Keras Intro.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Demo of the Keras (https://keras.io/) library. To run the code, download the T1 images of the IXI dataset (http://brain-development.org/ixi-dataset/) as well as the demographic information and convert it to .csv."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nibabel as nib\n",
    "import numpy as np\n",
    "\n",
    "import os, csv\n",
    "\n",
    "import matplotlib as mpl\n",
    "mpl.use('Agg')\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline  \n",
    "\n",
    "\n",
    "data_dir = 'E:/brains/IXI/'\n",
    "labels_file = 'IXI.csv'\n",
    "\n",
    "image_size = (256, 256, 150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def load_data(data_dir, labels_file):\n",
    "    '''Loading data usually has 2 parts, the inputs (images) and ouptuts (labels)'''    \n",
    "    \n",
    "    ids = []\n",
    "    subjects = {}\n",
    "    \n",
    "    ixi_reader = csv.reader(open(data_dir + labels_file))\n",
    "    lines = list(ixi_reader)[1:200]\n",
    "    \n",
    "    for line in lines:\n",
    "        try:\n",
    "            ixi_id = int(line[0])\n",
    "            sex = int(line[1])\n",
    "            age = float(line[11])\n",
    "            subjects[ixi_id] = (sex, age)\n",
    "\n",
    "            ids.append(ixi_id)\n",
    "        except ValueError as e:\n",
    "            print('Uh oh:', e)\n",
    "                \n",
    "    # Now load the data\n",
    "    # We're going to load just slices\n",
    "    print('We have', len(ids), 'subjects with sex/age information.')\n",
    "    imaging_slices = {}\n",
    "    for filename in os.listdir(data_dir):\n",
    "        if '.nii.gz' in filename:\n",
    "            ixi_id = int(filename[3:6])\n",
    "            \n",
    "            if ixi_id in ids:\n",
    "                print('Loading image', ixi_id)\n",
    "                image = nib.load(data_dir + filename).get_data()\n",
    "                image_slice = image[:, :, 75]\n",
    "                image_slice = np.reshape(image_slice, (256, 256, 1))\n",
    "                \n",
    "                imaging_slices[ixi_id] = image_slice\n",
    "            \n",
    "    \n",
    "    good_images = np.empty((len(ids), 256, 256, 1))\n",
    "    good_labels = np.empty((len(ids), 1))\n",
    "    \n",
    "    # order all the data so that the labels array matches the data array\n",
    "    for i, good_id in enumerate(ids):\n",
    "        try:\n",
    "            good_labels[i, 0] = subjects[good_id][0] - 1       \n",
    "            good_images[i, ...] = imaging_slices[good_id]\n",
    "        except:\n",
    "            pass\n",
    "        \n",
    "    return good_images, good_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uh oh: could not convert string to float: \n",
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      "Uh oh: could not convert string to float: \n",
      "Uh oh: could not convert string to float: \n",
      "Uh oh: could not convert string to float: \n",
      "We have 590 subjects with sex/age information.\n",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "We have 590 data points\n"
     ]
    }
   ],
   "source": [
    "images, labels = load_data(data_dir, labels_file)\n",
    "print('We have', images.shape[0], 'data points')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BwMVAAGxj4aHhejoOhliR0wRahAWObVAqx9EMD8c1yM1mqE0ncPdHYEoXrn7I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\nzpD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vYHH5jjquwU6t8W9e38VCe2db36AENMiweeyk0QdzVAZWOLjjZyAlSe1/ryIPbgR5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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16980ede390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Example label [ 1.]\n",
      "[[ 0.  1.]]\n"
     ]
    }
   ],
   "source": [
    "from keras.utils import to_categorical\n",
    "\n",
    "random_example_number = np.random.randint(images.shape[0])\n",
    "\n",
    "example_image = images[random_example_number, :, :, 0]\n",
    "example_label = labels[random_example_number]\n",
    "\n",
    "plt.imshow(example_image.T, origin='lower')\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "plt.show()\n",
    "\n",
    "print('Example label', example_label)\n",
    "\n",
    "categorical_example_label = to_categorical([example_label], num_classes=2)\n",
