Convolutional Neural Networks.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import libarary\n",
    "\n",
    "import numpy as np\n",
    "from keras.utils import to_categorical\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import keras\n",
    "from keras.models import Sequential,Input,Model\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.layers.advanced_activations import LeakyReLU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz\n",
      "32768/29515 [=================================] - 0s 5us/step\n",
      "Downloading data from http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz\n",
      "26427392/26421880 [==============================] - 15s 1us/step\n",
      "Downloading data from http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz\n",
      "8192/5148 [===============================================] - 0s 1us/step\n",
      "Downloading data from http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz\n",
      "4423680/4422102 [==============================] - 7s 2us/step\n"
     ]
    }
   ],
   "source": [
    "#load data\n",
    "from keras.datasets import fashion_mnist\n",
    "(train_X,train_Y), (test_X,test_Y) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape :  (60000, 28, 28) (60000,)\n",
      "Testing data shape :  (10000, 28, 28) (10000,)\n"
     ]
    }
   ],
   "source": [
    "#show columan and row\n",
    "print('Training data shape : ', train_X.shape, train_Y.shape)\n",
    "print('Testing data shape : ', test_X.shape, test_Y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of outputs :  10\n",
      "Output classes :  [0 1 2 3 4 5 6 7 8 9]\n"
     ]
    }
   ],
   "source": [
    "# Find the unique numbers from the train labels\n",
    "\n",
    "classes = np.unique(train_Y)\n",
    "nClasses = len(classes)\n",
    "print('Total number of outputs : ', nClasses)\n",
    "print('Output classes : ', classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Ground Truth : 9')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[5,5])\n",
    "\n",
    "# Display the first image in training data\n",
    "plt.subplot(121)\n",
    "plt.imshow(train_X[0,:,:], cmap='gray')\n",
    "plt.title(\"Ground Truth : {}\".format(train_Y[0]))\n",
    "\n",
    "# Display the first image in testing data\n",
    "plt.subplot(122)\n",
    "plt.imshow(test_X[0,:,:], cmap='gray')\n",
    "plt.title(\"Ground Truth : {}\".format(test_Y[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((60000, 28, 28, 1), (10000, 28, 28, 1))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reshpe image \n",
    "train_X = train_X.reshape(-1, 28,28, 1)\n",
    "test_X = test_X.reshape(-1, 28,28, 1)\n",
    "train_X.shape, test_X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert float32\n",
    "train_X = train_X.astype('float32')\n",
    "test_X = test_X.astype('float32')\n",
    "train_X = train_X / 255.\n",
    "test_X = test_X / 255."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original label: 9\n",
      "After conversion to one-hot: [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n"
     ]
    }
   ],
   "source": [
    "# Change the labels from categorical to one-hot encoding\n",
    "train_Y_one_hot = to_categorical(train_Y)\n",
    "test_Y_one_hot = to_categorical(test_Y)\n",
    "\n",
    "# Display the change for category label using one-hot encoding\n",
    "print('Original label:', train_Y[0])\n",
    "print('After conversion to one-hot:', train_Y_one_hot[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#split data\n",
    "from sklearn.model_selection import train_test_split\n",
    "train_X,valid_X,train_label,valid_label = train_test_split(train_X, train_Y_one_hot, test_size=0.2, random_state=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((48000, 28, 28, 1), (12000, 28, 28, 1), (48000, 10), (12000, 10))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#show shape\n",
    "train_X.shape,valid_X.shape,train_label.shape,valid_label.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#define variable\n",
    "batch_size = 64\n",
    "epochs = 20\n",
    "num_classes = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "fashion_model = Sequential() #Conv2D() define as Convolutional Neural Networks\n",
    "fashion_model.add(Conv2D(32, kernel_size=(3, 3),activation='linear',input_shape=(28,28,1),padding='same'))\n",
