From c8068e8ace9df7e36df3a904d3340efb11c3af1d Mon Sep 17 00:00:00 2001 From: Sam Greydanus Date: Wed, 1 Mar 2017 12:49:41 -0500 Subject: [PATCH] free recall results look good --- .../free-recall-rnn-checkpoint.ipynb | 327 +++++++++++++----- free-recall/free-recall-rnn.ipynb | 303 ++++++++++++---- 2 files changed, 490 insertions(+), 140 deletions(-) diff --git a/free-recall/.ipynb_checkpoints/free-recall-rnn-checkpoint.ipynb b/free-recall/.ipynb_checkpoints/free-recall-rnn-checkpoint.ipynb index 83b4d01..f79f047 100644 --- a/free-recall/.ipynb_checkpoints/free-recall-rnn-checkpoint.ipynb +++ b/free-recall/.ipynb_checkpoints/free-recall-rnn-checkpoint.ipynb @@ -98,9 +98,9 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -179,7 +179,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 77, "metadata": { "collapsed": false }, @@ -188,29 +188,71 @@ "name": "stdout", "output_type": "stream", "text": [ - "6.07968e-06\n" + "permute by: [4 2 1 0 3]\n", + "guessed permutation: [4 2 1 0 3]\n" ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ + "def remove_duplicates(k, l):\n", + " i = 0 ; removed_duplicate = False\n", + " while i < len(k) and not removed_duplicate: \n", + " if k[-i] == k[-i-1]:\n", + " ri = np.random.randint(2)\n", + " k = np.delete(k,-i-ri) ; l = np.delete(l,-i-ri) ; removed_duplicate = True\n", + " k,l = remove_duplicates(k,l)\n", + " i+=1\n", + " return k,l\n", + "\n", + "def guess_recall_order(real_y, pred_y, tsteps):\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,tsteps,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_mins = np.amin(y_minus, axis=1)\n", + " \n", + " k, l = np.where(y_minus == np.tile(y_mins,[tsteps,1]).T)\n", + " k, l = remove_duplicates(k, l)\n", + " return l\n", + "\n", + "# test\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", + "p = np.random.permutation(y_i.shape[0]) ; print 'permute by: ', p\n", + "pred_y = [y_i[p,:] 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", + "plt.figure(0, figsize=[1,1]) ; plt.imshow(real_y[0,:,:].T, interpolation='none')\n", + "plt.figure(1, figsize=[1,1]) ; plt.imshow(pred_y[0,:,:].T, interpolation='none')\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)" + "print \"guessed permutation: \", guess_recall_order(real_y, pred_y, FLAGS.length)" ] }, { @@ -302,7 +344,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "collapsed": false }, @@ -311,7 +353,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "loaded model: rnn_models/model.ckpt-2000\n" + "loaded model: rnn_models/model.ckpt-30000\n" ] } ], @@ -342,31 +384,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "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" - ] - } - ], + "outputs": [], "source": [ "loss_history = []\n", "for i in xrange(global_step, FLAGS.iterations + 1):\n", @@ -393,11 +415,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 130, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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jdgjUbItExCYRcRiwADi/jyzGoS0ija1NGyx7W2AexVX7VtdTDEEaufLK03uA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\naXLuPqhpgIBrmxS7EPhdw7rfZuuH6rSZmZlZE57/xsys5iL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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", @@ -428,21 +461,135 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 123, "metadata": { - "collapsed": true + "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting free recall trials: \n", + "\ttrial #0\n", + "\ttrial #100\n", + "\ttrial #200\n", + "\ttrial #300\n", + "\ttrial #400\n", + "\ttrial #500\n", + "\ttrial #600\n", + "\ttrial #700\n", + "\ttrial #800\n", + "\ttrial #900\n" + ] + } + ], + "source": [ + "recall_orders = []\n", + "trials = 1000 ; print \"starting free recall trials: \"\n", + "for i in range(trials):\n", + " X, y = next_batch(FLAGS.batch_size, FLAGS.length, FLAGS.xlen)\n", + " tsteps = 