MNIST contrastive

MNIST contrastive.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "\n",
    "import gzip\n",
    "import itertools\n",
    "import pickle\n",
    "import os\n",
    "import sys\n",
    "import numpy as np\n",
    "import lasagne\n",
    "import theano\n",
    "from lasagne.layers import dnn  # fails early if not available\n",
    "import theano.tensor as T\n",
    "import time\n",
    "\n",
    "PY2 = sys.version_info[0] == 2\n",
    "\n",
    "if PY2:\n",
    "    from urllib import urlretrieve\n",
    "\n",
    "    def pickle_load(f, encoding):\n",
    "        return pickle.load(f)\n",
    "else:\n",
    "    from urllib.request import urlretrieve\n",
    "\n",
    "    def pickle_load(f, encoding):\n",
    "        return pickle.load(f, encoding=encoding)\n",
    "\n",
    "\n",
    "NUM_EPOCHS = 500\n",
    "BATCH_SIZE = 600\n",
    "LEARNING_RATE = 0.01\n",
    "MOMENTUM = 0.9\n",
    "\n",
    "\n",
    "DATA_URL = 'http://deeplearning.net/data/mnist/mnist.pkl.gz'\n",
    "DATA_FILENAME = 'mnist.pkl.gz'\n",
    "\n",
    "\n",
    "def _load_data(url=DATA_URL, filename=DATA_FILENAME):\n",
    "    if not os.path.exists(filename):\n",
    "        print(\"Downloading MNIST\")\n",
    "        urlretrieve(url, filename)\n",
    "\n",
    "    with gzip.open(filename, 'rb') as f:\n",
    "        return pickle_load(f, encoding='latin-1')\n",
    "\n",
    "def aug_data(X, y):\n",
    "    X_aug = np.concatenate((X, X), axis=1)\n",
    "\n",
    "    idx = range(len(X))\n",
    "    np.random.shuffle(idx)\n",
    "    X_aug[:, 1, :, :] = X[idx, 0]\n",
    "    \n",
    "    y_aug = y[idx] == y\n",
    "    return X_aug, y_aug\n",
    "\n",
    "def load_data():\n",
    "    data = _load_data()\n",
    "    X_train, y_train = data[0]\n",
    "    X_valid, y_valid = data[1]\n",
    "    X_test, y_test = data[2]\n",
    "\n",
    "    # reshape for convolutions\n",
    "    X_train = X_train.reshape((X_train.shape[0], 1, 28, 28))\n",
    "    X_valid = X_valid.reshape((X_valid.shape[0], 1, 28, 28))\n",
    "    X_test = X_test.reshape((X_test.shape[0], 1, 28, 28))\n",
    "    \n",
    "    X_train, y_train_pair = aug_data(X_train, y_train)\n",
    "    X_valid, y_valid_pair = aug_data(X_valid, y_valid)\n",
    "\n",
    "    return dict(\n",
    "        X_train=lasagne.utils.floatX(X_train),\n",
    "        y_train_pair=y_train_pair.astype('int32'),\n",
    "        y_train=y_train.astype('int32'),\n",
    "        X_valid=lasagne.utils.floatX(X_valid),\n",
    "        y_valid_pair=y_valid_pair.astype('int32'),\n",
    "        y_valid=y_valid.astype('int32'),\n",
    "        num_examples_train=X_train.shape[0],\n",
    "        num_examples_valid=X_valid.shape[0],\n",
    "        input_height=X_train.shape[2],\n",
    "        input_width=X_train.shape[3],\n",
    "    )\n",
    "\n",
    "\n",
    "def build_model(input_width, input_height,\n",
    "                batch_size=BATCH_SIZE):\n",
    "    l_in = lasagne.layers.InputLayer(\n",
    "        shape=(None, 2, input_width, input_height),\n",
    "    )\n",
    "\n",
    "    l_c2b = lasagne.layers.ReshapeLayer(l_in, (-1, 1, [2], [3]))\n",
    "    \n",
    "    l_conv1 = dnn.Conv2DDNNLayer(\n",
    "        l_c2b,\n",
    "        num_filters=20,\n",
    "        filter_size=(5, 5),\n",
    "        nonlinearity=lasagne.nonlinearities.rectify,\n",
    "        W=lasagne.init.Uniform(),\n",
    "    )\n",
    "    l_pool1 = dnn.MaxPool2DDNNLayer(l_conv1, ds=(2, 2))\n",
    "\n",
    "    l_conv2 = dnn.Conv2DDNNLayer(\n",
    "        l_pool1,\n",
