examples/Spatial Transformer Networks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example was adapted from skaae's Lasagne [recipe](https://github.com/skaae/Recipes/blob/spatial_transform/examples/spatial_transformer_network.ipynb).  \n",
    "You can download skaae's Cluttered MNIST dataset [here](https://s3.amazonaws.com/lasagne/recipes/datasets/mnist_cluttered_60x60_6distortions.npz).\n",
    "\n",
    "Reference at [arXiv](http://arxiv.org/abs/1506.02025)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using Theano backend.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using gpu device 0: GeForce GTX 680 (CNMeM is disabled)\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from __future__ import absolute_import\n",
    "from __future__ import print_function\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import theano\n",
    "np.random.seed(1337)  # for reproducibility\n",
    "\n",
    "from scipy.misc import imresize\n",
    "\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Dropout, Activation, Flatten\n",
    "from keras.layers.convolutional import Convolution2D, MaxPooling2D\n",
    "from keras.utils import np_utils\n",
    "from keras.utils import np_utils, generic_utils\n",
    "from keras.optimizers import Adam, SGD\n",
    "\n",
    "from seya.layers.attention import SpatialTransformer, ST2\n",
    "\n",
    "batch_size = 128\n",
    "nb_classes = 10\n",
    "nb_epoch = 12\n",
    "\n",
    "DIM = 60\n",
    "mnist_cluttered = \"mnist_cluttered_60x60_6distortions.npz\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train samples: (50000, 1, 60, 60)\n",
      "Validation samples: (10000, 1, 60, 60)\n",
      "Test samples: (10000, 1, 60, 60)\n"
     ]
    }
   ],
   "source": [
    "data = np.load(mnist_cluttered)\n",
    "X_train, y_train = data['x_train'], np.argmax(data['y_train'], axis=-1)\n",
    "X_valid, y_valid = data['x_valid'], np.argmax(data['y_valid'], axis=-1)\n",
    "X_test, y_test = data['x_test'], np.argmax(data['y_test'], axis=-1)\n",
    "# reshape for convolutions\n",
    "X_train = X_train.reshape((X_train.shape[0], 1, DIM, DIM))\n",
    "X_valid = X_valid.reshape((X_valid.shape[0], 1, DIM, DIM))\n",
    "X_test = X_test.reshape((X_test.shape[0], 1, DIM, DIM))\n",
    "\n",
    "y_train = np_utils.to_categorical(y_train, nb_classes)\n",
    "y_valid = np_utils.to_categorical(y_valid, nb_classes)\n",
    "y_test = np_utils.to_categorical(y_test, nb_classes)\n",
    "\n",
    "print(\"Train samples: {}\".format(X_train.shape))\n",
    "print(\"Validation samples: {}\".format(X_valid.shape))\n",
    "print(\"Test samples: {}\".format(X_test.shape))\n",
    "\n",
    "input_shape = X_train.shape[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5f11c95910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "plt.imshow(X_train[101].reshape(DIM, DIM), cmap='gray', interpolation='none')\n",
    "plt.title('Cluttered MNIST', fontsize=20)\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# initial weights\n",
    "b = np.zeros((2, 3), dtype='float32')\n",
    "b[0, 0] = 1\n",
    "b[1, 1] = 1\n",
    "W = np.zeros((50, 6), dtype='float32')\n",
    "weights = [W, b.flatten()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "locnet = Sequential()\n",
    "locnet.add(MaxPooling2D(pool_size=(2,2), input_shape=input_shape))\n",
    "locnet.add(Convolution2D(20, 5, 5))\n",
    "locnet.add(MaxPooling2D(pool_size=(2,2)))\n",
    "locnet.add(Convolution2D(20, 5, 5))\n",
    "\n",
    "locnet.add(Flatten())\n",
    "locnet.add(Dense(50))\n",
    "locnet.add(Activation('relu'))\n",
    "locnet.add(Dense(6, weights=weights))\n",
    "#locnet.add(Activation('sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(SpatialTransformer(localization_net=locnet,\n",
    "                             downsample_factor=3, input_shape=input_shape))\n",
    "\n",
    "model.add(Convolution2D(32, 3, 3, border_mode='same'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Convolution2D(32, 3, 3))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(256))\n",
