pw04/CNN-final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HkLUJVa1bfNW"
   },
   "source": [
    "# Convolutional Neural Networks\n",
    "This notebook will guide you through the use of the `keras` package to train convolutional neural networks for handwritten digits classification. You are going to use the `mnist` dataset from LeCun et al. 1998."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SjQUQygFbfNb"
   },
   "source": [
    "## Loading the packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "h7O8MewabfNj",
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-30 17:06:23.670201: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-04-30 17:06:24.091420: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from keras.datasets import mnist\n",
    "from keras.layers import Input, Dropout\n",
    "from keras.layers.convolutional import Conv2D, MaxPooling2D\n",
    "from keras.layers.core import Dense, Flatten\n",
    "from keras.models import Sequential\n",
    "from keras.optimizers import RMSprop\n",
    "from keras.utils import np_utils, plot_model\n",
    "from matplotlib import pyplot as pl\n",
    "from sklearn import metrics as me\n",
    "\n",
    "from utils import plot_confusion_matrix\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vbttXt8SbfOH"
   },
   "source": [
    "First, create some useful functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "jAR9jPgybfOO",
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "def build_grid_of_images(array):\n",
    "    assert len(array.shape) == 3\n",
    "    dim_0 = np.sqrt(array.shape[0])\n",
    "    assert dim_0.is_integer()\n",
    "\n",
    "    temp_out = np.reshape(array, (dim_0, dim_0, array.shape[1], array.shape[2]))\n",
    "    temp_out = np.rollaxis(temp_out, 1, 3)\n",
    "    return np.reshape(temp_out, (dim_0 * array.shape[1], dim_0 * array.shape[2]))\n",
    "\n",
    "\n",
    "def plot_conv_layer_output(temp_out, title):\n",
    "    temp_to_plot = build_grid_of_images(temp_out)\n",
    "    pl.imshow(temp_to_plot, interpolation='nearest', cmap=pl.get_cmap('Greys'))\n",
    "    ax = pl.gca()\n",
    "    ax.set_xticks(np.arange(-0.5, temp_to_plot.shape[0] + 0.5, temp_out.shape[1]))\n",
    "    ax.set_yticks(np.arange(-0.5, temp_to_plot.shape[0] + 0.5, temp_out.shape[2]))\n",
    "    pl.grid()\n",
    "    pl.tick_params(axis='both', which='both', bottom='off', top='off', left='off', right='off', labelbottom='off',\n",
    "                   labelleft='off')\n",
    "    pl.title(title)\n",
    "\n",
    "\n",
    "def plot_dense_layer_output(temp_out, title):\n",
    "    pl.bar(np.arange(temp_out.shape[1]) - 0.4, temp_out[0, :])\n",
    "    pl.xlim(-0.5, temp_out.shape[1])\n",
    "    pl.grid()\n",
    "    pl.title(title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3fVOW4jGbfOb"
   },
   "source": [
    "Load the `mnist` dataset and normalize in the range [0, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "id": "2H2K3S4MbfOf",
    "outputId": "87306b90-6068-49ae-d1c2-5d5ea69927e2",
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000 train samples\n",
      "10000 test samples\n"
     ]
    }
   ],
   "source": [
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
    "\n",
    "n_train, height, width = X_train.shape\n",
    "n_test, _, _ = X_test.shape\n",
    "\n",
    "X_train = X_train.reshape(n_train, height, width, 1).astype('float32')\n",
    "X_test = X_test.reshape(n_test, height, width, 1).astype('float32')\n",
    "\n",
    "X_train /= 255.0\n",
    "X_test /= 255.0\n",
    "\n",
    "n_classes = 10\n",
    "\n",
    "print(n_train, 'train samples')\n",
    "print(n_test, 'test samples')\n",
    "\n",
    "# convert class vectors to binary class matrices\n",
    "Y_train = np_utils.to_categorical(y_train, n_classes)\n",
    "Y_test = np_utils.to_categorical(y_test, n_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train.shape =  (60000, 28, 28, 1)\n",
      "Y_train.shape =  (60000, 10)\n",
      "X_test.shape =  (10000, 28, 28, 1)\n",
      "Y_test.shape =  (10000, 10)\n"
     ]
    }
   ],
   "source": [
    "print('X_train.shape = ', X_train.shape)\n",
    "print('Y_train.shape = ', Y_train.shape)\n",
    "print('X_test.shape = ', X_test.shape)\n",
    "print('Y_test.shape = ', Y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2B-WNiQUbfOq"
   },
   "source": [
    "Create the CNN and show its architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 493
    },
    "id": "yhOscSNPbfOt",
    "outputId": "6e1a0da0-e48f-4fd1-eb5c-dadacdad548a",
