Week 3 - Class 3 LAB SOL.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "0bd64156",
      "metadata": {
        "id": "0bd64156"
      },
      "source": [
        "# Week 3 - Class 3\n",
        "\n",
        "This notebook have the most important exercises data wrangling operations for the data used in this course\n",
        "\n",
        "- Text\n",
        "- Numbers\n",
        "- Images Segmentation\n",
        "- Images"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d5512175",
      "metadata": {
        "id": "d5512175"
      },
      "source": [
        "# Text"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "da184482",
      "metadata": {
        "id": "da184482"
      },
      "source": [
        "Your dataset information:\n",
        "\n",
        "https://keras.io/api/datasets/imdb/"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c45a2caf",
      "metadata": {
        "id": "c45a2caf"
      },
      "source": [
        "dataset of 25,000 movies reviews from IMDB\n",
        "abeled by sentiment (positive/negative): see set(train_labels)\n",
        "each review is encoded as a list of word indexes (integers): see train_data[0] for example\n",
        "\n",
        "the integer \"5\" encodes the 5th most frequent word in the data\n",
        "### Zero (0)\n",
        "\"0\" does not stand for a specific word, but instead is used to encode the pad token\n",
        "Padding is add the zeros at the end of the sequence to make the samples in the same size\n",
        "\n",
        "maxlen: int or None. Maximum sequence length. Any longer sequence will be truncated. Defaults to None, which means no truncation. None== length of the longest sentence, then truncation never happens\n",
        "\n",
        "### One (1)\n",
        "Start of sentence"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "04a32efc",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "04a32efc",
        "outputId": "ce1a791c-c194-4437-b9e5-e6f8da9da708"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz\n",
            "17464789/17464789 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "from tensorflow.keras.datasets import imdb\n",
        "# These are the default parameters\n",
        "start_char = 1\n",
        "oov_char = 2\n",
        "index_from = 3 #start_char+oov_char\n",
        "\n",
        "(train_data, train_labels), (test_data, test_labels) = imdb.load_data(\n",
        "    num_words=10000)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "96d04665",
      "metadata": {
        "id": "96d04665"
      },
      "source": [
        "The '3-index shift issue': see the load_data() and the get_word_index() as two separate functions. \n",
        "\n",
        "The load_data has special parameters, such as having reserved the 1 and 2 indexes for start_char and oov_char, respectively. \n",
        "\n",
        "The get_word_index gives the indexes of the words by overall frequency in the dataset, being the 1, the most frequent word in the dataset. This is independent from the load_data() function. \n",
        "\n",
        "When we load the data, we can choose to drop x-first indexes, the most frequent words. With a index_from, are saying to the function: 'give me the indexes of words shift +3'. Because of this, when converting the index to words, we need to subtract 3. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "688ca973",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "688ca973",
        "outputId": "0354a969-6cec-45c7-bea8-ea4d13ce9fec"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "zero: [1, 11, 19, 13]\n",
            "3-indx: [1, 14, 22, 16]\n"
          ]
        }
      ],
      "source": [
        "start_char = 1\n",
        "oov_char = 2\n",
        "index_from = 0 \n",
        "\n",
        "(x_train0, _), _ = imdb.load_data(start_char=start_char, oov_char=oov_char, index_from=index_from)\n",
        "\n",
        "index_from = 3 #re assigning \n",
        "(x_train3, _), _ = imdb.load_data(start_char=start_char, oov_char=oov_char, index_from=index_from)\n",
        "\n",
        "print('zero:',x_train0[0][0:4]) #print first sample of train, for the first 5 words\n",
        "print('3-indx:',x_train3[0][0:4])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "c622b22c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c622b22c",
        "outputId": "cc105675-8272-44d7-d2c4-6db986f37899"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[1, 11, 19, 13]"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "start_char = 1\n",
        "oov_char = 2\n",
        "index_from = 0 #start_char+oov_char\n",
