addition.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import numpy as np\n",
    "import theano\n",
    "import theano.tensor as T\n",
    "import lasagne\n",
    "import time\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from rnn_lib import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%reload_ext autoreload"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CROSS VALIDATION FOR HYPERPARAMETERS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "N_HIDDEN = 100\n",
    "NDIM = 2\n",
    "TRAIN_SIZE = int(1e5)\n",
    "TEST_SIZE = int(1e4)\n",
    "SEQ_LENS = [10, 50, 70, 100, 150, 200, 300, 400]\n",
    "\n",
    "LEARNING_RATE = 0.001\n",
    "GRAD_CLIP = 10\n",
    "BATCH_SIZE = 20\n",
    "NUM_EPOCHS = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def gen_add_data(length, n_batch=BATCH_SIZE):\n",
    "    X = np.concatenate([np.random.uniform(size=(n_batch, length, 1)),\n",
    "                        np.zeros((n_batch, length, 1))],\n",
    "                        axis=-1)\n",
    "    y = np.zeros((n_batch,))\n",
    "    \n",
    "    X[np.arange(n_batch), np.random.randint(length/10, size=n_batch), 1] = 1\n",
    "    X[np.arange(n_batch), np.random.randint(length/2, length, size=n_batch), 1] = 1\n",
    "    # Multiply and sum the dimensions of X to get the target value\n",
    "    y = np.sum(X[:, :, 0] * X[:, :, 1], axis=1)\n",
    "    \n",
    "    return (X.astype(theano.config.floatX), y.astype(theano.config.floatX))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_data = {T : gen_add_data(T, TRAIN_SIZE) for T in SEQ_LENS}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for l in train_data:\n",
    "    x = train_data[l][0]\n",
    "    y = train_data[l][1]\n",
    "    train_data[l] = {\"x\" : x, \"y\" : y}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "test_data = {T : gen_add_data(T, TEST_SIZE) for T in SEQ_LENS}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for l in test_data:\n",
    "    x = test_data[l][0]\n",
    "    y = test_data[l][1]\n",
    "    test_data[l] = {\"x\" : x, \"y\" : y}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Use: train_data[seq_len][\"x\"/\"y\"][obj_idx]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В статье \"Improving performance of RNN with ReLU\" обучали на 10 эпох с BATCH_SIZE = 1.\n",
    "\n",
    "В статье \"Simple way to initialize RNN\" до $8 * 10^6$ шагов с BATCH_SIZE = 16, причём лосс значительно менялся до $3 * 10^6$ шагов, что есть примерно 480 эпох."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Вспомогательная функция для запаковки результата обучения \n",
    "def pack(network, inp, target, train_err, test_err, train_fn, test_fn):\n",
    "    return {'network':network,\n",
    "            'inp':inp,\n",
    "            'target':target,\n",
    "            \n",
    "            'train_err':train_err,\n",
    "            'test_err':test_err,\n",
    "            'train_fn':train_fn, \n",
    "            'test_fn':test_fn\n",
    "            } "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create a network with particular initialization for parameters\n",
    "def create_network(seq_len, rnn_type, dim, nhidden, nout, nonlin):\n",
    "    print(\"Building network ...\")\n",
    "    inp = T.tensor3('input', dtype='float32')\n",
    "    # Matrix of size BATCH_SIZE * 1 to match network's output\n",
    "    target_values = T.matrix('target_output', dtype='float32')\n",
    "    network = build_network(inp, seq_len, dim, nhidden, nout, nonlin, rnn_type)\n",
    "    print(\"The network has {} params\".format(lasagne.layers.count_params(network)))\n",
    "    return (network, inp, target_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def train(network, inp, target_values, Xtrain, ytrain, Xtest, ytest, filename, num_epochs=NUM_EPOCHS):\n",
    "    train_err = np.zeros(num_epochs)\n",
    "    test_err = np.zeros(num_epochs)\n",
    "    \n",
    "    network_output = lasagne.layers.get_output(network)\n",
    "\n",
    "    # Cost is MSE\n",
    "    cost = T.mean((network_output - target_values)**2)\n",
    "    all_params = lasagne.layers.get_all_params(network)\n",
    "    print(\"Computing updates ...\")\n",
    "    all_grads = T.grad(cost, all_params)\n",
    "    scaled_grads, total_norm = lasagne.updates.total_norm_constraint(all_grads, GRAD_CLIP, return_norm=True)\n",
    "    updates = lasagne.updates.sgd(scaled_grads, all_params, LEARNING_RATE)\n",
    "    \n",
    "    print(\"Compiling functions ...\")\n",
    "    train_fn = theano.function([inp, target_values], [cost, total_norm], updates=updates, allow_input_downcast=True)\n",
    "    compute_cost = theano.function([inp, target_values], cost, allow_input_downcast=True)\n",
    "    \n",
    "    num_batches = TRAIN_SIZE // BATCH_SIZE\n",
    "    train_idxs = np.arange(TRAIN_SIZE)\n",
    "    for epoch in range(num_epochs):\n",
    "        start_time = time.time()\n",
    "        np.random.shuffle(train_idxs)\n",
    "        for batch in range(num_batches):\n",
    "            idx = train_idxs[BATCH_SIZE * batch : BATCH_SIZE * (batch + 1)]\n",
    "            err, norm = train_fn(Xtrain[idx, :, :], ytrain[idx, np.newaxis])\n",
    "            train_err[epoch] += err\n",
    "            \n",
    "            #if batch == num_batches // 4:\n",
    "            #    print(\"25%\", end=\",\")\n",
    "            #if batch == num_batches // 2:\n",
    "            #    print(\"50%\", end=\",\")\n",
    "            #if batch == num_batches // 4 * 3:\n",
    "            #    print(\"75%\")\n",
    "                \n",
    "        train_err[epoch] /= num_batches\n",
    "        test_err[epoch] = compute_cost(Xtest, ytest[:, np.newaxis])\n",
    "        print(\"Epoch {} test loss = {:.4f} \\t train = {:.4f} \\t norm = {:.4f} \\t time = {:.2f}s\".format(\n",
    "                epoch, test_err[epoch], train_err[epoch], norm.item(), time.time() - start_time))\n",
