red neuronal multicapa\n", 115 | "\n", 116 | "Ahora pasaremos a la creación de la red neuronal, como ejemplo utilizaremos un perceptrón multicapa para clasificar las imagenes del conjunto MNIST. Como entrada tendremos 784 nodos = 28 * 28, en seguida tendremos una capa oculta de 128 nodos, con una función de activación tipo RELU, despúes tendremos una segunda capa oculta con 64 nodos y función de activación RELU, en seguida tendremos 10 nodos de salida los cuales pasan por una función softmax que convierte los valores a probabilidades. En el siguiente ejercicio incluiremos la pérdida (loss) con la función de entropía cruzada. \n", 117 | "\n", 118 | "\n", 119 | "\n", 120 | "El modulo que contiene las herramientas para crear la RN es **pytorch.nn**. La red neuronal en sí se crea como una clase que hereda la estructura de **pytorch.nn.Module**. Cada una de las capas de la red se define de forma independiente. e.g. Para crear una capa con 784 entradas y 128 nodos utilizamos *nn.Linear(784, 128)*\n", 121 | "\n", 122 | "La red implementa la función *forward* que realiza el paso frontal (fowdward pass). Esta función miembro recibe un tensor como entrada y calcula la salida de la red.\n", 123 | "\n", 124 | "Varias funciones de activación se encuntran en el módulo *nn.functional*. Dicho módulo usualmente se importa como *F*. \n" 125 | ] 126 | }, 127 | { 128 | "cell_type": "code", 129 | "execution_count": null, 130 | "metadata": {}, 131 | "outputs": [], 132 | "source": [ 133 | "# importamos paquetes de pytorch\n", 134 | "from torch import nn\n", 135 | "import torch.nn.functional as F" 136 | ] 137 | }, 138 | { 139 | "attachments": {}, 140 | "cell_type": "markdown", 141 | "metadata": {}, 142 | "source": [ 143 | "En general las redes implementan a partir de la clase nn.Module que provee la clase base. Por lo tanto en este ejercicio declararemos una clase denominada red neuronal que hereda de nn.Module. Una vez declarada nuestra red neuronal es necesario includir como atributos de la case las capas que se requieren, esto por que cada capa incluye los parámetros (pesos) que se entrenarán y deben tener permanencia mientras exista la red. Dichas capas se incluirán dentro del constructor __init__ . \n", 144 | "\n", 145 | "De acuerdo a pytorch.org\n", 146 | "\n", 147 | " nn.Linear(in_features, out_features, bias=True, device=None, dtype=None)\n", 148 | "\n", 149 | "Applies a linear transformation to the incoming data.\n", 150 | "\n", 151 | "Por lo tanto si queremos uncluir una sola capa podríamos codificar lo siguiente:\n", 152 | "\n", 153 | " self.fc = nn.Linear(n_inputs, n_outputs)\n", 154 | "\n", 155 | "A continuación definiremos el comportamiento de inferencia de la red dentro de la funcion forward. En el comportamiento indicaremos el orden en el que se van ejecutando las capas y las funciones de activación. Recuerda que las funciones de activación solo se llaman pero no se instancían. Un ejemplo de una sola capa sería:\n", 156 | "\n", 157 | " def forward(self, x):\n", 158 | " y = F.relu(self.fc(x))\n", 159 | " return y\n" 160 | ] 161 | }, 162 | { 163 | "cell_type": "code", 164 | "execution_count": null, 165 | "metadata": {}, 166 | "outputs": [], 167 | "source": [ 168 | "# Implementación de la red neuronal\n", 169 | "class RedNeuronal(nn.Module):\n", 170 | " def __init__(self):\n", 171 | " super().__init__()\n", 172 | " # TODO: Definir las capas. Cada una con 128, 64 y 10 unidades respectivamente\n", 173 | " # 1 punto\n", 174 | " self.fc1 = None\n", 175 | " self.fc2 = None\n", 176 | " self.fc3 = None\n", 177 | " \n", 178 | " def forward(self, x):\n", 179 | " ''' Pase frontal de la red, regresamos las probabilidades '''\n", 180 | " # TODO: Define el comportamiento de inferencia, Recuerda que al final esta función debe retornar probabilidades y no logits.\n", 181 | " # 1 punto\n", 182 | " y = None\n", 183 | "\n", 184 | " return y" 185 | ] 186 | }, 187 | { 188 | "cell_type": "code", 189 | "execution_count": null, 190 | "metadata": {}, 191 | "outputs": [], 192 | "source": [ 193 | "model = RedNeuronal()\n", 194 | "print(model)" 195 | ] 196 | }, 197 | { 198 | "cell_type": "markdown", 199 | "metadata": {}, 200 | "source": [ 201 | "### Inicializamos pesos y sesgos\n", 202 | "\n", 203 | "Cuando creas las capas se crean también los tensores correspondientes a los pesos y sesgos. Éstos son inicializados por ti, aunque pudes modificarlos usando funciones extra. Para observar sus valores puedes llamar a *model.fc1.weight* \n" 204 | ] 205 | }, 206 | { 207 | "cell_type": "code", 208 | "execution_count": null, 209 | "metadata": {}, 210 | "outputs": [], 211 | "source": [ 212 | "print(model.fc1.weight)\n", 213 | "print(model.fc1.bias)" 214 | ] 215 | }, 216 | { 217 | "cell_type": "markdown", 218 | "metadata": {}, 219 | "source": [ 220 | "Supongamos que deseamos inicializar los pesos con algunos valores personalizados. Dado que los pesos y sesgos en sí son variables de autograd (Preparadas para el cálculo del gradiente automático) estos solo se pueden modificar cuando no estan en modo de autogradiente." 221 | ] 222 | }, 223 | { 224 | "cell_type": "code", 225 | "execution_count": null, 226 | "metadata": {}, 227 | "outputs": [], 228 | "source": [ 229 | "# Colocamos ceros\n", 230 | "model.fc1.bias.data.fill_(0)" 231 | ] 232 | }, 233 | { 234 | "cell_type": "code", 235 | "execution_count": null, 236 | "metadata": {}, 237 | "outputs": [], 238 | "source": [ 239 | "# muestreamos desde una distribución normal con media cero y desv. estandar = 0.01\n", 240 | "model.fc1.weight.data.normal_(std=0.01)" 241 | ] 242 | }, 243 | { 244 | "cell_type": "markdown", 245 | "metadata": {}, 246 | "source": [ 247 | "### Pase frontal\n", 248 | "\n", 249 | "Hasta el momento la red no está entrenada y solo tenemos los pesos aleatorios. Hagamos un pase frontal para ver que pasa. Primero debemos convertir la imagen a un tensor y pasarla a través de la red. " 250 | ] 251 | }, 252 | { 253 | "cell_type": "code", 254 | "execution_count": null, 255 | "metadata": {}, 256 | "outputs": [], 257 | "source": [ 258 | "# Obtengamos el siguiente lote de imágenes\n", 259 | "#dataiter = iter(trainloader)\n", 260 | "images, labels = next(dataiter)\n", 261 | "\n", 262 | "# Reestructuremos el lote a un vector de una dimensión, hay quien le llama a esta operación \"aplanado\".\n", 263 | "# La nueva forma será (batch size, color channels, image pixels) \n", 264 | "images.resize_(batch_size, 1, 784)\n", 265 | "# alternativa: images.resize_(images.shape[0], 1, 784) to not automatically get batch size\n", 266 | "\n", 267 | "# Pase frontal de la red\n", 268 | "img_idx = 0\n", 269 | "prediction = model.forward(images[img_idx,:])\n", 270 | "\n", 271 | "print(prediction)" 272 | ] 273 | }, 274 | { 275 | "cell_type": "code", 276 | "execution_count": null, 277 | "metadata": {}, 278 | "outputs": [], 279 | "source": [ 280 | "img = images[img_idx]\n", 281 | "helper.view_classify(img.view(1, 28, 28), prediction)" 282 | ] 283 | }, 284 | { 285 | "cell_type": "markdown", 286 | "metadata": {}, 287 | "source": [ 288 | "Seguro ninguna de las clases tiene una probabilidad grande con respecto de las otras, esto se debe a que todavía no hemos entrenado la red. En el siguiente ejercicio entrenaremos la red.\n" 289 | ] 290 | }, 291 | { 292 | "cell_type": "code", 293 | "execution_count": null, 294 | "metadata": {}, 295 | "outputs": [], 296 | "source": [] 297 | } 298 | ], 299 | "metadata": { 300 | "kernelspec": { 301 | "display_name": "pytorch_1_12", 302 | "language": "python", 303 | "name": "python3" 304 | }, 305 | "language_info": { 306 | "codemirror_mode": { 307 | "name": "ipython", 308 | "version": 3 309 | }, 310 | "file_extension": ".py", 311 | "mimetype": "text/x-python", 312 | "name": "python", 313 | "nbconvert_exporter": "python", 314 | "pygments_lexer": "ipython3", 315 | "version": "3.10.4" 316 | }, 317 | "widgets": { 318 | "state": {}, 319 | "version": "1.1.2" 320 | } 321 | }, 322 | "nbformat": 4, 323 | "nbformat_minor": 2 324 | } 325 | -------------------------------------------------------------------------------- /05_validacion.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Validación\n", 8 | "\n", 9 | "Hasta el momento hemos logrado entrenar la red, es decir, encontrar los pesos tal que se reduce el error entre la salida de la red y la salida esperada. Con los pesos entrenados, la red pude predecir la clase que corresponde a una entrada que no ha visto. Este proceso de predicción conocido como inferencia es la parte más interesante de las redes ya que permite generalizar su uso a casos no vistos. Sin embargo, durante el entrenamiento se puede suscitar un fenómeno conocido como sobre-ajuste (overfitting), donde la red reduce de manera adecuada la pérdida con respecto a los datos de entrenamiento, pero al momento de encontrarse con ejemplos desconocidos, ésta tiene un rendimiento pobre. Este efecto es contrario, por que buscamos **generalizar** el rendimiento de tal forma que clasifique bien ejemplos que no han sido vistos. \n", 10 | "\n", 11 | "Para saber que tan bien está generalizando la red se realiza un proceso de validación. En este proceso de validación se verifican las predicciones de la red en un conjunto de datos de validación (En la literatura a este conjunto también se le denomina de prueba).\n", 12 | "\n", 13 | "En el resto del notebook veremos como realizar la validación de la red durante el entrenamiento.\n", 14 | "\n", 15 | "También haremos uso del *dropout*\n", 16 | "\n", 17 | "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/irvingvasquez/practicas_pytorch/blob/master/05_validacion.ipynb)\n" 18 | ] 19 | }, 20 | { 21 | "cell_type": "code", 22 | "execution_count": 1, 23 | "metadata": {}, 24 | "outputs": [], 25 | "source": [ 26 | "# Cargamos paquetes necesarios\n", 27 | "\n", 28 | "%matplotlib inline\n", 29 | "%config InlineBackend.figure_format = 'retina'\n", 30 | "\n", 31 | "import matplotlib.pyplot as plt\n", 32 | "import numpy as np\n", 33 | "import time\n", 34 | "\n", 35 | "import torch\n", 36 | "from torch import nn\n", 37 | "from torch import optim\n", 38 | "import torch.nn.functional as F\n", 39 | "from torchvision import datasets, transforms\n", 40 | "\n", 41 | "#helper was developed by Udacity under MIT license\n", 42 | "import helper" 43 | ] 44 | }, 45 | { 46 | "cell_type": "markdown", 47 | "metadata": {}, 48 | "source": [ 49 | "## Conjunto de datos (Dataset)\n", 50 | "\n", 51 | "Para este ejemplo utilizaremos el Fashion MNIST. Este dataset esta constituido por imágenes de 28 x 28 pixeles y cada imagen contiene prendas como ropa o zapatos. " 52 | ] 53 | }, 54 | { 55 | "cell_type": "code", 56 | "execution_count": 2, 57 | "metadata": {}, 58 | "outputs": [], 59 | "source": [ 60 | "# Definimos una transformación de los datos\n", 61 | "transform = transforms.Compose([transforms.ToTensor(),\n", 62 | " transforms.Normalize((0.5), (0.5))])\n", 63 | "# Descargamos el conjunto de entrenamiento y cargamos mediante un dataLoader\n", 64 | "trainset = datasets.FashionMNIST('F_MNIST_data/', download=True, train=True, transform=transform)\n", 65 | "trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)\n", 66 | "\n", 67 | "# Descargamos el conjunto de validación\n", 68 | "validationset = datasets.FashionMNIST('F_MNIST_data/', download=True, train=False, transform=transform)\n", 69 | "validationloader = torch.utils.data.DataLoader(validationset, batch_size=64, shuffle=True)" 70 | ] 71 | }, 72 | { 73 | "cell_type": "markdown", 74 | "metadata": {}, 75 | "source": [ 76 | "## Red Neuronal\n", 77 | "\n", 78 | "En esta ocasión, construiremos la red de forma más general, donde las capas ocultas se generarán a partir de un vector que indica el número de capas y el número de nodos de cada capa. Haremos uso del módulo *nn.ModuleList* que nos permitirá crear un número arbitrario de capas. En cierta forma *nn.ModuleList* se comporta como una lista simple de python. " 79 | ] 80 | }, 81 | { 82 | "cell_type": "code", 83 | "execution_count": 3, 84 | "metadata": {}, 85 | "outputs": [], 86 | "source": [ 87 | "class RedNeuronal(nn.Module):\n", 88 | " def __init__(self, input_size, output_size, hidden_layers, drop_p = 0.5):\n", 89 | " '''\n", 90 | " Construye una red de tamaño arbitrario.\n", 91 | " \n", 92 | " Parámetros:\n", 93 | " input_size: cantidad de elementos en la entrada\n", 94 | " output_size: cantidada de elementos en la salida \n", 95 | " hidden_layers: cantidad de elementos por cada capa oculta\n", 96 | " drop_p: probabilidad de \"tirar\" (drop) una neurona [0,1] \n", 97 | " '''\n", 98 | " # llamamos al constructor de la superclase\n", 99 | " super().