mnist_keras_cnn.ipynb

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      "name": "mnist-keras-cnn.ipynb",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/rahuldave/StartingWithPython-ai1-fall2019/blob/master/mnist_keras_cnn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S48WfyP-eWLT",
        "colab_type": "text"
      },
      "source": [
        "## Importing"
      ]
    },
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          "height": 1000
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      },
      "source": [
        "!pip install wandb"
      ],
      "execution_count": 0,
      "outputs": [
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            "\u001b[?25hBuilding wheels for collected packages: shortuuid, watchdog, subprocess32, gql, pathtools\n",
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            "Successfully built shortuuid watchdog subprocess32 gql pathtools\n",
            "\u001b[31mERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Installing collected packages: sentry-sdk, python-dateutil, smmap2, gitdb2, GitPython, shortuuid, argh, pathtools, watchdog, subprocess32, docker-pycreds, configparser, rx, graphql-core, gql, wandb\n",
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            "    Uninstalling python-dateutil-2.5.3:\n",
            "      Successfully uninstalled python-dateutil-2.5.3\n",
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                "packages": [
                  "dateutil"
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        "colab": {
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          "height": 34
        }
      },
      "source": [
        "# from lukas/mlclass\n",
        "from keras.datasets import mnist\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Conv2D, MaxPooling2D, Dropout, Dense, Flatten\n",
        "from keras.utils import np_utils\n",
        "from wandb.keras import WandbCallback\n",
        "import wandb\n",
        "import os\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
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        "run = wandb.init()"
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      "cell_type": "markdown",
      "metadata": {
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        "colab_type": "text"
      },
      "source": [
        "## Config"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xG8lUxuKd0uw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# \n",
        "config = run.config\n",
        "# class Config:\n",
        "#   pass\n",
        "# config = Config()\n",
        "config.first_layer_convs = 32\n",
        "config.first_layer_conv_width = 3\n",
        "config.first_layer_conv_height = 3\n",
        "config.dropout = 0.2\n",
        "config.dense_layer_size = 100\n",
        "config.img_width = 28\n",
        "config.img_height = 28\n",
        "config.epochs = 10"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vdRqoeq_ehuu",
        "colab_type": "text"
      },
      "source": [
        "## Setup"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SrkYbDtzdw4u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
        "\n",
        "X_train = X_train.astype('float32')\n",
        "X_train /= 255.\n",
        "X_test = X_test.astype('float32')\n",
        "X_test /= 255.\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cEtXh66KgtsP",
        "colab_type": "code",
        "outputId": "ad014be5-bd08-411c-cc67-b0bb3990aa09",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X_train.shape, y_test.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((60000, 28, 28), (10000,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "agWhTJudgoOS",
        "colab_type": "code",
        "outputId": "994697e5-5ee0-49a7-d002-76cc005fa5f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# reshape input data\n",
        "X_train = X_train.reshape(\n",
        "    X_train.shape[0], config.img_width, config.img_height, 1)\n",
        "X_test = X_test.reshape(\n",
        "    X_test.shape[0], config.img_width, config.img_height, 1)\n",
        "\n",
        "# one hot encode outputs\n",
        "y_train = np_utils.to_categorical(y_train)\n",
        "y_test = np_utils.to_categorical(y_test)\n",
        "print(y_train.shape)\n",
        "num_classes = y_test.shape[1]\n",
        "labels = range(10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(60000, 10)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ar0JcDPvem_6",
        "colab_type": "text"
      },
      "source": [
        "## Model Building"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TTZ0o7TreEDX",
        "colab_type": "code",
        "outputId": "8e7cd204-0165-48f2-d3b9-5326dec27706",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        }
      },
      "source": [
        "# build model\n",
        "model = Sequential()\n",
        "model.add(Conv2D(32,\n",
        "                 (config.first_layer_conv_width, config.first_layer_conv_height),\n",
        "                 input_shape=(28, 28, 1),\n",
        "                 activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "#model.add(Dropout(0.4))\n",
        "model.add(Conv2D(64,\n",
        "                 (3, 3),\n",