    "\n",
    "    np.save(file=filename, arr=lasagne.layers.get_all_param_values(network))\n",
    "    return pack(network, inp, target_values, train_err, test_err, train_fn, compute_cost)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[10, 50, 70, 100, 150, 200, 300, 400]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SEQ_LENS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sequence length is 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building network ...\n",
      "The network has 10501 params\n",
      "Computing updates ...\n",
      "Compiling functions ...\n",
      "Epoch 0 test loss = 0.1507 \t train = 0.1741 \t norm = 0.4287 \t time = 4.73s\n",
      "Epoch 1 test loss = 0.1430 \t train = 0.1480 \t norm = 0.7486 \t time = 4.71s\n",
      "Epoch 2 test loss = 0.1327 \t train = 0.1395 \t norm = 0.1800 \t time = 4.70s\n",
      "Epoch 3 test loss = 0.1077 \t train = 0.1240 \t norm = 0.6160 \t time = 4.71s\n",
      "Epoch 4 test loss = 0.0041 \t train = 0.0422 \t norm = 0.0330 \t time = 4.71s\n",
      "Epoch 5 test loss = 0.0024 \t train = 0.0031 \t norm = 0.1428 \t time = 4.71s\n",
      "Epoch 6 test loss = 0.0016 \t train = 0.0020 \t norm = 0.2094 \t time = 4.71s\n",
      "Epoch 7 test loss = 0.0012 \t train = 0.0014 \t norm = 0.2304 \t time = 4.70s\n",
      "Epoch 8 test loss = 0.0009 \t train = 0.0011 \t norm = 0.1724 \t time = 4.71s\n",
      "Epoch 9 test loss = 0.0007 \t train = 0.0008 \t norm = 0.0400 \t time = 4.71s\n"
     ]
    }
   ],
   "source": [
    "net, inp, tar = create_network(10, 'srnn', 2, N_HIDDEN, 1, lasagne.nonlinearities.linear)\n",
    "srnn10 = train(net, inp, tar, train_data[10][\"x\"], train_data[10][\"y\"],\n",
    "                      test_data[10][\"x\"], test_data[10][\"y\"], \"srnn10\", num_epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building network ...\n",
      "The network has 10501 params\n",
      "Computing updates ...\n",
      "Compiling functions ...\n",
      "Epoch 0 test loss = 0.0421 \t train = 0.0645 \t norm = 0.6257 \t time = 4.68s\n",
      "Epoch 1 test loss = 0.0173 \t train = 0.0282 \t norm = 3.7273 \t time = 4.68s\n",
      "Epoch 2 test loss = 0.0100 \t train = 0.0148 \t norm = 2.1797 \t time = 4.69s\n",
      "Epoch 3 test loss = 0.0066 \t train = 0.0091 \t norm = 0.4012 \t time = 4.68s\n",
      "Epoch 4 test loss = 0.0051 \t train = 0.0061 \t norm = 2.1134 \t time = 4.69s\n",
      "Epoch 5 test loss = 0.0035 \t train = 0.0043 \t norm = 2.1512 \t time = 4.69s\n",
      "Epoch 6 test loss = 0.0036 \t train = 0.0032 \t norm = 2.1496 \t time = 4.69s\n",
      "Epoch 7 test loss = 0.0023 \t train = 0.0024 \t norm = 1.0365 \t time = 4.69s\n",
      "Epoch 8 test loss = 0.0016 \t train = 0.0018 \t norm = 0.6697 \t time = 4.69s\n",
      "Epoch 9 test loss = 0.0012 \t train = 0.0015 \t norm = 0.4027 \t time = 4.69s\n"
     ]
    }
   ],
   "source": [
    "net, inp, tar = create_network(10, 'irnn', 2, N_HIDDEN, 1, lasagne.nonlinearities.linear)\n",
    "irnn10 = train(net, inp, tar, train_data[10][\"x\"], train_data[10][\"y\"],\n",
    "                      test_data[10][\"x\"], test_data[10][\"y\"], \"irnn10\", num_epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building network ...\n",
      "The network has 10501 params\n",
      "Computing updates ...\n",
      "Compiling functions ...\n",
      "Epoch 0 test loss = 0.0193 \t train = 0.0823 \t norm = 0.5962 \t time = 4.77s\n",
      "Epoch 1 test loss = 0.0063 \t train = 0.0116 \t norm = 0.1921 \t time = 4.75s\n",
      "Epoch 2 test loss = 0.0032 \t train = 0.0048 \t norm = 0.3831 \t time = 4.77s\n",
      "Epoch 3 test loss = 0.0023 \t train = 0.0028 \t norm = 1.4269 \t time = 4.76s\n",
      "Epoch 4 test loss = 0.0014 \t train = 0.0018 \t norm = 0.3212 \t time = 4.77s\n",
      "Epoch 5 test loss = 0.0010 \t train = 0.0013 \t norm = 0.6557 \t time = 4.78s\n",
      "Epoch 6 test loss = 0.0007 \t train = 0.0009 \t norm = 0.2185 \t time = 4.76s\n",
      "Epoch 7 test loss = 0.0005 \t train = 0.0006 \t norm = 0.0654 \t time = 4.76s\n",
      "Epoch 8 test loss = 0.0004 \t train = 0.0005 \t norm = 0.0466 \t time = 4.76s\n",
      "Epoch 9 test loss = 0.0003 \t train = 0.0004 \t norm = 0.0598 \t time = 4.76s\n"
     ]
    }
   ],
   "source": [
    "net, inp, tar = create_network(10, 'nprnn', 2, N_HIDDEN, 1, lasagne.nonlinearities.linear)\n",
    "nprnn10 = train(net, inp, tar, train_data[10][\"x\"], train_data[10][\"y\"],\n",
    "                      test_data[10][\"x\"], test_data[10][\"y\"], \"nprnn10\", num_epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building network ...\n",
      "The network has 10501 params\n",
      "Computing updates ...\n",
      "Compiling functions ...\n",
      "Epoch 0 test loss = 0.1090 \t train = 0.1495 \t norm = 0.7413 \t time = 4.82s\n",
      "Epoch 1 test loss = 0.0213 \t train = 0.0520 \t norm = 0.8571 \t time = 4.81s\n",
      "Epoch 2 test loss = 0.0078 \t train = 0.0138 \t norm = 1.2122 \t time = 4.81s\n",
      "Epoch 3 test loss = 0.0040 \t train = 0.0058 \t norm = 0.8790 \t time = 4.81s\n",
      "Epoch 4 test loss = 0.0024 \t train = 0.0032 \t norm = 1.0662 \t time = 4.81s\n",
      "Epoch 5 test loss = 0.0015 \t train = 0.0020 \t norm = 0.1503 \t time = 4.81s\n",
      "Epoch 6 test loss = 0.0011 \t train = 0.0014 \t norm = 0.0407 \t time = 4.81s\n",
      "Epoch 7 test loss = 0.0008 \t train = 0.0010 \t norm = 0.0908 \t time = 4.81s\n",
      "Epoch 8 test loss = 0.0006 \t train = 0.0007 \t norm = 0.0391 \t time = 4.82s\n",
      "Epoch 9 test loss = 0.0005 \t train = 0.0005 \t norm = 0.1335 \t time = 4.81s\n"
     ]
    }
   ],
   "source": [
    "net, inp, tar = create_network(10, 'nrnn', 2, N_HIDDEN, 1, lasagne.nonlinearities.linear)\n",
    "nrnn10 = train(net, inp, tar, train_data[10][\"x\"], train_data[10][\"y\"],\n",
