Facial-Emotion-Classification_MONA.ipynb - mlearn - Mathematics of Machine Learning

Facial-Emotion-Classification_MONA.ipynb (292392B)
      1 {
      2  "cells": [
      3   {
      4    "cell_type": "code",
      5    "execution_count": 1,
      6    "metadata": {},
      7    "outputs": [
      8     {
      9      "name": "stderr",
     10      "output_type": "stream",
     11      "text": [
     12       "2022-06-15 15:42:45.558873: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
     13       "2022-06-15 15:42:45.558916: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
     14      ]
     15     }
     16    ],
     17    "source": [
     18     "import numpy as np\n",
     19     "import matplotlib.pyplot as plt\n",
     20     "from matplotlib import colors\n",
     21     "from scipy import ndimage, signal\n",
     22     "import pandas as pd\n",
     23     "\n",
     24     "from sklearn.model_selection import train_test_split\n",
     25     "\n",
     26     "import tensorflow as tf\n",
     27     "from tensorflow import keras\n",
     28     "from tensorflow.keras import layers\n",
     29     "\n",
     30     "from keras.constraints import maxnorm\n",
     31     "from keras.utils import np_utils"
     32    ]
     33   },
     34   {
     35    "cell_type": "markdown",
     36    "metadata": {},
     37    "source": [
     38     "Let us load the data first"
     39    ]
     40   },
     41   {
     42    "cell_type": "code",
     43    "execution_count": 85,
     44    "metadata": {},
     45    "outputs": [
     46     {
     47      "name": "stdout",
     48      "output_type": "stream",
     49      "text": [
     50       "(20000, 35, 35)\n",
     51       "(20000, 35, 35)\n"
     52      ]
     53     }
     54    ],
     55    "source": [
     56     "labels = np.loadtxt('labels_Facial_train.csv', delimiter=',')\n",
     57     "data_train = np.load('data_train_Facial.npy', allow_pickle=True)\n",
     58     "data_test = np.load('data_test_Facial.npy', allow_pickle=True)\n",
     59     "print(data_train.shape)\n",
     60     "print(data_test.shape)"
     61    ]
     62   },
     63   {
     64    "cell_type": "markdown",
     65    "metadata": {},
     66    "source": [
     67     "This is an image data set in form of a numpy array.\n",
     68     "\n",
     69     "It contains images of 35x35 pixels. The images are of faces and the labels correspond to their emotional state:\n",
     70     "\n",
     71     "0: happy\n",
     72     "\n",
     73     "1: sad\n",
     74     "\n",
     75     "2: angry"
     76    ]
     77   },
     78   {
     79    "cell_type": "markdown",
     80    "metadata": {},
     81    "source": [
     82     "Lets have a look at the images:"
     83    ]
     84   },
     85   {
     86    "cell_type": "code",
     87    "execution_count": 3,
     88    "metadata": {},
     89    "outputs": [
     90     {
     91      "data": {
     92       "image/png": 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\n",
     93       "text/plain": [
     94        "<Figure size 1080x1080 with 16 Axes>"
     95       ]
     96      },
     97      "metadata": {
     98       "needs_background": "light"
     99      },
    100      "output_type": "display_data"
    101     }
    102    ],
    103    "source": [
    104     "cmap = colors.ListedColormap(['white','yellow', 'black'])\n",
    105     "Emotions = ['Happy', 'Sad', 'Angry']\n",
    106     "\n",
    107     "plt.figure(figsize = (15,15))\n",
    108     "for k in range(16):\n",
    109     "    plt.subplot(4,4,k+1)\n",
    110     "    plt.imshow(data_train[k,:,:], cmap= cmap)\n",
    111     "    plt.title(Emotions[int(labels[k])])"
    112    ]
    113   },
    114   {
    115    "cell_type": "code",
    116    "execution_count": null,
    117    "metadata": {},
    118    "outputs": [],
    119    "source": []
    120   },
    121   {
    122    "cell_type": "markdown",
    123    "metadata": {},
    124    "source": [
    125     "Our biggest problem is to deal with the massive input dimension of 35*35. \n",
    126     "\n",
    127     "My solution is to use a very simple algorithm. Other solutions could involve reducing the dimension in a smart way and then applying tools from earlier in the lecture. "
    128    ]
    129   },
    130   {
    131    "cell_type": "code",
    132    "execution_count": 31,
    133    "metadata": {},
    134    "outputs": [],
    135    "source": [
    136     "from sklearn.neighbors import KNeighborsClassifier"
    137    ]
    138   },
    139   {
    140    "cell_type": "code",
    141    "execution_count": 32,
    142    "metadata": {},
    143    "outputs": [
