pw04/MLP_HOG-final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X_wPegydbafH"
   },
   "source": [
    "# Computing features to train a MLP\n",
    "This notebook will guide you through the use of the `keras` package to train a multilayer perceptron for handwritten digits classification. You are going to computing the histogram of gradients from the images in the `mnist` dataset (LeCun et al. 1998)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "61oGWRyMbafJ"
   },
   "source": [
    "## Loading the packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "duI9Ar-8bafS"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from keras.datasets import mnist\n",
    "from keras.layers import Dropout\n",
    "from keras.layers.core import Dense\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 skimage import exposure\n",
    "from skimage.feature import hog\n",
    "from sklearn import metrics as me\n",
    "\n",
    "from utils import plot_confusion_matrix\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KpPmUqiRbafz"
   },
   "source": [
    "Load the `mnist` dataset and normalize in the range [0, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "id": "verKKFaFbaf6",
    "outputId": "29e842da-35fa-49b6-a7f7-f35d9e8a71b9"
   },
   "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": "markdown",
   "metadata": {
    "id": "TazeB7LLbagE"
   },
   "source": [
    "### An example of how to compute the histogram of gradients\n",
    "Let's compute the HOG for one image in the test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "cL-609oKbagI"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "392\n"
     ]
    }
   ],
   "source": [
    "n_orientations = 8\n",
    "pix_p_cell = 4\n",
    "hog_size = int(height * width * n_orientations / (pix_p_cell * pix_p_cell))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "r2DA2Da_bage"
   },
   "outputs": [],
   "source": [
    "fd, hog_image = hog(X_test[0,:,:,0], orientations=n_orientations, pixels_per_cell=(pix_p_cell, pix_p_cell), cells_per_block=(1, 1), visualize=True)\n",
    "hog_image_rescaled = exposure.rescale_intensity(hog_image, in_range=(0, 0.02))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "s2Ow4uJtbago"
   },
   "source": [
    "Show the image and the corresponding gradients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 351
    },
    "id": "83RZeljKbagu",
    "outputId": "e1507d91-5cd9-484c-e0bc-0673b046a274"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = pl.subplots(1, 2, figsize=(12, 6), sharex=True, sharey=True)\n",
    "ax1.axis('off')\n",
    "ax1.imshow(X_test[0,:,:,0], cmap=pl.get_cmap('Greys'), interpolation='nearest')\n",
    "ax1.set_title('Input image')\n",
    "ax1.set_adjustable('box')\n",
    "\n",
    "ax2.axis('off')\n",
    "ax2.imshow(hog_image_rescaled, cmap=pl.get_cmap('Greys'), interpolation='nearest')\n",
    "ax2.set_title('Histogram of Oriented Gradients')\n",
    "ax1.set_adjustable('box')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3Yv-ep9ybag4"
   },
   "source": [
    "Compute the HOGs for the train and test datasets\n",
    "\n",
    "> Indented block\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "id": "y8Q3cdrebag6",
    "outputId": "4c21e6ce-56d2-4287-eab6-634cda1ad94b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train done\n",
      "X_test done\n"
     ]
    }
   ],
   "source": [
    "X_train_hog = np.zeros((X_train.shape[0], hog_size))\n",
    "X_test_hog = np.zeros((X_test.shape[0], hog_size))\n",
    "\n",
    "for i in np.arange(X_train_hog.shape[0]):\n",
    "    X_train_hog[i,:] = hog(X_train[i,:,:,0], orientations=n_orientations, pixels_per_cell=(pix_p_cell, pix_p_cell), cells_per_block=(1, 1), visualize=False)\n",
    "print('X_train done')\n",
    "\n",
    "for i in np.arange(X_test_hog.shape[0]):\n",
    "    X_test_hog[i,:] = hog(X_test[i,:,:,0], orientations=n_orientations, pixels_per_cell=(pix_p_cell, pix_p_cell), cells_per_block=(1, 1), visualize=False)\n",
    "print('X_test done')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kos0aTYmbahU"
   },
   "source": [
    "Create the MLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "id": "NnawtqztbahV",
    "outputId": "c99078e7-4754-41c7-dbe9-e953931c5f6c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-28 15:37:13.993444: 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 256)               100608    \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 256)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 10)                2570      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 103,178\n",
      "Trainable params: 103,178\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-28 15:37:14.006850: 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",
      "2023-04-28 15:37:14.006902: 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",