        "\n",
        "(x_train, _), _ = imdb.load_data(start_char=start_char, oov_char=oov_char, index_from=index_from)\n",
        "\n",
        "x_train[0][0:4]\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1adaab85",
      "metadata": {
        "id": "1adaab85"
      },
      "source": [
        "To obtain the words, from the word index, we can subtract 3 from the train data index, or sum 3 from the word index"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "d0648f77",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d0648f77",
        "outputId": "7d29de1e-b0a2-4ce6-c06e-3132a5513bde"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb_word_index.json\n",
            "1641221/1641221 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "# Retrieve the word index file mapping words to indices\n",
        "word_index = imdb.get_word_index()\n",
        "# see the word index sorted\n",
        "#{k: v for k, v in sorted(word_index.items(), key=lambda item: item[1])}\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "45f24960",
      "metadata": {
        "id": "45f24960"
      },
      "source": [
        "### Sum to word index example"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "b5c4e686",
      "metadata": {
        "id": "b5c4e686"
      },
      "outputs": [],
      "source": [
        "# Retrieve the word index file mapping words to indices\n",
        "word_index = imdb.get_word_index()\n",
        "# Reverse the word index to obtain a dict mapping indices to words\n",
        "# And add `index_from` to indices to sync with `x_train`\n",
        "inverted_word_index = dict(\n",
        "    (i + index_from, word) for (word, i) in word_index.items()\n",
        ")\n",
        "# Update `inverted_word_index` to include `start_char` and `oov_char`\n",
        "inverted_word_index[start_char] = \"[START]\"\n",
        "inverted_word_index[oov_char] = \"[OOV]\"\n",
        "# Decode the first sequence in the dataset\n",
        "decoded_sequence = \" \".join(inverted_word_index[i] for i in x_train[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "3c0216eb",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "id": "3c0216eb",
        "outputId": "99f059a9-f0c8-4eef-ddb3-f9dfb2d5bcb8"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"[START] this film was just brilliant casting location scenery story direction everyone's really suited [START] part they played [OOV] you could just imagine being there robert redford's is an amazing actor [OOV] now [START] same being director norman's father came from [START] same scottish island as myself so i loved [START] fact there was a real connection with this film [START] witty remarks throughout [START] film were great it was just brilliant so much that i bought [START] film as soon as it was released for retail [OOV] would recommend it to everyone to watch [OOV] [START] fly fishing was amazing really cried at [START] end it was so sad [OOV] you know what they say if you cry at a film it must have been good [OOV] this definitely was also congratulations to [START] two little boy's that played [START] part's of norman [OOV] paul they were just brilliant children are often left out of [START] praising list i think because [START] stars that play them all grown up are such a big profile for [START] whole film but these children are amazing [OOV] should be praised for what they have done don't you think [START] whole story was so lovely because it was true [OOV] was someone's life after all that was shared with us all\""
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "decoded_sequence"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3fe9fc41",
      "metadata": {
        "id": "3fe9fc41"
      },
      "source": [
        "### Substract to train_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "64f1b875",
      "metadata": {
        "id": "64f1b875"
      },
      "outputs": [],
      "source": [
        "word_index = imdb.get_word_index()\n",
        "reverse_word_index = dict(\n",
        "    [(value, key) for (key, value) in word_index.items()])\n",
        "decoded_review = \" \".join(\n",
        "    [reverse_word_index.get(i - 3, \"?\") for i in train_data[0]])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "be9bae25",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "id": "be9bae25",