__init__()\n", 100 | " \n", 101 | " # Agregamos la primera capa\n", 102 | " self.hidden_layers = nn.ModuleList([nn.Linear(input_size, hidden_layers[0])])\n", 103 | " \n", 104 | " # agregamos cada una de las capas, zip empareja el número de entradas con las salidas\n", 105 | " layer_sizes = zip(hidden_layers[:-1], hidden_layers[1:])\n", 106 | " self.hidden_layers.extend([nn.Linear(h1, h2) for h1, h2 in layer_sizes])\n", 107 | " \n", 108 | " # agregamos la capa de salida final de la red\n", 109 | " self.output = nn.Linear(hidden_layers[-1], output_size)\n", 110 | " \n", 111 | " # Incluimos drop-out en la red\n", 112 | " self.dropout = nn.Dropout(p=drop_p)\n", 113 | " \n", 114 | " def forward(self, x):\n", 115 | " ''' Pase hacia adelante en la red, el regreso son las probabilidades en el dominio log '''\n", 116 | " \n", 117 | " # Hacemos un pase frontal en cada una de las capas ocultas, \n", 118 | " # La funció de activación es un RELU combinado con dropout\n", 119 | " for linear in self.hidden_layers:\n", 120 | " x = F.relu(linear(x))\n", 121 | " x = self.dropout(x)\n", 122 | " \n", 123 | " x = self.output(x)\n", 124 | " \n", 125 | " return F.log_softmax(x, dim=1)" 126 | ] 127 | }, 128 | { 129 | "cell_type": "markdown", 130 | "metadata": {}, 131 | "source": [ 132 | "## Entrenamiento\n", 133 | "\n", 134 | "La pérdida (loss) nos indica que tan bien o mal está clasificando nuestra red. Dado que estamos utilizando el dominio log para calcular las salidas de la red, utilizaremos el criterio negative log loss *nn.NLLLoss()*. Como optimizador utilzaremos ADAM optimzer, el cual combina el gradiente descendiente estocástico con el momentum. " 135 | ] 136 | }, 137 | { 138 | "cell_type": "code", 139 | "execution_count": 4, 140 | "metadata": {}, 141 | "outputs": [], 142 | "source": [ 143 | "# Create the network, define the criterion and optimizer\n", 144 | "model = RedNeuronal(784, 10, [516, 256], drop_p=0.5)\n", 145 | "criterion = nn.NLLLoss()\n", 146 | "optimizer = optim.Adam(model.parameters(), lr=0.001)" 147 | ] 148 | }, 149 | { 150 | "cell_type": "markdown", 151 | "metadata": {}, 152 | "source": [ 153 | "Ahora programaremos la validación. En esta etapa se mide la exactitud de la red en el conjunto de prueba a fin de ver que tan bien está generalizando la red. Como estamos utilizando drop out durante el entrenamiento al momento de hacer una inferencia este proceso se debe desactivar. Afortunadamente pytorch provee dos modos de funcionamiento para la red: entrenamiento o evaluación, model.train() y model.eval() respectivamente. " 154 | ] 155 | }, 156 | { 157 | "cell_type": "code", 158 | "execution_count": 5, 159 | "metadata": {}, 160 | "outputs": [], 161 | "source": [ 162 | "# Implementamos una función de evaluación\n", 163 | "def validation(model, validationloader, criterion):\n", 164 | " test_loss = 0\n", 165 | " accuracy = 0\n", 166 | " for images, labels in validationloader:\n", 167 | "\n", 168 | " images.resize_(images.shape[0], 784)\n", 169 | "\n", 170 | " output = model.forward(images)\n", 171 | " test_loss += criterion(output, labels).item()\n", 172 | "\n", 173 | " ps = torch.exp(output)\n", 174 | " equality = (labels.data == ps.max(dim=1)[1])\n", 175 | " accuracy += equality.type(torch.FloatTensor).mean()\n", 176 | " \n", 177 | " return test_loss, accuracy" 178 | ] 179 | }, 180 | { 181 | "cell_type": "markdown", 182 | "metadata": {}, 183 | "source": [ 184 | "### Implementación del entrenamiento y validación\n", 185 | "\n" 186 | ] 187 | }, 188 | { 189 | "cell_type": "code", 190 | "execution_count": 7, 191 | "metadata": {}, 192 | "outputs": [ 193 | { 194 | "name": "stdout", 195 | "output_type": "stream", 196 | "text": [ 197 | "Epoch: 1/2.. Pérdida de entrenamiento: 0.487.. Pérdida de validación: 0.429.. Exactitud de validación: 0.837\n", 198 | "Epoch: 1/2.. Pérdida de entrenamiento: 0.446.. Pérdida de validación: 0.435.. Exactitud de validación: 0.838\n", 199 | "Epoch: 1/2.. Pérdida de entrenamiento: 0.458.. Pérdida de validación: 0.425.. Exactitud de validación: 0.845\n", 200 | "Epoch: 1/2.. Pérdida de entrenamiento: 0.455.. Pérdida de validación: 0.449.. Exactitud de validación: 0.834\n", 201 | "Epoch: 1/2.. Pérdida de entrenamiento: 0.425.. Pérdida de validación: 0.445.. Exactitud de validación: 0.834\n", 202 | "Epoch: 1/2.. Pérdida de entrenamiento: 0.494.. Pérdida de validación: 0.434.. Exactitud de validación: 0.842\n", 203 | "Epoch: 1/2.. Pérdida de entrenamiento: 0.506.. Pérdida de validación: 0.431.. Exactitud de validación: 0.841\n", 204 | "Epoch: 1/2.. Pérdida de entrenamiento: 0.443.. Pérdida de validación: 0.422.. Exactitud de validación: 0.844\n", 205 | "Epoch: 1/2.. Pérdida de entrenamiento: 0.474.. Pérdida de validación: 0.432.. Exactitud de validación: 0.837\n", 206 | "Epoch: 1/2.. Pérdida de entrenamiento: 0.510.. Pérdida de validación: 0.437.. Exactitud de validación: 0.840\n", 207 | "Epoch: 1/2.. Pérdida de entrenamiento: 0.489.. Pérdida de validación: 0.425.. Exactitud de validación: 0.847\n", 208 | "Epoch: 1/2.. Pérdida de entrenamiento: 0.475.. Pérdida de validación: 0.431.. Exactitud de validación: 0.844\n", 209 | "Epoch: 1/2.. Pérdida de entrenamiento: 0.458.. Pérdida de validación: 0.417.. Exactitud de validación: 0.848\n", 210 | "Epoch: 1/2.. Pérdida de entrenamiento: 0.472.. Pérdida de validación: 0.427.. Exactitud de validación: 0.838\n", 211 | "Epoch: 1/2.. Pérdida de entrenamiento: 0.478.. Pérdida de validación: 0.413.. Exactitud de validación: 0.848\n", 212 | "Epoch: 1/2.. Pérdida de entrenamiento: 0.446.. Pérdida de validación: 0.415.. Exactitud de validación: 0.848\n", 213 | "Epoch: 1/2.. Pérdida de entrenamiento: 0.471.. Pérdida de validación: 0.404.. Exactitud de validación: 0.855\n", 214 | "Epoch: 1/2.. Pérdida de entrenamiento: 0.449.. Pérdida de validación: 0.417.. Exactitud de validación: 0.846\n", 215 | "Epoch: 1/2.. Pérdida de entrenamiento: 0.464.. Pérdida de validación: 0.418.. Exactitud de validación: 0.846\n", 216 | "Epoch: 1/2.. Pérdida de entrenamiento: 0.446.. Pérdida de validación: 0.421.. Exactitud de validación: 0.845\n", 217 | "Epoch: 1/2.. Pérdida de entrenamiento: 0.436.. Pérdida de validación: 0.405.. Exactitud de validación: 0.852\n", 218 | "Epoch: 1/2.. Pérdida de entrenamiento: 0.477.. Pérdida de validación: 0.413.. Exactitud de validación: 0.848\n", 219 | "Epoch: 1/2.. Pérdida de entrenamiento: 0.472.. Pérdida de validación: 0.410.. Exactitud de validación: 0.848\n", 220 | "Epoch: 2/2.. Pérdida de entrenamiento: 0.439.. Pérdida de validación: 0.408.. Exactitud de validación: 0.851\n", 221 | "Epoch: 2/2.. Pérdida de entrenamiento: 0.425.. Pérdida de validación: 0.405.. Exactitud de validación: 0.854\n", 222 | "Epoch: 2/2.. Pérdida de entrenamiento: 0.429.. Pérdida de validación: 0.416.. Exactitud de validación: 0.848\n", 223 | "Epoch: 2/2.. Pérdida de entrenamiento: 0.486.. Pérdida de validación: 0.406.. Exactitud de validación: 0.853\n", 224 | "Epoch: 2/2.. Pérdida de entrenamiento: 0.479.. Pérdida de validación: 0.406.. Exactitud de validación: 0.851\n", 225 | "Epoch: 2/2.. Pérdida de entrenamiento: 0.460.. Pérdida de validación: 0.406.. Exactitud de validación: 0.855\n", 226 | "Epoch: 2/2.. Pérdida de entrenamiento: 0.490.. Pérdida de validación: 0.412.. Exactitud de validación: 0.852\n", 227 | "Epoch: 2/2.. Pérdida de entrenamiento: 0.445.. Pérdida de validación: 0.419.. Exactitud de validación: 0.849\n", 228 | "Epoch: 2/2.. Pérdida de entrenamiento: 0.444.. Pérdida de validación: 0.405.. Exactitud de validación: 0.856\n", 229 | "Epoch: 2/2.. Pérdida de entrenamiento: 0.428.. Pérdida de validación: 0.403.. Exactitud de validación: 0.852\n", 230 | "Epoch: 2/2.. Pérdida de entrenamiento: 0.464.. Pérdida de validación: 0.407.. Exactitud de validación: 0.848\n", 231 | "Epoch: 2/2.. Pérdida de entrenamiento: 0.428.. Pérdida de validación: 0.426.. Exactitud de validación: 0.841\n", 232 | "Epoch: 2/2.. Pérdida de entrenamiento: 0.435.. Pérdida de validación: 0.411.. Exactitud de validación: 0.850\n", 233 | "Epoch: 2/2.. Pérdida de entrenamiento: 0.438.. Pérdida de validación: 0.393.. Exactitud de validación: 0.855\n", 234 | "Epoch: 2/2.. Pérdida de entrenamiento: 0.433.. Pérdida de validación: 0.397.. Exactitud de validación: 0.859\n", 235 | "Epoch: 2/2.. Pérdida de entrenamiento: 0.424.. Pérdida de validación: 0.423.. Exactitud de validación: 0.845\n", 236 | "Epoch: 2/2.. Pérdida de entrenamiento: 0.432.. Pérdida de validación: 0.400.. Exactitud de validación: 0.854\n", 237 | "Epoch: 2/2.. Pérdida de entrenamiento: 0.423.. Pérdida de validación: 0.406.. Exactitud de validación: 0.853\n", 238 | "Epoch: 2/2.. Pérdida de entrenamiento: 0.458.. Pérdida de validación: 0.420.. Exactitud de validación: 0.838\n", 239 | "Epoch: 2/2.. Pérdida de entrenamiento: 0.453.. Pérdida de validación: 0.411.. Exactitud de validación: 0.851\n", 240 | "Epoch: 2/2.. Pérdida de entrenamiento: 0.462.. Pérdida de validación: 0.400.. Exactitud de validación: 0.850\n", 241 | "Epoch: 2/2.. Pérdida de entrenamiento: 0.401.. Pérdida de validación: 0.403.. Exactitud de validación: 0.852\n", 242 | "Epoch: 2/2.. Pérdida de entrenamiento: 0.435.. Pérdida de validación: 0.401.. Exactitud de validación: 0.855\n" 243 | ] 244 | } 245 | ], 246 | "source": [ 247 | "#hiperparámetro\n", 248 | "epochs = 2\n", 249 | "steps = 0\n", 250 | "running_loss = 0\n", 251 | "print_every = 40\n", 252 | "for e in range(epochs):\n", 253 | " # Cambiamos a modo entrenamiento\n", 254 | " model.train()\n", 255 | " for images, labels in trainloader:\n", 256 | " steps += 1\n", 257 | " \n", 258 | " # Aplanar imágenes a un vector de 784 elementos\n", 259 | " images.resize_(images.size()[0], 784)\n", 260 | " \n", 261 | " optimizer.zero_grad()\n", 262 | " \n", 263 | " output = model.forward(images)\n", 264 | " loss = criterion(output, labels)\n", 265 | " # Backprogamation\n", 266 | " loss.backward()\n", 267 | " # Optimización\n", 268 | " optimizer.step()\n", 269 | " \n", 270 | " running_loss += loss.item()\n", 271 | " \n", 272 | " if steps % print_every == 0:\n", 273 | " # Cambiamos a modo de evaluación\n", 274 | " model.eval()\n", 275 | " \n", 276 | " # Apagamos los gradientes, reduce memoria y cálculos\n", 277 | " with torch.no_grad():\n", 278 | " test_loss, accuracy = validation(model, validationloader, criterion)\n", 279 | " \n", 280 | " print(\"Epoch: {}/{}.. \".format(e+1, epochs),\n", 281 | " \"Pérdida de entrenamiento: {:.3f}.. \".format(running_loss/print_every),\n", 282 | " \"Pérdida de validación: {:.3f}.. \".format(test_loss/len(validationloader)),\n", 283 | " \"Exactitud de validación: {:.3f}\".format(accuracy/len(validationloader)))\n", 284 | " \n", 285 | " running_loss = 0\n", 286 | " \n", 287 | " # Make sure training is back on\n", 288 | " model.train()" 289 | ] 290 | }, 291 | { 292 | "cell_type": "markdown", 293 | "metadata": {}, 294 | "source": [ 295 | "## Inferencia\n", 296 | "\n", 297 | "Ahora que ya hemos entrenado la red vamos a probar cual es su rendimiento en la clasificación de las prendas de vestir. " 298 | ] 299 | }, 300 | { 301 | "cell_type": "code", 302 | "execution_count": 8, 303 | "metadata": {}, 304 | "outputs": [ 305 | { 306 | "data": { 307 | "image/png": "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", 308 | "text/plain": [ 309 | "
" 310 | ] 311 | }, 312 | "metadata": { 313 | "image/png": { 314 | "height": 216, 315 | "width": 424 316 | }, 317 | "needs_background": "light" 318 | }, 319 | "output_type": "display_data" 320 | } 321 | ], 322 | "source": [ 323 | "# Probemos la red!\n", 324 | "\n", 325 | "model.eval()\n", 326 | "\n", 327 | "dataiter = iter(validationloader)\n", 328 | "images, labels = next(dataiter)\n", 329 | "img = images[0]\n", 330 | "# Aplanamos la imagenes\n", 331 | "img = img.view(1, 784)\n", 332 | "\n", 333 | "# Estimamos para cada imagen la probabilidad de pertenencia a una clase (softmax)\n", 334 | "with torch.no_grad():\n", 335 | " output = model.forward(img)\n", 336 | "\n", 337 | "ps = torch.exp(output)\n", 338 | "\n", 339 | "# Graficamos\n", 340 | "helper.view_classify(img.view(1, 28, 28), ps, version='Fashion')" 341 | ] 342 | }, 343 | { 344 | "cell_type": "code", 345 | "execution_count": null, 346 | "metadata": {}, 347 | "outputs": [], 348 | "source": [] 349 | } 350 | ], 351 | "metadata": { 352 | "kernelspec": { 353 | "display_name": "practicas_pt", 354 | "language": "python", 355 | "name": "python3" 356 | }, 357 | "language_info": { 358 | "codemirror_mode": { 359 | "name": "ipython", 360 | "version": 3 361 | }, 362 | "file_extension": ".py", 363 | "mimetype": "text/x-python", 364 | "name": "python", 365 | "nbconvert_exporter": "python", 366 | "pygments_lexer": "ipython3", 367 | "version": "3.10.4" 368 | }, 369 | "vscode": { 370 | "interpreter": { 371 | "hash": "e22d029f5570ef7df543599926afc42bb090457ba5a887f8aae20fd6018d0da0" 372 | } 373 | }, 374 | "widgets": { 375 | "state": {}, 376 | "version": "1.1.2" 377 | } 378 | }, 379 | "nbformat": 4, 380 | "nbformat_minor": 2 381 | } 382 | -------------------------------------------------------------------------------- /06_doe_una_variable.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Diseño de experimento\n", 8 | "\n", 9 | "El diseño de experimentos en el entrenamiento de redes neuronales juega un papel crucial en la optimización del rendimiento del modelo. Este enfoque sistemático permite explorar y ajustar los hiperparámetros (como la tasa de aprendizaje, el número de capas y neuronas, y el tamaño del lote) de manera eficiente y efectiva. Al planificar y estructurar los experimentos, se pueden identificar las combinaciones óptimas de hiperparámetros que mejoran la precisión y la generalización del modelo, mientras se minimiza el tiempo y los recursos computacionales necesarios. Además, un buen diseño de experimentos reduce el impacto ecológico del entrenamiento de redes neuronales al disminuir el número de ejecuciones necesarias, optimizando así el consumo de energía y hardware.\n", 10 | "\n", 11 | "## Una variable a la vez\n", 12 | "\n", 13 | "El enfoque de diseño de experimentos de una variable a la vez (One Variable At a Time, OVAT) es una metodología simple en la que se varía un solo factor o variable experimental mientras se mantienen constantes todos los demás factores. Este enfoque permite observar cómo cambios en esa única variable afectan el resultado del experimento. \n", 14 | "\n", 15 | "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/irvingvasquez/practicas_pytorch/blob/master/06_doe_una_variable.ipynb)\n", 16 | "\n", 17 | "Si ejecutas en COLAB debes copiar los archivos extra de este repositorio.\n", 18 | "\n", 19 | "@juan1rving\n" 20 | ] 21 | }, 22 | { 23 | "cell_type": "code", 24 | "execution_count": 2, 25 | "metadata": {}, 26 | "outputs": [], 27 | "source": [ 28 | "# Cargamos paquetes necesarios\n", 29 | "\n", 30 | "%matplotlib inline\n", 31 | "%config InlineBackend.figure_format = 'retina'\n", 32 | "\n", 33 | "import matplotlib.pyplot as plt\n", 34 | "import numpy as np\n", 35 | "import time\n", 36 | "\n", 37 | "import torch\n", 38 | "from torch import nn\n", 39 | "from torch import optim\n", 40 | "import torch.nn.functional as F\n", 41 | "from torchvision import datasets, transforms\n", 42 | "\n", 43 | "#helper was developed by Udacity under MIT license\n", 44 | "import helper" 45 | ] 46 | }, 47 | { 48 | "cell_type": "markdown", 49 | "metadata": {}, 50 | "source": [ 51 | "### 1. Definición de variables y rango de valores:\n", 52 | "\n", 53 | "Identificar los factores o variables experimentales clave que se desean estudiar. En el contexto de redes neuronales, estas variables pueden incluir la tasa de aprendizaje, el número de capas, el número de neuronas por capa, el tamaño del lote, entre otros.\n", 54 | "\n", 55 | "Variables que tomaremos en cuenta:\n", 56 | "\n", 57 | "- Tasa de aprendizaje (eta)\n", 58 | "- Número de épocas (n_epocas)\n", 59 | "- Tamaño de lote (batch_size)\n", 60 | "\n", 61 | "Una vez definidas las variables independientes definimos un rango de valores posibles para cada variable.\n", 62 | "\n", 63 | "> TODO: Define un rango de valores posibles para cada variable. Incluye el valor mínimo y el valor máximo. Se sugiere utilizar una lista de valores obtenida con una separación uniforme. Probar almenos 5 valores por variable.\n", 64 | "\n", 65 | "\n" 66 | ] 67 | }, 68 | { 69 | "cell_type": "code", 70 | "execution_count": null, 71 | "metadata": {}, 72 | "outputs": [], 73 | "source": [ 74 | "#TODO: Define rangos para los hiperparámetros" 75 | ] 76 | }, 77 | { 78 | "cell_type": "markdown", 79 | "metadata": {}, 80 | "source": [ 81 | "### 2. Configuración inicial:\n", 82 | "\n", 83 | "Establecer una configuración inicial para la red neuronal con valores predeterminados para todos los hiperparámetros. \n", 84 | "\n", 85 | "> TODO: Define la configuración inicial. Se sugiere usar un diccionario para contener dicha configuración.