2.2. mnist_cnn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MNIST CNN\n",
    "![title](https://ml4a.github.io/images/figures/mnist-input.png)\n",
    "\n",
    "<video controls src=\"data/Neural Network 3D Simulation.mp4\" />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from keras.datasets import mnist\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "import keras\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras import backend as K\n",
    "\n",
    "from keras.backend.tensorflow_backend import set_session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mendefinisikan Fungsi\n",
    "def set_gpu_mem_alloc(mem_use):\n",
    "    avail  = 4041\n",
    "    percent = mem_use / avail\n",
    "    config = tf.ConfigProto()\n",
    "    config.gpu_options.per_process_gpu_memory_fraction = percent\n",
    "    config.gpu_options.visible_device_list = \"0\"\n",
    "    set_session(tf.Session(config=config))\n",
    "\n",
    "set_gpu_mem_alloc(2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 128\n",
    "num_classes = 10\n",
    "epochs = 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# input image dimensions\n",
    "img_rows, img_cols = 28, 28"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the data, split between train and test sets\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f289c01cef0>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2878712208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# apa sih isinya\n",
    "f,ax = plt.subplots(2,2)\n",
    "ax[0][0].imshow(x_train[0], cmap='gray')\n",
    "ax[0][1].imshow(x_train[1], cmap='gray')\n",
    "ax[1][0].imshow(x_train[2], cmap='gray')\n",
    "ax[1][1].imshow(x_train[3], cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshape struktur datanya\n",
    "x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n",
    "x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n",
    "input_shape = (img_rows, img_cols, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_train.astype('float32')\n",
    "x_test = x_test.astype('float32')\n",
    "x_train /= 255\n",
    "x_test /= 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train shape: (60000, 28, 28, 1)\n",
      "60000 train samples\n",
      "10000 test samples\n"
     ]
    }
   ],
   "source": [
    "print('x_train shape:', x_train.shape)\n",
    "print(x_train.shape[0], 'train samples')\n",
    "print(x_test.shape[0], 'test samples')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert class vectors to binary class matrices\n",
    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = keras.utils.to_categorical(y_test, num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_10 (Conv2D)           (None, 26, 26, 32)        320       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_7 (MaxPooling2 (None, 13, 13, 32)        0         \n",
      "_________________________________________________________________\n",
      "flatten_7 (Flatten)          (None, 5408)              0         \n",
      "_________________________________________________________________\n",
      "dense_13 (Dense)             (None, 128)               692352    \n",
      "_________________________________________________________________\n",
      "dense_14 (Dense)             (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 693,962\n",
      "Trainable params: 693,962\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "ksize = (3,3)\n",
    "psize = (2,2)\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Conv2D(32, kernel_size=ksize, activation='relu', input_shape=input_shape))\n",
    "model.add(MaxPooling2D(pool_size=psize))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(128, activation='relu'))\n",
    "model.add(Dense(num_classes, activation='softmax'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
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       "<g class=\"graph\" id=\"graph0\" transform=\"scale(1 1) rotate(0) translate(4 406)\">\n",
       "<title>G</title>\n",
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       "<!-- 139811795952080 -->\n",
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       "<polygon fill=\"none\" points=\"14,-365.5 14,-401.5 193,-401.5 193,-365.5 14,-365.5\" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"103.5\" y=\"-379.8\">conv2d_10_input: InputLayer</text>\n",
       "</g>\n",
       "<!-- 139811796125512 -->\n",
       "<g class=\"node\" id=\"node2\"><title>139811796125512</title>\n",
       "<polygon fill=\"none\" points=\"38.5,-292.5 38.5,-328.5 168.5,-328.5 168.5,-292.5 38.5,-292.5\" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"103.5\" y=\"-306.8\">conv2d_10: Conv2D</text>\n",
       "</g>\n",
       "<!-- 139811795952080&#45;&gt;139811796125512 -->\n",
       "<g class=\"edge\" id=\"edge1\"><title>139811795952080-&gt;139811796125512</title>\n",
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       "<polygon fill=\"none\" points=\"0,-219.5 0,-255.5 207,-255.5 207,-219.5 0,-219.5\" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"103.5\" y=\"-233.8\">max_pooling2d_7: MaxPooling2D</text>\n",
       "</g>\n",
       "<!-- 139811796125512&#45;&gt;139811796099928 -->\n",
       "<g class=\"edge\" id=\"edge2\"><title>139811796125512-&gt;139811796099928</title>\n",