    "                      test_data[10][\"x\"], test_data[10][\"y\"], \"nrnn10\", num_epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd96425fd50>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ALUIvQYaY3MxEVu44iDHG36UopQKINy218cAWY8w2Y0wd8BZwrucGxphiY8xy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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd95f23e450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7, 5))\n",
    "plt.title(\"Test loss MSE seq len = 10\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.plot(srnn10[\"test_err\"], label=\"sRNN\")\n",
    "plt.plot(nrnn10[\"test_err\"], label=\"nRNN\")\n",
    "plt.plot(irnn10[\"test_err\"], label=\"IRNN\")\n",
    "plt.plot(nprnn10[\"test_err\"], label=\"npRNN\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sequence length is 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building network ...\n",
      "The network has 10501 params\n",
      "Computing updates ...\n",
      "Compiling functions ...\n",
      "Epoch 0 test loss = 0.1660 \t train = 0.2008 \t norm = 0.3547 \t time = 65.54s\n",
      "Epoch 1 test loss = 0.1651 \t train = 0.1677 \t norm = 0.4645 \t time = 65.84s\n",
      "Epoch 2 test loss = 0.1648 \t train = 0.1670 \t norm = 0.3573 \t time = 67.39s\n",
      "Epoch 3 test loss = 0.1644 \t train = 0.1666 \t norm = 0.2876 \t time = 65.95s\n",
      "Epoch 4 test loss = 0.1640 \t train = 0.1663 \t norm = 0.2862 \t time = 65.74s\n",
      "Epoch 5 test loss = 0.1637 \t train = 0.1660 \t norm = 0.1410 \t time = 66.68s\n",
      "Epoch 6 test loss = 0.1635 \t train = 0.1658 \t norm = 0.7400 \t time = 66.55s\n",
      "Epoch 7 test loss = 0.1632 \t train = 0.1655 \t norm = 0.1489 \t time = 66.58s\n",
      "Epoch 8 test loss = 0.1630 \t train = 0.1652 \t norm = 0.1862 \t time = 65.44s\n",
      "Epoch 9 test loss = 0.1628 \t train = 0.1650 \t norm = 0.3058 \t time = 61.53s\n"
     ]
    }
   ],
   "source": [
    "net, inp, tar = create_network(50, 'srnn', 2, N_HIDDEN, 1, lasagne.nonlinearities.linear)\n",
    "srnn50 = train(net, inp, tar, train_data[50][\"x\"], train_data[50][\"y\"],\n",
    "                      test_data[50][\"x\"], test_data[50][\"y\"], \"srnn50\", num_epochs=10)\n",
    "np.save(\"srnn50-err\", srnn50[\"test_err\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building network ...\n",
      "The network has 10501 params\n",
      "Computing updates ...\n",
      "Compiling functions ...\n",
      "Epoch 0 test loss = 0.0441 \t train = 0.1487 \t norm = 22.6715 \t time = 61.54s\n"
     ]
    }
   ],
   "source": [
    "net, inp, tar = create_network(50, 'irnn', 2, N_HIDDEN, 1, lasagne.nonlinearities.linear)\n",
    "irnn50 = train(net, inp, tar, train_data[50][\"x\"], train_data[50][\"y\"],\n",
    "                      test_data[50][\"x\"], test_data[50][\"y\"], \"add-data/irnn50\", num_epochs=10)\n",
    "np.save(\"irnn50-err\", irnn50[\"test_err\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building network ...\n",
      "The network has 10501 params\n",
      "Computing updates ...\n",
      "Compiling functions ...\n",
      "Epoch 0 test loss = 0.1633 \t train = 0.1734 \t norm = 2.4496 \t time = 61.16s\n",
      "Epoch 1 test loss = 0.1595 \t train = 0.1638 \t norm = 2.9404 \t time = 61.04s\n",
      "Epoch 2 test loss = 0.1936 \t train = 0.1598 \t norm = 6.3831 \t time = 61.04s\n",
      "Epoch 3 test loss = 0.1121 \t train = 0.1437 \t norm = 22.6664 \t time = 61.20s\n",
      "Epoch 4 test loss = 0.0451 \t train = 0.0692 \t norm = 25.6607 \t time = 62.26s\n",
      "Epoch 5 test loss = 0.0180 \t train = 0.0381 \t norm = 7.8952 \t time = 61.81s\n",
      "Epoch 6 test loss = 0.0111 \t train = 0.0256 \t norm = 1.3784 \t time = 61.77s\n",
      "Epoch 7 test loss = 0.0430 \t train = 0.0180 \t norm = 10.4029 \t time = 61.78s\n",
      "Epoch 8 test loss = 0.0114 \t train = 0.0144 \t norm = 5.7776 \t time = 61.77s\n",
      "Epoch 9 test loss = 0.0054 \t train = 0.0119 \t norm = 0.4052 \t time = 61.78s\n"
     ]
    }
   ],
   "source": [
    "net, inp, tar = create_network(50, 'nprnn', 2, N_HIDDEN, 1, lasagne.nonlinearities.linear)\n",
    "nprnn50 = train(net, inp, tar, train_data[50][\"x\"], train_data[50][\"y\"],\n",
    "                      test_data[50][\"x\"], test_data[50][\"y\"], \"add-data/nprnn50\", num_epochs=10)\n",
    "np.save(\"add-data/nprnn50-err\", nprnn50[\"test_err\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building network ...\n",
      "The network has 10501 params\n",
      "Computing updates ...\n",
      "Compiling functions ...\n",
      "Epoch 0 test loss = 0.1626 \t train = 0.1732 \t norm = 0.8093 \t time = 61.31s\n",
      "Epoch 1 test loss = 0.1642 \t train = 0.1636 \t norm = 0.3212 \t time = 61.22s\n",
      "Epoch 2 test loss = 0.1551 \t train = 0.1577 \t norm = 15.9034 \t time = 62.06s\n",
      "Epoch 3 test loss = 0.0471 \t train = 0.1121 \t norm = 5.9784 \t time = 61.33s\n",
      "Epoch 4 test loss = 0.0744 \t train = 0.0602 \t norm = 15.5705 \t time = 61.47s\n",
      "Epoch 5 test loss = 0.0566 \t train = 0.0442 \t norm = 8.7112 \t time = 61.84s\n",
      "Epoch 6 test loss = 0.0134 \t train = 0.0339 \t norm = 2.5434 \t time = 62.29s\n",
      "Epoch 7 test loss = 0.0256 \t train = 0.0262 \t norm = 7.4569 \t time = 62.40s\n",
      "Epoch 8 test loss = 0.0343 \t train = 0.0192 \t norm = 12.2994 \t time = 61.93s\n",
      "Epoch 9 test loss = 0.0067 \t train = 0.0154 \t norm = 3.0685 \t time = 61.94s\n"
     ]
    }
   ],
   "source": [
    "net, inp, tar = create_network(50, 'nrnn', 2, N_HIDDEN, 1, lasagne.nonlinearities.linear)\n",
    "nrnn50 = train(net, inp, tar, train_data[50][\"x\"], train_data[50][\"y\"],\n",
    "                      test_data[50][\"x\"], test_data[50][\"y\"], \"add-data/nrnn50\", num_epochs=10)\n",