\n" 86 | ] 87 | }, 88 | { 89 | "cell_type": "code", 90 | "execution_count": 12, 91 | "metadata": {}, 92 | "outputs": [], 93 | "source": [ 94 | "\n", 95 | "configuracion = {'eta': 0, 'epochs': 0, 'batch_size': 0}" 96 | ] 97 | }, 98 | { 99 | "cell_type": "markdown", 100 | "metadata": {}, 101 | "source": [ 102 | "### 3. Variación de una variable a la vez:\n", 103 | "\n", 104 | "- Seleccionar la primera variable a estudiar (por ejemplo, la tasa de aprendizaje).\n", 105 | "- Realizar una serie de experimentos donde se varía únicamente la tasa de aprendizaje, mientras se mantienen constantes todos los demás hiperparámetros.\n", 106 | "- Registrar el rendimiento del modelo para cada valor de la tasa de aprendizaje.\n", 107 | "\n", 108 | "> TODO: Modifica el código para que pueda aceptar la configuración deseada" 109 | ] 110 | }, 111 | { 112 | "cell_type": "code", 113 | "execution_count": 3, 114 | "metadata": {}, 115 | "outputs": [], 116 | "source": [ 117 | "# Definimos una transformación de los datos\n", 118 | "transform = transforms.Compose([transforms.ToTensor(),\n", 119 | " transforms.Normalize((0.5), (0.5))])\n", 120 | "# Descargamos el conjunto de entrenamiento y cargamos mediante un dataLoader\n", 121 | "trainset = datasets.FashionMNIST('F_MNIST_data/', download=True, train=True, transform=transform)\n", 122 | "trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)\n", 123 | "\n", 124 | "# Descargamos el conjunto de validación\n", 125 | "validationset = datasets.FashionMNIST('F_MNIST_data/', download=True, train=False, transform=transform)\n", 126 | "validationloader = torch.utils.data.DataLoader(validationset, batch_size=64, shuffle=True)" 127 | ] 128 | }, 129 | { 130 | "cell_type": "code", 131 | "execution_count": 5, 132 | "metadata": {}, 133 | "outputs": [], 134 | "source": [ 135 | "class RedNeuronal(nn.Module):\n", 136 | " def __init__(self, input_size, output_size, hidden_layers, drop_p = 0.5):\n", 137 | " '''\n", 138 | " Construye una red de tamaño arbitrario.\n", 139 | " \n", 140 | " Parámetros:\n", 141 | " input_size: cantidad de elementos en la entrada\n", 142 | " output_size: cantidada de elementos en la salida \n", 143 | " hidden_layers: cantidad de elementos por cada capa oculta\n", 144 | " drop_p: probabilidad de \"tirar\" (drop) una neurona [0,1] \n", 145 | " '''\n", 146 | " # llamamos al constructor de la superclase\n", 147 | " super().__init__()\n", 148 | " \n", 149 | " # Agregamos la primera capa\n", 150 | " self.hidden_layers = nn.ModuleList([nn.Linear(input_size, hidden_layers[0])])\n", 151 | " \n", 152 | " # agregamos cada una de las capas, zip empareja el número de entradas con las salidas\n", 153 | " layer_sizes = zip(hidden_layers[:-1], hidden_layers[1:])\n", 154 | " self.hidden_layers.extend([nn.Linear(h1, h2) for h1, h2 in layer_sizes])\n", 155 | " \n", 156 | " # agregamos la capa de salida final de la red\n", 157 | " self.output = nn.Linear(hidden_layers[-1], output_size)\n", 158 | " \n", 159 | " # Incluimos drop-out en la red\n", 160 | " self.dropout = nn.Dropout(p=drop_p)\n", 161 | " \n", 162 | " def forward(self, x):\n", 163 | " ''' Pase hacia adelante en la red, el regreso son las probabilidades en el dominio log '''\n", 164 | " \n", 165 | " # Hacemos un pase frontal en cada una de las capas ocultas, \n", 166 | " # La funció de activación es un RELU combinado con dropout\n", 167 | " for linear in self.hidden_layers:\n", 168 | " x = F.relu(linear(x))\n", 169 | " x = self.dropout(x)\n", 170 | " \n", 171 | " x = self.output(x)\n", 172 | " \n", 173 | " return F.log_softmax(x, dim=1)" 174 | ] 175 | }, 176 | { 177 | "cell_type": "code", 178 | "execution_count": 7, 179 | "metadata": {}, 180 | "outputs": [], 181 | "source": [ 182 | "# Create the network, define the criterion and optimizer\n", 183 | "model = RedNeuronal(784, 10, [516, 256], drop_p=0.5)\n", 184 | "criterion = nn.NLLLoss()\n", 185 | "optimizer = optim.Adam(model.parameters(), lr=0.001)" 186 | ] 187 | }, 188 | { 189 | "cell_type": "code", 190 | "execution_count": 8, 191 | "metadata": {}, 192 | "outputs": [], 193 | "source": [ 194 | "# Implementamos una función de evaluación\n", 195 | "def validation(model, validationloader, criterion):\n", 196 | " test_loss = 0\n", 197 | " accuracy = 0\n", 198 | " for images, labels in validationloader:\n", 199 | "\n", 200 | " images.resize_(images.shape[0], 784)\n", 201 | "\n", 202 | " output = model.forward(images)\n", 203 | " test_loss += criterion(output, labels).item()\n", 204 | "\n", 205 | " ps = torch.exp(output)\n", 206 | " equality = (labels.data == ps.max(dim=1)[1])\n", 207 | " accuracy += equality.type(torch.FloatTensor).mean()\n", 208 | " \n", 209 | " return test_loss, accuracy" 210 | ] 211 | }, 212 | { 213 | "cell_type": "code", 214 | "execution_count": null, 215 | "metadata": {}, 216 | "outputs": [], 217 | "source": [ 218 | "#hiperparámetro\n", 219 | "epochs = 2\n", 220 | "steps = 0\n", 221 | "running_loss = 0\n", 222 | "print_every = 40\n", 223 | "for e in range(epochs):\n", 224 | " # Cambiamos a modo entrenamiento\n", 225 | " model.train()\n", 226 | " for images, labels in trainloader:\n", 227 | " steps += 1\n", 228 | " \n", 229 | " # Aplanar imágenes a un vector de 784 elementos\n", 230 | " images.resize_(images.size()[0], 784)\n", 231 | " \n", 232 | " optimizer.zero_grad()\n", 233 | " \n", 234 | " output = model.forward(images)\n", 235 | " loss = criterion(output, labels)\n", 236 | " # Backprogamation\n", 237 | " loss.backward()\n", 238 | " # Optimización\n", 239 | " optimizer.step()\n", 240 | " \n", 241 | " running_loss += loss.item()\n", 242 | " \n", 243 | " if steps % print_every == 0:\n", 244 | " # Cambiamos a modo de evaluación\n", 245 | " model.eval()\n", 246 | " \n", 247 | " # Apagamos los gradientes, reduce memoria y cálculos\n", 248 | " with torch.no_grad():\n", 249 | " test_loss, accuracy = validation(model, validationloader, criterion)\n", 250 | " \n", 251 | " print(\"Epoch: {}/{}.. \".format(e+1, epochs),\n", 252 | " \"Pérdida de entrenamiento: {:.3f}.. \".format(running_loss/print_every),\n", 253 | " \"Pérdida de validación: {:.3f}.. \".format(test_loss/len(validationloader)),\n", 254 | " \"Exactitud de validación: {:.3f}\".format(accuracy/len(validationloader)))\n", 255 | " \n", 256 | " running_loss = 0\n", 257 | " \n", 258 | " # Make sure training is back on\n", 259 | " model.train()" 260 | ] 261 | }, 262 | { 263 | "cell_type": "markdown", 264 | "metadata": {}, 265 | "source": [ 266 | "### 4. Selección del mejor valor:\n", 267 | "\n", 268 | "Analizar los resultados y seleccionar el valor de la tasa de aprendizaje que produce el mejor rendimiento del modelo.\n" 269 | ] 270 | }, 271 | { 272 | "cell_type": "markdown", 273 | "metadata": {}, 274 | "source": [ 275 | "### 5. Repetición para otras variables:\n", 276 | "\n", 277 | "Proceder con la siguiente variable (por ejemplo, el número de capas) y repetir el proceso: variar solo esta variable mientras se mantienen constantes todos los demás hiperparámetros, utilizando el mejor valor encontrado para la tasa de aprendizaje. Continuar este proceso para cada variable en la lista.\n", 278 | "\n", 279 | "> TODO: Escribe una tabla con el resultado de cada experimento. Las columnas deben ser: ID, Configuración, Exactitud obtenida." 280 | ] 281 | }, 282 | { 283 | "cell_type": "markdown", 284 | "metadata": {}, 285 | "source": [ 286 | "## Conclusiones\n", 287 | "\n", 288 | "¿Cual fue el mejor valor encontrado?\n", 289 | "¿Cuantas ejecuciones se realizaron?\n", 290 | "¿Que tiempo tomó realizar todos los experimentos?" 291 | ] 292 | }, 293 | { 294 | "cell_type": "markdown", 295 | "metadata": {}, 296 | "source": [] 297 | } 298 | ], 299 | "metadata": { 300 | "kernelspec": { 301 | "display_name": "pytorch_1_12", 302 | "language": "python", 303 | "name": "python3" 304 | }, 305 | "language_info": { 306 | "codemirror_mode": { 307 | "name": "ipython", 308 | "version": 3 309 | }, 310 | "file_extension": ".py", 311 | "mimetype": "text/x-python", 312 | "name": "python", 313 | "nbconvert_exporter": "python", 314 | "pygments_lexer": "ipython3", 315 | "version": "3.10.4" 316 | } 317 | }, 318 | "nbformat": 4, 319 | "nbformat_minor": 2 320 | } 321 | -------------------------------------------------------------------------------- /07_lectura_de_imagenes.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Lectura de imágenes\n", 8 | "\n", 9 | "En este notebook veremos como introducir imágenes a nuestra red neuronal.\n", 10 | "\n", 11 | "\n", 12 | "### Dataset\n", 13 | "\n", 14 | "Existen multiples formas para hacerlo, pero en este tutorial utilizaremos la función *dataset.ImageFolder* incorporada en el paquete *torchvision*. La forma rápida de leer un conjunto de imágenes es\n", 15 | "\n", 16 | "```python\n", 17 | "dataset = datasets.ImageFolder('directorio/', transform=transformacion)\n", 18 | "```\n", 19 | "\n", 20 | "la función *ImageFolder* espera que dentro del directorio los archivos esté organizados por folderes, donde cada folder contiene las imagenes de una clase en específico.\n", 21 | "\n", 22 | "En este ejemplo usaré una base de datos propia que contiene dos clases, cactus y no_cactus. La base de datos tiene dos carpetas con los nombres *cactus* y *no_cactus*.\n", 23 | "\n", 24 | "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/irvingvasquez/practicas_pytorch/blob/master/07_lectura_de_imagenes.ipynb)\n" 25 | ] 26 | }, 27 | { 28 | "cell_type": "code", 29 | "execution_count": 1, 30 | "metadata": {}, 31 | "outputs": [], 32 | "source": [ 33 | "# cargamos paquetes\n", 34 | "%matplotlib inline\n", 35 | "%config InlineBackend.figure_format = 'retina'\n", 36 | "\n", 37 | "import matplotlib.pyplot as plt\n", 38 | "\n", 39 | "import torch\n", 40 | "from torchvision import datasets, transforms\n", 41 | "\n", 42 | "import helper" 43 | ] 44 | }, 45 | { 46 | "cell_type": "markdown", 47 | "metadata": {}, 48 | "source": [ 49 | "El conjunto de datos que usaremos será el de cactus" 50 | ] 51 | }, 52 | { 53 | "cell_type": "code", 54 | "execution_count": 3, 55 | "metadata": {}, 56 | "outputs": [ 57 | { 58 | "name": "stderr", 59 | "output_type": "stream", 60 | "text": [ 61 | " % Total % Received % Xferd Average Speed Time Time Time Current\n", 62 | " Dload Upload Total Spent Left Speed\n", 63 | "\n", 64 | " 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0\n", 65 | " 14 52.2M 14 7499k 0 0 9631k 0 0:00:05 --:--:-- 0:00:05 9639k\n", 66 | " 65 52.2M 65 34.3M 0 0 19.2M 0 0:00:02 0:00:01 0:00:01 19.2M\n", 67 | "100 52.2M 100 52.2M 0 0 22.8M 0 0:00:02 0:00:02 --:--:-- 22.8M\n" 68 | ] 69 | } 70 | ], 71 | "source": [ 72 | "# para linux\n", 73 | "#!wget https://jivg.org/wp-content/uploads/2024/07/cactus_course_dataset.zip\n", 74 | "\n", 75 | "# para windows\n", 76 | "!curl -o cactus_course_dataset.zip https://jivg.org/wp-content/uploads/2024/07/cactus_course_dataset.zip" 77 | ] 78 | }, 79 | { 80 | "cell_type": "code", 81 | "execution_count": 8, 82 | "metadata": {}, 83 | "outputs": [], 84 | "source": [ 85 | "# Para linux\n", 86 | "#!unzip cactus_course_dataset.zip\n", 87 | "\n", 88 | "# Para windows\n", 89 | "import zipfile\n", 90 | "\n", 91 | "with zipfile.ZipFile(\"cactus_course_dataset.zip\", 'r') as zip_ref:\n", 92 | " zip_ref.extractall(\"cactus_dataset\")" 93 | ] 94 | }, 95 | { 96 | "cell_type": "markdown", 97 | "metadata": {}, 98 | "source": [ 99 | "### Transformaciones\n", 100 | "\n", 101 | "Usualmente las imagenes que leas estarán en un tamaño diferente al que necesitas para la red, asi que es necesario convertirlas a un formato adecuado, además será necesario convertirlas a tensores de pytorch. Todo esto lo podemos hacer con la función *transforms.Compose()*, en la cual podemos apilar las transformaciones necesarias. Por ejemplo,\n", 102 | "\n", 103 | "```python\n", 104 | "transforms = transforms.Compose([transforms.Resize(255),\n", 105 | " transforms.CenterCrop(224),\n", 106 | " transforms.ToTensor()])\n", 107 | "\n", 108 | "```\n" 109 | ] 110 | }, 111 | { 112 | "cell_type": "code", 113 | "execution_count": 9, 114 | "metadata": {}, 115 | "outputs": [], 116 | "source": [ 117 | "# directorio de la carpeta\n", 118 | "directorio = 'cactus_dataset/cactus_course_dataset/'\n", 119 | "\n", 120 | "# aplicaré una serie de transformaciones\n", 121 | "# 1. escalar las imágenes a 32 x 32 pixeles\n", 122 | "# 2. convertir a tensores\n", 123 | "transformaciones = transforms.Compose([transforms.Resize(32),\n", 124 | " transforms.CenterCrop(32),\n", 125 | " transforms.ToTensor()]) \n", 126 | "datos = datasets.ImageFolder(directorio, transformaciones) \n" 127 | ] 128 | }, 129 | { 130 | "cell_type": "markdown", 131 | "metadata": {}, 132 | "source": [ 133 | "### Data Loader\n", 134 | "\n", 135 | "Image folder se encarga de las imágenes, sin embargo todavía es necesario otro objeto, el *DataLoader* quien se encarga de leer los ejemplos por lotes (batches) junto con su correspondiente etiqueta. En el objeto *DataLoader* puedes especificar diversos parametros como el tamaño del lote si los datos son mezclados (*suffled*) despúes de cada época, entre otras opciones. Por ejemplo,\n", 136 | "\n", 137 | "```python\n", 138 | "dataloader = torch.utils.data.DataLoader(dataset, batch_size=32, shuffle=True)\n", 139 | "```\n", 140 | "\n", 141 | "Para iterar sobre los datos podemos hacer:\n", 142 | "\n", 143 | "```python\n", 144 | "# Looping through it, get a batch on each loop \n", 145 | "for images, labels in dataloader:\n", 146 | " pass\n", 147 | "\n", 148 | "# Get one batch\n", 149 | "images, labels = next(iter(dataloader))\n", 150 | "```" 151 | ] 152 | }, 153 | { 154 | "cell_type": "code", 155 | "execution_count": 10, 156 | "metadata": {}, 157 | "outputs": [], 158 | "source": [ 159 | "#crear el objeto dataloader\n", 160 | "cargador = torch.utils.data.DataLoader(datos, batch_size=32, shuffle=True)\n" 161 | ] 162 | }, 163 | { 164 | "cell_type": "code", 165 | "execution_count": null, 166 | "metadata": {}, 167 | "outputs": [ 168 | { 169 | "data": { 170 | "text/plain": [ 171 | "" 172 | ] 173 | }, 174 | "execution_count": 14, 175 | "metadata": {}, 176 | "output_type": "execute_result" 177 | }, 178 | { 179 | "data": { 180 | "image/png": "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", 181 | "text/plain": [ 182 | "