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       "<polygon fill=\"none\" points=\"48.5,-146.5 48.5,-182.5 158.5,-182.5 158.5,-146.5 48.5,-146.5\" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"103.5\" y=\"-160.8\">flatten_7: Flatten</text>\n",
       "</g>\n",
       "<!-- 139811796099928&#45;&gt;139811796732336 -->\n",
       "<g class=\"edge\" id=\"edge3\"><title>139811796099928-&gt;139811796732336</title>\n",
       "<path d=\"M103.5,-219.313C103.5,-211.289 103.5,-201.547 103.5,-192.569\" fill=\"none\" stroke=\"black\"/>\n",
       "<polygon fill=\"black\" points=\"107,-192.529 103.5,-182.529 100,-192.529 107,-192.529\" stroke=\"black\"/>\n",
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       "<polygon fill=\"none\" points=\"49,-73.5 49,-109.5 158,-109.5 158,-73.5 49,-73.5\" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"103.5\" y=\"-87.8\">dense_13: Dense</text>\n",
       "</g>\n",
       "<!-- 139811796732336&#45;&gt;139812393106456 -->\n",
       "<g class=\"edge\" id=\"edge4\"><title>139811796732336-&gt;139812393106456</title>\n",
       "<path d=\"M103.5,-146.313C103.5,-138.289 103.5,-128.547 103.5,-119.569\" fill=\"none\" stroke=\"black\"/>\n",
       "<polygon fill=\"black\" points=\"107,-119.529 103.5,-109.529 100,-119.529 107,-119.529\" stroke=\"black\"/>\n",
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       "<polygon fill=\"none\" points=\"49,-0.5 49,-36.5 158,-36.5 158,-0.5 49,-0.5\" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"103.5\" y=\"-14.8\">dense_14: Dense</text>\n",
       "</g>\n",
       "<!-- 139812393106456&#45;&gt;139812393189672 -->\n",
       "<g class=\"edge\" id=\"edge5\"><title>139812393106456-&gt;139812393189672</title>\n",
       "<path d=\"M103.5,-73.3129C103.5,-65.2895 103.5,-55.5475 103.5,-46.5691\" fill=\"none\" stroke=\"black\"/>\n",
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       "</g>\n",
       "</g>\n",
       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import SVG\n",
    "from keras.utils.vis_utils import model_to_dot\n",
    "SVG(model_to_dot(model).create(prog='dot', format='svg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/12\n",
      "60000/60000 [==============================] - 6s 102us/step - loss: 0.2676 - acc: 0.9188 - val_loss: 0.1151 - val_acc: 0.9616\n",
      "Epoch 2/12\n",
      "60000/60000 [==============================] - 5s 91us/step - loss: 0.0763 - acc: 0.9775 - val_loss: 0.0751 - val_acc: 0.9756\n",
      "Epoch 3/12\n",
      "60000/60000 [==============================] - 5s 90us/step - loss: 0.0502 - acc: 0.9852 - val_loss: 0.0570 - val_acc: 0.9812\n",
      "Epoch 4/12\n",
      "60000/60000 [==============================] - 6s 92us/step - loss: 0.0376 - acc: 0.9889 - val_loss: 0.0415 - val_acc: 0.9851\n",
      "Epoch 5/12\n",
      "60000/60000 [==============================] - 5s 91us/step - loss: 0.0291 - acc: 0.9914 - val_loss: 0.0400 - val_acc: 0.9863\n",
      "Epoch 6/12\n",
      "60000/60000 [==============================] - 6s 93us/step - loss: 0.0226 - acc: 0.9936 - val_loss: 0.0412 - val_acc: 0.9847\n",
      "Epoch 7/12\n",
      "60000/60000 [==============================] - 6s 100us/step - loss: 0.0179 - acc: 0.9949 - val_loss: 0.0410 - val_acc: 0.9863\n",
      "Epoch 8/12\n",
      "60000/60000 [==============================] - 6s 93us/step - loss: 0.0144 - acc: 0.9960 - val_loss: 0.0391 - val_acc: 0.9859\n",
      "Epoch 9/12\n",
      "60000/60000 [==============================] - 5s 91us/step - loss: 0.0111 - acc: 0.9968 - val_loss: 0.0399 - val_acc: 0.9862\n",
      "Epoch 10/12\n",
      "60000/60000 [==============================] - 5s 90us/step - loss: 0.0083 - acc: 0.9980 - val_loss: 0.0377 - val_acc: 0.9882\n",
      "Epoch 11/12\n",
      "60000/60000 [==============================] - 5s 91us/step - loss: 0.0066 - acc: 0.9984 - val_loss: 0.0378 - val_acc: 0.9888\n",
      "Epoch 12/12\n",
      "60000/60000 [==============================] - 6s 95us/step - loss: 0.0049 - acc: 0.9989 - val_loss: 0.0411 - val_acc: 0.9874\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f285d8c1e48>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train,\n",
    "          batch_size=batch_size,\n",
    "          epochs=epochs,\n",
    "          verbose=1,\n",
    "          validation_data=(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.0411370140263\n",
      "Test accuracy: 0.9874\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(x_test, y_test, verbose=0)\n",
    "print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('saved/mnist_cnn.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "(train_x, train_y), (test_x, test_y) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict = model.predict(x_test, batch_size=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction :  0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f285d55ac18>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f285d5c6710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx = 10\n",
    "print('Prediction : ', np.argmax(predict[idx]))\n",
    "plt.imshow(test_x[idx], cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:dlearn]",
   "language": "python",
   "name": "conda-env-dlearn-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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