Exercise_8_Question_with_kaggle_api.ipynb

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      "name": "Exercise 8 - Question.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/EgorBEremeev/SoloLearmML-coursera-deeplearning.ai/blob/master/Exercise_8_Question_with_kaggle_api.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wYtuKeK0dImp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import csv\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.optimizers import RMSprop\n",
        "from google.colab import files"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EmMyh9_mkDHF",
        "colab_type": "text"
      },
      "source": [
        "The data for this exercise is available at: https://www.kaggle.com/datamunge/sign-language-mnist/home\n",
        "\n",
        "Sign up and download to find 2 CSV files: sign_mnist_test.csv and sign_mnist_train.csv -- You will upload both of them using this button before you can continue.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mR7aOWztdbgf",
        "colab_type": "code",
        "outputId": "a338d03d-7134-4529-df70-12f2688fc7b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        }
      },
      "source": [
        "!pip install kaggle"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.6/dist-packages (1.5.5)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.6/dist-packages (from kaggle) (1.12.0)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.6/dist-packages (from kaggle) (2.5.3)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.6/dist-packages (from kaggle) (3.0.3)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.6/dist-packages (from kaggle) (2019.6.16)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from kaggle) (2.21.0)\n",
            "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from kaggle) (1.24.3)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from kaggle) (4.28.1)\n",
            "Requirement already satisfied: text-unidecode==1.2 in /usr/local/lib/python3.6/dist-packages (from python-slugify->kaggle) (1.2)\n",
            "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->kaggle) (2.8)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->kaggle) (3.0.4)\n"
          ],
          "name": "stdout"
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      "cell_type": "code",
      "metadata": {
        "id": "XgPzfS6xglWI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e1849ff2-1433-4b43-b196-ffc87a3119d8"
      },
      "source": [
        "!mkdir ~/.kaggle\n",
        "!touch ~/.kaggle/kaggle.json\n",
        "\n",
        "api_token = {\"username\":\"egorberemeev\",\"key\":\"13d8a9eacd02b125d0404596b4555c44\"}\n",
        "\n",
        "import json\n",
        "\n",
        "with open('/root/.kaggle/kaggle.json', 'w') as file:\n",
        "    json.dump(api_token, file)\n",
        "\n",
        "!chmod 600 ~/.kaggle/kaggle.json"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "mkdir: cannot create directory ‘/root/.kaggle’: File exists\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UWx4LyAVdjLC",
        "colab_type": "code",
        "outputId": "ca5732bd-8447-494e-8025-cde00f24e94e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "!kaggle datasets download -d datamunge/sign-language-mnist\n",
        "\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading sign-language-mnist.zip to /content\n",
            " 88% 27.0M/30.8M [00:00<00:00, 21.5MB/s]\n",
            "100% 30.8M/30.8M [00:00<00:00, 48.4MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IcLOZlnnc_N7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#uploaded=files.upload()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oL7CE0Ue6RAr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "import zipfile\n",
        "\n",
        "local_zip = '/content/sign-language-mnist.zip'\n",
        "zip_ref = zipfile.ZipFile(local_zip, 'r')\n",
        "zip_ref.extractall('/tmp/sign-language-mnist')\n",
        "zip_ref.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4kxw-_rmcnVu",
        "colab_type": "code",
        "outputId": "63939fed-0522-4bbb-fc75-e44d47303f41",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "def get_data(filename):\n",
        "  # You will need to write code that will read the file passed\n",
        "  # into this function. The first line contains the column headers\n",
        "  # so you should ignore it\n",
        "  # Each successive line contians 785 comma separated values between 0 and 255\n",
        "  # The first value is the label\n",
        "  # The rest are the pixel values for that picture\n",
        "  # The function will return 2 np.array types. One with all the labels\n",
        "  # One with all the images\n",
        "  #\n",
        "  # Tips: \n",
        "  # If you read a full line (as 'row') then row[0] has the label\n",
        "  # and row[1:785] has the 784 pixel values\n",