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-30 17:06:25.094420: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:982] could not open file to read NUMA node: /sys/bus/pci/devices/0000:0c:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-04-30 17:06:25.103130: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:982] could not open file to read NUMA node: /sys/bus/pci/devices/0000:0c:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-04-30 17:06:25.103177: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:982] could not open file to read NUMA node: /sys/bus/pci/devices/0000:0c:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-04-30 17:06:25.104782: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:982] could not open file to read NUMA node: /sys/bus/pci/devices/0000:0c:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-04-30 17:06:25.104823: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:982] could not open file to read NUMA node: /sys/bus/pci/devices/0000:0c:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-04-30 17:06:25.104851: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:982] could not open file to read NUMA node: /sys/bus/pci/devices/0000:0c:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-04-30 17:06:25.616875: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:982] could not open file to read NUMA node: /sys/bus/pci/devices/0000:0c:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-04-30 17:06:25.616949: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:982] could not open file to read NUMA node: /sys/bus/pci/devices/0000:0c:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-04-30 17:06:25.616958: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1722] Could not identify NUMA node of platform GPU id 0, defaulting to 0.  Your kernel may not have been built with NUMA support.\n",
      "2023-04-30 17:06:25.616999: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:982] could not open file to read NUMA node: /sys/bus/pci/devices/0000:0c:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-04-30 17:06:25.617021: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 21568 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:0c:00.0, compute capability: 8.6\n"
     ]
    }
   ],
   "source": [
    "model = Sequential([\n",
    "    Input(shape=(height, width, 1), name='l0'),\n",
    "    \n",
    "    Conv2D(32, kernel_size=(3, 3), padding='same', activation='relu', name='l1'),\n",
    "    MaxPooling2D(pool_size=(2, 2), name='l1_mp'),\n",
    "    Dropout(0.1, name='l1_drop'),\n",
    "    \n",
    "    Conv2D(64, kernel_size=(3, 3), padding='same', activation='relu', name='l2'),\n",
    "    MaxPooling2D(pool_size=(2, 2), name='l2_mp'),\n",
    "    Dropout(0.1, name='l2_drop'),\n",
    "    \n",
    "    Flatten(name='flat'),\n",
    "    Dropout(0.2, name='drop'),\n",
    "    \n",
    "    Dense(n_classes, activation='softmax', name='l3'),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " l1 (Conv2D)                 (None, 28, 28, 32)        320       \n",
      "                                                                 \n",
      " l1_mp (MaxPooling2D)        (None, 14, 14, 32)        0         \n",
      "                                                                 \n",
      " l1_drop (Dropout)           (None, 14, 14, 32)        0         \n",
      "                                                                 \n",
      " l2 (Conv2D)                 (None, 14, 14, 64)        18496     \n",
      "                                                                 \n",
      " l2_mp (MaxPooling2D)        (None, 7, 7, 64)          0         \n",
      "                                                                 \n",
      " l2_drop (Dropout)           (None, 7, 7, 64)          0         \n",
      "                                                                 \n",
      " flat (Flatten)              (None, 3136)              0         \n",
      "                                                                 \n",
      " drop (Dropout)              (None, 3136)              0         \n",
      "                                                                 \n",
      " l3 (Dense)                  (None, 10)                31370     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 50,186\n",
      "Trainable params: 50,186\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_model(\n",
    "    model,\n",
    "    show_shapes=True,\n",
    "    show_layer_names=True,\n",
    "    show_layer_activations=True,\n",
    "    show_trainable=True,\n",
    "    to_file='mnist-model.png'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sP_cugPCbfO6"
   },
   "source": [
    "Define some constants and train de CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 374
    },
    "id": "l8eKqD80bfPB",
    "outputId": "bb20620f-70c6-4330-8c5f-d67cb5176464",
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-30 17:06:26.719638: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:954] layout failed: INVALID_ARGUMENT: Size of values 0 does not match size of permutation 4 @ fanin shape insequential/l1_drop/dropout/SelectV2-2-TransposeNHWCToNCHW-LayoutOptimizer\n",