    144     {
    145      "data": {
    146       "text/plain": [
    147        "KNeighborsClassifier(n_neighbors=1)"
    148       ]
    149      },
    150      "execution_count": 32,
    151      "metadata": {},
    152      "output_type": "execute_result"
    153     }
    154    ],
    155    "source": [
    156     "# we split the training set into a train and a validation set:\n",
    157     "data_train_split = data_train[0:int(3*data_train.shape[0]/4), :, :]\n",
    158     "lab_train_split  = labels[0:int(3*data_train.shape[0]/4)]\n",
    159     "data_validation_split = data_train[int(3*data_train.shape[0]/4)::, :, :]\n",
    160     "lab_validation_split  = labels[int(3*data_train.shape[0]/4)::]\n",
    161     "\n",
    162     "#we train the nearest neighbor classifier on the training set:\n",
    163     "neigh = KNeighborsClassifier(n_neighbors=1)\n",
    164     "neigh.fit(np.reshape(data_train_split, [data_train_split.shape[0], data_train_split.shape[1]*data_train.shape[2]]), lab_train_split)"
    165    ]
    166   },
    167   {
    168    "cell_type": "markdown",
    169    "metadata": {},
    170    "source": [
    171     "Next we compute the accuracy of our algorithm on the validation set:"
    172    ]
    173   },
    174   {
    175    "cell_type": "code",
    176    "execution_count": 33,
    177    "metadata": {},
    178    "outputs": [
    179     {
    180      "name": "stdout",
    181      "output_type": "stream",
    182      "text": [
    183       "Accuracy: 0.8266\n"
    184      ]
    185     }
    186    ],
    187    "source": [
    188     "# make prediction:\n",
    189     "\n",
    190     "validation_pred_labels = neigh.predict(np.reshape(data_validation_split, [data_validation_split.shape[0], data_validation_split.shape[1]*data_validation_split.shape[2]]))\n",
    191     "\n",
    192     "# validation accuracy:\n",
    193     "\n",
    194     "accuracy = np.sum(lab_validation_split == validation_pred_labels)/lab_validation_split.shape[0]\n",
    195     "print('Accuracy: ' + str(accuracy))"
    196    ]
    197   },
    198   {
    199    "cell_type": "markdown",
    200    "metadata": {},
    201    "source": [
    202     "Let us have a look at the misclassified data points to see if there is something conspicuous about them."
    203    ]
    204   },
    205   {
    206    "cell_type": "code",
    207    "execution_count": 34,
    208    "metadata": {},
    209    "outputs": [
    210     {
    211      "name": "stdout",
    212      "output_type": "stream",
    213      "text": [
    214       "(867,)\n"
    215      ]
    216     },
    217     {
    218      "data": {
    219       "image/png": 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\n",
    220       "text/plain": [
    221        "<Figure size 1080x1080 with 16 Axes>"
    222       ]
    223      },
    224      "metadata": {
    225       "needs_background": "light"
    226      },
    227      "output_type": "display_data"
    228     }
    229    ],
    230    "source": [
    231     "# Let's look at some of the misclassified examples:\n",
    232     "mistakes = np.where(lab_validation_split != validation_pred_labels)[0]\n",
    233     "print(np.shape(mistakes))\n",
    234     "\n",
    235     "cmap = colors.ListedColormap(['white', 'yellow', 'black'])\n",
    236     "Emotions = ['Happy', 'Sad', 'Angry']\n",
    237     "\n",
    238     "plt.figure(figsize = (15,15))\n",
    239     "for k in range(16):\n",
    240     "    plt.subplot(4,4,k+1)\n",
    241     "    plt.imshow(data_validation_split[mistakes[k],:,:], cmap= cmap)\n",
    242     "    plt.title(Emotions[int(validation_pred_labels[mistakes[k]])])"
    243    ]
    244   },
    245   {
    246    "cell_type": "markdown",
    247    "metadata": {},
    248    "source": [
    249     "I am very happy with the accuracy on the validation set (which is not a good thing because the accuracy is terrible). I also have no simple explanation why the faces above were misclassified and therefore no direct way of improving my algorithm. Hence I choose to proceed.\n",
    250     "\n",
    251     "I apply this algorithm to the test set now:"
    252    ]
    253   },
    254   {
    255    "cell_type": "code",
    256    "execution_count": 8,
    257    "metadata": {},
    258    "outputs": [],
    259    "source": [
    260     "labels_test = neigh.predict(np.reshape(data_test, [data_test.shape[0], data_test.shape[1]*data_test.shape[2]]))"
    261    ]
    262   },
    263   {
    264    "cell_type": "markdown",
    265    "metadata": {},
    266    "source": [
    267     "Finally we store the prediction to enter the competition."
