From 05508e312444ce52895e179b89b42f2920442db6 Mon Sep 17 00:00:00 2001 From: Sam Greydanus Date: Wed, 1 Mar 2017 09:29:54 -0500 Subject: [PATCH] free recall works --- .../.ipynb_checkpoints/debug-checkpoint.ipynb | 0 .../free-recall-rnn-checkpoint.ipynb | 514 ++++++++++++++++ .../repeat-copy-nn-checkpoint.ipynb | 0 .../visualization-checkpoint.ipynb | 0 free-recall/free-recall-rnn.ipynb | 506 ++++++++++++++++ .../nn_controller.pyc | Bin .../rnn_controller.py | 0 .../rnn_controller.pyc | Bin free-recall/rnn_models/checkpoint | 6 + .../model.ckpt-30000.data-00000-of-00001 | Bin 0 -> 977616 bytes free-recall/rnn_models/model.ckpt-30000.index | Bin 0 -> 715 bytes free-recall/rnn_models/model.ckpt-30000.meta | Bin 0 -> 788541 bytes .../serial-recall-rnn-checkpoint.ipynb | 495 ---------------- serial-recall/rnn_models/checkpoint | 4 - .../model.ckpt-10000.data-00000-of-00001 | Bin 374480 -> 0 bytes .../rnn_models/model.ckpt-10000.index | Bin 792 -> 0 bytes .../rnn_models/model.ckpt-10000.meta | Bin 750759 -> 0 bytes serial-recall/serial-recall-rnn.ipynb | 557 ------------------ 18 files changed, 1026 insertions(+), 1056 deletions(-) rename {serial-recall => free-recall}/.ipynb_checkpoints/debug-checkpoint.ipynb (100%) create mode 100644 free-recall/.ipynb_checkpoints/free-recall-rnn-checkpoint.ipynb rename {serial-recall => free-recall}/.ipynb_checkpoints/repeat-copy-nn-checkpoint.ipynb (100%) rename {serial-recall => free-recall}/.ipynb_checkpoints/visualization-checkpoint.ipynb (100%) create mode 100644 free-recall/free-recall-rnn.ipynb rename {serial-recall => free-recall}/nn_controller.pyc (100%) rename {serial-recall => free-recall}/rnn_controller.py (100%) rename {serial-recall => free-recall}/rnn_controller.pyc (100%) create mode 100644 free-recall/rnn_models/checkpoint create mode 100644 free-recall/rnn_models/model.ckpt-30000.data-00000-of-00001 create mode 100644 free-recall/rnn_models/model.ckpt-30000.index create mode 100644 free-recall/rnn_models/model.ckpt-30000.meta delete mode 100644 serial-recall/.ipynb_checkpoints/serial-recall-rnn-checkpoint.ipynb delete mode 100644 serial-recall/rnn_models/checkpoint delete mode 100644 serial-recall/rnn_models/model.ckpt-10000.data-00000-of-00001 delete mode 100644 serial-recall/rnn_models/model.ckpt-10000.index delete mode 100644 serial-recall/rnn_models/model.ckpt-10000.meta delete mode 100644 serial-recall/serial-recall-rnn.ipynb diff --git a/serial-recall/.ipynb_checkpoints/debug-checkpoint.ipynb b/free-recall/.ipynb_checkpoints/debug-checkpoint.ipynb similarity index 100% rename from serial-recall/.ipynb_checkpoints/debug-checkpoint.ipynb rename to free-recall/.ipynb_checkpoints/debug-checkpoint.ipynb diff --git a/free-recall/.ipynb_checkpoints/free-recall-rnn-checkpoint.ipynb b/free-recall/.ipynb_checkpoints/free-recall-rnn-checkpoint.ipynb new file mode 100644 index 0000000..83b4d01 --- /dev/null +++ b/free-recall/.ipynb_checkpoints/free-recall-rnn-checkpoint.ipynb @@ -0,0 +1,514 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Repeat Copy Task\n", + "### Differentiable Neural Computer (DNC) using a RNN Controller\n", + "\n", + "\"DNC\n", + "\n", + "**Sam Greydanus $\\cdot$ February 2017 $\\cdot$ MIT License.**\n", + "\n", + "Represents the state of the art in differentiable memory. Inspired by this [Nature paper](https://goo.gl/6eiJFc). Some ideas taken from [this Gihub repo](https://github.com/Mostafa-Samir/DNC-tensorflow)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Brain analogy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " However, there are interesting parallels between the memory mechanisms of a DNC and the functional capabilities of the mammalian hippocampus. DNC memory modification is fast and can be one-shot, resembling the associative long-term potentiation of hippocampal CA3 and CA1 synapses. The hippocampal dentate gyrus, a region known to support neurogenesis, has been proposed to increase representational sparsity, thereby enhancing memory capacity: usage- based memory allocation and sparse weightings may provide similar facilities in our model. Human 'free recall' experiments demonstrate the increased probability of item recall in the same order as first presented—a hippocampus-dependent phenomenon accounted for by the temporal context model, bearing some similarity to the formation of temporal links." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "import numpy as np\n", + "import sys\n", + "sys.path.insert(0, '../dnc')\n", + "\n", + "from dnc import DNC\n", + "from rnn_controller import RNNController\n", + "\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Hyperparameters" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "xydim = 6\n", + "tf.app.flags.DEFINE_integer(\"xlen\", xydim, \"Input dimension\")\n", + "tf.app.flags.DEFINE_integer(\"ylen\", xydim, \"output dimension\")\n", + "tf.app.flags.DEFINE_integer(\"length\", 5, \"Sequence length\")\n", + "tf.app.flags.DEFINE_integer(\"batch_size\", 3, \"Size of batch in minibatch gradient descent\")\n", + "\n", + "tf.app.flags.DEFINE_integer(\"R\", 1, \"Number of DNC read heads\")\n", + "tf.app.flags.DEFINE_integer(\"W\", 10, \"Word length for DNC memory\")\n", + "tf.app.flags.DEFINE_integer(\"N\", 7, \"Number of words the DNC memory can store\")\n", + "\n", + "tf.app.flags.DEFINE_integer(\"print_every\", 100, \"Print training info after this number of train steps\")\n", + "tf.app.flags.DEFINE_integer(\"iterations\", 30000, \"Number of training iterations\")\n", + "tf.app.flags.DEFINE_float(\"lr\", 1e-4, \"Learning rate (alpha) for the model\")\n", + "tf.app.flags.DEFINE_float(\"momentum\", .9, \"RMSProp momentum\")\n", + "tf.app.flags.DEFINE_integer(\"save_every\", 1000, \"Save model after this number of train steps\")\n", + "tf.app.flags.DEFINE_string(\"save_path\", \"rnn_models/model.ckpt\", \"Where to save checkpoints\")\n", + "FLAGS = tf.app.flags.FLAGS" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data functions" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def get_sequence(length, dim):\n", + " X = np.concatenate((np.random.randint(2, size=(length,dim)), np.zeros((length + 3,dim))))\n", + " X = np.vstack(X) ; X[:,dim-1] = 0\n", + " \n", + " X = np.concatenate((X[-1:,:],X[:-1,:]))\n", + " y = np.concatenate((X[-(length + 2):,:],X[:-(length + 2),:]))\n", + " markers = range(length+1, X.shape[0], 2*length+3)\n", + " X[markers,dim-1] = 1\n", + " return X, y\n", + " \n", + "def next_batch(batch_size, length, dim):\n", + " X_batch = []\n", + " y_batch = []\n", + " for _ in range(batch_size):\n", + " X, y = get_sequence(length, dim)\n", + " X_batch.append(X) ; y_batch.append(y)\n", + " return [X_batch, y_batch]\n", + "\n", + "batch = next_batch(1, FLAGS.length, FLAGS.xlen)\n", + "plt.imshow(batch[0][0].T - batch[1][0].T, interpolation='none')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Helper functions" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def binary_cross_entropy(y, y_hat):\n", + " return tf.reduce_mean(-y*tf.log(y_hat) - (1-y)*tf.log(1-y_hat))\n", + "\n", + "def llprint(message):\n", + " sys.stdout.write(message)\n", + " sys.stdout.flush()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def free_recall_loss(y, y_hat, tsteps): \n", + " # sorry this dimension stuff is uuuuugly but we have to because it's batched\n", + " y = tf.expand_dims(y, [1])\n", + " y_hat = tf.expand_dims(y_hat, [1])\n", + " \n", + " y_hat = tf.tile(y_hat,[1,tsteps,1,1])\n", + " y_hat = tf.transpose(y_hat, [0,2,1,3])\n", + " \n", + " y_minus = -y*tf.log(y_hat) - (1-y)*tf.log(1-y_hat) # binary cross entropy loss\n", + " y_minus = tf.reduce_sum(y_minus, axis=-1)\n", + " y_minus = tf.reduce_min(y_minus, axis=1)\n", + " \n", + " return tf.reduce_sum(y_minus) / (FLAGS.batch_size*FLAGS.length)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6.07968e-06\n" + ] + } + ], + "source": [ + "X, real_y = next_batch(FLAGS.batch_size, FLAGS.length, FLAGS.xlen)\n", + "real_y = np.stack(real_y)[:,-FLAGS.length:,:]\n", + "\n", + "pred_y = [y_i[np.random.permutation(y_i.shape[0]),:] for y_i in real_y]\n", + "pred_y = np.stack(pred_y)\n", + "# pred_y[:,-2:,-1] = 4\n", + "\n", + "tf_real_y = tf.placeholder(tf.float32, [FLAGS.batch_size, None, FLAGS.ylen], name='real_y')\n", + "tf_pred_y = tf.placeholder(tf.float32, [FLAGS.batch_size, None, FLAGS.ylen], name='pred_y')\n", + "sess = tf.InteractiveSession()\n", + "\n", + "\n", + "# clip predictions to range (0,1) to avoid infinite losses\n", + "_tf_pred_y = tf.clip_by_value(tf_pred_y, 1e-6, 1-1e-6)\n", + " \n", + "l = free_recall_loss(tf_real_y, _tf_pred_y, FLAGS.length)\n", + "feed = {tf_real_y: real_y, tf_pred_y: pred_y}\n", + "print l.eval(feed)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Build graph, initialize everything" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "building graph...\n", + "defining loss...\n", + "computing gradients...\n", + "init variables... \n", + "ready to train..." + ] + } + ], + "source": [ + "sess = tf.InteractiveSession()\n", + "\n", + "llprint(\"building graph...\\n\")\n", + "optimizer = tf.train.RMSPropOptimizer(FLAGS.lr, momentum=FLAGS.momentum)\n", + "dnc = DNC(RNNController, FLAGS)\n", + "\n", + "llprint(\"defining loss...\\n\")\n", + "y_hat, outputs = dnc.get_outputs()\n", + "y_hat = tf.clip_by_value(tf.sigmoid(y_hat), 1e-6, 1. - 1e-6) # avoid infinity\n", + "rlen = (dnc.tsteps-3)/2\n", + "loss = free_recall_loss(dnc.y[:,-rlen:,:], y_hat[:,-rlen:,:], tsteps=rlen)\n", + "\n", + "llprint(\"computing gradients...\\n\")\n", + "gradients = optimizer.compute_gradients(loss)\n", + "for i, (grad, var) in enumerate(gradients):\n", + " if grad is not None:\n", + " gradients[i] = (tf.clip_by_value(grad, -10, 10), var)\n", + " \n", + "grad_op = optimizer.apply_gradients(gradients)\n", + "\n", + "llprint(\"init variables... \\n\")\n", + "sess.run(tf.global_variables_initializer())\n", + "llprint(\"ready to train...\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "model overview...\n", + "\tvariable \"dnc_scope/basic_lstm_cell/weights:0\" has 73728 parameters\n", + "\tvariable \"dnc_scope/basic_lstm_cell/biases:0\" has 512 parameters\n", + "\tvariable \"W_z:0\" has 6144 parameters\n", + "\tvariable \"W_v:0\" has 768 parameters\n", + "\tvariable \"W_r:0\" has 60 parameters\n", + "total of 81212 parameters\n" + ] + } + ], + "source": [ + "# tf parameter overview\n", + "total_parameters = 0 ; print \"model overview...\"\n", + "for variable in tf.trainable_variables():\n", + " shape = variable.get_shape()\n", + " variable_parameters = 1\n", + " for dim in shape:\n", + " variable_parameters *= dim.value\n", + " print '\\tvariable \"{}\" has {} parameters' \\\n", + " .format(variable.name, variable_parameters)\n", + " total_parameters += variable_parameters\n", + "print \"total of {} parameters\".format(total_parameters)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loaded model: rnn_models/model.ckpt-2000\n" + ] + } + ], + "source": [ + "global_step = 0\n", + "saver = tf.train.Saver(tf.global_variables())\n", + "load_was_success = True # yes, I'm being optimistic\n", + "try:\n", + " save_dir = '/'.join(FLAGS.save_path.split('/')[:-1])\n", + " ckpt = tf.train.get_checkpoint_state(save_dir)\n", + " load_path = ckpt.model_checkpoint_path\n", + " saver.restore(sess, load_path)\n", + "except:\n", + " print \"no saved model to load.\"\n", + " load_was_success = False\n", + "else:\n", + " print \"loaded model: {}\".format(load_path)\n", + " saver = tf.train.Saver(tf.global_variables())\n", + " global_step = int(load_path.split('-')[-1]) + 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train loop" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 2100/10000\n", + "\tloss: 1.1709\n", + "Iteration 2200/10000\n", + "\tloss: 1.1148\n", + "Iteration 2300/10000\n", + "\tloss: 1.0109\n", + "Iteration 2400/10000\n", + "\tloss: 1.0461\n", + "Iteration 2500/10000\n", + "\tloss: 1.1705\n", + "Iteration 2600/10000\n", + "\tloss: 0.9667\n", + "Iteration 2685/10000" + ] + } + ], + "source": [ + "loss_history = []\n", + "for i in xrange(global_step, FLAGS.iterations + 1):\n", + " llprint(\"\\rIteration {}/{}\".format(i, FLAGS.iterations))\n", + "\n", + " rlen = np.random.randint(1, FLAGS.length + 1)\n", + " X, y = next_batch(FLAGS.batch_size, rlen, FLAGS.xlen)\n", + " tsteps = 2*rlen+3\n", + "\n", + " fetch = [loss, grad_op]\n", + " feed = {dnc.X: X, dnc.y: y, dnc.tsteps: tsteps}\n", + "\n", + " step_loss, _ = sess.run(fetch, feed_dict=feed)\n", + " loss_history.append(step_loss)\n", + " global_step = i\n", + "\n", + " if i % 100 == 0:\n", + " llprint(\"\\n\\tloss: {:03.4f}\\n\".format(np.mean(loss_history)))\n", + " loss_history = []\n", + " if i % FLAGS.save_every == 0 and i is not 0:\n", + " llprint(\"\\n\\tSAVING MODEL\\n\")\n", + " saver.save(sess, FLAGS.save_path, global_step=global_step)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "X, y = next_batch(FLAGS.batch_size, FLAGS.length, FLAGS.xlen)\n", + "tsteps = 2*FLAGS.length+3\n", + "\n", + "feed = {dnc.X: X, dnc.y: y, dnc.tsteps: tsteps}\n", + "fetch = [outputs['y_hat'], outputs['w_w'], outputs['w_r'], outputs['f'], outputs['g_a']]\n", + "[_y_hat, _w_w, _w_r, _f, _g_a] = sess.run(fetch, feed)\n", + "_y_hat = np.clip(_y_hat, 1e-6, 1-1e-6)\n", + "_y = y[0] ; _X = X[0]\n", + "\n", + "fig, ((ax1,ax2),(ax3,ax5),(ax4,ax6),) = plt.subplots(nrows=3, ncols=2)\n", + "plt.rcParams['savefig.facecolor'] = \"0.8\"\n", + "fs = 12 # font size\n", + "fig.set_figwidth(10)\n", + "fig.set_figheight(5)\n", + "\n", + "ax1.imshow(_X.T - _y.T, interpolation='none') ; ax1.set_title('input ($X$) and target ($y$)')\n", + "ax2.imshow(_y_hat[0,-FLAGS.length:,:].T, interpolation='none') ; ax2.set_title('prediction ($\\hat y$)')\n", + "\n", + "ax3.imshow(_w_w[0,:,:].T, interpolation='none') ; ax3.set_title('write weighting ($w_w$)')\n", + "ax4.imshow(_w_r[0,:,:,0].T, interpolation='none') ; ax4.set_title('read weighting ($w_r$)')\n", + "\n", + "ax5.imshow(_f[0,:,:].T, interpolation='none') ; ax5.set_title('free gate ($f$)') ; ax5.set_aspect(3)\n", + "ax6.imshow(_g_a[0,:,:].T, interpolation='none') ; ax6.set_title('allocation gate ($g_a$)') ; ax6.set_aspect(3)\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "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": false + }, + "outputs": [], + "source": [ + "# X, y = next_batch(FLAGS.batch_size, FLAGS.length, FLAGS.xlen)\n", + "# y = np.stack(y)\n", + "# y = y[:,FLAGS.length + 3:,:]\n", + "# y_shuffle = y[:,np.random.permutation(FLAGS.length),:]\n", + "# y_shuffle[:,-1,-1] = 4\n", + "\n", + "# plt.figure(0, figsize=[2,2])\n", + "# plt.imshow(y[0,:,:].T, interpolation='none')\n", + "# plt.figure(1, figsize=[2,2])\n", + "# plt.imshow(y_shuffle[0,:,:].T, interpolation='none')\n", + "# plt.show()\n", + "\n", + "# def np_loss(real_y, pred_y, FLAGS):\n", + "# # sorry this is uuuuugly but we have to because it's batched\n", + "# real_y = np.tile(real_y, [1,1,1,1]) ; real_y = np.transpose(real_y, (1,0,2,3))\n", + "# pred_y = np.tile(pred_y,[1,1,1,1]) ; pred_y = np.transpose(pred_y, (1,0,2,3))\n", + " \n", + "# pred_y = np.tile(pred_y,[1,FLAGS.xlen-1,1,1])\n", + "# pred_y = np.transpose(pred_y, (0,2,1,3))\n", + " \n", + "# real_y = real_y[0,:,:,:] ; pred_y = pred_y[0,:,:,:]\n", + "# y_minus = .5*(real_y - pred_y)**2\n", + "# y_minus = np.sum(y_minus, axis=-1)\n", + "# y_minus = np.min(y_minus, axis=1)\n", + " \n", + "# return np.sum(y_minus) / (FLAGS.batch_size*FLAGS.length)\n", + "\n", + "# X, real_y = next_batch(FLAGS.batch_size, FLAGS.length, FLAGS.xlen)\n", + "# real_y = np.stack(real_y)[:,-FLAGS.length:,:]\n", + "\n", + "# pred_y = [y_i[np.random.permutation(y_i.shape[0]),:] for y_i in real_y]\n", + "# pred_y = np.stack(pred_y)\n", + "# # pred_y[:,-2:,-1] = 4\n", + "\n", + "# np_loss(real_y, pred_y, FLAGS)" + ] + } + ], + "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": 1 +} diff --git a/serial-recall/.ipynb_checkpoints/repeat-copy-nn-checkpoint.ipynb b/free-recall/.ipynb_checkpoints/repeat-copy-nn-checkpoint.ipynb similarity index 100% rename from serial-recall/.ipynb_checkpoints/repeat-copy-nn-checkpoint.ipynb rename to free-recall/.ipynb_checkpoints/repeat-copy-nn-checkpoint.ipynb diff --git a/serial-recall/.ipynb_checkpoints/visualization-checkpoint.ipynb b/free-recall/.ipynb_checkpoints/visualization-checkpoint.ipynb similarity index 100% rename from serial-recall/.ipynb_checkpoints/visualization-checkpoint.ipynb rename to free-recall/.ipynb_checkpoints/visualization-checkpoint.ipynb diff --git a/free-recall/free-recall-rnn.ipynb b/free-recall/free-recall-rnn.ipynb new file mode 100644 index 0000000..41fb80a --- /dev/null +++ b/free-recall/free-recall-rnn.ipynb @@ -0,0 +1,506 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Repeat Copy Task\n", + "### Differentiable Neural Computer (DNC) using a RNN Controller\n", + "\n", + "\"DNC\n", + "\n", + "**Sam Greydanus $\\cdot$ February 2017 $\\cdot$ MIT License.**\n", + "\n", + "Represents the state of the art in differentiable memory. Inspired by this [Nature paper](https://goo.gl/6eiJFc). Some ideas taken from [this Gihub repo](https://github.com/Mostafa-Samir/DNC-tensorflow)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Brain analogy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " However, there are interesting parallels between the memory mechanisms of a DNC and the functional capabilities of the mammalian hippocampus. DNC memory modification is fast and can be one-shot, resembling the associative long-term potentiation of hippocampal CA3 and CA1 synapses. The hippocampal dentate gyrus, a region known to support neurogenesis, has been proposed to increase representational sparsity, thereby enhancing memory capacity: usage- based memory allocation and sparse weightings may provide similar facilities in our model. Human 'free recall' experiments demonstrate the increased probability of item recall in the same order as first presented—a hippocampus-dependent phenomenon accounted for by the temporal context model, bearing some similarity to the formation of temporal links." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "import numpy as np\n", + "import sys\n", + "sys.path.insert(0, '../dnc')\n", + "\n", + "from dnc import DNC\n", + "from rnn_controller import RNNController\n", + "\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Hyperparameters" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "xydim = 6\n", + "tf.app.flags.DEFINE_integer(\"xlen\", xydim, \"Input dimension\")\n", + "tf.app.flags.DEFINE_integer(\"ylen\", xydim, \"output dimension\")\n", + "tf.app.flags.DEFINE_integer(\"length\", 5, \"Sequence length\")\n", + "tf.app.flags.DEFINE_integer(\"batch_size\", 3, \"Size of batch in minibatch gradient descent\")\n", + "\n", + "tf.app.flags.DEFINE_integer(\"R\", 1, \"Number of DNC read heads\")\n", + "tf.app.flags.DEFINE_integer(\"W\", 10, \"Word length for DNC memory\")\n", + "tf.app.flags.DEFINE_integer(\"N\", 7, \"Number of words the DNC memory can store\")\n", + "\n", + "tf.app.flags.DEFINE_integer(\"print_every\", 100, \"Print training info after this number of train steps\")\n", + "tf.app.flags.DEFINE_integer(\"iterations\", 30000, \"Number of training iterations\")\n", + "tf.app.flags.DEFINE_float(\"lr\", 1e-4, \"Learning rate (alpha) for the model\")\n", + "tf.app.flags.DEFINE_float(\"momentum\", .9, \"RMSProp momentum\")\n", + "tf.app.flags.DEFINE_integer(\"save_every\", 1000, \"Save model after this number of train steps\")\n", + "tf.app.flags.DEFINE_string(\"save_path\", \"rnn_models/model.ckpt\", \"Where to save checkpoints\")\n", + "FLAGS = tf.app.flags.FLAGS" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data functions" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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alWQD8KpSysltZ+miJNcBO0opb9tj2T8Bj5RS/ri9ZO1LMgv8Xinl2j2W3Qd8pJSyce76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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def get_sequence(length, dim):\n", + " X = np.concatenate((np.random.randint(2, size=(length,dim)), np.zeros((length + 3,dim))))\n", + " X = np.vstack(X) ; X[:,dim-1] = 0\n", + " \n", + " X = np.concatenate((X[-1:,:],X[:-1,:]))\n", + " y = np.concatenate((X[-(length + 2):,:],X[:-(length + 2),:]))\n", + " markers = range(length+1, X.shape[0], 2*length+3)\n", + " X[markers,dim-1] = 1\n", + " return