    "        num_filters=50,\n",
    "        filter_size=(5, 5),\n",
    "        nonlinearity=lasagne.nonlinearities.rectify,\n",
    "        W=lasagne.init.Uniform(),\n",
    "    )\n",
    "    l_pool2 = dnn.MaxPool2DDNNLayer(l_conv2, ds=(2, 2))\n",
    "\n",
    "    l_hidden1 = lasagne.layers.DenseLayer(\n",
    "        l_pool2,\n",
    "        num_units=500,\n",
    "        nonlinearity=lasagne.nonlinearities.rectify,\n",
    "        W=lasagne.init.Uniform(),\n",
    "    )\n",
    "\n",
    "    l_hidden2 = lasagne.layers.DenseLayer(\n",
    "        l_pool1,\n",
    "        num_units=2,\n",
    "        nonlinearity=lasagne.nonlinearities.identity,\n",
    "        W=lasagne.init.Uniform(),\n",
    "    )\n",
    "        \n",
    "    l_out = lasagne.layers.ReshapeLayer(l_hidden2, (-1, 2, [1]))\n",
    "\n",
    "    return l_out\n",
    "\n",
    "\n",
    "def create_iter_functions(output_layer,\n",
    "                          learning_rate=LEARNING_RATE, \n",
    "                          momentum=MOMENTUM):    \n",
    "    X = T.tensor4()\n",
    "    y = T.ivector()\n",
    "    a = output_layer.get_output(X)\n",
    "    \n",
    "    d = T.sum((a[:, 0] - a[:, 1])**2, -1)\n",
    "    loss = T.mean(y * d + \n",
    "                  (1 - y) * T.maximum(0, 1 - d))\n",
    "\n",
    "    all_params = lasagne.layers.get_all_params(output_layer)\n",
    "    updates = lasagne.updates.nesterov_momentum(\n",
    "        loss, all_params, learning_rate, momentum)\n",
    "\n",
    "    iter_train = theano.function(\n",
    "        [X, y], loss,\n",
    "        updates=updates,\n",
    "    )\n",
    "    \n",
    "    iter_valid = theano.function(\n",
    "        [X, y], loss,\n",
    "        )\n",
    "\n",
    "    return dict(\n",
    "        train=iter_train,\n",
    "        valid=iter_valid,\n",
    "        )\n",
    "\n",
    "\n",
    "def train(iter_funcs, dataset, batch_size=BATCH_SIZE):\n",
    "    num_batches_train = dataset['num_examples_train'] // batch_size\n",
    "    num_batches_valid = dataset['num_examples_valid'] // batch_size\n",
    "\n",
    "    batch_train_losses = []\n",
    "    for batch_index in range(num_batches_train):\n",
    "        batch_slice = slice(\n",
    "            batch_index * batch_size, (batch_index + 1) * batch_size)\n",
    "        batch_train_loss = iter_funcs['train'](dataset['X_train'][batch_slice], \n",
    "                                               dataset['y_train_pair'][batch_slice])\n",
    "        batch_train_losses.append(batch_train_loss)\n",
    "\n",
    "    avg_train_loss = np.mean(batch_train_losses)\n",
    "\n",
    "    batch_valid_losses = []\n",
    "    for batch_index in range(num_batches_valid):\n",
    "        batch_slice = slice(\n",
    "            batch_index * batch_size, (batch_index + 1) * batch_size)\n",
    "        batch_valid_loss = iter_funcs['valid'](dataset['X_valid'][batch_slice], \n",
    "                                               dataset['y_valid_pair'][batch_slice])\n",
    "        batch_valid_losses.append(batch_valid_loss)\n",
    "\n",
    "    avg_valid_loss = np.mean(batch_valid_losses)\n",
    "\n",
    "    return {\n",
    "        'train_loss': avg_train_loss,\n",
    "        'valid_loss': avg_valid_loss,\n",
    "        }\n",
    "\n",
    "\n",
    "def main(num_epochs=NUM_EPOCHS):\n",
    "    print(\"Loading data...\")\n",
    "    dataset = load_data()\n",
    "\n",
    "    print(\"Building model and compiling functions...\")\n",
    "    output_layer = build_model(28, 28)\n",