" 183 | ] 184 | }, 185 | "metadata": { 186 | "image/png": { 187 | "height": 389, 188 | "width": 389 189 | } 190 | }, 191 | "output_type": "display_data" 192 | } 193 | ], 194 | "source": [ 195 | "# Run this to test your data loader\n", 196 | "images, labels = next(iter(cargador))\n", 197 | "helper.imshow(images[0], normalize=False)" 198 | ] 199 | }, 200 | { 201 | "cell_type": "code", 202 | "execution_count": null, 203 | "metadata": {}, 204 | "outputs": [], 205 | "source": [ 206 | "data_dir = 'data'\n", 207 | "\n", 208 | "# TODO: Define transforms for the training data and testing data\n", 209 | "train_transforms = \n", 210 | "\n", 211 | "test_transforms = \n", 212 | "\n", 213 | "\n", 214 | "# Pass transforms in here, then run the next cell to see how the transforms look\n", 215 | "train_data = datasets.ImageFolder(data_dir + '/train', transform=train_transforms)\n", 216 | "test_data = datasets.ImageFolder(data_dir + '/test', transform=test_transforms)\n", 217 | "\n", 218 | "trainloader = torch.utils.data.DataLoader(train_data, batch_size=32)\n", 219 | "testloader = torch.utils.data.DataLoader(test_data, batch_size=32)" 220 | ] 221 | }, 222 | { 223 | "cell_type": "code", 224 | "execution_count": null, 225 | "metadata": {}, 226 | "outputs": [], 227 | "source": [ 228 | "# change this to the trainloader or testloader \n", 229 | "data_iter = iter(testloader)\n", 230 | "\n", 231 | "images, labels = next(data_iter)\n", 232 | "fig, axes = plt.subplots(figsize=(10,4), ncols=4)\n", 233 | "for ii in range(4):\n", 234 | " ax = axes[ii]\n", 235 | " helper.imshow(images[ii], ax=ax)" 236 | ] 237 | } 238 | ], 239 | "metadata": { 240 | "kernelspec": { 241 | "display_name": "pytorch", 242 | "language": "python", 243 | "name": "python3" 244 | }, 245 | "language_info": { 246 | "codemirror_mode": { 247 | "name": "ipython", 248 | "version": 3 249 | }, 250 | "file_extension": ".py", 251 | "mimetype": "text/x-python", 252 | "name": "python", 253 | "nbconvert_exporter": "python", 254 | "pygments_lexer": "ipython3", 255 | "version": "3.13.2" 256 | } 257 | }, 258 | "nbformat": 4, 259 | "nbformat_minor": 2 260 | } 261 | -------------------------------------------------------------------------------- /08_respaldo_de_modelos.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Respaldo de Modelos y Datos\n", 8 | "\n", 9 | "Como ya te puedes imaginar es impráctico entrenar una red cada que la vas a utilizar, sobre todo con conjuntos de datos grandes. En este notebook veremos como guardar modelos y datos. Ya sea por que vas a hacer predicciones en otro conjunto de datos o por que vas a dividir el entrenamiento en diversas etapas." 10 | ] 11 | }, 12 | { 13 | "cell_type": "code", 14 | "execution_count": null, 15 | "metadata": {}, 16 | "outputs": [], 17 | "source": [ 18 | "%matplotlib inline\n", 19 | "%config InlineBackend.figure_format = 'retina'\n", 20 | "\n", 21 | "import matplotlib.pyplot as plt\n", 22 | "\n", 23 | "import torch\n", 24 | "from torch import nn\n", 25 | "from torch import optim\n", 26 | "import torch.nn.functional as F\n", 27 | "from torchvision import datasets, transforms\n", 28 | "\n", 29 | "import helper\n", 30 | "\n", 31 | "# fc_model es propiedad de Udacity bajo licencia MIT\n", 32 | "import fc_model" 33 | ] 34 | }, 35 | { 36 | "cell_type": "code", 37 | "execution_count": null, 38 | "metadata": {}, 39 | "outputs": [], 40 | "source": [ 41 | "# Definimos una transformación de los datos\n", 42 | "transform = transforms.Compose([transforms.ToTensor(),\n", 43 | " transforms.Normalize((0.5), (0.5))])\n", 44 | "# Descargamos el conjunto de entrenamiento y cargamos mediante un dataLoader\n", 45 | "trainset = datasets.FashionMNIST('F_MNIST_data/', download=True, train=True, transform=transform)\n", 46 | "trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)\n", 47 | "\n", 48 | "# Descargamos el conjunto de validación\n", 49 | "testset = datasets.FashionMNIST('F_MNIST_data/', download=True, train=False, transform=transform)\n", 50 | "testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=True)" 51 | ] 52 | }, 53 | { 54 | "cell_type": "markdown", 55 | "metadata": {}, 56 | "source": [ 57 | "Nuevamente estamos utilizando el fashion MNIST, " 58 | ] 59 | }, 60 | { 61 | "cell_type": "code", 62 | "execution_count": null, 63 | "metadata": {}, 64 | "outputs": [], 65 | "source": [ 66 | "# visualizar los datos\n", 67 | "image, label = next(iter(trainloader))\n", 68 | "helper.imshow(image[0,:]);" 69 | ] 70 | }, 71 | { 72 | "cell_type": "markdown", 73 | "metadata": {}, 74 | "source": [ 75 | "# Entrenamiento\n", 76 | "\n", 77 | "En esta ocasión el modelo de la red está definido en el archivo *fc_model*. Esto nos permite reutilizarlo en donde queramos. A continuación cargaremos el modelo y lo entrenaremos." 78 | ] 79 | }, 80 | { 81 | "cell_type": "code", 82 | "execution_count": null, 83 | "metadata": {}, 84 | "outputs": [], 85 | "source": [ 86 | "# Cargar el modelo de red, definir el criterio a utilizar y el optimizador\n", 87 | "model = fc_model.Network(784, 10, [512, 256, 128])\n", 88 | "criterion = nn.NLLLoss()\n", 89 | "optimizer = optim.Adam(model.parameters(), lr=0.001)" 90 | ] 91 | }, 92 | { 93 | "cell_type": "code", 94 | "execution_count": null, 95 | "metadata": {}, 96 | "outputs": [], 97 | "source": [ 98 | "fc_model.train(model, trainloader, testloader, criterion, optimizer, epochs=2)" 99 | ] 100 | }, 101 | { 102 | "cell_type": "markdown", 103 | "metadata": {}, 104 | "source": [ 105 | "## Respaldo y carga\n", 106 | "\n", 107 | "Los parámetros, pesos y sesgos, de redes creadas en PyTorch se almacenan en un objeto state_dict. Veamos que contiene el objeto para la red que cargamos.\n" 108 | ] 109 | }, 110 | { 111 | "cell_type": "code", 112 | "execution_count": null, 113 | "metadata": {}, 114 | "outputs": [], 115 | "source": [ 116 | "print(\"Modelo: \\n\\n\", model, '\\n')\n", 117 | "print(\"State dict keys: \\n\\n\", model.state_dict().keys())" 118 | ] 119 | }, 120 | { 121 | "cell_type": "markdown", 122 | "metadata": {}, 123 | "source": [ 124 | "La forma más sencilla es simplemente guardar el objeto static_dict. En este caso, podemos guardarlo a un archivo \"respaldo.pth\" usando *torch.save*\n" 125 | ] 126 | }, 127 | { 128 | "cell_type": "code", 129 | "execution_count": null, 130 | "metadata": {}, 131 | "outputs": [], 132 | "source": [ 133 | "torch.save(model.state_dict(), 'respaldo.pth')" 134 | ] 135 | }, 136 | { 137 | "cell_type": "markdown", 138 | "metadata": {}, 139 | "source": [ 140 | "Y obviamente la operación contraria es cargarlo." 141 | ] 142 | }, 143 | { 144 | "cell_type": "code", 145 | "execution_count": null, 146 | "metadata": {}, 147 | "outputs": [], 148 | "source": [ 149 | "state_dict = torch.load('respaldo.pth')\n", 150 | "print(state_dict.keys())" 151 | ] 152 | }, 153 | { 154 | "cell_type": "markdown", 155 | "metadata": {}, 156 | "source": [ 157 | "Para insertar los parámetros en la red es necesario hacer `model.load_state_dict(state_dict)`." 158 | ] 159 | }, 160 | { 161 | "cell_type": "code", 162 | "execution_count": null, 163 | "metadata": {}, 164 | "outputs": [], 165 | "source": [ 166 | "model.load_state_dict(state_dict)" 167 | ] 168 | }, 169 | { 170 | "cell_type": "markdown", 171 | "metadata": {}, 172 | "source": [ 173 | "## Diferente arquitectura\n", 174 | "\n", 175 | "En el ejemplo anterior parace que todo funciona bien, sin embargo, el ejemplo está limitado a casos cuando la arquitectura en la que se cargan los parámetros es exactamente igual a la arquitectura entrenada. Para casos más generales es necesario también almacenar información de la arquitectura. Esto último se realiza con un diccionario." 176 | ] 177 | }, 178 | { 179 | "cell_type": "code", 180 | "execution_count": null, 181 | "metadata": {}, 182 | "outputs": [], 183 | "source": [ 184 | "checkpoint = {'input_size': 784,\n", 185 | " 'output_size': 10,\n", 186 | " 'hidden_layers': [each.out_features for each in model.hidden_layers],\n", 187 | " 'state_dict': model.state_dict()}\n", 188 | "\n", 189 | "torch.save(checkpoint, 'checkpoint.pth')" 190 | ] 191 | }, 192 | { 193 | "cell_type": "code", 194 | "execution_count": null, 195 | "metadata": {}, 196 | "outputs": [], 197 | "source": [ 198 | "# función para cargar el modelo\n", 199 | "def load_checkpoint(filepath):\n", 200 | " checkpoint = torch.load(filepath)\n", 201 | " model = fc_model.Network(checkpoint['input_size'],\n", 202 | " checkpoint['output_size'],\n", 203 | " checkpoint['hidden_layers'])\n", 204 | " model.load_state_dict(checkpoint['state_dict'])\n", 205 | " \n", 206 | " return model" 207 | ] 208 | }, 209 | { 210 | "cell_type": "code", 211 | "execution_count": null, 212 | "metadata": {}, 213 | "outputs": [], 214 | "source": [ 215 | "# cargar parámetros a la red\n", 216 | "model = load_checkpoint('checkpoint.pth')\n", 217 | "print(model)" 218 | ] 219 | }, 220 | { 221 | "cell_type": "code", 222 | "execution_count": null, 223 | "metadata": {}, 224 | "outputs": [], 225 | "source": [] 226 | } 227 | ], 228 | "metadata": { 229 | "kernelspec": { 230 | "display_name": "practicas_pt", 231 | "language": "python", 232 | "name": "python3" 233 | }, 234 | "language_info": { 235 | "codemirror_mode": { 236 | "name": "ipython", 237 | "version": 3 238 | }, 239 | "file_extension": ".py", 240 | "mimetype": "text/x-python", 241 | "name": "python", 242 | "nbconvert_exporter": "python", 243 | "pygments_lexer": "ipython3", 244 | "version": "3.7.15" 245 | }, 246 | "vscode": { 247 | "interpreter": { 248 | "hash": "e22d029f5570ef7df543599926afc42bb090457ba5a887f8aae20fd6018d0da0" 249 | } 250 | } 251 | }, 252 | "nbformat": 4, 253 | "nbformat_minor": 2 254 | } 255 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. By contrast, 15 | the GNU General Public License is intended to guarantee your freedom to 16 | share and change all versions of a program--to make sure it remains free 17 | software for all its users. We, the Free Software Foundation, use the 18 | GNU General Public License for most of our software; it applies also to 19 | any other work released this way by its authors. You can apply it to 20 | your programs, too. 21 | 22 | When we speak of free software, we are referring to freedom, not 23 | price. Our General Public Licenses are designed to make sure that you 24 | have the freedom to distribute copies of free software (and charge for 25 | them if you wish), that you receive source code or can get it if you 26 | want it, that you can change the software or use pieces of it in new 27 | free programs, and that you know you can do these things. 28 | 29 | To protect your rights, we need to prevent others from denying you 30 | these rights or asking you to surrender the rights. Therefore, you have 31 | certain responsibilities if you distribute copies of the software, or if 32 | you modify it: responsibilities to respect the freedom of others. 33 | 34 | For example, if you distribute copies of such a program, whether 35 | gratis or for a fee, you must pass on to the recipients the same 36 | freedoms that you received. You must make sure that they, too, receive 37 | or can get the source code. And you must show them these terms so they 38 | know their rights. 39 | 40 | Developers that use the GNU GPL protect your rights with two steps: 41 | (1) assert copyright on the software, and (2) offer you this License 42 | giving you legal permission to copy, distribute and/or modify it. 43 | 44 | For the developers' and authors' protection, the GPL clearly explains 45 | that there is no warranty for this free software. For both users' and 46 | authors' sake, the GPL requires that modified versions be marked as 47 | changed, so that their problems will not be attributed erroneously to 48 | authors of previous versions. 49 | 50 | Some devices are designed to deny users access to install or run 51 | modified versions of the software inside them, although the manufacturer 52 | can do so. This is fundamentally incompatible with the aim of 53 | protecting users' freedom to change the software. The systematic 54 | pattern of such abuse occurs in the area of products for individuals to 55 | use, which is precisely where it is most unacceptable. Therefore, we 56 | have designed this version of the GPL to prohibit the practice for those 57 | products. If such problems arise substantially in other domains, we 58 | stand ready to extend this provision to those domains in future versions 59 | of the GPL, as needed to protect the freedom of users. 60 | 61 | Finally, every program is threatened constantly by software patents. 62 | States should not allow patents to restrict development and use of 63 | software on general-purpose computers, but in those that do, we wish to 64 | avoid the special danger that patents applied to a free program could 65 | make it effectively proprietary. To prevent this, the GPL assures that 66 | patents cannot be used to render the program non-free. 67 | 68 | The precise terms and conditions for copying, distribution and 69 | modification follow. 70 | 71 | TERMS AND CONDITIONS 72 | 73 | 0. Definitions. 74 | 75 | "This License" refers to version 3 of the GNU General Public License. 76 | 77 | "Copyright" also means copyright-like laws that apply to other kinds of 78 | works, such as semiconductor masks. 79 | 80 | "The Program" refers to any copyrightable work licensed under this 81 | License. Each licensee is addressed as "you". "Licensees" and 82 | "recipients" may be individuals or organizations. 83 | 84 | To "modify" a work means to copy from or adapt all or part of the work 85 | in a fashion requiring copyright permission, other than the making of an 86 | exact copy. The resulting work is called a "modified version" of the 87 | earlier work or a work "based on" the earlier work. 88 | 89 | A "covered work" means either the unmodified Program or a work based 90 | on the Program. 91 | 92 | To "propagate" a work means to do anything with it that, without 93 | permission, would make you directly or secondarily liable for 94 | infringement under applicable copyright law, except executing it on a 95 | computer or modifying a private copy. Propagation includes copying, 96 | distribution (with or without modification), making available to the 97 | public, and in some countries other activities as well. 98 | 99 | To "convey" a work means any kind of propagation that enables other 100 | parties to make or receive copies. Mere interaction with a user through 101 | a computer network, with no transfer of a copy, is not conveying. 102 | 103 | An interactive user interface displays "Appropriate Legal Notices" 104 | to the extent that it includes a convenient and prominently visible 105 | feature that (1) displays an appropriate copyright notice, and (2) 106 | tells the user that there is no warranty for the work (except to the 107 | extent that warranties are provided), that licensees may convey the 108 | work under this License, and how to view a copy of this License. If 109 | the interface presents a list of user commands or options, such as a 110 | menu, a prominent item in the list meets this criterion. 