    "np.save(\"add-data/nrnn50-err\", nrnn50[\"test_err\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2b010fa91d90>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LCtg+YQKlO3fS5pWXCe/QIWhtKR+Tl6tj8pRSDUytAc8Y4wBuwtUduR6YYYxZJyIPicgw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\np2mapnmF/w9vHw/ncShLyAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b010f9c8d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7, 5))\n",
    "plt.title(\"Test loss MSE seq len = 50\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.plot(srnn50[\"test_err\"], label=\"sRNN\")\n",
    "plt.plot(irnn50[\"test_err\"], label=\"IRNN\")\n",
    "plt.plot(nrnn50[\"test_err\"], label=\"nRNN\")\n",
    "plt.plot(nprnn50[\"test_err\"], label=\"npRNN\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sequence length is 70"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building network ...\n",
      "The network has 10501 params\n",
      "Computing updates ...\n",
      "Compiling functions ...\n",
      "Epoch 0 test loss = 0.1660 \t train = 0.1945 \t norm = 0.1958 \t time = 84.78s\n",
      "Epoch 1 test loss = 0.1649 \t train = 0.1669 \t norm = 0.5113 \t time = 84.59s\n",
      "Epoch 2 test loss = 0.1646 \t train = 0.1663 \t norm = 0.5218 \t time = 84.62s\n",
      "Epoch 3 test loss = 0.1645 \t train = 0.1661 \t norm = 0.7331 \t time = 85.27s\n"
     ]
    }
   ],
   "source": [
    "net, inp, tar = create_network(70, 'srnn', 2, N_HIDDEN, 1, lasagne.nonlinearities.linear)\n",
    "srnn70 = train(net, inp, tar, train_data[70][\"x\"], train_data[70][\"y\"],\n",
    "                      test_data[70][\"x\"], test_data[70][\"y\"], \"add-data/srnn70\", num_epochs=10)\n",
    "np.save(\"add-data/srnn70-err\", srnn70[\"test_err\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "net, inp, tar = create_network(70, 'irnn', 2, N_HIDDEN, 1, lasagne.nonlinearities.linear)\n",
    "irnn70 = train(net, inp, tar, train_data[70][\"x\"], train_data[70][\"y\"],\n",
    "                      test_data[70][\"x\"], test_data[70][\"y\"], \"add-data/irnn70\", num_epochs=10)\n",
    "np.save(\"add-data/irnn70-err\", irnn70[\"test_err\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "net, inp, tar = create_network(70, 'nprnn', 2, N_HIDDEN, 1, lasagne.nonlinearities.linear)\n",
    "nprnn70 = train(net, inp, tar, train_data[70][\"x\"], train_data[70][\"y\"],\n",
    "                      test_data[70][\"x\"], test_data[70][\"y\"], \"add-data/nprnn70\", num_epochs=10)\n",
    "np.save(\"add-data/nprnn70-err\", nprnn70[\"test_err\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "net, inp, tar = create_network(70, 'nrnn', 2, N_HIDDEN, 1, lasagne.nonlinearities.linear)\n",
    "nrnn70 = train(net, inp, tar, train_data[70][\"x\"], train_data[70][\"y\"],\n",
    "                      test_data[70][\"x\"], test_data[70][\"y\"], \"add-data/nrnn70\", num_epochs=10)\n",
    "np.save(\"add-data/nrnn70-err\", nrnn70[\"test_err\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fa026c3afd0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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q54UP6lmztZ7HX9vPo//aR0mek/OPm8qNZ84eU0XDhZiQBY1jljMNPv1nOP97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\npLqGaUt2MfnV5fx5ch+mDu96RKHqTgRqJLudYerbB21sbIu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cLKVc25pzG0OJWvvD/sN8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\nKRQKhaLNoERNoVAoFG2G/wcXjBlbuQZ5agAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa026e76588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7, 5))\n",
    "plt.title(\"Test loss MSE seq len = 70\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.plot(srnn70[\"test_err\"], label=\"sRNN\")\n",
    "plt.plot(irnn70[\"test_err\"], label=\"IRNN\")\n",
    "plt.plot(nrnn70[\"test_err\"], label=\"nRNN\")\n",
    "plt.plot(nprnn70[\"test_err\"], label=\"npRNN\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sequence length is 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "net, inp, tar = create_network(100, 'srnn', 2, N_HIDDEN, 1, lasagne.nonlinearities.linear)\n",
    "srnn100 = train(net, inp, tar, train_data[100][\"x\"], train_data[100][\"y\"],\n",
    "                      test_data[100][\"x\"], test_data[100][\"y\"], \"add-data/srnn100\", num_epochs=10)\n",
    "np.save(\"add-data/srnn100-err\", srnn100[\"test_err\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "net, inp, tar = create_network(100, 'irnn', 2, N_HIDDEN, 1, lasagne.nonlinearities.linear)\n",
    "irnn100 = train(net, inp, tar, train_data[100][\"x\"], train_data[100][\"y\"],\n",
    "                      test_data[100][\"x\"], test_data[100][\"y\"], \"add-data/irnn100\", num_epochs=10)\n",
    "np.save(\"add-data/irnn100-err\", irnn100[\"test_err\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "net, inp, tar = create_network(100, 'nprnn', 2, N_HIDDEN, 1, lasagne.nonlinearities.linear)\n",
    "nprnn100 = train(net, inp, tar, train_data[100][\"x\"], train_data[100][\"y\"],\n",
    "                      test_data[100][\"x\"], test_data[100][\"y\"], \"add-data/nprnn100\", num_epochs=10)\n",
    "np.save(\"add-data/nprnn100-err\", nprnn100[\"test_err\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "net, inp, tar = create_network(100, 'nrnn', 2, N_HIDDEN, 1, lasagne.nonlinearities.linear)\n",
    "nrnn100 = train(net, inp, tar, train_data[100][\"x\"], train_data[100][\"y\"],\n",
    "                      test_data[100][\"x\"], test_data[100][\"y\"], \"add-data/nrnn100\", num_epochs=10)\n",
    "np.save(\"add-data/nrnn100-err\", nrnn100[\"test_err\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc4a88fc898>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xtnb66bY62oGgFmv147JliStPR1pXP2Zm2r+7+nr7eyvRUqr6saOyLNq6iEvn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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc4ad39c668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7, 5))\n",
    "plt.title(\"Test loss MSE seq len = 100\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.plot(srnn100[\"test_err\"], label=\"sRNN\")\n",
