CUNY_ITS_MNIST_CNN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CUNY_ITS_MNIST_CNN.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "qoU32z5Gg1LR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "63f3c11f-7b6a-41f9-8fe5-bff7aa3aa4b8"
      },
      "source": [
        "# This code is adapted from\n",
        "# https://machinelearningmastery.com/handwritten-digit-recognition-using-convolutional-neural-networks-python-keras/\n",
        "\n",
        "from keras.datasets import mnist\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import Dropout\n",
        "from keras.layers import Flatten\n",
        "from keras.layers.convolutional import Conv2D\n",
        "from keras.layers.convolutional import MaxPooling2D\n",
        "from keras.utils import np_utils\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_hcIFu52rmzp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "#tf.logging.set_verbosity(tf.logging.ERROR)"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6nuBOKB5krCh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "9fed87d2-9d29-44f2-f950-48a3cd09a7ee"
      },
      "source": [
        "# load data\n",
        "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
        "\n",
        "# reshape to be [samples][width][height][channels]\n",
        "X_train = X_train.reshape((X_train.shape[0], 28, 28, 1)).astype('float32')\n",
        "X_test = X_test.reshape((X_test.shape[0], 28, 28, 1)).astype('float32')\n",
        "\n",
        "# normalize inputs from 0-255 to 0-1 to make neural network happy\n",
        "X_train = X_train / 255\n",
        "X_test = X_test / 255\n",
        "\n",
        "# one hot encode outputs (create ten possible outputs and assign a 0/1 label to each object)\n",
        "y_train = np_utils.to_categorical(y_train)\n",
        "y_test = np_utils.to_categorical(y_test)\n",
        "\n",
        "num_classes = y_test.shape[1]"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://s3.amazonaws.com/img-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 1s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dDmJbwFylHtd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "outputId": "f2107fed-2c2a-4a75-e8b0-1cc7d68349ee"
      },
      "source": [
        "#Visualize first few objects of training set\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "for i in range(9):\n",
        "  plt.subplot(3,3,i+1)\n",
        "  first_image = np.array(X_train[i], dtype='float')\n",
        "  pixels = first_image.reshape((28, 28))\n",
        "  plt.imshow(pixels, cmap='gray')"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y95ojrGdktHK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# define a simple CNN model\n",
        "def baseline_model():\n",
        "\t# create model\n",
        "\tmodel = Sequential()\n",
        "\tmodel.add(Conv2D(32, (5, 5), input_shape=(28, 28, 1), activation='relu'))\n",
        "\tmodel.add(MaxPooling2D())\n",
        "\tmodel.add(Dropout(0.2))\n",
        "\tmodel.add(Flatten())\n",
        "\tmodel.add(Dense(128, activation='relu'))\n",
        "\tmodel.add(Dense(num_classes, activation='softmax'))\n",
        "\t# Compile model\n",
        "\tmodel.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "\treturn model"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tY-fwnbFjL0i",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "outputId": "f5ee405c-f2bb-4c2d-c081-2950d13a227c"
      },
      "source": [
        "# build the model\n",
        "model = baseline_model()\n",
        "# Fit the model\n",
        "model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, batch_size=200)\n",
        "# Final evaluation of the model\n",
        "scores = model.evaluate(X_test, y_test, verbose=1)\n",
        "print(\"CNN Error: %.2f%%\" % (100-scores[1]*100))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/10\n",
            "60000/60000 [==============================] - 9s 144us/step - loss: 0.2359 - accuracy: 0.9316 - val_loss: 0.0758 - val_accuracy: 0.9765\n",
            "Epoch 2/10\n",
            "60000/60000 [==============================] - 2s 28us/step - loss: 0.0730 - accuracy: 0.9778 - val_loss: 0.0534 - val_accuracy: 0.9810\n",
            "Epoch 3/10\n",
            "60000/60000 [==============================] - 2s 28us/step - loss: 0.0518 - accuracy: 0.9843 - val_loss: 0.0416 - val_accuracy: 0.9863\n",
            "Epoch 4/10\n",
            "60000/60000 [==============================] - 2s 28us/step - loss: 0.0408 - accuracy: 0.9873 - val_loss: 0.0426 - val_accuracy: 0.9871\n",
            "Epoch 5/10\n",
            "60000/60000 [==============================] - 2s 28us/step - loss: 0.0327 - accuracy: 0.9901 - val_loss: 0.0347 - val_accuracy: 0.9886\n",
            "Epoch 6/10\n",
            "60000/60000 [==============================] - 2s 28us/step - loss: 0.0269 - accuracy: 0.9915 - val_loss: 0.0346 - val_accuracy: 0.9885\n",
            "Epoch 7/10\n",
            "60000/60000 [==============================] - 2s 28us/step - loss: 0.0234 - accuracy: 0.9926 - val_loss: 0.0327 - val_accuracy: 0.9884\n",