X, y\n", + " \n", + "def next_batch(batch_size, length, dim):\n", + " X_batch = []\n", + " y_batch = []\n", + " for _ in range(batch_size):\n", + " X, y = get_sequence(length, dim)\n", + " X_batch.append(X) ; y_batch.append(y)\n", + " return [X_batch, y_batch]\n", + "\n", + "batch = next_batch(1, FLAGS.length, FLAGS.xlen)\n", + "plt.imshow(batch[0][0].T - batch[1][0].T, interpolation='none')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Helper functions" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def binary_cross_entropy(y, y_hat):\n", + " return tf.reduce_mean(-y*tf.log(y_hat) - (1-y)*tf.log(1-y_hat))\n", + "\n", + "def llprint(message):\n", + " sys.stdout.write(message)\n", + " sys.stdout.flush()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def free_recall_loss(y, y_hat, tsteps): \n", + " # sorry this dimension stuff is uuuuugly but we have to because it's batched\n", + " y = tf.expand_dims(y, [1])\n", + " y_hat = tf.expand_dims(y_hat, [1])\n", + " \n", + " y_hat = tf.tile(y_hat,[1,tsteps,1,1])\n", + " y_hat = tf.transpose(y_hat, [0,2,1,3])\n", + " \n", + " y_minus = -y*tf.log(y_hat) - (1-y)*tf.log(1-y_hat) # binary cross entropy loss\n", + " y_minus = tf.reduce_sum(y_minus, axis=-1)\n", + " y_minus = tf.reduce_min(y_minus, axis=1)\n", + " \n", + " return tf.reduce_sum(y_minus) / (FLAGS.batch_size*FLAGS.length)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6.07968e-06\n" + ] + } + ], + "source": [ + "# test free recall loss\n", + "X, real_y = next_batch(FLAGS.batch_size, FLAGS.length, FLAGS.xlen)\n", + "real_y = np.stack(real_y)[:,-FLAGS.length:,:]\n", + "\n", + "pred_y = [y_i[np.random.permutation(y_i.shape[0]),:] for y_i in real_y]\n", + "pred_y = np.stack(pred_y)\n", + "# pred_y[:,-2:,-1] = 4\n", + "\n", + "tf_real_y = tf.placeholder(tf.float32, [FLAGS.batch_size, None, FLAGS.ylen], name='real_y')\n", + "tf_pred_y = tf.placeholder(tf.float32, [FLAGS.batch_size, None, FLAGS.ylen], name='pred_y')\n", + "sess = tf.InteractiveSession()\n", + "\n", + "\n", + "# clip predictions to range (0,1) to avoid infinite losses\n", + "_tf_pred_y = tf.clip_by_value(tf_pred_y, 1e-6, 1-1e-6)\n", + " \n", + "l = free_recall_loss(tf_real_y, _tf_pred_y, FLAGS.length)\n", + "feed = {tf_real_y: real_y, tf_pred_y: pred_y}\n", + "print l.eval(feed)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Build graph, initialize everything" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "building graph...\n", + "defining loss...\n", + "computing gradients...\n", + "init variables... \n", + "ready to train..." + ] + } + ], + "source": [ + "sess = tf.InteractiveSession()\n", + "\n", + "llprint(\"building graph...\\n\")\n", + "optimizer = tf.train.RMSPropOptimizer(FLAGS.lr, momentum=FLAGS.momentum)\n", + "dnc = DNC(RNNController, FLAGS)\n", + "\n", + "llprint(\"defining loss...\\n\")\n", + "y_hat, outputs = dnc.get_outputs()\n", + "y_hat = tf.clip_by_value(tf.sigmoid(y_hat), 1e-6, 1. - 1e-6) # avoid infinity\n", + "rlen = (dnc.tsteps-3)/2\n", + "loss = free_recall_loss(dnc.y[:,-rlen:,:], y_hat[:,-rlen:,:], tsteps=rlen)\n", + "\n", + "llprint(\"computing gradients...\\n\")\n", + "gradients = optimizer.compute_gradients(loss)\n", + "for i, (grad, var) in enumerate(gradients):\n", + " if grad is not None:\n", + " gradients[i] = (tf.clip_by_value(grad, -10, 10), var)\n", + " \n", + "grad_op = optimizer.apply_gradients(gradients)\n", + "\n", + "llprint(\"init variables... \\n\")\n", + "sess.run(tf.global_variables_initializer())\n", + "llprint(\"ready to train...\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "model overview...\n", + "\tvariable \"dnc_scope/basic_lstm_cell/weights:0\" has 73728 parameters\n", + "\tvariable \"dnc_scope/basic_lstm_cell/biases:0\" has 512 parameters\n", + "\tvariable \"W_z:0\" has 6144 parameters\n", + "\tvariable \"W_v:0\" has 768 parameters\n", + "\tvariable \"W_r:0\" has 60 parameters\n", + "total of 81212 parameters\n" + ] + } + ], + "source": [ + "# tf parameter overview\n", + "total_parameters = 0 ; print \"model overview...\"\n", + "for variable in tf.trainable_variables():\n", + " shape = variable.get_shape()\n", + " variable_parameters = 1\n", + " for dim in shape:\n", + " variable_parameters *= dim.value\n", + " print '\\tvariable \"{}\" has {} parameters' \\\n", + " .format(variable.name, variable_parameters)\n", + " total_parameters += variable_parameters\n", + "print \"total of {} parameters\".format(total_parameters)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loaded model: rnn_models/model.ckpt-30000\n" + ] + } + ], + "source": [ + "global_step = 0\n", + "saver = tf.train.Saver(tf.global_variables())\n", + "load_was_success = True # yes, I'm being optimistic\n", + "try:\n", + " save_dir = '/'.join(FLAGS.save_path.split('/')[:-1])\n", + " ckpt = tf.train.get_checkpoint_state(save_dir)\n", + " load_path = ckpt.model_checkpoint_path\n", + " saver.restore(sess, load_path)\n", + "except:\n", + " print \"no saved model to load.\"\n", + " load_was_success = False\n", + "else:\n", + " print \"loaded model: {}\".format(load_path)\n", + " saver = tf.train.Saver(tf.global_variables())\n", + " global_step = int(load_path.split('-')[-1]) + 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train loop" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "loss_history = []\n", + "for i in xrange(global_step, FLAGS.iterations + 1):\n", + " llprint(\"\\rIteration {}/{}\".format(i, FLAGS.iterations))\n", + "\n", + " rlen = np.random.randint(1, FLAGS.length + 1)\n", + " X, y = next_batch(FLAGS.batch_size, rlen, FLAGS.xlen)\n", + " tsteps = 2*rlen+3\n", + "\n", + " fetch = [loss, grad_op]\n", + " feed = {dnc.X: X, dnc.y: y, dnc.tsteps: tsteps}\n", + "\n", + " step_loss, _ = sess.run(fetch, feed_dict=feed)\n", + " loss_history.append(step_loss)\n", + " global_step = i\n", + "\n", + " if i % 100 == 0:\n", + " llprint(\"\\n\\tloss: {:03.4f}\\n\".format(np.mean(loss_history)))\n", + " loss_history = []\n", + " if i % FLAGS.save_every == 0 and i is not 0:\n", + " llprint(\"\\n\\tSAVING MODEL\\n\")\n", + " saver.save(sess, FLAGS.save_path, global_step=global_step)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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vD8yluGrf6EaKIUgjV155+gBwYWZeWUP5h1IMx9pn1GU3eSTFVaf3Af9KMUTt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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "X, y = next_batch(FLAGS.batch_size, FLAGS.length, FLAGS.xlen)\n", + "tsteps = 2*FLAGS.length+3\n", + "\n", + "feed = {dnc.X: X, dnc.y: y, dnc.tsteps: tsteps}\n", + "fetch = [outputs['y_hat'], outputs['w_w'], outputs['w_r'], outputs['f'], outputs['g_a']]\n", + "[_y_hat, _w_w, _w_r, _f, _g_a] = sess.run(fetch, feed)\n", + "_y_hat = np.clip(_y_hat, 1e-6, 1-1e-6)\n", + "_y = y[0] ; _X = X[0]\n", + "\n", + "fig, ((ax1,ax2),(ax3,ax5),(ax4,ax6),) = plt.subplots(nrows=3, ncols=2)\n", + "plt.rcParams['savefig.facecolor'] = \"0.8\"\n", + "fs = 12 # font size\n", + "fig.set_figwidth(10)\n", + "fig.set_figheight(5)\n", + "\n", + "ax1.imshow(_X.T - _y.T, interpolation='none') ; ax1.set_title('input ($X$) and target ($y$)')\n", + "ax2.imshow(_y_hat[0,-FLAGS.length:,:].T, interpolation='none') ; ax2.set_title('prediction ($\\hat y$)')\n", + "\n", + "ax3.imshow(_w_w[0,:,:].T, interpolation='none') ; ax3.set_title('write weighting ($w_w$)')\n", + "ax4.imshow(_w_r[0,:,:,0].T, interpolation='none') ; ax4.set_title('read weighting ($w_r$)')\n", + "\n", + "ax5.imshow(_f[0,:,:].T, interpolation='none') ; ax5.set_title('free gate ($f$)') ; ax5.set_aspect(3)\n", + "ax6.imshow(_g_a[0,:,:].T, interpolation='none') ; ax6.set_title('allocation gate ($g_a$)') ; ax6.set_aspect(3)\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "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": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# X, y = next_batch(FLAGS.batch_size, FLAGS.length, FLAGS.xlen)\n", + "# y = np.stack(y)\n", + "# y = y[:,FLAGS.length + 3:,:]\n", + "# y_shuffle = y[:,np.random.permutation(FLAGS.length),:]\n", + "# y_shuffle[:,-1,-1] = 4\n", + "\n", + "# plt.figure(0, figsize=[2,2])\n", + "# plt.imshow(y[0,:,:].T, interpolation='none')\n", + "# plt.figure(1, figsize=[2,2])\n", + "# plt.imshow(y_shuffle[0,:,:].T, interpolation='none')\n", + "# plt.show()\n", + "\n", + "# def np_loss(real_y, pred_y, FLAGS):\n", + "# # sorry this is uuuuugly but we have to because it's batched\n", + "# real_y = np.tile(real_y, [1,1,1,1]) ; real_y = np.transpose(real_y, (1,0,2,3))\n", + "# pred_y = np.tile(pred_y,[1,1,1,1]) ; pred_y = np.transpose(pred_y, (1,0,2,3))\n", + " \n", + "# pred_y = np.tile(pred_y,[1,FLAGS.xlen-1,1,1])\n", + "# pred_y = np.transpose(pred_y, (0,2,1,3))\n", + " \n", + "# real_y = real_y[0,:,:,:] ; pred_y = pred_y[0,:,:,:]\n", + "# y_minus = .5*(real_y - pred_y)**2\n", + "# y_minus = np.sum(y_minus, axis=-1)\n", + "# y_minus = np.min(y_minus, axis=1)\n", + " \n", + "# return np.sum(y_minus) / (FLAGS.batch_size*FLAGS.length)\n", + "\n", + "# X, real_y = next_batch(FLAGS.batch_size, FLAGS.length, FLAGS.xlen)\n", + "# real_y = np.stack(real_y)[:,-FLAGS.length:,:]\n", + "\n", + "# pred_y = [y_i[np.random.permutation(y_i.shape[0]),:] for y_i in real_y]\n", + "# pred_y = np.stack(pred_y)\n", + "# # pred_y[:,-2:,-1] = 4\n", + "\n", + "# np_loss(real_y, pred_y, FLAGS)" + ] + } + ], + "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": 1 +} diff --git a/serial-recall/nn_controller.pyc b/free-recall/nn_controller.pyc similarity index 100% rename from serial-recall/nn_controller.pyc rename to free-recall/nn_controller.pyc diff --git a/serial-recall/rnn_controller.py b/free-recall/rnn_controller.py similarity index 100% rename from serial-recall/rnn_controller.py rename to free-recall/rnn_controller.py diff --git a/serial-recall/rnn_controller.pyc b/free-recall/rnn_controller.pyc similarity index 100% rename from serial-recall/rnn_controller.pyc rename to free-recall/rnn_controller.pyc diff --git a/free-recall/rnn_models/checkpoint b/free-recall/rnn_models/checkpoint new file mode 100644 index 0000000..33af155 --- /dev/null +++ b/free-recall/rnn_models/checkpoint @@ -0,0 +1,6 @@ +model_checkpoint_path: "model.ckpt-30000" +all_model_checkpoint_paths: "model.ckpt-26000" +all_model_checkpoint_paths: "model.ckpt-27000" +all_model_checkpoint_paths: "model.ckpt-28000" +all_model_checkpoint_paths: "model.ckpt-29000" +all_model_checkpoint_paths: "model.ckpt-30000" diff --git a/free-recall/rnn_models/model.ckpt-30000.data-00000-of-00001 b/free-recall/rnn_models/model.ckpt-30000.data-00000-of-00001 new file mode 100644 index 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zf1)SepPr&0mHU6g@{Tr9oa+uHqLpM)G+9-OubczHq$s2rRsnOex>RMeCZ#y@PoZ*X zBbVGcxJRne@v4$%3<0s^d=N9(sfGLhd`8>`#h>xk@L526dAK{4|CscDbEf}mGvcav zeI0T6ilUFufU7JxXY\"DNC\n", - "\n", - "**Sam Greydanus $\\cdot$ February 2017 $\\cdot$ MIT License.**\n", - "\n", - "Represents the state of the art in differentiable memory. Inspired by this [Nature paper](https://goo.gl/6eiJFc). Some ideas taken from [this Gihub repo](https://github.com/Mostafa-Samir/DNC-tensorflow)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Brain analogy" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " However, there are interesting parallels between the memory mechanisms of a DNC and the functional capabilities of the mammalian hippocampus. DNC memory modification is fast and can be one-shot, resembling the associative long-term potentiation of hippocampal CA3 and CA1 synapses. The hippocampal dentate gyrus, a region known to support neurogenesis, has been proposed to increase representational sparsity, thereby enhancing memory capacity: usage- based memory allocation and sparse weightings may provide similar facilities in our model. Human 'free recall' experiments demonstrate the increased probability of item recall in the same order as first presented—a hippocampus-dependent phenomenon accounted for by the temporal context model, bearing some similarity to the formation of temporal links." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import tensorflow as tf\n", - "import numpy as np\n", - "import sys\n", - "sys.path.insert(0, '../dnc')\n", - "\n", - "from dnc import DNC\n", - "from rnn_controller import RNNController\n", - "\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hyperparameters" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "xydim = 6\n", - "tf.app.flags.DEFINE_integer(\"xlen\", xydim, \"Input dimension\")\n", - "tf.app.flags.DEFINE_integer(\"ylen\", xydim, \"output dimension\")\n", - "tf.app.flags.DEFINE_integer(\"length\", 5, \"Sequence length\")\n", - "tf.app.flags.DEFINE_integer(\"batch_size\", 1, \"Size of batch in minibatch gradient descent\")\n", - "\n", - "tf.app.flags.DEFINE_integer(\"R\", 1, \"Number of DNC read heads\")\n", - "tf.app.flags.DEFINE_integer(\"W\", 10, \"Word length for DNC memory\")\n", - "tf.app.flags.DEFINE_integer(\"N\", 7, \"Number of words the DNC memory can store\")\n", - "\n", - "tf.app.flags.DEFINE_integer(\"print_every\", 100, \"Print training info after this number of train steps\")\n", - "tf.app.flags.DEFINE_integer(\"iterations\", 10000, \"Number of training iterations\")\n", - "tf.app.flags.DEFINE_float(\"lr\", 1e-4, \"Learning rate (alpha) for the model\")\n", - "tf.app.flags.DEFINE_float(\"momentum\", .9, \"RMSProp momentum\")\n", - "tf.app.flags.DEFINE_integer(\"save_every\", 1000, \"Save model after this number of train steps\")\n", - "tf.app.flags.DEFINE_string(\"save_path\", \"rnn_models/model.ckpt\", \"Where to save checkpoints\")\n", - "FLAGS = tf.app.flags.FLAGS" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Data