    "\n",
    "    iter_funcs = create_iter_functions(output_layer)\n",
    "\n",
    "    print(\"Starting training...\")\n",
    "    now = time.time()\n",
    "    try:\n",
    "        i = 0\n",
    "        while 1:\n",
    "            epoch = train(iter_funcs, dataset)\n",
    "            i += 1\n",
    "            epoch['number'] = i\n",
    "            print(\"Epoch {} of {} took {:.3f}s\".format(epoch['number'], num_epochs, time.time() - now))\n",
    "            now = time.time()\n",
    "            print(\"  training loss:\\t\\t{:.6f}\".format(epoch['train_loss']))\n",
    "            print(\"  validation loss:\\t\\t{:.6f}\".format(epoch['valid_loss']))\n",
    "#            print(\"  validation accuracy:\\t\\t{:.2f} %%\".format(\n",
    "#                epoch['valid_accuracy'] * 100))\n",
    "\n",
    "            if epoch['number'] >= num_epochs:\n",
    "                break\n",
    "\n",
    "    except KeyboardInterrupt:\n",
    "        pass\n",
    "\n",
    "    return output_layer\n",
    "\n",
    "\n",
    "#if __name__ == '__main__':\n",
    "#    main()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dataset = load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "l_out = build_model(28,28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "iter_funcs = create_iter_functions(l_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data...\n",
      "Building model and compiling functions...\n",
      "Starting training...\n",
      "Epoch 1 of 25 took 1.523s\n",
      "  training loss:\t\t0.266293\n",
      "  validation loss:\t\t0.184540\n",
      "Epoch 2 of 25 took 1.551s\n",
      "  training loss:\t\t0.174943\n",
      "  validation loss:\t\t0.170360\n",
      "Epoch 3 of 25 took 1.536s\n",
      "  training loss:\t\t0.160561\n",
      "  validation loss:\t\t0.154442\n",
      "Epoch 4 of 25 took 1.500s\n",
      "  training loss:\t\t0.146417\n",
      "  validation loss:\t\t0.137413\n",
      "Epoch 5 of 25 took 1.521s\n",
      "  training loss:\t\t0.132241\n",
      "  validation loss:\t\t0.122444\n",
      "Epoch 6 of 25 took 1.509s\n",
      "  training loss:\t\t0.121499\n",
      "  validation loss:\t\t0.113801\n",
      "Epoch 7 of 25 took 1.576s\n",
      "  training loss:\t\t0.114960\n",
      "  validation loss:\t\t0.108911\n",
      "Epoch 8 of 25 took 1.510s\n",
      "  training loss:\t\t0.110110\n",
      "  validation loss:\t\t0.105396\n",
      "Epoch 9 of 25 took 1.513s\n",
      "  training loss:\t\t0.106369\n",
      "  validation loss:\t\t0.102437\n",
      "Epoch 10 of 25 took 1.503s\n",
      "  training loss:\t\t0.103087\n",
      "  validation loss:\t\t0.100060\n",
      "Epoch 11 of 25 took 1.530s\n",
      "  training loss:\t\t0.100183\n",
      "  validation loss:\t\t0.097917\n",
      "Epoch 12 of 25 took 1.548s\n",
      "  training loss:\t\t0.097523\n",
      "  validation loss:\t\t0.095948\n",
      "Epoch 13 of 25 took 1.501s\n",
      "  training loss:\t\t0.095170\n",
      "  validation loss:\t\t0.094461\n",
      "Epoch 14 of 25 took 1.519s\n",
      "  training loss:\t\t0.093103\n",
      "  validation loss:\t\t0.092939\n",
      "Epoch 15 of 25 took 1.510s\n",
      "  training loss:\t\t0.091082\n",
      "  validation loss:\t\t0.091414\n",
      "Epoch 16 of 25 took 1.501s\n",
      "  training loss:\t\t0.089276\n",
      "  validation loss:\t\t0.090439\n",
      "Epoch 17 of 25 took 1.500s\n",
      "  training loss:\t\t0.087537\n",
      "  validation loss:\t\t0.088928\n",
      "Epoch 18 of 25 took 1.515s\n",
      "  training loss:\t\t0.086015\n",