            "Epoch 8/10\n",
            "60000/60000 [==============================] - 2s 28us/step - loss: 0.0190 - accuracy: 0.9940 - val_loss: 0.0319 - val_accuracy: 0.9891\n",
            "Epoch 9/10\n",
            "60000/60000 [==============================] - 2s 28us/step - loss: 0.0166 - accuracy: 0.9947 - val_loss: 0.0339 - val_accuracy: 0.9885\n",
            "Epoch 10/10\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.0136 - accuracy: 0.9959 - val_loss: 0.0281 - val_accuracy: 0.9908\n",
            "10000/10000 [==============================] - 1s 57us/step\n",
            "CNN Error: 0.92%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4JS9CqVvsr_4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bfff3f7c-6ebc-4605-c0c7-6a165ddc8318"
      },
      "source": [
        "ix = np.random.random_integers(0,1000)\n",
        "\n",
        "print(ix)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "599\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6mC9lu4vsDWI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "8a080377-a23c-47a8-c4ba-13ae17b70e7a"
      },
      "source": [
        "plt.imshow((X_test[ix]).reshape(28,28), cmap='gray')"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f42bc0f70b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5WwO7-gxsjAI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c658557a-5a1b-4448-a0f1-f16b463c5db2"
      },
      "source": [
        "np.argmax(model.predict(X_test)[ix])"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ucdF5Qe_EVI_",
        "colab_type": "text"
      },
      "source": [
        "Save predictions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sMws4_hfqSv4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cpred = model.predict(X_test)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "an9-xCpDqWPJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "outputId": "e95b3e18-07dc-4786-b419-ce47f06a572d"
      },
      "source": [
        "cpred[:5,:]"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[5.67698555e-09, 1.31546174e-09, 4.38644321e-08, 2.23753943e-07,\n",
              "        1.03610222e-13, 4.85163298e-10, 9.80369803e-16, 9.99999523e-01,\n",
              "        9.79238357e-10, 2.47531602e-07],\n",
              "       [1.16325500e-11, 2.05638031e-07, 9.99999762e-01, 3.50056599e-13,\n",
              "        6.85380150e-17, 1.59562711e-13, 2.80226731e-09, 1.73752711e-12,\n",
              "        2.08512114e-11, 1.14324350e-14],\n",
              "       [3.79332687e-06, 9.99940157e-01, 8.64784397e-07, 6.33288000e-09,\n",
              "        9.03234013e-06, 1.16626488e-07, 7.13712211e-07, 3.32387244e-05,\n",
              "        1.18732851e-05, 2.01126397e-07],\n",
              "       [9.99991894e-01, 1.24327293e-09, 1.61182729e-08, 5.33905697e-10,\n",
              "        8.03082947e-11, 2.90731901e-08, 8.01771057e-06, 9.04584923e-08,\n",
              "        1.05261876e-10, 2.21193623e-08],\n",
              "       [5.57906876e-09, 2.71640985e-08, 9.55088897e-10, 8.59711635e-10,\n",
              "        9.99856830e-01, 1.47942025e-09, 2.45235423e-08, 3.13351762e-08,\n",
              "        1.58300875e-08, 1.43132609e-04]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I4a9bp_mwLWa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Save predicted class for the first 1000 objects\n",
        "\n",
        "pred = [np.argmax(cpred[i]) for i in range(1000)]"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ARaaVtAqwOM-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Save true class for the first 1000 objects\n",
        "\n",
        "true = [np.argmax(y_test[i]) for i in range(1000)]"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oFHHPIGnwEB_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Find differences\n",
        "\n",
        "err = np.where(np.array(pred) != np.array(true))"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SU91LbGrq0NQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "10edf039-8614-4cfc-930c-52460ca9237b"
      },
      "source": [
        "err"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 18, 115, 247, 340, 445, 582, 659, 740, 947]),)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O-ErLCzIrqNB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d71216e0-8e95-47ac-bc09-c6980e0b70af"
      },
      "source": [
        "len(err[0])"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W8XY8yM7LWsJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "dcad5199-1881-48f3-baf8-442f8a5bdb37"
      },
      "source": [
        "#Visualize wrongly classified examples\n",
        "plt.figure(figsize=(10,10))\n",
        "for i in range(len(err[0])):\n",
        "  plt.subplot(4,5,i+1)\n",
        "  first_image = np.array(X_test[err[0][i]], dtype='float')\n",
        "  pixels = first_image.reshape((28, 28))\n",
        "  plt.imshow(pixels, cmap='gray')\n",
        "  plt.title('T/P:'+str(true[err[0][i]])+','+str(pred[err[0][i]]))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xky4DzEkadaz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}