    "print(categorical_example_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 254, 254, 8)       80        \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 254, 254, 8)       32        \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 127, 127, 8)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 125, 125, 16)      1168      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 62, 62, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 60, 60, 32)        4640      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 30, 30, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 28, 28, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 14, 14, 64)        0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 12544)             0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 128)               1605760   \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 2)                 258       \n",
      "=================================================================\n",
      "Total params: 1,646,946\n",
      "Trainable params: 1,646,930\n",
      "Non-trainable params: 16\n",
      "_________________________________________________________________\n",
      "Epoch 1/10\n",
      "590/590 [==============================] - 5s - loss: 0.7881 - acc: 0.5254     \n",
      "Epoch 2/10\n",
      "590/590 [==============================] - 4s - loss: 0.7116 - acc: 0.6068     \n",
      "Epoch 3/10\n",
      "590/590 [==============================] - 4s - loss: 0.7410 - acc: 0.5712     \n",
      "Epoch 4/10\n",
      "590/590 [==============================] - 4s - loss: 0.6674 - acc: 0.6186     \n",
      "Epoch 5/10\n",
      "590/590 [==============================] - 4s - loss: 0.6439 - acc: 0.6254     \n",
      "Epoch 6/10\n",
      "590/590 [==============================] - 4s - loss: 0.6454 - acc: 0.6305     \n",
      "Epoch 7/10\n",
      "590/590 [==============================] - 4s - loss: 0.6447 - acc: 0.6576     \n",
      "Epoch 8/10\n",
      "590/590 [==============================] - 4s - loss: 0.6130 - acc: 0.6746     \n",
      "Epoch 9/10\n",
      "590/590 [==============================] - 4s - loss: 0.5691 - acc: 0.7085     \n",
      "Epoch 10/10\n",
      "590/590 [==============================] - 4s - loss: 0.5752 - acc: 0.7000     \n",
      "<keras.callbacks.History object at 0x0000016A0C7E19B0>\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "'History' object is not subscriptable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-51-39ca68c5e1d5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'accuracy'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'History' object is not subscriptable"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "tf.device('cpu')\n",
    "from keras.models import Sequential\n",
    "\n",
    "from keras.layers import Dense, Flatten, Dropout\n",
    "from keras.layers import Conv2D, BatchNormalization, MaxPooling2D\n",
    "\n",
    "from keras.optimizers import Adam\n",
    "\n",
    "from keras import backend as K\n",
    "K.clear_session()\n",
    "\n",
    "\n",
    "kernel_size = (3, 3)\n",
    "n_classes = 2\n",
    "\n",
    "filters = 8\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(filters, kernel_size, activation='relu', input_shape=(256, 256, 1)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(MaxPooling2D())\n",
    "model.add(Conv2D(filters*2, kernel_size, activation='relu'))\n",
    "model.add(MaxPooling2D())\n",
    "model.add(Conv2D(filters*4, kernel_size, activation='relu'))\n",
    "model.add(MaxPooling2D())\n",
    "model.add(Conv2D(filters*8, kernel_size, activation='relu'))\n",
    "model.add(MaxPooling2D())\n",
    "model.add(Flatten())\n",
    "model.add(Dense(128, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(128, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(n_classes, activation='softmax'))\n",
    "\n",
    "adam = Adam(lr=0.00002)\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "                 optimizer=adam,\n",
    "                 metrics=['accuracy'])\n",
    "\n",
    "categorical_labels = to_categorical(labels, num_classes=2)\n",
    "\n",
    "model.summary()\n",
    "\n",
    "model.fit(images, categorical_labels, epochs=10, batch_size=32)\n",
    "\n",
    "\n",
    "K.clear_session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def mlp():\n",
    "    model = Sequential()\n",
    "\n",
    "    model.add(Flatten(input_shape=(256, 256, 1)))\n",
    "    model.add(Dense(units=256, activation='relu'))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(units=256, activation='relu'))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(units=256, activation='relu'))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(units=n_classes, activation='softmax'))\n",
    "\n",
    "\n",
    "    adam = Adam(lr=0.0002)\n",
    "\n",
    "    model.compile(loss='categorical_crossentropy',\n",
    "                 optimizer=adam,\n",
    "                 metrics=['accuracy'])\n",
    "\n",
    "    return model"
   ]
  }
 ],
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