MNIST_ANN.ipynb

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      "name": "MNIST_ANN.ipynb",
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      "cell_type": "code",
      "metadata": {
        "id": "cEUq8cIBTeiX",
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        "outputId": "ecb56659-6540-4ae5-aaea-8dbff9a38922"
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      "source": [
        "# Description: This program classifies the MNIST handwritten digit images\n",
        "#              as a number 0 - 9\n",
        "\n",
        "# Install packages\n",
        "pip install tensorflow keras numpy mnist matplotlib"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: tensorflow in /usr/local/lib/python3.6/dist-packages (1.14.0rc1)\n",
            "Requirement already satisfied: keras in /usr/local/lib/python3.6/dist-packages (2.2.4)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (1.16.4)\n",
            "Requirement already satisfied: mnist in /usr/local/lib/python3.6/dist-packages (0.2.2)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (3.0.3)\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ehnsueDUUI1r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#import the packages / dependecies\n",
        "import numpy as np \n",
        "import mnist # Get data set from\n",
        "from keras.models import Sequential #ANN architecture\n",
        "from keras. layers import Dense # The layers in the ANN\n",
        "from keras.utils import to_categorical\n",
        "import matplotlib.pyplot as plt # Graph"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bNiQWH_NUhTy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Load the data set\n",
        "train_images = mnist.train_images() # training data of images\n",
        "train_labels = mnist.train_labels() # training data of the labels\n",
        "test_images = mnist. test_images()  # testing data images\n",
        "test_labels = mnist.test_labels()   # testing data labels"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VY0-HNI-VDgR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "49555bf2-1dd4-407e-8579-e8824dd48b07"
      },
      "source": [
        "#Normalize the images \n",
        "#Normalize the pixel values from [0, 255] to [-0.5 to 0.5]\n",
        "#This make the network easier to train\n",
        "train_images = (train_images / 255) - 0.5\n",
        "test_images = (test_images/ 255) - 0.5\n",
        "\n",
        "#Flatten the images. Flatten each 28 x 28 image into a 784= 28^2 \n",
        "#dimensional vector and pass into the neural network\n",
        "train_images = train_images.reshape((-1, 784))\n",
        "test_images = test_images.reshape((-1,784))\n",
        "#print the new image shape\n",
        "print(train_images.shape) #60,000 rows and 784 cols\n",
        "print(test_images.shape)  #10,000 rows and 784 cols"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(60000, 784)\n",
            "(10000, 784)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eYpbz8nsWerD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Build the ANN model\n",
        "#With 3 layers, 2 with 64 neurons and activation function = relu\n",
        "#    and  1 layer with 10 neurons with activation function= softmax\n",
        "model = Sequential()\n",
        "model.add(Dense(64, activation='relu', input_dim=784))\n",
        "model.add(Dense(64, activation='relu'))\n",
        "model.add(Dense(10, activation='softmax'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NG9dfatZXlTk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Compile the model\n",
        "# loss measures how well the model did on training, and then tries to improve on\n",
        "# it using the optimizer\n",
        "model.compile(\n",
        "  optimizer= 'adam',\n",
        "    loss = 'categorical_crossentropy', #loss function for classes > 2\n",
        "    metrics = ['accuracy']\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yhMQSC4AYBjt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "18664389-7bd8-498f-90a4-b8033efd8d8e"
      },
      "source": [
        "#Train the model\n",
        "model.fit(\n",
        "    train_images, #The training data images\n",
        "    to_categorical(train_labels),#The trainind data labels, label data only returns a single digit representing the class of each label Ex: train_labels = 2,to_categorical(2)= [0,0,1,0,0,0,0,0,0,0]\n",
        "    epochs=5, #Number of iterations over the entire data set to train on\n",
        "    batch_size = 3 #The number of samples per gradient update for training\n",
        ")"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "60000/60000 [==============================] - 31s 518us/step - loss: 0.3158 - acc: 0.9014\n",
            "Epoch 2/5\n",
            "60000/60000 [==============================] - 31s 513us/step - loss: 0.1876 - acc: 0.9433\n",
            "Epoch 3/5\n",
            "60000/60000 [==============================] - 31s 517us/step - loss: 0.1603 - acc: 0.9516\n",
            "Epoch 4/5\n",
            "60000/60000 [==============================] - 31s 511us/step - loss: 0.1446 - acc: 0.9570\n",
            "Epoch 5/5\n",
            "60000/60000 [==============================] - 30s 498us/step - loss: 0.1365 - acc: 0.9601\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f167e5e36d8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_6Df_KcYaPsd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "2a0472dc-1a26-4e58-c350-c276553361a5"
      },
      "source": [
        "#Evaluate the model\n",
        "model.evaluate(\n",
        "  test_images,\n",
        "  to_categorical(test_labels)\n",
        ")"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 0s 28us/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.15349477015165613, 0.9551]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3kTAapL9akr0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#save the model to disk\n",
        "model.save_weights('model.h5')\n",
        "# Load the model from disk later using:\n",
        "# Build the model.\n",
        "#model = Sequential([\n",
        "#  Dense(64, activation='relu', input_shape=(784,)),\n",
        "#  Dense(64, activation='relu'),\n",
        "#  Dense(10, activation='softmax'),\n",
        "#])\n",
        "# model.load_weights('model.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LBcV8923a0MW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "c04291a7-92ae-4b06-a1e7-39849fd5712e"
      },
      "source": [
        "#Make predictions\n",
        "# Predict on the first 5 test images.\n",
        "# Keep in mind that the output of our network is 10 probabilities, \n",
        "#   so we'll use np.argmax()to turn those into actual digits\n",
        "predictions = model.predict(test_images[:5])\n",
        "#print(predictions)\n",
        "print (np.argmax(predictions, axis =1))\n",
        "print(test_labels[:5])"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[7 2 1 0 4]\n",
            "[7 2 1 0 4]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zm4BrF2obdyr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1277
        },
        "outputId": "b53000a7-733a-495d-b67c-4a97dd9ef900"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "for i in range(0,5):\n",
        "  first_image = test_images[i]\n",
        "  first_image = np.array(first_image, dtype='float')\n",
        "  pixels = first_image.reshape((28, 28))\n",
        "  plt.imshow(pixels, cmap='gray')\n",
        "  plt.show()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nWv8QfFP9AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FAfaK+yOWZZUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}