    "plt.plot(irnn100[\"test_err\"], label=\"IRNN\")\n",
    "plt.plot(nprnn100[\"test_err\"], label=\"npRNN\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sequence length is 150"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "net, inp, tar = create_network(150, 'srnn', 2, N_HIDDEN, 1, lasagne.nonlinearities.linear)\n",
    "srnn150 = train(net, inp, tar, train_data[150][\"x\"], train_data[150][\"y\"],\n",
    "                      test_data[150][\"x\"], test_data[150][\"y\"], \"add-data/srnn150\", num_epochs=10)\n",
    "np.save(\"add-data/srnn150-err\", srnn150[\"test_err\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "net, inp, tar = create_network(150, 'irnn', 2, N_HIDDEN, 1, lasagne.nonlinearities.linear)\n",
    "irnn150 = train(net, inp, tar, train_data[150][\"x\"], train_data[150][\"y\"],\n",
    "                      test_data[150][\"x\"], test_data[150][\"y\"], \"add-data/irnn150\", num_epochs=10)\n",
    "np.save(\"add-data/irnn150-err\", irnn150[\"test_err\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "net, inp, tar = create_network(150, 'nrnn', 2, N_HIDDEN, 1, lasagne.nonlinearities.linear)\n",
    "nrnn150 = train(net, inp, tar, train_data[150][\"x\"], train_data[150][\"y\"],\n",
    "                      test_data[150][\"x\"], test_data[150][\"y\"], \"add-data/nrnn150\", num_epochs=10)\n",
    "np.save(\"add-data/nrnn150-err\", nrnn150[\"test_err\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "net, inp, tar = create_network(150, 'nprnn', 2, N_HIDDEN, 1, lasagne.nonlinearities.linear)\n",
    "nprnn150 = train(net, inp, tar, train_data[150][\"x\"], train_data[150][\"y\"],\n",
    "                      test_data[150][\"x\"], test_data[150][\"y\"], \"add-data/nprnn150\", num_epochs=10)\n",
    "np.save(\"add-data/nprnn150-err\", nprnn150[\"test_err\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fa01891ef60>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rrbe44447Bk2uzs/P58c/Dj/v0+Fw8Mwzz9DU1DRltomoCclF91BRk3lqQnIzUuuZU045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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0189c5ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7, 5))\n",
    "plt.title(\"Test loss MSE seq len = 150\")\n",
    "plt.xlabel(\"epoch\")\n",
    "#plt.plot(srnn150[\"test_err\"], label=\"sRNN\")\n",
    "plt.plot(irnn150[\"test_err\"], label=\"IRNN\")\n",
    "plt.plot(nrnn150[\"test_err\"], label=\"nRNN\")\n",
    "plt.plot(nprnn150[\"test_err\"], label=\"npRNN\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sequence length is 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'init_posdef_w' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-38364211fb4f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0minit_posdef_w\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'init_posdef_w' is not defined"
     ]
    }
   ],
   "source": [
    "init_posdef_w(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building network ...\n",
      "The network has 10501 params\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  1.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  1., ...,  0.,  0.,  0.],\n",
       "       ..., \n",
       "       [ 0.,  0.,  0., ...,  1.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  1.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  1.]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net, inp, tar = create_network(200, NDIM, N_HIDDEN, 'nprnn')\n",
    "lasagne.layers.get_all_param_values(net)[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sys.version_info(major=2, minor=7, micro=12, releaselevel='final', serial=0)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "sys.version_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building network ...\n",
      "The network has 10501 params\n",
      "Computing updates ...\n",
      "Compiling functions ...\n",
      "Proc 25% Proc 50% Proc 75%\n",
      "Epoch 0 test loss = 0.1716 \t train = 0.1914 \t norm = 0.2907 \t time = 239.90s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 1 test loss = 0.1701 \t train = 0.1669 \t norm = 0.1872 \t time = 253.26s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 2 test loss = 0.1698 \t train = 0.1663 \t norm = 0.8929 \t time = 252.80s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 3 test loss = 0.1697 \t train = 0.1662 \t norm = 0.3048 \t time = 252.93s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 4 test loss = 0.1697 \t train = 0.1661 \t norm = 0.2101 \t time = 252.10s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 5 test loss = 0.1697 \t train = 0.1661 \t norm = 0.1482 \t time = 251.86s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 6 test loss = 0.1696 \t train = 0.1661 \t norm = 0.3822 \t time = 251.89s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 7 test loss = 0.1697 \t train = 0.1661 \t norm = 0.0904 \t time = 252.01s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 8 test loss = 0.1696 \t train = 0.1661 \t norm = 0.5230 \t time = 251.88s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 9 test loss = 0.1696 \t train = 0.1661 \t norm = 0.0370 \t time = 251.79s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 10 test loss = 0.1696 \t train = 0.1660 \t norm = 0.1804 \t time = 251.74s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 11 test loss = 0.1696 \t train = 0.1660 \t norm = 0.1722 \t time = 251.71s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 12 test loss = 0.1695 \t train = 0.1660 \t norm = 0.4353 \t time = 251.62s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 13 test loss = 0.1698 \t train = 0.1660 \t norm = 0.5421 \t time = 251.80s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 14 test loss = 0.1695 \t train = 0.1660 \t norm = 0.3038 \t time = 251.97s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 15 test loss = 0.1695 \t train = 0.1660 \t norm = 0.3857 \t time = 