111 | 112 | 1. Source Code. 113 | 114 | The "source code" for a work means the preferred form of the work 115 | for making modifications to it. "Object code" means any non-source 116 | form of a work. 117 | 118 | A "Standard Interface" means an interface that either is an official 119 | standard defined by a recognized standards body, or, in the case of 120 | interfaces specified for a particular programming language, one that 121 | is widely used among developers working in that language. 122 | 123 | The "System Libraries" of an executable work include anything, other 124 | than the work as a whole, that (a) is included in the normal form of 125 | packaging a Major Component, but which is not part of that Major 126 | Component, and (b) serves only to enable use of the work with that 127 | Major Component, or to implement a Standard Interface for which an 128 | implementation is available to the public in source code form. A 129 | "Major Component", in this context, means a major essential component 130 | (kernel, window system, and so on) of the specific operating system 131 | (if any) on which the executable work runs, or a compiler used to 132 | produce the work, or an object code interpreter used to run it. 133 | 134 | The "Corresponding Source" for a work in object code form means all 135 | the source code needed to generate, install, and (for an executable 136 | work) run the object code and to modify the work, including scripts to 137 | control those activities. However, it does not include the work's 138 | System Libraries, or general-purpose tools or generally available free 139 | programs which are used unmodified in performing those activities but 140 | which are not part of the work. For example, Corresponding Source 141 | includes interface definition files associated with source files for 142 | the work, and the source code for shared libraries and dynamically 143 | linked subprograms that the work is specifically designed to require, 144 | such as by intimate data communication or control flow between those 145 | subprograms and other parts of the work. 146 | 147 | The Corresponding Source need not include anything that users 148 | can regenerate automatically from other parts of the Corresponding 149 | Source. 150 | 151 | The Corresponding Source for a work in source code form is that 152 | same work. 153 | 154 | 2. Basic Permissions. 155 | 156 | All rights granted under this License are granted for the term of 157 | copyright on the Program, and are irrevocable provided the stated 158 | conditions are met. This License explicitly affirms your unlimited 159 | permission to run the unmodified Program. The output from running a 160 | covered work is covered by this License only if the output, given its 161 | content, constitutes a covered work. This License acknowledges your 162 | rights of fair use or other equivalent, as provided by copyright law. 163 | 164 | You may make, run and propagate covered works that you do not 165 | convey, without conditions so long as your license otherwise remains 166 | in force. You may convey covered works to others for the sole purpose 167 | of having them make modifications exclusively for you, or provide you 168 | with facilities for running those works, provided that you comply with 169 | the terms of this License in conveying all material for which you do 170 | not control copyright. Those thus making or running the covered works 171 | for you must do so exclusively on your behalf, under your direction 172 | and control, on terms that prohibit them from making any copies of 173 | your copyrighted material outside their relationship with you. 174 | 175 | Conveying under any other circumstances is permitted solely under 176 | the conditions stated below. Sublicensing is not allowed; section 10 177 | makes it unnecessary. 178 | 179 | 3. Protecting Users' Legal Rights From Anti-Circumvention Law. 180 | 181 | No covered work shall be deemed part of an effective technological 182 | measure under any applicable law fulfilling obligations under article 183 | 11 of the WIPO copyright treaty adopted on 20 December 1996, or 184 | similar laws prohibiting or restricting circumvention of such 185 | measures. 186 | 187 | When you convey a covered work, you waive any legal power to forbid 188 | circumvention of technological measures to the extent such circumvention 189 | is effected by exercising rights under this License with respect to 190 | the covered work, and you disclaim any intention to limit operation or 191 | modification of the work as a means of enforcing, against the work's 192 | users, your or third parties' legal rights to forbid circumvention of 193 | technological measures. 194 | 195 | 4. Conveying Verbatim Copies. 196 | 197 | You may convey verbatim copies of the Program's source code as you 198 | receive it, in any medium, provided that you conspicuously and 199 | appropriately publish on each copy an appropriate copyright notice; 200 | keep intact all notices stating that this License and any 201 | non-permissive terms added in accord with section 7 apply to the code; 202 | keep intact all notices of the absence of any warranty; and give all 203 | recipients a copy of this License along with the Program. 204 | 205 | You may charge any price or no price for each copy that you convey, 206 | and you may offer support or warranty protection for a fee. 207 | 208 | 5. Conveying Modified Source Versions. 209 | 210 | You may convey a work based on the Program, or the modifications to 211 | produce it from the Program, in the form of source code under the 212 | terms of section 4, provided that you also meet all of these conditions: 213 | 214 | a) The work must carry prominent notices stating that you modified 215 | it, and giving a relevant date. 216 | 217 | b) The work must carry prominent notices stating that it is 218 | released under this License and any conditions added under section 219 | 7. This requirement modifies the requirement in section 4 to 220 | "keep intact all notices". 221 | 222 | c) You must license the entire work, as a whole, under this 223 | License to anyone who comes into possession of a copy. This 224 | License will therefore apply, along with any applicable section 7 225 | additional terms, to the whole of the work, and all its parts, 226 | regardless of how they are packaged. This License gives no 227 | permission to license the work in any other way, but it does not 228 | invalidate such permission if you have separately received it. 229 | 230 | d) If the work has interactive user interfaces, each must display 231 | Appropriate Legal Notices; however, if the Program has interactive 232 | interfaces that do not display Appropriate Legal Notices, your 233 | work need not make them do so. 234 | 235 | A compilation of a covered work with other separate and independent 236 | works, which are not by their nature extensions of the covered work, 237 | and which are not combined with it such as to form a larger program, 238 | in or on a volume of a storage or distribution medium, is called an 239 | "aggregate" if the compilation and its resulting copyright are not 240 | used to limit the access or legal rights of the compilation's users 241 | beyond what the individual works permit. Inclusion of a covered work 242 | in an aggregate does not cause this License to apply to the other 243 | parts of the aggregate. 244 | 245 | 6. Conveying Non-Source Forms. 246 | 247 | You may convey a covered work in object code form under the terms 248 | of sections 4 and 5, provided that you also convey the 249 | machine-readable Corresponding Source under the terms of this License, 250 | in one of these ways: 251 | 252 | a) Convey the object code in, or embodied in, a physical product 253 | (including a physical distribution medium), accompanied by the 254 | Corresponding Source fixed on a durable physical medium 255 | customarily used for software interchange. 256 | 257 | b) Convey the object code in, or embodied in, a physical product 258 | (including a physical distribution medium), accompanied by a 259 | written offer, valid for at least three years and valid for as 260 | long as you offer spare parts or customer support for that product 261 | model, to give anyone who possesses the object code either (1) a 262 | copy of the Corresponding Source for all the software in the 263 | product that is covered by this License, on a durable physical 264 | medium customarily used for software interchange, for a price no 265 | more than your reasonable cost of physically performing this 266 | conveying of source, or (2) access to copy the 267 | Corresponding Source from a network server at no charge. 268 | 269 | c) Convey individual copies of the object code with a copy of the 270 | written offer to provide the Corresponding Source. This 271 | alternative is allowed only occasionally and noncommercially, and 272 | only if you received the object code with such an offer, in accord 273 | with subsection 6b. 274 | 275 | d) Convey the object code by offering access from a designated 276 | place (gratis or for a charge), and offer equivalent access to the 277 | Corresponding Source in the same way through the same place at no 278 | further charge. You need not require recipients to copy the 279 | Corresponding Source along with the object code. If the place to 280 | copy the object code is a network server, the Corresponding Source 281 | may be on a different server (operated by you or a third party) 282 | that supports equivalent copying facilities, provided you maintain 283 | clear directions next to the object code saying where to find the 284 | Corresponding Source. Regardless of what server hosts the 285 | Corresponding Source, you remain obligated to ensure that it is 286 | available for as long as needed to satisfy these requirements. 287 | 288 | e) Convey the object code using peer-to-peer transmission, provided 289 | you inform other peers where the object code and Corresponding 290 | Source of the work are being offered to the general public at no 291 | charge under subsection 6d. 292 | 293 | A separable portion of the object code, whose source code is excluded 294 | from the Corresponding Source as a System Library, need not be 295 | included in conveying the object code work. 296 | 297 | A "User Product" is either (1) a "consumer product", which means any 298 | tangible personal property which is normally used for personal, family, 299 | or household purposes, or (2) anything designed or sold for incorporation 300 | into a dwelling. In determining whether a product is a consumer product, 301 | doubtful cases shall be resolved in favor of coverage. For a particular 302 | product received by a particular user, "normally used" refers to a 303 | typical or common use of that class of product, regardless of the status 304 | of the particular user or of the way in which the particular user 305 | actually uses, or expects or is expected to use, the product. A product 306 | is a consumer product regardless of whether the product has substantial 307 | commercial, industrial or non-consumer uses, unless such uses represent 308 | the only significant mode of use of the product. 309 | 310 | "Installation Information" for a User Product means any methods, 311 | procedures, authorization keys, or other information required to install 312 | and execute modified versions of a covered work in that User Product from 313 | a modified version of its Corresponding Source. The information must 314 | suffice to ensure that the continued functioning of the modified object 315 | code is in no case prevented or interfered with solely because 316 | modification has been made. 317 | 318 | If you convey an object code work under this section in, or with, or 319 | specifically for use in, a User Product, and the conveying occurs as 320 | part of a transaction in which the right of possession and use of the 321 | User Product is transferred to the recipient in perpetuity or for a 322 | fixed term (regardless of how the transaction is characterized), the 323 | Corresponding Source conveyed under this section must be accompanied 324 | by the Installation Information. But this requirement does not apply 325 | if neither you nor any third party retains the ability to install 326 | modified object code on the User Product (for example, the work has 327 | been installed in ROM). 328 | 329 | The requirement to provide Installation Information does not include a 330 | requirement to continue to provide support service, warranty, or updates 331 | for a work that has been modified or installed by the recipient, or for 332 | the User Product in which it has been modified or installed. Access to a 333 | network may be denied when the modification itself materially and 334 | adversely affects the operation of the network or violates the rules and 335 | protocols for communication across the network. 336 | 337 | Corresponding Source conveyed, and Installation Information provided, 338 | in accord with this section must be in a format that is publicly 339 | documented (and with an implementation available to the public in 340 | source code form), and must require no special password or key for 341 | unpacking, reading or copying. 342 | 343 | 7. Additional Terms. 344 | 345 | "Additional permissions" are terms that supplement the terms of this 346 | License by making exceptions from one or more of its conditions. 347 | Additional permissions that are applicable to the entire Program shall 348 | be treated as though they were included in this License, to the extent 349 | that they are valid under applicable law. If additional permissions 350 | apply only to part of the Program, that part may be used separately 351 | under those permissions, but the entire Program remains governed by 352 | this License without regard to the additional permissions. 353 | 354 | When you convey a copy of a covered work, you may at your option 355 | remove any additional permissions from that copy, or from any part of 356 | it. (Additional permissions may be written to require their own 357 | removal in certain cases when you modify the work.) You may place 358 | additional permissions on material, added by you to a covered work, 359 | for which you have or can give appropriate copyright permission. 