functions" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def get_sequence(length, dim):\n", - " X = np.concatenate((np.random.randint(2, size=(length,dim)), np.zeros((length + 3,dim))))\n", - " X = np.vstack(X) ; X[:,dim-1] = 0\n", - " \n", - " X = np.concatenate((X[-1:,:],X[:-1,:]))\n", - " y = np.concatenate((X[-(length + 2):,:],X[:-(length + 2),:]))\n", - " markers = range(length+1, X.shape[0], 2*length+3)\n", - " X[markers,dim-1] = 1\n", - " return X, y\n", - " \n", - "def next_batch(batch_size, length, dim):\n", - " X_batch = []\n", - " y_batch = []\n", - " for _ in range(batch_size):\n", - " X, y = get_sequence(length, dim)\n", - " X_batch.append(X) ; y_batch.append(y)\n", - " return [X_batch, y_batch]\n", - "\n", - "batch = next_batch(1, FLAGS.length, FLAGS.xlen)\n", - "plt.imshow(batch[0][0].T - batch[1][0].T, interpolation='none')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Helper functions" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def binary_cross_entropy(y_hat, y):\n", - " return tf.reduce_mean(-y*tf.log(y_hat) - (1-y)*tf.log(1-y_hat))\n", - "\n", - "def llprint(message):\n", - " sys.stdout.write(message)\n", - " sys.stdout.flush()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "real_y = tf.placeholder(tf.float32, [batch_size, None, ylen], name='real_y')\n", - "pred_y = tf.placeholder(tf.float32, [batch_size, None, ylen], name='pred_y')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Build graph, initialize everything" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "building graph...\n", - "defining loss...\n", - "computing gradients...\n", - "init variables... \n", - "ready to train..." - ] - } - ], - "source": [ - "sess = tf.InteractiveSession()\n", - "\n", - "llprint(\"building graph...\\n\")\n", - "optimizer = tf.train.RMSPropOptimizer(FLAGS.lr, momentum=FLAGS.momentum)\n", - "dnc = DNC(RNNController, FLAGS)\n", - "\n", - "llprint(\"defining loss...\\n\")\n", - "y_hat, outputs = dnc.get_outputs()\n", - "y_hat = tf.clip_by_value(tf.sigmoid(y_hat), 1e-6, 1. - 1e-6)\n", - "loss = binary_cross_entropy(y_hat, dnc.y)\n", - "\n", - "llprint(\"computing gradients...\\n\")\n", - "gradients = optimizer.compute_gradients(loss)\n", - "for i, (grad, var) in enumerate(gradients):\n", - " if grad is not None:\n", - " gradients[i] = (tf.clip_by_value(grad, -10, 10), var)\n", - " \n", - "grad_op = optimizer.apply_gradients(gradients)\n", - "\n", - "llprint(\"init variables... \\n\")\n", - "sess.run(tf.global_variables_initializer())\n", - "llprint(\"ready to train...\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "model overview...\n", - "\tvariable \"basic_lstm_cell/weights:0\" has 20480 parameters\n", - "\tvariable \"basic_lstm_cell/biases:0\" has 256 parameters\n", - "\tvariable \"W_z:0\" has 3072 parameters\n", - "\tvariable \"W_v:0\" has 384 parameters\n", - "\tvariable \"W_r:0\" has 60 parameters\n", - "\tvariable \"while_loop/basic_lstm_cell/weights:0\" has 20480 parameters\n", - "\tvariable \"while_loop/basic_lstm_cell/biases:0\" has 256 parameters\n", - "total of 44988 parameters\n" - ] - } - ], - "source": [ - "# tf parameter overview\n", - "total_parameters = 0 ; print \"model overview...\"\n", - "for variable in tf.trainable_variables():\n", - " shape = variable.get_shape()\n", - " variable_parameters = 1\n", - " for dim in shape:\n", - " variable_parameters *= dim.value\n", - " print '\\tvariable \"{}\" has {} parameters' \\\n", - " .format(variable.name, variable_parameters)\n", - " total_parameters += variable_parameters\n", - "print \"total of {} parameters\".format(total_parameters)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loaded model: rnn_models/model.ckpt-7000\n" - ] - } - ], - "source": [ - "global_step = 0\n", - "saver = tf.train.Saver(tf.global_variables())\n", - "load_was_success = True # yes, I'm being optimistic\n", - "try:\n", - " save_dir = '/'.join(FLAGS.save_path.split('/')[:-1])\n", - " ckpt = tf.train.get_checkpoint_state(save_dir)\n", - " load_path = ckpt.model_checkpoint_path\n", - " saver.restore(sess, load_path)\n", - "except:\n", - " print \"no saved model to load.\"\n", - " load_was_success = False\n", - "else:\n", - " print \"loaded model: {}\".format(load_path)\n", - " saver = tf.train.Saver(tf.global_variables())\n", - " global_step = int(load_path.split('-')[-1]) + 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Train loop" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iteration 7100/10000\n", - "\tloss: 0.0787\n", - "Iteration 7200/10000\n", - "\tloss: 0.0709\n", - "Iteration 7300/10000\n", - "\tloss: 0.0706\n", - "Iteration 7400/10000\n", - "\tloss: 0.0714\n", - "Iteration 7500/10000\n", - "\tloss: 0.0717\n", - "Iteration 7600/10000\n", - "\tloss: 0.0647\n", - "Iteration 7700/10000\n", - "\tloss: 0.0683\n", - "Iteration 7800/10000\n", - "\tloss: 0.0782\n", - "Iteration 7900/10000\n", - "\tloss: 0.0673\n", - "Iteration 8000/10000\n", - "\tloss: 0.0617\n", - "\n", - "\tSAVING MODEL\n", - "Iteration 8100/10000\n", - "\tloss: 0.0674\n", - "Iteration 8200/10000\n", - "\tloss: 0.0643\n", - "Iteration 8300/10000\n", - "\tloss: 0.0597\n", - "Iteration 8400/10000\n", - "\tloss: 0.0608\n", - "Iteration 8500/10000\n", - "\tloss: 0.0623\n", - "Iteration 8600/10000\n", - "\tloss: 0.0595\n", - "Iteration 8700/10000\n", - "\tloss: 0.0595\n", - "Iteration 8800/10000\n", - "\tloss: 0.0600\n", - "Iteration 8900/10000\n", - "\tloss: 0.0601\n", - "Iteration 9000/10000\n", - "\tloss: 0.0540\n", - "\n", - "\tSAVING MODEL\n", - "Iteration 9100/10000\n", - "\tloss: 0.0488\n", - "Iteration 9200/10000\n", - "\tloss: 0.0554\n", - "Iteration 9300/10000\n", - "\tloss: 0.0595\n", - "Iteration 9400/10000\n", - "\tloss: 0.0482\n", - "Iteration 9500/10000\n", - "\tloss: 0.0554\n", - "Iteration 9600/10000\n", - "\tloss: 0.0491\n", - "Iteration 9700/10000\n", - "\tloss: 0.0504\n", - "Iteration 9800/10000\n", - "\tloss: 0.0575\n", - "Iteration 9900/10000\n", - "\tloss: 0.0460\n", - "Iteration 10000/10000\n", - "\tloss: 0.0419\n", - "\n", - "\tSAVING MODEL\n" - ] - } - ], - "source": [ - "loss_history = []\n", - "for i in xrange(global_step, FLAGS.iterations + 1):\n", - " llprint(\"\\rIteration {}/{}\".format(i, FLAGS.iterations))\n", - "\n", - " rlen = np.random.randint(1, FLAGS.length + 1)\n", - " X, y = next_batch(FLAGS.batch_size, rlen, FLAGS.xlen)\n", - " tsteps = 2*rlen+3\n", - "\n", - " fetch = [loss, grad_op]\n", - " feed = {dnc.X: X, dnc.y: y, dnc.tsteps: tsteps}\n", - "\n", - " step_loss, _ = sess.run(fetch, feed_dict=feed)\n", - " loss_history.append(step_loss)\n", - " global_step = i\n", - "\n", - " if i % 100 == 0:\n", - " llprint(\"\\n\\tloss: {:03.4f}\\n\".format(np.mean(loss_history)))\n", - " loss_history = []\n", - " if i % FLAGS.save_every == 0 and i is not 