    268    ]
    269   },
    270   {
    271    "cell_type": "code",
    272    "execution_count": 88,
    273    "metadata": {},
    274    "outputs": [],
    275    "source": [
    276     "np.savetxt('prediction_facial_recognition_PhilippPetersen.csv', labels_test, delimiter=',')"
    277    ]
    278   },
    279   {
    280    "cell_type": "code",
    281    "execution_count": 89,
    282    "metadata": {},
    283    "outputs": [
    284     {
    285      "name": "stdout",
    286      "output_type": "stream",
    287      "text": [
    288       "[2.90876061e-16 2.00000000e+00]\n"
    289      ]
    290     }
    291    ],
    292    "source": [
    293     "print(data_train[4][11:13,34])"
    294    ]
    295   },
    296   {
    297    "cell_type": "code",
    298    "execution_count": 33,
    299    "metadata": {
    300     "scrolled": true
    301    },
    302    "outputs": [
    303     {
    304      "name": "stdout",
    305      "output_type": "stream",
    306      "text": [
    307       "(24500000,)\n"
    308      ]
    309     },
    310     {
    311      "data": {
    312       "image/png": "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\n",
    313       "text/plain": [
    314        "<Figure size 720x720 with 1 Axes>"
    315       ]
    316      },
    317      "metadata": {
    318       "needs_background": "light"
    319      },
    320      "output_type": "display_data"
    321     }
    322    ],
    323    "source": [
    324     "pixels = data_train.flatten()\n",
    325     "\n",
    326     "\n",
    327     "plt.figure(figsize = (10,10))\n",
    328     "plt.hist(pixels, bins = 100)\n",
    329     "print(np.shape(pixels))\n"
    330    ]
    331   },
    332   {
    333    "cell_type": "code",
    334    "execution_count": 102,
    335    "metadata": {},
    336    "outputs": [],
    337    "source": [
    338     "def remove_boundary(emoji): \n",
    339     "    for i in range(emoji.shape[0]): \n",
    340     "        for j in range(emoji.shape[1]): \n",
    341     "            \n",
    342     "            if emoji[i][j] >= 1.4: # schwarzes Pixel \n",
    343     "                #boundary\n",
    344     "                if i == 0 or i == 34 or j == 0 or j == 34: \n",
    345     "                    emoji[i,j] = 1\n",
    346     "                    \n",
    347     "                elif emoji[i,j-1] <= 0.5 or emoji[i, j+1] <= 0.5 or emoji[i+1, j] <= 0.5 or emoji[i-1,j] <= 0.5: #Kante mit weißem Pixel\n",
    348     "                    emoji[i,j] = 1\n",
    349     "                elif emoji[i+1, j+1] <= 0.5 or emoji[i-1, j-1] <= 0.5 or emoji[i-1, j+1] <= 0.5 or emoji[i+1, j-1] <= 0.5: #Diagonale mit weißem Pixel\n",
    350     "                    emoji[i,j] = 1\n",
    351     "    return emoji > 1.5\n",
    352     "\n",
    353     "data_train_new = []\n",
    354     "for i in range(20000): \n",
    355     "    data_train_new.append(remove_boundary(data_train[i]))"
    356    ]
    357   },
    358   {
    359    "cell_type": "code",
    360    "execution_count": 91,
    361    "metadata": {},
    362    "outputs": [],
    363    "source": [
    364     "X_train, X_valid, y_train, y_valid = train_test_split(data_train_new,\\\n",
    365     "                                                      labels,\\\n",
    366     "                                                      test_size=0.25,\\\n",
    367     "                                                      random_state=40)\n",
    368     "\n",
    369     "X_train = np.round(np.reshape(X_train, (15000, 35, 35, 1)))\n",
    370     "X_valid = np.round(np.reshape(X_valid, (5000, 35, 35, 1)))\n",
    371     "\n",
    372     "y_train = np_utils.to_categorical(y_train)\n",
    373     "y_valid = np_utils.to_categorical(y_valid)"
    374    ]
    375   },
    376   {
    377    "cell_type": "code",
    378    "execution_count": 92,
    379    "metadata": {},
    380    "outputs": [],
    381    "source": [
    382     "#data_train_split = data_train[0:int(3*data_train.shape[0]/4), :, :]\n",
    383     "#\n",
    384     "#lab_train_split  = labels[0:int(3*data_train.shape[0]/4)]\n",
    385     "#data_validation_split = data_train[int(3*data_train.shape[0]/4)::, :, :]\n",
    386     "#lab_validation_split  = labels[int(3*data_train.shape[0]/4)::]\n",