        "outputId": "4d3eb7e0-fc12-4cbc-9016-dffbd4ddba33"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"[START] this film was just brilliant casting location scenery story direction everyone's really suited [START] part they played [OOV] you could just imagine being there robert redford's is an amazing actor [OOV] now [START] same being director norman's father came from [START] same scottish island as myself so i loved [START] fact there was a real connection with this film [START] witty remarks throughout [START] film were great it was just brilliant so much that i bought [START] film as soon as it was released for retail [OOV] would recommend it to everyone to watch [OOV] [START] fly fishing was amazing really cried at [START] end it was so sad [OOV] you know what they say if you cry at a film it must have been good [OOV] this definitely was also congratulations to [START] two little boy's that played [START] part's of norman [OOV] paul they were just brilliant children are often left out of [START] praising list i think because [START] stars that play them all grown up are such a big profile for [START] whole film but these children are amazing [OOV] should be praised for what they have done don't you think [START] whole story was so lovely because it was true [OOV] was someone's life after all that was shared with us all\""
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "decoded_sequence"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "074c5036",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "074c5036",
        "outputId": "de3cc596-06a0-48bd-f957-50a21f52a265"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(25000,) (25000,)\n",
            "(25000,) (25000,)\n"
          ]
        }
      ],
      "source": [
        "# check the number of samples in training and testing dataset\n",
        "\n",
        "print(train_data.shape,train_labels.shape)\n",
        "print(test_data.shape,test_labels.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "df0b60a6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "df0b60a6",
        "outputId": "f68f7a53-ffb4-46a7-c1ab-52babeed10b9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "189 1\n",
            "260 0\n"
          ]
        }
      ],
      "source": [
        "# check the size of training and testing dataset for one sample\n",
        "\n",
        "print(len(train_data[1]), train_labels[0])\n",
        "print(len(test_data[1]), test_labels[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "e8e7ed85",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e8e7ed85",
        "outputId": "d7e343c9-6452-426f-d1d8-d4689ee63b44"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "9999"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# check the maximum length in the data\n",
        "\n",
        "max([max(sequence) for sequence in train_data])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "744763e9",
      "metadata": {
        "id": "744763e9"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "\n",
        "#create a function to transfor list of word-indexes to vectors\n",
        "def vectorize_sequences(sequences, dimension=10000):\n",
        "    #start by create the placeholder for the word vectors\n",
        "    #np.zeros(rows, columns)-> array with zeros with rows=nb of samples and columns=vocabulary dimension\n",
        "    results = np.zeros((len(sequences), dimension))\n",
        "    #for each row, give an i (index) and the sequence itself\n",
        "    for i, sequence in enumerate(sequences):\n",
        "        #inside the sequence, j will go through the columns\n",
        "        for j in sequence:\n",
        "            #for i (the sequence number) and j (the column in that sequence), assign 1 to that cell in results\n",
        "            #results for row i, in j column will have a 1, if sequence i has j in its values\n",
        "            results[i, j] = 1.\n",
        "    return results\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "50e90040",
      "metadata": {
        "id": "50e90040"
      },
      "outputs": [],
      "source": [
        "x_train = vectorize_sequences(train_data)\n",
        "x_test = vectorize_sequences(test_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "c858a331",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c858a331",
        "outputId": "99c6bbbf-02ce-477b-8447-bd7cfa89fdcd"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0., 1., 1., ..., 0., 0., 0.])"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_train[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "d66543ef",
      "metadata": {
        "id": "d66543ef"
      },
      "outputs": [],
      "source": [
        "y_train = np.asarray(train_labels).astype(\"float32\")\n",
        "y_test = np.asarray(test_labels).astype(\"float32\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7072f1b4",