      "2023-04-28 15:37:14.010303: 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",
      "2023-04-28 15:37:14.010350: 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",
      "2023-04-28 15:37:14.010383: 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",
      "2023-04-28 15:37:14.501278: 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",
      "2023-04-28 15:37:14.501334: 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",
      "2023-04-28 15:37:14.501341: 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",
      "2023-04-28 15:37:14.501374: 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",
      "2023-04-28 15:37:14.501394: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 3649 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",
    "    Dense(256, input_shape=(hog_size,), activation='relu'),\n",
    "    Dropout(0.4),\n",
    "    Dense(n_classes, activation='softmax'),\n",
    "])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 14,
     "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='mlpf-model.png'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EGeviXzJbahY"
   },
   "source": [
    "Define some constants and train the MLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 374
    },
    "id": "laKFY8vcbahZ",
    "outputId": "5849045b-3563-4afb-edfa-5658aac89ec8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      " 33/422 [=>............................] - ETA: 1s - loss: 1.1475 - accuracy: 0.7540"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-28 09:50:20.506554: 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 [==============================] - 3s 6ms/step - loss: 0.2561 - accuracy: 0.9341 - val_loss: 0.0789 - val_accuracy: 0.9767\n",
      "Epoch 2/10\n",
      "422/422 [==============================] - 2s 5ms/step - loss: 0.1035 - accuracy: 0.9672 - val_loss: 0.0705 - val_accuracy: 0.9768\n",
      "Epoch 3/10\n",
      "422/422 [==============================] - 3s 7ms/step - loss: 0.0832 - accuracy: 0.9734 - val_loss: 0.0655 - val_accuracy: 0.9780\n",
      "Epoch 4/10\n",
      "422/422 [==============================] - 3s 8ms/step - loss: 0.0705 - accuracy: 0.9772 - val_loss: 0.0631 - val_accuracy: 0.9793\n",
      "Epoch 5/10\n",
      "422/422 [==============================] - 3s 8ms/step - loss: 0.0599 - accuracy: 0.9813 - val_loss: 0.0638 - val_accuracy: 0.9787\n",
      "Epoch 6/10\n",
      "422/422 [==============================] - 3s 8ms/step - loss: 0.0528 - accuracy: 0.9826 - val_loss: 0.0575 - val_accuracy: 0.9833\n",
      "Epoch 7/10\n",
      "422/422 [==============================] - 3s 8ms/step - loss: 0.0471 - accuracy: 0.9849 - val_loss: 0.0608 - val_accuracy: 0.9802\n",
      "Epoch 8/10\n",
      "422/422 [==============================] - 3s 8ms/step - loss: 0.0413 - accuracy: 0.9869 - val_loss: 0.0581 - val_accuracy: 0.9842\n",
      "Epoch 9/10\n",
      "422/422 [==============================] - 3s 8ms/step - loss: 0.0371 - accuracy: 0.9883 - val_loss: 0.0561 - val_accuracy: 0.9832\n",
      "Epoch 10/10\n",
      "422/422 [==============================] - 3s 8ms/step - loss: 0.0337 - accuracy: 0.9895 - val_loss: 0.0613 - val_accuracy: 0.9828\n"
     ]
    }
   ],
   "source": [
    "batch_size = 128\n",
    "n_epoch = 10\n",
    "\n",
    "model.compile(loss='categorical_crossentropy', optimizer=RMSprop(), metrics=['accuracy'])\n",
    "\n",
    "history = model.fit(X_train_hog, Y_train,\n",
    "                    batch_size=batch_size, epochs=n_epoch,\n",
    "                    verbose=1, validation_split=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aezTZuCMbahf"
   },
   "source": [
    "Show the performance of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 299
    },
    "id": "u6E66I52bahg",
    "outputId": "52139b93-823a-4fca-de59-a64f77f3b0ab"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test score: 0.062235455960035324\n",
      "Test accuracy: 0.982200026512146\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_hog, Y_test, verbose=0)\n",
    "print('Test score:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "axdxLHYMbahu"
   },
   "source": [
    "Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "id": "Mhm1wjNfbahv",
    "outputId": "8366db2a-2162-4c03-d1d3-de0a4114c3cb",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 0s 1ms/step\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: 0.9863429438543246\n",
      "F-score 1: 0.9868651488616462\n",
      "F-score 2: 0.9849441476444877\n",
      "F-score 3: 0.9773844641101278\n",
      "F-score 4: 0.985670419651996\n",
      "F-score 5: 0.9819819819819819\n",
      "F-score 6: 0.9905857740585775\n",
      "F-score 7: 0.9780594831789371\n",
      "F-score 8: 0.9765066394279877\n",
      "F-score 9: 0.9734335839598998\n"
     ]
    }
   ],
   "source": [
    "pred = np.argmax(model.predict(X_test_hog), 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
}