    387     "#\n",
    388     "#data_train_split = np.reshape(data_train_split, (15000, 35, 35, 1))\n",
    389     "#data_validation_split = np.reshape(data_validation_split, (5000, 35, 35, 1))\n",
    390     "#\n",
    391     "#print(np.shape(data_train_split))"
    392    ]
    393   },
    394   {
    395    "cell_type": "code",
    396    "execution_count": 93,
    397    "metadata": {},
    398    "outputs": [
    399     {
    400      "name": "stdout",
    401      "output_type": "stream",
    402      "text": [
    403       "Epoch 1/5\n",
    404       "235/235 [==============================] - 85s 358ms/step - loss: 0.3108 - accuracy: 0.8841 - val_loss: 0.1051 - val_accuracy: 0.9780\n",
    405       "Epoch 2/5\n",
    406       "235/235 [==============================] - 85s 362ms/step - loss: 0.0999 - accuracy: 0.9790 - val_loss: 0.0898 - val_accuracy: 0.9862\n",
    407       "Epoch 3/5\n",
    408       "235/235 [==============================] - 84s 358ms/step - loss: 0.0780 - accuracy: 0.9857 - val_loss: 0.0831 - val_accuracy: 0.9900\n",
    409       "Epoch 4/5\n",
    410       "235/235 [==============================] - 85s 360ms/step - loss: 0.0663 - accuracy: 0.9882 - val_loss: 0.0856 - val_accuracy: 0.9904\n",
    411       "Epoch 5/5\n",
    412       "235/235 [==============================] - 116s 495ms/step - loss: 0.0575 - accuracy: 0.9903 - val_loss: 0.0860 - val_accuracy: 0.9884\n"
    413      ]
    414     }
    415    ],
    416    "source": [
    417     "model = tf.keras.Sequential()\n",
    418     "#add layers ? how many ? \n",
    419     "#first layer is convolutional layer -> runs concolutional filters over input data\n",
    420     "#conv.learns about features of the image\n",
    421     "\n",
    422     "#(3,3) filter size, like shining a spotlight: 3x3 area of pixels\n",
    423     "#schwarß weiß bild thus only 2 dimensional \n",
    424     "#32 number of filters \n",
    425     "model.add(tf.keras.layers.Conv2D(32, (3, 3), input_shape= (35, 35, 1) , activation = 'relu', padding='same'))\n",
    426     "\n",
    427     "#add dropout layer to prevent overfitting , 0.2 means dropping out 20 percent of existing connections\n",
    428     "model.add(tf.keras.layers.Dropout(0.2))\n",
    429     "\n",
    430     "#2nd concolutional layer, higher filter for more accuracy\n",
    431     "model.add(tf.keras.layers.Conv2D(64, (3,3), activation='relu', padding='same'))\n",
    432     "model.add(tf.keras.layers.MaxPooling2D(2))\n",
    433     "model.add(tf.keras.layers.Dropout(0.2))\n",
    434     "\n",
    435     "model.add(tf.keras.layers.Conv2D(128, 3, activation='relu', padding='same'))\n",
    436     "model.add(tf.keras.layers.Dropout(0.2))\n",
    437     "\n",
    438     "#next layer needs data in form of a vector, so we need to flatten it \n",
    439     "model.add(tf.keras.layers.Flatten())\n",
    440     "model.add(tf.keras.layers.Dropout(0.2))\n",
    441     "\n",
    442     "#dense layer, classifies data (32- neurons, number decreases with each layer)\n",
    443     "model.add(tf.keras.layers.Dense(32, activation='relu'))\n",
    444     "model.add(tf.keras.layers.Dropout(0.3))\n",
    445     "\n",
    446     "#softmax selects neuron with highest probability as its output, to classify\n",
    447     "#3 number of classes in our data set, here happy, sad, angry -> 3 classes\n",
    448     "model.add(tf.keras.layers.Dense(3, activation='softmax'))\n",
    449     "\n",
    450     "#created model, now we need to compile it- WIE FUNKTIONIERT DAS; WAS PASSIERT DA\n",
    451     "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    452     "\n",
    453     "#fit to our data\n",
    454     "\n",
    455     "\n",
    456     "#epoch = length of training of a network \n",
    457     "history = model.fit(X_train,\\\n",
    458     "                    y_train,\\\n",
    459     "                    validation_data=(X_valid, y_valid),\\\n",
    460     "                    epochs= 5,\\\n",
    461     "                    batch_size = 64)"
    462    ]
    463   },
    464   {
    465    "cell_type": "code",
    466    "execution_count": 98,
    467    "metadata": {},
    468    "outputs": [
    469     {
    470      "data": {
    471       "text/plain": [