    "fashion_model.add(LeakyReLU(alpha=0.1))\n",
    "fashion_model.add(MaxPooling2D((2, 2),padding='same'))\n",
    "fashion_model.add(Conv2D(64, (3, 3), activation='linear',padding='same'))\n",
    "fashion_model.add(LeakyReLU(alpha=0.1))\n",
    "fashion_model.add(MaxPooling2D(pool_size=(2, 2),padding='same'))\n",
    "fashion_model.add(Conv2D(128, (3, 3), activation='linear',padding='same'))\n",
    "fashion_model.add(LeakyReLU(alpha=0.1))                  \n",
    "fashion_model.add(MaxPooling2D(pool_size=(2, 2),padding='same'))\n",
    "fashion_model.add(Flatten())\n",
    "fashion_model.add(Dense(128, activation='linear'))\n",
    "fashion_model.add(LeakyReLU(alpha=0.1))                  \n",
    "fashion_model.add(Dense(num_classes, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Compile the Model\n",
    "\n",
    "fashion_model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.Adam(),metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 28, 28, 32)        320       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_1 (LeakyReLU)    (None, 28, 28, 32)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 14, 14, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 14, 14, 64)        18496     \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_2 (LeakyReLU)    (None, 14, 14, 64)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 7, 7, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 7, 7, 128)         73856     \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_3 (LeakyReLU)    (None, 7, 7, 128)         0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 2048)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 128)               262272    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_4 (LeakyReLU)    (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 356,234\n",
      "Trainable params: 356,234\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "fashion_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 48000 samples, validate on 12000 samples\n",
      "Epoch 1/20\n",
      "48000/48000 [==============================] - 79s 2ms/step - loss: 0.4670 - accuracy: 0.8285 - val_loss: 0.3558 - val_accuracy: 0.8722\n",
      "Epoch 2/20\n",
      "48000/48000 [==============================] - 73s 2ms/step - loss: 0.2900 - accuracy: 0.8942 - val_loss: 0.2743 - val_accuracy: 0.8992\n",
      "Epoch 3/20\n",
      "48000/48000 [==============================] - 67s 1ms/step - loss: 0.2421 - accuracy: 0.9101 - val_loss: 0.2588 - val_accuracy: 0.9055\n",
      "Epoch 4/20\n",
      "48000/48000 [==============================] - 68s 1ms/step - loss: 0.2103 - accuracy: 0.9231 - val_loss: 0.2324 - val_accuracy: 0.9147\n",
      "Epoch 5/20\n",
      "48000/48000 [==============================] - 66s 1ms/step - loss: 0.1838 - accuracy: 0.9320 - val_loss: 0.2470 - val_accuracy: 0.9135\n",
      "Epoch 6/20\n",
      "48000/48000 [==============================] - 67s 1ms/step - loss: 0.1607 - accuracy: 0.9410 - val_loss: 0.2531 - val_accuracy: 0.9103\n",
      "Epoch 7/20\n",
      "48000/48000 [==============================] - 65s 1ms/step - loss: 0.1406 - accuracy: 0.9481 - val_loss: 0.2268 - val_accuracy: 0.9214\n",
      "Epoch 8/20\n",
      "48000/48000 [==============================] - 62s 1ms/step - loss: 0.1160 - accuracy: 0.9564 - val_loss: 0.2525 - val_accuracy: 0.9200\n",
      "Epoch 9/20\n",
      "48000/48000 [==============================] - 73s 2ms/step - loss: 0.1000 - accuracy: 0.9631 - val_loss: 0.2455 - val_accuracy: 0.9206\n",
      "Epoch 10/20\n",
      "48000/48000 [==============================] - 66s 1ms/step - loss: 0.0856 - accuracy: 0.9674 - val_loss: 0.2612 - val_accuracy: 0.9242\n",
      "Epoch 11/20\n",
      "48000/48000 [==============================] - 72s 1ms/step - loss: 0.0740 - accuracy: 0.9722 - val_loss: 0.3428 - val_accuracy: 0.9092\n",
      "Epoch 12/20\n",
      "48000/48000 [==============================] - 69s 1ms/step - loss: 0.0601 - accuracy: 0.9768 - val_loss: 0.3254 - val_accuracy: 0.9141\n",
      "Epoch 13/20\n",
      "48000/48000 [==============================] - 65s 1ms/step - loss: 0.0557 - accuracy: 0.9795 - val_loss: 0.3214 - val_accuracy: 0.9214\n",
      "Epoch 14/20\n",
      "48000/48000 [==============================] - 66s 1ms/step - loss: 0.0465 - accuracy: 0.9826 - val_loss: 0.3735 - val_accuracy: 0.9178\n",