      "metadata": {
        "id": "7072f1b4"
      },
      "source": [
        "# Numbers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "c4fdf5cf",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c4fdf5cf",
        "outputId": "51da6868-a87a-4cf9-ad0e-6b26fe5c1f14"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/boston_housing.npz\n",
            "57026/57026 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "from tensorflow.keras.datasets import boston_housing\n",
        "(train_data, train_targets), (test_data, test_targets) = boston_housing.load_data()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "3983c186",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3983c186",
        "outputId": "fb7dbf09-010b-4e01-f8d3-5a79c5ed3a5c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(404, 13)"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "688c024a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "688c024a",
        "outputId": "c34ca140-24ce-46ce-a780-c6607ef41a53"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(102, 13)"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "c0b6c3ee",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c0b6c3ee",
        "outputId": "ea51e36c-ab18-4ad9-edd3-1da317feb984"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([15.2, 42.3, 50. , 21.1, 17.7, 18.5, 11.3, 15.6, 15.6, 14.4, 12.1,\n",
              "       17.9, 23.1, 19.9, 15.7,  8.8, 50. , 22.5, 24.1, 27.5, 10.9, 30.8,\n",
              "       32.9, 24. , 18.5, 13.3, 22.9, 34.7, 16.6, 17.5, 22.3, 16.1, 14.9,\n",
              "       23.1, 34.9, 25. , 13.9, 13.1, 20.4, 20. , 15.2, 24.7, 22.2, 16.7,\n",
              "       12.7, 15.6, 18.4, 21. , 30.1, 15.1, 18.7,  9.6, 31.5, 24.8, 19.1,\n",
              "       22. , 14.5, 11. , 32. , 29.4, 20.3, 24.4, 14.6, 19.5, 14.1, 14.3,\n",
              "       15.6, 10.5,  6.3, 19.3, 19.3, 13.4, 36.4, 17.8, 13.5, 16.5,  8.3,\n",
              "       14.3, 16. , 13.4, 28.6, 43.5, 20.2, 22. , 23. , 20.7, 12.5, 48.5,\n",
              "       14.6, 13.4, 23.7, 50. , 21.7, 39.8, 38.7, 22.2, 34.9, 22.5, 31.1,\n",
              "       28.7, 46. , 41.7, 21. , 26.6, 15. , 24.4, 13.3, 21.2, 11.7, 21.7,\n",
              "       19.4, 50. , 22.8, 19.7, 24.7, 36.2, 14.2, 18.9, 18.3, 20.6, 24.6,\n",
              "       18.2,  8.7, 44. , 10.4, 13.2, 21.2, 37. , 30.7, 22.9, 20. , 19.3,\n",
              "       31.7, 32. , 23.1, 18.8, 10.9, 50. , 19.6,  5. , 14.4, 19.8, 13.8,\n",
              "       19.6, 23.9, 24.5, 25. , 19.9, 17.2, 24.6, 13.5, 26.6, 21.4, 11.9,\n",
              "       22.6, 19.6,  8.5, 23.7, 23.1, 22.4, 20.5, 23.6, 18.4, 35.2, 23.1,\n",
              "       27.9, 20.6, 23.7, 28. , 13.6, 27.1, 23.6, 20.6, 18.2, 21.7, 17.1,\n",
              "        8.4, 25.3, 13.8, 22.2, 18.4, 20.7, 31.6, 30.5, 20.3,  8.8, 19.2,\n",
              "       19.4, 23.1, 23. , 14.8, 48.8, 22.6, 33.4, 21.1, 13.6, 32.2, 13.1,\n",
              "       23.4, 18.9, 23.9, 11.8, 23.3, 22.8, 19.6, 16.7, 13.4, 22.2, 20.4,\n",
              "       21.8, 26.4, 14.9, 24.1, 23.8, 12.3, 29.1, 21. , 19.5, 23.3, 23.8,\n",
              "       17.8, 11.5, 21.7, 19.9, 25. , 33.4, 28.5, 21.4, 24.3, 27.5, 33.1,\n",
              "       16.2, 23.3, 48.3, 22.9, 22.8, 13.1, 12.7, 22.6, 15. , 15.3, 10.5,\n",
              "       24. , 18.5, 21.7, 19.5, 33.2, 23.2,  5. , 19.1, 12.7, 22.3, 10.2,\n",
              "       13.9, 16.3, 17. , 20.1, 29.9, 17.2, 37.3, 45.4, 17.8, 23.2, 29. ,\n",
              "       22. , 18. , 17.4, 34.6, 20.1, 25. , 15.6, 24.8, 28.2, 21.2, 21.4,\n",
              "       23.8, 31. , 26.2, 17.4, 37.9, 17.5, 20. ,  8.3, 23.9,  8.4, 13.8,\n",
              "        7.2, 11.7, 17.1, 21.6, 50. , 16.1, 20.4, 20.6, 21.4, 20.6, 36.5,\n",
              "        8.5, 24.8, 10.8, 21.9, 17.3, 18.9, 36.2, 14.9, 18.2, 33.3, 21.8,\n",
              "       19.7, 31.6, 24.8, 19.4, 22.8,  7.5, 44.8, 16.8, 18.7, 50. , 50. ,\n",
              "       19.5, 20.1, 50. , 17.2, 20.8, 19.3, 41.3, 20.4, 20.5, 13.8, 16.5,\n",
              "       23.9, 20.6, 31.5, 23.3, 16.8, 14. , 33.8, 36.1, 12.8, 18.3, 18.7,\n",
              "       19.1, 29. , 30.1, 50. , 50. , 22. , 11.9, 37.6, 50. , 22.7, 20.8,\n",
              "       23.5, 27.9, 50. , 19.3, 23.9, 22.6, 15.2, 21.7, 19.2, 43.8, 20.3,\n",
              "       33.2, 19.9, 22.5, 32.7, 22. , 17.1, 19. , 15. , 16.1, 25.1, 23.7,\n",
              "       28.7, 37.2, 22.6, 16.4, 25. , 29.8, 22.1, 17.4, 18.1, 30.3, 17.5,\n",
              "       24.7, 12.6, 26.5, 28.7, 13.3, 10.4, 24.4, 23. , 20. , 17.8,  7. ,\n",