        "  # Take a look at np.array_split to turn the 784 pixels into 28x28\n",
        "  # You are reading in strings, but need the values to be floats\n",
        "  # Check out np.array().astype for a conversion\n",
        "    labels = np.empty(1)\n",
        "    images = np.empty([1,28,28])\n",
        "    with open(filename) as training_file:\n",
        "      filereader = csv.reader(training_file, delimiter=',')\n",
        "      temp_labels = []\n",
        "      temp_images = []\n",
        "      header_row = filereader.__next__()\n",
        "      #print(header_row)\n",
        "      for row in filereader:\n",
        "            temp_labels.append(row[0])\n",
        "            pixels = row[1:785]\n",
        "            image = np.array_split(pixels,28)\n",
        "            temp_images.append(image)\n",
        "      images = np.array(temp_images).astype(float)\n",
        "      labels = np.array(temp_labels).astype(float)\n",
        "    return images, labels\n",
        "\n",
        "\n",
        "training_images, training_labels = get_data('/tmp/sign-language-mnist/sign_mnist_train.csv')\n",
        "testing_images, testing_labels = get_data('/tmp/sign-language-mnist/sign_mnist_test.csv')\n",
        "\n",
        "# Keep these\n",
        "print(training_images.shape)\n",
        "print(training_labels.shape)\n",
        "print(testing_images.shape)\n",
        "print(testing_labels.shape)\n",
        "\n",
        "# Their output should be:\n",
        "# (27455, 28, 28)\n",
        "# (27455,)\n",
        "# (7172, 28, 28)\n",
        "# (7172,)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(27455, 28, 28)\n",
            "(27455,)\n",
            "(7172, 28, 28)\n",
            "(7172,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VVqAuSYRkXoX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# In this section you will have to add another dimension to the data\n",
        "# So, for example, if your array is (10000, 28, 28)\n",
        "# You will need to make it (10000, 28, 28, 1)\n",
        "# Hint: np.expand_dims\n",
        "\n",
        "training_images = np.expand_dims(training_images,3)\n",
        "testing_images = np.expand_dims(testing_images,3)\n",
        "\n",
        "# Keep These\n",
        "print(training_images.shape)\n",
        "print(testing_images.shape)\n",
        "    \n",
        "# Their output should be:\n",
        "# (27455, 28, 28, 1)\n",
        "# (7172, 28, 28, 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "awoqRpyZdQkD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "\n",
        "# Create an ImageDataGenerator and do Image Augmentation\n",
        "train_datagen = ImageDataGenerator(rescale = 1./255.,\n",
        "                                   rotation_range = 30,\n",
        "                                   width_shift_range = 0.2,\n",
        "                                   height_shift_range = 0.2,\n",
        "                                   shear_range = 0.2,\n",
        "                                   zoom_range = 0.2,\n",
        "                                   horizontal_flip = True)\n",
        "train_generator = train_datagen.flow(training_images, training_labels, batch_size=32)\n",
        "\n",
        "validation_datagen = ImageDataGenerator(rescale = 1./255.,\n",
        "                                   rotation_range = 30,\n",
        "                                   width_shift_range = 0.2,\n",
        "                                   height_shift_range = 0.2,\n",
        "                                   shear_range = 0.2,\n",
        "                                   zoom_range = 0.2,\n",
        "                                   horizontal_flip = True)\n",
        "validation_generator = validation_datagen.flow(testing_images, testing_labels, batch_size=32)\n",
        "    \n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rmb7S32cgRqS",
        "colab_type": "code",
        "outputId": "d87b3c04-a2c1-42f2-cef5-d341a4e56e64",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        }
      },
      "source": [
        "# Define the model\n",
        "# Use no more than 2 Conv2D and 2 MaxPooling2D\n",
        "model = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(28,28,1)),\n",
        "    tf.keras.layers.MaxPool2D(2,2),\n",
        "    tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\n",
        "    tf.keras.layers.MaxPool2D(2,2),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(256, activation='relu'),\n",
        "    tf.keras.layers.Dense(26, activation='softmax')\n",
        "    ])\n",
        "\n",
        "# Compile Model. \n",
        "model.compile(optimizer=RMSprop(lr=1e-3),\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['acc'])\n",
        "\n",
        "# Train the Model\n",
        "batch_size = 64\n",