      "Epoch 15/20\n",
      "48000/48000 [==============================] - 68s 1ms/step - loss: 0.0416 - accuracy: 0.9845 - val_loss: 0.3718 - val_accuracy: 0.9158\n",
      "Epoch 16/20\n",
      "48000/48000 [==============================] - 69s 1ms/step - loss: 0.0434 - accuracy: 0.9835 - val_loss: 0.3806 - val_accuracy: 0.9184\n",
      "Epoch 17/20\n",
      "48000/48000 [==============================] - 69s 1ms/step - loss: 0.0320 - accuracy: 0.9878 - val_loss: 0.4532 - val_accuracy: 0.9212\n",
      "Epoch 18/20\n",
      "48000/48000 [==============================] - 71s 1ms/step - loss: 0.0302 - accuracy: 0.9887 - val_loss: 0.4132 - val_accuracy: 0.9183\n",
      "Epoch 19/20\n",
      "48000/48000 [==============================] - 69s 1ms/step - loss: 0.0303 - accuracy: 0.9890 - val_loss: 0.4765 - val_accuracy: 0.9143\n",
      "Epoch 20/20\n",
      "48000/48000 [==============================] - 64s 1ms/step - loss: 0.0289 - accuracy: 0.9891 - val_loss: 0.4714 - val_accuracy: 0.9172\n"
     ]
    }
   ],
   "source": [
    "#Train Model\n",
    "fashion_train = fashion_model.fit(train_X, train_label, batch_size=batch_size,epochs=epochs,verbose=1,validation_data=(valid_X, valid_label))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Test Model\n",
    "test_eval = fashion_model.evaluate(test_X, test_Y_one_hot, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.5022541431802325\n",
      "Test accuracy: 0.9150999784469604\n"
     ]
    }
   ],
   "source": [
    "#Model Evaluation on Test Model\n",
    "\n",
    "print('Test loss:', test_eval[0])\n",
    "print('Test accuracy:', test_eval[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predict Labels\n",
    "predicted_classes = fashion_model.predict(test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_classes = np.argmax(np.round(predicted_classes),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((10000,), (10000,))"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_classes.shape, test_Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 9144 correct labels\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot correct labels\n",
    "correct = np.where(predicted_classes==test_Y)[0]\n",
    "print (\"Found %d correct labels\" % len(correct))\n",
    "for i, correct in enumerate(correct[:9]):\n",
    "    plt.subplot(3,3,i+1)\n",
    "    plt.imshow(test_X[correct].reshape(28,28), cmap='gray', interpolation='none')\n",
    "    plt.title(\"Predicted {}, Class {}\".format(predicted_classes[correct], test_Y[correct]))\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 856 incorrect labels\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#in plot correct labels\n",
    "incorrect = np.where(predicted_classes!=test_Y)[0]\n",
    "print (\"Found %d incorrect labels\" % len(incorrect))\n",
    "for i, incorrect in enumerate(incorrect[:9]):\n",
    "    plt.subplot(3,3,i+1)\n",
    "    plt.imshow(test_X[incorrect].reshape(28,28), cmap='gray', interpolation='none')\n",
    "    plt.title(\"Predicted {}, Class {}\".format(predicted_classes[incorrect], test_Y[incorrect]))\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "     Class 0       0.87      0.85      0.86      1000\n",
      "     Class 1       0.98      0.99      0.98      1000\n",
      "     Class 2       0.85      0.88      0.87      1000\n",
      "     Class 3       0.90      0.93      0.91      1000\n",
      "     Class 4       0.87      0.86      0.87      1000\n",
      "     Class 5       0.99      0.98      0.98      1000\n",
      "     Class 6       0.78      0.74      0.76      1000\n",
      "     Class 7       0.97      0.96      0.96      1000\n",
      "     Class 8       0.98      0.98      0.98      1000\n",
      "     Class 9       0.96      0.97      0.97      1000\n",
      "\n",
      "    accuracy                           0.91     10000\n",
      "   macro avg       0.91      0.91      0.91     10000\n",
      "weighted avg       0.91      0.91      0.91     10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Classification Report\n",
    "from sklearn.metrics import classification_report\n",
    "target_names = [\"Class {}\".format(i) for i in range(num_classes)]\n",
    "print(classification_report(test_Y, predicted_classes, target_names=target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}