pw04/CNN-3c0.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 23:48:58.912214: 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 23:48:59.342864: 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 23:49:00.327505: 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 23:49:00.337290: 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 23:49:00.337342: 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 23:49:00.339345: 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 23:49:00.339392: 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 23:49:00.339423: 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 23:49:00.782552: 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 23:49:00.782610: 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 23:49:00.782617: 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 23:49:00.782648: 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 23:49:00.782670: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 9294 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)),\n",
    "    \n",
    "    Conv2D(9, kernel_size=(5, 5), padding='same', activation='relu'),\n",
    "    MaxPooling2D(pool_size=(2, 2)),\n",
    "    #Dropout(0.1, name='l1_drop'),\n",
    "    \n",
    "    Conv2D(9, kernel_size=(5, 5), padding='same', activation='relu'),\n",
    "    MaxPooling2D(pool_size=(2, 2)),\n",
    "    #Dropout(0.1),\n",
    "    \n",
    "    Conv2D(16, kernel_size=(3, 3), padding='same', activation='relu'),\n",
    "    MaxPooling2D(pool_size=(2, 2)),\n",
    "    #Dropout(0.25),\n",
    "    \n",
    "    Flatten(),\n",
    "    #Dropout(0.2),\n",
    "    \n",
    "    Dense(n_classes, activation='softmax'),\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",
      " conv2d (Conv2D)             (None, 28, 28, 9)         234       \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 14, 14, 9)        0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 14, 14, 9)         2034      \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 7, 7, 9)          0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 7, 7, 16)          1312      \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 3, 3, 16)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 144)               0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 10)                1450      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 5,030\n",
      "Trainable params: 5,030\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 23:49:03.737763: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:424] Loaded cuDNN version 8900\n",
      "2023-04-30 23:49:05.570511: 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 [==============================] - 8s 9ms/step - loss: 0.5296 - accuracy: 0.8410 - val_loss: 0.1576 - val_accuracy: 0.9500\n",
      "Epoch 2/15\n",
      "422/422 [==============================] - 3s 8ms/step - loss: 0.1621 - accuracy: 0.9505 - val_loss: 0.1032 - val_accuracy: 0.9673\n",
      "Epoch 3/15\n",
      "422/422 [==============================] - 4s 9ms/step - loss: 0.1139 - accuracy: 0.9646 - val_loss: 0.0770 - val_accuracy: 0.9760\n",
      "Epoch 4/15\n",
      "422/422 [==============================] - 4s 9ms/step - loss: 0.0922 - accuracy: 0.9718 - val_loss: 0.0764 - val_accuracy: 0.9775\n",
      "Epoch 5/15\n",
      "422/422 [==============================] - 4s 9ms/step - loss: 0.0796 - accuracy: 0.9750 - val_loss: 0.0678 - val_accuracy: 0.9805\n",
      "Epoch 6/15\n",
      "422/422 [==============================] - 4s 9ms/step - loss: 0.0700 - accuracy: 0.9783 - val_loss: 0.0659 - val_accuracy: 0.9817\n",
      "Epoch 7/15\n",
      "422/422 [==============================] - 4s 9ms/step - loss: 0.0623 - accuracy: 0.9810 - val_loss: 0.0676 - val_accuracy: 0.9795\n",
      "Epoch 8/15\n",
      "422/422 [==============================] - 4s 9ms/step - loss: 0.0567 - accuracy: 0.9820 - val_loss: 0.0544 - val_accuracy: 0.9828\n",
      "Epoch 9/15\n",
      "422/422 [==============================] - 4s 9ms/step - loss: 0.0520 - accuracy: 0.9840 - val_loss: 0.0552 - val_accuracy: 0.9850\n",
      "Epoch 10/15\n",
      "422/422 [==============================] - 4s 9ms/step - loss: 0.0487 - accuracy: 0.9843 - val_loss: 0.0481 - val_accuracy: 0.9863\n",
      "Epoch 11/15\n",
      "422/422 [==============================] - 4s 9ms/step - loss: 0.0450 - accuracy: 0.9862 - val_loss: 0.0570 - val_accuracy: 0.9850\n",
      "Epoch 12/15\n",
      "422/422 [==============================] - 4s 9ms/step - loss: 0.0418 - accuracy: 0.9872 - val_loss: 0.0509 - val_accuracy: 0.9865\n",
      "Epoch 13/15\n",
      "422/422 [==============================] - 4s 9ms/step - loss: 0.0394 - accuracy: 0.9874 - val_loss: 0.0514 - val_accuracy: 0.9852\n",
      "Epoch 14/15\n",
      "422/422 [==============================] - 4s 9ms/step - loss: 0.0365 - accuracy: 0.9883 - val_loss: 0.0530 - val_accuracy: 0.9855\n",
      "Epoch 15/15\n",
      "422/422 [==============================] - 4s 9ms/step - loss: 0.0346 - accuracy: 0.9892 - val_loss: 0.0556 - val_accuracy: 0.9857\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: 0.04211839661002159\n",
      "Test accuracy: 0.9860000014305115\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 975us/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: 0.9928861788617886\n",
      "F-score 1: 0.9920424403183022\n",
      "F-score 2: 0.9883268482490272\n",
      "F-score 3: 0.9867452135493373\n",
      "F-score 4: 0.9861609431060994\n",
      "F-score 5: 0.9829157175398633\n",
      "F-score 6: 0.9859007832898171\n",
      "F-score 7: 0.9838788470933073\n",
      "F-score 8: 0.9800918836140888\n",
      "F-score 9: 0.9799902391410443\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])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}