script.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num GPUs Available:  0\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow_datasets as tfds\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tfds.core.DatasetInfo(\n",
       "    name='emnist',\n",
       "    full_name='emnist/letters/3.0.0',\n",
       "    description=\"\"\"\n",
       "    The EMNIST dataset is a set of handwritten character digits derived from the NIST Special Database 19 and converted to a 28x28 pixel image format and dataset structure that directly matches the MNIST dataset.\n",
       "    \n",
       "    Note: Like the original EMNIST data, images provided here are inverted horizontally and rotated 90 anti-clockwise. You can use `tf.transpose` within `ds.map` to convert the images to a human-friendlier format.\n",
       "    \"\"\",\n",
       "    config_description=\"\"\"\n",
       "    EMNIST Letters\n",
       "    \"\"\",\n",
       "    homepage='https://www.nist.gov/itl/products-and-services/emnist-dataset',\n",
       "    data_dir='C:\\\\Users\\\\feder\\\\tensorflow_datasets\\\\emnist\\\\letters\\\\3.0.0',\n",
       "    file_format=tfrecord,\n",
       "    download_size=535.73 MiB,\n",
       "    dataset_size=44.14 MiB,\n",
       "    features=FeaturesDict({\n",
       "        'image': Image(shape=(28, 28, 1), dtype=uint8),\n",
       "        'label': ClassLabel(shape=(), dtype=int64, num_classes=37),\n",
       "    }),\n",
       "    supervised_keys=('image', 'label'),\n",
       "    disable_shuffling=False,\n",
       "    splits={\n",
       "        'test': <SplitInfo num_examples=14800, num_shards=1>,\n",
       "        'train': <SplitInfo num_examples=88800, num_shards=1>,\n",
       "    },\n",
       "    citation=\"\"\"@article{cohen_afshar_tapson_schaik_2017,\n",
       "        title={EMNIST: Extending MNIST to handwritten letters},\n",
       "        DOI={10.1109/ijcnn.2017.7966217},\n",
       "        journal={2017 International Joint Conference on Neural Networks (IJCNN)},\n",
       "        author={Cohen, Gregory and Afshar, Saeed and Tapson, Jonathan and Schaik, Andre Van},\n",
       "        year={2017}\n",
       "    }\"\"\",\n",
       ")"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data, metadata = tfds.load('emnist/letters', as_supervised=True, with_info=True)\n",
    "metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_shape = (28, 28, 1)\n",
    "\n",
    "classes = metadata.features[\"label\"].names\n",
    "num_classes = len(classes)\n",
    "\n",
    "tam_training_data= metadata.splits[\"train\"].num_examples\n",
    "tam_testing_data = metadata.splits[\"test\"].num_examples\n",
    "\n",
    "batch_size = 64\n",
    "epochs = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalizar datos\n",
    "def normalize(images, labels):\n",
    "    images = tf.cast(images, tf.float32)\n",
    "    images /= 255\n",
    "    return images, labels\n",
    "\n",
    "training_data, testing_data = data[\"train\"], data[\"test\"]\n",
    "\n",
    "training_data = training_data.map(normalize)\n",
    "testing_data = testing_data.map(normalize)\n",
    "\n",
    "training_data = training_data.cache()\n",
    "testing_data = testing_data.cache()\n",
    "\n",
    "training_data = training_data.repeat().shuffle(tam_training_data).batch(batch_size)\n",
    "testing_data = testing_data.batch(batch_size)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 26, 26, 32)        320       \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2  (None, 13, 13, 32)        0         \n",
      " D)                                                              \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 11, 11, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPoolin  (None, 5, 5, 64)          0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 1600)              0         \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 1600)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 512)               819712    \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 256)               131328    \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 37)                9509      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 979365 (3.74 MB)\n",
      "Trainable params: 979365 (3.74 MB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = tf.keras.Sequential(\n",
    "    [\n",
    "        tf.keras.Input(shape=input_shape),\n",
    "        tf.keras.layers.Conv2D(32, kernel_size=(3, 3), activation=\"relu\"),\n",
    "        tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),\n",
    "        tf.keras.layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\"),\n",
    "        tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),\n",
    "        tf.keras.layers.Flatten(),\n",
    "        tf.keras.layers.Dropout(0.5),\n",
    "        tf.keras.layers.Dense(512, activation=\"relu\"),\n",
    "        tf.keras.layers.Dense(256, activation=\"relu\"),\n",
    "        tf.keras.layers.Dense(num_classes, activation=\"softmax\"),\n",
    "    ]\n",
    ")\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(), optimizer=\"adam\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "1388/1388 [==============================] - 61s 40ms/step - loss: 0.5391 - accuracy: 0.8283 - val_loss: 0.3105 - val_accuracy: 0.8912\n",
      "Epoch 2/20\n",
      "1388/1388 [==============================] - 50s 36ms/step - loss: 0.2646 - accuracy: 0.9102 - val_loss: 0.2822 - val_accuracy: 0.9032\n",
      "Epoch 3/20\n",