360 | 361 | Notwithstanding any other provision of this License, for material you 362 | add to a covered work, you may (if authorized by the copyright holders of 363 | that material) supplement the terms of this License with terms: 364 | 365 | a) Disclaiming warranty or limiting liability differently from the 366 | terms of sections 15 and 16 of this License; or 367 | 368 | b) Requiring preservation of specified reasonable legal notices or 369 | author attributions in that material or in the Appropriate Legal 370 | Notices displayed by works containing it; or 371 | 372 | c) Prohibiting misrepresentation of the origin of that material, or 373 | requiring that modified versions of such material be marked in 374 | reasonable ways as different from the original version; or 375 | 376 | d) Limiting the use for publicity purposes of names of licensors or 377 | authors of the material; or 378 | 379 | e) Declining to grant rights under trademark law for use of some 380 | trade names, trademarks, or service marks; or 381 | 382 | f) Requiring indemnification of licensors and authors of that 383 | material by anyone who conveys the material (or modified versions of 384 | it) with contractual assumptions of liability to the recipient, for 385 | any liability that these contractual assumptions directly impose on 386 | those licensors and authors. 387 | 388 | All other non-permissive additional terms are considered "further 389 | restrictions" within the meaning of section 10. If the Program as you 390 | received it, or any part of it, contains a notice stating that it is 391 | governed by this License along with a term that is a further 392 | restriction, you may remove that term. If a license document contains 393 | a further restriction but permits relicensing or conveying under this 394 | License, you may add to a covered work material governed by the terms 395 | of that license document, provided that the further restriction does 396 | not survive such relicensing or conveying. 397 | 398 | If you add terms to a covered work in accord with this section, you 399 | must place, in the relevant source files, a statement of the 400 | additional terms that apply to those files, or a notice indicating 401 | where to find the applicable terms. 402 | 403 | Additional terms, permissive or non-permissive, may be stated in the 404 | form of a separately written license, or stated as exceptions; 405 | the above requirements apply either way. 406 | 407 | 8. Termination. 408 | 409 | You may not propagate or modify a covered work except as expressly 410 | provided under this License. Any attempt otherwise to propagate or 411 | modify it is void, and will automatically terminate your rights under 412 | this License (including any patent licenses granted under the third 413 | paragraph of section 11). 414 | 415 | However, if you cease all violation of this License, then your 416 | license from a particular copyright holder is reinstated (a) 417 | provisionally, unless and until the copyright holder explicitly and 418 | finally terminates your license, and (b) permanently, if the copyright 419 | holder fails to notify you of the violation by some reasonable means 420 | prior to 60 days after the cessation. 421 | 422 | Moreover, your license from a particular copyright holder is 423 | reinstated permanently if the copyright holder notifies you of the 424 | violation by some reasonable means, this is the first time you have 425 | received notice of violation of this License (for any work) from that 426 | copyright holder, and you cure the violation prior to 30 days after 427 | your receipt of the notice. 428 | 429 | Termination of your rights under this section does not terminate the 430 | licenses of parties who have received copies or rights from you under 431 | this License. If your rights have been terminated and not permanently 432 | reinstated, you do not qualify to receive new licenses for the same 433 | material under section 10. 434 | 435 | 9. Acceptance Not Required for Having Copies. 436 | 437 | You are not required to accept this License in order to receive or 438 | run a copy of the Program. Ancillary propagation of a covered work 439 | occurring solely as a consequence of using peer-to-peer transmission 440 | to receive a copy likewise does not require acceptance. However, 441 | nothing other than this License grants you permission to propagate or 442 | modify any covered work. These actions infringe copyright if you do 443 | not accept this License. Therefore, by modifying or propagating a 444 | covered work, you indicate your acceptance of this License to do so. 445 | 446 | 10. Automatic Licensing of Downstream Recipients. 447 | 448 | Each time you convey a covered work, the recipient automatically 449 | receives a license from the original licensors, to run, modify and 450 | propagate that work, subject to this License. You are not responsible 451 | for enforcing compliance by third parties with this License. 452 | 453 | An "entity transaction" is a transaction transferring control of an 454 | organization, or substantially all assets of one, or subdividing an 455 | organization, or merging organizations. If propagation of a covered 456 | work results from an entity transaction, each party to that 457 | transaction who receives a copy of the work also receives whatever 458 | licenses to the work the party's predecessor in interest had or could 459 | give under the previous paragraph, plus a right to possession of the 460 | Corresponding Source of the work from the predecessor in interest, if 461 | the predecessor has it or can get it with reasonable efforts. 462 | 463 | You may not impose any further restrictions on the exercise of the 464 | rights granted or affirmed under this License. For example, you may 465 | not impose a license fee, royalty, or other charge for exercise of 466 | rights granted under this License, and you may not initiate litigation 467 | (including a cross-claim or counterclaim in a lawsuit) alleging that 468 | any patent claim is infringed by making, using, selling, offering for 469 | sale, or importing the Program or any portion of it. 470 | 471 | 11. Patents. 472 | 473 | A "contributor" is a copyright holder who authorizes use under this 474 | License of the Program or a work on which the Program is based. The 475 | work thus licensed is called the contributor's "contributor version". 476 | 477 | A contributor's "essential patent claims" are all patent claims 478 | owned or controlled by the contributor, whether already acquired or 479 | hereafter acquired, that would be infringed by some manner, permitted 480 | by this License, of making, using, or selling its contributor version, 481 | but do not include claims that would be infringed only as a 482 | consequence of further modification of the contributor version. For 483 | purposes of this definition, "control" includes the right to grant 484 | patent sublicenses in a manner consistent with the requirements of 485 | this License. 486 | 487 | Each contributor grants you a non-exclusive, worldwide, royalty-free 488 | patent license under the contributor's essential patent claims, to 489 | make, use, sell, offer for sale, import and otherwise run, modify and 490 | propagate the contents of its contributor version. 491 | 492 | In the following three paragraphs, a "patent license" is any express 493 | agreement or commitment, however denominated, not to enforce a patent 494 | (such as an express permission to practice a patent or covenant not to 495 | sue for patent infringement). To "grant" such a patent license to a 496 | party means to make such an agreement or commitment not to enforce a 497 | patent against the party. 498 | 499 | If you convey a covered work, knowingly relying on a patent license, 500 | and the Corresponding Source of the work is not available for anyone 501 | to copy, free of charge and under the terms of this License, through a 502 | publicly available network server or other readily accessible means, 503 | then you must either (1) cause the Corresponding Source to be so 504 | available, or (2) arrange to deprive yourself of the benefit of the 505 | patent license for this particular work, or (3) arrange, in a manner 506 | consistent with the requirements of this License, to extend the patent 507 | license to downstream recipients. "Knowingly relying" means you have 508 | actual knowledge that, but for the patent license, your conveying the 509 | covered work in a country, or your recipient's use of the covered work 510 | in a country, would infringe one or more identifiable patents in that 511 | country that you have reason to believe are valid. 512 | 513 | If, pursuant to or in connection with a single transaction or 514 | arrangement, you convey, or propagate by procuring conveyance of, a 515 | covered work, and grant a patent license to some of the parties 516 | receiving the covered work authorizing them to use, propagate, modify 517 | or convey a specific copy of the covered work, then the patent license 518 | you grant is automatically extended to all recipients of the covered 519 | work and works based on it. 520 | 521 | A patent license is "discriminatory" if it does not include within 522 | the scope of its coverage, prohibits the exercise of, or is 523 | conditioned on the non-exercise of one or more of the rights that are 524 | specifically granted under this License. You may not convey a covered 525 | work if you are a party to an arrangement with a third party that is 526 | in the business of distributing software, under which you make payment 527 | to the third party based on the extent of your activity of conveying 528 | the work, and under which the third party grants, to any of the 529 | parties who would receive the covered work from you, a discriminatory 530 | patent license (a) in connection with copies of the covered work 531 | conveyed by you (or copies made from those copies), or (b) primarily 532 | for and in connection with specific products or compilations that 533 | contain the covered work, unless you entered into that arrangement, 534 | or that patent license was granted, prior to 28 March 2007. 535 | 536 | Nothing in this License shall be construed as excluding or limiting 537 | any implied license or other defenses to infringement that may 538 | otherwise be available to you under applicable patent law. 539 | 540 | 12. No Surrender of Others' Freedom. 541 | 542 | If conditions are imposed on you (whether by court order, agreement or 543 | otherwise) that contradict the conditions of this License, they do not 544 | excuse you from the conditions of this License. If you cannot convey a 545 | covered work so as to satisfy simultaneously your obligations under this 546 | License and any other pertinent obligations, then as a consequence you may 547 | not convey it at all. For example, if you agree to terms that obligate you 548 | to collect a royalty for further conveying from those to whom you convey 549 | the Program, the only way you could satisfy both those terms and this 550 | License would be to refrain entirely from conveying the Program. 551 | 552 | 13. Use with the GNU Affero General Public License. 553 | 554 | Notwithstanding any other provision of this License, you have 555 | permission to link or combine any covered work with a work licensed 556 | under version 3 of the GNU Affero General Public License into a single 557 | combined work, and to convey the resulting work. The terms of this 558 | License will continue to apply to the part which is the covered work, 559 | but the special requirements of the GNU Affero General Public License, 560 | section 13, concerning interaction through a network will apply to the 561 | combination as such. 562 | 563 | 14. Revised Versions of this License. 564 | 565 | The Free Software Foundation may publish revised and/or new versions of 566 | the GNU General Public License from time to time. Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Prácticas de Pytorch 2 | 3 | 4 | Pytorch es una librería reciente que se está esparciendo en la comunidad académina e industrial para desarrollar sistemas basados en aprendizaje automático, en especial en aprendizaje profundo. En este repositorio encontrarás una serie de ejercicios para aprender o reforzar tu conocimiento acerca de la implementación de redes neuronales profundas. Este repositorio es complemento al curso de introducción a las redes neuronales. Para más detalles visita mi página web. 5 | 6 | Irving Vasquez 7 | [Sitio Web] 8 | 9 | 10 | ## Configurar ambiente con conda 11 | 12 | Para facilitar la instalación y ejecución de los ejercicios se utiliza de manejador conda. En dependencia de que distribución se use se puede llamar minicomda o anaconda. Para simplicidad en este repositorio se usa minconda. 13 | 14 | De la definición de conda [docs](http://conda.pydata.org/docs): 15 | 16 | > Conda is an open source package management system and environment management system for installing multiple versions of software packages and their dependencies and switching easily between them. It works on Linux, OS X and Windows, and was created for Python programs but can package and distribute any software. 17 | 18 | ### Pasos 19 | Para tener los programas listos haremos dos cosas: 20 | 21 | 1. Install [`miniconda`](http://conda.pydata.org/miniconda.html) on your computer, by selecting the latest Python version for your operating system. If you already have `conda` or `miniconda` installed, you should be able to skip this step and move on to step 2. 22 | 2. Create and activate * a new `conda` [environment](http://conda.pydata.org/docs/using/envs.html). 23 | 24 | \* Nota que cada que vayamos a usar los programas debemos de activar el ambiente de `conda`! 25 | 26 | --- 27 | 28 | ## 1. Instalación de miniconda 29 | 30 | **Download** the latest version of `miniconda` that matches your system. 31 | 32 | **NOTE**: There have been reports of issues creating an environment using miniconda `v4.3.13`. If it gives you issues try versions `4.3.11` or `4.2.12` from [here](https://repo.continuum.io/miniconda/). 33 | 34 | | | Linux | Mac | Windows | 35 | |--------|-------|-----|---------| 36 | | 64-bit | [64-bit (bash installer)][lin64] | [64-bit (bash installer)][mac64] | [64-bit (exe installer)][win64] 37 | | 32-bit | [32-bit (bash installer)][lin32] | | [32-bit (exe installer)][win32] 38 | 39 | [win64]: https://repo.continuum.io/miniconda/Miniconda3-latest-Windows-x86_64.exe 40 | [win32]: https://repo.continuum.io/miniconda/Miniconda3-latest-Windows-x86.exe 41 | [mac64]: https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh 42 | [lin64]: https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh 43 | [lin32]: https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86.sh 44 | 45 | **Install** [miniconda](http://conda.pydata.org/miniconda.html) on your machine. Detailed instructions: 46 | 47 | - **Linux:** http://conda.pydata.org/docs/install/quick.html#linux-miniconda-install 48 | - **Mac:** http://conda.pydata.org/docs/install/quick.html#os-x-miniconda-install 49 | - **Windows:** http://conda.pydata.org/docs/install/quick.html#windows-miniconda-install 50 | 51 | ## 2. Crear y activar el ambiente 52 | 53 | For Windows users, these following commands need to be executed from the **Anaconda prompt** as opposed to a Windows terminal window. For Mac, a normal terminal window will work. 54 | 55 | #### Git and version control 56 | These instructions also assume you have `git` installed for working with Github from a terminal window, but if you do not, you can download that first with the command: 57 | ``` 58 | conda install git 59 | ``` 60 | 61 | 62 | **Now, we're ready to create our local environment!