        "                 activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.4))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(config.dense_layer_size, activation='relu'))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(num_classes, activation='softmax'))\n",
        "\n",
        "model.compile(loss='categorical_crossentropy', optimizer='adam',\n",
        "              metrics=['accuracy'])\n",
        "model.summary()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3576: The name tf.log is deprecated. Please use tf.math.log instead.\n",
            "\n",
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_3 (Conv2D)            (None, 26, 26, 32)        320       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 13, 13, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 11, 11, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2 (None, 5, 5, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 5, 5, 64)          0         \n",
            "_________________________________________________________________\n",
            "flatten_2 (Flatten)          (None, 1600)              0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 100)               160100    \n",
            "_________________________________________________________________\n",
            "dropout_4 (Dropout)          (None, 100)               0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 10)                1010      \n",
            "=================================================================\n",
            "Total params: 179,926\n",
            "Trainable params: 179,926\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3WZnUmbTeESk",
        "colab_type": "code",
        "outputId": "c5d97edf-5f03-4f24-c0a9-0f18d4431693",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 880
        }
      },
      "source": [
        "%%wandb\n",
        "## Model Running\n",
        "history = model.fit(X_train, y_train, validation_data=(X_test, y_test),\n",
        "          epochs=config.epochs,\n",
        "          callbacks=[WandbCallback(data_type=\"image\", save_model=False)])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<iframe src=\"https://app.wandb.ai/univai-ss2019/uncategorized/runs/v39i55q1?jupyter=true&state=paused\" style=\"border:none;width:100%;height:420px\">\n",
              "        </iframe>"
            ],
            "text/plain": [
              "<wandb.jupyter.Run at 0x7f6cba8696d8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/10\n",
            "60000/60000 [==============================] - 18s 302us/step - loss: 0.1821 - acc: 0.9429 - val_loss: 0.0422 - val_acc: 0.9855\n",
            "Epoch 2/10\n",
            "60000/60000 [==============================] - 16s 272us/step - loss: 0.0687 - acc: 0.9791 - val_loss: 0.0306 - val_acc: 0.9885\n",
            "Epoch 3/10\n",
            "60000/60000 [==============================] - 16s 269us/step - loss: 0.0544 - acc: 0.9832 - val_loss: 0.0276 - val_acc: 0.9911\n",
            "Epoch 4/10\n",
            "60000/60000 [==============================] - 16s 268us/step - loss: 0.0466 - acc: 0.9858 - val_loss: 0.0246 - val_acc: 0.9918\n",
            "Epoch 5/10\n",
            "60000/60000 [==============================] - 16s 268us/step - loss: 0.0374 - acc: 0.9882 - val_loss: 0.0284 - val_acc: 0.9911\n",
            "Epoch 6/10\n",
            "60000/60000 [==============================] - 16s 264us/step - loss: 0.0332 - acc: 0.9892 - val_loss: 0.0278 - val_acc: 0.9908\n",
            "Epoch 7/10\n",
            "60000/60000 [==============================] - 16s 268us/step - loss: 0.0310 - acc: 0.9904 - val_loss: 0.0248 - val_acc: 0.9913\n",
            "Epoch 8/10\n",
            "60000/60000 [==============================] - 16s 269us/step - loss: 0.0295 - acc: 0.9904 - val_loss: 0.0219 - val_acc: 0.9930\n",
            "Epoch 9/10\n",
            "60000/60000 [==============================] - 17s 279us/step - loss: 0.0291 - acc: 0.9909 - val_loss: 0.0234 - val_acc: 0.9926\n",
            "Epoch 10/10\n",
            "60000/60000 [==============================] - 17s 277us/step - loss: 0.0242 - acc: 0.9921 - val_loss: 0.0229 - val_acc: 0.9925\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3hX4B-stlU_t",
        "colab_type": "code",
        "outputId": "2d143a2d-a48e-4264-d3e9-351a9cee1f66",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "# Plot training & validation accuracy values\n",
        "plt.plot(history.history['acc'])\n",
        "plt.plot(history.history['val_acc'])\n",
        "plt.title('Model accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Test'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "# Plot training & validation loss values\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Model loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Test'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8XyO/HE65DCbMhezC/r/cD3zp5w+dw0uHqHjcad7XRWPbfhpa91PbdDAMahrb2RrbS3fC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\nRGRACgsRERnQ/w/aH0WxVsJF9wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ERJcjIjJiknmyfVTJy0xj/rQ8nU8iIuOOgmQYVZQVs3Z3Ha3t/TotRkRkTFCQDKOK8iJa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\nDImCREREhkRBIiIiQ6IgERGRIfn/AUW3kg4LI3g2AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wN-nX-ZWlnOQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}