              "       11.8, 24.4, 13.8, 19.4, 25.2, 19.4, 19.4, 29.1])"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_targets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "4556c164",
      "metadata": {
        "id": "4556c164"
      },
      "outputs": [],
      "source": [
        "mean = train_data.mean(axis=0)\n",
        "train_data -= mean\n",
        "std = train_data.std(axis=0)\n",
        "train_data /= std\n",
        "test_data -= mean\n",
        "test_data /= std"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c534c0e9",
      "metadata": {
        "id": "c534c0e9"
      },
      "source": [
        "# Images Segmentation"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8e4c6e85",
      "metadata": {
        "id": "8e4c6e85"
      },
      "source": [
        "Get the images from these two links and extract them:\n",
        "http://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz\n",
        "http://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "4131c510",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4131c510",
        "outputId": "e4487400-cb82-42b3-da92-58e4d12fc2e0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2023-03-28 15:37:10--  http://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz\n",
            "Resolving www.robots.ox.ac.uk (www.robots.ox.ac.uk)... 129.67.94.2\n",
            "Connecting to www.robots.ox.ac.uk (www.robots.ox.ac.uk)|129.67.94.2|:80... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: https://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz [following]\n",
            "--2023-03-28 15:37:11--  https://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz\n",
            "Connecting to www.robots.ox.ac.uk (www.robots.ox.ac.uk)|129.67.94.2|:443... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: https://thor.robots.ox.ac.uk/~vgg/data/pets/images.tar.gz [following]\n",
            "--2023-03-28 15:37:11--  https://thor.robots.ox.ac.uk/~vgg/data/pets/images.tar.gz\n",
            "Resolving thor.robots.ox.ac.uk (thor.robots.ox.ac.uk)... 129.67.95.98\n",
            "Connecting to thor.robots.ox.ac.uk (thor.robots.ox.ac.uk)|129.67.95.98|:443... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: https://thor.robots.ox.ac.uk/datasets/pets/images.tar.gz [following]\n",
            "--2023-03-28 15:37:12--  https://thor.robots.ox.ac.uk/datasets/pets/images.tar.gz\n",
            "Reusing existing connection to thor.robots.ox.ac.uk:443.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 791918971 (755M) [application/octet-stream]\n",
            "Saving to: ‘images.tar.gz’\n",
            "\n",
            "images.tar.gz       100%[===================>] 755.23M  28.6MB/s    in 28s     \n",
            "\n",
            "2023-03-28 15:37:40 (27.3 MB/s) - ‘images.tar.gz’ saved [791918971/791918971]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget http://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "d5e639b8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d5e639b8",
        "outputId": "7463bc15-9554-43de-efc6-48a318411957"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2023-03-28 15:37:40--  http://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz\n",
            "Resolving www.robots.ox.ac.uk (www.robots.ox.ac.uk)... 129.67.94.2\n",
            "Connecting to www.robots.ox.ac.uk (www.robots.ox.ac.uk)|129.67.94.2|:80... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: https://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz [following]\n",
            "--2023-03-28 15:37:40--  https://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz\n",
            "Connecting to www.robots.ox.ac.uk (www.robots.ox.ac.uk)|129.67.94.2|:443... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: https://thor.robots.ox.ac.uk/~vgg/data/pets/images.tar.gz [following]\n",
            "--2023-03-28 15:37:40--  https://thor.robots.ox.ac.uk/~vgg/data/pets/images.tar.gz\n",
            "Resolving thor.robots.ox.ac.uk (thor.robots.ox.ac.uk)... 129.67.95.98\n",
            "Connecting to thor.robots.ox.ac.uk (thor.robots.ox.ac.uk)|129.67.95.98|:443... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: https://thor.robots.ox.ac.uk/datasets/pets/images.tar.gz [following]\n",
            "--2023-03-28 15:37:41--  https://thor.robots.ox.ac.uk/datasets/pets/images.tar.gz\n",
            "Reusing existing connection to thor.robots.ox.ac.uk:443.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 791918971 (755M) [application/octet-stream]\n",
            "Saving to: ‘images.tar.gz.1’\n",
            "\n",
            "images.tar.gz.1     100%[===================>] 755.23M  28.6MB/s    in 27s     \n",