** 63 | 64 | 1. Clone the repository, and navigate to the downloaded folder. This may take a minute or two to clone due to the included image data. 65 | ``` 66 | git clone https://github.com/irvingvasquez/practicas_pytorch.git 67 | cd practicas_pytorch 68 | ``` 69 | 70 | 2. Create (and activate) a new environment, named `pptorch` with Python 3.7. If prompted to proceed with the install `(Proceed [y]/n)` type y. 71 | 72 | - __Linux__ or __Mac__: 73 | ``` 74 | conda create -n pptorch python=3.7 75 | source activate pptorch 76 | ``` 77 | - __Windows__: 78 | ``` 79 | conda create --name pptorch python=3.7 80 | conda activate pptorch 81 | ``` 82 | 83 | At this point your command line should look something like: 84 | 85 | `(pptorch) :practicas_pytorch $`. 86 | 87 | The `(pptorch)` indicates that your environment has been activated, and you can proceed with further package installations. 88 | 89 | 3. Install PyTorch and torchvision; this should install the latest version of PyTorch. Mi recomendación es revisar antes la [documentación oficial](https://pytorch.org/get-started/locally/) de pytorch y verificar los comandos en dependencia de si se va a utilizar GPU o no. Los siguientes comandos son para usar CPU. 90 | 91 | - __Linux__ or __Mac__: 92 | ``` 93 | conda install pytorch=1 torchvision cpuonly -c pytorch 94 | ``` 95 | - __Windows__: 96 | ``` 97 | conda install pytorch=1 torchvision cpuonly -c pytorch 98 | ``` 99 | 100 | 6. Install a few required pip packages, which are specified in the requirements text file (including OpenCV). 101 | ``` 102 | pip install -r requirements.txt 103 | ``` 104 | 105 | 7. That's it! 106 | 107 | Now all of the `pptorch` libraries are available to you. Assuming you're environment is still activated, you can navigate to the Exercises repo and start looking at the notebooks: 108 | 109 | ``` 110 | cd 111 | cd practicas_pytorch 112 | jupyter notebook 113 | ``` 114 | 115 | To exit the environment when you have completed your work session, simply close the terminal window. 116 | 117 | Referencias usadas: 118 | Udacity, Deep Learning with PyTorch 119 | 120 | [Sitio Web]: 121 | -------------------------------------------------------------------------------- /archivos/function_approx.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/irvingvasquez/practicas_pytorch/b63b5b8b5a3f54cc7d87ca9bc9d0fafe88b31671/archivos/function_approx.png -------------------------------------------------------------------------------- /archivos/lenet.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/irvingvasquez/practicas_pytorch/b63b5b8b5a3f54cc7d87ca9bc9d0fafe88b31671/archivos/lenet.png -------------------------------------------------------------------------------- /archivos/net.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/irvingvasquez/practicas_pytorch/b63b5b8b5a3f54cc7d87ca9bc9d0fafe88b31671/archivos/net.png -------------------------------------------------------------------------------- /archivos/simple_neuron.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/irvingvasquez/practicas_pytorch/b63b5b8b5a3f54cc7d87ca9bc9d0fafe88b31671/archivos/simple_neuron.png -------------------------------------------------------------------------------- /fc_model.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch import nn 3 | import torch.nn.functional as F 4 | 5 | 6 | class Network(nn.Module): 7 | def __init__(self, input_size, output_size, hidden_layers, drop_p=0.5): 8 | ''' Builds a feedforward network with arbitrary hidden layers. 9 | 10 | Arguments 11 | --------- 12 | input_size: integer, size of the input layer 13 | output_size: integer, size of the output layer 14 | hidden_layers: list of integers, the sizes of the hidden layers 15 | 16 | ''' 17 | super().__init__() 18 | # Input to a hidden layer 19 | self.hidden_layers = nn.ModuleList([nn.Linear(input_size, hidden_layers[0])]) 20 | 21 | # Add a variable number of more hidden layers 22 | layer_sizes = zip(hidden_layers[:-1], hidden_layers[1:]) 23 | self.hidden_layers.extend([nn.Linear(h1, h2) for h1, h2 in layer_sizes]) 24 | 25 | self.output = nn.Linear(hidden_layers[-1], output_size) 26 | 27 | self.dropout = nn.Dropout(p=drop_p) 28 | 29 | def forward(self, x): 30 | ''' Forward pass through the network, returns the output logits ''' 31 | 32 | for each in self.hidden_layers: 33 | x = F.relu(each(x)) 34 | x = self.dropout(x) 35 | x = self.output(x) 36 | 37 | return F.log_softmax(x, dim=1) 38 | 39 | 40 | def validation(model, testloader, criterion): 41 | accuracy = 0 42 | test_loss = 0 43 | for images, labels in testloader: 44 | 45 | images = images.resize_(images.size()[0], 784) 46 | 47 | output = model.forward(images) 48 | test_loss += criterion(output, labels).item() 49 | 50 | ## Calculating the accuracy 51 | # Model's output is log-softmax, take exponential to get the probabilities 52 | ps = torch.exp(output) 53 | # Class with highest probability is our predicted class, compare with true label 54 | equality = (labels.data == ps.max(1)[1]) 55 | # Accuracy is number of correct predictions divided by all predictions, just take the mean 56 | accuracy += equality.type_as(torch.FloatTensor()).mean() 57 | 58 | return test_loss, accuracy 59 | 60 | 61 | def train(model, trainloader, testloader, criterion, optimizer, epochs=5, print_every=40): 62 | 63 | steps = 0 64 | running_loss = 0 65 | for e in range(epochs): 66 | # Model in training mode, dropout is on 67 | model.train() 68 | for images, labels in trainloader: 69 | steps += 1 70 | 71 | # Flatten images into a 784 long vector 72 | images.resize_(images.size()[0], 784) 73 | 74 | optimizer.zero_grad() 75 | 76 | output = model.forward(images) 77 | loss = criterion(output, labels) 78 | loss.backward() 79 | optimizer.step() 80 | 81 | running_loss += loss.item() 82 | 83 | if steps % print_every == 0: 84 | # Model in inference mode, dropout is off 85 | model.eval() 86 | 87 | # Turn off gradients for validation, will speed up inference 88 | with torch.no_grad(): 89 | test_loss, accuracy = validation(model, testloader, criterion) 90 | 91 | print("Epoch: {}/{}.. ".format(e+1, epochs), 92 | "Training Loss: {:.3f}.. ".format(running_loss/print_every), 93 | "Test Loss: {:.3f}.. ".format(test_loss/len(testloader)), 94 | "Test Accuracy: {:.3f}".format(accuracy/len(testloader))) 95 | 96 | running_loss = 0 97 | 98 | # Make sure dropout and grads are on for training 99 | model.train() -------------------------------------------------------------------------------- /helper.py: -------------------------------------------------------------------------------- 1 | import matplotlib.pyplot as plt 2 | import numpy as np 3 | from torch import nn, optim 4 | from torch.autograd import Variable 5 | 6 | 7 | def test_network(net, trainloader): 8 | 9 | criterion = nn.MSELoss() 10 | optimizer = optim.Adam(net.parameters(), lr=0.001) 11 | 12 | dataiter = iter(trainloader) 13 | images, labels = dataiter.next() 14 | 15 | # Create Variables for the inputs and targets 16 | inputs = Variable(images) 17 | targets = Variable(images) 18 | 19 | # Clear the gradients from all Variables 20 | optimizer.zero_grad() 21 | 22 | # Forward pass, then backward pass, then update weights 23 | output = net.forward(inputs) 24 | loss = criterion(output, targets) 25 | loss.backward() 26 | optimizer.step() 27 | 28 | return True 29 | 30 | 31 | def imshow(image, ax=None, title=None, normalize=True): 32 | """Imshow for Tensor.""" 33 | if ax is None: 34 | fig, ax = plt.subplots() 35 | image = image.numpy().transpose((1, 2, 0)) 36 | 37 | if normalize: 38 | mean = np.array([0.485, 0.456, 0.406]) 39 | std = np.array([0.229, 0.224, 0.225]) 40 | image = std * image + mean 41 | image = np.clip(image, 0, 1) 42 | 43 | ax.imshow(image) 44 | ax.spines['top'].set_visible(False) 45 | ax.spines['right'].set_visible(False) 46 | ax.spines['left'].set_visible(False) 47 | ax.spines['bottom'].set_visible(False) 48 | ax.tick_params(axis='both', length=0) 49 | ax.set_xticklabels('') 50 | ax.set_yticklabels('') 51 | 52 | return ax 53 | 54 | 55 | def view_recon(img, recon): 56 | ''' Function for displaying an image (as a PyTorch Tensor) and its 57 | reconstruction also a PyTorch Tensor 58 | ''' 59 | 60 | fig, axes = plt.subplots(ncols=2, sharex=True, sharey=True) 61 | axes[0].imshow(img.numpy().squeeze()) 62 | axes[1].imshow(recon.data.numpy().squeeze()) 63 | for ax in axes: 64 | ax.axis('off') 65 | ax.set_adjustable('box-forced') 66 | 67 | def view_classify(img, ps, version="MNIST"): 68 | ''' Function for viewing an image and it's predicted classes. 69 | ''' 70 | ps = ps.data.numpy().squeeze() 71 | 72 | fig, (ax1, ax2) = plt.subplots(figsize=(6,9), ncols=2) 73 | ax1.imshow(img.resize_(1, 28, 28).numpy().squeeze()) 74 | ax1.axis('off') 75 | ax2.barh(np.arange(10), ps) 76 | ax2.set_aspect(0.1) 77 | ax2.set_yticks(np.arange(10)) 78 | if version == "MNIST": 79 | ax2.set_yticklabels(np.arange(10)) 80 | elif version == "Fashion": 81 | ax2.set_yticklabels(['T-shirt/top', 82 | 'Trouser', 83 | 'Pullover', 84 | 'Dress', 85 | 'Coat', 86 | 'Sandal', 87 | 'Shirt', 88 | 'Sneaker', 89 | 'Bag', 90 | 'Ankle Boot'], size='small'); 91 | ax2.set_title('Class Probability') 92 | ax2.set_xlim(0, 1.1) 93 | 94 | plt.tight_layout() 95 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | jupyter==1.0.0 2 | matplotlib==3.5.3 -------------------------------------------------------------------------------- /soluciones/02_red_neuronal_solucion.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Red neuronal multicapa en PyTorch (solución)\n", 8 | "\n", 9 | "En este notebook veremos como implementar una red neuronal usando PyTorch. En particular, implementaremos un perceptrón multicapa (MLP por sus siglas en inglés) para la classificación de dígitos del conjunto de datos MNIST.\n", 10 | "\n", 11 | "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/irvingvasquez/practicas_pytorch/blob/master/soluciones/02_red_neuronal.ipynb)\n", 12 | "\n", 13 | "@juan1rving" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": 2, 19 | "metadata": {}, 20 | "outputs": [], 21 | "source": [ 22 | "# Primero llamamos a los paquetes necesarios\n", 23 | "%matplotlib inline\n", 24 | "%config InlineBackend.figure_format = 'retina'\n", 25 | "\n", 26 | "import helper\n", 27 | "import matplotlib.pyplot as plt\n", 28 | "\n", 29 | "import numpy as np\n", 30 | "import torch\n", 31 | "from torchvision import datasets, transforms" 32 | ] 33 | }, 34 | { 35 | "cell_type": "markdown", 36 | "metadata": {}, 37 | "source": [ 38 | "## Conjunto de datos\n", 39 | "\n", 40 | "Para la práctica necesitaremos un conjunto de datos (dataset). Afortunadamente el paquete **torchvision** provee diversos conjuntos de datos de ejemplo. En este ejercicio, utilizaremos MNIST, el cual contiene ejemplos de letras escritas a mano. El siguiente código lee el conjunto de datos y lo separa en un conjunto de entrenamiendo y uno de prueba. " 41 | ] 42 | }, 43 | { 44 | "cell_type": "code", 45 | "execution_count": 6, 46 | "metadata": {}, 47 | "outputs": [], 48 | "source": [ 49 | "# Generaramos una transformación para normalizar el conjunto de datos\n", 50 | "transform = transforms.Compose([transforms.ToTensor(),\n", 51 | " #transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n", 52 | " transforms.Normalize([0.5],[0.5]) \n", 53 | " ])\n", 54 | "# Descargamos el conjunto de datos de entrenamiento\n", 55 | "trainset = datasets.MNIST('MNIST_data/', download=True, train=True, transform=transform)\n", 56 | "# Cargamos el conjunto\n", 57 | "batch_size=64\n", 58 | "trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True)\n", 59 | "\n", 60 | "# Descargamos y cargamos el conjunto de prueba\n", 61 | "testset = datasets.MNIST('MNIST_data/', download=True, train=False, transform=transform)\n", 62 | "testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=True)" 63 | ] 64 | }, 65 | { 66 | "cell_type": "code", 67 | "execution_count": 7, 68 | "metadata": {}, 69 | "outputs": [], 70 | "source": [ 71 | "# Ordenamos los datos para tener parejas de imágenes con su respectiva clase\n", 72 | "\n", 73 | "# Los datos se encuentran en trainloader asi que generamos un iterador para extraerlos uno por uno\n", 74 | "dataiter = iter(trainloader)\n", 75 | "\n", 76 | "# TODO: Obtén un lote de ejemplos y sus respectivas etiquetas\n", 77 | "images, labels = next(dataiter)" 78 | ] 79 | }, 80 | { 81 | "cell_type": "markdown", 82 | "metadata": {}, 83 | "source": [ 84 | "Es recomendable verificar que estamos cargando bien el conjunto de datos. Asi que a continuación imprimeremos uno." 85 | ] 86 | }, 87 | { 88 | "cell_type": "code", 89 | "execution_count": 8, 90 | "metadata": {}, 91 | "outputs": [ 92 | { 93 | "data": { 94 | "text/plain": [ 95 | "torch.Size([1, 28, 28])" 96 | ] 97 | }, 98 | "execution_count": 8, 99 | "metadata": {}, 100 | "output_type": "execute_result" 101 | }, 102 | { 103 | "data": { 104 | "image/png": "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", 105 | "text/plain": [ 106 | "