250.83s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 16 test loss = 0.1695 \t train = 0.1659 \t norm = 0.1167 \t time = 250.96s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 17 test loss = 0.1695 \t train = 0.1659 \t norm = 0.3147 \t time = 250.96s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 18 test loss = 0.1695 \t train = 0.1659 \t norm = 0.0980 \t time = 252.07s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 19 test loss = 0.1694 \t train = 0.1659 \t norm = 0.1420 \t time = 251.07s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 20 test loss = 0.1695 \t train = 0.1659 \t norm = 0.0858 \t time = 251.00s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 21 test loss = 0.1695 \t train = 0.1659 \t norm = 0.1349 \t time = 251.21s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 22 test loss = 0.1694 \t train = 0.1659 \t norm = 0.2509 \t time = 251.06s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 23 test loss = 0.1694 \t train = 0.1658 \t norm = 0.7206 \t time = 251.02s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 24 test loss = 0.1694 \t train = 0.1658 \t norm = 0.1199 \t time = 251.17s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 25 test loss = 0.1695 \t train = 0.1658 \t norm = 0.1393 \t time = 251.05s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 26 test loss = 0.1693 \t train = 0.1658 \t norm = 0.2539 \t time = 251.12s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 27 test loss = 0.1693 \t train = 0.1658 \t norm = 0.0789 \t time = 251.03s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 28 test loss = 0.1694 \t train = 0.1658 \t norm = 0.4036 \t time = 251.40s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 29 test loss = 0.1694 \t train = 0.1657 \t norm = 0.0703 \t time = 251.53s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 30 test loss = 0.1693 \t train = 0.1657 \t norm = 0.7675 \t time = 251.07s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 31 test loss = 0.1693 \t train = 0.1657 \t norm = 0.6727 \t time = 251.46s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 32 test loss = 0.1693 \t train = 0.1657 \t norm = 0.7192 \t time = 252.21s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 33 test loss = 0.1693 \t train = 0.1657 \t norm = 0.3002 \t time = 252.31s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 34 test loss = 0.1693 \t train = 0.1657 \t norm = 0.4559 \t time = 252.33s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 35 test loss = 0.1692 \t train = 0.1657 \t norm = 0.3827 \t time = 252.32s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 36 test loss = 0.1692 \t train = 0.1657 \t norm = 0.5942 \t time = 252.59s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 37 test loss = 0.1693 \t train = 0.1656 \t norm = 0.3752 \t time = 252.25s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 38 test loss = 0.1692 \t train = 0.1656 \t norm = 0.6060 \t time = 252.28s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 39 test loss = 0.1692 \t train = 0.1656 \t norm = 0.6375 \t time = 251.63s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 40 test loss = 0.1691 \t train = 0.1656 \t norm = 0.4532 \t time = 248.57s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 41 test loss = 0.1691 \t train = 0.1656 \t norm = 0.3290 \t time = 247.56s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 42 test loss = 0.1691 \t train = 0.1655 \t norm = 0.2773 \t time = 247.37s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 43 test loss = 0.1691 \t train = 0.1655 \t norm = 0.0518 \t time = 247.51s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 44 test loss = 0.1691 \t train = 0.1655 \t norm = 0.6234 \t time = 247.04s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 45 test loss = 0.1691 \t train = 0.1655 \t norm = 0.1504 \t time = 247.15s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 46 test loss = 0.1691 \t train = 0.1655 \t norm = 0.4382 \t time = 247.02s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 47 test loss = 0.1690 \t train = 0.1655 \t norm = 0.1161 \t time = 247.14s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 48 test loss = 0.1690 \t train = 0.1655 \t norm = 0.5206 \t time = 247.49s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 49 test loss = 0.1690 \t train = 0.1654 \t norm = 1.0354 \t time = 247.34s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 50 test loss = 0.1690 \t train = 0.1654 \t norm = 0.1405 \t time = 247.54s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 51 test loss = 0.1690 \t train = 0.1654 \t norm = 0.1101 \t time = 247.01s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 52 test loss = 0.1689 \t train = 0.1654 \t norm = 0.0455 \t time = 247.03s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 53 test loss = 0.1689 \t train = 0.1654 \t norm = 0.5206 \t time = 247.04s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 54 test loss = 0.1689 \t train = 0.1654 \t norm = 0.4066 \t time = 247.01s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 55 test loss = 0.1689 \t train = 0.1653 \t norm = 0.5558 \t time = 247.00s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 56 test loss = 0.1689 \t train = 0.1653 \t norm = 0.7235 \t time = 247.02s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 57 test loss = 0.1689 \t train = 0.1653 \t norm = 0.9460 \t time = 247.43s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 58 test loss = 0.1689 \t train = 0.1653 \t norm = 0.1334 \t time = 247.03s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 59 test loss = 0.1689 \t train = 0.1653 \t norm = 0.1470 \t time = 247.03s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 60 test loss = 0.1688 \t train = 0.1652 \t norm = 1.0433 \t time = 247.02s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 61 test loss = 0.1688 \t train = 0.1652 \t norm = 0.9081 \t time = 247.03s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 62 test