0:\n", - " llprint(\"\\n\\tSAVING MODEL\\n\")\n", - " saver.save(sess, FLAGS.save_path, global_step=global_step)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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iIpKfqsfxLqBfwEVERFqT6ngREZEaiO7VvJi7f6AWiYiIiMjAojpeRESkNizm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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "X, y = next_batch(FLAGS.batch_size, FLAGS.length, FLAGS.xlen)\n", - "tsteps = 2*FLAGS.length+3\n", - "\n", - "feed = {dnc.X: X, dnc.y: y, dnc.tsteps: tsteps}\n", - "fetch = [outputs['y_hat'], outputs['w_w'], outputs['w_r'], outputs['f'], outputs['g_a']]\n", - "[_y_hat, _w_w, _w_r, _f, _g_a] = sess.run(fetch, feed)\n", - "_y = y[0] ; _X = X[0]\n", - "\n", - "fig, ((ax1,ax2),(ax3,ax5),(ax4,ax6),) = plt.subplots(nrows=3, ncols=2)\n", - "plt.rcParams['savefig.facecolor'] = \"0.8\"\n", - "fs = 12 # font size\n", - "fig.set_figwidth(10)\n", - "fig.set_figheight(5)\n", - "\n", - "ax1.imshow(_X.T - _y.T, interpolation='none') ; ax1.set_title('input ($X$) and target ($y$)')\n", - "ax2.imshow(_y_hat[0,:,:].T, interpolation='none') ; ax2.set_title('prediction ($\\hat y$)')\n", - "\n", - "ax3.imshow(_w_w[0,:,:].T, interpolation='none') ; ax3.set_title('write weighting ($w_w$)')\n", - "ax4.imshow(_w_r[0,:,:,0].T, interpolation='none') ; ax4.set_title('read weighting ($w_r$)')\n", - "\n", - "ax5.imshow(_f[0,:,:].T, interpolation='none') ; ax5.set_title('free gate ($f$)') ; ax5.set_aspect(3)\n", - "ax6.imshow(_g_a[0,:,:].T, interpolation='none') ; ax6.set_title('allocation gate ($g_a$)') ; ax6.set_aspect(3)\n", - "\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "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": 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Inspired by this [Nature paper](https://goo.gl/6eiJFc). Some ideas taken from [this Gihub repo](https://github.com/Mostafa-Samir/DNC-tensorflow)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Brain analogy" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " However, there are interesting parallels between the memory mechanisms of a DNC and the functional capabilities of the mammalian hippocampus. DNC memory modification is fast and can be one-shot, resembling the associative long-term potentiation of hippocampal CA3 and CA1 synapses. The hippocampal dentate gyrus, a region known to support neurogenesis, has been proposed to increase representational sparsity, thereby enhancing memory capacity: usage- based memory allocation and sparse weightings may provide similar facilities in our model. Human 'free recall' experiments demonstrate the increased probability of item recall in the same order as first presented—a hippocampus-dependent phenomenon accounted for by the temporal context model, bearing some similarity to the formation of temporal links." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import tensorflow as tf\n", - "import numpy as np\n", - "import sys\n", - "sys.path.insert(0, '../dnc')\n", - "\n", - "from dnc import DNC\n", - "from rnn_controller import RNNController\n", - "\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hyperparameters" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "xydim = 6\n", - "tf.app.flags.DEFINE_integer(\"xlen\", xydim, \"Input dimension\")\n", - "tf.app.flags.DEFINE_integer(\"ylen\", xydim, \"output dimension\")\n", - "tf.app.flags.DEFINE_integer(\"length\", 5, \"Sequence length\")\n", - "tf.app.flags.DEFINE_integer(\"batch_size\", 1, \"Size of batch in minibatch gradient descent\")\n", - "\n", - "tf.app.flags.DEFINE_integer(\"R\", 1, \"Number of DNC read heads\")\n", - "tf.app.flags.DEFINE_integer(\"W\", 10, \"Word length for DNC memory\")\n", - "tf.app.flags.DEFINE_integer(\"N\", 7, \"Number of words the DNC memory can store\")\n", - "\n", - "tf.app.flags.DEFINE_integer(\"print_every\", 100, \"Print training info after this number of train steps\")\n", - "tf.app.flags.DEFINE_integer(\"iterations\", 10000, \"Number of training iterations\")\n", - "tf.app.flags.DEFINE_float(\"lr\", 1e-4, \"Learning rate (alpha) for the model\")\n", - "tf.app.flags.DEFINE_float(\"momentum\", .9, \"RMSProp momentum\")\n", - "tf.app.flags.DEFINE_integer(\"save_every\", 1000, \"Save model after this number of train steps\")\n", - "tf.app.flags.DEFINE_string(\"save_path\", \"rnn_models/model.ckpt\", \"Where to save checkpoints\")\n", - "FLAGS = tf.app.flags.FLAGS" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Data functions" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def get_sequence(length, dim):\n", - " X = np.concatenate((np.random.randint(2, size=(length,dim)), np.zeros((length + 3,dim))))\n", - " X = np.vstack(X) ; X[:,dim-1] = 0\n", - " \n", - " X = np.concatenate((X[-1:,:],X[:-1,:]))\n", - " y = np.concatenate((X[-(length + 2):,:],X[:-(length + 2),:]))\n", - " markers = range(length+1, X.shape[0], 2*length+3)\n", - " X[markers,dim-1] = 1\n", - " return X, y\n", - " \n", - "def next_batch(batch_size, length, dim):\n", - " X_batch = []\n", - " y_batch = []\n", - " for _ in range(batch_size):\n", - " X, y = get_sequence(length, dim)\n", - " X_batch.append(X) ; y_batch.append(y)\n", - " return [X_batch, y_batch]\n", - "\n", - "batch = next_batch(1, FLAGS.length, FLAGS.xlen)\n", - "plt.imshow(batch[0][0].T - batch[1][0].T, interpolation='none')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Helper functions" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def binary_cross_entropy(y_hat, y):\n", - " return tf.reduce_mean(-y*tf.log(y_hat) - (1-y)*tf.log(1-y_hat))\n", - "\n", - "def llprint(message):\n", - " sys.stdout.write(message)\n", - " sys.stdout.flush()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(13, 6)\n" - ] - } - ], - "source": [ - "print y_shuffle[0].shape" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "X, y = next_batch(FLAGS.batch_size, FLAGS.length, FLAGS.xlen)\n", - "\n", - "y = y[0][FLAGS.length + 3:,:]\n", - "y_shuffle = y[np.random.permutation(FLAGS.length),:]\n", - "\n", - "plt.figure(0, figsize=[2,2])\n", - "plt.imshow(y.T, interpolation='none')\n", - "plt.figure(1, figsize=[2,2])\n", - "plt.imshow(y_shuffle.T, interpolation='none')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "np.tile(y_shuffle" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "real_y = tf.placeholder(tf.float32, [batch_size, None, ylen], name='real_y')\n", - "pred_y = tf.placeholder(tf.float32, [batch_size, None, ylen], name='pred_y')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Build graph, initialize everything" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "building graph...\n", - "defining loss...\n", - "computing gradients...\n", - "init variables... \n", - "ready to train..." - ] - } - ], - "source": [ - "sess = tf.InteractiveSession()\n", - "\n", - "llprint(\"building graph...