            "\n",
            "2023-03-28 15:38:08 (28.2 MB/s) - ‘images.tar.gz.1’ saved [791918971/791918971]\n",
            "\n",
            "--2023-03-28 15:38:08--  http://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz\n",
            "Resolving www.robots.ox.ac.uk (www.robots.ox.ac.uk)... 129.67.94.2\n",
            "Connecting to www.robots.ox.ac.uk (www.robots.ox.ac.uk)|129.67.94.2|:80... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: https://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz [following]\n",
            "--2023-03-28 15:38:08--  https://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz\n",
            "Connecting to www.robots.ox.ac.uk (www.robots.ox.ac.uk)|129.67.94.2|:443... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: https://thor.robots.ox.ac.uk/~vgg/data/pets/annotations.tar.gz [following]\n",
            "--2023-03-28 15:38:09--  https://thor.robots.ox.ac.uk/~vgg/data/pets/annotations.tar.gz\n",
            "Resolving thor.robots.ox.ac.uk (thor.robots.ox.ac.uk)... 129.67.95.98\n",
            "Connecting to thor.robots.ox.ac.uk (thor.robots.ox.ac.uk)|129.67.95.98|:443... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: https://thor.robots.ox.ac.uk/datasets/pets/annotations.tar.gz [following]\n",
            "--2023-03-28 15:38:09--  https://thor.robots.ox.ac.uk/datasets/pets/annotations.tar.gz\n",
            "Reusing existing connection to thor.robots.ox.ac.uk:443.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 19173078 (18M) [application/octet-stream]\n",
            "Saving to: ‘annotations.tar.gz’\n",
            "\n",
            "annotations.tar.gz  100%[===================>]  18.28M  14.9MB/s    in 1.2s    \n",
            "\n",
            "2023-03-28 15:38:11 (14.9 MB/s) - ‘annotations.tar.gz’ saved [19173078/19173078]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget http://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz\n",
        "!wget http://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz\n",
        "!tar -xf images.tar.gz\n",
        "!tar -xf annotations.tar.gz"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3a448c22",
      "metadata": {
        "id": "3a448c22"
      },
      "source": [
        "Do a list of images paths and of target paths"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "f421752b",
      "metadata": {
        "id": "f421752b"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "input_dir = \"images/\"\n",
        "target_dir = \"annotations/trimaps/\"\n",
        "\n",
        "input_img_paths = sorted(\n",
        "    [os.path.join(input_dir, fname)\n",
        "     for fname in os.listdir(input_dir)\n",
        "     if fname.endswith(\".jpg\")])\n",
        "target_paths = sorted(\n",
        "    [os.path.join(target_dir, fname)\n",
        "     for fname in os.listdir(target_dir)\n",
        "     if fname.endswith(\".png\") and not fname.startswith(\".\")])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c17695ba",
      "metadata": {
        "id": "c17695ba"
      },
      "source": [
        "Plot the image 10th"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "c42bb269",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "c42bb269",
        "outputId": "dae53177-3531-4bee-8b57-9e1d9a4b3b7e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7ff4d4ae8430>"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras.utils import load_img, img_to_array\n",
        "\n",
        "plt.axis(\"off\")\n",
        "plt.imshow(load_img(input_img_paths[9]))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "806d9a75",
      "metadata": {
        "id": "806d9a75"
      },
      "source": [
        "Plot its target"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "7a9d2d5d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "7a9d2d5d",
        "outputId": "77e38837-d996-40da-a818-8db9a9b72669"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "def display_target(target_array):\n",
        "    normalized_array = (target_array.astype(\"uint8\") - 1) * 127\n",
        "    plt.axis(\"off\")\n",
        "    plt.imshow(normalized_array[:, :, 0])\n",
        "\n",
        "img = img_to_array(load_img(target_paths[9], color_mode=\"grayscale\"))\n",
        "display_target(img)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bc92f073",
      "metadata": {
        "id": "bc92f073"
      },
      "source": [
        "How to load an image to a numeric tensor!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "f5a56eb4",
      "metadata": {
        "id": "f5a56eb4"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import random\n",