      "2023-04-30 17:06:27.631547: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:424] Loaded cuDNN version 8900\n",
      "2023-04-30 17:06:28.783912: I tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:637] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "422/422 [==============================] - 6s 8ms/step - loss: 0.2824 - accuracy: 0.9137 - val_loss: 0.0854 - val_accuracy: 0.9738\n",
      "Epoch 2/15\n",
      "422/422 [==============================] - 3s 8ms/step - loss: 0.0888 - accuracy: 0.9728 - val_loss: 0.0568 - val_accuracy: 0.9838\n",
      "Epoch 3/15\n",
      "422/422 [==============================] - 4s 9ms/step - loss: 0.0662 - accuracy: 0.9797 - val_loss: 0.0474 - val_accuracy: 0.9867\n",
      "Epoch 4/15\n",
      "422/422 [==============================] - 4s 9ms/step - loss: 0.0539 - accuracy: 0.9833 - val_loss: 0.0421 - val_accuracy: 0.9890\n",
      "Epoch 5/15\n",
      "422/422 [==============================] - 3s 8ms/step - loss: 0.0487 - accuracy: 0.9851 - val_loss: 0.0384 - val_accuracy: 0.9900\n",
      "Epoch 6/15\n",
      "422/422 [==============================] - 3s 8ms/step - loss: 0.0433 - accuracy: 0.9871 - val_loss: 0.0386 - val_accuracy: 0.9900\n",
      "Epoch 7/15\n",
      "422/422 [==============================] - 4s 9ms/step - loss: 0.0387 - accuracy: 0.9886 - val_loss: 0.0375 - val_accuracy: 0.9902\n",
      "Epoch 8/15\n",
      "422/422 [==============================] - 3s 8ms/step - loss: 0.0350 - accuracy: 0.9891 - val_loss: 0.0376 - val_accuracy: 0.9907\n",
      "Epoch 9/15\n",
      "422/422 [==============================] - 4s 8ms/step - loss: 0.0329 - accuracy: 0.9905 - val_loss: 0.0361 - val_accuracy: 0.9913\n",
      "Epoch 10/15\n",
      "422/422 [==============================] - 3s 8ms/step - loss: 0.0302 - accuracy: 0.9909 - val_loss: 0.0324 - val_accuracy: 0.9917\n",
      "Epoch 11/15\n",
      "422/422 [==============================] - 4s 9ms/step - loss: 0.0285 - accuracy: 0.9916 - val_loss: 0.0343 - val_accuracy: 0.9912\n",
      "Epoch 12/15\n",
      "422/422 [==============================] - 3s 8ms/step - loss: 0.0272 - accuracy: 0.9914 - val_loss: 0.0314 - val_accuracy: 0.9912\n",
      "Epoch 13/15\n",
      "422/422 [==============================] - 4s 8ms/step - loss: 0.0261 - accuracy: 0.9921 - val_loss: 0.0368 - val_accuracy: 0.9905\n",
      "Epoch 14/15\n",
      "422/422 [==============================] - 3s 8ms/step - loss: 0.0250 - accuracy: 0.9921 - val_loss: 0.0389 - val_accuracy: 0.9905\n",
      "Epoch 15/15\n",
      "422/422 [==============================] - 3s 8ms/step - loss: 0.0239 - accuracy: 0.9922 - val_loss: 0.0351 - val_accuracy: 0.9913\n"
     ]
    }
   ],
   "source": [
    "batch_size = 128\n",
    "n_epoch = 15\n",
    "\n",
    "model.compile(loss='categorical_crossentropy', optimizer=RMSprop(), metrics=['accuracy'])\n",
    "\n",
    "history = model.fit(X_train, Y_train, batch_size=batch_size, epochs=n_epoch, verbose=1, validation_split=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IBX2Tc_0bfPK"
   },
   "source": [
    "Show the performance of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 299
    },
    "id": "WeaMjl-EbfPS",
    "outputId": "e219b778-8eb2-4fd9-a2f5-baaa3d936a3f",
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test score: 0.023659802973270416\n",
      "Test accuracy: 0.9926000237464905\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pl.plot(history.history['loss'], label='Training')\n",
    "pl.plot(history.history['val_loss'], label='Testing')\n",
    "pl.legend()\n",
    "pl.grid()\n",
    "\n",
    "score = model.evaluate(X_test, Y_test, verbose=0)\n",
    "print('Test score:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rXNZ2ZHSbfPm"
   },
   "source": [
    "Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "id": "XfgRipFebfPm",
    "outputId": "b435b340-a409-423f-bd5a-d989f6f76a8a",
    "pycharm": {
     "is_executing": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 0s 1ms/step\n",
      "pred.shape =  (10000, 10)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F-score 0: 0.9933976637887252\n",
      "F-score 1: 0.9942857142857143\n",
      "F-score 2: 0.9922705314009662\n",
      "F-score 3: 0.9955511616411271\n",
      "F-score 4: 0.9928644240570846\n",
      "F-score 5: 0.9921436588103256\n",
      "F-score 6: 0.9921507064364208\n",
      "F-score 7: 0.991695163654128\n",
      "F-score 8: 0.9922720247295209\n",
      "F-score 9: 0.9891089108910891\n"
     ]
    }
   ],
   "source": [
    "pred = model.predict(X_test)\n",
    "print('pred.shape = ', pred.shape)\n",
    "pred = np.argmax(pred, axis=-1)\n",
    "cm = me.confusion_matrix(y_test, pred)\n",
    "\n",
    "plot_confusion_matrix(cm, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
   ]
  }
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