    "model.add(Activation('relu'))\n",
    "\n",
    "model.add(Dense(nb_classes))\n",
    "model.add(Activation('softmax'))\n",
    "\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "XX = model.get_input()\n",
    "YY = model.layers[0].get_output()\n",
    "F = theano.function([XX], YY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------\n",
      "Epoch: 0 | Valid: 0.7162 | Test: 0.7054\n"
     ]
    },
    {
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       "AABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5eb43c1f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------\n",
      "Epoch: 1 | Valid: 0.7967 | Test: 0.7948\n",
      "----------------------------------------\n",
      "Epoch: 2 | Valid: 0.8243 | Test: 0.8239\n",
      "----------------------------------------\n",
      "Epoch: 3 | Valid: 0.8562 | Test: 0.8519\n",
      "----------------------------------------\n",
      "Epoch: 4 | Valid: 0.8633 | Test: 0.861\n",
      "----------------------------------------\n",
      "Epoch: 5 | Valid: 0.8682 | Test: 0.8639\n"
     ]
    },
    {
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       "TkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5eb3fcad50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------\n",
      "Epoch: 6 | Valid: 0.87 | Test: 0.8701\n",
      "----------------------------------------\n",
      "Epoch: 7 | Valid: 0.8834 | Test: 0.8832\n",
      "----------------------------------------\n",
      "Epoch: 8 | Valid: 0.8608 | Test: 0.8672\n",
      "----------------------------------------\n",
      "Epoch: 9 | Valid: 0.8978 | Test: 0.8975\n"
     ]
    }
   ],
   "source": [
    "nb_epochs = 10 # you probably want to go longer than this\n",
    "batch_size = 256\n",
    "fig = plt.figure()\n",
    "try:\n",
    "    for e in range(nb_epochs):\n",
    "        print('-'*40)\n",
    "        #progbar = generic_utils.Progbar(X_train.shape[0])\n",
    "        for b in range(X_train.shape[0]/batch_size):\n",
    "            f = b * batch_size\n",
    "            l = (b+1) * batch_size\n",
    "            X_batch = X_train[f:l].astype('float32')\n",
    "            y_batch = y_train[f:l].astype('float32')\n",
    "            loss = model.train_on_batch(X_batch, y_batch)\n",
    "            #progbar.add(X_batch.shape[0], values=[(\"train loss\", loss)])\n",
    "        scorev = model.evaluate(X_valid, y_valid, show_accuracy=True, verbose=0)[1]\n",
    "        scoret = model.evaluate(X_test, y_test, show_accuracy=True, verbose=0)[1]\n",
    "        print('Epoch: {0} | Valid: {1} | Test: {2}'.format(e, scorev, scoret))\n",
    "\n",
    "        if e % 5 == 0:\n",
    "            Xresult = F(X_batch[:9])\n",
    "            plt.clf()\n",
    "            for i in range(9):\n",
    "                plt.subplot(3, 3, i+1)\n",
    "                plt.imshow(Xresult[i, 0], cmap='gray')\n",
    "                plt.axis('off')\n",
    "            fig.canvas.draw()\n",
    "            plt.show()\n",
    "except KeyboardInterrupt:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing what the Spatial Transformer learned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Xaug = X_train[:9]\n",
    "Xresult = F(Xaug.astype('float32'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe727717ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# input\n",
    "for i in range(9):\n",
    "    plt.subplot(3, 3, i+1)\n",
    "    plt.imshow(Xaug[i, 0], cmap='gray')\n",
    "    plt.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "AAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe7265d9b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# output\n",
    "for i in range(9):\n",
    "    plt.subplot(3, 3, i+1)\n",
    "    plt.imshow(Xresult[i, 0], cmap='gray')\n",
    "    plt.axis('off')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}