loss = 0.1688 \t train = 0.1652 \t norm = 0.1564 \t time = 247.03s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 63 test loss = 0.1689 \t train = 0.1652 \t norm = 0.5768 \t time = 247.23s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 64 test loss = 0.1687 \t train = 0.1651 \t norm = 0.4427 \t time = 247.28s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 65 test loss = 0.1687 \t train = 0.1651 \t norm = 0.7434 \t time = 247.24s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 66 test loss = 0.1687 \t train = 0.1651 \t norm = 0.2405 \t time = 247.04s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 67 test loss = 0.1686 \t train = 0.1651 \t norm = 0.6598 \t time = 247.07s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 68 test loss = 0.1686 \t train = 0.1650 \t norm = 0.4806 \t time = 247.02s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 69 test loss = 0.1686 \t train = 0.1650 \t norm = 0.6745 \t time = 247.15s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 70 test loss = 0.1685 \t train = 0.1650 \t norm = 0.5953 \t time = 247.03s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 71 test loss = 0.1685 \t train = 0.1650 \t norm = 0.5159 \t time = 247.01s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 72 test loss = 0.1685 \t train = 0.1650 \t norm = 0.1997 \t time = 247.40s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 73 test loss = 0.1685 \t train = 0.1649 \t norm = 0.7778 \t time = 247.02s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 74 test loss = 0.1684 \t train = 0.1649 \t norm = 0.0684 \t time = 247.09s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 75 test loss = 0.1683 \t train = 0.1648 \t norm = 0.3993 \t time = 247.06s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 76 test loss = 0.1683 \t train = 0.1648 \t norm = 0.1704 \t time = 247.03s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 77 test loss = 0.1683 \t train = 0.1648 \t norm = 0.5877 \t time = 247.04s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 78 test loss = 0.1686 \t train = 0.1647 \t norm = 0.7530 \t time = 247.01s\tProc 25% Proc 50% Proc 75%\n",
      "Epoch 79 test loss = 0.1682 \t train = 0.1647 \t norm = 0.3107 \t time = 247.44s\t"
     ]
    }
   ],
   "source": [
    "net, inp, tar = create_network(200, NDIM, N_HIDDEN, 'srnn')\n",
    "srnn200 = train(net, inp, tar, train_data[200][\"x\"], train_data[200][\"y\"],\n",
    "                      test_data[200][\"x\"], test_data[200][\"y\"], \"data/srnn200\", num_epochs=50)\n",
    "np.save(\"data/srnn200-err\", srnn200[\"test_err\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building network ...\n",
      "The network has 10501 params\n",
      "Computing updates ...\n",
      "Compiling functions ...\n"
     ]
    }
   ],
   "source": [
    "net, inp, tar = create_network(200, NDIM, N_HIDDEN, 'irnn')\n",
    "irnn200 = train(net, inp, tar, train_data[200][\"x\"], train_data[200][\"y\"],\n",
    "                      test_data[200][\"x\"], test_data[200][\"y\"], \"data/irnn200\", num_epochs=50)\n",
    "np.save(\"data/irnn200-err\", irnn200[\"test_err\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "net, inp, tar = create_network(200, NDIM, N_HIDDEN, 'nrnn')\n",
    "nrnn200 = train(net, inp, tar, train_data[200][\"x\"], train_data[200][\"y\"],\n",
    "                      test_data[200][\"x\"], test_data[200][\"y\"], \"data/nrnn200\", num_epochs=50)\n",
    "np.save(\"data/nrnn200-err\", nrnn200[\"test_err\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "net, inp, tar = create_network(200, NDIM, N_HIDDEN, 'nprnn')\n",
    "nprnn200 = train(net, inp, tar, train_data[200][\"x\"], train_data[200][\"y\"],\n",
    "                      test_data[200][\"x\"], test_data[200][\"y\"], \"data/nprnn200\", num_epochs=50)\n",
    "np.save(\"data/nprnn200-err\", nprnn200[\"test_err\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f16ab7b1fd0>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/JzlVO0h1VWP3uLB53di9VkCJ19P4P1SMB/F4EK8Hm9eFzePG5rVeHnHgtqXg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\nNTMzqw0XNTMzqw0XNTMzq40/ASgcvIxYypTyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f16ab807ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7, 5))\n",
    "plt.title(\"Test loss MSE seq len = 200\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.plot(temp_srnn, label=\"sRNN\")\n",
    "plt.plot(irnn200[\"test_err\"], label=\"IRNN\")\n",
    "plt.plot(nprnn200[\"test_err\"], label=\"npRNN\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Sequence length is 300"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building network ...\n",
      "The network has 10501 params\n",
      "Computing updates ...\n",
      "Compiling functions ...\n"
     ]
    }
   ],
   "source": [
    "net, inp, tar = create_network(300, NDIM, N_HIDDEN, 'irnn')\n",
    "irnn300 = train(net, inp, tar, train_data[300][\"x\"], train_data[300][\"y\"],\n",
    "                      test_data[300][\"x\"], test_data[300][\"y\"], \"data/irnn300\", num_epochs=10)\n",
    "np.save(\"data/irnn300-err\", irnn300[\"test_err\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "net, inp, tar = create_network(300, NDIM, N_HIDDEN, 'nrnn')\n",
    "nrnn300 = train(net, inp, tar, train_data[300][\"x\"], train_data[300][\"y\"],\n",
    "                      test_data[300][\"x\"], test_data[300][\"y\"], \"data/nrnn300\", num_epochs=10)\n",
    "np.save(\"data/nrnn300-err\", nrnn300[\"test_err\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "net, inp, tar = create_network(300, NDIM, N_HIDDEN, 'nprnn')\n",
    "nprnn300 = train(net, inp, tar, train_data[300][\"x\"], train_data[300][\"y\"],\n",
    "                      test_data[300][\"x\"], test_data[300][\"y\"], \"data/nprnn300\", num_epochs=10)\n",
    "np.save(\"data/nprnn300-err\", nprnn300[\"test_err\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Seq len 400"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building network ...\n",