    472        "[<matplotlib.lines.Line2D at 0x7f801f0df0d0>]"
    473       ]
    474      },
    475      "execution_count": 98,
    476      "metadata": {},
    477      "output_type": "execute_result"
    478     },
    479     {
    480      "data": {
    481       "image/png": 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\n",
    482       "text/plain": [
    483        "<Figure size 432x288 with 1 Axes>"
    484       ]
    485      },
    486      "metadata": {
    487       "needs_background": "light"
    488      },
    489      "output_type": "display_data"
    490     }
    491    ],
    492    "source": [
    493     "plt.plot(history.history['loss'])\n",
    494     "plt.plot(history.history['val_loss'])"
    495    ]
    496   },
    497   {
    498    "cell_type": "code",
    499    "execution_count": 99,
    500    "metadata": {},
    501    "outputs": [
    502     {
    503      "name": "stdout",
    504      "output_type": "stream",
    505      "text": [
    506       "157/157 - 5s - loss: 0.0860 - accuracy: 0.9884 - 5s/epoch - 32ms/step\n",
    507       "Accuracy: 98.84%\n"
    508      ]
    509     }
    510    ],
    511    "source": [
    512     "scores = model.evaluate(X_valid, y_valid, verbose=2)\n",
    513     "print(\"Accuracy: %.2f%%\" % (scores[1]*100))"
    514    ]
    515   },
    516   {
    517    "cell_type": "code",
    518    "execution_count": 100,
    519    "metadata": {},
    520    "outputs": [],
    521    "source": [
    522     "predictions = model.predict(X_valid)"
    523    ]
    524   },
    525   {
    526    "cell_type": "code",
    527    "execution_count": 101,
    528    "metadata": {},
    529    "outputs": [
    530     {
    531      "data": {
    532       "image/png": 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\n",
    533       "text/plain": [
    534        "<Figure size 1080x10800 with 60 Axes>"
    535       ]
    536      },
    537      "metadata": {
    538       "needs_background": "light"
    539      },
    540      "output_type": "display_data"
    541     }
    542    ],
    543    "source": [
    544     "mistakes = np.where(np.argmax(np.round(predictions), axis=1) != np.argmax(y_valid, axis=1))[0]\n",
    545     "labels_mistake = np.argmax(predictions[mistakes], axis=1)\n",
    546     "\n",
    547     "plt.figure(figsize = (15,150))\n",
    548     "for k in range(len(mistakes)):\n",
    549     "    plt.subplot(len(mistakes)//4+1,4,k+1)\n",
    550     "    plt.imshow(np.reshape(X_valid, (5000,35,35))[mistakes[k]], cmap= cmap)\n",
    551     "    plt.title(f\"Predicted:{Emotions[labels_mistake[k]]} True: {Emotions[np.argmax(y_valid[mistakes[k]])]}\", color='white')"
    552    ]
    553   },
    554   {
    555    "cell_type": "code",
    556    "execution_count": null,
    557    "metadata": {},
    558    "outputs": [],
    559    "source": []
    560   },
    561   {
    562    "cell_type": "code",
    563    "execution_count": null,
    564    "metadata": {},
    565    "outputs": [],
    566    "source": []
    567   },
    568   {
    569    "cell_type": "code",
    570    "execution_count": null,
    571    "metadata": {},
    572    "outputs": [],
    573    "source": []
    574   },
    575   {
    576    "cell_type": "code",
    577    "execution_count": null,
    578    "metadata": {},
    579    "outputs": [],
    580    "source": []
    581   },
    582   {
    583    "cell_type": "code",
    584    "execution_count": null,
    585    "metadata": {},
    586    "outputs": [],
    587    "source": []
    588   }
    589  ],
    590  "metadata": {
    591   "kernelspec": {
    592    "display_name": "Python 3 (ipykernel)",
    593    "language": "python",
    594    "name": "python3"
    595   },
    596   "language_info": {
    597    "codemirror_mode": {
    598     "name": "ipython",
    599     "version": 3
    600    },
    601    "file_extension": ".py",
    602    "mimetype": "text/x-python",
    603    "name": "python",
    604    "nbconvert_exporter": "python",
    605    "pygments_lexer": "ipython3",
    606    "version": "3.10.5"
    607   }
    608  },
    609  "nbformat": 4,
    610  "nbformat_minor": 4
    611 }
	mlearn Mathematics of Machine Learning
	git clone git://popovic.xyz/mlearn.git
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