Copy_of_Exercise_8_Question.ipynb

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      "name": "Copy of Exercise 8 - Question.ipynb",
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    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/RhuthuHegde/Tensorflow-coursera/blob/master/Copy_of_Exercise_8_Question.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zX4Kg8DUTKWO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "wYtuKeK0dImp",
        "colab": {}
      },
      "source": [
        "import csv\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from google.colab import files"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "EmMyh9_mkDHF"
      },
      "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": {
        "colab_type": "code",
        "id": "IcLOZlnnc_N7",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "b2d395e5-8600-4aa0-a10d-9127e995c741"
      },
      "source": [
        "uploaded=files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-d5c3f089-92c0-421d-87ac-676a969008f6\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-d5c3f089-92c0-421d-87ac-676a969008f6\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving sign_mnist_test.csv to sign_mnist_test.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "4kxw-_rmcnVu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "outputId": "a05090a3-e08a-4490-d6ca-9760af98477f"
      },
      "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",
        "    with open(filename) as training_file:\n",
        "      # Your code starts here\n",
        "        read=csv.reader(training_file,delimiter=',')\n",
        "        first_line=True\n",
        "        temp_images=[]\n",
        "        temp_labels=[]\n",
        "        for row in read:\n",
        "            if first_line:\n",
        "                first_line=False\n",
        "            else:\n",
        "                temp_labels.append(row[0])\n",
        "                images_data=row[1:785]\n",
        "                as_array=np.array_split(images_data,28)\n",
        "                temp_images.append(as_array)\n",
        "        \n",
        "        images=np.array(temp_images).astype('float')\n",
        "        labels=np.array(temp_labels).astype('float')\n",
        "      # Your code ends here\n",
        "    return images, labels\n",
        "\n",
        "\n",
        "training_images, training_labels = get_data('sign_mnist_train.csv')\n",
        "testing_images, testing_labels = get_data('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": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;31mTypeError\u001b[0m: only size-1 arrays can be converted to Python scalars",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-14-617d8d81a2af>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m \u001b[0mtraining_images\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraining_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'sign_mnist_train.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     39\u001b[0m \u001b[0mtesting_images\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtesting_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'sign_mnist_test.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-14-617d8d81a2af>\u001b[0m in \u001b[0;36mget_data\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m     30\u001b[0m                 \u001b[0mtemp_images\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mas_array\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m         \u001b[0mimages\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemp_images\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'float'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     33\u001b[0m         \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemp_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'float'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m       \u001b[0;31m# Your code ends here\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: setting an array element with a sequence."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "awoqRpyZdQkD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        },
        "outputId": "6743ee5a-6758-44a4-e1e0-879c2240f087"
      },
      "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,axis=3)# Your Code Here\n",
        "testing_images = np.expand_dims(testing_images,axis=3)# Your Code Here\n",
        "\n",
        "# Create an ImageDataGenerator and do Image Augmentation\n",
        "train_datagen = ImageDataGenerator(\n",
        "    # Your Code Here\n",
        "    rescale=1/255,\n",
        "    rotation_range=40,\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",
        "      fill_mode='nearest'\n",
        "    )\n",
        "\n",
        "validation_datagen = ImageDataGenerator(\n",
        "    \n",
        "    # Your Code Here\n",
        "    rescale=1/255\n",
        "    )\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": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-16-61c697f7ace0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# Hint: np.expand_dims\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mtraining_images\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpand_dims\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtraining_images\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;31m# Your Code Here\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0mtesting_images\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpand_dims\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtesting_images\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;31m# Your Code Here\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'training_images' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "16CL6RbfDY56",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Rmb7S32cgRqS",
        "colab": {}
      },
      "source": [
        "# Define the model\n",
        "# Use no more than 2 Conv2D and 2 MaxPooling2D\n",
        "model = tf.keras.models.Sequential([\n",
        "    # Your Code Here\n",
        "    tf.keras.layers.Conv2D(32,(3,3),activation='relu',input_shape=(28,28)),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "\n",
        "    tf.keras.layers.Conv2D(64,(3,3),activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "\n",
        "    tf.keras.layers.Flatten()\n",
        "    tf.keras.Dropout(0.2),\n",
        "    tf.keras.layers.Dense(512,activation='relu'),\n",
        "    tf.keras.layers.Dense(26,activation='softmax')\n",
        "    )\n",
        "train_generator=train_datagen.flow(\n",
        "    training_images,\n",
        "    training_labels,\n",
        "    batch_size=20\n",
        ")\n",
        "validation_generator=validation_datagen.flow(\n",
        "    testing_images,\n",
        "    testing_labels,\n",
        "    batch_size=20\n",
        ")\n",
        "\n",
        "class mycallback(tf.keras.callbacks.Callback):\n",
        "  def on_epoch_end(self,epoch,logs={}):\n",
        "    if(logs.get('acc')>0.99):\n",
        "      print('\\n Reached 99% accuracy\\n')\n",
        "      self.model.stop_training=True\n",
        "callbacks=mycallback()\n",
        "# Compile Model. \n",
        "model.compile(# Your Code Here\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              optimizer='rmsprop',\n",
        "              metrics=['acc']\n",
        "              \n",
        "              )\n",
        "\n",
        "# Train the Model\n",
        "history = model.fit(# Your Code Here\\\n",
        "  train_generator,\n",
        "  validation_data=validation_generator,\n",
        "  epochs=10,\n",
        "  steps_per_epoch=len(training_images)/20,\n",
        "  validation_steps=len(testing_images)/20,\n",
        "  callbacks=[callbacks]\n",
        "                    )\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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "_Q3Zpr46dsij",
        "colab": {}
      },
      "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']# Your Code Here\n",
        "val_acc = history.history['val_acc']# Your Code Here\n",
        "loss = history.history['loss']# Your Code Here\n",
        "val_loss = history.history['val_loss']# Your Code Here\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": null,
      "outputs": []
    }
  ]
}