      "  validation loss:\t\t0.087879\n",
      "Epoch 19 of 25 took 1.499s\n",
      "  training loss:\t\t0.084656\n",
      "  validation loss:\t\t0.087149\n",
      "Epoch 20 of 25 took 1.501s\n",
      "  training loss:\t\t0.083339\n",
      "  validation loss:\t\t0.086347\n",
      "Epoch 21 of 25 took 1.505s\n",
      "  training loss:\t\t0.082137\n",
      "  validation loss:\t\t0.085612\n",
      "Epoch 22 of 25 took 1.510s\n",
      "  training loss:\t\t0.080948\n",
      "  validation loss:\t\t0.085032\n",
      "Epoch 23 of 25 took 1.502s\n",
      "  training loss:\t\t0.079899\n",
      "  validation loss:\t\t0.084310\n",
      "Epoch 24 of 25 took 1.515s\n",
      "  training loss:\t\t0.078841\n",
      "  validation loss:\t\t0.083636\n",
      "Epoch 25 of 25 took 1.500s\n",
      "  training loss:\t\t0.077810\n",
      "  validation loss:\t\t0.083232\n"
     ]
    }
   ],
   "source": [
    "l_out = main(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    },
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       "bKttgPoZRvD4rWeD1i4McYbfCfwmM09k5jvA94BPDfB1z9VoNsfMzKeA11ccvgU40Nw+wOJfkplY\n",
       "ZT4YwWOYma9k5tPN7QVgaQPUUTx+LfPBAI/fEMFfCby47OOT/N9/4Fgk8P2IOBoRn531MKvYsmyz\n",
       "j3lgyyyHWcWoNhhdtgHqjxnh4zeLDVqHCP58uO53Q2ZeB3wC+HzzlHW0cvF12Nge11FtMNo8XX6M\n",
       "xQ1Q31y+NobHb1YbtA4R/EvA3LKP51g8y49GZp5qfj8DPM7iy5CxmW9e/y3t7Xd6xvP8P5l5OhvA\n",
       "t5nhY9hsgPoY8J3MPNQcHs3jt2y+f1iab6jHb4jgjwIfiohtEbER+DRweICvuyYR8f6I+EBzexPw\n",
       "cca5OeZhYG9zey9wqOW+gxvLBqOrbYDKSB6/WW/QOsg77SLiE8BDwEXAvsz8au9fdI0i4moWz+oA\n",
       "7wO+O+v5IuIR4GPAB1l8vfl3wD8BB4E/BE4Ad2TmGyOZ737gRiZsMDrQbGfbAPVe4CeM4PFbzwat\n",
       "nXx931or1eE77aRCDF4qxOClQgxeKsTgpUIMXirE4KVCDF4q5HehqAPrklK1+AAAAABJRU5ErkJg\n",
       "gg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6760e0910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(dataset['X_train'][i,1], cmap='gray', interpolation='nearest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<lasagne.layers.shape.ReshapeLayer at 0x7fe67c846210>,\n",
       " <lasagne.layers.dense.DenseLayer at 0x7fe67ab72ed0>,\n",
       " <lasagne.layers.dnn.MaxPool2DDNNLayer at 0x7fe685ee8f50>,\n",
       " <lasagne.layers.dnn.Conv2DDNNLayer at 0x7fe67c846190>,\n",
       " <lasagne.layers.dnn.MaxPool2DDNNLayer at 0x7fe685ee8650>,\n",
       " <lasagne.layers.dnn.Conv2DDNNLayer at 0x7fe685ee8390>,\n",
       " <lasagne.layers.shape.ReshapeLayer at 0x7fe685ee8710>,\n",
       " <lasagne.layers.input.InputLayer at 0x7fe675be5750>]"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasagne.layers.get_all_layers(l_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X = T.tensor4()\n",
    "out = theano.function([X],l_out.get_output(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "p = out(dataset['X_valid'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from tsne import bh_sne"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X0 = dataset['X_valid'][:,0,:,:].reshape((10000,-1)).astype('float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       ..., \n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.]], dtype=float32)"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "p1 = bh_sne(X0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe6ce3d2610>]"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "cUXvOI5Tc1zRO47j1BxX9I7jODXHFb3jOE7N+X+YRy+38bQNmwAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6ce296e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(p1[:,0], p1[:,1],'.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}