" 107 | ] 108 | }, 109 | "metadata": { 110 | "image/png": { 111 | "height": 248, 112 | "width": 251 113 | }, 114 | "needs_background": "light" 115 | }, 116 | "output_type": "display_data" 117 | } 118 | ], 119 | "source": [ 120 | "plt.imshow(images[1].numpy().squeeze(), cmap='Greys_r');\n", 121 | "images[1].size()" 122 | ] 123 | }, 124 | { 125 | "cell_type": "markdown", 126 | "metadata": {}, 127 | "source": [ 128 | "## Implementación de la red neuronal multicapa\n", 129 | "\n", 130 | "Ahora pasaremos a la creación de la red neuronal, como ejemplo utilizaremos un perceptrón multicapa para clasificar las imagenes del conjunto MNIST. Como entrada tendremos 784 nodos = 28 * 28, en seguida tendremos una capa oculta de 128 nodos, con una función de activación tipo RELU, despúes tendremos una segunda capa oculta con 64 nodos y función de activación RELU, en seguida tendremos 10 nodos de salida los cuales pasan por una función softmax que convierte los valores a probabilidades. En el siguiente ejercicio incluiremos la pérdida (loss) con la función de entropía cruzada. \n", 131 | "\n", 132 | "\n", 133 | "\n", 134 | "El modulo que contiene las herramientas para crear la RN es **pytorch.nn**. La red neuronal en sí se crea como una clase que hereda la estructura de **pytorch.nn.Module**. Cada una de las capas de la red se define de forma independiente. e.g. Para crear una capa con 784 entradas y 128 nodos utilizamos *nn.Linear(784, 128)*\n", 135 | "\n", 136 | "La red implementa la función *forward* que realiza el paso frontal (fowdward pass). Esta función miembro recibe un tensor como entrada y calcula la salida de la red.\n", 137 | "\n", 138 | "Varias funciones de activación se encuntran en el módulo *nn.functional*. Dicho módulo usualmente se importa como *F*. \n" 139 | ] 140 | }, 141 | { 142 | "cell_type": "code", 143 | "execution_count": 9, 144 | "metadata": {}, 145 | "outputs": [], 146 | "source": [ 147 | "# importamos paquetes de pytorch\n", 148 | "from torch import nn\n", 149 | "import torch.nn.functional as F" 150 | ] 151 | }, 152 | { 153 | "attachments": {}, 154 | "cell_type": "markdown", 155 | "metadata": {}, 156 | "source": [ 157 | "En general las redes implementan a partir de la clase nn.Module que provee la clase base. Por lo tanto en este ejercicio declararemos una clase denominada red neuronal que hereda de nn.Module. Una vez declarada nuestra red neuronal es necesario includir como atributos de la case las capas que se requieren, esto por que cada capa incluye los parámetros (pesos) que se entrenarán y deben tener permanencia mientras exista la red. Dichas capas se incluirán dentro del constructor __init__ . \n", 158 | "\n", 159 | "De acuerdo a pytorch.org\n", 160 | "\n", 161 | " nn.Linear(in_features, out_features, bias=True, device=None, dtype=None)\n", 162 | "\n", 163 | "Applies a linear transformation to the incoming data.\n", 164 | "\n", 165 | "Por lo tanto si queremos uncluir una sola capa podríamos codificar lo siguiente:\n", 166 | "\n", 167 | " self.fc = nn.Lnear(n_inputs, n_outputs)\n", 168 | "\n", 169 | "A continuación definiremos el comportamiento de inferencia de la red dentro de la funcion forward. En el comportamiento indicaremos el orden en el que se van ejecutando las capas y las funciones de activación. Recuerda que las funciones de activación solo se llaman pero no se instancían. Un ejemplo de una sola capa sería:\n", 170 | "\n", 171 | " def forward(self, x):\n", 172 | " y = F.relu(self.fc(x))\n", 173 | " return y\n" 174 | ] 175 | }, 176 | { 177 | "cell_type": "code", 178 | "execution_count": 10, 179 | "metadata": {}, 180 | "outputs": [], 181 | "source": [ 182 | "# Implementación de la red neuronal\n", 183 | "class RedNeuronal(nn.Module):\n", 184 | " def __init__(self):\n", 185 | " super().__init__()\n", 186 | " # Definir las capas. Cada una con 128, 64 y 10 unidades respectivamente\n", 187 | " self.fc1 = nn.Linear(784, 128)\n", 188 | " self.fc2 = nn.Linear(128, 64)\n", 189 | " # Capa de salida con 10 units (una para cada dígito)\n", 190 | " self.fc3 = nn.Linear(64, 10)\n", 191 | " \n", 192 | " def forward(self, x):\n", 193 | " ''' Pase frontal de la red, regresamos las probabilidades '''\n", 194 | " x = self.fc1(x)\n", 195 | " x = F.relu(x)\n", 196 | " x = self.fc2(x)\n", 197 | " x = F.relu(x)\n", 198 | " x = self.fc3(x)\n", 199 | " y = F.softmax(x, dim=1)\n", 200 | " \n", 201 | " return y" 202 | ] 203 | }, 204 | { 205 | "cell_type": "code", 206 | "execution_count": 11, 207 | "metadata": {}, 208 | "outputs": [ 209 | { 210 | "name": "stdout", 211 | "output_type": "stream", 212 | "text": [ 213 | "RedNeuronal(\n", 214 | " (fc1): Linear(in_features=784, out_features=128, bias=True)\n", 215 | " (fc2): Linear(in_features=128, out_features=64, bias=True)\n", 216 | " (fc3): Linear(in_features=64, out_features=10, bias=True)\n", 217 | ")\n" 218 | ] 219 | } 220 | ], 221 | "source": [ 222 | "model = RedNeuronal()\n", 223 | "print(model)" 224 | ] 225 | }, 226 | { 227 | "cell_type": "markdown", 228 | "metadata": {}, 229 | "source": [ 230 | "### Inicializamos pesos y sesgos\n", 231 | "\n", 232 | "Cuando creas las capas se crean también los tensores correspondientes a los pesos y sesgos. Éstos son inicializados por ti, aunque pudes modificarlos usando funciones extra. Para observar sus valores puedes llamar a *model.fc1.weight* \n" 233 | ] 234 | }, 235 | { 236 | "cell_type": "code", 237 | "execution_count": 16, 238 | "metadata": {}, 239 | "outputs": [ 240 | { 241 | "name": "stdout", 242 | "output_type": "stream", 243 | "text": [ 244 | "Parameter containing:\n", 245 | "tensor([[ 9.9723e-03, -7.5474e-06, 1.3265e-02, ..., 2.8805e-02,\n", 246 | " -1.3914e-02, -2.6288e-02],\n", 247 | " [-1.4172e-02, 3.2646e-02, 2.6562e-02, ..., 2.5904e-02,\n", 248 | " -2.0148e-02, -2.6238e-02],\n", 249 | " [ 1.2665e-03, 3.7877e-03, 3.1762e-02, ..., 2.7686e-02,\n", 250 | " 2.1482e-02, -3.0605e-02],\n", 251 | " ...,\n", 252 | " [ 2.1352e-02, 2.1146e-02, -1.1668e-02, ..., -1.8667e-02,\n", 253 | " -2.4304e-02, 2.3473e-02],\n", 254 | " [-6.7076e-03, 8.5811e-03, -2.4602e-02, ..., 1.1613e-02,\n", 255 | " 2.4198e-02, -3.2462e-02],\n", 256 | " [-9.6219e-04, -5.3277e-03, -1.5103e-02, ..., 3.5086e-02,\n", 257 | " 2.2136e-02, 3.0636e-02]], requires_grad=True)\n", 258 | "Parameter containing:\n", 259 | "tensor([ 0.0349, 0.0023, -0.0086, 0.0190, 0.0051, 0.0182, -0.0353, -0.0009,\n", 260 | " -0.0342, -0.0043, 0.0160, -0.0184, -0.0305, 0.0119, 0.0131, -0.0157,\n", 261 | " -0.0218, 0.0288, -0.0278, -0.0194, -0.0157, 0.0270, -0.0241, -0.0325,\n", 262 | " -0.0132, -0.0357, -0.0295, 0.0220, -0.0218, 0.0268, -0.0026, 0.0192,\n", 263 | " 0.0229, 0.0033, -0.0030, -0.0314, 0.0354, -0.0109, -0.0126, -0.0142,\n", 264 | " 0.0325, -0.0075, -0.0103, -0.0078, -0.0181, -0.0082, 0.0095, 0.0218,\n", 265 | " 0.0337, -0.0282, 0.0135, 0.0187, -0.0104, -0.0006, 0.0257, -0.0350,\n", 266 | " -0.0040, 0.0209, -0.0128, -0.0106, -0.0087, -0.0037, 0.0193, 0.0169,\n", 267 | " 0.0201, 0.0223, 0.0057, -0.0271, -0.0241, -0.0045, -0.0246, 0.0154,\n", 268 | " 0.0128, 0.0141, 0.0097, 0.0346, -0.0292, -0.0106, -0.0139, 0.0125,\n", 269 | " -0.0105, -0.0005, -0.0287, 0.0115, 0.0084, -0.0031, 0.0094, 0.0008,\n", 270 | " 0.0237, -0.0286, -0.0313, -0.0075, 0.0177, -0.0002, 0.0190, -0.0159,\n", 271 | " 0.0022, -0.0011, -0.0124, -0.0098, -0.0297, 0.0255, -0.0038, -0.0013,\n", 272 | " -0.0186, -0.0313, 0.0075, 0.0113, 0.0238, -0.0069, 0.0280, 0.0047,\n", 273 | " 0.0354, 0.0199, 0.0342, 0.0130, -0.0019, 0.0069, 0.0136, -0.0238,\n", 274 | " 0.0255, 0.0230, 0.0204, -0.0318, -0.0006, 0.0300, -0.0352, 0.0164],\n", 275 | " requires_grad=True)\n" 276 | ] 277 | } 278 | ], 279 | "source": [ 280 | "print(model.fc1.weight)\n", 281 | "print(model.fc1.bias)" 282 | ] 283 | }, 284 | { 285 | "cell_type": "markdown", 286 | "metadata": {}, 287 | "source": [ 288 | "Supongamos que deseamos inicializar los pesos con algunos valores personalizados. Dado que los pesos y sesgos en sí son variables de autograd (Preparadas para el cálculo del gradiente automático) estos solo se pueden modificar cuando no estan en modo de autogradiente." 289 | ] 290 | }, 291 | { 292 | "cell_type": "code", 293 | "execution_count": 19, 294 | "metadata": {}, 295 | "outputs": [ 296 | { 297 | "data": { 298 | "text/plain": [ 299 | "tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", 300 | " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", 301 | " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", 302 | " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", 303 | " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", 304 | " 0., 0., 0., 0., 0., 0., 0., 0.])" 305 | ] 306 | }, 307 | "execution_count": 19, 308 | "metadata": {}, 309 | "output_type": "execute_result" 310 | } 311 | ], 312 | "source": [ 313 | "# Colocamos ceros\n", 314 | "model.fc1.bias.data.fill_(0)" 315 | ] 316 | }, 317 | { 318 | "cell_type": "code", 319 | "execution_count": 20, 320 | "metadata": {}, 321 | "outputs": [ 322 | { 323 | "data": { 324 | "text/plain": [ 325 | "tensor([[-0.0062, 0.0080, 0.0015, ..., 0.0103, 0.0073, 0.0207],\n", 326 | " [ 0.0013, 0.0206, -0.0056, ..., 0.0069, -0.0008, 0.0100],\n", 327 | " [-0.0058, 0.0032, 0.0119, ..., -0.0003, -0.0244, -0.0037],\n", 328 | " ...,\n", 329 | " [ 0.0064, 0.0067, -0.0066, ..., 0.0012, 0.0075, -0.0086],\n", 330 | " [ 0.0044, 0.0110, -0.0208, ..., 0.0008, 0.0018, 0.0105],\n", 331 | " [-0.0169, 0.0023, -0.0010, ..., 0.0023, -0.0071, -0.0023]])" 332 | ] 333 | }, 334 | "execution_count": 20, 335 | "metadata": {}, 336 | "output_type": "execute_result" 337 | } 338 | ], 339 | "source": [ 340 | "# muestreamos desde una distribución normal con media cero y desv. estandar = 0.01\n", 341 | "model.fc1.weight.data.normal_(std=0.01)" 342 | ] 343 | }, 344 | { 345 | "cell_type": "markdown", 346 | "metadata": {}, 347 | "source": [ 348 | "### Pase frontal\n", 349 | "\n", 350 | "Hasta el momento la red no está entrenada y solo tenemos los pesos aleatorios. Hagamos un pase frontal para ver que pasa. Primero debemos convertir la imagen a un tensor y pasarla a través de la red. " 351 | ] 352 | }, 353 | { 354 | "cell_type": "code", 355 | "execution_count": 13, 356 | "metadata": {}, 357 | "outputs": [ 358 | { 359 | "name": "stdout", 360 | "output_type": "stream", 361 | "text": [ 362 | "tensor([[0.0984, 0.1052, 0.0964, 0.1013, 0.0908, 0.0788, 0.1160, 0.1081, 0.1227,\n", 363 | " 0.0823]], grad_fn=)\n" 364 | ] 365 | } 366 | ], 367 | "source": [ 368 | "# Obtengamos el siguiente lote de imágenes\n", 369 | "#dataiter = iter(trainloader)\n", 370 | "images, labels = dataiter.next()\n", 371 | "\n", 372 | "# Reestructuremos el lote a un vector de una dimensión, hay quien le llama a esta operación \"aplanado\".\n", 373 | "# La nueva forma será (batch size, color channels, image pixels) \n", 374 | "images.resize_(batch_size, 1, 784)\n", 375 | "# alternativa: images.resize_(images.shape[0], 1, 784) to not automatically get batch size\n", 376 | "\n", 377 | "# Pase frontal de la red\n", 378 | "img_idx = 0\n", 379 | "prediction = model.forward(images[img_idx,:])\n", 380 | "\n", 381 | "print(prediction)" 382 | ] 383 | }, 384 | { 385 | "cell_type": "code", 386 | "execution_count": 15, 387 | "metadata": {}, 388 | "outputs": [ 389 | { 390 | "data": { 391 | "image/png": "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", 392 | "text/plain": [ 393 | "
" 394 | ] 395 | }, 396 | "metadata": { 397 | "image/png": { 398 | "height": 235, 399 | "width": 424 400 | }, 401 | "needs_background": "light" 402 | }, 403 | "output_type": "display_data" 404 | } 405 | ], 406 | "source": [ 407 | "img = images[img_idx]\n", 408 | "helper.view_classify(img.view(1, 28, 28), prediction)" 409 | ] 410 | }, 411 | { 412 | "cell_type": "markdown", 413 | "metadata": {}, 414 | "source": [ 415 | "Seguro ninguna de las clases tiene una probabilidad grande con respecto de las otras, esto se debe a que todavía no hemos entrenado la red. En el siguiente ejercicio entrenaremos la red.\n" 416 | ] 417 | }, 418 | { 419 | "cell_type": "code", 420 | "execution_count": null, 421 | "metadata": {}, 422 | "outputs": [], 423 | "source": [] 424 | } 425 | ], 426 | "metadata": { 427 | "kernelspec": { 428 | "display_name": "practicas_pt", 429 | "language": "python", 430 | "name": "python3" 431 | }, 432 | "language_info": { 433 | "codemirror_mode": { 434 | "name": "ipython", 435 | "version": 3 436 | }, 437 | "file_extension": ".py", 438 | "mimetype": "text/x-python", 439 | "name": "python", 440 | "nbconvert_exporter": "python", 441 | "pygments_lexer": "ipython3", 442 | "version": "3.10.4" 443 | }, 444 | "vscode": { 445 | "interpreter": { 446 | "hash": "e22d029f5570ef7df543599926afc42bb090457ba5a887f8aae20fd6018d0da0" 447 | } 448 | }, 449 | "widgets": { 450 | "state": {}, 451 | "version": "1.1.2" 452 | } 453 | }, 454 | "nbformat": 4, 455 | "nbformat_minor": 2 456 | } 457 | -------------------------------------------------------------------------------- /soluciones/helper.py: -------------------------------------------------------------------------------- 1 | import matplotlib.pyplot as plt 2 | import numpy as np 3 | from torch import nn, optim 4 | from torch.autograd import Variable 5 | 6 | 7 | def test_network(net, trainloader): 8 | 9 | criterion = nn.MSELoss() 10 | optimizer = optim.Adam(net.parameters(), lr=0.001) 11 | 12 | dataiter = iter(trainloader) 13 | images, labels = dataiter.next() 14 | 15 | # Create Variables for the inputs and targets 16 | inputs = Variable(images) 17 | targets = Variable(images) 18 | 19 | # Clear the gradients from all Variables 20 | optimizer.zero_grad() 21 | 22 | # Forward pass, then backward pass, then update weights 23 | output = net.forward(inputs) 24 | loss = criterion(output, targets) 25 | loss.backward() 26 | optimizer.step() 27 | 28 | return True 29 | 30 | 31 | def imshow(image, ax=None, title=None, normalize=True): 32 | """Imshow for Tensor.""" 33 | if ax is None: 34 | fig, ax = plt.subplots() 35 | image = image.numpy().transpose((1, 2, 0)) 36 | 37 | if normalize: 38 | mean = np.array([0.485, 0.456, 0.406]) 39 | std = np.array([0.229, 0.224, 0.225]) 40 | image = std * image + mean 41 | image = np.clip(image, 0, 1) 42 | 43 | ax.imshow(image) 44 | ax.spines['top'].set_visible(False) 45 | ax.spines['right'].set_visible(False) 46 | ax.spines['left'].set_visible(False) 47 | ax.spines['bottom'].set_visible(False) 48 | ax.tick_params(axis='both', length=0) 49 | ax.set_xticklabels('') 50 | ax.set_yticklabels('') 51 | 52 | return ax 53 | 54 | 55 | def view_recon(img, recon): 56 | ''' Function for displaying an image (as a PyTorch Tensor) and its 57 | reconstruction also a PyTorch Tensor 58 | ''' 59 | 60 | fig, axes = plt.subplots(ncols=2, sharex=True, sharey=True) 61 | axes[0].imshow(img.numpy().squeeze()) 62 | axes[1].imshow(recon.data.numpy().squeeze()) 63 | for ax in axes: 64 | ax.axis('off') 65 | ax.set_adjustable('box-forced') 66 | 67 | def view_classify(img, ps, version="MNIST"): 68 | ''' Function for viewing an image and it's predicted classes. 69 | ''' 70 | ps = ps.data.numpy().squeeze() 71 | 72 | fig, (ax1, ax2) = plt.subplots(figsize=(6,9), ncols=2) 73 | ax1.imshow(img.resize_(1, 28, 28).numpy().squeeze()) 74 | ax1.axis('off') 75 | ax2.barh(np.arange(10), ps) 76 | ax2.set_aspect(0.1) 77 | ax2.set_yticks(np.arange(10)) 78 | if version == "MNIST": 79 | ax2.set_yticklabels(np.arange(10)) 80 | elif version == "Fashion": 81 | ax2.set_yticklabels(['T-shirt/top', 82 | 'Trouser', 83 | 'Pullover', 84 | 'Dress', 85 | 'Coat', 86 | 'Sandal', 87 | 'Shirt', 88 | 'Sneaker', 89 | 'Bag', 90 | 'Ankle Boot'], size='small'); 91 | ax2.set_title('Class Probability') 92 | ax2.set_xlim(0, 1.1) 93 | 94 | plt.tight_layout() 95 | --------------------------------------------------------------------------------