        "\n",
        "img_size = (200, 200)\n",
        "num_imgs = len(input_img_paths)\n",
        "\n",
        "random.Random(1337).shuffle(input_img_paths)\n",
        "random.Random(1337).shuffle(target_paths)\n",
        "\n",
        "def path_to_input_image(path):\n",
        "    return img_to_array(load_img(path, target_size=img_size))\n",
        "\n",
        "def path_to_target(path):\n",
        "    img = img_to_array(\n",
        "        load_img(path, target_size=img_size, color_mode=\"grayscale\"))\n",
        "    img = img.astype(\"uint8\") - 1\n",
        "    return img\n",
        "\n",
        "input_imgs = np.zeros((num_imgs,) + img_size + (3,), dtype=\"float32\")\n",
        "targets = np.zeros((num_imgs,) + img_size + (1,), dtype=\"uint8\")\n",
        "for i in range(num_imgs):\n",
        "    input_imgs[i] = path_to_input_image(input_img_paths[i])\n",
        "    targets[i] = path_to_target(target_paths[i])\n",
        "\n",
        "num_val_samples = 1000\n",
        "train_input_imgs = input_imgs[:-num_val_samples]\n",
        "train_targets = targets[:-num_val_samples]\n",
        "val_input_imgs = input_imgs[-num_val_samples:]\n",
        "val_targets = targets[-num_val_samples:]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9a7da3c7",
      "metadata": {
        "id": "9a7da3c7"
      },
      "source": [
        "# Images"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "6dd6ec56",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6dd6ec56",
        "outputId": "c4d1c143-8baf-46d7-8402-0824f50c3bb4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading dogs-vs-cats.zip to /content\n",
            " 98% 793M/812M [00:04<00:00, 244MB/s]\n",
            "100% 812M/812M [00:04<00:00, 204MB/s]\n"
          ]
        }
      ],
      "source": [
        "!mkdir ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "!chmod 600 ~/.kaggle/kaggle.json\n",
        "!kaggle competitions download -c dogs-vs-cats\n",
        "!unzip -qq dogs-vs-cats.zip\n",
        "!unzip -qq train.zip\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0cf18c27",
      "metadata": {
        "id": "0cf18c27"
      },
      "source": [
        "Create directories and datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "02ca3fde",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "02ca3fde",
        "outputId": "dc58a5e0-c8d1-447b-f06a-8e6a2e130ce2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 2000 files belonging to 2 classes.\n",
            "Found 1000 files belonging to 2 classes.\n",
            "Found 2000 files belonging to 2 classes.\n"
          ]
        }
      ],
      "source": [
        "import os, shutil, pathlib\n",
        "from tensorflow.keras.utils import image_dataset_from_directory\n",
        "\n",
        "original_dir = pathlib.Path(\"train\")\n",
        "new_base_dir = pathlib.Path(\"cats_vs_dogs_small\")\n",
        "\n",
        "def make_subset(subset_name, start_index, end_index):\n",
        "    for category in (\"cat\", \"dog\"):\n",
        "        dir = new_base_dir / subset_name / category\n",
        "        os.makedirs(dir)\n",
        "        fnames = [f\"{category}.{i}.jpg\" for i in range(start_index, end_index)]\n",
        "        for fname in fnames:\n",
        "            shutil.copyfile(src=original_dir / fname,\n",
        "                            dst=dir / fname)\n",
        "\n",
        "make_subset(\"train\", start_index=0, end_index=1000)\n",
        "make_subset(\"validation\", start_index=1000, end_index=1500)\n",
        "make_subset(\"test\", start_index=1500, end_index=2500)\n",
        "\n",
        "train_dataset = image_dataset_from_directory(\n",
        "    new_base_dir / \"train\",\n",
        "    image_size=(180, 180),\n",
        "    batch_size=32)\n",
        "validation_dataset = image_dataset_from_directory(\n",
        "    new_base_dir / \"validation\",\n",
        "    image_size=(180, 180),\n",
        "    batch_size=32)\n",
        "test_dataset = image_dataset_from_directory(\n",
        "    new_base_dir / \"test\",\n",
        "    image_size=(180, 180),\n",
        "    batch_size=32)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "0de2f9fb",
      "metadata": {
        "id": "0de2f9fb"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3.9.13",
      "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": "3.9.13"
    },
    "vscode": {
      "interpreter": {
        "hash": "d33a1388bb95f6f47390937b44de6829ea073fadb3ed2a95296fdc5e4fdfba80"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}