2*FLAGS.length+3\n", + "\n", + " feed = {dnc.X: X, dnc.tsteps: tsteps}\n", + " _y_hat = outputs['y_hat'].eval(feed)\n", + " _y_hat = np.clip(_y_hat, 1e-6, 1-1e-6)\n", + " _y = y[0] ; _X = X[0]\n", + " \n", + " real_y = np.tile(_y[-FLAGS.length:,:], [1,1,1])\n", + " pred_y = _y_hat[0:1,-FLAGS.length:,:]\n", + " order = guess_recall_order(real_y, pred_y, FLAGS.length)\n", + " \n", + " recall_orders.append(order)\n", + " if (i%100 == 0): print(\"\\ttrial #{}\".format(i))\n", + "recall_orders = np.stack(recall_orders)" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 131, "metadata": { - "collapsed": true + "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "image/png": 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Q0phaqoYKURyZTuW/lEoIfLbW0ESBTMBnTLXDRx1IGgSsa2YnpU5I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\nEAQFEYojCIIgKIhQHEEQBEFB/D9Drt8lJx2O5gAAAABJRU5ErkJggg==\n", 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cA8wDzgduSOpmAVfiu8qDIG++/dYDFb70kke47d690hIFQVAXBSmOZNF6XeAu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\nQrmKpVzjVihlHzNJAm4DXja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R0ivLoosu2pByQciWhUaVC0K2rDSybFR4qT/Thr6ZzTazG8xsJ2BpfCp1GvCapJuS2Fbl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\n1YlytpldbWbHmNmhZnapmX3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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_recall(probs, pos):\n", + " # print \"Is normalized: \", np.abs(1-np.sum(position_probs)) < 1e-6\n", + " plt.figure(pos, figsize=[4,1.5])\n", + "# plt.axis((0,4,0.15,.3))\n", + " plt.title(\"DNC free recall (position={})\".format(pos))\n", + " plt.xlabel(\"Serial position\") ; plt.ylabel(\"Recall probability\")\n", + " plt.plot(range(len(probs)), probs) ; plt.show()\n", + "\n", + "for pos in range(FLAGS.length):\n", + " position_probs = [np.sum(i == recall_orders[:,pos])/float(trials) for i in range(FLAGS.length)]\n", + " plot_recall(position_probs, pos=pos)\n", + " \n", + "p0_probs = np.asarray([np.sum(i == recall_orders[:,0])/float(trials) for i in range(FLAGS.length)])\n", + "p1_probs = np.asarray([np.sum(i == recall_orders[:,1])/float(trials) for i in range(FLAGS.length)])\n", + "plot_recall(.5*(p0_probs + p1_probs), pos=12)" + ] }, { "cell_type": "code", @@ -452,42 +599,78 @@ }, "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", + "# # X, y = next_batch(FLAGS.batch_size, FLAGS.length, FLAGS.xlen)\n", + "# # y = np.stack(y)\n", + "# # y = y[:,FLAGS.length + 3:,:]\n", + "# # # p = np.random.permutation(FLAGS.length)\n", + "# # y_shuffle = y #y[:,p,:] ; print 'permute by: ', p\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", + "# # plt.figure(0, figsize=[1,1])\n", + "# # plt.imshow(y[0,:,:].T, interpolation='none')\n", + "# # plt.figure(1, figsize=[1,1])\n", + "# # plt.imshow(y_shuffle[0,:,:].T, interpolation='none')\n", + "# # plt.show()\n", "\n", - "# def np_loss(real_y, pred_y, FLAGS):\n", + "# def guess_recall_order(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.tile(pred_y,[1,FLAGS.length,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", + "# # 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", + "# y_mins = np.amin(y_minus, axis=1)\n", " \n", - "# return np.sum(y_minus) / (FLAGS.batch_size*FLAGS.length)\n", + "# k, l = np.where(y_minus == y_mins)\n", + "# i = 0\n", + "# while i < len(k): \n", + "# if k[-i] == k[-i-1]:\n", + "# k = np.delete(k,-i) ; l = np.delete(l,-i)\n", + "# i+=1\n", + "# return l\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", + "# p = np.random.permutation(y_i.shape[0]) ; print 'permute by: ', p\n", + "# pred_y = [y_i[p,:] for y_i in real_y]\n", "# pred_y = np.stack(pred_y)\n", "# # pred_y[:,-2:,-1] = 4\n", "\n", + "# plt.figure(0, figsize=[1,1])\n", + "# plt.imshow(real_y[0,:,:].T, interpolation='none')\n", + "# plt.figure(1, figsize=[1,1])\n", + "# plt.imshow(pred_y[0,:,:].T, interpolation='none')\n", + "# plt.show()\n", + "\n", "# np_loss(real_y, pred_y, FLAGS)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def