      "1388/1388 [==============================] - 52s 37ms/step - loss: 0.2214 - accuracy: 0.9244 - val_loss: 0.2245 - val_accuracy: 0.9230\n",
      "Epoch 4/20\n",
      "1388/1388 [==============================] - 47s 34ms/step - loss: 0.1941 - accuracy: 0.9313 - val_loss: 0.2128 - val_accuracy: 0.9247\n",
      "Epoch 5/20\n",
      "1388/1388 [==============================] - 50s 36ms/step - loss: 0.1729 - accuracy: 0.9380 - val_loss: 0.2252 - val_accuracy: 0.9220\n",
      "Epoch 6/20\n",
      "1388/1388 [==============================] - 51s 37ms/step - loss: 0.1677 - accuracy: 0.9390 - val_loss: 0.2280 - val_accuracy: 0.9202\n",
      "Epoch 7/20\n",
      "1388/1388 [==============================] - 48s 35ms/step - loss: 0.1521 - accuracy: 0.9436 - val_loss: 0.2045 - val_accuracy: 0.9286\n",
      "Epoch 8/20\n",
      "1388/1388 [==============================] - 47s 34ms/step - loss: 0.1440 - accuracy: 0.9463 - val_loss: 0.2109 - val_accuracy: 0.9290\n",
      "Epoch 9/20\n",
      "1388/1388 [==============================] - 48s 34ms/step - loss: 0.1378 - accuracy: 0.9471 - val_loss: 0.2096 - val_accuracy: 0.9297\n",
      "Epoch 10/20\n",
      "1388/1388 [==============================] - 47s 34ms/step - loss: 0.1282 - accuracy: 0.9516 - val_loss: 0.2121 - val_accuracy: 0.9285\n",
      "Epoch 11/20\n",
      "1388/1388 [==============================] - 47s 34ms/step - loss: 0.1259 - accuracy: 0.9523 - val_loss: 0.2134 - val_accuracy: 0.9294\n",
      "Epoch 12/20\n",
      "1388/1388 [==============================] - 49s 35ms/step - loss: 0.1202 - accuracy: 0.9533 - val_loss: 0.2187 - val_accuracy: 0.9284\n",
      "Epoch 13/20\n",
      "1388/1388 [==============================] - 49s 35ms/step - loss: 0.1128 - accuracy: 0.9556 - val_loss: 0.2204 - val_accuracy: 0.9288\n",
      "Epoch 14/20\n",
      "1388/1388 [==============================] - 46s 33ms/step - loss: 0.1119 - accuracy: 0.9563 - val_loss: 0.2242 - val_accuracy: 0.9303\n",
      "Epoch 15/20\n",
      "1388/1388 [==============================] - 46s 33ms/step - loss: 0.1042 - accuracy: 0.9584 - val_loss: 0.2228 - val_accuracy: 0.9317\n",
      "Epoch 16/20\n",
      "1388/1388 [==============================] - 46s 33ms/step - loss: 0.1046 - accuracy: 0.9587 - val_loss: 0.2231 - val_accuracy: 0.9290\n",
      "Epoch 17/20\n",
      "1388/1388 [==============================] - 46s 33ms/step - loss: 0.1007 - accuracy: 0.9596 - val_loss: 0.2296 - val_accuracy: 0.9295\n",
      "Epoch 18/20\n",
      "1388/1388 [==============================] - 47s 34ms/step - loss: 0.0989 - accuracy: 0.9605 - val_loss: 0.2229 - val_accuracy: 0.9316\n",
      "Epoch 19/20\n",
      "1388/1388 [==============================] - 47s 34ms/step - loss: 0.0955 - accuracy: 0.9609 - val_loss: 0.2499 - val_accuracy: 0.9282\n",
      "Epoch 20/20\n",
      "1388/1388 [==============================] - 46s 33ms/step - loss: 0.0963 - accuracy: 0.9618 - val_loss: 0.2490 - val_accuracy: 0.9283\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(training_data, epochs=epochs, steps_per_epoch=math.ceil(tam_training_data/batch_size), validation_data=testing_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Accuracy')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(ncols=2, figsize=(15, 5))\n",
    "\n",
    "ax[0].plot(history.history[\"loss\"], color=\"r\")\n",
    "ax[0].set_title(\"Loss\")\n",
    "\n",
    "ax[1].plot(history.history[\"accuracy\"])\n",
    "ax[1].set_title(\"Accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 8ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x1440 with 100 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for test_images, test_labels in testing_data.take(1):\n",
    "  test_images = test_images.numpy()\n",
    "  test_labels = test_labels.numpy()\n",
    "  predictions = model.predict(test_images)\n",
    "  \n",
    "def draw_images(i, arr_predictions, labels, images):\n",
    "  arr_predictions, label, img = arr_predictions[i], labels[i], images[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks([])\n",
    "  plt.yticks([])\n",
    "  \n",
    "  plt.imshow(img[...,0].T, cmap=plt.cm.binary)\n",
    "\n",
    "  prediction_label = np.argmax(arr_predictions)\n",
    "  if prediction_label == label:\n",
    "    color = 'blue'\n",
    "  else:\n",
    "    color = 'red'\n",
    "  \n",
    "  plt.xlabel(\"{} {:2.0f}% ({})\".format(chr(int(classes[prediction_label])+64),\n",
    "                                100*np.max(arr_predictions),\n",
    "                                chr(int(classes[label])+64)),\n",
    "                                color=color)\n",
    "  \n",
    "def draw_prediction_graphic(i, arr_predictions, label):\n",
    "  arr_predictions, label = arr_predictions[i], label[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks([])\n",
    "  plt.yticks([])\n",
    "  graphic = plt.bar(range(num_classes), arr_predictions, color=\"#777777\")\n",
    "  plt.ylim([0, 1]) \n",
    "  prediction_label = np.argmax(arr_predictions)\n",
    "  \n",
    "  graphic[prediction_label].set_color('red')\n",
    "  graphic[label].set_color('blue')\n",
    "  \n",
    "rows = 10\n",
    "columns = 5\n",
    "num_images = rows*columns\n",
    "plt.figure(figsize=(2*2*columns, 2*rows))\n",
    "for i in range(num_images):\n",
    "  plt.subplot(rows, 2*columns, 2*i+1)\n",
    "  draw_images(i, predictions, test_labels, test_images)\n",
    "  plt.subplot(rows, 2*columns, 2*i+2)\n",
    "  draw_prediction_graphic(i, predictions, test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.4"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}