        "history = model.fit_generator(train_datagen.flow(training_images, training_labels, batch_size=batch_size),\n",
        "                              steps_per_epoch=len(training_images) / batch_size,\n",
        "                              epochs=15,\n",
        "                              validation_data=validation_datagen.flow(testing_images, testing_labels, batch_size=batch_size),\n",
        "                              validation_steps=len(testing_images) / batch_size)\n",
        "\n",
        "model.evaluate(testing_images, testing_labels)\n",
        "    \n",
        "# The output from model.evaluate should be close to:\n",
        "[6.92426086682151, 0.56609035]\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0807 19:33:42.591364 139757305481088 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "429/428 [==============================] - 18s 41ms/step - loss: 2.7570 - acc: 0.1705 - val_loss: 2.3785 - val_acc: 0.2772\n",
            "Epoch 2/15\n",
            "429/428 [==============================] - 15s 35ms/step - loss: 2.1242 - acc: 0.3382 - val_loss: 2.0898 - val_acc: 0.3370\n",
            "Epoch 3/15\n",
            "429/428 [==============================] - 15s 34ms/step - loss: 1.7205 - acc: 0.4498 - val_loss: 1.6151 - val_acc: 0.4782\n",
            "Epoch 4/15\n",
            "429/428 [==============================] - 15s 34ms/step - loss: 1.4471 - acc: 0.5361 - val_loss: 1.3870 - val_acc: 0.5442\n",
            "Epoch 5/15\n",
            "429/428 [==============================] - 15s 35ms/step - loss: 1.2298 - acc: 0.6025 - val_loss: 1.3122 - val_acc: 0.5714\n",
            "Epoch 6/15\n",
            "429/428 [==============================] - 15s 34ms/step - loss: 1.0683 - acc: 0.6522 - val_loss: 1.0669 - val_acc: 0.6486\n",
            "Epoch 7/15\n",
            "429/428 [==============================] - 15s 35ms/step - loss: 0.9286 - acc: 0.6985 - val_loss: 0.9868 - val_acc: 0.6606\n",
            "Epoch 8/15\n",
            "429/428 [==============================] - 15s 34ms/step - loss: 0.8133 - acc: 0.7361 - val_loss: 0.9099 - val_acc: 0.6966\n",
            "Epoch 9/15\n",
            "429/428 [==============================] - 15s 35ms/step - loss: 0.7252 - acc: 0.7621 - val_loss: 0.8142 - val_acc: 0.7313\n",
            "Epoch 10/15\n",
            "429/428 [==============================] - 15s 35ms/step - loss: 0.6512 - acc: 0.7860 - val_loss: 0.7149 - val_acc: 0.7634\n",
            "Epoch 11/15\n",
            "429/428 [==============================] - 15s 35ms/step - loss: 0.5774 - acc: 0.8113 - val_loss: 0.7167 - val_acc: 0.7584\n",
            "Epoch 12/15\n",
            "429/428 [==============================] - 15s 34ms/step - loss: 0.5263 - acc: 0.8266 - val_loss: 0.6393 - val_acc: 0.7798\n",
            "Epoch 13/15\n",
            "429/428 [==============================] - 15s 34ms/step - loss: 0.4959 - acc: 0.8376 - val_loss: 0.5855 - val_acc: 0.8061\n",
            "Epoch 14/15\n",
            "429/428 [==============================] - 15s 34ms/step - loss: 0.4491 - acc: 0.8509 - val_loss: 0.6130 - val_acc: 0.8005\n",
            "Epoch 15/15\n",
            "429/428 [==============================] - 15s 34ms/step - loss: 0.4259 - acc: 0.8610 - val_loss: 0.5344 - val_acc: 0.8235\n",
            "7172/7172 [==============================] - 1s 93us/sample - loss: 133.6164 - acc: 0.7616\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[6.92426086682151, 0.56609035]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Q3Zpr46dsij",
        "colab_type": "code",
        "outputId": "64424910-b506-44fa-aa28-e0456a855aa9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        }
      },
      "source": [
        "# Plot the chart for accuracy and loss on both training and validation\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "acc = history.history['acc']\n",
        "val_acc = history.history['val_acc']\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs = range(len(acc))\n",
        "\n",
        "plt.plot(epochs, acc, 'r', label='Training accuracy')\n",
        "plt.plot(epochs, val_acc, 'b', label='Validation accuracy')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.legend()\n",
        "plt.figure()\n",
        "\n",
        "plt.plot(epochs, loss, 'r', label='Training Loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation Loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ol9R37nRj5p07u4TeoUO2Tvc/ccLt+vXXXdGsEiXg3nvdJY0Tgo1JkyV3Y9Kj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\nCUH/D/XhYjtlsGCuAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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WL0+xUQQmT9bsf++98MornsVpjAlultw90Lo1fPWVXmhN6qJJVqiQXlSNjdU+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\nLLkbY0wIsuRujDEhyJK7McaEoP8Hf32yayn/8EsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}