scatter(idx, vals, target):\n", + " \"\"\"target[idx] += vals, but allowing for repeats in idx\"\"\"\n", + " np.add.at(target, idx.ravel(), vals.ravel())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] } ], "metadata": { @@ -495,18 +678,6 @@ "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, diff --git a/free-recall/free-recall-rnn.ipynb b/free-recall/free-recall-rnn.ipynb index 41fb80a..f79f047 100644 --- a/free-recall/free-recall-rnn.ipynb +++ b/free-recall/free-recall-rnn.ipynb @@ -98,9 +98,9 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -179,7 +179,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 77, "metadata": { "collapsed": false }, @@ -188,30 +188,71 @@ "name": "stdout", "output_type": "stream", "text": [ - "6.07968e-06\n" + "permute by: [4 2 1 0 3]\n", + "guessed permutation: [4 2 1 0 3]\n" ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "# test free recall loss\n", + "def remove_duplicates(k, l):\n", + " i = 0 ; removed_duplicate = False\n", + " while i < len(k) and not removed_duplicate: \n", + " if k[-i] == k[-i-1]:\n", + " ri = np.random.randint(2)\n", + " k = np.delete(k,-i-ri) ; l = np.delete(l,-i-ri) ; removed_duplicate = True\n", + " k,l = remove_duplicates(k,l)\n", + " i+=1\n", + " return k,l\n", + "\n", + "def guess_recall_order(real_y, pred_y, tsteps):\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,tsteps,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_mins = np.amin(y_minus, axis=1)\n", + " \n", + " k, l = np.where(y_minus == np.tile(y_mins,[tsteps,1]).T)\n", + " k, l = remove_duplicates(k, l)\n", + " return l\n", + "\n", + "# test\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", + "p = np.random.permutation(y_i.shape[0]) ; print 'permute by: ', p\n", + "pred_y = [y_i[p,:] 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", + "plt.figure(0, figsize=[1,1]) ; plt.imshow(real_y[0,:,:].T, interpolation='none')\n", + "plt.figure(1, figsize=[1,1]) ; plt.imshow(pred_y[0,:,:].T, interpolation='none')\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)" + "print \"guessed permutation: \", guess_recall_order(real_y, pred_y, FLAGS.length)" ] }, { @@ -343,7 +384,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 10, "metadata": { "collapsed": false }, @@ -374,16 +415,16 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 130, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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vD8yluGrf6EaKIUgjV155+gBwYWZeWUP5h1IMx9pn1GU3eSTFVaf3Af9KMUTt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\n", 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jdgjUbItExCYRcRiwADi/jyzGoS0ija1NGyx7W2AexVX7VtdTDEEaufLK03uA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\naXLuPqhpgIBrmxS7EPhdw7rfZuuH6rSZmZlZE57/xsys5iL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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -420,66 +461,216 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 123, "metadata": { - "collapsed": true + "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting free recall trials: \n", + "\ttrial #0\n", + "\ttrial #100\n", + "\ttrial #200\n", + "\ttrial #300\n", + "\ttrial #400\n", + "\ttrial #500\n", + "\ttrial #600\n", + "\ttrial #700\n", + "\ttrial #800\n", + "\ttrial #900\n" + ] + } + ], + "source": [ + "recall_orders = []\n", + "trials = 1000 ; print \"starting free recall trials: \"\n", + "for i in range(trials):\n", + " X, y = next_batch(FLAGS.batch_size, FLAGS.length, FLAGS.xlen)\n", + " tsteps = 2*FLAGS.length+3\n", + "\n", + " feed = {dnc.X: X, dnc.tsteps: tsteps}\n", + " _y_hat = outputs['y_hat'].eval(feed)\n", + " _y_hat = np.clip(_y_hat, 1e-6, 1-1e-6)\n", + " _y = y[0] ; _X = X[0]\n", + " \n", + " real_y = np.tile(_y[-FLAGS.length:,:], [1,1,1])\n", + " pred_y = _y_hat[0:1,-FLAGS.length:,:]\n", + " order = guess_recall_order(real_y, pred_y, FLAGS.length)\n", + " \n", + " recall_orders.append(order)\n", + " if (i%100 == 0): print(\"\\ttrial #{}\".format(i))\n", + "recall_orders = np.stack(recall_orders)" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Q0phaqoYKURyZTuW/lEoIfLbW0ESBTMBnTLXDRx1IGgSsa2YnpU5I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\nEAQFEYojCIIgKIhQHEEQBEFB/D9Drt8lJx2O5gAAAABJRU5ErkJggg==\n", 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cA8wDzgduSOpmAVfiu8qDIG++/dYDFb70kke47d690hIFQVAXBSmOZNF6XeAu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\nQrmKpVzjVihlHzNJAm4DXja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R0ivLoosu2pByQciWhUaVC0K2rDSybFR4qT/Thr6ZzTazG8xsJ2BpfCp1GvCapJuS2Fbl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\n1YlytpldbWbHmNmhZnapmX3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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_recall(probs, pos):\n", + " # print \"Is normalized: \", np.abs(1-np.sum(position_probs)) < 1e-6\n", + " plt.figure(pos, figsize=[4,1.5])\n", + "# plt.axis((0,4,0.15,.3))\n", + " plt.title(\"DNC free recall (position={})\".format(pos))\n", + " plt.xlabel(\"Serial position\") ; plt.ylabel(\"Recall probability\")\n", + " plt.plot(range(len(probs)), probs) ; plt.show()\n", + "\n", + "for pos in range(FLAGS.length):\n", + " position_probs = [np.sum(i == recall_orders[:,pos])/float(trials) for i in range(FLAGS.length)]\n", + " plot_recall(position_probs, pos=pos)\n", + " \n", + "p0_probs = np.asarray([np.sum(i == recall_orders[:,0])/float(trials) for i in range(FLAGS.length)])\n", + "p1_probs = np.asarray([np.sum(i == recall_orders[:,1])/float(trials) for i in range(FLAGS.length)])\n", + "plot_recall(.5*(p0_probs + p1_probs), pos=12)" + ] }, { "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", + "# # X, y = next_batch(FLAGS.batch_size, FLAGS.length, FLAGS.xlen)\n", + "# # y = np.stack(y)\n", + "# # y = y[:,FLAGS.length + 3:,:]\n", + "# # # p = np.random.permutation(FLAGS.length)\n", + "# # y_shuffle = y #y[:,p,:] ; print 'permute by: ', p\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", + "# # plt.figure(0, figsize=[1,1])\n", + "# # plt.imshow(y[0,:,:].T, interpolation='none')\n", + "# # plt.figure(1, figsize=[1,1])\n", + "# # plt.imshow(y_shuffle[0,:,:].T, interpolation='none')\n", + "# # plt.show()\n", "\n", - "# def np_loss(real_y, pred_y, FLAGS):\n", + "# def guess_recall_order(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.tile(pred_y,[1,FLAGS.length,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", + "# # 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", + "# y_mins = np.amin(y_minus, axis=1)\n", " \n", - "# return np.sum(y_minus) / (FLAGS.batch_size*FLAGS.length)\n", + "# k, l = np.where(y_minus == y_mins)\n", + "# i = 0\n", + "# while i < len(k): \n", + "# if k[-i] == k[-i-1]:\n", + "# k = np.delete(k,-i) ; l = np.delete(l,-i)\n", + "# i+=1\n", + "# return l\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", + "# p = np.random.permutation(y_i.shape[0]) ; print 'permute by: ', p\n", + "# pred_y = [y_i[p,:] for y_i in real_y]\n", "# pred_y = np.stack(pred_y)\n", "# # pred_y[:,-2:,-1] = 4\n", "\n", + "# plt.figure(0, figsize=[1,1])\n", + "# plt.imshow(real_y[0,:,:].T, interpolation='none')\n", + "# plt.figure(1, figsize=[1,1])\n", + "# plt.imshow(pred_y[0,:,:].T, interpolation='none')\n", + "# plt.show()\n", + "\n", "# np_loss(real_y, pred_y, FLAGS)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def scatter(idx, vals, target):\n", + " \"\"\"target[idx] += vals, but allowing for repeats in idx\"\"\"\n", + " np.add.at(target, idx.ravel(), vals.ravel())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] } ], "metadata": { @@ -487,18 +678,6 @@ "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,