      "The network has 10501 params\n",
      "Computing updates ...\n",
      "Compiling functions ...\n",
      "Epoch 0 test loss = 0.1692 \t train = 0.1924 \t norm = 2.5057 \t time = 496.64s\n",
      "Epoch 1 test loss = 0.1719 \t train = 0.1673 \t norm = 0.1283 \t time = 498.29s\n",
      "Epoch 2 test loss = 0.1686 \t train = 0.1671 \t norm = 2.2412 \t time = 497.53s\n",
      "Epoch 3 test loss = 0.1695 \t train = 0.1670 \t norm = 3.1827 \t time = 496.47s\n",
      "Epoch 4 test loss = 0.1682 \t train = 0.1667 \t norm = 0.6971 \t time = 497.22s\n",
      "Epoch 5 test loss = 0.1678 \t train = 0.1667 \t norm = 1.0240 \t time = 499.63s\n",
      "Epoch 6 test loss = 0.1677 \t train = 0.1666 \t norm = 1.9427 \t time = 497.92s\n",
      "Epoch 7 test loss = 0.1689 \t train = 0.1666 \t norm = 0.0902 \t time = 497.48s\n",
      "Epoch 8 test loss = 0.1677 \t train = 0.1665 \t norm = 0.5591 \t time = 500.98s\n",
      "Epoch 9 test loss = 0.1678 \t train = 0.1665 \t norm = 1.8536 \t time = 497.05s\n"
     ]
    }
   ],
   "source": [
    "net, inp, tar = create_network(400, NDIM, N_HIDDEN, 'irnn')\n",
    "irnn400 = train(net, inp, tar, train_data[400][\"x\"], train_data[400][\"y\"],\n",
    "                      test_data[400][\"x\"], test_data[400][\"y\"], \"data/irnn400\", num_epochs=10)\n",
    "np.save(\"data/irnn400-err\", irnn400[\"test_err\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building network ...\n",
      "The network has 10501 params\n",
      "Computing updates ...\n",
      "Compiling functions ...\n",
      "Epoch 0 test loss = 0.1678 \t train = 0.1769 \t norm = 0.5829 \t time = 506.20s\n",
      "Epoch 1 test loss = 0.1674 \t train = 0.1666 \t norm = 2.4825 \t time = 509.79s\n",
      "Epoch 2 test loss = 0.1686 \t train = 0.1663 \t norm = 1.1828 \t time = 502.77s\n",
      "Epoch 3 test loss = 0.1683 \t train = 0.1663 \t norm = 0.2922 \t time = 505.48s\n",
      "Epoch 4 test loss = 0.1674 \t train = 0.1663 \t norm = 0.3555 \t time = 502.42s\n",
      "Epoch 5 test loss = 0.1675 \t train = 0.1662 \t norm = 0.5281 \t time = 504.33s\n",
      "Epoch 6 test loss = 0.1675 \t train = 0.1662 \t norm = 1.1894 \t time = 504.73s\n",
      "Epoch 7 test loss = 0.1678 \t train = 0.1661 \t norm = 2.0760 \t time = 505.61s\n",
      "Epoch 8 test loss = 0.1673 \t train = 0.1661 \t norm = 0.1917 \t time = 503.57s\n",
      "Epoch 9 test loss = 0.1683 \t train = 0.1661 \t norm = 0.5542 \t time = 504.69s\n"
     ]
    }
   ],
   "source": [
    "net, inp, tar = create_network(400, NDIM, N_HIDDEN, 'nrnn')\n",
    "nrnn400 = train(net, inp, tar, train_data[400][\"x\"], train_data[400][\"y\"],\n",
    "                      test_data[400][\"x\"], test_data[400][\"y\"], \"data/nrnn400\", num_epochs=10)\n",
    "np.save(\"data/nrnn400-err\", nrnn400[\"test_err\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building network ...\n",
      "The network has 10501 params\n",
      "Computing updates ...\n",
      "Compiling functions ...\n"
     ]
    }
   ],
   "source": [
    "net, inp, tar = create_network(400, NDIM, N_HIDDEN, 'nprnn')\n",
    "nprnn400 = train(net, inp, tar, train_data[400][\"x\"], train_data[400][\"y\"],\n",
    "                      test_data[400][\"x\"], test_data[400][\"y\"], \"data/nprnn400\", num_epochs=10)\n",
    "np.save(\"data/nprnn400-err\", nprnn300[\"test_err\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def train_adam(network, inp, target_values, Xtrain, ytrain, Xtest, ytest, filename, num_epochs=NUM_EPOCHS):\n",
    "    train_err = np.zeros(num_epochs)\n",
    "    test_err = np.zeros(num_epochs)\n",
    "    \n",
    "    network_output = lasagne.layers.get_output(network)\n",
    "\n",
    "    # Cost is MSE\n",
    "    cost = T.mean((network_output - target_values)**2)\n",
    "    all_params = lasagne.layers.get_all_params(network)\n",
    "    print(\"Computing updates ...\")\n",
    "    all_grads = T.grad(cost, all_params)\n",
    "    scaled_grads = lasagne.updates.total_norm_constraint(all_grads, GRAD_CLIP)\n",
    "    updates = lasagne.updates.adam(scaled_grads, all_params, LEARNING_RATE)\n",
    "    \n",
    "    print(\"Compiling functions ...\")\n",
    "    train_fn = theano.function([inp, target_values], cost, updates=updates, allow_input_downcast=True)\n",
    "    compute_cost = theano.function([inp, target_values], cost, allow_input_downcast=True)\n",
    "    \n",
    "    num_batches = TRAIN_SIZE // BATCH_SIZE\n",
    "    train_idxs = np.arange(TRAIN_SIZE)\n",
    "    for epoch in range(num_epochs):\n",
    "        start_time = time.time()\n",
    "        np.random.shuffle(train_idxs)\n",
    "        for batch in range(num_batches):\n",
    "            idx = train_idxs[BATCH_SIZE * batch : BATCH_SIZE * (batch + 1)]\n",
    "            train_err[epoch] += train_fn(Xtrain[idx, :, :], ytrain[idx, np.newaxis])\n",
    "            \n",
    "            if batch == num_batches // 4:\n",
    "                print(\"Proc 25%\", end=\"\\t\")\n",
    "            if batch == num_batches // 2:\n",
    "                print(\"Proc 50%\", end=\"\\t\")\n",
    "            if batch == num_batches // 4 * 3:\n",
    "                print(\"Proc 75%\")\n",
    "                \n",
    "        train_err[epoch] /= num_batches\n",
    "        test_err[epoch] = compute_cost(Xtest, ytest[:, np.newaxis])\n",
    "        print(\"Epoch {} test loss = {:.4f} \\t train = {:.4f} \\t time = {:.2f}s\".format(\n",
    "                epoch, test_err[epoch], train_err[epoch], time.time() - start_time), end=\"\\t\")\n",
    "    np.save(file=filename, arr=lasagne.layers.get_all_param_values(network))\n",
    "    return pack(network, inp, target_values, train_err, test_err, train_fn, compute_cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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