\\n\")\n", - "optimizer = tf.train.RMSPropOptimizer(FLAGS.lr, momentum=FLAGS.momentum)\n", - "dnc = DNC(RNNController, FLAGS)\n", - "\n", - "llprint(\"defining loss...\\n\")\n", - "y_hat, outputs = dnc.get_outputs()\n", - "y_hat = tf.clip_by_value(tf.sigmoid(y_hat), 1e-6, 1. - 1e-6)\n", - "loss = binary_cross_entropy(y_hat, dnc.y)\n", - "\n", - "llprint(\"computing gradients...\\n\")\n", - "gradients = optimizer.compute_gradients(loss)\n", - "for i, (grad, var) in enumerate(gradients):\n", - " if grad is not None:\n", - " gradients[i] = (tf.clip_by_value(grad, -10, 10), var)\n", - " \n", - "grad_op = optimizer.apply_gradients(gradients)\n", - "\n", - "llprint(\"init variables... \\n\")\n", - "sess.run(tf.global_variables_initializer())\n", - "llprint(\"ready to train...\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "model overview...\n", - "\tvariable \"basic_lstm_cell/weights:0\" has 20480 parameters\n", - "\tvariable \"basic_lstm_cell/biases:0\" has 256 parameters\n", - "\tvariable \"W_z:0\" has 3072 parameters\n", - "\tvariable \"W_v:0\" has 384 parameters\n", - "\tvariable \"W_r:0\" has 60 parameters\n", - "\tvariable \"while_loop/basic_lstm_cell/weights:0\" has 20480 parameters\n", - "\tvariable \"while_loop/basic_lstm_cell/biases:0\" has 256 parameters\n", - "total of 44988 parameters\n" - ] - } - ], - "source": [ - "# tf parameter overview\n", - "total_parameters = 0 ; print \"model overview...\"\n", - "for variable in tf.trainable_variables():\n", - " shape = variable.get_shape()\n", - " variable_parameters = 1\n", - " for dim in shape:\n", - " variable_parameters *= dim.value\n", - " print '\\tvariable \"{}\" has {} parameters' \\\n", - " .format(variable.name, variable_parameters)\n", - " total_parameters += variable_parameters\n", - "print \"total of {} parameters\".format(total_parameters)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loaded model: rnn_models/model.ckpt-7000\n" - ] - } - ], - "source": [ - "global_step = 0\n", - "saver = tf.train.Saver(tf.global_variables())\n", - "load_was_success = True # yes, I'm being optimistic\n", - "try:\n", - " save_dir = '/'.join(FLAGS.save_path.split('/')[:-1])\n", - " ckpt = tf.train.get_checkpoint_state(save_dir)\n", - " load_path = ckpt.model_checkpoint_path\n", - " saver.restore(sess, load_path)\n", - "except:\n", - " print \"no saved model to load.\"\n", - " load_was_success = False\n", - "else:\n", - " print \"loaded model: {}\".format(load_path)\n", - " saver = tf.train.Saver(tf.global_variables())\n", - " global_step = int(load_path.split('-')[-1]) + 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Train loop" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iteration 7100/10000\n", - "\tloss: 0.0787\n", - "Iteration 7200/10000\n", - "\tloss: 0.0709\n", - "Iteration 7300/10000\n", - "\tloss: 0.0706\n", - "Iteration 7400/10000\n", - "\tloss: 0.0714\n", - "Iteration 7500/10000\n", - "\tloss: 0.0717\n", - "Iteration 7600/10000\n", - "\tloss: 0.0647\n", - "Iteration 7700/10000\n", - "\tloss: 0.0683\n", - "Iteration 7800/10000\n", - "\tloss: 0.0782\n", - "Iteration 7900/10000\n", - "\tloss: 0.0673\n", - "Iteration 8000/10000\n", - "\tloss: 0.0617\n", - "\n", - "\tSAVING MODEL\n", - "Iteration 8100/10000\n", - "\tloss: 0.0674\n", - "Iteration 8200/10000\n", - "\tloss: 0.0643\n", - "Iteration 8300/10000\n", - "\tloss: 0.0597\n", - "Iteration 8400/10000\n", - "\tloss: 0.0608\n", - "Iteration 8500/10000\n", - "\tloss: 0.0623\n", - "Iteration 8600/10000\n", - "\tloss: 0.0595\n", - "Iteration 8700/10000\n", - "\tloss: 0.0595\n", - "Iteration 8800/10000\n", - "\tloss: 0.0600\n", - "Iteration 8900/10000\n", - "\tloss: 0.0601\n", - "Iteration 9000/10000\n", - "\tloss: 0.0540\n", - "\n", - "\tSAVING MODEL\n", - "Iteration 9100/10000\n", - "\tloss: 0.0488\n", - "Iteration 9200/10000\n", - "\tloss: 0.0554\n", - "Iteration 9300/10000\n", - "\tloss: 0.0595\n", - "Iteration 9400/10000\n", - "\tloss: 0.0482\n", - "Iteration 9500/10000\n", - "\tloss: 0.0554\n", - "Iteration 9600/10000\n", - "\tloss: 0.0491\n", - "Iteration 9700/10000\n", - "\tloss: 0.0504\n", - "Iteration 9800/10000\n", - "\tloss: 0.0575\n", - "Iteration 9900/10000\n", - "\tloss: 0.0460\n", - "Iteration 10000/10000\n", - "\tloss: 0.0419\n", - "\n", - "\tSAVING MODEL\n" - ] - } - ], - "source": [ - "loss_history = []\n", - "for i in xrange(global_step, FLAGS.iterations + 1):\n", - " llprint(\"\\rIteration {}/{}\".format(i, FLAGS.iterations))\n", - "\n", - " rlen = np.random.randint(1, FLAGS.length + 1)\n", - " X, y = next_batch(FLAGS.batch_size, rlen, FLAGS.xlen)\n", - " tsteps = 2*rlen+3\n", - "\n", - " fetch = [loss, grad_op]\n", - " feed = {dnc.X: X, dnc.y: y, dnc.tsteps: tsteps}\n", - "\n", - " step_loss, _ = sess.run(fetch, feed_dict=feed)\n", - " loss_history.append(step_loss)\n", - " global_step = i\n", - "\n", - " if i % 100 == 0:\n", - " llprint(\"\\n\\tloss: {:03.4f}\\n\".format(np.mean(loss_history)))\n", - " loss_history = []\n", - " if i % FLAGS.save_every == 0 and i is not 0:\n", - " llprint(\"\\n\\tSAVING MODEL\\n\")\n", - " saver.save(sess, FLAGS.save_path, global_step=global_step)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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iIpKfqsfxLqBfwEVERFqT6ngREZEaiO7VvJi7f6AWiYiIiMjAojpeRESkNizm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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "X, y = next_batch(FLAGS.batch_size, FLAGS.length, FLAGS.xlen)\n", - "tsteps = 2*FLAGS.length+3\n", - "\n", - "feed = {dnc.X: X, dnc.y: y, dnc.tsteps: tsteps}\n", - "fetch = [outputs['y_hat'], outputs['w_w'], outputs['w_r'], outputs['f'], outputs['g_a']]\n", - "[_y_hat, _w_w, _w_r, _f, _g_a] = sess.run(fetch, feed)\n", - "_y = y[0] ; _X = X[0]\n", - "\n", - "fig, ((ax1,ax2),(ax3,ax5),(ax4,ax6),) = plt.subplots(nrows=3, ncols=2)\n", - "plt.rcParams['savefig.facecolor'] = \"0.8\"\n", - "fs = 12 # font size\n", - "fig.set_figwidth(10)\n", - "fig.set_figheight(5)\n", - "\n", - "ax1.imshow(_X.T - _y.T, interpolation='none') ; ax1.set_title('input ($X$) and target ($y$)')\n", - "ax2.imshow(_y_hat[0,:,:].T, interpolation='none') ; ax2.set_title('prediction ($\\hat y$)')\n", - "\n", - "ax3.imshow(_w_w[0,:,:].T, interpolation='none') ; ax3.set_title('write weighting ($w_w$)')\n", - "ax4.imshow(_w_r[0,:,:,0].T, interpolation='none') ; ax4.set_title('read weighting ($w_r$)')\n", - "\n", - "ax5.imshow(_f[0,:,:].T, interpolation='none') ; ax5.set_title('free gate ($f$)') ; ax5.set_aspect(3)\n", - "ax6.imshow(_g_a[0,:,:].T, interpolation='none') ; ax6.set_title('allocation gate ($g_a$)') ; ax6.set_aspect(3)\n", - "\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "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": "2.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -}