From 9938d0686b56c6d74a2fcc8159f48c3c026e24cc Mon Sep 17 00:00:00 2001 From: Aurelien Geron Date: Wed, 7 Jun 2017 17:52:59 +0200 Subject: [PATCH] Use np.random.set_seed(42) and tf.set_random_seed(42) to make notebook's output constant, and simplify code in notebook 15 --- 12_distributed_tensorflow.ipynb | 34 +- 14_recurrent_neural_networks.ipynb | 1020 ++++++++-------- 15_autoencoders.ipynb | 1818 +++++++++++++++++----------- 3 files changed, 1660 insertions(+), 1212 deletions(-) diff --git a/12_distributed_tensorflow.ipynb b/12_distributed_tensorflow.ipynb index 354cac3ea..2dd3c998f 100644 --- a/12_distributed_tensorflow.ipynb +++ b/12_distributed_tensorflow.ipynb @@ -55,11 +55,13 @@ "\n", "# Common imports\n", "import numpy as np\n", - "import numpy.random as rnd\n", "import os\n", "\n", "# to make this notebook's output stable across runs\n", - "rnd.seed(42)\n", + "def reset_graph(seed=42):\n", + " tf.reset_default_graph()\n", + " tf.set_random_seed(seed)\n", + " np.random.seed(seed)\n", "\n", "# To plot pretty figures\n", "%matplotlib inline\n", @@ -209,7 +211,7 @@ }, "outputs": [], "source": [ - "tf.reset_default_graph()\n", + "reset_graph()\n", "\n", "with tf.device(\"/job:ps\"):\n", " a = tf.Variable(1.0, name=\"a\")\n", @@ -254,7 +256,7 @@ }, "outputs": [], "source": [ - "tf.reset_default_graph()\n", + "reset_graph()\n", "\n", "with tf.device(tf.train.replica_device_setter(\n", " ps_tasks=2,\n", @@ -300,15 +302,15 @@ "output_type": "stream", "text": [ "No more files to read\n", - "[array([[ 4. , 5. ],\n", - " [ 1. , -508.17480469]], dtype=float32), array([1, 0], dtype=int32)]\n", + "[array([[ 4.00000000e+00, 5.00000000e+00],\n", + " [ 1.00000000e+00, 8.62997533e-19]], dtype=float32), array([1, 0], dtype=int32)]\n", "[array([[ 7., 8.]], dtype=float32), array([0], dtype=int32)]\n", "No more training instances\n" ] } ], "source": [ - "tf.reset_default_graph()\n", + "reset_graph()\n", "\n", "test_csv = open(\"my_test.csv\", \"w\")\n", "test_csv.write(\"x1, x2 , target\\n\")\n", @@ -393,15 +395,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "[array([[ 1. , -508.17480469],\n", - " [ 7. , 8. ]], dtype=float32), array([0, 0], dtype=int32)]\n", - "[array([[ 4., 5.]], dtype=float32), array([1], dtype=int32)]\n", + "[array([[ 7., 8.],\n", + " [ 4., 5.]], dtype=float32), array([0, 1], dtype=int32)]\n", + "[array([[ 1.00000000e+00, 8.62997533e-19]], dtype=float32), array([0], dtype=int32)]\n", "No more training instances\n" ] } ], "source": [ - "tf.reset_default_graph()\n", + "reset_graph()\n", "\n", "filename_queue = tf.FIFOQueue(capacity=10, dtypes=[tf.string], shapes=[()])\n", "filename = tf.placeholder(tf.string)\n", @@ -451,15 +453,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "[array([[ 1. , -508.17480469],\n", - " [ 4. , 5. ]], dtype=float32), array([0, 1], dtype=int32)]\n", + "[array([[ 4.00000000e+00, 5.00000000e+00],\n", + " [ 1.00000000e+00, 8.62997533e-19]], dtype=float32), array([1, 0], dtype=int32)]\n", "[array([[ 7., 8.]], dtype=float32), array([0], dtype=int32)]\n", "No more training instances\n" ] } ], "source": [ - "tf.reset_default_graph()\n", + "reset_graph()\n", "\n", "def read_and_push_instance(filename_queue, instance_queue):\n", " reader = tf.TextLineReader(skip_header_lines=1)\n", @@ -529,7 +531,7 @@ } ], "source": [ - "tf.reset_default_graph()\n", + "reset_graph()\n", "\n", "q = tf.FIFOQueue(capacity=10, dtypes=[tf.float32], shapes=[()])\n", "v = tf.placeholder(tf.float32)\n", @@ -577,7 +579,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": { "collapsed": true, "deletable": true, diff --git a/14_recurrent_neural_networks.ipynb b/14_recurrent_neural_networks.ipynb index c28bfbd49..bc16d31d4 100644 --- a/14_recurrent_neural_networks.ipynb +++ b/14_recurrent_neural_networks.ipynb @@ -55,11 +55,13 @@ "\n", "# Common imports\n", "import numpy as np\n", - "import numpy.random as rnd\n", "import os\n", "\n", "# to make this notebook's output stable across runs\n", - "rnd.seed(42)\n", + "def reset_graph(seed=42):\n", + " tf.reset_default_graph()\n", + " tf.set_random_seed(seed)\n", + " np.random.seed(seed)\n", "\n", "# To plot pretty figures\n", "%matplotlib inline\n", @@ -134,7 +136,7 @@ }, "outputs": [], "source": [ - "tf.reset_default_graph()\n", + "reset_graph()\n", "\n", "n_inputs = 3\n", "n_neurons = 5\n", @@ -185,10 +187,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[ 0.96223319 0.54390049 0.99944168 0.22135893 0.95317668]\n", - " [ 0.99987721 0.9999615 0.99999988 -0.12834749 1.00000012]\n", - " [ 0.99999964 1. 1. -0.4488056 1. ]\n", - " [-0.27392432 1. -0.99962711 0.78032929 1. ]]\n" + "[[-0.0664006 0.96257669 0.68105787 0.70918542 -0.89821595]\n", + " [ 0.9977755 -0.71978885 -0.99657625 0.9673925 -0.99989718]\n", + " [ 0.99999774 -0.99898815 -0.99999893 0.99677622 -0.99999988]\n", + " [ 1. -1. -1. -0.99818915 0.99950868]]\n" ] } ], @@ -209,10 +211,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[ 0.99999964 1. 1. -0.34297404 1. ]\n", - " [ 0.30002448 -0.90767932 0.26926002 0.95352989 0.05459169]\n", - " [ 0.99859399 0.99999899 0.99954653 0.86539894 1. ]\n", - " [ 0.99212319 0.99994123 0.97276199 -0.87521726 0.99968809]]\n" + "[[ 1. -1. -1. 0.40200216 -1. ]\n", + " [-0.12210433 0.62805319 0.96718419 -0.99371207 -0.25839335]\n", + " [ 0.99999827 -0.9999994 -0.9999975 -0.85943311 -0.9999879 ]\n", + " [ 0.99928284 -0.99999815 -0.99990582 0.98579615 -0.92205751]]\n" ] } ], @@ -227,7 +229,7 @@ "editable": true }, "source": [ - "## Using `rnn()`" + "## Using `static_rnn()`" ] }, { @@ -240,8 +242,6 @@ }, "outputs": [], "source": [ - "tf.reset_default_graph()\n", - "\n", "n_inputs = 3\n", "n_neurons = 5" ] @@ -256,6 +256,8 @@ }, "outputs": [], "source": [ + "reset_graph()\n", + "\n", "X0 = tf.placeholder(tf.float32, [None, n_inputs])\n", "X1 = tf.placeholder(tf.float32, [None, n_inputs])\n", "\n", @@ -308,10 +310,10 @@ { "data": { "text/plain": [ - "array([[-0.01483451, -0.49605229, 0.75308919, -0.78006792, -0.14246999],\n", - " [-0.41594887, -0.95152193, 0.99829108, -0.94347018, -0.99635023],\n", - " [-0.70174533, -0.99634278, 0.99998957, -0.98639774, -0.99999106],\n", - " [-0.99520677, 0.84450072, 0.989519 , 0.99703455, -0.99995816]], dtype=float32)" + "array([[-0.81393629, -0.43182844, -0.40150994, 0.7043609 , 0.89640522],\n", + " [-0.9915663 , -0.95103657, 0.19996507, 0.98335052, 0.99998963],\n", + " [-0.99965042, -0.99683058, 0.68092704, 0.99918783, 1. ],\n", + " [ 0.64988363, -0.16740513, 0.99994725, 0.81680971, 0.99995029]], dtype=float32)" ] }, "execution_count": 11, @@ -335,10 +337,10 @@ { "data": { "text/plain": [ - "array([[-0.95365602, -0.999026 , 0.99998683, -0.81465274, -1. ],\n", - " [-0.62058419, -0.08800258, -0.88539422, -0.46115425, 0.67685097],\n", - " [-0.91839862, -0.9652319 , 0.99197757, -0.70452976, -0.9999088 ],\n", - " [-0.51075095, -0.0602669 , 0.81636053, -0.57558066, -0.92382717]], dtype=float32)" + "array([[-0.99959785, -0.99861717, 0.98714638, 0.99745673, 1. ],\n", + " [-0.72472596, 0.17925572, 0.53362155, -0.65215266, -0.08035918],\n", + " [-0.9957462 , -0.96851194, 0.9874723 , 0.84106421, 0.99999976],\n", + " [-0.72859728, -0.27958852, 0.80567408, -0.20587993, 0.9995411 ]], dtype=float32)" ] }, "execution_count": 12, @@ -414,7 +416,7 @@ " \n", " " @@ -688,7 +730,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 27, "metadata": { "collapsed": true, "deletable": true, @@ -696,19 +738,19 @@ }, "outputs": [], "source": [ - "tf.reset_default_graph()\n", - "\n", "n_steps = 2\n", "n_inputs = 3\n", "n_neurons = 5\n", "\n", + "reset_graph()\n", + "\n", "X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\n", "basic_cell = tf.contrib.rnn.BasicRNNCell(num_units=n_neurons)" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 28, "metadata": { "collapsed": true, "deletable": true, @@ -723,7 +765,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 29, "metadata": { "collapsed": true, "deletable": true, @@ -736,7 +778,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 30, "metadata": { "collapsed": false, "deletable": true, @@ -756,7 +798,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 31, "metadata": { "collapsed": true, "deletable": true, @@ -772,7 +814,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 32, "metadata": { "collapsed": false, "deletable": true, @@ -783,17 +825,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.53053296 -0.54991364 -0.87955254 0.86993307 0.55490023]\n", - " [ 0.65844423 -0.99999821 -0.98302919 0.99999547 0.95430201]]\n", + "[[[-0.68579948 -0.25901747 -0.80249101 -0.18141513 -0.37491536]\n", + " [-0.99996698 -0.94501185 0.98072106 -0.9689762 0.99966913]]\n", "\n", - " [[ 0.78509521 -0.9961158 -0.99012053 0.99925238 0.90460461]\n", + " [[-0.99099374 -0.64768541 -0.67801034 -0.7415446 0.7719509 ]\n", " [ 0. 0. 0. 0. 0. ]]\n", "\n", - " [[ 0.90976256 -0.99997383 -0.99923128 0.99999595 0.98262477]\n", - " [ 0.47883341 -0.99976891 -0.79789418 0.99825007 0.63609445]]\n", + " [[-0.99978048 -0.85583007 -0.49696958 -0.93838578 0.98505187]\n", + " [-0.99951065 -0.89148796 0.94170523 -0.38407657 0.97499216]]\n", "\n", - " [[-0.99599224 -0.9998247 0.99989974 0.9872781 -0.89491421]\n", - " [-0.22703208 -0.98906386 -0.79345828 0.83942837 0.89154387]]]\n" + " [[-0.02052618 -0.94588047 0.99935204 0.37283331 0.9998163 ]\n", + " [-0.91052347 0.05769409 0.47446665 -0.44611037 0.89394671]]]\n" ] } ], @@ -803,7 +845,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 33, "metadata": { "collapsed": false, "deletable": true, @@ -814,10 +856,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[ 0.65844423 -0.99999821 -0.98302919 0.99999547 0.95430201]\n", - " [ 0.78509521 -0.9961158 -0.99012053 0.99925238 0.90460461]\n", - " [ 0.47883341 -0.99976891 -0.79789418 0.99825007 0.63609445]\n", - " [-0.22703208 -0.98906386 -0.79345828 0.83942837 0.89154387]]\n" + "[[-0.99996698 -0.94501185 0.98072106 -0.9689762 0.99966913]\n", + " [-0.99099374 -0.64768541 -0.67801034 -0.7415446 0.7719509 ]\n", + " [-0.99951065 -0.89148796 0.94170523 -0.38407657 0.97499216]\n", + " [-0.91052347 0.05769409 0.47446665 -0.44611037 0.89394671]]\n" ] } ], @@ -849,7 +891,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 34, "metadata": { "collapsed": false, "deletable": true, @@ -857,7 +899,7 @@ }, "outputs": [], "source": [ - "tf.reset_default_graph()\n", + "reset_graph()\n", "\n", "n_steps = 28\n", "n_inputs = 28\n", @@ -886,7 +928,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 35, "metadata": { "collapsed": false, "deletable": true, @@ -913,7 +955,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 36, "metadata": { "collapsed": false, "deletable": true, @@ -924,106 +966,106 @@ "name": "stdout", "output_type": "stream", "text": [ - "0 Train accuracy: 0.966667 Test accuracy: 0.8913\n", - "1 Train accuracy: 0.946667 Test accuracy: 0.9431\n", - "2 Train accuracy: 0.953333 Test accuracy: 0.9561\n", - "3 Train accuracy: 0.96 Test accuracy: 0.9613\n", - "4 Train accuracy: 0.986667 Test accuracy: 0.9651\n", - "5 Train accuracy: 0.966667 Test accuracy: 0.9597\n", - "6 Train accuracy: 0.973333 Test accuracy: 0.9707\n", - "7 Train accuracy: 0.973333 Test accuracy: 0.9649\n", - "8 Train accuracy: 0.973333 Test accuracy: 0.9671\n", - "9 Train accuracy: 0.986667 Test accuracy: 0.9744\n", - "10 Train accuracy: 0.96 Test accuracy: 0.9601\n", - "11 Train accuracy: 0.993333 Test accuracy: 0.9748\n", - "12 Train accuracy: 0.986667 Test accuracy: 0.9762\n", - "13 Train accuracy: 0.986667 Test accuracy: 0.973\n", - "14 Train accuracy: 0.96 Test accuracy: 0.9743\n", - "15 Train accuracy: 1.0 Test accuracy: 0.973\n", - "16 Train 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0.993333 Test accuracy: 0.9766\n", - "87 Train accuracy: 0.986667 Test accuracy: 0.9799\n", - "88 Train accuracy: 1.0 Test accuracy: 0.9801\n", - "89 Train accuracy: 0.986667 Test accuracy: 0.9773\n", - "90 Train accuracy: 0.993333 Test accuracy: 0.9772\n", - "91 Train accuracy: 1.0 Test accuracy: 0.9806\n", - "92 Train accuracy: 0.98 Test accuracy: 0.9705\n", - "93 Train accuracy: 1.0 Test accuracy: 0.9742\n", - "94 Train accuracy: 1.0 Test accuracy: 0.9772\n", - "95 Train accuracy: 0.993333 Test accuracy: 0.9755\n", - "96 Train accuracy: 0.986667 Test accuracy: 0.9686\n", - "97 Train accuracy: 1.0 Test accuracy: 0.9761\n", - "98 Train accuracy: 1.0 Test accuracy: 0.9781\n", - "99 Train accuracy: 1.0 Test accuracy: 0.9807\n" + "40 Train accuracy: 0.993333 Test accuracy: 0.9791\n", + "41 Train accuracy: 0.993333 Test accuracy: 0.9787\n", + "42 Train accuracy: 0.986667 Test accuracy: 0.9821\n", + "43 Train accuracy: 0.993333 Test accuracy: 0.9777\n", + "44 Train accuracy: 0.986667 Test accuracy: 0.975\n", + "45 Train accuracy: 0.986667 Test accuracy: 0.98\n", + "46 Train accuracy: 0.986667 Test accuracy: 0.9786\n", + "47 Train accuracy: 0.993333 Test accuracy: 0.9809\n", + "48 Train accuracy: 0.973333 Test accuracy: 0.9787\n", + "49 Train accuracy: 0.986667 Test accuracy: 0.9815\n", + "50 Train accuracy: 1.0 Test accuracy: 0.9774\n", + "51 Train accuracy: 0.98 Test accuracy: 0.9713\n", + "52 Train accuracy: 1.0 Test accuracy: 0.9803\n", + "53 Train accuracy: 0.993333 Test accuracy: 0.9789\n", + "54 Train accuracy: 1.0 Test accuracy: 0.9805\n", + "55 Train accuracy: 1.0 Test accuracy: 0.9786\n", + "56 Train accuracy: 0.986667 Test accuracy: 0.9758\n", + "57 Train accuracy: 0.993333 Test accuracy: 0.9788\n", + "58 Train accuracy: 0.98 Test accuracy: 0.9811\n", + "59 Train accuracy: 0.986667 Test accuracy: 0.9765\n", + "60 Train accuracy: 1.0 Test accuracy: 0.979\n", + "61 Train accuracy: 0.993333 Test accuracy: 0.976\n", + "62 Train accuracy: 0.993333 Test accuracy: 0.9787\n", + "63 Train accuracy: 0.98 Test accuracy: 0.977\n", + "64 Train accuracy: 0.993333 Test accuracy: 0.9822\n", + "65 Train accuracy: 0.993333 Test accuracy: 0.9719\n", + "66 Train accuracy: 1.0 Test accuracy: 0.9782\n", + "67 Train accuracy: 0.986667 Test accuracy: 0.9788\n", + "68 Train accuracy: 0.993333 Test accuracy: 0.9807\n", + "69 Train accuracy: 1.0 Test accuracy: 0.978\n", + "70 Train accuracy: 0.973333 Test accuracy: 0.9806\n", + "71 Train accuracy: 1.0 Test accuracy: 0.9786\n", + "72 Train accuracy: 0.993333 Test accuracy: 0.9782\n", + "73 Train accuracy: 0.986667 Test accuracy: 0.976\n", + "74 Train accuracy: 1.0 Test accuracy: 0.9784\n", + "75 Train accuracy: 0.993333 Test accuracy: 0.9758\n", + "76 Train accuracy: 0.986667 Test accuracy: 0.9779\n", + "77 Train accuracy: 1.0 Test accuracy: 0.9741\n", + "78 Train accuracy: 0.986667 Test accuracy: 0.9737\n", + "79 Train accuracy: 0.986667 Test accuracy: 0.9754\n", + "80 Train accuracy: 0.986667 Test accuracy: 0.98\n", + "81 Train accuracy: 0.986667 Test accuracy: 0.9807\n", + "82 Train accuracy: 0.993333 Test accuracy: 0.979\n", + "83 Train accuracy: 1.0 Test accuracy: 0.979\n", + "84 Train accuracy: 0.993333 Test accuracy: 0.9752\n", + "85 Train accuracy: 0.993333 Test accuracy: 0.9775\n", + "86 Train accuracy: 0.986667 Test accuracy: 0.975\n", + "87 Train accuracy: 0.993333 Test accuracy: 0.9763\n", + "88 Train accuracy: 0.993333 Test accuracy: 0.972\n", + "89 Train accuracy: 1.0 Test accuracy: 0.9782\n", + "90 Train accuracy: 1.0 Test accuracy: 0.9795\n", + "91 Train accuracy: 0.986667 Test accuracy: 0.9742\n", + "92 Train accuracy: 0.986667 Test accuracy: 0.9775\n", + "93 Train accuracy: 0.986667 Test accuracy: 0.9803\n", + "94 Train accuracy: 1.0 Test accuracy: 0.9806\n", + "95 Train accuracy: 0.993333 Test accuracy: 0.977\n", + "96 Train accuracy: 0.993333 Test accuracy: 0.9781\n", + "97 Train accuracy: 0.993333 Test accuracy: 0.9751\n", + "98 Train accuracy: 0.98 Test accuracy: 0.9794\n", + "99 Train accuracy: 1.0 Test accuracy: 0.9804\n" ] } ], @@ -1055,7 +1097,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 37, "metadata": { "collapsed": true, "deletable": true, @@ -1063,7 +1105,7 @@ }, "outputs": [], "source": [ - "tf.reset_default_graph()\n", + "reset_graph()\n", "\n", "n_steps = 28\n", "n_inputs = 28\n", @@ -1077,7 +1119,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 38, "metadata": { "collapsed": false, "deletable": true, @@ -1097,7 +1139,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 39, "metadata": { "collapsed": true, "deletable": true, @@ -1119,7 +1161,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 40, "metadata": { "collapsed": false, "deletable": true, @@ -1130,16 +1172,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "0 Train accuracy: 0.953333 Test accuracy: 0.9398\n", - "1 Train accuracy: 0.933333 Test accuracy: 0.9631\n", - "2 Train accuracy: 0.966667 Test accuracy: 0.9654\n", - "3 Train accuracy: 0.986667 Test accuracy: 0.9713\n", - "4 Train accuracy: 0.993333 Test accuracy: 0.9748\n", - "5 Train accuracy: 0.993333 Test accuracy: 0.9769\n", - "6 Train accuracy: 0.973333 Test accuracy: 0.9744\n", - "7 Train accuracy: 0.986667 Test accuracy: 0.9764\n", - "8 Train accuracy: 0.986667 Test accuracy: 0.9826\n", - "9 Train accuracy: 1.0 Test accuracy: 0.9819\n" + "0 Train accuracy: 0.96 Test accuracy: 0.9418\n", + "1 Train accuracy: 0.98 Test accuracy: 0.9686\n", + "2 Train accuracy: 0.94 Test accuracy: 0.9693\n", + "3 Train accuracy: 0.973333 Test accuracy: 0.9715\n", + "4 Train accuracy: 0.986667 Test accuracy: 0.9758\n", + "5 Train accuracy: 0.993333 Test accuracy: 0.9774\n", + "6 Train accuracy: 0.993333 Test accuracy: 0.9815\n", + "7 Train accuracy: 1.0 Test accuracy: 0.9765\n", + "8 Train accuracy: 0.986667 Test accuracy: 0.9831\n", + "9 Train accuracy: 0.986667 Test accuracy: 0.9804\n" ] } ], @@ -1171,7 +1213,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 41, "metadata": { "collapsed": false, "deletable": true, @@ -1194,7 +1236,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 42, "metadata": { "collapsed": false, "deletable": true, @@ -1212,7 +1254,7 @@ "data": { "image/png": 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smNu+QGf5gtGHJ9plpv7++++nRo0aVKlShVdeeYUNGzZw8uTJC/tvvvlmrrzy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\nW9OtWzduuOEG7rnnnpImPa5iKLZ//37atm1b8vzUqVOcOXPmnM7RnTt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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1249,7 +1291,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 43, "metadata": { "collapsed": false, "deletable": true, @@ -1262,7 +1304,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 44, "metadata": { "collapsed": false, "deletable": true, @@ -1272,29 +1314,29 @@ { "data": { "text/plain": [ - "array([[ 3.99711896, 4.42605082],\n", - " [ 4.42605082, 4.67138363],\n", - " [ 4.67138363, 4.53085825],\n", - " [ 4.53085825, 3.8877217 ],\n", - " [ 3.8877217 , 2.74075883],\n", - " [ 2.74075883, 1.20612611],\n", - " [ 1.20612611, -0.50947652],\n", - " [-0.50947652, -2.15774285],\n", - " [-2.15774285, -3.50784175],\n", - " [-3.50784175, -4.4011945 ],\n", - " [-4.4011945 , -4.7885542 ],\n", - " [-4.7885542 , -4.7403267 ],\n", - " [-4.7403267 , -4.42762383],\n", - " [-4.42762383, -4.07870547],\n", - " [-4.07870547, -3.92149231],\n", - " [-3.92149231, -4.12624098],\n", - " [-4.12624098, -4.76243294],\n", - " [-4.76243294, -5.7804475 ],\n", - " [-5.7804475 , -7.02252044],\n", - " [-7.02252044, -8.26031845]])" + "array([[ 1.38452097, 2.05081182],\n", + " [ 2.05081182, 2.29742291],\n", + " [ 2.29742291, 2.0465599 ],\n", + " [ 2.0465599 , 1.34009916],\n", + " [ 1.34009916, 0.32948704],\n", + " [ 0.32948704, -0.76115235],\n", + " [-0.76115235, -1.68967022],\n", + " [-1.68967022, -2.25492776],\n", + " [-2.25492776, -2.34576159],\n", + " [-2.34576159, -1.96789418],\n", + " [-1.96789418, -1.24220428],\n", + " [-1.24220428, -0.37478448],\n", + " [-0.37478448, 0.39387907],\n", + " [ 0.39387907, 0.84815766],\n", + " [ 0.84815766, 0.85045064],\n", + " [ 0.85045064, 0.3752526 ],\n", + " [ 0.3752526 , -0.48422846],\n", + " [-0.48422846, -1.53852738],\n", + " [-1.53852738, -2.54795941],\n", + " [-2.54795941, -3.28097239]])" ] }, - "execution_count": 42, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -1325,7 +1367,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 45, "metadata": { "collapsed": true, "deletable": true, @@ -1333,7 +1375,7 @@ }, "outputs": [], "source": [ - "tf.reset_default_graph()\n", + "reset_graph()\n", "\n", "n_steps = 20\n", "n_inputs = 1\n", @@ -1359,7 +1401,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 46, "metadata": { "collapsed": true, "deletable": true, @@ -1367,7 +1409,7 @@ }, "outputs": [], "source": [ - "tf.reset_default_graph()\n", + "reset_graph()\n", "\n", "n_steps = 20\n", "n_inputs = 1\n", @@ -1380,7 +1422,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 47, "metadata": { "collapsed": false, "deletable": true, @@ -1395,7 +1437,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 48, "metadata": { "collapsed": true, "deletable": true, @@ -1408,7 +1450,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 49, "metadata": { "collapsed": true, "deletable": true, @@ -1427,7 +1469,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 50, "metadata": { "collapsed": true, "deletable": true, @@ -1440,7 +1482,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 51, "metadata": { "collapsed": false, "deletable": true, @@ -1451,21 +1493,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "0 \tMSE: 12.5781\n", - "100 \tMSE: 0.547315\n", - "200 \tMSE: 0.184477\n", - "300 \tMSE: 0.0903497\n", - "400 \tMSE: 0.0818634\n", - "500 \tMSE: 0.0419608\n", - "600 \tMSE: 0.0585126\n", - "700 \tMSE: 0.0592046\n", - "800 \tMSE: 0.0534744\n", - "900 \tMSE: 0.0471234\n", - "1000 \tMSE: 0.0530561\n", - "1100 \tMSE: 0.0418988\n", - "1200 \tMSE: 0.0444884\n", - "1300 \tMSE: 0.0483815\n", - "1400 \tMSE: 0.0423761\n" + "0 \tMSE: 13.6543\n", + "100 \tMSE: 0.538476\n", + "200 \tMSE: 0.168532\n", + "300 \tMSE: 0.0879579\n", + "400 \tMSE: 0.0633425\n", + "500 \tMSE: 0.061859\n", + "600 \tMSE: 0.0558801\n", + "700 \tMSE: 0.0498718\n", + "800 \tMSE: 0.0518417\n", + "900 \tMSE: 0.0482838\n", + "1000 \tMSE: 0.0483549\n", + "1100 \tMSE: 0.0503321\n", + "1200 \tMSE: 0.0412116\n", + "1300 \tMSE: 0.0488435\n", + "1400 \tMSE: 0.0426057\n" ] } ], @@ -1487,7 +1529,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 52, "metadata": { "collapsed": false, "deletable": true, @@ -1512,7 +1554,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 53, "metadata": { "collapsed": false, "deletable": true, @@ -1522,29 +1564,29 @@ { "data": { "text/plain": [ - "array([[[-3.44862223],\n", - " [-2.45397472],\n", - " [-1.17047215],\n", - " [ 0.69015497],\n", - " [ 2.09089303],\n", - " [ 3.09212542],\n", - " [ 3.38644886],\n", - " [ 3.39212108],\n", - " [ 2.87099624],\n", - " [ 2.22925496],\n", - " [ 1.68792105],\n", - " [ 1.53225946],\n", - " [ 1.85546851],\n", - " [ 2.74033833],\n", - " [ 3.88769126],\n", - " [ 5.0760498 ],\n", - " [ 6.01084471],\n", - " [ 6.65660477],\n", - " [ 6.67408991],\n", - " [ 6.07125759]]], dtype=float32)" + "array([[[-3.42596436],\n", + " [-2.48950148],\n", + " [-1.1358937 ],\n", + " [ 0.75142008],\n", + " [ 2.19939661],\n", + " [ 3.14104176],\n", + " [ 3.54801917],\n", + " [ 3.34113908],\n", + " [ 2.82566142],\n", + " [ 2.17759967],\n", + " [ 1.65191436],\n", + " [ 1.55619645],\n", + " [ 1.94783175],\n", + " [ 2.74632907],\n", + " [ 3.89091802],\n", + " [ 5.11678171],\n", + " [ 6.13101864],\n", + " [ 6.67043686],\n", + " [ 6.62354612],\n", + " [ 6.05428839]]], dtype=float32)" ] }, - "execution_count": 51, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } @@ -1555,7 +1597,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 54, "metadata": { "collapsed": false, "deletable": true, @@ -1571,9 +1613,9 @@ }, { "data": { - "image/png": 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5gBde+IjMzK+JjT2FUaP6MnToIE1X1ACF+hO/D5jmnPuu0iNFRIJkZWVx3nk3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\n4EBodsWXNAAAAABJRU5ErkJggg==\n", 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FKBERCUs1suS7mXUDvgBmOedGVPW42NhYdUYIY+VNySQiUhNqpJOEmS0CIoCM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\n5xoUwM1AJJANvA7cqC7mIiJ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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1604,7 +1646,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 55, "metadata": { "collapsed": true, "deletable": true, @@ -1612,7 +1654,7 @@ }, "outputs": [], "source": [ - "tf.reset_default_graph()\n", + "reset_graph()\n", "\n", "n_steps = 20\n", "n_inputs = 1\n", @@ -1624,7 +1666,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 56, "metadata": { "collapsed": false, "deletable": true, @@ -1638,7 +1680,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 57, "metadata": { "collapsed": true, "deletable": true, @@ -1652,7 +1694,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 58, "metadata": { "collapsed": true, "deletable": true, @@ -1667,7 +1709,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 59, "metadata": { "collapsed": true, "deletable": true, @@ -1685,7 +1727,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 60, "metadata": { "collapsed": false, "deletable": true, @@ -1696,21 +1738,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "0 \tMSE: 13.7628\n", - "100 \tMSE: 0.497152\n", - "200 \tMSE: 0.158743\n", - "300 \tMSE: 0.0830925\n", - "400 \tMSE: 0.0534122\n", - "500 \tMSE: 0.0579881\n", - "600 \tMSE: 0.0503276\n", - "700 \tMSE: 0.0500856\n", - "800 \tMSE: 0.0553265\n", - "900 \tMSE: 0.0521492\n", - "1000 \tMSE: 0.0520231\n", - "1100 \tMSE: 0.0592064\n", - "1200 \tMSE: 0.0437843\n", - "1300 \tMSE: 0.0451353\n", - "1400 \tMSE: 0.0390866\n" + "0 \tMSE: 11.6768\n", + "100 \tMSE: 0.51119\n", + "200 \tMSE: 0.14452\n", + "300 \tMSE: 0.0760974\n", + "400 \tMSE: 0.063713\n", + "500 \tMSE: 0.0601674\n", + "600 \tMSE: 0.0531676\n", + "700 \tMSE: 0.0493623\n", + "800 \tMSE: 0.0519282\n", + "900 \tMSE: 0.0482475\n", + "1000 \tMSE: 0.048083\n", + "1100 \tMSE: 0.0484352\n", + "1200 \tMSE: 0.0418098\n", + "1300 \tMSE: 0.0477387\n", + "1400 \tMSE: 0.0419062\n" ] } ], @@ -1735,7 +1777,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 61, "metadata": { "collapsed": false, "deletable": true, @@ -1745,29 +1787,29 @@ { "data": { "text/plain": [ - "array([[[-3.42541599],\n", - " [-2.47213984],\n", - " [-1.18770957],\n", - " [ 0.65560889],\n", - " [ 2.28769326],\n", - " [ 3.15006804],\n", - " [ 3.36928892],\n", - " [ 3.1990695 ],\n", - " [ 2.78958797],\n", - " [ 2.17565632],\n", - " [ 1.6632216 ],\n", - " [ 1.44775581],\n", - " [ 1.79704356],\n", - " [ 2.62301469],\n", - " [ 3.84780502],\n", - " [ 5.08452415],\n", - " [ 6.05238008],\n", - " [ 6.56187773],\n", - " [ 6.58187437],\n", - " [ 6.04165268]]], dtype=float32)" + "array([[[-3.42077947],\n", + " [-2.47134852],\n", + " [-1.14368439],\n", + " [ 0.75839251],\n", + " [ 2.15983796],\n", + " [ 3.11996722],\n", + " [ 3.52640414],\n", + " [ 3.43011165],\n", + " [ 2.8376286 ],\n", + " [ 2.18515253],\n", + " [ 1.6659894 ],\n", + " [ 1.54036307],\n", + " [ 1.89834416],\n", + " [ 2.73356843],\n", + " [ 3.9192028 ],\n", + " [ 5.16150093],\n", + " [ 6.10899305],\n", + " [ 6.66055822],\n", + " [ 6.65600348],\n", + " [ 6.09106874]]], dtype=float32)" ] }, - "execution_count": 59, + "execution_count": 61, "metadata": {}, "output_type": "execute_result" } @@ -1778,7 +1820,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 62, "metadata": { "collapsed": false, "deletable": true, @@ -1787,9 +1829,9 @@ "outputs": [ { "data": { - "image/png": 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2coqLd9G3bxueeuov/i5u0AjkfgD/AM7GfQeQAPwdeBOwqX+MaWR27GhGSUk4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\nTBdtqQWeWAAAAABJRU5ErkJggg==\n", 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Y06tcWu0HoJQPGzduApmZh2gtbZmz4SU6HD9Qum5PcAvuuPRO/mf20bNnc557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\ngg==\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1819,7 +1861,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 63, "metadata": { "collapsed": false, "deletable": true, @@ -1847,7 +1889,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 64, "metadata": { "collapsed": false, "deletable": true, @@ -1856,9 +1898,9 @@ "outputs": [ { "data": { - "image/png": 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A3bpBTU3r57zE01BLcpmeTjsGuBi4Cqg3s6eAO4BH3b25Pd/Q3duzCsZpmfPx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\nssdDREREKlO59niIiIhIBVL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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1876,7 +1918,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 65, "metadata": { "collapsed": false, "deletable": true, @@ -1893,9 +1935,9 @@ }, { "data": { - "image/png": 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7S3Gb2QTgfuBTwDBgN3BfXNt7gT3AEOBq4D4zG59lWxHpRE44AVatCivj1dfD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\nO0ub6QAAAABJRU5ErkJggg==\n", 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zoaAgDGv8QURaAB2BSUBX4Jf/cefcemBm0eOGEQv/+AdcdJF2K65XD26/HR59NG6rwmPW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\n5lec6dy5Gg8ZZkfikiQEhC/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z9abgQ7WXqD0QzhXH28ZFXGFMcQmIqlyF6b33ipslNmmi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\ntEsaKKkyHeYmYKKk0yUNBD4L3FureDAz6ykJLrig/kvgRZn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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1955,7 +1997,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 66, "metadata": { "collapsed": true, "deletable": true, @@ -1963,7 +2005,7 @@ }, "outputs": [], "source": [ - "tf.reset_default_graph()\n", + "reset_graph()\n", "\n", "n_inputs = 2\n", "n_steps = 5\n", @@ -1973,7 +2015,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 67, "metadata": { "collapsed": true, "deletable": true, @@ -1992,7 +2034,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 68, "metadata": { "collapsed": true, "deletable": true, @@ -2005,7 +2047,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 69, "metadata": { "collapsed": true, "deletable": true, @@ -2018,7 +2060,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 70, "metadata": { "collapsed": true, "deletable": true, @@ -2033,7 +2075,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 71, "metadata": { "collapsed": false, "deletable": true, @@ -2046,7 +2088,7 @@ "(2, 5, 100)" ] }, - "execution_count": 69, + "execution_count": 71, "metadata": {}, "output_type": "execute_result" } @@ -2077,7 +2119,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 72, "metadata": { "collapsed": true, "deletable": true, @@ -2104,7 +2146,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 73, "metadata": { "collapsed": false, "deletable": true, @@ -2134,7 +2176,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 74, "metadata": { "collapsed": true, "deletable": true, @@ -2142,7 +2184,7 @@ }, "outputs": [], "source": [ - "tf.reset_default_graph()\n", + "reset_graph()\n", "\n", "n_inputs = 5\n", "n_steps = 20\n", @@ -2153,7 +2195,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 75, "metadata": { "collapsed": false, "deletable": true, @@ -2170,7 +2212,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 76, "metadata": { "collapsed": true, "deletable": true, @@ -2183,7 +2225,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 77, "metadata": { "collapsed": false, "deletable": true, @@ -2195,33 +2237,33 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ -4.02673557e-02 -1.32697192e-03 5.89309596e-02 ..., 3.01364507e-03\n", - " 1.13810204e-01 -2.95128021e-03]\n", - " [ 5.07392362e-02 -2.27102004e-02 9.08590779e-02 ..., -7.28313485e-03\n", - " 7.38209486e-02 -1.04581699e-01]\n", - " [ -2.63734370e-01 7.45113939e-02 -1.59692034e-01 ..., -2.26709619e-02\n", - " 1.94620773e-01 -2.74280488e-01]\n", + "[[[-0.06683909 -0.06814943 0.12806301 ..., -0.04951219 0.0169118\n", + " 0.09129722]\n", + " [-0.03898398 -0.32816607 0.25709429 ..., -0.22360352 -0.00203764\n", + " 0.18901907]\n", + " [-0.14598769 -0.03324183 0.06588719 ..., -0.36336255 -0.117153\n", + " 0.39544109]\n", " ..., \n", - " [ -1.57276452e-01 -2.25722805e-01 -2.83314496e-01 ..., -2.01246798e-01\n", - " -2.75413662e-01 -3.03873122e-01]\n", - " [ -2.17203677e-01 -1.56792626e-01 -8.67026821e-02 ..., -1.56499967e-02\n", - " -2.58552223e-01 1.44222811e-01]\n", - " [ -2.23659739e-01 -1.20433375e-01 -1.25079557e-01 ..., -9.19605419e-02\n", - " -2.63631225e-01 -5.52971773e-02]]\n", + " [-0.52596134 0.04002573 0.14033252 ..., 0.18522167 0.25101244\n", + " -0.05308188]\n", + " [-0.45618156 -0.11686647 -0.09905577 ..., -0.17943858 0.27567461\n", + " -0.04363405]\n", + " [-0.55723065 0.13874871 -0.14983818 ..., 0.04673974 0.10338999\n", + " -0.03823486]]\n", "\n", - " [[ -7.01689422e-02 7.87304118e-02 -4.61662567e-04 ..., -7.18819797e-02\n", - " 1.39971092e-01 -1.15815736e-01]\n", - " [ -8.13159868e-02 3.60450037e-02 9.81829762e-02 ..., 1.03328772e-01\n", - " 7.71655664e-02 -1.34362206e-01]\n", - " [ -8.32017362e-02 4.47433516e-02 5.96695021e-02 ..., -2.00068895e-02\n", - " -6.25158921e-02 -7.78452605e-02]\n", + " [[-0.0191099 -0.06458578 0.08206855 ..., -0.07772326 -0.05498064\n", + " 0.01358664]\n", + " [-0.05150904 -0.36381066 0.0913103 ..., -0.12480559 -0.03924585\n", + " 0.06585156]\n", + " [-0.29961377 -0.00120922 0.06789977 ..., -0.27556923 -0.15278165\n", + " 0.21452278]\n", " ..., \n", - " [ 2.92190045e-01 4.59108129e-02 -1.35491624e-01 ..., -5.05926490e-01\n", - " -7.15270042e-01 6.77146986e-02]\n", - " [ 5.72165012e-01 -1.87340751e-01 -2.77077109e-01 ..., -4.05810833e-01\n", - " -5.08782566e-01 1.87231988e-01]\n", - " [ 2.48714276e-02 -2.44836390e-01 3.51739705e-01 ..., 6.02148138e-02\n", - " 1.54227063e-01 1.12923510e-01]]]\n" + " [-0.6460501 0.17479922 0.14066698 ..., -0.08995064 -0.03049678\n", + " 0.05738082]\n", + " [-0.61097401 -0.17900243 -0.23193845 ..., -0.2500132 0.25146627\n", + " 0.36902413]\n", + " [-0.25920284 -0.08149087 0.19740498 ..., -0.32611009 -0.02686078\n", + " 0.11232778]]]\n" ] } ], @@ -2243,7 +2285,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 78, "metadata": { "collapsed": true, "deletable": true, @@ -2251,7 +2293,7 @@ }, "outputs": [], "source": [ - "tf.reset_default_graph()\n", + "reset_graph()\n", "\n", "n_inputs = 1\n", "n_neurons = 100\n", @@ -2265,7 +2307,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 79, "metadata": { "collapsed": false, "deletable": true, @@ -2285,7 +2327,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 80, "metadata": { "collapsed": true, "deletable": true, @@ -2319,7 +2361,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 81, "metadata": { "collapsed": false, "deletable": true, @@ -2330,16 +2372,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "0 Training MSE: 19.0452\n", - "100 Training MSE: 5.08043\n", - "200 Training MSE: 3.33357\n", - "300 Training MSE: 4.07959\n", - "400 Training MSE: 2.8691\n", - "500 Training MSE: 3.28825\n", - "600 Training MSE: 3.5159\n", - "700 Training MSE: 3.04527\n", - "800 Training MSE: 3.65255\n", - "900 Training MSE: 2.37376\n" + "0 Training MSE: 13.7079\n", + "100 Training MSE: 4.25301\n", + "200 Training MSE: 3.3346\n", + "300 Training MSE: 3.62894\n", + "400 Training MSE: 3.29399\n", + "500 Training MSE: 3.88701\n", + "600 Training MSE: 3.38845\n", + "700 Training MSE: 3.05871\n", + "800 Training MSE: 3.84628\n", + "900 Training MSE: 4.78431\n" ] } ], @@ -2370,7 +2412,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 82, "metadata": { "collapsed": true, "deletable": true, @@ -2378,7 +2420,7 @@ }, "outputs": [], "source": [ - "tf.reset_default_graph()\n", + "reset_graph()\n", "\n", "n_inputs = 1\n", "n_neurons = 100\n", @@ -2410,7 +2452,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 83, "metadata": { "collapsed": false, "deletable": true, @@ -2426,9 +2468,9 @@ }, { "data": { - "image/png": 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t2w5GjryF7dt30q9fInCE6Oj1TJ58SZBLGiTlnce+QqjzuK5sMTjTJAoKCkhL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\n", 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0FBYWMnLkRD7++AqOHPk1TuM+JSZmIX36LGHt2gUWxAPMAoAJec1pQmhPDqHF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\nGXhZNt5ldhIAAAAASUVORK5CYII=\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2474,7 +2516,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 84, "metadata": { "collapsed": false, "deletable": true, @@ -2485,21 +2527,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "0 Training MSE: 18.5711\n", - "100 Training MSE: 5.00803\n", - "200 Training MSE: 3.57105\n", - "300 Training MSE: 4.78687\n", - "400 Training MSE: 3.2551\n", - "500 Training MSE: 3.66258\n", - "600 Training MSE: 2.91071\n", - "700 Training MSE: 3.82467\n", - "800 Training MSE: 3.92405\n", - "900 Training MSE: 3.27747\n" + "0 Training MSE: 13.6546\n", + "100 Training MSE: 4.41883\n", + "200 Training MSE: 3.23384\n", + "300 Training MSE: 3.71355\n", + "400 Training MSE: 2.6646\n", + "500 Training MSE: 3.77632\n", + "600 Training MSE: 3.0631\n", + "700 Training MSE: 3.56676\n", + "800 Training MSE: 3.84577\n", + "900 Training MSE: 4.79746\n" ] } ], "source": [ - "tf.reset_default_graph()\n", + "reset_graph()\n", "\n", "import sys\n", "training = True # in a script, this would be (sys.argv[-1] == \"train\") instead\n", @@ -2551,7 +2593,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 85, "metadata": { "collapsed": true, "deletable": true, @@ -2559,12 +2601,14 @@ }, "outputs": [], "source": [ + "reset_graph()\n", + "\n", "lstm_cell = tf.contrib.rnn.BasicLSTMCell(num_units=n_neurons)" ] }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 86, "metadata": { "collapsed": true, "deletable": true, @@ -2572,8 +2616,6 @@ }, "outputs": [], "source": [ - "tf.reset_default_graph()\n", - "\n", "n_steps = 28\n", "n_inputs = 28\n", "n_neurons = 150\n", @@ -2603,7 +2645,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 87, "metadata": { "collapsed": false, "deletable": true, @@ -2618,7 +2660,7 @@ " LSTMStateTuple(c=, h=))" ] }, - "execution_count": 85, + "execution_count": 87, "metadata": {}, "output_type": "execute_result" } @@ -2629,7 +2671,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 88, "metadata": { "collapsed": false, "deletable": true, @@ -2642,7 +2684,7 @@ "" ] }, - "execution_count": 86, + "execution_count": 88, "metadata": {}, "output_type": "execute_result" } @@ -2653,7 +2695,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 89, "metadata": { "collapsed": false, "deletable": true, @@ -2665,16 +2707,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 0 Train accuracy = 0.98 Test accuracy = 0.9564\n", - "Epoch 1 Train accuracy = 0.98 Test accuracy = 0.9647\n", - "Epoch 2 Train accuracy = 0.98 Test accuracy = 0.9733\n", - "Epoch 3 Train accuracy = 1.0 Test accuracy = 0.9809\n", - "Epoch 4 Train accuracy = 1.0 Test accuracy = 0.9788\n", - "Epoch 5 Train accuracy = 0.993333 Test accuracy = 0.9864\n", - "Epoch 6 Train accuracy = 0.993333 Test accuracy = 0.9873\n", - "Epoch 7 Train accuracy = 0.993333 Test accuracy = 0.987\n", - "Epoch 8 Train accuracy = 0.993333 Test accuracy = 0.9851\n", - "Epoch 9 Train accuracy = 1.0 Test accuracy = 0.9848\n" + "Epoch 0 Train accuracy = 0.966667 Test accuracy = 0.9525\n", + "Epoch 1 Train accuracy = 0.993333 Test accuracy = 0.9747\n", + "Epoch 2 Train accuracy = 0.993333 Test accuracy = 0.9775\n", + "Epoch 3 Train accuracy = 0.993333 Test accuracy = 0.9813\n", + "Epoch 4 Train accuracy = 0.986667 Test accuracy = 0.9837\n", + "Epoch 5 Train accuracy = 1.0 Test accuracy = 0.9831\n", + "Epoch 6 Train accuracy = 1.0 Test accuracy = 0.9834\n", + "Epoch 7 Train accuracy = 0.993333 Test accuracy = 0.9862\n", + "Epoch 8 Train accuracy = 1.0 Test accuracy = 0.9863\n", + "Epoch 9 Train accuracy = 0.993333 Test accuracy = 0.9863\n" ] } ], @@ -2696,7 +2738,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 90, "metadata": { "collapsed": true, "deletable": true, @@ -2709,7 +2751,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 91, "metadata": { "collapsed": true, "deletable": true, @@ -2752,7 +2794,7 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 92, "metadata": { "collapsed": true, "deletable": true, @@ -2795,7 +2837,7 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 93, "metadata": { "collapsed": false, "deletable": true, @@ -2808,7 +2850,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 94, "metadata": { "collapsed": false, "deletable": true, @@ -2821,7 +2863,7 @@ "['anarchism', 'originated', 'as', 'a', 'term']" ] }, - "execution_count": 92, + "execution_count": 94, "metadata": {}, "output_type": "execute_result" } @@ -2842,7 +2884,7 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 95, "metadata": { "collapsed": false, "deletable": true, @@ -2862,7 +2904,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 96, "metadata": { "collapsed": false, "deletable": true, @@ -2873,10 +2915,10 @@ "data": { "text/plain": [ "('anarchism originated as a term of abuse first used',\n", - " array([5237, 3082, 12, 6, 195, 2, 3134, 46, 59]))" + " array([5241, 3082, 12, 6, 195, 2, 3136, 46, 59]))" ] }, - "execution_count": 94, + "execution_count": 96, "metadata": {}, "output_type": "execute_result" } @@ -2887,7 +2929,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 97, "metadata": { "collapsed": false, "deletable": true, @@ -2897,10 +2939,10 @@ { "data": { "text/plain": [ - "'guided didn as a term of abuse first used'" + "'anarchism didn as a term of presidency first used'" ] }, - "execution_count": 95, + "execution_count": 97, "metadata": {}, "output_type": "execute_result" } @@ -2911,7 +2953,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 98, "metadata": { "collapsed": false, "deletable": true, @@ -2924,7 +2966,7 @@ "('culottes', 0)" ] }, - "execution_count": 96, + "execution_count": 98, "metadata": {}, "output_type": "execute_result" } @@ -2945,7 +2987,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 99, "metadata": { "collapsed": true, "deletable": true, @@ -2983,7 +3025,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 100, "metadata": { "collapsed": false, "deletable": true, @@ -2997,7 +3039,7 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 101, "metadata": { "collapsed": false, "deletable": true, @@ -3011,7 +3053,7 @@ " ['originated', 'originated', 'as', 'as', 'a', 'a', 'term', 'term'])" ] }, - "execution_count": 99, + "execution_count": 101, "metadata": {}, "output_type": "execute_result" } @@ -3022,7 +3064,7 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 102, "metadata": { "collapsed": false, "deletable": true, @@ -3033,7 +3075,7 @@ "data": { "text/plain": [ "(array([[ 12],\n", - " [5237],\n", + " [5241],\n", " [3082],\n", " [ 6],\n", " [ 195],\n", @@ -3043,7 +3085,7 @@ " ['as', 'anarchism', 'originated', 'a', 'term', 'as', 'of', 'a'])" ] }, - "execution_count": 100, + "execution_count": 102, "metadata": {}, "output_type": "execute_result" } @@ -3064,7 +3106,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 103, "metadata": { "collapsed": true, "deletable": true, @@ -3090,7 +3132,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 104, "metadata": { "collapsed": true, "deletable": true, @@ -3098,7 +3140,7 @@ }, "outputs": [], "source": [ - "tf.reset_default_graph()\n", + "reset_graph()\n", "\n", "# Input data.\n", "train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])\n", @@ -3107,7 +3149,7 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 105, "metadata": { "collapsed": false, "deletable": true, @@ -3125,7 +3167,7 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 106, "metadata": { "collapsed": true, "deletable": true, @@ -3139,7 +3181,7 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 107, "metadata": { "collapsed": true, "deletable": true, @@ -3186,7 +3228,7 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 108, "metadata": { "collapsed": false, "deletable": true, @@ -3197,44 +3239,44 @@ "name": "stdout", "output_type": "stream", "text": [ - "Iteration: 0\tAverage loss at step 0 : 276.952941895\n", - "Nearest to i: cathar, binder, rout, applying, plastic, atlantis, panini, detainee,\n", - "Nearest to states: pekah, tempered, fournier, dantzig, tangents, manual, ephraem, tollens,\n", - "Nearest to war: hacker, mamet, scaled, fearless, progressives, born, duvalier, dirkjan,\n", - "Nearest to however: enfield, capitals, stunning, tbilisi, ghibli, antonine, creutzfeldt, garp,\n", - "Nearest to by: bagapsh, dry, agenda, whiteface, untouched, doctrinal, libels, pete,\n", - "Nearest to only: langmuir, cherokee, schneider, rawlinson, intoxicated, radiometric, adair, midlands,\n", - "Nearest to into: kword, abstractly, copenhagen, psa, goalkeepers, bernardino, nicolau, wong,\n", - "Nearest to he: athabasca, lutheran, bess, bootlegs, bro, dramatists, conjugate, organ,\n", - "Nearest to may: agilent, flows, sandstorms, jain, microbial, eduardo, intercal, swampy,\n", - "Nearest to two: negotiator, anastasia, benford, mercantile, ambulance, goidelic, planckian, classmate,\n", - "Nearest to so: norepinephrine, reginae, evaluate, shooters, damme, meson, bats, hydrofoil,\n", - "Nearest to of: aleut, augusti, geezer, conti, divide, executable, rivets, pamphilus,\n", - "Nearest to d: legions, longwave, genitals, daoxuan, appoints, ocampo, swordsmen, profusion,\n", - "Nearest to first: jonathon, electronica, fanning, courant, inevitably, squadron, timeframe, ouaddai,\n", - "Nearest to up: talkie, snack, android, achieves, chiuchow, rameau, prostitute, boldsymbol,\n", - "Nearest to were: widgery, arnauld, gaia, lays, indirect, microscopy, manson, vending,\n", - "Iteration: 2000\tAverage loss at step 2000 : 132.210864033\n", - "Iteration: 4000\t\t\t\t\t\t\t\t\t\t\t\t\t\tAverage loss at step 4000 : 62.4988844576\n", - "Iteration: 6000\t\t\t\t\t\t\t\t\t\t\t\t\tAverage loss at step 6000 : 41.4175263124\n", - "Iteration: 8000\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tAverage loss at step 8000 : 30.9688069243\n", - "Iteration: 10000\t\t\t\t\t\t\t\t\t\t\tAverage loss at step 10000 : 25.463088474\n", - "Nearest to i: onset, please, justices, recall, t, heard, hochschule, hoxha,\n", - "Nearest to states: its, omotic, goto, lips, streams, the, aardvarks, practitioners,\n", - "Nearest to war: phobias, alum, katrina, int, downfall, aristotle, enhance, repulsed,\n", - "Nearest to however: rope, and, clara, aruba, bremer, for, dean, ginsberg,\n", - "Nearest to by: and, intel, fork, aldous, cave, persephone, ehret, in,\n", - "Nearest to only: temperatures, injustice, chun, but, alhazred, fernando, wozniak, devonian,\n", - "Nearest to into: loosening, be, sweeping, achill, hep, balliol, protests, entrances,\n", - "Nearest to he: rope, guggenheim, who, suffolk, spanning, she, manzikert, him,\n", - "Nearest to may: fleeting, feeling, pregnancy, not, requested, retailers, concubine, wodehouse,\n", - "Nearest to two: zero, three, one, five, nine, four, six, seven,\n", - "Nearest to so: bellows, ros, snow, propositional, two, completeness, blore, arif,\n", - "Nearest to of: in, and, the, arches, hebrides, korah, anointed, ampere,\n", - "Nearest to d: one, eight, purchased, circular, eridanus, fernando, seven, volcanism,\n", - "Nearest to first: tarleton, asparagales, nominations, feeling, seo, behaviours, achill, kick,\n", - "Nearest to up: computes, difficulty, striking, conversion, anatoly, question, raskin, allah,\n", - "Nearest to were: bicycle, four, cca, nine, blacks, ankh, abdicated, are,\n" + "Iteration: 0\tAverage loss at step 0 : 285.899108887\n", + "Nearest to would: employee, fayed, ladino, sadr, northamptonshire, epa, presidents, stiff,\n", + "Nearest to on: vigesimal, dim, mbit, conscientious, musics, molina, tarn, seminar,\n", + "Nearest to four: arches, evacuation, laser, alia, galveston, raced, latch, bandar,\n", + "Nearest to his: colloquial, mast, themes, someone, noir, streamline, value, merwara,\n", + "Nearest to often: milestone, mega, mboxx, antigen, vicki, overriding, adorno, anthony,\n", + "Nearest to in: sheltering, virtualization, petersen, appeals, weill, examine, compassion, browser,\n", + "Nearest to an: patchwork, orang, bethune, archaeological, sweat, mislead, keystroke, changeover,\n", + "Nearest to eight: notch, churchyard, mayfair, brightly, exertion, processing, monuc, reggae,\n", + "Nearest to these: aphrodite, malignancies, desired, eocene, bg, grandmother, checkpoint, nakano,\n", + "Nearest to nine: imr, blocks, lucy, learners, rett, recognising, aspects, relating,\n", + "Nearest to called: electrolyte, thompson, lojban, haken, tapestry, eutyches, mojo, plunge,\n", + "Nearest to about: gael, bravo, walsingham, octagonal, authorship, declarations, resettlement, fughetta,\n", + "Nearest to up: ifad, drives, nee, holmes, caligula, impulse, safeties, havel,\n", + "Nearest to one: indecent, egon, unequivocally, oppenheim, tla, alan, psyche, ellington,\n", + "Nearest to and: canal, berbers, secluded, leh, huac, etiquette, tajikistan, honneur,\n", + "Nearest to been: subdivided, unamended, vanes, memorandum, justifying, welwyn, linear, automation,\n", + "Iteration: 2000\tAverage loss at step 2000 : 130.957044463\n", + "Iteration: 4000\tAverage loss at step 4000 : 62.5069862733\n", + "Iteration: 6000\tAverage loss at step 6000 : 42.0973700013\n", + "Iteration: 8000\tAverage loss at step 8000 : 31.6292150426\n", + "Iteration: 10000\tAverage loss at step 10000 : 25.6433333195\n", + "Nearest to would: to, wrongly, floppy, was, bj, expenditure, mossad, int,\n", + "Nearest to on: in, four, seo, odessa, abscess, sqrt, satisfies, defunct,\n", + "Nearest to four: nine, zero, five, six, one, two, three, seven,\n", + "Nearest to his: the, s, chiefly, gage, botany, somali, arslan, died,\n", + "Nearest to often: cards, revolutionaries, bypasses, crm, carved, gide, mistakenly, and,\n", + "Nearest to in: of, and, on, two, accredited, the, nine, for,\n", + "Nearest to an: altaic, chlorine, achill, expedition, trilobites, columbus, depressed, a,\n", + "Nearest to eight: nine, one, seven, six, five, zero, three, four,\n", + "Nearest to these: delicate, bambaataa, appropriation, hanson, confirm, mathbb, columbus, contributes,\n", + "Nearest to nine: zero, one, six, seven, four, three, eight, five,\n", + "Nearest to called: used, insisting, handed, gallon, rematch, respondent, bind, victorious,\n", + "Nearest to about: honoria, pa, diet, finds, cosmos, holmes, ataxia, abstraction,\n", + "Nearest to up: silurian, condom, the, auld, archie, with, seo, raf,\n", + "Nearest to one: nine, three, eight, two, six, seven, five, four,\n", + "Nearest to and: astatine, in, the, of, zero, topalov, abdicated, UNK,\n", + "Nearest to been: have, archie, by, stg, pedals, was, ambients, it,\n" ] } ], @@ -3290,7 +3332,7 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 109, "metadata": { "collapsed": false, "deletable": true, @@ -3313,7 +3355,7 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 110, "metadata": { "collapsed": true, "deletable": true, @@ -3337,7 +3379,7 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 111, "metadata": { "collapsed": false, "deletable": true, @@ -3346,9 +3388,9 @@ "outputs": [ { "data": { - "image/png": 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FxsYqMjJSkZGR2rx5s11rsidXGuJTl3DdAAAXorGzCwCAk61du1Y5OTmKioqS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ADS0tIQERFR7rO/sbNs2TK0a9cOM2fOBCAW3uvQoQM+fPiAY8eO4fHjx+jevTsUFRWhqKgI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\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++UcIIcTw4cPFsmXLRGpqqmjdurW0bXp6uhBCCHNzc3Ht2rVi84qKwc6ZM0eoVCqRm5sr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\ngOMttWnwQZIkSZIkHZXjDT647EKSJEmSJEWUwQdJkiR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69VNERISGDx+uQ4cOSZLWr1+vpKQkSdKgQYO0du1a+1j9+vUrM+79998vSbrz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\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wATc3N3Q6nbLvzZs36dWrF25ubri5uXH48OGyuTnFTE4x082bNzNz5kxcXV3RarWKeC2A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\nhmF4Goax0DAMm2EYJwzD2GUYxoALjulrGMYBwzBOGYaRcq7iQuS6YRjGo4Z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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3387,7 +3429,7 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 112, "metadata": { "collapsed": false, "deletable": true, @@ -3396,7 +3438,7 @@ "outputs": [], "source": [ "import tensorflow as tf\n", - "tf.reset_default_graph()\n", + "reset_graph()\n", "\n", "n_steps = 50\n", "n_neurons = 200\n", @@ -3432,7 +3474,7 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 113, "metadata": { "collapsed": false, "deletable": true, diff --git a/15_autoencoders.ipynb b/15_autoencoders.ipynb index b667249c9..d423d078f 100644 --- a/15_autoencoders.ipynb +++ b/15_autoencoders.ipynb @@ -55,12 +55,14 @@ "\n", "# Common imports\n", "import numpy as np\n", - "import numpy.random as rnd\n", "import os\n", "import sys\n", "\n", "# to make this notebook's output stable across runs\n", - "rnd.seed(42)\n", + "def reset_graph(seed=42):\n", + " tf.reset_default_graph()\n", + " tf.set_random_seed(seed)\n", + " np.random.seed(seed)\n", "\n", "# To plot pretty figures\n", "%matplotlib inline\n", @@ -159,15 +161,15 @@ "outputs": [], "source": [ "rnd.seed(4)\n", - "m = 100\n", + "m = 200\n", "w1, w2 = 0.1, 0.3\n", "noise = 0.1\n", "\n", "angles = rnd.rand(m) * 3 * np.pi / 2 - 0.5\n", - "X_train = np.empty((m, 3))\n", - "X_train[:, 0] = np.cos(angles) + np.sin(angles)/2 + noise * rnd.randn(m) / 2\n", - "X_train[:, 1] = np.sin(angles) * 0.7 + noise * rnd.randn(m) / 2\n", - "X_train[:, 2] = X_train[:, 0] * w1 + X_train[:, 1] * w2 + noise * rnd.randn(m)" + "data = np.empty((m, 3))\n", + "data[:, 0] = np.cos(angles) + np.sin(angles)/2 + noise * rnd.randn(m) / 2\n", + "data[:, 1] = np.sin(angles) * 0.7 + noise * rnd.randn(m) / 2\n", + "data[:, 2] = data[:, 0] * w1 + data[:, 1] * w2 + noise * rnd.randn(m)" ] }, { @@ -192,30 +194,8 @@ "source": [ "from sklearn.preprocessing import StandardScaler\n", "scaler = StandardScaler()\n", - "X_train = scaler.fit_transform(X_train)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, - "source": [ - "Going to need TensorFlow..." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], - "source": [ - "import tensorflow as tf" + "X_train = scaler.fit_transform(data[:100])\n", + "X_test = scaler.transform(data[100:])" ] }, { @@ -246,7 +226,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, @@ -254,7 +234,9 @@ }, "outputs": [], "source": [ - "tf.reset_default_graph()\n", + "import tensorflow as tf\n", + "\n", + "reset_graph()\n", "\n", "n_inputs = 3\n", "n_hidden = 2 # codings\n", @@ -266,17 +248,17 @@ "hidden = tf.layers.dense(X, n_hidden)\n", "outputs = tf.layers.dense(hidden, n_outputs)\n", "\n", - "mse = tf.reduce_mean(tf.square(outputs - X))\n", + "reconstruction_loss = tf.reduce_mean(tf.square(outputs - X))\n", "\n", "optimizer = tf.train.AdamOptimizer(learning_rate)\n", - "training_op = optimizer.minimize(mse)\n", + "training_op = optimizer.minimize(reconstruction_loss)\n", "\n", "init = tf.global_variables_initializer()" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, @@ -284,19 +266,19 @@ }, "outputs": [], "source": [ - "n_iterations = 10000\n", + "n_iterations = 1000\n", "codings = hidden\n", "\n", "with tf.Session() as sess:\n", " init.run()\n", " for iteration in range(n_iterations):\n", " training_op.run(feed_dict={X: X_train})\n", - " codings_val = codings.eval(feed_dict={X: X_train})" + " codings_val = codings.eval(feed_dict={X: X_test})" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, @@ -312,9 +294,9 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -352,7 +334,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, @@ -407,7 +389,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": { "collapsed": false, "deletable": true, @@ -415,11 +397,11 @@ }, "outputs": [], "source": [ - "tf.reset_default_graph()\n", + "reset_graph()\n", "\n", "from functools import partial\n", "\n", - "n_inputs = 28*28\n", + "n_inputs = 28 * 28\n", "n_hidden1 = 300\n", "n_hidden2 = 150 # codings\n", "n_hidden3 = n_hidden1\n", @@ -428,33 +410,32 @@ "learning_rate = 0.01\n", "l2_reg = 0.0001\n", "\n", - "initializer = tf.contrib.layers.variance_scaling_initializer() # He initialization\n", - "#Equivalent to:\n", - "#initializer = lambda shape, dtype=tf.float32: tf.truncated_normal(shape, 0., stddev=np.sqrt(2/shape[0]))\n", - "\n", "X = tf.placeholder(tf.float32, shape=[None, n_inputs])\n", "\n", - "my_dense_layer = partial(\n", - " tf.layers.dense,\n", - " activation=tf.nn.elu,\n", - " kernel_initializer=initializer,\n", - " kernel_regularizer=tf.contrib.layers.l2_regularizer(l2_reg))\n", + "he_init = tf.contrib.layers.variance_scaling_initializer() # He initialization\n", + "#Equivalent to:\n", + "#he_init = lambda shape, dtype=tf.float32: tf.truncated_normal(shape, 0., stddev=np.sqrt(2/shape[0]))\n", + "l2_regularizer = tf.contrib.layers.l2_regularizer(l2_reg)\n", + "my_dense_layer = partial(tf.layers.dense,\n", + " activation=tf.nn.elu,\n", + " kernel_initializer=he_init,\n", + " kernel_regularizer=l2_regularizer)\n", "\n", "hidden1 = my_dense_layer(X, n_hidden1)\n", "hidden2 = my_dense_layer(hidden1, n_hidden2)\n", "hidden3 = my_dense_layer(hidden2, n_hidden3)\n", "outputs = my_dense_layer(hidden3, n_outputs, activation=None)\n", "\n", - "mse = tf.reduce_mean(tf.square(outputs - X))\n", + "reconstruction_loss = tf.reduce_mean(tf.square(outputs - X))\n", "\n", "reg_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)\n", - "loss = tf.add_n([mse] + reg_losses)\n", + "loss = tf.add_n([reconstruction_loss] + reg_losses)\n", "\n", "optimizer = tf.train.AdamOptimizer(learning_rate)\n", "training_op = optimizer.minimize(loss)\n", "\n", "init = tf.global_variables_initializer()\n", - "saver = tf.train.Saver()" + "saver = tf.train.Saver() # not shown in the book" ] }, { @@ -469,7 +450,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": { "collapsed": false, "deletable": true, @@ -480,15 +461,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "0 Train MSE: 0.025843\n", - "1 Train MSE: 0.0132539\n", - "2 Train MSE: 0.0115193\n", - "3 Train MSE: 0.0110359\n" + "0 Train MSE: 0.0203432\n", + "1 Train MSE: 0.011473\n", + "2 Train MSE: 0.010229\n", + "3 Train MSE: 0.00990439\n", + "4 Train MSE: 0.0103764\n" ] } ], "source": [ - "n_epochs = 4\n", + "n_epochs = 5\n", "batch_size = 150\n", "\n", "with tf.Session() as sess:\n", @@ -496,13 +478,13 @@ " for epoch in range(n_epochs):\n", " n_batches = mnist.train.num_examples // batch_size\n", " for iteration in range(n_batches):\n", - " print(\"\\r{}%\".format(100 * iteration // n_batches), end=\"\")\n", - " sys.stdout.flush()\n", + " print(\"\\r{}%\".format(100 * iteration // n_batches), end=\"\") # not shown in the book\n", + " sys.stdout.flush() # not shown\n", " X_batch, y_batch = mnist.train.next_batch(batch_size)\n", " sess.run(training_op, feed_dict={X: X_batch})\n", - " mse_train = mse.eval(feed_dict={X: X_batch})\n", - " print(\"\\r{}\".format(epoch), \"Train MSE:\", mse_train)\n", - " saver.save(sess, \"./my_model_all_layers.ckpt\")" + " loss_train = reconstruction_loss.eval(feed_dict={X: X_batch}) # not shown\n", + " print(\"\\r{}\".format(epoch), \"Train MSE:\", loss_train) # not shown\n", + " saver.save(sess, \"./my_model_all_layers.ckpt\") # not shown" ] }, { @@ -517,7 +499,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": { "collapsed": false, "deletable": true, @@ -542,7 +524,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": { "collapsed": false, "deletable": true, @@ -553,14 +535,15 @@ "name": "stdout", "output_type": "stream", "text": [ + "INFO:tensorflow:Restoring parameters from ./my_model_all_layers.ckpt\n", "Saving figure reconstruction_plot\n" ] }, { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -572,6 +555,176 @@ "save_fig(\"reconstruction_plot\")" ] }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Tying weights" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "It is common to tie the weights of the encoder and the decoder (`weights_decoder = tf.transpose(weights_encoder)`). Unfortunately this makes it impossible (or very tricky) to use the `tf.layers.dense()` function, so we need to build the Autoencoder manually:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "reset_graph()\n", + "\n", + "n_inputs = 28 * 28\n", + "n_hidden1 = 300\n", + "n_hidden2 = 150 # codings\n", + "n_hidden3 = n_hidden1\n", + "n_outputs = n_inputs\n", + "\n", + "learning_rate = 0.01\n", + "l2_reg = 0.0005" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "activation = tf.nn.elu\n", + "regularizer = tf.contrib.layers.l2_regularizer(l2_reg)\n", + "initializer = tf.contrib.layers.variance_scaling_initializer()\n", + "\n", + "X = tf.placeholder(tf.float32, shape=[None, n_inputs])\n", + "\n", + "weights1_init = initializer([n_inputs, n_hidden1])\n", + "weights2_init = initializer([n_hidden1, n_hidden2])\n", + "\n", + "weights1 = tf.Variable(weights1_init, dtype=tf.float32, name=\"weights1\")\n", + "weights2 = tf.Variable(weights2_init, dtype=tf.float32, name=\"weights2\")\n", + "weights3 = tf.transpose(weights2, name=\"weights3\") # tied weights\n", + "weights4 = tf.transpose(weights1, name=\"weights4\") # tied weights\n", + "\n", + "biases1 = tf.Variable(tf.zeros(n_hidden1), name=\"biases1\")\n", + "biases2 = tf.Variable(tf.zeros(n_hidden2), name=\"biases2\")\n", + "biases3 = tf.Variable(tf.zeros(n_hidden3), name=\"biases3\")\n", + "biases4 = tf.Variable(tf.zeros(n_outputs), name=\"biases4\")\n", + "\n", + "hidden1 = activation(tf.matmul(X, weights1) + biases1)\n", + "hidden2 = activation(tf.matmul(hidden1, weights2) + biases2)\n", + "hidden3 = activation(tf.matmul(hidden2, weights3) + biases3)\n", + "outputs = tf.matmul(hidden3, weights4) + biases4\n", + "\n", + "reconstruction_loss = tf.reduce_mean(tf.square(outputs - X))\n", + "reg_loss = regularizer(weights1) + regularizer(weights2)\n", + "loss = reconstruction_loss + reg_loss\n", + "\n", + "optimizer = tf.train.AdamOptimizer(learning_rate)\n", + "training_op = optimizer.minimize(loss)\n", + "\n", + "init = tf.global_variables_initializer()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "saver = tf.train.Saver()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 Train MSE: 0.0150667\n", + "1 Train MSE: 0.0164884\n", + "2 Train MSE: 0.0173757\n", + "3 Train MSE: 0.0168781\n", + "4 Train MSE: 0.0155875\n" + ] + } + ], + "source": [ + "n_epochs = 5\n", + "batch_size = 150\n", + "\n", + "with tf.Session() as sess:\n", + " init.run()\n", + " for epoch in range(n_epochs):\n", + " n_batches = mnist.train.num_examples // batch_size\n", + " for iteration in range(n_batches):\n", + " print(\"\\r{}%\".format(100 * iteration // n_batches), end=\"\")\n", + " sys.stdout.flush()\n", + " X_batch, y_batch = mnist.train.next_batch(batch_size)\n", + " sess.run(training_op, feed_dict={X: X_batch})\n", + " loss_train = reconstruction_loss.eval(feed_dict={X: X_batch})\n", + " print(\"\\r{}\".format(epoch), \"Train MSE:\", loss_train)\n", + " saver.save(sess, \"./my_model_tying_weights.ckpt\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Restoring parameters from ./my_model_tying_weights.ckpt\n" + ] + }, + { + "data": { + "image/png": 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AhMHQAgCEUcnPtADgv8r5PEjxPuPJ+TxopJ8dqd6Rfl+luAePurecz55ylOIzOJ60AABh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\nMLQAAGEwtAAAYTC0AABhMLQAAGH8B+VGgpqOIIDlAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "show_reconstructed_digits(X, outputs, \"./my_model_tying_weights.ckpt\")" + ] + }, { "cell_type": "markdown", "metadata": { @@ -599,12 +752,12 @@ "editable": true }, "source": [ - "Let's create a function that will train one autoencoder and return the transformed training set (ie. the output of the hidden layer) and the model parameters." + "Let's create a function that will train one autoencoder and return the transformed training set (i.e., the output of the hidden layer) and the model parameters." ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 19, "metadata": { "collapsed": true, "deletable": true, @@ -612,11 +765,17 @@ }, "outputs": [], "source": [ + "reset_graph()\n", + "\n", "from functools import partial\n", "\n", - "def train_autoencoder(X_train, n_neurons, n_epochs, batch_size, learning_rate = 0.01, l2_reg = 0.0005, activation=tf.nn.elu):\n", + "def train_autoencoder(X_train, n_neurons, n_epochs, batch_size,\n", + " learning_rate = 0.01, l2_reg = 0.0005,\n", + " activation=tf.nn.elu, seed=42):\n", " graph = tf.Graph()\n", " with graph.as_default():\n", + " tf.set_random_seed(seed)\n", + "\n", " n_inputs = X_train.shape[1]\n", "\n", " X = tf.placeholder(tf.float32, shape=[None, n_inputs])\n", @@ -630,10 +789,10 @@ " hidden = my_dense_layer(X, n_neurons, name=\"hidden\")\n", " outputs = my_dense_layer(hidden, n_inputs, activation=None, name=\"outputs\")\n", "\n", - " mse = tf.reduce_mean(tf.square(outputs - X))\n", + " reconstruction_loss = tf.reduce_mean(tf.square(outputs - X))\n", "\n", " reg_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)\n", - " loss = tf.add_n([mse] + reg_losses)\n", + " loss = tf.add_n([reconstruction_loss] + reg_losses)\n", "\n", " optimizer = tf.train.AdamOptimizer(learning_rate)\n", " training_op = optimizer.minimize(loss)\n", @@ -650,8 +809,8 @@ " indices = rnd.permutation(len(X_train))[:batch_size]\n", " X_batch = X_train[indices]\n", " sess.run(training_op, feed_dict={X: X_batch})\n", - " mse_train = mse.eval(feed_dict={X: X_batch})\n", - " print(\"\\r{}\".format(epoch), \"Train MSE:\", mse_train)\n", + " loss_train = reconstruction_loss.eval(feed_dict={X: X_batch})\n", + " print(\"\\r{}\".format(epoch), \"Train MSE:\", loss_train)\n", " params = dict([(var.name, var.eval()) for var in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)])\n", " hidden_val = hidden.eval(feed_dict={X: X_train})\n", " return hidden_val, params[\"hidden/kernel:0\"], params[\"hidden/bias:0\"], params[\"outputs/kernel:0\"], params[\"outputs/bias:0\"]" @@ -669,7 +828,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 20, "metadata": { "collapsed": false, "deletable": true, @@ -680,14 +839,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "0 Train MSE: 0.0193756\n", - "1 Train MSE: 0.019141\n", - "2 Train MSE: 0.018939\n", - "3 Train MSE: 0.0192285\n", - "0 Train MSE: 0.00419925\n", - "1 Train MSE: 0.00431123\n", - "2 Train MSE: 0.00460423\n", - "3 Train MSE: 0.00459113\n" + "0 Train MSE: 0.0185175\n", + "1 Train MSE: 0.0186825\n", + "2 Train MSE: 0.0184675\n", + "3 Train MSE: 0.0192315\n", + "0 Train MSE: 0.00410999\n", + "1 Train MSE: 0.00461446\n", + "2 Train MSE: 0.00455271\n", + "3 Train MSE: 0.00431479\n" ] } ], @@ -708,7 +867,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 21, "metadata": { "collapsed": false, "deletable": true, @@ -716,7 +875,7 @@ }, "outputs": [], "source": [ - "tf.reset_default_graph()\n", + "reset_graph()\n", "\n", "n_inputs = 28*28\n", "\n", @@ -729,7 +888,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 22, "metadata": { "collapsed": false, "deletable": true, @@ -738,9 +897,9 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -773,15 +932,15 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 23, "metadata": { - "collapsed": false, + "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ - "tf.reset_default_graph()\n", + "reset_graph()\n", "\n", "n_inputs = 28 * 28\n", "n_hidden1 = 300\n", @@ -818,29 +977,53 @@ "hidden3 = activation(tf.matmul(hidden2, weights3) + biases3)\n", "outputs = tf.matmul(hidden3, weights4) + biases4\n", "\n", + "reconstruction_loss = tf.reduce_mean(tf.square(outputs - X))" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "optimizer = tf.train.AdamOptimizer(learning_rate)\n", "\n", "with tf.name_scope(\"phase1\"):\n", - " optimizer = tf.train.AdamOptimizer(learning_rate)\n", " phase1_outputs = tf.matmul(hidden1, weights4) + biases4 # bypass hidden2 and hidden3\n", - " phase1_mse = tf.reduce_mean(tf.square(phase1_outputs - X))\n", + " phase1_reconstruction_loss = tf.reduce_mean(tf.square(phase1_outputs - X))\n", " phase1_reg_loss = regularizer(weights1) + regularizer(weights4)\n", - " phase1_loss = phase1_mse + phase1_reg_loss\n", + " phase1_loss = phase1_reconstruction_loss + phase1_reg_loss\n", " phase1_training_op = optimizer.minimize(phase1_loss)\n", "\n", "with tf.name_scope(\"phase2\"):\n", - " optimizer = tf.train.AdamOptimizer(learning_rate)\n", - " phase2_mse = tf.reduce_mean(tf.square(hidden3 - hidden1))\n", + " phase2_reconstruction_loss = tf.reduce_mean(tf.square(hidden3 - hidden1))\n", " phase2_reg_loss = regularizer(weights2) + regularizer(weights3)\n", - " phase2_loss = phase2_mse + phase2_reg_loss\n", - " phase2_training_op = optimizer.minimize(phase2_loss, var_list=[weights2, biases2, weights3, biases3]) # freeze hidden1\n", - " \n", + " phase2_loss = phase2_reconstruction_loss + phase2_reg_loss\n", + " train_vars = [weights2, biases2, weights3, biases3]\n", + " phase2_training_op = optimizer.minimize(phase2_loss, var_list=train_vars) # freeze hidden1" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ "init = tf.global_variables_initializer()\n", "saver = tf.train.Saver()" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 26, "metadata": { "collapsed": false, "deletable": true, @@ -852,22 +1035,22 @@ "output_type": "stream", "text": [ "Training phase #1\n", - "0 Train MSE: 0.00777324\n", - "1 Train MSE: 0.0075254\n", - "2 Train MSE: 0.00773622\n", - "3 Train MSE: 0.00787562\n", + "0 Train MSE: 0.0074068\n", + "1 Train MSE: 0.00782866\n", + "2 Train MSE: 0.00772802\n", + "3 Train MSE: 0.00740893\n", "Training phase #2\n", - "0 Train MSE: 0.00219898\n", - "1 Train MSE: 0.0025481\n", - "2 Train MSE: 0.00238456\n", - "3 Train MSE: 0.00263778\n", - "Test MSE: 0.00289071\n" + "0 Train MSE: 0.279671\n", + "1 Train MSE: 0.00553525\n", + "2 Train MSE: 0.00291541\n", + "3 Train MSE: 0.00238866\n", + "Test MSE: 0.00976381\n" ] } ], "source": [ "training_ops = [phase1_training_op, phase2_training_op]\n", - "mses = [phase1_mse, phase2_mse]\n", + "reconstruction_losses = [phase1_reconstruction_loss, phase2_reconstruction_loss]\n", "n_epochs = [4, 4]\n", "batch_sizes = [150, 150]\n", "\n", @@ -882,39 +1065,15 @@ " sys.stdout.flush()\n", " X_batch, y_batch = mnist.train.next_batch(batch_sizes[phase])\n", " sess.run(training_ops[phase], feed_dict={X: X_batch})\n", - " mse_train = mses[phase].eval(feed_dict={X: X_batch})\n", - " print(\"\\r{}\".format(epoch), \"Train MSE:\", mse_train)\n", + " loss_train = reconstruction_losses[phase].eval(feed_dict={X: X_batch})\n", + " print(\"\\r{}\".format(epoch), \"Train MSE:\", loss_train)\n", " saver.save(sess, \"./my_model_one_at_a_time.ckpt\")\n", - " mse_test = mses[phase].eval(feed_dict={X: mnist.test.images})\n", - " print(\"Test MSE:\", mse_test)" + " loss_test = reconstruction_loss.eval(feed_dict={X: mnist.test.images})\n", + " print(\"Test MSE:\", loss_test)" ] }, { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "show_reconstructed_digits(X, outputs, \"./my_model_one_at_a_time.ckpt\")" - ] - }, - { - "cell_type": "markdown", + "cell_type": "markdown", "metadata": { "deletable": true, "editable": true @@ -925,11 +1084,12 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 27, "metadata": { "collapsed": false, "deletable": true, - "editable": true + "editable": true, + "scrolled": true }, "outputs": [ { @@ -937,22 +1097,22 @@ "output_type": "stream", "text": [ "Training phase #1\n", - "0 Train MSE: 0.00725573\n", - "1 Train MSE: 0.00784995\n", - "2 Train MSE: 0.00773007\n", - "3 Train MSE: 0.00777166\n", + "0 Train MSE: 0.00753817\n", + "1 Train MSE: 0.00775457\n", + "2 Train MSE: 0.00734359\n", + "3 Train MSE: 0.00783768\n", "Training phase #2\n", - "0 Train MSE: 0.002398\n", - "1 Train MSE: 0.00257562\n", - "2 Train MSE: 0.00279413\n", - "3 Train MSE: 0.00285046\n", - "Test MSE: 0.00302882\n" + "0 Train MSE: 0.229686\n", + "1 Train MSE: 0.00474355\n", + "2 Train MSE: 0.00255261\n", + "3 Train MSE: 0.00206523\n", + "Test MSE: 0.00978721\n" ] } ], "source": [ - "training_ops = [phase1_training_op, phase2_training_op, training_op]\n", - "mses = [phase1_mse, phase2_mse, mse]\n", + "training_ops = [phase1_training_op, phase2_training_op]\n", + "reconstruction_losses = [phase1_reconstruction_loss, phase2_reconstruction_loss]\n", "n_epochs = [4, 4]\n", "batch_sizes = [150, 150]\n", "\n", @@ -961,43 +1121,59 @@ " for phase in range(2):\n", " print(\"Training phase #{}\".format(phase + 1))\n", " if phase == 1:\n", - " mnist_hidden1 = hidden1.eval(feed_dict={X: mnist.train.images})\n", + " hidden1_cache = hidden1.eval(feed_dict={X: mnist.train.images})\n", " for epoch in range(n_epochs[phase]):\n", " n_batches = mnist.train.num_examples // batch_sizes[phase]\n", " for iteration in range(n_batches):\n", " print(\"\\r{}%\".format(100 * iteration // n_batches), end=\"\")\n", " sys.stdout.flush()\n", " if phase == 1:\n", - " indices = rnd.permutation(len(mnist_hidden1))\n", - " hidden1_batch = mnist_hidden1[indices[:batch_sizes[phase]]]\n", + " indices = rnd.permutation(mnist.train.num_examples)\n", + " hidden1_batch = hidden1_cache[indices[:batch_sizes[phase]]]\n", " feed_dict = {hidden1: hidden1_batch}\n", " sess.run(training_ops[phase], feed_dict=feed_dict)\n", " else:\n", " X_batch, y_batch = mnist.train.next_batch(batch_sizes[phase])\n", " feed_dict = {X: X_batch}\n", " sess.run(training_ops[phase], feed_dict=feed_dict)\n", - " mse_train = mses[phase].eval(feed_dict=feed_dict)\n", - " print(\"\\r{}\".format(epoch), \"Train MSE:\", mse_train)\n", + " loss_train = reconstruction_losses[phase].eval(feed_dict=feed_dict)\n", + " print(\"\\r{}\".format(epoch), \"Train MSE:\", loss_train)\n", " saver.save(sess, \"./my_model_cache_frozen.ckpt\")\n", - " mse_test = mses[phase].eval(feed_dict={X: mnist.test.images})\n", - " print(\"Test MSE:\", mse_test)" + " loss_test = reconstruction_loss.eval(feed_dict={X: mnist.test.images})\n", + " print(\"Test MSE:\", loss_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Visualizing the Reconstructions" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 28, "metadata": { "collapsed": false, "deletable": true, - "editable": true, - "scrolled": true + "editable": true }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Restoring parameters from ./my_model_one_at_a_time.ckpt\n" + ] + }, { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1005,17 +1181,22 @@ } ], "source": [ - "show_reconstructed_digits(X, outputs, \"./my_model_cache_frozen.ckpt\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, - "source": [ - "## Tying weights" + "n_test_digits = 2\n", + "X_test = mnist.test.images[:n_test_digits]\n", + "\n", + "with tf.Session() as sess:\n", + " saver.restore(sess, \"./my_model_one_at_a_time.ckpt\") # not shown in the book\n", + " outputs_val = outputs.eval(feed_dict={X: X_test})\n", + "\n", + "def plot_image(image, shape=[28, 28]):\n", + " plt.imshow(image.reshape(shape), cmap=\"Greys\", interpolation=\"nearest\")\n", + " plt.axis(\"off\")\n", + "\n", + "for digit_index in range(n_test_digits):\n", + " plt.subplot(n_test_digits, 2, digit_index * 2 + 1)\n", + " plot_image(X_test[digit_index])\n", + " plt.subplot(n_test_digits, 2, digit_index * 2 + 2)\n", + " plot_image(outputs_val[digit_index])" ] }, { @@ -1025,68 +1206,12 @@ "editable": true }, "source": [ - "It is common to tie the weights of the encoder and the decoder (`weights_decoder = tf.transpose(weights_encoder)`). Unfortunately this makes it impossible (or very tricky) to use the `tf.layers.dense()` function, so we need to build the Autoencoder manually:" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], - "source": [ - "tf.reset_default_graph()\n", - "\n", - "n_inputs = 28 * 28\n", - "n_hidden1 = 300\n", - "n_hidden2 = 150 # codings\n", - "n_hidden3 = n_hidden1\n", - "n_outputs = n_inputs\n", - "\n", - "learning_rate = 0.01\n", - "l2_reg = 0.0005\n", - "\n", - "activation = tf.nn.elu\n", - "regularizer = tf.contrib.layers.l2_regularizer(l2_reg)\n", - "initializer = tf.contrib.layers.variance_scaling_initializer()\n", - "\n", - "X = tf.placeholder(tf.float32, shape=[None, n_inputs])\n", - "\n", - "weights1_init = initializer([n_inputs, n_hidden1])\n", - "weights2_init = initializer([n_hidden1, n_hidden2])\n", - "\n", - "weights1 = tf.Variable(weights1_init, dtype=tf.float32, name=\"weights1\")\n", - "weights2 = tf.Variable(weights2_init, dtype=tf.float32, name=\"weights2\")\n", - "weights3 = tf.transpose(weights2, name=\"weights3\") # tied weights\n", - "weights4 = tf.transpose(weights1, name=\"weights4\") # tied weights\n", - "\n", - "biases1 = tf.Variable(tf.zeros(n_hidden1), name=\"biases1\")\n", - "biases2 = tf.Variable(tf.zeros(n_hidden2), name=\"biases2\")\n", - "biases3 = tf.Variable(tf.zeros(n_hidden3), name=\"biases3\")\n", - "biases4 = tf.Variable(tf.zeros(n_outputs), name=\"biases4\")\n", - "\n", - "hidden1 = activation(tf.matmul(X, weights1) + biases1)\n", - "hidden2 = activation(tf.matmul(hidden1, weights2) + biases2)\n", - "hidden3 = activation(tf.matmul(hidden2, weights3) + biases3)\n", - "outputs = tf.matmul(hidden3, weights4) + biases4\n", - "\n", - "mse = tf.reduce_mean(tf.square(outputs - X))\n", - "reg_loss = regularizer(weights1) + regularizer(weights2)\n", - "loss = mse + reg_loss\n", - "\n", - "optimizer = tf.train.AdamOptimizer(learning_rate)\n", - "training_op = optimizer.minimize(loss)\n", - "\n", - "init = tf.global_variables_initializer()\n", - "saver = tf.train.Saver()" + "## Visualizing the extracted features" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 29, "metadata": { "collapsed": false, "deletable": true, @@ -1097,54 +1222,42 @@ "name": "stdout", "output_type": "stream", "text": [ - "0 Train MSE: 0.016999\n", - "1 Train MSE: 0.0166273\n", - "2 Train MSE: 0.0163764\n", - "3 Train MSE: 0.0172502\n", - "4 Train MSE: 0.016203\n" + "INFO:tensorflow:Restoring parameters from ./my_model_one_at_a_time.ckpt\n", + "Saving figure extracted_features_plot\n" ] + }, + { + "data": { + "image/png": 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jspSoekbEMbzCFmAexGyvFnjG4pk+apQ8U14RuNmimjCnfLFYmC3/w77koDaq\n5NtpCgoMBoOKT/pJu34pJb/RX7PZrCgL1AOv+dYL+XfJCfj2UCo2U1AgV5VEPHzbBuRJUAR5pAA1\nz/XITfnFgLmGr2K9nK3MS0Ze2emjIKhMfAwb5EupSev9AVElH1nBTv47tHXO/d9u86GkAoFAIFAs\najsxBhsIBAKB/w2EkgoEAoFAsYhBKhAIBALFIgapQCAQCBSLGKQCgUAgUCxikAoEAoFAsYhBKhAI\nBALFIgapQCAQCBSLGKQCgUAgUCxikAoEAoFAsYhBKhAIBALFIgapQCAQCBSLGKQCgUAgUCxikAoE\nAoFAsYhBKhAIBALFIgapQCAQCBSLGKQCgUAgUCxikAoEAoFAsYhBKhAIBALFIgapQCAQCBSLGKQC\ngUAgUCxikAoEAoFAsYhBKhAIBALFIgapQCAQCBSL/wN7k7sE/apoVAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "n_epochs = 5\n", - "batch_size = 150\n", - "\n", "with tf.Session() as sess:\n", - " init.run()\n", - " for epoch in range(n_epochs):\n", - " n_batches = mnist.train.num_examples // batch_size\n", - " for iteration in range(n_batches):\n", - " print(\"\\r{}%\".format(100 * iteration // n_batches), end=\"\")\n", - " sys.stdout.flush()\n", - " X_batch, y_batch = mnist.train.next_batch(batch_size)\n", - " sess.run(training_op, feed_dict={X: X_batch})\n", - " mse_train = mse.eval(feed_dict={X: X_batch})\n", - " print(\"\\r{}\".format(epoch), \"Train MSE:\", mse_train)\n", - " saver.save(sess, \"./my_model_tying_weights.ckpt\")" + " saver.restore(sess, \"./my_model_one_at_a_time.ckpt\") # not shown in the book\n", + " weights1_val = weights1.eval()\n", + "\n", + "for i in range(5):\n", + " plt.subplot(1, 5, i + 1)\n", + " plot_image(weights1_val.T[i])\n", + "\n", + "save_fig(\"extracted_features_plot\") # not shown\n", + "plt.show() # not shown" ] }, { - "cell_type": "code", - "execution_count": 26, + "cell_type": "markdown", "metadata": { - "collapsed": false, "deletable": true, "editable": true }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ - "show_reconstructed_digits(X, outputs, \"./my_model_tying_weights.ckpt\")" + "# Unsupervised pretraining" ] }, { @@ -1154,12 +1267,12 @@ "editable": true }, "source": [ - "# Unsupervised pretraining" + "Let's create a small neural network for MNIST classification:" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 30, "metadata": { "collapsed": false, "deletable": true, @@ -1167,7 +1280,7 @@ }, "outputs": [], "source": [ - "tf.reset_default_graph()\n", + "reset_graph()\n", "\n", "n_inputs = 28 * 28\n", "n_hidden1 = 300\n", @@ -1226,7 +1339,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 31, "metadata": { "collapsed": false, "deletable": true, @@ -1237,10 +1350,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "0 Train accuracy: 0.946667 Test accuracy: 0.9344\n", - "1 Train accuracy: 0.973333 Test accuracy: 0.9274\n", - "2 Train accuracy: 0.953333 Test accuracy: 0.9421\n", - "3 Train accuracy: 0.98 Test accuracy: 0.9435\n" + "0 Train accuracy: 0.94 Test accuracy: 0.9247\n", + "1 Train accuracy: 0.973333 Test accuracy: 0.9328\n", + "2 Train accuracy: 0.986667 Test accuracy: 0.9406\n", + "3 Train accuracy: 0.98 Test accuracy: 0.9425\n" ] } ], @@ -1278,7 +1391,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 32, "metadata": { "collapsed": false, "deletable": true, @@ -1289,10 +1402,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "0 Train accuracy: 0.913333\tTest accuracy: 0.9105\n", - "1 Train accuracy: 0.986667\tTest accuracy: 0.9366\n", - "2 Train accuracy: 0.946667\tTest accuracy: 0.9386\n", - "3 Train accuracy: 0.98\tTest accuracy: 0.9503\n" + "INFO:tensorflow:Restoring parameters from ./my_model_cache_frozen.ckpt\n", + "09% Train accuracy: 0.94\tTest accuracy: 0.9255\n", + "1 Train accuracy: 0.973333\tTest accuracy: 0.9423\n", + "2 Train accuracy: 0.993333\tTest accuracy: 0.941\n", + "3 Train accuracy: 0.973333\tTest accuracy: 0.9458\n" ] } ], @@ -1333,24 +1447,37 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "Note: the book uses `tf.contrib.layers.dropout()` rather than `tf.layers.dropout()` (which did not exist when this chapter was written). It is now preferable to use `tf.layers.dropout()`, because anything in the contrib module may change or be deleted without notice. The `tf.layers.dropout()` function is almost identical to the `tf.contrib.layers.dropout()` function, except for a few minor differences. Most importantly:\n", "* you must specify the dropout rate (`rate`) rather than the keep probability (`keep_prob`), where `rate` is simply equal to `1 - keep_prob`,\n", "* the `is_training` parameter is renamed to `training`." ] }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Using Gaussian noise:" + ] + }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 33, "metadata": { - "collapsed": false, + "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ - "tf.reset_default_graph()\n", + "reset_graph()\n", "\n", "n_inputs = 28 * 28\n", "n_hidden1 = 300\n", @@ -1358,42 +1485,47 @@ "n_hidden3 = n_hidden1\n", "n_outputs = n_inputs\n", "\n", - "learning_rate = 0.01\n", - "l2_reg = 0.00001\n", - "dropout_rate = 0.3\n", - "\n", - "activation = tf.nn.elu\n", - "regularizer = tf.contrib.layers.l2_regularizer(l2_reg)\n", - "initializer = tf.contrib.layers.variance_scaling_initializer()\n", + "learning_rate = 0.01" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "noise_level = 1.0\n", "\n", "X = tf.placeholder(tf.float32, shape=[None, n_inputs])\n", - "is_training = tf.placeholder_with_default(False, shape=(), name='is_training')\n", - "\n", - "X_drop = tf.layers.dropout(X, dropout_rate, training=is_training)\n", - "\n", - "weights1_init = initializer([n_inputs, n_hidden1])\n", - "weights2_init = initializer([n_hidden1, n_hidden2])\n", - "\n", - "weights1 = tf.Variable(weights1_init, dtype=tf.float32, name=\"weights1\")\n", - "weights2 = tf.Variable(weights2_init, dtype=tf.float32, name=\"weights2\")\n", - "weights3 = tf.transpose(weights2, name=\"weights3\") # tied weights\n", - "weights4 = tf.transpose(weights1, name=\"weights4\") # tied weights\n", + "X_noisy = X + noise_level * tf.random_normal(tf.shape(X))\n", "\n", - "biases1 = tf.Variable(tf.zeros(n_hidden1), name=\"biases1\")\n", - "biases2 = tf.Variable(tf.zeros(n_hidden2), name=\"biases2\")\n", - "biases3 = tf.Variable(tf.zeros(n_hidden3), name=\"biases3\")\n", - "biases4 = tf.Variable(tf.zeros(n_outputs), name=\"biases4\")\n", - "\n", - "hidden1 = activation(tf.matmul(X_drop, weights1) + biases1)\n", - "hidden2 = activation(tf.matmul(hidden1, weights2) + biases2)\n", - "hidden3 = activation(tf.matmul(hidden2, weights3) + biases3)\n", - "outputs = tf.matmul(hidden3, weights4) + biases4\n", + "hidden1 = tf.layers.dense(X_noisy, n_hidden1, activation=tf.nn.relu,\n", + " name=\"hidden1\")\n", + "hidden2 = tf.layers.dense(hidden1, n_hidden2, activation=tf.nn.relu, # not shown in the book\n", + " name=\"hidden2\") # not shown\n", + "hidden3 = tf.layers.dense(hidden2, n_hidden3, activation=tf.nn.relu, # not shown\n", + " name=\"hidden3\") # not shown\n", + "outputs = tf.layers.dense(hidden3, n_outputs, name=\"outputs\") # not shown\n", "\n", + "reconstruction_loss = tf.reduce_mean(tf.square(outputs - X)) # MSE" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ "optimizer = tf.train.AdamOptimizer(learning_rate)\n", - "mse = tf.reduce_mean(tf.square(outputs - X))\n", - "reg_loss = regularizer(weights1) + regularizer(weights2)\n", - "loss = mse + reg_loss\n", - "training_op = optimizer.minimize(loss)\n", + "training_op = optimizer.minimize(reconstruction_loss)\n", " \n", "init = tf.global_variables_initializer()\n", "saver = tf.train.Saver()" @@ -1401,7 +1533,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 36, "metadata": { "collapsed": false, "deletable": true, @@ -1412,16 +1544,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "0 Train MSE: 0.019915\n", - "1 Train MSE: 0.0135197\n", - "2 Train MSE: 0.011092\n", - "3 Train MSE: 0.0103048\n", - "4 Train MSE: 0.00956749\n", - "5 Train MSE: 0.00912805\n", - "6 Train MSE: 0.00907707\n", - "7 Train MSE: 0.00908674\n", - "8 Train MSE: 0.00848128\n", - "9 Train MSE: 0.00875844\n" + "0 Train MSE: 0.0440489\n", + "1 Train MSE: 0.0432517\n", + "2 Train MSE: 0.042057\n", + "3 Train MSE: 0.0409477\n", + "4 Train MSE: 0.0402107\n", + "5 Train MSE: 0.0388787\n", + "6 Train MSE: 0.0391096\n", + "7 Train MSE: 0.0421885\n", + "8 Train MSE: 0.0398648\n", + "9 Train MSE: 0.0408181\n" ] } ], @@ -1437,49 +1569,74 @@ " print(\"\\r{}%\".format(100 * iteration // n_batches), end=\"\")\n", " sys.stdout.flush()\n", " X_batch, y_batch = mnist.train.next_batch(batch_size)\n", - " sess.run(training_op, feed_dict={X: X_batch, is_training: True})\n", - " mse_train = mse.eval(feed_dict={X: X_batch, is_training: False})\n", - " print(\"\\r{}\".format(epoch), \"Train MSE:\", mse_train)\n", - " saver.save(sess, \"./my_model_stacked_denoising.ckpt\")" + " sess.run(training_op, feed_dict={X: X_batch})\n", + " loss_train = reconstruction_loss.eval(feed_dict={X: X_batch})\n", + " print(\"\\r{}\".format(epoch), \"Train MSE:\", loss_train)\n", + " saver.save(sess, \"./my_model_stacked_denoising_gaussian.ckpt\")" ] }, { - "cell_type": "code", - "execution_count": 33, + "cell_type": "markdown", "metadata": { - "collapsed": false, "deletable": true, "editable": true }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ - "show_reconstructed_digits(X, outputs, \"./my_model_stacked_denoising.ckpt\")" + "Using dropout:" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 37, "metadata": { + "collapsed": true, "deletable": true, "editable": true }, + "outputs": [], "source": [ - "## Visualizing the extracted features" - ] - }, - { + "reset_graph()\n", + "\n", + "n_inputs = 28 * 28\n", + "n_hidden1 = 300\n", + "n_hidden2 = 150 # codings\n", + "n_hidden3 = n_hidden1\n", + "n_outputs = n_inputs\n", + "\n", + "learning_rate = 0.01" + ] + }, + { "cell_type": "code", - "execution_count": 34, + "execution_count": 38, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "dropout_rate = 0.3\n", + "\n", + "training = tf.placeholder_with_default(False, shape=(), name='training')\n", + "\n", + "X = tf.placeholder(tf.float32, shape=[None, n_inputs])\n", + "X_drop = tf.layers.dropout(X, dropout_rate, training=training)\n", + "\n", + "hidden1 = tf.layers.dense(X_drop, n_hidden1, activation=tf.nn.relu,\n", + " name=\"hidden1\")\n", + "hidden2 = tf.layers.dense(hidden1, n_hidden2, activation=tf.nn.relu, # not shown in the book\n", + " name=\"hidden2\") # not shown\n", + "hidden3 = tf.layers.dense(hidden2, n_hidden3, activation=tf.nn.relu, # not shown\n", + " name=\"hidden3\") # not shown\n", + "outputs = tf.layers.dense(hidden3, n_outputs, name=\"outputs\") # not shown\n", + "\n", + "reconstruction_loss = tf.reduce_mean(tf.square(outputs - X)) # MSE" + ] + }, + { + "cell_type": "code", + "execution_count": 39, "metadata": { "collapsed": false, "deletable": true, @@ -1487,14 +1644,60 @@ }, "outputs": [], "source": [ + "optimizer = tf.train.AdamOptimizer(learning_rate)\n", + "training_op = optimizer.minimize(reconstruction_loss)\n", + " \n", + "init = tf.global_variables_initializer()\n", + "saver = tf.train.Saver()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 Train MSE: 0.0296476\n", + "1 Train MSE: 0.0275545\n", + "2 Train MSE: 0.0250731\n", + "3 Train MSE: 0.0254317\n", + "4 Train MSE: 0.0249076\n", + "5 Train MSE: 0.0250501\n", + "6 Train MSE: 0.024483\n", + "7 Train MSE: 0.0251505\n", + "8 Train MSE: 0.0243836\n", + "9 Train MSE: 0.0242349\n" + ] + } + ], + "source": [ + "n_epochs = 10\n", + "batch_size = 150\n", + "\n", "with tf.Session() as sess:\n", - " saver.restore(sess, \"./my_model_stacked_denoising.ckpt\")\n", - " weights1_val = weights1.eval()" + " init.run()\n", + " for epoch in range(n_epochs):\n", + " n_batches = mnist.train.num_examples // batch_size\n", + " for iteration in range(n_batches):\n", + " print(\"\\r{}%\".format(100 * iteration // n_batches), end=\"\")\n", + " sys.stdout.flush()\n", + " X_batch, y_batch = mnist.train.next_batch(batch_size)\n", + " sess.run(training_op, feed_dict={X: X_batch, training: True})\n", + " loss_train = reconstruction_loss.eval(feed_dict={X: X_batch})\n", + " print(\"\\r{}\".format(epoch), \"Train MSE:\", loss_train)\n", + " saver.save(sess, \"./my_model_stacked_denoising_dropout.ckpt\")" ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 41, "metadata": { "collapsed": false, "deletable": true, @@ -1505,14 +1708,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Saving figure extracted_features_plot\n" + "INFO:tensorflow:Restoring parameters from ./my_model_stacked_denoising_dropout.ckpt\n" ] }, { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1520,12 +1723,7 @@ } ], "source": [ - "for i in range(5):\n", - " plt.subplot(1, 5, i + 1)\n", - " plot_image(weights1_val.T[i])\n", - "\n", - "save_fig(\"extracted_features_plot\")\n", - "plt.show()" + "show_reconstructed_digits(X, outputs, \"./my_model_stacked_denoising_dropout.ckpt\")" ] }, { @@ -1540,21 +1738,13 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 42, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/ageron/dev/py/envs/ml/lib/python3.5/site-packages/ipykernel/__main__.py:3: RuntimeWarning: divide by zero encountered in true_divide\n", - " app.launch_new_instance()\n" - ] - }, { "name": "stdout", "output_type": "stream", @@ -1564,9 +1754,9 @@ }, { "data": { - "image/png": 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piudSn0/5h6goaNUKBg6E9u2djsaLiNipjsotli5dytKlS3PtfE4lqJ3ANSJS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\naYJSSinlkf4fSRC3hLflMqQAAAAASUVORK5CYII=\n", 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Vqmly8gjjxsG6dbByZbaTU27QFpRSynGRkVCrFoSHQ506Tkfj5+bNg2eftaXjK1bM0al0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\nEYl2rYeakt5JtQXlv5YutVt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QazMDmINtHRQCXgYOOR1/Hl+bAkARYCx28khhbBd56uPu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\nAABJRU5ErkJggg==\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1575,7 +1765,7 @@ ], "source": [ "p = 0.1\n", - "q = np.linspace(0, 1, 500)\n", + "q = np.linspace(0.001, 0.999, 500)\n", "kl_div = p * np.log(p / q) + (1 - p) * np.log((1 - p) / (1 - q))\n", "mse = (p - q)**2\n", "plt.plot([p, p], [0, 0.3], \"k:\")\n", @@ -1591,7 +1781,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 43, "metadata": { "collapsed": true, "deletable": true, @@ -1599,14 +1789,16 @@ }, "outputs": [], "source": [ - "def kl_divergence(p, q):\n", - " \"\"\"Kullback Leibler divergence\"\"\"\n", - " return p * tf.log(p / q) + (1 - p) * tf.log((1 - p) / (1 - q))" + "reset_graph()\n", + "\n", + "n_inputs = 28 * 28\n", + "n_hidden1 = 1000 # sparse codings\n", + "n_outputs = n_inputs" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 44, "metadata": { "collapsed": false, "deletable": true, @@ -1614,49 +1806,45 @@ }, "outputs": [], "source": [ - "tf.reset_default_graph()\n", - "\n", - "n_inputs = 28 * 28\n", - "n_hidden1 = 1000 # sparse codings\n", - "n_outputs = n_inputs\n", + "def kl_divergence(p, q):\n", + " # Kullback Leibler divergence\n", + " return p * tf.log(p / q) + (1 - p) * tf.log((1 - p) / (1 - q))\n", "\n", "learning_rate = 0.01\n", "sparsity_target = 0.1\n", "sparsity_weight = 0.2\n", "\n", - "#activation = tf.nn.softplus # soft variant of ReLU\n", - "activation = tf.nn.sigmoid\n", - "initializer = tf.contrib.layers.variance_scaling_initializer()\n", - "\n", - "X = tf.placeholder(tf.float32, shape=[None, n_inputs])\n", - "\n", - "weights1_init = initializer([n_inputs, n_hidden1])\n", - "weights2_init = initializer([n_hidden1, n_outputs])\n", + "X = tf.placeholder(tf.float32, shape=[None, n_inputs]) # not shown in the book\n", "\n", - "weights1 = tf.Variable(weights1_init, dtype=tf.float32, name=\"weights1\")\n", - "weights2 = tf.Variable(weights2_init, dtype=tf.float32, name=\"weights2\")\n", - "\n", - "biases1 = tf.Variable(tf.zeros(n_hidden1), name=\"biases1\")\n", - "biases2 = tf.Variable(tf.zeros(n_outputs), name=\"biases2\")\n", - "\n", - "hidden1 = activation(tf.matmul(X, weights1) + biases1)\n", - "outputs = tf.matmul(hidden1, weights2) + biases2\n", - "\n", - "optimizer = tf.train.AdamOptimizer(learning_rate)\n", - "mse = tf.reduce_mean(tf.square(outputs - X))\n", + "hidden1 = tf.layers.dense(X, n_hidden1, activation=tf.nn.sigmoid) # not shown\n", + "outputs = tf.layers.dense(hidden1, n_outputs) # not shown\n", "\n", "hidden1_mean = tf.reduce_mean(hidden1, axis=0) # batch mean\n", "sparsity_loss = tf.reduce_sum(kl_divergence(sparsity_target, hidden1_mean))\n", - "loss = mse + sparsity_weight * sparsity_loss\n", - "training_op = optimizer.minimize(loss)\n", + "reconstruction_loss = tf.reduce_mean(tf.square(outputs - X)) # MSE\n", + "loss = reconstruction_loss + sparsity_weight * sparsity_loss\n", "\n", + "optimizer = tf.train.AdamOptimizer(learning_rate)\n", + "training_op = optimizer.minimize(loss)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ "init = tf.global_variables_initializer()\n", "saver = tf.train.Saver()" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 46, "metadata": { "collapsed": false, "deletable": true, @@ -1667,106 +1855,106 @@ "name": "stdout", "output_type": "stream", "text": [ - "0 Train MSE: 0.0808103 \tSparsity loss: 0.415093 \tTotal loss: 0.163829\n", - "1 Train MSE: 0.0544481 \tSparsity loss: 0.118671 \tTotal loss: 0.0781823\n", - "2 Train MSE: 0.0489213 \tSparsity loss: 0.104058 \tTotal loss: 0.0697329\n", - "3 Train MSE: 0.042664 \tSparsity loss: 0.0575809 \tTotal loss: 0.0541802\n", - "4 Train MSE: 0.0390694 \tSparsity loss: 0.259185 \tTotal loss: 0.0909064\n", - "5 Train MSE: 0.0361558 \tSparsity loss: 0.0221424 \tTotal loss: 0.0405843\n", - "6 Train MSE: 0.0328707 \tSparsity loss: 0.0500909 \tTotal loss: 0.0428889\n", - "7 Train MSE: 0.0294563 \tSparsity loss: 0.0537512 \tTotal loss: 0.0402066\n", - "8 Train MSE: 0.027672 \tSparsity loss: 0.0859079 \tTotal loss: 0.0448536\n", - "9 Train MSE: 0.0257369 \tSparsity loss: 0.0981425 \tTotal loss: 0.0453654\n", - "10 Train MSE: 0.0230508 \tSparsity loss: 0.142276 \tTotal loss: 0.0515059\n", - "11 Train MSE: 0.0230676 \tSparsity loss: 0.181762 \tTotal loss: 0.05942\n", - "12 Train MSE: 0.020677 \tSparsity loss: 0.0929343 \tTotal loss: 0.0392639\n", - "13 Train MSE: 0.0190676 \tSparsity loss: 0.0617354 \tTotal loss: 0.0314146\n", - "14 Train MSE: 0.0211431 \tSparsity loss: 0.0756601 \tTotal loss: 0.0362751\n", - "15 Train MSE: 0.0172402 \tSparsity loss: 0.106335 \tTotal loss: 0.0385071\n", - "16 Train MSE: 0.0173277 \tSparsity loss: 0.0966078 \tTotal loss: 0.0366493\n", - "17 Train MSE: 0.0160216 \tSparsity loss: 0.0766543 \tTotal loss: 0.0313525\n", - "18 Train MSE: 0.0152365 \tSparsity loss: 0.0632296 \tTotal loss: 0.0278824\n", - "19 Train MSE: 0.0190664 \tSparsity loss: 0.383607 \tTotal loss: 0.0957877\n", - "20 Train MSE: 0.0150545 \tSparsity loss: 0.0546298 \tTotal loss: 0.0259805\n", - "21 Train MSE: 0.0148168 \tSparsity loss: 0.0451466 \tTotal loss: 0.0238461\n", - "22 Train MSE: 0.0146978 \tSparsity loss: 0.227617 \tTotal loss: 0.0602212\n", - "23 Train MSE: 0.0150732 \tSparsity loss: 0.135743 \tTotal loss: 0.0422218\n", - "24 Train MSE: 0.0177874 \tSparsity loss: 0.194969 \tTotal loss: 0.0567811\n", - "25 Train MSE: 0.0142951 \tSparsity loss: 0.10445 \tTotal loss: 0.035185\n", - "26 Train MSE: 0.0142418 \tSparsity loss: 0.0933622 \tTotal loss: 0.0329143\n", - "27 Train MSE: 0.0149857 \tSparsity loss: 0.100697 \tTotal loss: 0.0351252\n", - "28 Train MSE: 0.0161774 \tSparsity loss: 0.0489748 \tTotal loss: 0.0259723\n", - "29 Train MSE: 0.0158215 \tSparsity loss: 0.375802 \tTotal loss: 0.0909819\n", - "30 Train MSE: 0.0159297 \tSparsity loss: 0.0585507 \tTotal loss: 0.0276398\n", - "31 Train MSE: 0.0131048 \tSparsity loss: 0.0708771 \tTotal loss: 0.0272802\n", - "32 Train MSE: 0.0133353 \tSparsity loss: 0.274087 \tTotal loss: 0.0681527\n", - "33 Train MSE: 0.0134352 \tSparsity loss: 0.089336 \tTotal loss: 0.0313024\n", - "34 Train MSE: 0.011721 \tSparsity loss: 0.0931978 \tTotal loss: 0.0303606\n", - "35 Train MSE: 0.0134944 \tSparsity loss: 0.121721 \tTotal loss: 0.0378386\n", - "36 Train MSE: 0.0130126 \tSparsity loss: 0.0709383 \tTotal loss: 0.0272003\n", - "37 Train MSE: 0.0137884 \tSparsity loss: 0.0827571 \tTotal loss: 0.0303398\n", - "38 Train MSE: 0.0138806 \tSparsity loss: 0.192237 \tTotal loss: 0.0523281\n", - "39 Train MSE: 0.012782 \tSparsity loss: 0.162988 \tTotal loss: 0.0453796\n", - "40 Train MSE: 0.0129845 \tSparsity loss: 0.184135 \tTotal loss: 0.0498114\n", - "41 Train MSE: 0.0141138 \tSparsity loss: 0.145071 \tTotal loss: 0.043128\n", - "42 Train MSE: 0.0137911 \tSparsity loss: 0.106032 \tTotal loss: 0.0349975\n", - "43 Train MSE: 0.0121966 \tSparsity loss: 0.129571 \tTotal loss: 0.0381109\n", - "44 Train MSE: 0.0146962 \tSparsity loss: 0.406566 \tTotal loss: 0.0960094\n", - "45 Train MSE: 0.0124659 \tSparsity loss: 0.202119 \tTotal loss: 0.0528897\n", - "46 Train MSE: 0.0119216 \tSparsity loss: 0.0971551 \tTotal loss: 0.0313527\n", - "47 Train MSE: 0.0107826 \tSparsity loss: 0.132033 \tTotal loss: 0.0371893\n", - "48 Train MSE: 0.0119806 \tSparsity loss: 0.118638 \tTotal loss: 0.0357083\n", - "49 Train MSE: 0.0113738 \tSparsity loss: 0.317469 \tTotal loss: 0.0748676\n", - "50 Train MSE: 0.0126131 \tSparsity loss: 0.130214 \tTotal loss: 0.038656\n", - "51 Train MSE: 0.0115075 \tSparsity loss: 0.126815 \tTotal loss: 0.0368705\n", - "52 Train MSE: 0.0131578 \tSparsity loss: 0.239686 \tTotal loss: 0.061095\n", - "53 Train MSE: 0.011203 \tSparsity loss: 0.144113 \tTotal loss: 0.0400257\n", - "54 Train MSE: 0.0126501 \tSparsity loss: 0.373799 \tTotal loss: 0.0874099\n", - "55 Train MSE: 0.0119187 \tSparsity loss: 0.371901 \tTotal loss: 0.0862989\n", - "56 Train MSE: 0.0112231 \tSparsity loss: 0.225509 \tTotal loss: 0.0563249\n", - "57 Train MSE: 0.0113873 \tSparsity loss: 0.107863 \tTotal loss: 0.0329598\n", - "58 Train MSE: 0.0108124 \tSparsity loss: 0.0912845 \tTotal loss: 0.0290693\n", - "59 Train MSE: 0.0117589 \tSparsity loss: 0.257519 \tTotal loss: 0.0632627\n", - "60 Train MSE: 0.0111196 \tSparsity loss: 0.232198 \tTotal loss: 0.0575592\n", - "61 Train MSE: 0.0114572 \tSparsity loss: 0.138591 \tTotal loss: 0.0391755\n", - "62 Train MSE: 0.0117489 \tSparsity loss: 0.116193 \tTotal loss: 0.0349875\n", - "63 Train MSE: 0.0111976 \tSparsity loss: 0.262772 \tTotal loss: 0.063752\n", - "64 Train MSE: 0.0117205 \tSparsity loss: 0.145903 \tTotal loss: 0.0409011\n", - "65 Train MSE: 0.0166962 \tSparsity loss: 1.01099 \tTotal loss: 0.218893\n", - "66 Train MSE: 0.0207524 \tSparsity loss: 0.392595 \tTotal loss: 0.0992715\n", - "67 Train MSE: 0.0156945 \tSparsity loss: 0.187199 \tTotal loss: 0.0531343\n", - "68 Train MSE: 0.0194048 \tSparsity loss: 0.515735 \tTotal loss: 0.122552\n", - "69 Train MSE: 0.0116519 \tSparsity loss: 0.835861 \tTotal loss: 0.178824\n", - "70 Train MSE: 0.0209426 \tSparsity loss: 0.292206 \tTotal loss: 0.0793837\n", - "71 Train MSE: 0.0134064 \tSparsity loss: 0.542741 \tTotal loss: 0.121955\n", - "72 Train MSE: 0.0151789 \tSparsity loss: 0.376449 \tTotal loss: 0.0904687\n", - "73 Train MSE: 0.0152982 \tSparsity loss: 0.37529 \tTotal loss: 0.0903562\n", - "74 Train MSE: 0.023566 \tSparsity loss: 0.454576 \tTotal loss: 0.114481\n", - "75 Train MSE: 0.0164631 \tSparsity loss: 0.456268 \tTotal loss: 0.107717\n", - "76 Train MSE: 0.0164126 \tSparsity loss: 0.214973 \tTotal loss: 0.0594073\n", - "77 Train MSE: 0.0159823 \tSparsity loss: 0.330593 \tTotal loss: 0.0821008\n", - "78 Train MSE: 0.0297457 \tSparsity loss: 0.148935 \tTotal loss: 0.0595327\n", - "79 Train MSE: 0.017431 \tSparsity loss: 0.273009 \tTotal loss: 0.0720328\n", - "80 Train MSE: 0.0280243 \tSparsity loss: 0.491862 \tTotal loss: 0.126397\n", - "81 Train MSE: 0.0199488 \tSparsity loss: 0.279786 \tTotal loss: 0.075906\n", - "82 Train MSE: 0.020573 \tSparsity loss: 0.319931 \tTotal loss: 0.0845592\n", - "83 Train MSE: 0.0156649 \tSparsity loss: 0.153916 \tTotal loss: 0.0464482\n", - "84 Train MSE: 0.0145016 \tSparsity loss: 0.989233 \tTotal loss: 0.212348\n", - "85 Train MSE: 0.0287968 \tSparsity loss: 0.544559 \tTotal loss: 0.137709\n", - "86 Train MSE: 0.0122126 \tSparsity loss: 0.0891289 \tTotal loss: 0.0300384\n", - "87 Train MSE: 0.0145746 \tSparsity loss: 1.48072 \tTotal loss: 0.310719\n", - "88 Train MSE: 0.0183811 \tSparsity loss: 1.096 \tTotal loss: 0.237581\n", - "89 Train MSE: 0.0294373 \tSparsity loss: 1.21178 \tTotal loss: 0.271793\n", - "90 Train MSE: 0.0211941 \tSparsity loss: 0.141069 \tTotal loss: 0.0494079\n", - "91 Train MSE: 0.0131894 \tSparsity loss: 0.263666 \tTotal loss: 0.0659225\n", - "92 Train MSE: 0.0178111 \tSparsity loss: 0.566228 \tTotal loss: 0.131057\n", - "93 Train MSE: 0.0142184 \tSparsity loss: 0.368309 \tTotal loss: 0.0878801\n", - "94 Train MSE: 0.0133273 \tSparsity loss: 0.133334 \tTotal loss: 0.039994\n", - "95 Train MSE: 0.0148034 \tSparsity loss: 0.167435 \tTotal loss: 0.0482905\n", - "96 Train MSE: 0.0207172 \tSparsity loss: 0.236838 \tTotal loss: 0.0680848\n", - "97 Train MSE: 0.0379609 \tSparsity loss: 0.521251 \tTotal loss: 0.142211\n", - "98 Train MSE: 0.0187403 \tSparsity loss: 0.370602 \tTotal loss: 0.0928608\n", - "99 Train MSE: 0.0170631 \tSparsity loss: 1.34448 \tTotal loss: 0.285958\n" + "0 Train MSE: 0.134832 \tSparsity loss: 0.421739 \tTotal loss: 0.21918\n", + "1 Train MSE: 0.0587859 \tSparsity loss: 0.0108979 \tTotal loss: 0.0609655\n", + "2 Train MSE: 0.053738 \tSparsity loss: 0.0201038 \tTotal loss: 0.0577588\n", + "3 Train MSE: 0.0476169 \tSparsity loss: 0.0399679 \tTotal loss: 0.0556105\n", + "4 Train MSE: 0.0447499 \tSparsity loss: 0.0116199 \tTotal loss: 0.0470739\n", + "5 Train MSE: 0.0403685 \tSparsity loss: 0.0930409 \tTotal loss: 0.0589767\n", + "6 Train MSE: 0.0388338 \tSparsity loss: 0.0462908 \tTotal loss: 0.048092\n", + "7 Train MSE: 0.0378196 \tSparsity loss: 0.0758871 \tTotal loss: 0.052997\n", + "8 Train MSE: 0.0332092 \tSparsity loss: 0.0200693 \tTotal loss: 0.037223\n", + "9 Train MSE: 0.0314318 \tSparsity loss: 0.0965061 \tTotal loss: 0.050733\n", + "10 Train MSE: 0.0273777 \tSparsity loss: 0.0670885 \tTotal loss: 0.0407954\n", + "11 Train MSE: 0.0246779 \tSparsity loss: 0.0900828 \tTotal loss: 0.0426945\n", + "12 Train MSE: 0.0233311 \tSparsity loss: 0.0577432 \tTotal loss: 0.0348797\n", + "13 Train MSE: 0.0228954 \tSparsity loss: 0.0623308 \tTotal loss: 0.0353615\n", + "14 Train MSE: 0.0210913 \tSparsity loss: 0.0258186 \tTotal loss: 0.026255\n", + "15 Train MSE: 0.0220006 \tSparsity loss: 0.483207 \tTotal loss: 0.118642\n", + "16 Train MSE: 0.0190526 \tSparsity loss: 0.0361403 \tTotal loss: 0.0262806\n", + "17 Train MSE: 0.0188885 \tSparsity loss: 0.132695 \tTotal loss: 0.0454275\n", + "18 Train MSE: 0.0174156 \tSparsity loss: 0.0403093 \tTotal loss: 0.0254774\n", + "19 Train MSE: 0.0178612 \tSparsity loss: 0.110486 \tTotal loss: 0.0399584\n", + "20 Train MSE: 0.0168293 \tSparsity loss: 0.0291402 \tTotal loss: 0.0226573\n", + "21 Train MSE: 0.0183871 \tSparsity loss: 0.364209 \tTotal loss: 0.0912289\n", + "22 Train MSE: 0.0161226 \tSparsity loss: 0.0556278 \tTotal loss: 0.0272482\n", + "23 Train MSE: 0.0158919 \tSparsity loss: 0.0792573 \tTotal loss: 0.0317434\n", + "24 Train MSE: 0.0157006 \tSparsity loss: 0.149254 \tTotal loss: 0.0455514\n", + "25 Train MSE: 0.0145307 \tSparsity loss: 0.136184 \tTotal loss: 0.0417676\n", + "26 Train MSE: 0.0144209 \tSparsity loss: 0.110554 \tTotal loss: 0.0365316\n", + "27 Train MSE: 0.0138508 \tSparsity loss: 0.0744676 \tTotal loss: 0.0287443\n", + "28 Train MSE: 0.0139305 \tSparsity loss: 0.158476 \tTotal loss: 0.0456257\n", + "29 Train MSE: 0.0133762 \tSparsity loss: 0.143838 \tTotal loss: 0.0421438\n", + "30 Train MSE: 0.0137258 \tSparsity loss: 0.185643 \tTotal loss: 0.0508544\n", + "31 Train MSE: 0.0139518 \tSparsity loss: 0.0635133 \tTotal loss: 0.0266544\n", + "32 Train MSE: 0.013692 \tSparsity loss: 0.0577956 \tTotal loss: 0.0252512\n", + "33 Train MSE: 0.0134704 \tSparsity loss: 0.104171 \tTotal loss: 0.0343045\n", + "34 Train MSE: 0.0124406 \tSparsity loss: 0.136569 \tTotal loss: 0.0397544\n", + "35 Train MSE: 0.0126563 \tSparsity loss: 0.162903 \tTotal loss: 0.0452369\n", + "36 Train MSE: 0.0128764 \tSparsity loss: 0.0948648 \tTotal loss: 0.0318493\n", + "37 Train MSE: 0.0123458 \tSparsity loss: 0.108087 \tTotal loss: 0.0339632\n", + "38 Train MSE: 0.0121672 \tSparsity loss: 0.20089 \tTotal loss: 0.0523451\n", + "39 Train MSE: 0.0122532 \tSparsity loss: 0.149409 \tTotal loss: 0.0421351\n", + "40 Train MSE: 0.0125975 \tSparsity loss: 0.230649 \tTotal loss: 0.0587274\n", + "41 Train MSE: 0.0124657 \tSparsity loss: 0.100664 \tTotal loss: 0.0325984\n", + "42 Train MSE: 0.0116947 \tSparsity loss: 0.101108 \tTotal loss: 0.0319164\n", + "43 Train MSE: 0.0122123 \tSparsity loss: 0.125789 \tTotal loss: 0.0373701\n", + "44 Train MSE: 0.0117173 \tSparsity loss: 0.110301 \tTotal loss: 0.0337774\n", + "45 Train MSE: 0.0116897 \tSparsity loss: 0.165081 \tTotal loss: 0.0447058\n", + "46 Train MSE: 0.011611 \tSparsity loss: 0.130426 \tTotal loss: 0.0376962\n", + "47 Train MSE: 0.0117358 \tSparsity loss: 0.163508 \tTotal loss: 0.0444374\n", + "48 Train MSE: 0.0116507 \tSparsity loss: 0.459586 \tTotal loss: 0.103568\n", + "49 Train MSE: 0.0116655 \tSparsity loss: 0.188114 \tTotal loss: 0.0492883\n", + "50 Train MSE: 0.0114275 \tSparsity loss: 0.19221 \tTotal loss: 0.0498695\n", + "51 Train MSE: 0.0113954 \tSparsity loss: 0.266457 \tTotal loss: 0.0646867\n", + "52 Train MSE: 0.0119332 \tSparsity loss: 0.379985 \tTotal loss: 0.0879303\n", + "53 Train MSE: 0.0113018 \tSparsity loss: 0.129771 \tTotal loss: 0.037256\n", + "54 Train MSE: 0.0153057 \tSparsity loss: 0.434827 \tTotal loss: 0.102271\n", + "55 Train MSE: 0.0134004 \tSparsity loss: 0.0833025 \tTotal loss: 0.0300609\n", + "56 Train MSE: 0.0123188 \tSparsity loss: 0.297605 \tTotal loss: 0.0718399\n", + "57 Train MSE: 0.0122943 \tSparsity loss: 0.247148 \tTotal loss: 0.061724\n", + "58 Train MSE: 0.0239939 \tSparsity loss: 0.215717 \tTotal loss: 0.0671373\n", + "59 Train MSE: 0.013596 \tSparsity loss: 0.203553 \tTotal loss: 0.0543065\n", + "60 Train MSE: 0.0179108 \tSparsity loss: 0.180449 \tTotal loss: 0.0540006\n", + "61 Train MSE: 0.0132258 \tSparsity loss: 0.231824 \tTotal loss: 0.0595906\n", + "62 Train MSE: 0.0136533 \tSparsity loss: 0.498429 \tTotal loss: 0.113339\n", + "63 Train MSE: 0.0143277 \tSparsity loss: 0.333901 \tTotal loss: 0.0811079\n", + "64 Train MSE: 0.0119968 \tSparsity loss: 0.176848 \tTotal loss: 0.0473664\n", + "65 Train MSE: 0.0156422 \tSparsity loss: 0.173917 \tTotal loss: 0.0504256\n", + "66 Train MSE: 0.0150095 \tSparsity loss: 1.02187 \tTotal loss: 0.219383\n", + "67 Train MSE: 0.036823 \tSparsity loss: 0.323619 \tTotal loss: 0.101547\n", + "68 Train MSE: 0.0148193 \tSparsity loss: 0.230714 \tTotal loss: 0.060962\n", + "69 Train MSE: 0.0126409 \tSparsity loss: 0.454552 \tTotal loss: 0.103551\n", + "70 Train MSE: 0.045501 \tSparsity loss: 0.745102 \tTotal loss: 0.194521\n", + "71 Train MSE: 0.0143786 \tSparsity loss: 0.229362 \tTotal loss: 0.060251\n", + "72 Train MSE: 0.0151026 \tSparsity loss: 0.826014 \tTotal loss: 0.180306\n", + "73 Train MSE: 0.0136122 \tSparsity loss: 0.316737 \tTotal loss: 0.0769596\n", + "74 Train MSE: 0.0309757 \tSparsity loss: 0.289552 \tTotal loss: 0.0888861\n", + "75 Train MSE: 0.0304744 \tSparsity loss: 0.489417 \tTotal loss: 0.128358\n", + "76 Train MSE: 0.0204102 \tSparsity loss: 0.201982 \tTotal loss: 0.0608067\n", + "77 Train MSE: 0.0211023 \tSparsity loss: 0.32347 \tTotal loss: 0.0857964\n", + "78 Train MSE: 0.0178777 \tSparsity loss: 0.533425 \tTotal loss: 0.124563\n", + "79 Train MSE: 0.018841 \tSparsity loss: 0.424661 \tTotal loss: 0.103773\n", + "80 Train MSE: 0.0159234 \tSparsity loss: 0.115559 \tTotal loss: 0.0390352\n", + "81 Train MSE: 0.0129649 \tSparsity loss: 0.912508 \tTotal loss: 0.195467\n", + "82 Train MSE: 0.0162278 \tSparsity loss: 2.17347 \tTotal loss: 0.450922\n", + "83 Train MSE: 0.0146708 \tSparsity loss: 0.681089 \tTotal loss: 0.150889\n", + "84 Train MSE: 0.0150686 \tSparsity loss: 0.292309 \tTotal loss: 0.0735305\n", + "85 Train MSE: 0.0250247 \tSparsity loss: 0.949989 \tTotal loss: 0.215023\n", + "86 Train MSE: 0.0146914 \tSparsity loss: 0.685326 \tTotal loss: 0.151757\n", + "87 Train MSE: 0.0122667 \tSparsity loss: 1.44823 \tTotal loss: 0.301912\n", + "88 Train MSE: 0.0197259 \tSparsity loss: 0.861047 \tTotal loss: 0.191935\n", + "89 Train MSE: 0.0331342 \tSparsity loss: 0.291833 \tTotal loss: 0.0915009\n", + "90 Train MSE: 0.0295548 \tSparsity loss: 0.445159 \tTotal loss: 0.118587\n", + "91 Train MSE: 0.0145762 \tSparsity loss: 0.0887034 \tTotal loss: 0.0323169\n", + "92 Train MSE: 0.0147775 \tSparsity loss: 0.390856 \tTotal loss: 0.0929486\n", + "93 Train MSE: 0.0166543 \tSparsity loss: 0.155326 \tTotal loss: 0.0477195\n", + "94 Train MSE: 0.012198 \tSparsity loss: 0.12071 \tTotal loss: 0.03634\n", + "95 Train MSE: 0.0141104 \tSparsity loss: 0.107212 \tTotal loss: 0.0355529\n", + "96 Train MSE: 0.018834 \tSparsity loss: 0.230255 \tTotal loss: 0.0648851\n", + "97 Train MSE: 0.0134663 \tSparsity loss: 0.102045 \tTotal loss: 0.0338754\n", + "98 Train MSE: 0.013678 \tSparsity loss: 0.0839055 \tTotal loss: 0.0304591\n", + "99 Train MSE: 0.0245401 \tSparsity loss: 0.335841 \tTotal loss: 0.0917084\n" ] } ], @@ -1783,25 +1971,32 @@ " sys.stdout.flush()\n", " X_batch, y_batch = mnist.train.next_batch(batch_size)\n", " sess.run(training_op, feed_dict={X: X_batch})\n", - " mse_val, sparsity_loss_val, loss_val = sess.run([mse, sparsity_loss, loss], feed_dict={X: X_batch})\n", - " print(\"\\r{}\".format(epoch), \"Train MSE:\", mse_val, \"\\tSparsity loss:\", sparsity_loss_val, \"\\tTotal loss:\", loss_val)\n", + " reconstruction_loss_val, sparsity_loss_val, loss_val = sess.run([reconstruction_loss, sparsity_loss, loss], feed_dict={X: X_batch})\n", + " print(\"\\r{}\".format(epoch), \"Train MSE:\", reconstruction_loss_val, \"\\tSparsity loss:\", sparsity_loss_val, \"\\tTotal loss:\", loss_val)\n", " saver.save(sess, \"./my_model_sparse.ckpt\")" ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 47, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Restoring parameters from ./my_model_sparse.ckpt\n" + ] + }, { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1812,6 +2007,56 @@ "show_reconstructed_digits(X, outputs, \"./my_model_sparse.ckpt\")" ] }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Note that the coding layer must output values from 0 to 1, which is why we use the sigmoid activation function:" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "hidden1 = tf.layers.dense(X, n_hidden1, activation=tf.nn.sigmoid)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "To speed up training, you can normalize the inputs between 0 and 1, and use the cross entropy instead of the MSE for the cost function:" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "logits = tf.layers.dense(hidden1, n_outputs)\n", + "outputs = tf.nn.sigmoid(logits)\n", + "\n", + "xentropy = tf.nn.sigmoid_cross_entropy_with_logits(labels=X, logits=logits)\n", + "reconstruction_loss = tf.reduce_mean(xentropy)" + ] + }, { "cell_type": "markdown", "metadata": { @@ -1824,7 +2069,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 50, "metadata": { "collapsed": false, "deletable": true, @@ -1832,59 +2077,72 @@ }, "outputs": [], "source": [ - "tf.reset_default_graph()\n", + "reset_graph()\n", "\n", - "n_inputs = 28*28\n", + "from functools import partial\n", + "\n", + "n_inputs = 28 * 28\n", "n_hidden1 = 500\n", "n_hidden2 = 500\n", "n_hidden3 = 20 # codings\n", "n_hidden4 = n_hidden2\n", "n_hidden5 = n_hidden1\n", "n_outputs = n_inputs\n", - "\n", "learning_rate = 0.001\n", "\n", - "activation = tf.nn.elu\n", - "initializer = tf.contrib.layers.variance_scaling_initializer(mode=\"FAN_AVG\",\n", - " uniform=True)\n", - "\n", - "X = tf.placeholder(tf.float32, [None, n_inputs])\n", - "\n", - "weights1 = tf.Variable(initializer([n_inputs, n_hidden1]))\n", - "weights2 = tf.Variable(initializer([n_hidden1, n_hidden2]))\n", - "weights3_mean = tf.Variable(initializer([n_hidden2, n_hidden3]))\n", - "weights3_log_sigma = tf.Variable(initializer([n_hidden2, n_hidden3]))\n", - "weights4 = tf.Variable(initializer([n_hidden3, n_hidden4]))\n", - "weights5 = tf.Variable(initializer([n_hidden4, n_hidden5]))\n", - "weights6 = tf.Variable(initializer([n_hidden5, n_inputs]))\n", - "\n", - "biases1 = tf.Variable(tf.zeros([n_hidden1], dtype=tf.float32))\n", - "biases2 = tf.Variable(tf.zeros([n_hidden2], dtype=tf.float32))\n", - "biases3_mean = tf.Variable(tf.zeros([n_hidden3], dtype=tf.float32))\n", - "biases3_log_sigma = tf.Variable(tf.zeros([n_hidden3], dtype=tf.float32))\n", - "biases4 = tf.Variable(tf.zeros([n_hidden4], dtype=tf.float32))\n", - "biases5 = tf.Variable(tf.zeros([n_hidden5], dtype=tf.float32))\n", - "biases6 = tf.Variable(tf.zeros([n_inputs], dtype=tf.float32))\n", - "\n", - "hidden1 = activation(tf.matmul(X, weights1) + biases1)\n", - "hidden2 = activation(tf.matmul(hidden1, weights2) + biases2)\n", + "initializer = tf.contrib.layers.variance_scaling_initializer()\n", "\n", - "hidden3_mean = tf.matmul(hidden2, weights3_mean) + biases3_mean\n", - "hidden3_log_sigma = tf.matmul(hidden2, weights3_log_sigma) + biases3_log_sigma\n", - "noise = tf.random_normal(tf.shape(hidden3_log_sigma), dtype=tf.float32)\n", - "hidden3 = hidden3_mean + tf.sqrt(tf.exp(hidden3_log_sigma)) * noise\n", + "my_dense_layer = partial(\n", + " tf.layers.dense,\n", + " activation=tf.nn.elu,\n", + " kernel_initializer=initializer)\n", "\n", - "hidden4 = activation(tf.matmul(hidden3, weights4) + biases4)\n", - "hidden5 = activation(tf.matmul(hidden4, weights5) + biases5)\n", - "logits = tf.matmul(hidden5, weights6) + biases6\n", + "X = tf.placeholder(tf.float32, [None, n_inputs])\n", + "hidden1 = my_dense_layer(X, n_hidden1)\n", + "hidden2 = my_dense_layer(hidden1, n_hidden2)\n", + "hidden3_mean = my_dense_layer(hidden2, n_hidden3, activation=None)\n", + "hidden3_sigma = my_dense_layer(hidden2, n_hidden3, activation=None)\n", + "noise = tf.random_normal(tf.shape(hidden3_sigma), dtype=tf.float32)\n", + "hidden3 = hidden3_mean + hidden3_sigma * noise\n", + "hidden4 = my_dense_layer(hidden3, n_hidden4)\n", + "hidden5 = my_dense_layer(hidden4, n_hidden5)\n", + "logits = my_dense_layer(hidden5, n_outputs, activation=None)\n", "outputs = tf.sigmoid(logits)\n", "\n", - "reconstruction_loss = tf.reduce_sum(tf.nn.sigmoid_cross_entropy_with_logits(labels=X, logits=logits))\n", - "latent_loss = 0.5 * tf.reduce_sum(tf.exp(hidden3_log_sigma) + tf.square(hidden3_mean) - 1 - hidden3_log_sigma)\n", - "cost = reconstruction_loss + latent_loss\n", + "xentropy = tf.nn.sigmoid_cross_entropy_with_logits(labels=X, logits=logits)\n", + "reconstruction_loss = tf.reduce_sum(xentropy)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "eps = 1e-10 # smoothing term to avoid computing log(0) which is NaN\n", + "latent_loss = 0.5 * tf.reduce_sum(\n", + " tf.square(hidden3_sigma) + tf.square(hidden3_mean)\n", + " - 1 - tf.log(eps + tf.square(hidden3_sigma)))" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "loss = reconstruction_loss + latent_loss\n", "\n", "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n", - "training_op = optimizer.minimize(cost)\n", + "training_op = optimizer.minimize(loss)\n", "\n", "init = tf.global_variables_initializer()\n", "saver = tf.train.Saver()" @@ -1892,7 +2150,91 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 53, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 Train total loss: 32440.1 \tReconstruction loss: 25031.5 \tLatent loss: 7408.61\n", + "1 Train total loss: 30017.4 \tReconstruction loss: 23093.3 \tLatent loss: 6924.14\n", + "2 Train total loss: 23337.9 \tReconstruction loss: 20221.0 \tLatent loss: 3116.88\n", + "3 Train total loss: 21724.7 \tReconstruction loss: 18698.8 \tLatent loss: 3025.89\n", + "4 Train total loss: 28219.0 \tReconstruction loss: 21493.3 \tLatent loss: 6725.66\n", + "5 Train total loss: 25906.5 \tReconstruction loss: 19582.4 \tLatent loss: 6324.09\n", + "6 Train total loss: 19198.3 \tReconstruction loss: 15831.6 \tLatent loss: 3366.69\n", + "7 Train total loss: 17638.8 \tReconstruction loss: 14539.6 \tLatent loss: 3099.17\n", + "8 Train total loss: 16688.3 \tReconstruction loss: 13615.9 \tLatent loss: 3072.4\n", + "9 Train total loss: 17007.3 \tReconstruction loss: 13783.2 \tLatent loss: 3224.1\n", + "10 Train total loss: 16550.5 \tReconstruction loss: 13333.8 \tLatent loss: 3216.75\n", + "11 Train total loss: 16248.7 \tReconstruction loss: 13009.1 \tLatent loss: 3239.6\n", + "12 Train total loss: 16346.3 \tReconstruction loss: 13150.0 \tLatent loss: 3196.26\n", + "13 Train total loss: 16067.2 \tReconstruction loss: 12777.2 \tLatent loss: 3290.02\n", + "14 Train total loss: 16512.1 \tReconstruction loss: 13058.1 \tLatent loss: 3454.07\n", + "15 Train total loss: 16099.5 \tReconstruction loss: 12739.1 \tLatent loss: 3360.35\n", + "16 Train total loss: 20827.6 \tReconstruction loss: 16602.6 \tLatent loss: 4224.96\n", + "17 Train total loss: 38965.4 \tReconstruction loss: 24849.1 \tLatent loss: 14116.2\n", + "18 Train total loss: 29396.9 \tReconstruction loss: 24286.1 \tLatent loss: 5110.81\n", + "19 Train total loss: 27910.6 \tReconstruction loss: 21005.3 \tLatent loss: 6905.23\n", + "20 Train total loss: 26797.9 \tReconstruction loss: 20202.2 \tLatent loss: 6595.64\n", + "21 Train total loss: 18686.1 \tReconstruction loss: 15251.4 \tLatent loss: 3434.69\n", + "22 Train total loss: 17034.8 \tReconstruction loss: 13890.0 \tLatent loss: 3144.77\n", + "23 Train total loss: 16404.0 \tReconstruction loss: 13102.6 \tLatent loss: 3301.37\n", + "24 Train total loss: 16214.5 \tReconstruction loss: 12803.4 \tLatent loss: 3411.13\n", + "25 Train total loss: 16253.4 \tReconstruction loss: 12823.9 \tLatent loss: 3429.48\n", + "26 Train total loss: 16326.2 \tReconstruction loss: 12934.0 \tLatent loss: 3392.18\n", + "27 Train total loss: 16161.3 \tReconstruction loss: 12767.4 \tLatent loss: 3393.91\n", + "28 Train total loss: 16990.3 \tReconstruction loss: 13471.8 \tLatent loss: 3518.54\n", + "29 Train total loss: 15728.4 \tReconstruction loss: 12465.1 \tLatent loss: 3263.28\n", + "30 Train total loss: 16505.3 \tReconstruction loss: 13219.9 \tLatent loss: 3285.37\n", + "31 Train total loss: 16961.6 \tReconstruction loss: 13379.0 \tLatent loss: 3582.55\n", + "32 Train total loss: 17671.7 \tReconstruction loss: 14372.1 \tLatent loss: 3299.55\n", + "33 Train total loss: 16640.7 \tReconstruction loss: 13332.3 \tLatent loss: 3308.39\n", + "34 Train total loss: 21943.6 \tReconstruction loss: 15878.3 \tLatent loss: 6065.31\n", + "35 Train total loss: 15656.0 \tReconstruction loss: 12254.1 \tLatent loss: 3401.86\n", + "36 Train total loss: 15697.0 \tReconstruction loss: 12231.0 \tLatent loss: 3465.93\n", + "37 Train total loss: 15769.4 \tReconstruction loss: 12409.7 \tLatent loss: 3359.68\n", + "38 Train total loss: 17182.6 \tReconstruction loss: 13943.9 \tLatent loss: 3238.67\n", + "39 Train total loss: 18285.6 \tReconstruction loss: 14796.2 \tLatent loss: 3489.34\n", + "40 Train total loss: 20053.2 \tReconstruction loss: 14899.0 \tLatent loss: 5154.25\n", + "41 Train total loss: 16290.2 \tReconstruction loss: 13008.1 \tLatent loss: 3282.09\n", + "42 Train total loss: 27364.1 \tReconstruction loss: 22713.0 \tLatent loss: 4651.08\n", + "43 Train total loss: 15450.8 \tReconstruction loss: 12009.0 \tLatent loss: 3441.87\n", + "44 Train total loss: 15567.6 \tReconstruction loss: 12068.6 \tLatent loss: 3499.0\n", + "45 Train total loss: 15348.8 \tReconstruction loss: 11840.9 \tLatent loss: 3507.95\n", + "46 Train total loss: 15435.6 \tReconstruction loss: 11949.6 \tLatent loss: 3486.03\n", + "47 Train total loss: 15210.5 \tReconstruction loss: 11804.4 \tLatent loss: 3406.18\n", + "48 Train total loss: 20627.8 \tReconstruction loss: 16485.7 \tLatent loss: 4142.07\n", + "49 Train total loss: 15147.4 \tReconstruction loss: 11587.1 \tLatent loss: 3560.29\n" + ] + } + ], + "source": [ + "n_epochs = 50\n", + "batch_size = 150\n", + "\n", + "with tf.Session() as sess:\n", + " init.run()\n", + " for epoch in range(n_epochs):\n", + " n_batches = mnist.train.num_examples // batch_size\n", + " for iteration in range(n_batches):\n", + " print(\"\\r{}%\".format(100 * iteration // n_batches), end=\"\")\n", + " sys.stdout.flush()\n", + " X_batch, y_batch = mnist.train.next_batch(batch_size)\n", + " sess.run(training_op, feed_dict={X: X_batch})\n", + " loss_val, reconstruction_loss_val, latent_loss_val = sess.run([loss, reconstruction_loss, latent_loss], feed_dict={X: X_batch})\n", + " print(\"\\r{}\".format(epoch), \"Train total loss:\", loss_val, \"\\tReconstruction loss:\", reconstruction_loss_val, \"\\tLatent loss:\", latent_loss_val)\n", + " saver.save(sess, \"./my_model_variational.ckpt\")" + ] + }, + { + "cell_type": "code", + "execution_count": 54, "metadata": { "collapsed": false, "deletable": true, @@ -1900,22 +2242,20 @@ }, "outputs": [], "source": [ - "tf.reset_default_graph()\n", + "reset_graph()\n", "\n", "from functools import partial\n", "\n", - "n_inputs = 28*28\n", + "n_inputs = 28 * 28\n", "n_hidden1 = 500\n", "n_hidden2 = 500\n", "n_hidden3 = 20 # codings\n", "n_hidden4 = n_hidden2\n", "n_hidden5 = n_hidden1\n", "n_outputs = n_inputs\n", - "\n", "learning_rate = 0.001\n", "\n", "initializer = tf.contrib.layers.variance_scaling_initializer()\n", - "\n", "my_dense_layer = partial(\n", " tf.layers.dense,\n", " activation=tf.nn.elu,\n", @@ -1933,20 +2273,42 @@ "logits = my_dense_layer(hidden5, n_outputs, activation=None)\n", "outputs = tf.sigmoid(logits)\n", "\n", - "reconstruction_loss = tf.reduce_sum(tf.nn.sigmoid_cross_entropy_with_logits(labels=X, logits=logits))\n", - "latent_loss = 0.5 * tf.reduce_sum(tf.exp(hidden3_gamma) + tf.square(hidden3_mean) - 1 - hidden3_gamma)\n", - "cost = reconstruction_loss + latent_loss\n", + "xentropy = tf.nn.sigmoid_cross_entropy_with_logits(labels=X, logits=logits)\n", + "reconstruction_loss = tf.reduce_sum(xentropy)\n", + "latent_loss = 0.5 * tf.reduce_sum(\n", + " tf.exp(hidden3_gamma) + tf.square(hidden3_mean) - 1 - hidden3_gamma)\n", + "loss = reconstruction_loss + latent_loss\n", "\n", "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n", - "training_op = optimizer.minimize(cost)\n", + "training_op = optimizer.minimize(loss)\n", "\n", "init = tf.global_variables_initializer()\n", "saver = tf.train.Saver()" ] }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Generate digits" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Let's train the model and generate a few random digits:" + ] + }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 55, "metadata": { "collapsed": false, "deletable": true, @@ -1957,60 +2319,63 @@ "name": "stdout", "output_type": "stream", "text": [ - "0 Train cost: 18604.3 \tReconstruction loss: 14818.0 \tLatent loss: 3786.32\n", - "1 Train cost: 16815.6 \tReconstruction loss: 13203.3 \tLatent loss: 3612.25\n", - "2 Train cost: 16529.8 \tReconstruction loss: 12717.1 \tLatent loss: 3812.65\n", - "3 Train cost: 16443.3 \tReconstruction loss: 12582.1 \tLatent loss: 3861.12\n", - "4 Train cost: 16256.6 \tReconstruction loss: 12489.2 \tLatent loss: 3767.41\n", - "5 Train cost: 15971.0 \tReconstruction loss: 12240.5 \tLatent loss: 3730.49\n", - "6 Train cost: 15806.8 \tReconstruction loss: 11980.4 \tLatent loss: 3826.42\n", - "7 Train cost: 16334.4 \tReconstruction loss: 12480.7 \tLatent loss: 3853.68\n", - "8 Train cost: 15835.0 \tReconstruction loss: 12082.8 \tLatent loss: 3752.25\n", - "9 Train cost: 15549.4 \tReconstruction loss: 11733.3 \tLatent loss: 3816.08\n", - "10 Train cost: 15970.6 \tReconstruction loss: 12221.3 \tLatent loss: 3749.32\n", - "11 Train cost: 15287.9 \tReconstruction loss: 11545.8 \tLatent loss: 3742.02\n", - "12 Train cost: 15450.1 \tReconstruction loss: 11634.0 \tLatent loss: 3816.12\n", - "13 Train cost: 15865.1 \tReconstruction loss: 11956.3 \tLatent loss: 3908.8\n", - "14 Train cost: 15065.9 \tReconstruction loss: 11274.5 \tLatent loss: 3791.37\n", - "15 Train cost: 15817.3 \tReconstruction loss: 11941.5 \tLatent loss: 3875.85\n", - "16 Train cost: 15068.3 \tReconstruction loss: 11291.6 \tLatent loss: 3776.76\n", - "17 Train cost: 15378.0 \tReconstruction loss: 11527.9 \tLatent loss: 3850.08\n", - "18 Train cost: 15467.5 \tReconstruction loss: 11681.3 \tLatent loss: 3786.2\n", - "19 Train cost: 15028.8 \tReconstruction loss: 11369.0 \tLatent loss: 3659.77\n", - "20 Train cost: 14593.2 \tReconstruction loss: 10931.3 \tLatent loss: 3661.84\n", - "21 Train cost: 15184.6 \tReconstruction loss: 11463.8 \tLatent loss: 3720.81\n", - "22 Train cost: 15549.5 \tReconstruction loss: 11719.6 \tLatent loss: 3829.93\n", - "23 Train cost: 15120.7 \tReconstruction loss: 11356.6 \tLatent loss: 3764.11\n", - "24 Train cost: 15043.3 \tReconstruction loss: 11271.0 \tLatent loss: 3772.25\n", - "25 Train cost: 14868.0 \tReconstruction loss: 11159.2 \tLatent loss: 3708.84\n", - "26 Train cost: 15204.3 \tReconstruction loss: 11325.8 \tLatent loss: 3878.51\n", - "27 Train cost: 14795.7 \tReconstruction loss: 11049.2 \tLatent loss: 3746.59\n", - "28 Train cost: 14319.1 \tReconstruction loss: 10681.5 \tLatent loss: 3637.58\n", - "29 Train cost: 15113.9 \tReconstruction loss: 11406.9 \tLatent loss: 3707.06\n", - "30 Train cost: 14934.5 \tReconstruction loss: 11208.4 \tLatent loss: 3726.05\n", - "31 Train cost: 15178.9 \tReconstruction loss: 11462.5 \tLatent loss: 3716.41\n", - "32 Train cost: 14835.1 \tReconstruction loss: 11094.8 \tLatent loss: 3740.24\n", - "33 Train cost: 14892.1 \tReconstruction loss: 11055.8 \tLatent loss: 3836.29\n", - "34 Train cost: 14663.1 \tReconstruction loss: 10960.1 \tLatent loss: 3703.0\n", - "35 Train cost: 14606.7 \tReconstruction loss: 10958.7 \tLatent loss: 3648.02\n", - "36 Train cost: 14877.4 \tReconstruction loss: 11219.5 \tLatent loss: 3657.99\n", - "37 Train cost: 14887.5 \tReconstruction loss: 11089.1 \tLatent loss: 3798.37\n", - "38 Train cost: 14376.7 \tReconstruction loss: 10733.5 \tLatent loss: 3643.15\n", - "39 Train cost: 14774.7 \tReconstruction loss: 11164.5 \tLatent loss: 3610.25\n", - "40 Train cost: 14465.2 \tReconstruction loss: 10808.1 \tLatent loss: 3657.06\n", - "41 Train cost: 14554.3 \tReconstruction loss: 10781.6 \tLatent loss: 3772.74\n", - "42 Train cost: 14828.5 \tReconstruction loss: 11083.7 \tLatent loss: 3744.73\n", - "43 Train cost: 14663.9 \tReconstruction loss: 10968.0 \tLatent loss: 3695.93\n", - "44 Train cost: 14264.3 \tReconstruction loss: 10717.4 \tLatent loss: 3546.83\n", - "45 Train cost: 14479.3 \tReconstruction loss: 10833.3 \tLatent loss: 3645.97\n", - "46 Train cost: 14839.5 \tReconstruction loss: 11099.8 \tLatent loss: 3739.75\n", - "47 Train cost: 14417.5 \tReconstruction loss: 10806.8 \tLatent loss: 3610.71\n", - "48 Train cost: 14933.3 \tReconstruction loss: 11270.8 \tLatent loss: 3662.51\n", - "49 Train cost: 15005.8 \tReconstruction loss: 11272.2 \tLatent loss: 3733.64\n" + "0 Train total loss: 17792.6 \tReconstruction loss: 14122.9 \tLatent loss: 3669.64\n", + "1 Train total loss: 17332.2 \tReconstruction loss: 13560.0 \tLatent loss: 3772.24\n", + "2 Train total loss: 16350.7 \tReconstruction loss: 12579.3 \tLatent loss: 3771.48\n", + "3 Train total loss: 16581.4 \tReconstruction loss: 12810.6 \tLatent loss: 3770.78\n", + "4 Train total loss: 16223.9 \tReconstruction loss: 12450.0 \tLatent loss: 3773.86\n", + "5 Train total loss: 15628.1 \tReconstruction loss: 11819.6 \tLatent loss: 3808.51\n", + "6 Train total loss: 16080.9 \tReconstruction loss: 12179.7 \tLatent loss: 3901.24\n", + "7 Train total loss: 15772.8 \tReconstruction loss: 12021.3 \tLatent loss: 3751.55\n", + "8 Train total loss: 16276.5 \tReconstruction loss: 12404.6 \tLatent loss: 3871.83\n", + "9 Train total loss: 15589.6 \tReconstruction loss: 11740.6 \tLatent loss: 3849.02\n", + "10 Train total loss: 15931.3 \tReconstruction loss: 12031.4 \tLatent loss: 3899.94\n", + "11 Train total loss: 16112.7 \tReconstruction loss: 12238.3 \tLatent loss: 3874.35\n", + "12 Train total loss: 16002.0 \tReconstruction loss: 12185.1 \tLatent loss: 3816.83\n", + "13 Train total loss: 15357.7 \tReconstruction loss: 11667.4 \tLatent loss: 3690.35\n", + "14 Train total loss: 16208.4 \tReconstruction loss: 12264.4 \tLatent loss: 3943.96\n", + "15 Train total loss: 15970.0 \tReconstruction loss: 12158.5 \tLatent loss: 3811.52\n", + "16 Train total loss: 15551.6 \tReconstruction loss: 11783.1 \tLatent loss: 3768.49\n", + "17 Train total loss: 15330.0 \tReconstruction loss: 11555.7 \tLatent loss: 3774.3\n", + "18 Train total loss: 15251.3 \tReconstruction loss: 11584.5 \tLatent loss: 3666.81\n", + "19 Train total loss: 15196.0 \tReconstruction loss: 11516.6 \tLatent loss: 3679.44\n", + "20 Train total loss: 15323.9 \tReconstruction loss: 11525.9 \tLatent loss: 3797.99\n", + "21 Train total loss: 15358.7 \tReconstruction loss: 11515.6 \tLatent loss: 3843.17\n", + "22 Train total loss: 15297.9 \tReconstruction loss: 11582.5 \tLatent loss: 3715.37\n", + "23 Train total loss: 14673.0 \tReconstruction loss: 10940.7 \tLatent loss: 3732.34\n", + "24 Train total loss: 15293.5 \tReconstruction loss: 11561.7 \tLatent loss: 3731.75\n", + "25 Train total loss: 15256.3 \tReconstruction loss: 11540.8 \tLatent loss: 3715.53\n", + "26 Train total loss: 15305.4 \tReconstruction loss: 11475.4 \tLatent loss: 3830.01\n", + "27 Train total loss: 15276.9 \tReconstruction loss: 11449.7 \tLatent loss: 3827.24\n", + "28 Train total loss: 14980.6 \tReconstruction loss: 11318.0 \tLatent loss: 3662.56\n", + "29 Train total loss: 15232.8 \tReconstruction loss: 11520.1 \tLatent loss: 3712.69\n", + "30 Train total loss: 14872.4 \tReconstruction loss: 11172.9 \tLatent loss: 3699.47\n", + "31 Train total loss: 14890.3 \tReconstruction loss: 11144.1 \tLatent loss: 3746.17\n", + "32 Train total loss: 15246.7 \tReconstruction loss: 11439.3 \tLatent loss: 3807.4\n", + "33 Train total loss: 15063.5 \tReconstruction loss: 11282.1 \tLatent loss: 3781.41\n", + "34 Train total loss: 15046.7 \tReconstruction loss: 11310.2 \tLatent loss: 3736.47\n", + "35 Train total loss: 15293.9 \tReconstruction loss: 11599.5 \tLatent loss: 3694.4\n", + "36 Train total loss: 15134.5 \tReconstruction loss: 11362.8 \tLatent loss: 3771.74\n", + "37 Train total loss: 14705.7 \tReconstruction loss: 11054.7 \tLatent loss: 3650.98\n", + "38 Train total loss: 14913.9 \tReconstruction loss: 11077.0 \tLatent loss: 3836.93\n", + "39 Train total loss: 14848.1 \tReconstruction loss: 11198.5 \tLatent loss: 3649.57\n", + "40 Train total loss: 14694.2 \tReconstruction loss: 10991.5 \tLatent loss: 3702.73\n", + "41 Train total loss: 15223.9 \tReconstruction loss: 11465.1 \tLatent loss: 3758.8\n", + "42 Train total loss: 14585.3 \tReconstruction loss: 11019.3 \tLatent loss: 3566.01\n", + "43 Train total loss: 14579.1 \tReconstruction loss: 10931.2 \tLatent loss: 3647.84\n", + "44 Train total loss: 15049.1 \tReconstruction loss: 11381.9 \tLatent loss: 3667.18\n", + "45 Train total loss: 14855.6 \tReconstruction loss: 11125.6 \tLatent loss: 3730.04\n", + "46 Train total loss: 14777.7 \tReconstruction loss: 11093.4 \tLatent loss: 3684.3\n", + "47 Train total loss: 14408.9 \tReconstruction loss: 10788.5 \tLatent loss: 3620.39\n", + "48 Train total loss: 14479.2 \tReconstruction loss: 10864.3 \tLatent loss: 3614.88\n", + "49 Train total loss: 14637.6 \tReconstruction loss: 10926.0 \tLatent loss: 3711.55\n" ] } ], "source": [ + "import numpy as np\n", + "\n", + "n_digits = 60\n", "n_epochs = 50\n", "batch_size = 150\n", "\n", @@ -2019,42 +2384,78 @@ " for epoch in range(n_epochs):\n", " n_batches = mnist.train.num_examples // batch_size\n", " for iteration in range(n_batches):\n", - " print(\"\\r{}%\".format(100 * iteration // n_batches), end=\"\")\n", - " sys.stdout.flush()\n", + " print(\"\\r{}%\".format(100 * iteration // n_batches), end=\"\") # not shown in the book\n", + " sys.stdout.flush() # not shown\n", " X_batch, y_batch = mnist.train.next_batch(batch_size)\n", " sess.run(training_op, feed_dict={X: X_batch})\n", - " cost_val, reconstruction_loss_val, latent_loss_val = sess.run([cost, reconstruction_loss, latent_loss], feed_dict={X: X_batch})\n", - " print(\"\\r{}\".format(epoch), \"Train cost:\", cost_val, \"\\tReconstruction loss:\", reconstruction_loss_val, \"\\tLatent loss:\", latent_loss_val)\n", - " saver.save(sess, \"./my_model_variational.ckpt\")" + " loss_val, reconstruction_loss_val, latent_loss_val = sess.run([loss, reconstruction_loss, latent_loss], feed_dict={X: X_batch}) # not shown\n", + " print(\"\\r{}\".format(epoch), \"Train total loss:\", loss_val, \"\\tReconstruction loss:\", reconstruction_loss_val, \"\\tLatent loss:\", latent_loss_val) # not shown\n", + " saver.save(sess, \"./my_model_variational.ckpt\") # not shown\n", + " \n", + " codings_rnd = np.random.normal(size=[n_digits, n_hidden3])\n", + " outputs_val = outputs.eval(feed_dict={hidden3: codings_rnd})" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 56, "metadata": { + "collapsed": false, "deletable": true, "editable": true }, + "outputs": [ + { + "data": { + "image/png": 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8KMZ/4OR8ySfjmCF+x30yjhn0+ogmX6e5PhnHDCfvuAXtstZoNBqNJg6I68Yg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\nGo3GBmiBrNFoNBqNDfgvmTYlgjgWm00AAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "Encode:" + "plt.figure(figsize=(8,50)) # not shown in the book\n", + "for iteration in range(n_digits):\n", + " plt.subplot(n_digits, 10, iteration + 1)\n", + " plot_image(outputs_val[iteration])" ] }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 57, "metadata": { "collapsed": false, "deletable": true, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure generated_digits_plot\n" + ] + }, + { + "data": { + "image/png": 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ii9jtdvLy8mSbmczMzJBaNsKKO9qW96HguBJQhyOcmpqaWLlypewPEx0dLQs1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\nPhz4ddG5XC6Z3PHuu+9yyim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tjcDs6F0Eq1xWsAnHmER8s7NnCuU/VG7Z30IXUCFATKrw\nbaekpJCUlEROTo48OCwWi9Qmw43JZKJ///5YLBaZOjp+/PhOrYNwbZT2B4jH42Hz5s1S+zabzWGJ\nF3i9Xt58801MJhPnnHMO4I9/dbVDTBwu4sBXVZXm5uY2l05DbfG1T2luH1tUVRWXyyVrLF5xxRUh\nGYdIcx88eDDgj0MfquA5VkrisUZcMD+U728w/NppIJwC3XAs3AGd0GUGEkrCUfLlUBEJEoJjvVHb\nCyhN02hqauLhhx8G/OV9Ro4cGfJMMJ/Px44dO4iJiZF3aI71uzkUNE3D7XZjsViOi/HqHF8cxdl1\nxIdd13Co6+jo6OjotEO3oHS6PF3J6tTROVEQ97/CwBFvXF1A6ejo6OiEEt3Fp6Ojo6NzYqELKB0d\nHR2dLokuoHR0dHR0uiS6gNLR0dHR6ZLoF3XDiKZpsmhmOKon6xzftO9qGpjQ9FvVEXR0TgR0ARUC\n2pd8EXXK6uvrZeVpgKFDh5KYmNhhozmd/200TZONNUVrksCGkqIRYH19vayuUVxcHPJLzCcKoirC\nsSrh055jdZUiUMHpisrO/1SaeTgmwOv14na7qampkRqwx+OhZ8+ebUrQbNmyhRdffBFN02SlhJ49\ne3a5BfK/jOhTBf5mk/X19ZSWllJbWwv4FYyUlBTi4+NDIhjaV/oQhyr4i/1u2LCBn376ib59+wL+\nShvHql6gKGsEdNgypaugqip2u13OYVZW1jErOaZpGi6Xi5aWFlkHMzo6OiR3k9qffaqq4na72bx5\nM3PmzAFgzJgx9O/fn/j4+GDPoZ5mrqOjo6NzYnFC+wM0TWP37t2Avxp1TEwMzzzzDDk5OSF5FkBT\nUxNPPPEEjY2N/PWvfwWgV69eB8Sb+vfvj8FgYPHixcyaNQuAu+66q0vUUGvvogxXrKx9JXNVVfF4\nPLLastlsDutY3G43GzduBOC1115j69ataJqG3W6XP+d2u7niiiu45ZZbANpUzz5axHcVLuLAiuEp\nKSmy0dzevXsBcLlcYbWgFEWR++ull16SDTpFl+ZrrrlGzpl4J6J6gdlsDrvVoigKdXV1fPDBB9Lq\nzMrKOiZuNfDP18qVK5k3b57stH3hhReSk5MT9HUe6MYDv1entLSUl19+mXnz5gHwzjvvMHz4cKZN\nmya7EB8r61JwwgooTdMoLy9nzJgxgL8VQXZ2dsg6xoqJ//LLL5kzZw6jRo0iOzsb6PiAj4iIID4+\nnsbGRtmy+2gXQvueLYczbp/PR21tLTt37pSH8p49e/D5fNx2222yTUgwBKjo8yTwer04nU4aGhpY\ns2YNAF999RVr1qxh37598iB+8MEHmTJlSsjbbYgx1tbWMnv2bABWrVpFQ0MDVqtVuv3q6+txu908\n/vjjuFwuAB599NGgb+hA15mYX4vFQq9evRg1ahRbt24F/IdOuFBVlQ0bNsj91dzcDPg7tTqdTgAG\nDBhAUlISTqeTffv2AX5hmpqaynnnnSeFabiUMrvdzh//+EfWrVsne2idddZZYXm2wO12M3fuXMC/\nVkpLS/F6vbJ33MKFC3nuuefIzc0NidtY7Dun08maNWtYsWKFVLg0TWPx4sXU19czZcoUAM477zxS\nUlKOiUIBx5mA6iheJg67wF5CoqX5p59+Sl1dnfy71NRUYmNjQzI2ES+oqKggMzOTadOmHXSBGQwG\nduzYgdPplAdeMDgUISWsE1VV5ft58803WbJkCRs3bpTjEVp7XV0dL774IuBvU300C1XTNDweD/X1\n9axatQqA7du3U1ZWxubNm9mzZw/g30A2mw2Xy0VTUxMATz31FOeeey5WqzUs/alcLpcU1o2NjVgs\nFrKysuTBWlpaSmVlJUajkbKyMuDIlITOEIKpfbt08M9NZGQkubm5MobQ0NBAampqWN7Ntm3bOOec\nc+TcaJpGREQEJ598MjfeeCMAJ510EgaDgfLycmbMmAHAxo0bKSgoYNCgQRQWFgKhb98g9uYbb7zB\nDz/8QEFBAZMnTwbC2+69tbWVa6+9VlosovJ8bGys9BKsW7eOl156iTvuuIPc3FyAkAiqiIgIEhIS\n6Nu3r2xnY7fbsVqt7Nmzh2effRaAOXPmMH78eK655hri4uKA8MYWjwsBFehyUhRFJiIA8uUmJCTI\niRTCymaztdE+b7/99pC5iMSkjRo1CrvdLi2OzvD5fGzatAlVVTvsJns0Y+gsGaS9C81ut/PZZ58B\n/q6/iYmJpKenywCyw+HA4/GwfPlyduzYAcDJJ5982BtGzBv43Rpbt27ls88+k5pkS0sLXq+3TaM9\nt9t9gFurvLyc8vJykpOTD+v5R4KmadTX10vN32g0kpmZSUZGhnQPFRUVMXv2bFpbW6WVF6zNqyiK\ntERqamqIiYkhPj6+zYFqNBrxeDxs2rQJgPnz59OrV6+QWSRiHhwOB3fffTd1dXVtur6effbZvPrq\nq1a2cZ8AACAASURBVHJ+LBYLmqaRmJgoFQ/RWXrjxo306NEjJONsj3A9zps3j4KCAh544AHy8vKA\n8PXLcjgcnH/++SxbtkzuhbS0NMaPH8+QIUNobGwEYNu2bWiaxoYNG+Saio+PD9q6Ep9jNpspKSkh\nMzNTrher1YrVapVtbgCWLl3KmjVrWLt2rRRagUkUoUZPktDR0dHR6ZIcFxZUYIBVURQqKytlh876\n+nr69u3LwIEDpVQ3Go14vV6qqqqkBRUZGcm5554b8rFGR0fLbp4Hw26343K5MJlMjBgxIijP7qgr\nbWc/J1rSX3jhhYDfoisvL+f999/nhx9+AMBms8murUcT99E0TVq6W7du5e233+a///2v9H3Hx8eT\nnJyM2WyWlrHVaiUqKoqGhgapAbtcLp555hneeeedkLtmVFXlu+++o6qqCvBrwPv27aNfv35kZGTI\ncYvmgMLaCYamKyzO/fv3A/4rCenp6fTu3buNl8BgMOB0OqXF8u2333L11VeTmJh41GPoCOFqfOih\nh1i0aFGbdg1jx47lgw8+ICoqqs3a1zSN6OhoeafL5XLJ9RT4XUKFoigyjhgTE8Nf//pXSkpKwhb3\nam1tBeCxxx5j9erVAEyaNAmAO++8k4KCAkwmExUVFQAMGTJEvldhVUVERAQtWSHQy9LS0kJ9fb20\n6IT1FPhzdrud5uZmPv74YxkeeeKJJ2RafKg5LgSUwGQyoaoq9fX1rF+/HvC7EVJSUvB4PDI2oCgK\nDQ0NrF+/XgqonJyckL5UMaFer5chQ4b8phusvr5etoXv169fm88IB+JZwr1os9l44403+Omnn+Sh\n7PP5SElJ4aqrrqJXr17A4Qe02y94i8WC3W5HURSZuZSdnU1qaip5eXkyJpaYmMioUaP49ttvmT59\nOuBPApg3bx42m43U1NSj+foHRSQAvPPOO9TX18vvIbLAYmJiAP9cK4qC0WhkwIABbb7nkSJiql6v\nV7rFvvvuO3r16kVGRoY8/EVQPT8/n/POOw+Ajz76iAULFnDOOecEXYBrmiYTWN544w0cDgdGo5Fh\nw4YB8O677x4gnMD/Pmw2m5xXTdOIjY3llFNOCct6r62tletn2LBhlJSUtFG2Qnk30uv1snTpUsCv\nPERGRjJ27Fief/55AOLi4qSSkZmZCfizCj0eD01NTVJw2Gy2NsoQHPk6CywesG7dOlasWCFd+haL\nhfj4eBISEtrExUU8dtGiRYD/XAjXpd7jSkAZDAbMZjP5+fkywLp3716ampqorKyUGkdZWRnLli2j\nrKxMbpisrKwOkyyChVhM0dHRqKqKz+frcDGJMSxZsgRFUWSQVPzbkUx64K34zmifOaeqKuXl5Sxc\nuBCAF154gdLSUqklg7+CwZ133skf/vCHI6p2Efi8QIEdERFBQUGBFFADBw4kJSWFk08+WR4effr0\nwWw2079/fxmrKi0txe12s3z5cmkNh2KT2O12Hn74YWpra9v47Hv16sXdd99Nz549Af8cCg1Z/F0w\n4oiqquJyuWTiRU1NDa2trSQmJjJ06FDAr3AZDAasVivl5eUArF69mo0bNzJz5kxuvvlmAEpKSjoU\nHIeLz+fjlVdeAX6N+yYmJvLWW28B/vT6jp6hqipz5syRGYZGo5H+/fuHLJu2/bOfeeYZqqurAfi/\n//s/IiMj21x4DhVCodmyZQsA3bt3Z+LEiVx77bUkJSUB/rjv7t27SU9PJz8/H/BbMaqqkpKSIr0J\nc+fOpUePHvTr10+eFUe7zrxeL8uWLaO+vl7OjTizcnNzpeKxfPlydu7ciaZp8gwIPCNCzXEloMC/\nwGNiYqQ1VFNTg6ZpLFu2TB4WDodDCitxMMbGxsoslVDcdBeHcW1tLbt27WLixInSFSQElaIocnPP\nmTMHt9tNr169iI+Pb/MZRzquzn5PCCexMRVFoaqqig8++IA333wTQAa8jUajzB66++67ue66647Y\nvRD4O2IeRPagSDgA/z2xpKQkBgwYIK0T8c5yc3NlpY1bbrkFn8/H559/zu9+97s2nxsMhJKxceNG\nNm7ciKqqUog+8sgjXH755fIAAb8gczgcWK1W+vTpE7RxCFetODjcbjc+n4/6+nqp7UZGRtLa2srq\n1aul4BDW3ldffcWCBQsASEpK4qKLLuKuu+6Se+ZIhJXX62X79u0Acp088sgjbTLxOsLtdvPCCy/I\ntR0TE8NNN93UJvlFfF6waWlp4bvvvuOcc84B/ALV4/Hg8/mk5yAwMzLYKIrCwIEDAb8S1rdvX+Lj\n46V1YrPZ6N69O9nZ2dIiVlVVrmkhCOx2O6tXryY5OVl6Mo7WgvH5fJSVleFyudpULImKiuKaa66R\nd9nmzJnDiy++iNfrlc8OZ8q5niSho6Ojo9MlOe4sKPBrayLoumfPHnbu3EleXp6M5QwaNIja2lrW\nr18vL1CWlJRQXV2N2+2W6ZuB1tTRIiyjb7/9lsrKSkpLS5k4cSLg1+RWrFjB/v37ZXLH7t27MRqN\npKamyu8i3DaHqx391ndof4vcZrPx3nvvMWvWLKnNiRTT4uJi3nvvPcAf3wiGthRoDcyePZvq6mps\nNpu01FpaWhg5cmSHdciMRiOjR4+WY/T5fGzbtq2NyyhYcyhcKosW/T/23jw+yvLc/3/PnkyWSUI2\nSAKEJWGJgKwWEAERFFEWezgVcan1pZyq1Z6e2tZWu6hdtPW0ao+tFY/LUVrLqiIKSAGRfU0ChiUh\nJGTfl0lmfeb3x/O7LycIKmQmgN/5/KOvYTJzz3Pf97V+ruvaisfjITo6moceegiAxYsXi2Wrvq+4\nuBiPx0N8fDypqakhWYOCpmlCDmltbWXEiBGkpaVJbmn79u28/fbb7NixQ6IFqmTBZrNJ4Wxrayuv\nvvoqiYmJ3HPPPcD5dwdQOTH1N1FRUQwYMIBFixZ9YU5E0zT+93//V/JPoFOr+/fvj8fjkfNosVjC\n4sksW7aMtrY2MjIyAP1ZOJ1OWlpaxIMaPnx42Aq/LRaLdGQIvkfKY1FEJYvF8rkwfXAY0uVyUVFR\n0SVM391nFRUVxdSpU9m+fbvIALPZjNVqZeDAgeJtl5SUSI1bbm4u0LO1Y5elgtI0TYS6yWQiMTGR\nadOmiYLq3bs3brebgoICiT/n5OSwdOlSTp06JfHVhQsX0qdPn24XfgYnkFesWCEhi7Vr1wJ6HYbT\n6SQQCIiQs9vtxMbGkpCQIMpNtbQJLjoO5aVVh/748eM4nU7i4+Npa2sD9EM3atQo/uM//kPi4aF0\n5VVR7o4dOygpKenyb7m5uaSmpp6TgKGej8FgwOfzoWma1GmkpqaGrCO1uqjV1dWkpqayYMECvve9\n7wFnDyW+9dZbaJpGbGxsyIWc2WyWs2uxWHC73RiNRsk3vf/++2zdulXC2qDXAi5cuJAbb7yRt99+\nG9CfuwoRKcZmenr6eT+vlpYWCfG0trYyYcIEfD5fl3xEcGsm9d1/+MMfhK0KesHzb37zG66++mrJ\npw0bNkzyy6E6b5qmsXLlShITE+UzKysrqauro6ioSEKlDz74IBkZGWEJWZlMJgndwWeKXp3n0tJS\nGhsbmTx5snSdUa2hAoGAMEMLCgooLS1lzpw55OTkAN1PA9hsNim+VbWQtbW12Gw2Dhw4IPd1586d\neL1e0tLSmDp1qvyunsJlp6CU4FYC44orrmDUqFGMGzdO6LYmk4nDhw+Tn59PS0sLoFvpe/fupb29\nXYpOp0yZInmQs33PVz0EHo+H999/H9BppepgKgtYHbTgz3Q4HAwfPpxBgwZJ0WB31/FlUM8uJSUF\nq9VKS0uLXNTk5GTmzZvHtdde222m0Jnw+Xy89NJLgJ7It1gsREVFSU/EESNGSFHn2f5WtfJRwrC2\ntlYEdVJSUkhyipqmidfRr18/nnnmGa655pqzKiblEZSWlmI0Glm8eHFIcyiaplFTUyOWalNTE2az\nmd69e4tiVt6SyWQS7+3xxx/nW9/6FiaTiQkTJgA6+WX9+vWYTKYuyux8EQgEJLmflpaGwWBgx44d\nsmdOpxODwUBzczPbtm0D9LZfShir59Pe3s6BAwfIzc2V/TIajUIYChUCgQADBw4kJyeHvLw8QI9a\nnD59mh07dogSbWxsFA8rnFDkl/b2dlauXAnAgQMHiIuLIy8vjz59+sj7VIG6klPl5eXExMTQv3//\nkPZ5TEpK4jvf+Q533HEHAPn5+Tz//PMsW7ZM5EJ9fT02m40pU6aIA9CTc+wuSwXl9XqZPHkyoFt2\nWVlZOBwOEaxVVVU8+eST7N27V5RWc3MznZ2dpKamsmjRIkBXbucKd3zVg6BpGsXFxcLWSUhIEMq0\nEggxMTFUVVXR1tYmn9u/f3/q6+vJy8sTynRw4vh81qEE95e1VlICICYmhtLSUo4dOyavzZ07l8WL\nFxMVFRVya7KxsZHi4mJAV9aapnHttdeKBa082DOZhqp+atmyZYAuBDVNo6OjQyivGRkZpKamdku4\nqRCk6hoxduxYxowZc9bP1DRN2uT4/X6io6N5+OGHQ/rMDAYDZWVlsp5evXqRmZlJTk6OCIe+ffti\nMBg4evQot9xyCwDf/OY3sdlsaJomZJOsrCyGDBnC4MGDv7A35JetJy4ursvokYKCAj766COJZHi9\nXhH66r/BXSbU2UxJSWHy5Mn06dNHCCiBQCDkVrnRaOSnP/1plxDwkSNH2L17NyaTSQxTpXRDjTPv\nsZJbK1as4Pnnnwf0fV2wYAEDBw4UD1w9B4/Hw/bt2wFdxo0YMSLkbayC60ZBNxRnzJjB7t27u+yr\n2ne1xp4sh4mQJCKIIIIIIrgkcdl6UIqafeTIEUwmEwkJCUKIeOmllzh48KDUXIBuSaq4q4ql22y2\nbrmrql/bP//5Tylu/cY3vsGoUaMYPXq0WJIJCQl0dnbi8Xg4evQooFOZT58+LZNSQc9LnZl7+iq5\nqPOlWufn57Nq1So0TePqq68G9OrwcHhPoP8G1WjSbreTkJDAwoULJWSgvlfTtC6NajVN4/Dhw/zr\nX/8CPmvEGh0dLaFbn8/X7ZBDIBCgpqZG8ojjx48/6zMNBAIcO3aMPXv2yGtDhgyRsxgqKI9C5aDa\n2towGo1ERUVJ2C87O5tnnnmGlpYW8UTMZjMdHR14vV7x3lWR56xZs7pVqG6z2SRqsWvXLpqamrp0\nagkEAuJxKgKLwWAgOTmZYcOGyZ375je/SWZmJlFRUeK9mM1mGWkfyvPXq1cvOjs7xRtQ9Tz9+/cX\n4o3D4QhL93mVK1XPR3noK1askL0ZMWIE06dPF28XdK/c5XKxefNmSRuYTCZuvvnmkIRAz1YzGVxE\nP2HCBIYNGyYjXNSQ1ZKSki6NinsKl52CMhgMREdHy4NuaGjA4XBQVVUlwtxutzNq1Cjy8vKkDiEx\nMZFBgwYxYMCAkLqqJ0+e5ODBgyIwOzs7GTduXJcYu81mkzoWlTQ9deoUXq8Xp9MpAvZs9SDnI3y/\n7IIrwfHUU0/hdruJjY3lqaeeAgibcgJdUKgGq5WVlYwYMYLhw4fLswgO7wQrZFXkqXJDqoP3kCFD\nuOqqqwA9B9VdkkQgEGDPnj288847ANLdw2QydWFVnT59muuvv75LSPWZZ54JeUzeaDQyevRoYZse\nPnyYDz/8kLlz50qYLjY2ltjYWFJSUmRf/X4/+fn57Ny5U9okVVVVMWTIkC7n8UJgMpkkQb9gwQIK\nCgqkESzo+3D8+HE2b97cJSR05ZVXct9993HllVcCeojPZDLh8XhE2SpyRKjPn8FgoK6uTro57Nu3\nj4yMDBITE8VwDVYOoYLX66WiooLW1tYutUz79+/HYrEISWvJkiWkpqZSV1cnTMySkhI2btzInj17\n5HlMmzaNsWPHdvucqflqoO+nOt/B9WjBylVBdbtQv6WnukjAZaigVKGuYprdfPPNxMfHdxkUN3jw\nYGpqaoiOjpbLcvLkSWlrH0rSQSAQwOFwCMNlzJgxDBo0iPj4eKGTut1uvF4vUVFRIoyjoqJoamqi\nvLxcEt92u71bCf8vOjiBQEA8kUOHDmEwGLjmmmvkoobzwJnNZr7//e8DOq03KyuL9PT0z/ViMxqN\nXYwHn8/XpbuF1Wqlf//+LFq0SOYQhWJAnyK5KDLGpk2bmDZtWhcCzd///ncef/xxqqqqZI9Gjx7N\npEmTuv39Z8JoNOJwOHjwwQcBnXXZ3NzMihUrWLx4MfCZoPf7/UKPP3r0KH/+8585fPiwCJ0hQ4Zw\n3333dZtlaLFYREENGjRIrHn1LOrq6vjJT34iIyTUGocPH050dHSXYnRN0zCZTF0Ebrg8d7fbLS2j\nqquryczMZMaMGZL3DWXuSwn11tZWiaqozg8mkwmLxcJ1110nXdwDgQDvvPMOlZWVkm/ct28ftbW1\n9OnTR8aW3HHHHd3uxae6kwTP7bJYLJjNZrlf6r4FN0JQ5JXujtm5UFx2CgrowlxKSUn5XOuSqKgo\n4uLiaG1tFUsyJSWlC+MrFDAYDAwdOpSHHnpImEsTJ07EYrHg8/lEQamQTXJysrCa3G43jY2NbN++\nXRTHt771rW5Rbc/12wKBAPX19fz4xz8G9MOamJjIj370o7DMmjkTBoNBLP9vfvObQqVVz+fMBqhq\njTU1NVRUVIhwVSMC5syZE1Jat8/no7i4WDoxbN68mRdeeIGbbrpJyBhLly6VMK4KV61evTqkzLNg\nBDPxVq1axVtvvcXp06d54oknAJ1kM378eCwWi6xx9erVlJSUoGma1Jj96Ec/OmcbovOBwWAQYav2\nzufziUVeWVnJp59+SlRUlHjLs2bNYtKkSVit1i6Ej9jYWAnpKYTaKlehqfb2dvlut9vNjBkzGDBg\nQFh6FarzvGXLFj766CMqKipEMScmJjJ69GgSExNlQGlZWRlHjx7FbrdLNGH06NFYrVa+/e1vn7WG\nqjtQ7c0AKioqaGxslJZioJOZjh8/jqZpoqC8Xi9GoxGfzyeyKzo6OtJJIoIIIogggv+3cVl6UPBZ\nSCDY6lYWmbLsWltbJXzWu3fvkHsLBoOBmJgYcnNzu0y/tNls+P1+sfKVlW0ymbrUavn9fpxOp9Qc\nKKsv+PeFAn6/n7///e9iSVqtVm699VZycnJ6xBIyGAwSSlGdkp1Op4Sm1OiFYJp5Q0MDy5cvp6ys\nTF4bMGAACxcuxG63hzRha7fbmTJlioxDaGpq4rXXXmPlypVSyKxCI2lpafz5z38GdG8gnM9PnZ/+\n/fvzwx/+kOrqamnuW11dzaZNm9i5cycHDhwA9Pyn0WgkNzeX5557DtC7qoQijHVmjigQCFBVVSWj\nLFatWkVNTY0M4QO9EL69vZ2mpiYhMBkMBoqKiqT/IoRnYqzBYKC9vZ39+/dLYXh2djbDhw8P20Tm\n4MnHpaWltLe3yxlXNUyKfAD6uZs4cSK9evUSb2nQoEE4HA5sNltIw48GgwG73S4hfZ/PR0VFBStX\nrhQZ2djYKPkwlRoJ7p6volEOh+OsQ1ZDOU1a4bJVUGeDcrFPnjzJ4cOHu2xyuIrLFLvqyxhSKoSh\nksp33nknq1atIjExUS5quLoEe71eDh48KIIgPj6e8ePHS8imJxCcZ7JYLERHR4tibm5ulma1FRUV\ngJ4nW7t2rXTxBnjggQeYPHlyyAWM0WjknnvukdHpx44do7W1ldbW1i5hqJSUFF588UXJO/VEeBT0\nZ2e1WsnKypIaPo/HQ0FBAW63W56j1+vl6quv5qmnnhKCRTgnSJtMJk6ePAnoDDmDwcDw4cO57rrr\n5H01NTVUVlbK2VaF2ikpKRL+UjmpUIf5ysrKePPNN0VJzJs3j5iYmLAoJ7VHABMmTODhhx/m5MmT\nwl6dPHmyGKdqb5xOJ3a7naSkpC6NfMO1PvgsZzt27Fj69evHnj17WLFiBaCH/UpLS/F4PJI2MZlM\n0spLGUzKkD7TYIkoqHNAMb6C8xc2m43ExEQRwuE8mF/lc9V7lMLMzs5m/fr1dHZ2dmlzEuo1qs7l\nbW1t8ix6MoZ8JtTzMpvNEndXHcELCgqkV+GuXbuor6/H4XDIgLcpU6Z08Z5CuabMzEz++te/AvDo\no49y5MiRLoXVgwcP5o9//CPjx48XQdTTzzDYE42Ojmb8+PGMHTuW3/72t8BnFmyoFee5xtS43e4u\nfeUMBgMpKSmiEE6cOCG5VrXXBoOBjRs3kpCQIK9lZmZ2YZOFas0NDQ2MGzeOGTNmAHpOLJxtepRw\nTk1NlXEnwb/pXMqnp85R8PeYzWbS09OZOXOmzDLbuHEj//znP2lubhZqfkxMDMOGDeOOO+4QYtrZ\niGaBQACfzxf6KFU4ZySdJ7q1kOAkZUtLCy6Xq4sgVl0kgqnDX2eofXW5XLzzzju89NJL0u3CZrNx\n44038sgjjwib6ULmPXUHmqbJfvn9fjRNo66uTkY6vPLKK9TW1pKamsqdd94JwNSpU4UeGw7PIHiY\nW0tLC36/X5RRdHR0WEczXE5Q1nJnZyeHDx8G4J///Ccul4tx48aRlpYG6IL69OnTXXr/HTx4kDlz\n5pCQkCB7GNxpIlRQUQOfzyekjbi4uB5t03O5IJhm7na7aWhokJCsw+EgOjq6SzTqXEb5F3jAF3xp\nIrsVQQQRRBDBJYmvjQd1JoKH8wFhs7ovNZxtOOGRI0dYtmyZNJ/s06cPCxcu5IorrhCLR1F/e+IZ\nBXsqwa8pmjfA3/72N5qamsjKyuK2224DupISIp7MxYOylFVYR8FoNHYJtauQ1tks6zPzFaHez7ON\npoicmYuGC37wX1sFFcFnOLPNf2xsbI8l+M8X6jyqli/BxcsRRBDBZYmIgooggggiiOCSRCQHFUEE\nEUQQwdcLEQUVQQQRRBDBJYmIgooggggiiOCSRERBRRBBBBFEcEkioqAiiCCCCCK4JBFRUCFCcN2F\ngtfrlcaokydPZtq0adTU1FBTUxPSti4RRBBBBF9HXJrFMBeI4AJQNRUyuJN4OAv1ztabyul0Avqw\nO7/fT0tLC8XFxQDSDuZiQc3zCZ5W6/f7SUpK+twgwQguL/TkxNNzfT/o91D161OttC7FgnllXF5q\n64og4kFFEEEEEURwieJr40Fpmiatcnbu3MnevXspLi6mT58+ACxevJiBAweetRv22SZ7hsICVVbj\n5s2baWhoIDExkYyMjG5/7vnA4/FIFwnVVv9f//oXe/bsoaOjQ+Yd+Xw+AoEAZrNZPKhp06bx0ksv\nkZqaesl4U+GYl6Us6DPPwZmteC6VZ6CgWlqp2T1qnz0ej4yyiImJ6fEGyeo5NjY28uyzz1JSUsLc\nuXMBfeTFxeymr6BaoFVUVPD+++/Tt29fpk+fDnx2b7+OCJ6Z19zczKeffioTvbds2YLD4WDy5Mnc\nfPPNgN4WLRwTBL4qvhYKStM0mpqaRAB/+OGHVFVV0bdvXwmznT59mj59+hAVFSWCJ7hHV3BfuFBs\nRiAQoKioCNDHDtTV1eF0OkM+avrLYDKZaG9vZ9myZWzatAnQL6XqYq6Glbndbnw+H52dnSLw9u7d\ny1NPPcXvf//7Hl93MNR6Nm3axMqVK/ne974nHaq7u1eqC35FRQVPPfUUAEeOHKGpqYmWlhb53f37\n92fRokXMmzdP5lNdjM746pw6nU6KiopYsWIFnZ2d8lp0dDSDBw9m1KhRAOTl5REXFxeWUS5nQ/BU\ngY6ODk6dOsWePXsYNGgQADfddNNFE3aqd2B5eTl/+tOfAFi/fj0dHR1cf/31XHvttT2+njPDseF+\nNkpB7d+/n9/+9rds3LhRWqApI+z999/nN7/5DQDTp0/nueeeIy0t7aLs22WvoDRN4/jx49x6662U\nlpYCuuAYOnQow4YNY/jw4YBuSTqdTmJjYyUvpRSV6lWnXguFtaxpmigov9+P0Wjskt8JN5Qg6+jo\n4B//+AfvvvsuHo8HgFtuuYV58+bRu3fvLmM2VM5g6dKlACxdupRTp07R2dnZ4wpKXd7Ozk7++Mc/\nAvDCCy8QHR3N/fffH7LvUXsdFxcnZ6C2tpbKyko8Ho+MGKipqeHw4cNs3bqVKVOmADBnzhxSU1N7\nLHfhcrlkb9566y0qKirQNI2YmBhAV6JTp05lzJgxXSY896QiDQQCcs5KSkqoqKggEAhIrvNsQjnc\nUAqzurqaF198kX379knkoKWlhejoaK644gqRC+GCykvv2rULgOXLl7Nt2zY0TZOZTH379uXhhx8m\nOTlZZEUoz5d67na7naNHj+Lz+eSMx8TE4HA4ZOQG6JOS9+7dS0FBgZyznsRlq6CCwwgPPPAAJ06c\nkH+bOHEiDz74IIMGDZKJljabTTorBysjTdM4ePAg1dXVAEyaNCkko7ydTicff/wxoHspsbGx5Obm\nysVQHky4oJ5Pc3MzgUCAWbNmMX/+fAD69et3TqFlsVhk/PSzzz7LgQMHZABduNeraZoIN9A9pzVr\n1vDCCy8A+hj4gQMHkp2dHfKJumazmTFjxgBw6tQpCZ0p61LTNNrb21m9ejXbt28HYMOGDfz4xz/m\niiuuCKvhoQT8kiVLWLt2rawnLi6O+fPnc/fddwP64D9N06iqqpJ5PuEeS3/mOhX5BnQrva2tDYfD\nIec93ErgTLhcLpmUrMhKQ4cOpb6+XtaclZXFzJkzw/Kc1D10u9384Q9/4IUXXqCxsRH4LKxuMBhk\n0oDFYmHNmjV85zvfYfTo0QAMHTqUmJgYvF6vTLW12Wzy/+cD9Rtzc3N57LHHOHr0qBjxasr26dOn\n+e53vwvAnj17qKurY+/evVxzzTXdeBIXhghJIoIIIogggksSl60Hpay0l19+WaZ65uTkAPD000/T\nt2/fLq6xsmRMJpO4/I2NjWzevJmnnnqK9vZ2AO6++27mzZsnLveFIBAI0NjYyI4dOwA9jDBs2DD6\n9+/fI/VPwSGUhIQE5s+fT2xsrLjo5woZqJDaL3/5S0APdcXHx4dlTHYwKcHn89HQ0MCxY8c42ct1\n0AAAIABJREFUdeoUAPX19ZSVlbFu3TpaWloA3fq79dZbwxJqsFqtYiE6nU7xqpuamgB9IrPb7SYu\nLo7ExERAz2v+8Y9/5KGHHmLEiBEAYfGknE4n9913HytXrpR9nTZtGq+++irJycld9tPj8eByuWTP\nwjni/GwIzueeOnVK8njqbvZULgz08PayZct47733AN0jf/jhhxk8eDB79+4F9Hz15MmT6du3b8jX\npWmayKb58+dTWlra5f6bzWZiYmKwWq1CarHZbOTm5uLxeHj//fcBePfdd3G5XGRlZVFVVQXAE088\ncUEelILVauXf//3f8fv9n5vflZCQwOrVqwF45JFHKCkpCXvE51y4LBVU8HC7zZs309bWhslkYvbs\n2QBkZGTI5qlQhxLaHo9HNvkvf/kLa9asobKyUoRebW2tJHS7gw8//JDa2lpAd9tzc3OJi4sTxRrO\nOHzw50ZFRWG1WrFYLF8Yyw4EAnR0dLBmzRq5VFarlfHjx3frIgQPtwt+zefzieJRQuT06dNiPNjt\ndkaOHCl1NABJSUk88MADIc/5GAwGrFYr/fr1A2DkyJHs2LGDkpISCW86HA5mz57NiBEj+PTTTwE4\nevQohw4d4k9/+hM//elPARg0aFDI1qfID88++ywbNmzAarWycOFCAP76179+TtirZ+zxeOQ893T+\nCXTFDfDxxx/T0tLCwIEDmThxIhAeBX42eDweli1bxrvvviu/f8mSJUyYMIFAICCGh9vtJi8vL+Q5\nVq/Xy69//WvJn6qzbrFYuPLKKwE9FTFjxgzGjh0rSt3r9eJwOPD5fBKaXL9+PXv37iUuLk5kUyiY\nhkajUdIcQBdFpYhA3/nOd6ioqLhodZuXpYKCz5hd7e3tIvQVI625uRlN02htbWX9+vUAFBQU4HK5\n0DSNsrIyQGepuVwuDAYDSUlJgJ6DWrduHQsWLLjgtfn9fqqrq0Wwjhw5kkWLFlFZWdmF1t0T8Xij\n0XjO4sjgybtOp5PCwkI++OADEdT9+/dnzpw5IREq6vAHAgFcLhelpaW8/fbbgJ7wr6urw2w2k5mZ\nCegWZ15eHh9++KF8Rl5enlycUMNoNGK32wEYMWIEbrebtrY2OVtms5m0tDSMRiPp6emA7oFXVFRQ\nUFDAypUrAXj44YdDIjz8fj8/+MEPAD13Yrfb+dWvfsW9994LnDuX43a7OXjwoEQAevfu3e21nA/a\n29uFDXns2DGMRiNXXXWVWOA9xVI7cuQIzz//PB0dHdx3330AXHXVVdhsNtra2jhw4ACgy5GsrKyQ\nrsvv9/PYY4/x/PPPi4FsNBpxOBzMmzdP1jNw4EDi4+M/d7/UvbzpppsAKC4upry8nPb2dvr37w+E\n7jkGsy6DPzd4uOnw4cMvGvX+slVQsbGxgM56KSws7MKaW7NmDc3NzWzbto19+/YB+sX1eDxdkouK\npZWUlMTUqVMBmDJligigC4WmadTV1TFw4EAAbr75ZoYOHUqfPn0klOhyuXok3OHz+T5Hq1cXoKOj\nQ9bjdDqJiYnhBz/4gdRFZGdnC/38QnG23+f3+ykrK5PEcGNjIzabjVGjRklydtq0aZw4cYLm5ma5\nQPPnzw8rY06FwxT5oK2tTRLaHR0dVFdXYzKZyMrKAnRDqKioiMrKSjGE5s+fT05OTrf2VdM03nzz\nTf7+978D+ll5+OGHuffee7/UqGltbWX37t3iEQwYMOCcYb5Q15L5fD5effVVIZG43W4SExO55557\nuuWFnw/UWf3lL39JRUUFs2bN4rbbbgP0cLfBYKCiokIiMEOGDCEmJiakz+LIkSMsXboUt9stn9uv\nXz+efPJJxowZIyHi6OjoLzzPynjMzMxk7ty51NbWsnjxYvnbUEEZ9nv37qWwsFAiP6CHHAcMGEB8\nfLx8Z0923IiQJCKIIIIIIrgkcdl6UMotnjFjBkVFRVRXV0sd1O9//3v8fn+XEI3BYCAqKoqEhARy\nc3MB3UPo6Ohg+PDhEttNSEjotoXQ3NxMTU2NJD4nTZoE6In/LVu2ADr91263S2IyXFDU7eAOEW63\nm+PHj1NeXi6hIEU97+zsFHf+gw8+oLq6mmnTpklOIxRr9Xq9lJSUyH6ZzWZmzZrFo48+SnZ2NqBb\naatWrcLtdosHcMstt3T7u78I6rdFR0dzww03YDabOXLkCACHDh1i+PDhXHvttRIKNJvN1NXVUVdX\nR0FBAQBvvPEGv/rVr7r1nOrr6/n9738vtUOpqak88sgjX+o9eb1eXnvtNY4fPy51UE6nU/JQyhNV\ntVGhRm1tLatWraKmpgbQPdK77rqLnJycHrG6A4EAx44dA/TctNVqZcmSJaSmpsp72tvbefLJJ2W/\nbrnlFkwmU0hzwh9//LGE9lSZyzPPPMPEiROxWCwSPlOh9+B99fv9n+vbOWbMGOLj4+ns7JQQX6jW\nq2kajz/+OACrV6+mpaUFg8EgZzwmJob4+Hjq6+slNBkfH99jec3LXkF94xvfoL6+njVr1ojAq6+v\nF3aKEqy9e/dm7NixTJs2TeoL+vbtS3t7u+SfANmYC0UgEKCgoICjR49KW6PY2FgaGxt5/fXXxX02\nmUwsWbKEuLi4LsIi1Bvv8Xiorq7G4XBI3q65uRm73c7MmTMlFKS+12azybPdt28fDocjLMzDqqoq\nqUXp168f1157Lf369ZPv9vv9rFu3DoPBQK9evYDw144pmEwmHA4HgwYNkrORnp5OZmYmGRkZssab\nbrqJpKQktmzZIgLpgw8+4Gc/+9kFx+wDgQBlZWU0NjbKnnz729/+whCZCgXt27ePTZs2UVZWJknt\nrVu30qtXLzRNk+4byggLdd7l2WefZe/evXLOUlNT+f73v99jRd4+n49f/OIX8v/9+/dn5MiR8u/t\n7e08+OCDfPTRR7Imp9Mp4fZQKe0RI0bgcDiw2WzcfvvtAEydOlVki/qv1+ulra0No9HYpV3Vzp07\n2bVrF3l5eYBuhPfq1QuTySRrDOXeqbxuZ2entDtTSt1isVBXV8fvfvc79uzZA8Brr73WY+2qLlsF\npQo66+vrKS8vp7q6WtoaKevCbDaLFzNt2jTuu+8++vTpI68p68VisYTsEgUCAXbs2EFVVZUcxMOH\nD3PixAmamppEKKtu6x0dHSJ8LBZLyDY9uHVTZ2cn9fX1Ilj79+8vB/7MtQeTBU6cOCGFn8FWaHcQ\nCASoqqqitLRU9iE1NZXo6Gja2trkNb/fT69evTCbzWK59RSUkti4cSNDhgwBYNiwYQwePBiTyST7\nFRUVJZ0bVq1aBej5h3379onXfL4wGAw4HA5iYmIk5v9lJQ8qSvDhhx9SXFxMfHw8FRUVAJLHMpvN\nPPTQQwCMGjUqZB6NMl6OHTvGG2+8QWdnp3z2nXfeSUpKSo9Z23V1dZJztlgsXHHFFdTV1Qkp6ne/\n+x0rVqxA0zQRyh6Ph9bWVmJiYkLW53Hs2LG89dZbfPLJJ9xxxx2AbqSemXNWJS+nTp2SRgNbtmyh\ntraWhIQEYfulpKSItxXqZ2k0GvnZz34G6H0Sm5qa6N+/v+ThGxoa+OUvf8nKlSslN/32229z++23\n90gJw2WnoAKBAG63W2ob1qxZw759+6isrJQQhqotsNlsktDOzs7GaDRitVq7jAPwer1ERUV9jq57\noQdBERCCO1YUFRVx4MABTp06JeSOQYMGSWgh2HpSYb/uQLUsAp3e6vP5SElJ6dJANHidgHicmqZx\n6NAhACorK6WXX3cvb/AzVzVQyiNKSUnhzTff5MUXXxRiicvlknUoWvepU6ewWCx4PB7ZVxW+CrU3\nsHPnTrxer3i8Q4cOpXfv3p9LbEdFRfHkk08KJdjj8bBhw4YLVlCge/vjxo2TOjqv13vWM6n2UNWO\nvfbaa7S0tGA0GoWAkp6ezsCBA3E4HDgcDiC0SW515+6//36hbivhf/fdd/dYHVYgEGD37t2irGNj\nY2lpaeH222+XsF9rayuaphEVFSXeSb9+/SQMFyrYbDZGjx5Nv3795L6f63wajUba2tp46aWXACgs\nLMThcHDvvfdKOC/YcA21ggqOMo0fP/5z/x4TE8N3v/td8vPzhSq/f/9+br311h7Z2whJIoIIIogg\ngksSl50HpWkaBQUF/O53vwPg+PHj+Hw+CY0A/Nu//Rt9+/YlPz9frMXm5maOHDlCQkKCWExRUVHE\nxsZ+zhLojpViMBjw+Xy4XC6xbNetW0drays5OTnceeedgB5mUcQFFZosLCxk8ODBpKamdqv2SNM0\nKfIsKSkhJiaG06dPS9Ld5XLR3NxMZ2enJLQtFgvXX389Bw4ckK7wHR0dxMfHd+mPd6EI9tQsFgtT\np06VsMahQ4coKCigsbFR+hcG12aoqvaPPvoIs9mMw+HgiSeeAODGG28M+TgAr9dLc3MzNptNLOCB\nAweelY5sMBgYMGCAkDuOHz9+To/nqyImJoYnn3ySF198UdbT0dHRJe6vaRpOp5N9+/bJs6ipqcFs\nNhMIBGTMTFpaGikpKVxxxRVdii1DlWQ/efIkAAcPHgT0c6RKBTIyMnq0Kazq5g6flTKUlZUJKUGF\nr6dOncqDDz4IIDU+oW7IarfbZS/UawrBhbGapvHuu++yc+dOQN/rXr16MWfOnC6NnHsCZ/ses9lM\nVlYWV199tfyWmTNn9sh64DJUUAaDga1btwohQgnPqKgoaVXzox/9CIvFwsqVK/noo48AaGtr4+TJ\nk6SkpEhTUJPJFPJaJIPBwO23386zzz4rRbmHDh0iIyODb3/724wbN07eZzQaaWxsZMOGDQC8+uqr\nWCwW/va3vwkL63zXpmkabW1tkoNYu3YtaWlpbN68WTpENDc343a7CQQCXToZFxUV4fV6KS8vB/QL\nPXbsWDIyMkRhXOhFDg5RpKSkMHv2bFFQSUlJjBkzhl27dkkiVoUVg0MQQ4YMISMjg969ezNhwgSA\nz4Vnu4NgJdrW1kZhYaGcqdjY2HN+j8lkkpBeaWmphB8vFAaDgaysLCkW/+CDD/j5z3/O+PHjJYx2\n/PhxKioqOHr0qCgJs9lMr169GDp0qIyO8Pl8XHXVVeTl5YVc4Gmaxl//+lcAqflJS0sTYkBPN4Yd\nNmyYnJWmpiacTidpaWlCdHE6naSkpPDAAw+IDLDZbFit1pCHiVV+W4Xv1f3RNK0L0cHlcrFmzZrP\nKdH4+Pgu0xZCvb6vCr/fj6ZppKeni2IdM2ZMj4VuLzsFBXo+KdgyURdjyZIlgC7wKisr2bdvH/v3\n7wd0S2/gwIFMmTJFktzhKJRVFvXIkSOlrb7T6aSlpYV9+/aJhVdXV4fb7WblypVCPXc6nTgcDg4c\nOCBC7nzX5/P5KCwsFM/I7XZTVlbG0KFD5bs//vhj/H4/drtdLlBcXBwWi4X4+HiGDh0K6Ey77Oxs\n4uLiQvaczGYzsbGxxMfHC/Fi0qRJYk0qYfuf//mf5Ofn4/V6+fa3vw3orWri4+OldROEtpWPEiIr\nV64kPz8fu90uRZVf9ndK2ar2Md2F0WiUfVixYgWbNm1iw4YNwhZsaGgQ6rjKN0ZFRZGamsrEiRNF\nQamxDWomFIRu+GJHRwfr1q0DEPaXxWKRVmLKyOopJCUlCbHA5XIxZswYBgwYIMSJt99+m8zMTCnx\ngPDkMBWMRqPIKVXoXVtbK+zezs5OHnvsMYqLi0X42+12RowYEdI7F4zgXDCcu1ejyuUVFxezatUq\njh07JoZHXFxcjxXrXnYKymAwkJOTI6w7VSdjNBrFuty9ezfvvPMO//rXv2SMhtvtxuFwkJqa+jlq\ndahhMplYt26dMMBqamqoqanhN7/5jQgJm80mjT3VYbFYLIwbN45Ro0ad19qCaeAHDhxg7dq1ooTH\njBkjc7HU+w4fPkxZWRnV1dV88skngO5BZWRkUFJSIoczKiqKhoYG2tvbu91dI9iDOhdj0mg0ilC7\n4447WLp0KWazmRkzZgB6wj9c3TcCgQCVlZWA7nU2NzczduxYSVSf6280TWP37t3SxcRisci+dwfB\ntSgPPvgg2dnZNDQ0yHnetm0b1dXVuFwuERZJSUnMnz+fu+66q4u3FBMTEzLFGYwTJ06Ip+71ejEa\njcTHx0sIXfVgDA4nhrP/ZHJysoQXLRYLvXv3xul08sYbbwB6FCUqKoq+ffuKYA6noDUYDGJI1dTU\n8Ic//IGNGzdK2NjpdFJZWYmmabKOYcOG8d3vfrfLOQ+VAlX9NkE32JOTk4UhGPwel8slhv2zzz7L\noUOHGDNmjBBL1OiinkCEJBFBBBFEEMElicvSg+rfv7+EP/bs2YPX66WqqkoSnyaTiY6ODiEfgN4d\n4LrrriMzM7NHtH98fDxHjx4F9BBWUVGRFMKBbuGpgjgVN7/tttu47bbbzrv1v3qvz+fD7XaTn58v\n4bNrr72WIUOGdMkH5ObmkpKS0qX9v9FopLW1lcTEROrq6gDdwuvo6KCiooIBAwbI+8IJZc2lpqaS\nkpKCx+P5XDgmHPB4PPzwhz8E9BBoWloao0eP7vLczuzM7vP5+PDDD3nooYck35iXl8fQoUNDsk7l\nbWdmZvKd73wHp9MpxJtBgwbx+uuvU1JSIuUDDz/8MLfffjtRUVHyt+Eo7FT45JNPxCJXIb6rrrpK\nurKo7w72oMLZxd9ut0sxsuqecujQIQoLCwE9rNWvXz/xKHsCah9SU1PJyclh+/bt0t+xra1Npm0r\ngte9997LgAEDPudBhQI+n0/Kc1avXs2CBQuYPXu2RDT8fj9NTU288sor0qS5rq6OPn36cMMNN8ga\ne3JkymWnoEAPRz377LMALFq0iFOnTuFyubpMflVdvFVNxuLFi3nwwQd71D1VoY6DBw9SXl7Oyy+/\nLIy0MWPGcOeddzJixAhRUGokRnfW53A4qKurkyawr7zyCmPHjmX48OHitjc2NlJVVYXRaJQ19u7d\nG6PR2IVBmJyczIQJExg4cGCPXWgl/B0OB4mJiRw6dEhyQ+rfQi3kAoEAzc3NkqtoaWnB5XLx1ltv\nSQ5q6NChUlipwmyrV6/m+eefp7GxURLxzz//vHS+CBVUqMhutws7z2Kx4PP5xPACWLBgAXa7vUv3\n+nCddTXaXSkhVfx67733fq6paHCIKpzz0IJDaj6fj23btvH0009LLVtSUhLXXXddj438CEZ0dDR3\n3nknjY2NbNu2DYD8/HzJ202ZMgXQO04otmY4usqoOVj79++npaWF8vJyUVAHDhzgwIEDVFdXy3f3\n79+fKVOmMG/ePEkb9CRZ47JUUAaDQcaSb9myhfvvv5+PP/5YEv6g97obN26cdP+dMWNGt9sYXSiM\nRiP9+vXjiSeeEEpwqKrWg6EE95AhQ4SuferUKdauXYvL5RJBrzq4x8fHSyuYWbNmMWDAAGw2mwgd\nNVCtJ5W6WqPBYBBChCIgXHnllWFZi2Iqqd/t8/nw+XysXbtWioSV1ejz+UTgtba2YjKZGDFiBH/4\nwx8AGD16dFiUueqMoqzvjRs3UlZWRkxMjBADEhISwtZx4GyYOHGi9LVUYy2UIldrPtvvCCeCy0qe\nfvppdu7cKTnVyZMn91hfwDOhcmT33nuv9NjLz8/HZDIRGxsr3UIUmSUcz8lkMokMVB14tm7dKjlw\nJT8SExNFvn7/+99nzJgxX8hiDScuSwUFnx3EjIwMVq5cicvlEoZTZ2cn8fHxXUIdF+PhfhHCsR6z\n2czgwYP5+c9/LuHN8vJyNm3aRGVlpXiYQ4YMIS8vj8GDB0uo9ItGDvTUs1OtluCzcQLJycls2rQJ\nQBq4hjrUZzQaiYmJ4dZbbwX0Kc3t7e1omibKEfQL7vF4hIAwZMgQbrvtNubOnSs1RuGk3wYTJ4YP\nH057eztDhgxh2rRpQGg88PNZy5VXXsmaNWsA/ZyFy4A433WBHmIPBAJYLBZhzT333HMhH61xPjCZ\nTMTHx4syOnr0KLW1teTk5DBv3jxA9/LCpUCtVqvUMK1du5aioiKpSwQ9DHnPPfcwb948mSMWExPz\nle5buEK3EZJEBBFEEEEElyQM4YwJnycumYVEcPGgwrRNTU188MEHlJSUSEjt17/+NVFRUWGx0s+k\n4BYVFVFXVycdOaqqqjh9+jR5eXnSADQxMVG6WPR0CFTV1sXFxQltuSfHu1/qCAQCdHZ20tbWJnnE\nnuqq/kVr6ujokLrH1atXc/jwYX7wgx9www03AKEtPP+ytai+mOr7wuh9X/CHRhRUBJcklIBpbGyU\n0K2aWXUxcgiXGoLvbUQpXV5QRlhlZSWnTp1i5MiREjZWXS2+ZogoqAgiiCCCyxHhpN5fIrjgHxcx\nRSOIIIIILiK+5sqpW4goqAgiiCCCCC5JRBRUBBFEEEEElyQiCiqCCCKIIIJLEhEFFUEEEUQQwSWJ\ny7aTRAQRRBDBV4GqHevs7CQ/P5+EhATpl5iSkgLovQQvRo++CL4YEQ8qgggiiCCCSxJfW5NBtbGP\nUDgjiOD/bahuIOPGjcNsNuPxePjFL34BwMKFCzEajRHv6RLFZbsrZ+sGrl5rbW3l9ddfl1lIEP45\nRhGEHmqkQ/CEXzV9NLg9SwQRnAuapkkLq4SEBGJjY3n00UeZOHEiENb2Ppct/H6/PDO/309sbOxF\nU+CXrYI626FSseZXXnmFTZs2kZWVFZLx2183BAIBfD4fzc3NMjcqKSmJ6OhoLBbLRb2wqvW/x+Oh\ntLSU5cuXywyb5ORkBg0aREpKioyBT0tLw2QydRks+HWD6pumWj+BPs8nMTGRjIwM6XCekJBAVFRU\nRGn//wgEAhw9epTHH38cgL59+/LUU08xYMCAsI+g/6pQe6ug/v9itPQKBAK43W7++Mc/8vLLLwN6\nz8f58+fzzDPPyNy6nsRlq6DOBo/HA+iTPg8cOEBbW9tFP4CXEoInwVZWVvLyyy/L1N/hw4czd+5c\nhg0bdtGaaiqPCfRRBB9//DFHjhyhoqJC3hMXF8exY8fo378/oCe5e2K9SpBomiZrrKurY+/evXR0\ndPCNb3wDgOzs7JA3bQ0EArS0tPDee+/x/PPPA1BWVobBYCA2NpZx48YBcPXVVzNv3jxR2uFE8PBI\noEcb5n5VqHM+evRoAO6//36ZPnwpwO/309LSwqFDh2SIYWxsLCNGjGD48OHSBNhut/eIstI0jfLy\nct577z3q6+sBvW/g5s2b6ezsFEOoJ/c5YmpFEEEEEURwSeJr5UE1NzcDsHv3bpxO50VxSb8ImqZJ\nZ+7t27fzt7/9jb1794pFctVVV/Hcc8+RmJgYVitF0zTq6+vxeDzU1NQAUFhYyLvvvsvixYt58MEH\ngfAO3zsTauz6zp07AdixYwd+v59evXrJdE+bzUZbWxslJSWsW7cO0Eexp6enf+WGm1/0vuDGyYFA\nAK/XK4Mf6+rqWLNmDZ988glbt24FkKGGNpuNnJwcAN5//33S09NDsn9qPe3t7SxdupQ///nPVFVV\nAbp3YDQaaWhooLy8HNCH0L3wwgvce++93HPPPcAXD6K8UHg8HjZt2sSyZcvYsWMHoA8OnTJlCgsW\nLJApu1ar9aKGG+vq6tiwYQOzZ88GEI/kYiM4V/6LX/yCjRs30tLSAujeSWZmJllZWWRmZgLwgx/8\ngD59+oTtWar1eL1edu/eLfkn+CxH53Q6hZrfk/jaKKhAICCXpaGhAdDHb/fUbJUv+p5AIEB7ezu3\n3367CFZN07BYLHg8Hok7l5aWYjKZeOWVV8KqHAKBAImJiSxatIgFCxYAumB98803ee6557jlllsA\nyMrKCtsazoTP52Pfvn3s27cPgIEDBzJy5EjS09NJSEiQ9+Tn57Nv3z6OHDkCQG1tLSkpKV85iXu2\nfTozD+Dz+WhqamLTpk1ypqqqqti8eTNtbW0yLkHtu9/vFyWalJQUsjOnBEdxcTF/+ctfqKmpkTyr\nyWTCZDKhaVqXGVHFxcU8/fTTlJaWAvoMrejo6JCsSYU2ly9fzkMPPURTU5N87unTpzl06BDLly/n\n9ttvB+DWW28lLS0No9Eov0Ux5sJ9L/1+P6+99hq7du1iyZIl8t2XAtRZKygo4NSpU3g8HhH+Pp9P\nRsyoac65ubncfffdYQtlq72pra1l+/btdHZ2yhrNZjO9evW6aOHbr5WC+te//gXolkBsbCxJSUk9\n8t1ftnknTpxgwoQJNDc3y2GIjo5m1qxZWCwWNm7cCOiWstfrDfthsFqtZGVldbmweXl5uFwuVq9e\nLQq+JxVUe3s7u3btYvDgwYA+Tj03N7fLfByj0Yjb7aa5uVkukGL2+Xw+UVIX8vyMRqMIerfbzbFj\nx8jPzxdSwpEjR+js7MRoNIrxYDAYiIqK4o477uCZZ54BCOkwxWBL2+VyYTQaRVkPGzaMvn37Ul9f\nz/HjxwFoaWnB4/HQ3t7OoUOHADh+/Dh5eXndNng0TeMvf/kLoCu9pqYmAoGAzDGKjY3F4XBgNBpZ\nvnw5oBM5RowYgdvtFuV2/fXXM3bsWGJjY8N6zuvq6njvvfewWq2kpaWd831nYwOHE4qIALpB2tzc\nzMiRI7nxxhvlPSUlJZSXl4tXpZRXuMZyqHNfXFxMc3MzLpdL7pf6znAMCf0q+NooqM7OTlavXg3o\nD9xmswE9fwCDUVhYCMCYMWPweDyYTCamT58OwNKlS0lLS8PlcvH0008D8M477/Doo4+GzdILfgbK\nilXPJyoqiuzs7IsSltE0jfXr19Pc3MzQoUMBPXR3plfk9XrZuXMnxcXFXHnllYBOkjAajSGhC6tL\n6Xa7xVpVFPfOzk4yMzNJS0uTupoBAwYwY8YMFi1aRHR0dLe++2xQ+5CZmcno0aPx+XxCiLj55psJ\nBAI0NDSQn58PwKpVq6ioqMDtdkt42+l0omlatxVUe3u7nNO6ujoMBgPp6enMmjULgGnTptG7d28K\nCgooKSkBoKioiNdff53q6mo5Z+vXr+evf/0rY8aMCUuUQO3h8uXLKSoq4lvf+pbIgnPax5i0AAAg\nAElEQVShp2REIBDA7/dTV1cHwMGDBzGZTAwZMoS4uDhAZ6X269ePkydPyvsGDx5MIBAIm4JSz6y1\ntRXQ71nwM4mLi7to5JJLw+eNIIIIIogggjPwtfCgNE3jjTfeoLq6Wl4bOXIkZrO5S8wees6TWrdu\nHTfffDPwWbHb//3f/3HTTTcBn1nHPp+PvLw8AMaOHSsJ5nDjzOfQ2dnJtm3bsNlsX2pxhhpOp5O1\na9cyduxYBg4cCHTdL7WH7733Hq+++ioOh0No3aEoIgz2JEG3IN9//30OHz4sFPdBgwZxyy23MHny\nZFlbcnIy0dHRQr8NNdQeZWRk8Mgjj1BWVkZiYqL8e1VVFUVFRRw+fBjQn1lGRgbJyclcc801sm71\n+y707AcCAUpLS8XCBoiPj+dvf/ubRATUHkycOFGe2a9+9St27dqFy+WSPSwtLZX/DweUd7thwwZi\nYmJ44IEHzpl3BKQQ3GazdVtGfFHzAFUw3NzczIEDBwDdE01PTyc7O1v2NTExkejoaOLi4ujXrx+g\nn/FQly4EQ8mi1NRUWlpa6OjokMiBxWLpkbKFc+FroaDa29v53e9+J65qTEwMTz31FBaLRS6Dpml4\nvV5JLgMSGgpGKA7BqVOnuPnmm2WT4+LiKCwspG/fvvKeQCBAZ2cnv/jFL+RCP/HEE2EnR4D+LBQx\nQOUG8vPz8Xq9jB49mj59+oRtDWeDz+cjNzeXG2+8UXIaqq7G7/dLPuXxxx/H5XJx0003cdtttwF0\nyVF1B4FAQNhL69ev58iRI5SWlkqobPDgwQwaNIj4+HgRxjabLWTf/0WwWCzk5ubS2NjImjVrACgv\nL+fTTz+lra1NFOSECRO4/vrryc7OZsSIEQCSF+rOGjVN4+mnn5bcidFo5LrrrmPatGlizKjPN5lM\nUqPWt29fLBYLTqdTzp7ZbA4bS9Xv9/Phhx8C+vO5+uqrSUlJ6XLu4bNQG+hK3mq1kpCQ0O0wbbCR\nA/ozUffL6/Xicrlob2+X+97W1obZbCYpKUlyr3a7Ha/Xi8PhkLNnNpvDGnZX57lfv374fD4hAal/\nmz17dqSTxIUiEAhQVVVFdXW1CPc777yTESNGdBHAXq+Xzs5OrFarUIfT09Olc0KoLkxbWxtjx47F\n5/PJgS8oKBDCgbokJSUl3HXXXRQWFoq1m5ycHNaDqA5eXV0dDQ0NWCwWUaJOp5Np06Yxfvz4Hqfn\n2+12rrnmmi6/32AwoGka+/btY9GiRYC+13feeSf333+/dKG+kH07mzfh9XqlCPbFF1+kubkZr9cr\nF/P48eOsXLkSh8MhXt4999zD9OnTw97LzWAwYDab+eijj3j77bcB6OjoEEJNW1sboOc8R40axaxZ\ns2QPQ2F5+/1+amtr5exaLBZGjx59VuUc7PGePHmSzs5OAoGA7OuwYcPo3bt3WM75iRMneOWVVwA9\nh3nfffdhNpulW0pNTQ1JSUn4fD4pr2hvb5dCWJV3vJC9DFZ6wSzFYGPYarUSExMjXVCMRiOJiYlM\nmzZNDDO/34/dbr8o3VHsdjuVlZW43W5Zf3JyMpMmTbpoDMjLXkFpmsaLL76I1+slOTkZgEceeQS/\n309FRYW40zt27MBisRAdHS3MrIULFzJy5Ejsdnu3L7ES9HPnzqWpqYnY2Fi2b98O6EluTdNoampi\n9+7dALz66qvk5+fj9/uFwRNuxaAsvPLycrZt24amaSJss7KyGDVqlISEehJWq5WhQ4fidrvlOWqa\nxqFDh1iyZIkI4B//+Mfcf//93W7HdObf+v1+ysvL+Z//+R9AV+BKGKv1qPq12tpaIQHs2rWLa6+9\nlv/6r/8S0kY4hIrBYMBqtXZ5Pj6fTxLn6rXi4mKee+45CgsLefTRRwHIycnpNj3ZYDDg9XpFSJnN\nZqFHB3u88FlJBcCxY8fEKFJ/269fv7CERF0uFy+99JKsY/bs2eTl5eHxeKRU4MiRI5jNZmpra6mt\nrQX052i1Wpk9e7aE3y9EqQcbuWeSkYJhsVgYNGgQoBMiFCFBnTfFEj3bGQ/3vTx16hSVlZX4/X5Z\nd15enjBHLwYiJIkIIoggggguSVz2HlRHRwf/93//RyAQkGr+trY29u/fzz/+8Q+psrfb7SQmJlJf\nXy9kisbGRv77v/+7255LcA3Wnj17sFgs3H333ZLk9Hq9FBUV8eabb0qoLyMjA5/Ph91uFzJFuBOR\nypK+8sor6dOnDxUVFVIM+MknnxAbG0tWVpZQXnsSXq+XkydPynevXbuW5cuX09raypNPPgnA3Xff\nHZZnZDAY6OjoEA+8tbVVQsNnhjbMZrPkXdxuNxs2bKCsrIxf//rXAEyaNCkseSmTycSkSZOkdOHI\nkSNSrxLcF6+pqYmVK1eK1/CPf/yDsWPHduu5mUwm7r77bvEcGxoaWLduHXl5eRJ+jY6Oljq1f/7z\nnwBSLBycA4uNjf1crqa70DSN/fv3U15eLn33brjhBux2OzU1NRQXFwN6GNvpdHYhxTQ0NIiHrggf\nyis8X3yVPTcYDLIX0dHRUrumajatVuvn0g7hpJjDZ2mHpUuX0tbWRiAQEA9qzJgxF3UUyWWvoJqa\nmujo6MBsNovwX758OW+88QbNzc2Sqxg/fjzR0dHU19fT1NQE6IchFOEGv9/PP/7xD0APGSiFt2vX\nLkAP3e3fv585c+ZISG3OnDm4XC5yc3NJTU3t9hq+CtQBt9lswvZSArioqIhPPvmE1NRUCTn21MFU\nRIhf//rX0rzW4/EwadIk1qxZQ+/evbusP9QwGo0MGzaMjz/+WL5bhdVUyK6jo0Nyl8eOHQPgt7/9\nLYWFhVRVVbF+/XpA714Sjo7wRqORadOmyeceP36c+Ph4BgwYIGd48+bNvP7669TX11NZWQnoebIN\nGzaQlpZ2wWsyGo3MnTtXunwsW7aMuro6fvazn0lRbnx8PK2trZSUlEj9jsfjwWaz4fF4JD+j2pCF\n4hkFFzK/+OKL1NfXSz43Li4Og8FAdHS0EBAMBgPDhg0jISFBitFXrFjB22+/TUFBQbfCs4qEBV/d\n0DSZTHg8HqqqqqQNU1RU1FlDjOEM7ylj7MMPP5SQo8qfX3PNNReNwQeXsYJSh9PpdBIfH09KSooU\nMe7cuZPa2losFovkBm644QY+/fRTrFareBIzZ84MSfsQg8HAHXfcAeiJ6oEDBzJ9+nSGDx8O6Fbj\n6NGjsdlsEp8/fPgwgUCAq6666qIkII1GIzabjWHDhgF6bsXlclFQUMDMmTOB8CsotYdHjx5lyZIl\nnD59WoTEFVdcwauvvtpj/b/MZvMXxtrtdjvJyckEAgFRmK2trTzzzDPU1NRI0t3lcuFwOEK6NmVB\nx8fHM2/ePEDPZShPTQmvmTNnMnv2bObPny+U8OLiYrZt2ybtqy4UcXFxPPbYY4BuULzzzjvU1tby\nySefAEin92DvyGw2YzabhSgBOnHixIkTjB49utuCT33mpk2bKCwsJCMjQ4xUdafi4uJEaZnNZlGM\nyktKT09H0zT69u17wZ6Tx+Ph3XfflUjIV2l9BroxW15ejt1uFwXV0x0bFMkMdG9SrV3dhQEDBvTY\nWs6Gy1ZBKaSkpHDzzTfj9/s5efIkgIQ3kpOTpeHi/v37efPNN2lvb2fq1KnAZ9ZBdw+ECr8AbNmy\n5XO00ODPf/PNNwH9kptMJrn0FwN+v18uy/Dhw3E6nXR0dEir/czMzLBeltOnTwNw4403UllZic1m\n4+qrrwbgueeeO6fCCBaCPU3oCA7RDBs2jLS0NFpbW0UhBPf06y6C6dGdnZ1dwotn80CMRiNXXnkl\niYmJ0ibH5/Oxfft2FixY0G1iiQpD/eY3v2Hu3Lk89thj4k2q2pngrhUZGRl4vV7a29vlt9hstpAR\nSRRxZevWrTQ2NpKZmdklImIwGLBYLF0UoWqLpajeL7/8MqdPn2bOnDkXrDBbWlro1auXeI69evU6\nZy1hsKfV1NRE3759SU9PF+Wo1nBm4+JwnXOPx8Orr74KfNZJwmg0kp6eDnDRRu8oREgSEUQQQQQR\nXJK47D0ou93O1KlTWb9+vVDK29vbsdlsJCcnU1BQAOh1R42NjeTk5PDQQw8BeuV0qCwTZfl8kRWm\naZrQfwOBAGlpaWKp9CTUYMDq6mrJqzgcDnJzc6moqAh5EvtsKCoqkjEI9fX1JCYmMnPmTK677joA\naXoaExPzOQtY5YhAt8iD62x6AiqfUldXR1tbGw6HQ8J+oexZpryxwsJC1q9fz3XXXScTos9m2fr9\nfvbt2yf5FdDDWpMnTw7JOQ8mOkyfPp2JEydKeKitrQ2Px8PJkyfl/OTk5PDss8+yYsUK8RocDgcJ\nCQkhWY96Bn379sVgMFBXV8emTZsA3Su3Wq24XC4pwK6pqaG0tJT9+/cLOej06dNkZ2dz1113XfAZ\niouLIzs7WyI3ioQUTJZRXefb29ulzEV5Kmcrcwke3qmaC4Tai/L7/Rw4cIAtW7bIaypkrORSuAka\nX4bLVkEFd7g2Go14vV4JGfn9fpl5pNzuxMREbrjhBn7+859L6KinH3plZaW40QaDgSVLlvSoYFUC\nz+VyUVdXJwlr0Jt9qpYmilgSDqi2Oddff73UoqSlpfHTn/4Ut9stRIVly5aRlZXF7NmzmTBhgvx9\nVFQUPp9PYvY+n+9zYZEv29fuhAg1TZMz9ac//Yljx46Rnp7OyJEjgdB2tlDf89hjj/Hpp5+yd+9e\nfvjDHwJ6Ky+TyYTf7xcl8aMf/Yi1a9fS0dEhwvvaa69l9uzZYSFt2O12If2oNY8dO1a+y+/3M2/e\nPFauXCnnPDo6OmQKSu37/fffT1ZWFh988IEI2x07dkhNlur2XlNTQ0JCAoFAQGqRFi5cyF133SVK\n7kJgNpuJj4+XMH9xcTGnT58mJSVFurKoYufOzk4JlcbHx2MymbqcRxUC9Hg8cjeTkpLCQlTw+Xxs\n2bKFsrIyeU0Vnav8c319PQkJCV1Cyj0pNy9bBRUMr9dLdXW1CH9lebjdbslp/OQnPyEnJ+eiUSZ9\nPh8zZ84UJREfHy/Cpqe+X1mSTU1NHDx4kKNHj3ah2DscDpKSkkS4heMgNjY2snDhQmpqaoQp9I1v\nfIN+/fqxZs0aGT1it9vJzs6mtbVVCAhJSUkkJCR0UQLB1t1XWa8qijxzrtIXvV9B0zRKS0tZvHgx\noJM7fD4fkyZNkiLPUBbqqvO8f/9+6urqqKqqEpr5uHHjSExMZOvWrcJ8dLlcBAIBTCaTEAPefvvt\nC07+ny/O7MhiMplk1EdwwXOovIFgL/qb3/wmc+bMYcOGDYCel1IlJer85ObmisGjSlKsVis2m61b\ncsFkMpGQkCA5MaPRSH5+fhfjqaGhgZaWFiZPniwyoKWlhYaGBjIyMuTceL1e8vPzcTgcZGdnA3ou\nPRx3UZE0gg07lbdTkYD29nb8fn+Pd7VQuOwVlNVq5frrr+fdd9/tUg9isViYPHky//3f/w1wUYdu\n+f1+XnrpJYqLi8WS/NnPftZjTVn9fj8dHR1Sx7J9+3Zqa2upr68XwkhiYiI2m+2s/QlDASWgfvzj\nH/Ppp5/i9/tFcNpsNv7+979z9OhRRo0aBejtqqZOnUpUVJRcDnWButtXzu/3S8gpWLkF/79SZOp9\n6vndcccdUlcTCARITk7m/vvvl1KBUJ2x4DBLWloadXV1uFwuISWcOHFC1qgEnmqJNH36dKlF6inl\ndC6oEFFwc9bGxsaQz4NSlHLFpLvpppvk2QRHW848P6EIX6m/D+6+0NLSQmFhIXv27AF0TyQ1NZW9\ne/d26RdYXFxMVFSU7NOAAQNIS0tj+vTpEskIR9mCWrfD4RBPTb1mtVrFe1d1Y6FsB3c+iJAkIogg\ngggiuCRx2XtQRqORpKQk/r/2zjy46TJ94J+kSdP0Pgm0QKEcArXQrVyCAlauEURBZkHFax1GWZEV\nWJB1WVfHc3Fh1AVXBI/xGkWqIggq1CooKnIv5aiFQgulBy29c3/z+yPzvpsgq/wkSYO+nxmGTiZt\n3nzf4znf53n88cely+jbb79l7NixPProo7KMfajvFng8Hmnyr1mzhoceegiPxyMrms+ZMydkY9I0\njdOnT/Pxxx8D3mrder2eYcOGSVdQbGxsUONhwu0qNLKYmBjZnLBHjx7k5OSQmZkp240Iay7QCE1Q\nxL+ioqIwGAxERUVJa6m1tZVTp06xf/9+6R4qKipi//79NDU1yXlLSUnh3Xff5corrwzKWEWc7dln\nn2X+/PkcOnRItpMQtfjgv7GYhIQEHnroIe6///52Tw8WxMTEYDQa5fw3NDRQUlJC586dg3oBVFwH\n+LnPCOQeFJa+Xq9nxIgR5OTkSDfdt99+i8PhoK2tTSaxnDhxgurqaiwWi6w+f9111zFy5EiSkpIu\nqkP0hRAZGcmQIUN45513AK/LUTw3Yb2np6e3W6FYAF0oMrYukIsaiO8lQU3T/CoKhxJR1bipqYk5\nc+YA3k6nokvrV199BSBLsoRiPA6Hg++++04uxMrKSvLy8rjvvvukAA/2IhRz09zczIEDBygvL5cZ\newkJCSHr1yXWiYjHVVZWsmXLFoxGo0w2qK2tZe/evZSVlcn3ud1u9Ho9KSkp8iLz4sWLSU9PD8mY\n6+rqWLNmDQUFBYA3/mUwGMjJyWH27NkAXHXVVcTExLSbK/tcxCXQ/v37y+otUVFRzJgxgxUrVrRr\nCZ1gc66SKiqRCDcseJW25uZmunfvLl8zGAxB7f107hhtNhvPP/884E1MMhqNzJs3T1aTMZvNgThL\nf/Ev/2oEVHvjezvc6XTyxBNPyPJHp0+fxmw28+abb8oW2aE6RITAtFqtsq+SXq8nNzcXs9kcNodZ\nqBHWUlNTE6Wlpbz++usyRmez2aioqJAtLcBb7b1///4sWLBAls4JVmzgf+EbyxG0V2zgQvB4PDQ2\nNjJ8+HD5bHU6HatXr2batGntWkKnPfhfZ224zl8A+cVf8NerwoQYschE6m9zc7M0+SMiIrjyyivJ\nz89vl8oHBoOBmJgYmQb7G9gQP4vQbOPj48nLy2PAgAGyaKjJZMLtdvu5GX0LeLbX8wtnYXQ+dDod\nsbGxLFy4kCVLlgDe0kITJky4pL5HoPgtfueLRSVJKBQKhSIsUS6+IOFwOGS7AZPJRJcuXdo12KhQ\ntBe+TRWFZaqsid8UKgalUCgUirDkFwsopdIrFAqFIixRAkqhUCgUYYkSUAqFQqEIS5SAUigUCkVY\nogSUQqFQKMISdVFX8SNEZmdDQwM7d+4kPj6egQMHAj/dkFGhCDfOV3lDcemgBFQ7caHN9UKJx+PB\narXyzDPPAPDCCy/IBmufffYZAL169QqrMSsUAt/WGaIslG+7jUutEke4Idq6QOiepXLxKRQKhSIs\n+VVZUELCC00q3DQmTdOoqqoC4OuvvyY7O5s+ffqERYUJt9tNfX09DzzwgLSW2traMJvN5ObmkpiY\n2M4jVCh+Ht+OBk6nk4aGBlkpPDo6moiICL+OzIqfxu12s3v3bubPn09FRQXg7Uw8evRo7rrrLtm+\nPljP85IXUKKMSkVFhawe7nK5yM7OZvTo0bKnTjgIAavVKjtsfvjhhxQXF/PQQw+1a+8eUYJm+/bt\nLFiwgKNHj8pnlpuby6233srUqVNla/j22NhC8XA4HHg8HqKion6zB4w4gNva2ti1axcFBQV+/amy\nsrLIzs6mS5cugLddQiir1muaJns/6XQ69Hp9SFvfiOdjs9n4/PPP2bBhA3a7HYDx48czfvz4kFeh\n//+gaRoul8uvkn5UVBRmszmkbThEl90FCxbw8ccf43K55LkQHx/PoEGDiI6O9jMKgjG+S1ZA+WpK\np06d4l//+pfU/G02G3l5eeTk5EjtyWg0/qj/TKhbJVRXV7NixQoA9u/fj91u/58l+EOBy+Vi3bp1\nAMyfP5+Ghgays7O58847AbjpppuIj48P2cY43/hqamo4fPgwAFu3biU/P5/hw4e3S7KGiGuIg8Pp\ndBIZGSkP4VB8fkNDAwDLli3js88+4+TJk1KLjYqKwuVyERERQc+ePQGYOXMmI0eODNocivXb2trK\n6tWrWbJkiWwXLvqyTZ8+nZUrV8oxBgPRVkYIx5UrV7J06VIaGxvla2vXrmX69OksXbrUT3FtT2El\nFOyWlhYASkpK2L59Oxs2bKC0tBSAsWPHsnjxYrp06RKSsba2tvLAAw8AsG7dOpKSkhg+fDhXXnkl\nABaLhaysLL/2L8EyAC5JASWCnwAtLS3s3r2bbdu2cerUKcDb9Ku4uJgVK1bI5nLx8fFYLBZiYmKk\nRpWYmIjRaCQyMlIeMB6PJyiL1ul0cvLkSY4cOSLHLTZye+B0Otm4cSPLli0DvIfJ0KFD+cc//uHX\n1TbUQlzMa2NjI2+++SaHDx/mhx9+ALxKxt133x3wzSA+17enl/gnBKGmafznP//h5ZdfprCwEPD2\nktLr9QwcOJC3334bgNTU1ICOzRen08natWsB2LRpExEREYwbN45OnToB3r5jJ06cwOVykZaWBkDX\nrl2Ddgh7PB7ZiPD6669n7969OJ1Ov+Z7DoeDgoICevfuDcBf/vKXoBxmwkW9c+dOAFatWsWZM2dw\nu93yPQ6Hg40bN3LZZZdx6623At6uxWL/h6pppu+Yy8rK+Otf/8qZM2cAsNvtnD17lsrKSilYv/rq\nK0pLS8nIyAi6J8hqtfLwww+zYcMGAJKSkli8eDETJ06U89rW1kZ0dDQGg0GOJ1jhlPb3eykUCoVC\ncR4uOQtKmJVCu9i1axeffPIJdXV1UlvS6XTU1NSwfv16aVUlJSXJxn3CnLZYLOTn55OWlkZTUxMA\nmZmZJCcnBzQupGkamqZhNpv9vodoIx5qXC4X33//PR988IHU3K666ioeffRRunXrJjWlUMYNnE4n\n1dXVfPTRRwC8//77JCUlYbFYGDZsGAB33nknGRkZAR+X0P7E+qmsrKSwsJDi4mL5HpfLRVFREadO\nncJqtcpx63Q6vvnmG5566ikAlixZEhT3o6ZpnDx5UrZ8N5lMTJkyhaFDh0rLr7m5mZiYGBISEqSL\nLyoqKmhrrLm5mfz8fMDbgj42NpbbbruNWbNmAd7417Jly3jttddkfHju3LlER0cHfCyaptHS0sLW\nrVsBpPXkm3ouxrxp0yba2trkGK+66ipMJhMpKSmA91yIjIwMyjyKLsMAjzzyCK+88gput5v4+HgA\n+vbtS2xsrJ9F0qFDBwYOHBh0t7bT6aSwsJA1a9ZgMpkAuOeee/j9739PZGSk9G4Id3EoXP+XlIAS\nrhin00lJSQkAH3zwATt37uTs2bPyAba1tWEymYiMjJSLITk5GYvFgsFgkMInLS0NvV5PS0uLTBao\nrq7GYrEEbLzgNeV1Oh11dXVyY3g8Hrp27RrSWIp4PidOnOCVV17hwIEDDB48GIC//e1vUjiFOqB9\n9uxZPv/8c7788kv52ZMnT2bYsGFERkbKGIvFYgnaYev7nW02Gz/88APFxcVyLRgMBlJTU7FYLHLc\nLS0tlJeX43Q6OXr0KODd5MGYU4fDwbp162QW6LBhw0hLS8NkMsnDJD4+noyMDOLj44OuZDgcDubN\nm8ehQ4cAiImJYdu2bfTq1Ut+f4/Hw8MPP8zXX38tuxW3trYGXEAJBefo0aMcOHAA8LqqhDLr2+06\nJiaG5uZmvvvuO8DrSv7oo49obW2ViQEul4u8vDyeeeYZunfvHrBxulwuysvLue222wBvHBq8bmHh\npk1OTub48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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "n_digits = 3\n", - "X_test, y_test = mnist.test.next_batch(batch_size)\n", - "codings = hidden3\n", - "\n", - "with tf.Session() as sess:\n", - " saver.restore(sess, \"./my_model_variational.ckpt\")\n", - " codings_val = codings.eval(feed_dict={X: X_test})" + "n_rows = 6\n", + "n_cols = 10\n", + "plot_multiple_images(outputs_val.reshape(-1, 28, 28), n_rows, n_cols)\n", + "save_fig(\"generated_digits_plot\")\n", + "plt.show()" ] }, { @@ -2064,22 +2465,21 @@ "editable": true }, "source": [ - "Decode:" + "Note that the latent loss is computed differently in this second variant:" ] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 58, "metadata": { - "collapsed": false, + "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ - "with tf.Session() as sess:\n", - " saver.restore(sess, \"./my_model_variational.ckpt\")\n", - " outputs_val = outputs.eval(feed_dict={codings: codings_val})" + "latent_loss = 0.5 * tf.reduce_sum(\n", + " tf.exp(hidden3_gamma) + tf.square(hidden3_mean) - 1 - hidden3_gamma)" ] }, { @@ -2089,12 +2489,22 @@ "editable": true }, "source": [ - "Let's plot the reconstructions:" + "## Encode & Decode" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Encode:" ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 59, "metadata": { "collapsed": false, "deletable": true, @@ -2102,23 +2512,21 @@ }, "outputs": [ { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Restoring parameters from ./my_model_variational.ckpt\n" + ] } ], "source": [ - "fig = plt.figure(figsize=(8, 2.5 * n_digits))\n", - "for iteration in range(n_digits):\n", - " plt.subplot(n_digits, 2, 1 + 2 * iteration)\n", - " plot_image(X_test[iteration])\n", - " plt.subplot(n_digits, 2, 2 + 2 * iteration)\n", - " plot_image(outputs_val[iteration])" + "n_digits = 3\n", + "X_test, y_test = mnist.test.next_batch(batch_size)\n", + "codings = hidden3\n", + "\n", + "with tf.Session() as sess:\n", + " saver.restore(sess, \"./my_model_variational.ckpt\")\n", + " codings_val = codings.eval(feed_dict={X: X_test})" ] }, { @@ -2128,50 +2536,56 @@ "editable": true }, "source": [ - "## Generate digits" + "Decode:" ] }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 60, "metadata": { "collapsed": false, "deletable": true, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Restoring parameters from ./my_model_variational.ckpt\n" + ] + } + ], "source": [ - "n_rows = 6\n", - "n_cols = 10\n", - "n_digits = n_rows * n_cols\n", - "codings_rnd = np.random.normal(size=[n_digits, n_hidden3])\n", - "\n", "with tf.Session() as sess:\n", " saver.restore(sess, \"./my_model_variational.ckpt\")\n", - " outputs_val = outputs.eval(feed_dict={codings: codings_rnd})" + " outputs_val = outputs.eval(feed_dict={codings: codings_val})" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Let's plot the reconstructions:" ] }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 61, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Saving figure generated_digits_plot\n" - ] - }, { "data": { - "image/png": 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dwYGBgQwZMoQnn3wScO+9nkrCuSgF1KmIuIZ4aAkJCSQmJvZoBbvnIRGNRUWT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bKysmTY8LXXXiM2NpY6derQs2dPAFavXk1RURGTJ09m2bJlgGcaRl8Nt52CstvtLFmyRLZYcebo\nX6nmQbCe3nvvPU6ePEloaKhkFLmz1ZF4aaGhodStW5e6dev+rkhQxLoBhgwZwpo1a2SiFHApcKwK\nNBqNJIeMGzeOl19+mSVLljBo0CAAOUfoSnA4HJw/fx5QL9C6deswGAwypxEWFlalPXMWalf6HHFZ\nzpw5Q1BQEFOmTCExMRFQ91jMrHL+vOqIjdvtds6fPy+Vk3i2GI/iTjgcDuLj44mIiADUEE+DBg2u\n2ED0jyAao3qqYFcYK4K1lpub6zJaIyYmhueee04KYHcpgSvddWeIfFhpaSn79u37//bOPDyq6nz8\nnztLtglJSMgeFtlRsIigAm6AWmVTBEGsCKLVx1JafdQKtvi0SuvyiGxqZXFBrVZcaQFpBSuKiMgi\niwkKJCQhIWL2hSwzc+/vj/md1wmgIt4ZBr/n8xfCmHty7pnz7u8LBAp8laBW37XLL7+cefPmhawO\nSa3L5/Nx1VVXkZ2dzTnnnANA9+7dRTlT+9jY2EhsbCwZGRnyufT0dFsaxQZnP0dHR0tX+pUrV8pn\nfvWrXzFw4EARUKtXr8ayLPbs2SPKvhZQ34NpmuTn50tsICcnp1VxoMLn81FcXCwdFXbv3o3T6eS2\n226Tz9v5pVUvrVevXgwaNIj//e9/oikFr099QZ1OJwcPHmTPnj1iNdgloIKfM3HiRJYuXcrBgwel\n28HIkSPp1q0bSUlJrZINvF4vc+bM4ZFHHgECVkNcXBxXXXWVjA6xM9ZzPEpLS4GAlXzOOee0srTU\n/xeuLvB+v180zj179jB79uxWhbpRUVF07949JJbJ4MGDmTBhAgC9e/f+UReo6pYAAas9Pj6ezMzM\nkFoI6mcfPHhQ/i4hIYHx48eTmpoa8hIBlRQRXEpRX1/Ppk2bxNuizlZ0dLRYYA8//LCt/QqPJlhA\nNTU1kZWVJW22XC4XXq8Xv98vHSe2b99Ohw4dGDduXCtF2i7UOVqwYAEXX3wxhw8flrOyfv16srOz\nSUtLE+9PSkoKFRUV4oUB5F4LB6edgHI4HPTu3ZtVq1YBga4Jubm5EjSHQIbKW2+9xbx588TdEBMT\nw/Tp07n88stD+mVJSEhgyJAhLFq0SLoQTJw4UdJMVZDyueeek3RTdTjVGAe71/POO+9w1113iYm+\nbNkyHA4HaWlpkv6bnJwsFfrqUk5ISGDmzJn87ne/C0uaqWmaLFu2DAgE2KOjo8M6HE2hakS2bdsm\nw9wKCgrIzc1tZY0kJycft+nuT8UwDNq2bcutt94KIFq/1+ttZbF813NbWlqke8OOHTuIjo7G5/O1\nynALBaWlpa1Sq3v27MmNN94Y1hRlpeXX1tby0Ucf8fzzz0vKvd/vJzo6msTERMaPHw8EmhKHcn1q\nz48cOUJTUxMJCQlSt3b48GHKysr4+OOPJauwX79+jB8/nri4uJDcU+rdd+jQgc2bNzNz5kwZZJqV\nlUVxcTErVqwQA0B9vrKyUtpIvfzyy2F7pzpJQqPRaDQRyWlpQQ0fPlwaRb7//vuMHj2auLg48XMf\nOnRImj127doVgEWLFnH++eeH3H9qGAadO3fm3nvvlc4WmzdvJj09nfj4eNHIlyxZQl1dHd27d5em\nsqFaT05ODq+88ooMUpw7dy6madLQ0CA1KwkJCVRXV5OYmChNd1955RUyMzPD5nP2+/0yCdayLPr3\n739SE3PtoL6+njvuuEMC7MoVA98WTl522WV07tw5JOtzOp3SnFM1ia2urhbrJCYmBqfTeYyWrYLx\nKk24tLSU0aNHk5qaGvI+kMXFxTQ3N4vbuHv37rRv3z68zUX//7tJSkqif//+fPnllxw4cAAIpKdH\nR0dz9dVXy0SDk4nr/RjU7x4XF0dJSQmbN2+W2OK+fftoaWlh4MCBTJ06FQi4z04kkeSnllaoDjNL\nly6VrjOqVKCqqkq6XcTHx1NWVobf7+eDDz6Qz9lZsvB9nHYCyjAMsrOz5YWWl5ezYcMGDh06JD5m\nl8tFamoqY8aMYcaMGcD3JwbYjcPhIDU1VSrBq6ur+eqrr9iwYQPPP/88EMhWS0pKYsaMGceNodmN\n2+3m3nvvBWDatGlUVFRQXFwsSQkHDhygU6dOUhAK9jU+PVHq6urYv38/ENjD4E4X4US5z1wulySw\nmKYpl4JSKB588MGQuiDVeVUTaRMTE0VIqg4IwZ9TjVDj4uIkZjhs2DCJ44Xy/FuWxb59+ygpKZHn\njBo1KqTxnaMJdnuqe+K2224TZbWwsJAzzjiDadOmifAP9Z2gfn5sbCw5OTls3LhROtm43W7Gjx/P\nlVdeKft0oufdrnU7HA7JRE1MTKRjx440NTWJUqpCE36/X9x+BQUFpKSk2D7h93icdgIKAhenylJb\nuHAhmzdvZuPGjRLsu/DCCznvvPPIzs4+ZS06DMOQgGRqaiputxuXyyVzXw4ePMgFF1zAuHHjwrbG\n4NRaj8cjY0ciBcuypNtFUVERI0eOPCXWEwQulF69ekkWqOqRlpSUxMMPPwwEEnTCsT518Z5IokRs\nbKxkgUH42kOpZ2dkZIgAV1lopwLVkT4hIYHp06cDgbhUamoqbdq0aRXLU9jZ6+5oXC4XvXv3pqqq\nSu6ps88++6SzM0OB2jOPx8Mvf/lLIDCA85///Ce7du2S99qnT5+wnSsjnNMRf4CIWUgoOLrtSrB2\nrAnQ0tJCVVUVEHCJxMfHn7IvbktLCytWrODuu+8GkPqr0aNHS4cQu3rb/RywLIvi4mLeeustyT4b\nOXKkLcMbfwqmacp3zrKsU+YyVpOOVaIGcMLtxn4GnPSG/5/YHY1Go9GcfmgLSqP5Dvx+v4yTeO21\n1xg+fDjt27eXerVIcMtEEk1NTXz++edSWK2GJf4fsRI0381Jf1G0gNJoNLYRzpiX5rRBu/g0Gs2p\nRwsnjZ1oAaXRaDSaiEQLKI1Go9FEJFpAaTQajSYi0QJKo9FoNBHJadlJQqPRnF4Ej8EI1wwvzemP\nFlAajcY2gmcywbcDKI8cOQJAfn4+nTp1wuPxSONbu5+vnut0OsM6XE9jP9rFp9FoNJqIRFtQGo3G\nVoKtmMbGRsrLy6XHYkJCAvHx8SFrkKwm+q5atQqv18vYsWOlce7JdLRQffxO9v//PkLZnPbnws9S\nQKmxA/X19dJqX42+DvXYge9az/GmmYZrHaZp0tjYKJM8q6qqKCsrIyUlhezsbCBwcZyqzu8Kn88n\n78uyLJqbm6mqqpJ1WZZFSkoKiYmJIXXdqAajXq+Xzz//HIBPP/2U8vJy8vPzxTU1YcIEhgwZQlRU\nVMjW8kPrbG5uFjdaVFQU0dHRp/yyU2e9qamJwsJCioqKZEpzp06daG5uxuVy2T7h1zRNdu3aBcDj\njz+O3+/H6XRy++23n/TPtFMoWZZFbW0tjz/+OBBon6VGBKnJAkuWLGHgwIER0x5KTWJWnc4hvML0\nZyWglLZTU1NDbm4ua9askflCOTk53HfffSEZq368dTQ2NpKXlwfAhx9+SGlpKSUlJdTV1QGBUQTT\np08nNTU1pC+8ubmZoqIiFi5cKOvJzc2lqamJxMREcnJyAJg5cyZDhw49oZEOocDn87Fz507mz58P\nBIbf+Xw+SkpKZK5Wamoqffr04Z577hGt2M69U7OWGhoa2LZtGy+++CLr168HAmeqoaFBRiUALF++\nnDFjxvDss8+GXUh5vV42bdrEggUL2Lt3LwBt27Zl8eLFdO3a1dZ9+THti0zTlDNeWFhIRUVFq/EW\nXq+X0tJSOnToYHs3eNM0ZdBedXU1ycnJIR0G+mMwTZP8/HzuuOMOGbHe3Nwse1tcXAzAY489xoIF\nC+jQoUNYBEGwFaf+7Pf7+eabbwCYPXs2u3fvpkePHsycOROA7OzssI0I+dkIKMuyOHz4MABPPPEE\nmzZtoqysTKbslpeX8+yzz/Lb3/5Wmn2GAtM0qaqq4v7775epprW1tcTFxVFVVUVTUxMAH3/8Mbt3\n7+aFF16QC9iuF25ZFl9//TUQmCS8Zs0a3G63TM5MTEzE4/Hg8/nYt28fANOnT+e2227jzjvvDPtl\n6/f7+eijj7jhhhuorKyU30FZBMo9VFxcTHl5uUyIBfuGKiqlAmD37t089NBDbN68WQYWOp1OvF5v\nK2u4sbGRN954gyFDhjB58mT5XChR1tKiRYt4+umnOXTokAhWt9vN3LlzeeKJJ2xNQDjRc+n1eikq\nKuKVV14BAlZBcnIyPXv2bGUFV1dXU1ZWJlZD8J791Cmx8fHxAKSnpxMbG4vL5RLFNdgqOdp1F6rL\nNtjd+dJLL/HZZ5/J4D81iDIpKUmGBrZt25adO3eSlZVl+zDMYGHk8/nw+/00NTVRXV0NBPakoqKC\nd955h9dffx0IuEz9fj/79+8XZXbq1KlkZmbicDhCbulFhh2p0Wg0Gs1RRLQFdaKuBcuyaGpq4qWX\nXgJg5cqVdOzYkeuuu04mQ37++eds3LiRHTt2yDTeUEh/v9/PwoULWblypVgsycnJ9OrVi8TERLZv\n3w4E3B9bt25lzZo1XHvttQC2aUzNzc08+eSTALz55pvy/LPOOgsITMSsra3lX//6F++99x4AX3/9\nNfPmzaNz586ynlBrR0qL3bt3L+PHj6eyslK0PKfTSVpaGikpKZSXlwNQUVHB4cOHKSgoYPDgwbat\nw7IsfD6fxOiWLl3K5s2baWhoaHX+oqKi8Hg8ssba2lp8Ph/333+/xPKuuOKKkGnjpmmydOlSAB5+\n+GGioqK46aabqKmpAeC9997jgw8+IDc3V6bZhite4PP52Lt3L3/729+oqKgAoH379gwYMIA2bdrI\nOvx+P5Zl4XQ6j3Ev2bFWdWbT0tLIzMzkzDPPlH8zTZOWlhZM0xSr0+l0EhMT84NWlM/nO6n4tfod\nCwoKePXVV6mvrxcPRfv27bn++uu54IIL8Hg8AHzzzTd4PB6am5vFsrTre6hilgBlZWXs2rULt9vd\nyuW4du1a8vLyxJUd/K6KioqAwDtU+6EI1V0R0QLqRA+Dz+dj165dvPvuu0Bgo0eMGMGUKVNk41JS\nUsjLyxPzOlQos7mxsVHiORMmTGDy5MlERUXxxRdfAPDoo49y6NAhVq9ezfDhwwF7BJS6bFWM5qKL\nLuKWW27hzDPPbOXaNE2T3r17S33Ke++9R2NjI8uXL+fyyy8HCOk0VBUwBrj11ltFOKnnZWZmMn36\ndBISEnjmmWeAQFzB5/PZ7v9WhaMqdvLJJ5/Q2NjYKjAcFxfHkCFDuOWWWzh06BAATz31FF999RVV\nVVUyZXfo0KG2u2YUBw4cYNasWfLfs2bN4vrrrxeBsG3bNg4cOMBTTz3FokWLAPtcoN+Fuug3btzI\nzJkz+eabb8TdOWbMGOLj449xrSm3e/A7tOOCMwxDZlFlZGSQnJxMTU0NaWlpQMDNNnfuXM4991z6\n9OkDQFZW1gm5+E52H5WA2rZtG5WVlbjdbomDX3DBBUydOpX09HTZx9LS0mPWY+cIExVieOeddygp\nKWkVDnA6nRQWFkpiBAT2NDo6mksuuYSxY8cCgbs02GUL354D9XPsIqIF1IlgWRaNjY289NJLIuGH\nDx/OLbfcQkxMjGxcTEwM0dHRxMTEtNLc7MbtdnPxxRfz2WefiY/91ltvJTs7G4fDIS+2V69eFBQU\nyMEFbBlxbhgGMTEx3HDDDUBAOCYkJOB2u1tdAg6Hg3bt2nHeeecBUFJSQlFREeXl5axduxaAyy67\njNjY2FYXrl1fFL/fLxbvtm3bME0TwzDEF//AAw/Qp08fXnjhBYmTeb1e2rdvT7du3WwXnIZhiPJQ\nVFQkmmNiYiIAgwYN4sEHH6RLly6iXXo8HmbMmEFZWRlbtmwBAoWoPXr0sHVtELjYFyxYIEJ02LBh\nTJ48mejoaFEyvF4vzc3NbN26VWJVoRRQpmmyceNGACZOnEhLSwu33347d955JxAQ6ke/J8MwcLvd\neL1e+Te73qVhGHTq1AkIWFAul4uDBw+KUvrcc8+xdu1aHA4Hl156KUDIg/3qZxihD/EAABOfSURB\nVHs8HnJycqiurmbYsGFAQEC1tLS0GuqYlpYmsSm79wcQa/ubb76Rc64EuIpnBj8vJiaGkSNH8vjj\nj5OSkgK03rOjO4TYbUmdtgJKuYfq6+tZtGgRq1evpl+/fgA88sgjxMTEtNqwgoICPB4P2dnZIT2Q\nTqeTCy+8kPj4eBFQmZmZEqxV2lOXLl3wer0cOXLEdqvO6XTKRQ/fWgjBqIOlMqni4+NJT0+ntraW\nF198EQhYEldeeSXnn3++fE5pdz9lDy3Lor6+nrlz5wLfanVut5srr7wSCGTsLV68mBUrVoil5XA4\nZB3HC3z/FIIzwCCwZ5mZmYwePRoIKBldunQhKipKNMT09HSam5vx+/2yxn/84x/8+c9/tv2LWl9f\nz/Lly8U99MADD4iFrlyT6owdOXJEEj7U98BuLMuitLSUSZMmAVBXV8f48eP54x//KAkax3uuUigr\nKytF+NuFYRjiKqurq2PXrl3k5eVJKvfXX39NUlISN998M23atAFO/PyYpnlS71TtweDBg5k2bRqW\nZcn+FBYWMnfuXM4//3zxosTExBAbGxuSchjDMOT74/V62bt3LxkZGeKeTk5OJiMjQ2rJAC655BJm\nz55Nenr6MZZRsKKv7gWfz2erUqSTJDQajUYTkZyWFpTf7xe/6dKlS1m2bBkxMTHin1cmqGVZYp1s\n3bqVlpaWsBQytmnThkGDBslzlOblcDhEC/nPf/6D3+8nMzNTTGw7XR3Hs5iCCyiLi4t5+eWXpbCx\nqqqKtm3bYpqmpJ1+8skndOjQgXPOOcfWtGXTNFm9ejVlZWXyd06nk+7du3PBBRcAsGHDBlasWEFV\nVZVYS4ZhUFRUxBtvvEGvXr2AgBvJDmvF6/WK5miaprj3lHaZkZGB2+0WCwACpQINDQ1YliWuZBWE\ntpstW7ZQXV1Nx44dAejbty+GYWCappRXFBcX43A4yMjICGlRpdqDu+66SwqrJ06cyLx5837QYvP5\nfBQUFNhuPSmUVVlTU0NRURFffvllq3hK//79SU1N/dFn5mTPmNqLhIQEzjjjDLZu3cqOHTuAwBmv\nqqri3XffFet9xowZIUt7NwxD4n+dOnUiPj6+VcJI586daWhooLa2VqygSZMmkZOT0+r3/74Qid0u\n5dNOQHm9XioqKnjssceAwCVRX19Pnz595NCrC8OyLAlo5+XlkZCQcFy/uN0YhnHcF2WaJhs2bABg\n+/bt+Hw++vfvL26JUGKapsToHn74Yd5///1WWXMej4e4uDh69eolRXo9evTg6quvpk2bNq0uvJ+6\nfz6fj1WrVskXw+Fw0KZNG8466yx27twJBIK41dXVrQLEhmFQX1/P6tWrJc4zYcIEW96pYRh89dVX\nsj7TNCkoKJD3dc4555CcnExhYSFvvfUWADt27MDv9+NwOORiTE9P/0nr+C62bt2KYRi0b98eCOyZ\nqt267777ACRDbMKECSE9U16vlyeffJLPPvuMvn37AjBnzpzvFU7qnDU0NLB7926GDBli+7qClbCs\nrCycTicOh0MUCofDQadOnU5JA1nLsli1ahVvvvmmuINVF5Di4mIWLlwIBJTre+65x5Z49NGo+B/A\nqFGjKCgoIDc3V4qEGxoaKCwspKGhgXbt2gFISOLojMujBXao7tTTSkCpDLU9e/aIBeXz+cjKyiIt\nLY1PPvkEgI4dO9KhQwd8Ph+ffvopEPD3jh49OiQdlE907UeOHOGuu+4CAt2dExMTmTJlSti+MOog\nFhUVUVZWRktLiwjShIQEYmNjMQxDBH16ejoejweXy2VrwLahoUE6fACS2VRcXExubi4QiCGoxAn1\nZTAMA7/fz6FDh6QF0YQJE2zJcnK5XGKVrVu3Ti5/dX72799PRkYGNTU1YrG4XC48Hg+1tbXyDoNb\n+NiFZVnU1dXhdrulqLukpIQjR44wf/58Nm/eLJ/LysriuuuuC8mZUr/X9u3bef7550lISJBsQY/H\n873vQCkjubm57NixgwsvvDAkl5qKsYwbN47i4mI+/fRTsWpdLhexsbGYpnlSbZZU66Qfg3qOSvFW\ncWkInPsDBw5QUVEhcdjHH3+cjRs38tprrx032/Gnon5WWloaHTt2ZP369VLGUV9fT2NjIz6fT4T6\n9u3b6dq1K9nZ2SLcjmfh2ZlpGMxpJaBUEC41NVU0sNGjRxMfH8/u3btFM9m3bx8FBQWkp6ezdetW\nILD5/fr1OyV9ylRvwKeffpoDBw7I348YMYJevXqFZU0Oh4PevXsDgTqovXv3Ul1dLRr5hAkTSExM\nZN++fVKr5fP5GDNmjK3zeyzLoqamhqqqqlYXeXV1NaWlpeKSDb5Agq1Rh8NBTEwMgwYNAgIapx0u\nPrfbLen1S5YskUtNZcjV1dXRu3dvrrrqKlGE4uLi2LlzJw0NDeKGbGpqEhehnXTr1g2XyyXPvuOO\nOzAMg08++aRVTc+yZctISUkJmWsPAh00PB4P9913n7gcv+95wW7RZ599loMHD7aqjbILwzAkiaR3\n796MHTsWv98v51ldrOXl5eJWPzq79Yd+/smsST3nr3/9K06n85hONk1NTSxevBiA+fPn8+mnn3LT\nTTeJpW5nZxe1nqioKPr378+LL74oCpeqEXM4HJIFmp+fLwkbwYrid/1cu9FJEhqNRqOJSE4rCwoC\nJnzXrl3p2rUr8K3kvuiii0STLCkpobq6mj179kitTUNDgwS07e6i/F2o56hGqM8884xYCLGxscye\nPTtszVmD+5TdcccdDB48mNraWkmjjomJoaWlhbfffpuVK1cC33ajzszMtG0dqhdbfX19q35odXV1\nrRqxqjUHa25t2rShffv2jBgxglGjRgH2BWUNw5DamJ49e0pNlDoj3bp144EHHiAzM5ORI0cCgbTl\np556iuLiYrG4lEvQTgzD4Nprr2XLli2S1GKaprhm1BpTUlLo27dvyFzG6n0VFRXRt29fhg0bdkLP\nMk1TYsGff/45SUlJP+gSPFnUWUlMTGTUqFE4nU7Zp9raWsrKynjzzTcZOHAgEKhHPLqY+Lt+h5+y\nXpfLJS67o4mPjxfX/6RJkxg8eDAbN27kv//9LxDwtITC2szJyZFYE9AqGUmttW/fvqSnp4etOezR\nnHYCyuVyHbdGILiYNCkpCZ/Px4EDB6RGRLU5CWWR7tEoM7moqIjZs2dTXl4uX4Srr75a3GvhQl3m\nWVlZUpsVfMG7XC6io6PFH15bW0tTU5OtB9Pv97Nu3Tpx+ai/UwRnPKo1qS/L1KlTufHGG8nJyZFY\ng51rUz9zxIgR7Nu3D7/fT8+ePYFA5/KEhAQsyxJBf8YZZ7BlyxbJyASks7jdxMXFMWvWLInR+f1+\n1qxZw7Jly2Qvp0yZYnuH8GDUc3w+H0OHDj2hRAzV3fzVV18FAokBY8aMkTokuzm6A4Lb7ZaaQJ/P\nR1xcHAUFBXz55ZcA3H777fTo0eMH9+14iQF2EqxkjB8/nnnz5nH33XcDcOWVV4ak4Lpt27Z07NhR\nlGTVXT06OlqKiUeMGHHCmc+hiEOddgIKftgHqrJ3unbtKofK7XZLB95waAKWZYnmlpeXJ10t1GG4\n+eabT9nMF7fbfdyqb8uyWL9+vXQPj4+P5xe/+IWtzzYMg7S0NOLi4qQrgorZxMfHy6Wnik2joqK4\n5JJLAPj9738vs6BClYYLgRRcFZt49NFHgcCXWfUkC66iv+iii+TPilC8V9VlQ2XNGUagNdPixYsl\npjFhwoSQninV8WTDhg0MGzbsOwtxFao79qOPPir93gYPHsyvf/3rkKxTzfACZB5csOYfFRVFv379\naG5ulvR4Nfrjh9Ljw5XIZBgGFRUVmKZ5jEfB7uc4HI5W3zmfz4fb7eaKK65g2rRpQKDJwKmwnBSn\npYA6EQzDaBWMjImJEWsq3HTr1o3o6GicTqdYesFNLMOBGsKn/nx0vzjLsti/fz/vvvuufG7o0KG2\n16u4XC5Gjx7NypUrpVGt1+slNTWVSy+9VCyEffv2odo2nX322UDgHYZy4KS6EPLz82nTpg2pqamc\ne+65sm5orQhZliV9+9TFfOaZZ4ZsfU6nUwLmPp+PlStXSvsnCNSxhBL1O37xxRfExcVxzTXXHHOO\nVC9KgE2bNjFv3jz2798vbuI5c+aExMpTGb5K8DQ1NWFZFhkZGSK0OnToQHZ2NjU1NZLRWlJSEtLx\nMic6NVd9rqqqitdffx2/3y/jLUKldDidTi666CK2bdsGBEYS9ezZk1mzZtGtWzeAY3rufd/vERKX\nre0/UaPRaDQaG/jZWlAKFRtoamoKe4GeilUcPnxY3Fhq1EdwcDLUqI4awenIwf8GgeaRkyZNoqam\nRmo1Fi5cGJI9S0xMZN68eVJD09DQQGpqKvv37xctTK3V6/WKFhfqLiBK89+wYQNNTU107tz5e5NY\n6urq+Pvf/y5+e0DS8kNBcKFlSUkJW7ZsISoqSrqHhyquo1A1WGlpaWzZsoXVq1dLrAICIxzefvtt\ndu/eDQRc29HR0Vx//fXcc889ACErIFbF+craiIuLwzRN0tPTxS1aU1NDQUEBlZWV5OfnA4EGqcOG\nDZMu6HajkmcqKytJTk4+pizCsiyam5vZtGkTADfccANVVVV4PB6WLFkChM5lDIHvvWogm5CQQKdO\nnSQ+rT6n6saUZ0V5MYLPuS7UPQn27Nkj5r3T6aS4uDhsSRKqmSbAmjVrqKiooHv37kydOhWwb/bT\nieDz+airq5MMwqSkJPx+P6ZpSueGa665hsrKSlJTU1mzZg3Ad2Yd/VRUI1Y1Qrq6upodO3awfv16\nCgsLgW8D8m63W9xsoRROlmVJ8e++ffuora0lPz9fumqodi/qQoFAw9aDBw/icrlE8Rg5cmTI1wmB\nprSFhYUkJSXJGIRQxzSVAJwyZQrz58/noYcekjEjTqeTkpISfD6fnJuxY8fym9/8hrZt24Z8bSop\nQj1HzStyuVwioPLy8mhsbKS5uVnaeWVnZ8t3wW5lzLIsOc9/+tOfcLvd3HzzzZJ44/V6WbNmDXPm\nzJE2Wz6fj+joaF577TVxs4UKp9OJx+MR5aq6upq4uLhjFEFVf6oUOJV8Eo7Y1M9aQOXl5YnUD+44\nHS7+/e9/A/DBBx9QXV3NiBEjpFt3uAKPKnsxPz9f2vZ89tln5OTk8OWXX8pY+paWFjp27MjatWvJ\nysoK+RrVhQKBdir19fUyeFIRHR1N37596dKlS8jXE6y41NbW0tLSwu7du/nDH/4ABGYvdejQgaqq\nKubPnw8E2jG5XC4GDBggWWqhblullIy1a9diGAbDhg2TYtlQo5Sqa665hkGDBvHkk09KESwEEiAm\nT54swjpUqeTHQ2n0Kp7kcrnw+Xw4HA4RUIcPH+bcc8+lurpa9rFz584ha08VnFCzbds2Dh06xPLl\ny1sppyo2rO6ptLQ0SYMP9d45nU4GDhwoZSW5ublkZma2Ss5Qlmlwzz6VjBIcDw7VWJefrYCyLIsP\nPvjgmJqncGakKNN5586dGIbBWWeddUpaLZWWlrJ06VJJSjh8+LDsi0okGT9+PE8++aS4JcNB8OEe\nOHAgGzdulBZIFRUVpKSkMGzYsJC0fDneWpSbp0uXLuTl5eH3+1mxYgUQGMiXlpZGeXm5aNrKunv6\n6adDdskdjXpvFRUVMqsn1EMJj8bpdJKZmcns2bNbrcvOjiMni3q+mr1mWZa408eNG0dKSor0wISA\nVRgXFxcSV7bD4ZDkkAEDBrBixQpM0xQlzDAMyV69+OKLgUDz63bt2oVtH6OiokQBbGxspLi4mIMH\nD0rCTUtLi9SQKo9Qnz59pNepUggSEhJCYiXrJAmNRqPRRCQ/WwsKAtqRSmkdMmQIo0ePDptmYlmW\njGpQAUbV/yucGIZBdnY2ffv25cMPPwQCCRqxsbGcf/75/PGPfwQCqfCnqi7L4XCQlpbG5MmTxUIo\nLCwkPT2dG2+8MSxWp4qLATzzzDP85S9/YefOnVL7U1FRwZEjR8jKypLEgC5dujBgwAB69OgRtnOl\nNP2+fftSWVkp2u+pIBxB8pNFWSdOp/O450clfMCx4yNUtxk7vg/KI/H000+TnZ3NunXr5ExlZmYy\nZcoUJk6cKPG9cCZyGUZggKEqU6itreWLL75g+fLl0i3FMAyqqqqOiUuZpklDQ4N4DtSe2d7xIpyd\nFX4A2xeybt06FixYAAS6BHfu3DlsB8Dv98t4izlz5tCvXz8mTZp03HqacOD1eiXxQLk0Iu1SgW/b\nrahzeSpGIwQT3I4JTv2lrPalqKgIt9tNu3btJKYRie9TE9moGDUgiUGGYUjNaE1NDR999BGjRo2S\npI3ExEQZcaPO4w+4mU/6YP6sBdSJFsmFmlC1otdoNBo7OZ4ldLzepX6//8fEPrWA0mg0Gk1EctIC\nSidJaDQajSYi0QJKo9FoNBGJFlAajUajiUgiKc1cZxFoNBqNRtAWlEaj0WgiEi2gNBqNRhORaAGl\n0Wg0mohECyiNRqPRRCRaQGk0Go0mItECSqPRaDQRiRZQGo1Go4lItIDSaDQaTUSiBZRGo9FoIhIt\noDQajUYTkWgBpdFoNJqIRAsojUaj0UQkWkBpNBqNJiLRAkqj0Wg0EYkWUBqNRqOJSLSA0mg0Gk1E\nogWURqPRaCISLaA0Go1GE5FoAaXRaDSaiEQLKI1Go9FEJFpAaTQajSYi0QJKo9FoNBGJFlAajUaj\niUj+H84bQOcjSV96AAAAAElFTkSuQmCC\n", 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2179,29 +2593,12 @@ } ], "source": [ - "plot_multiple_images(outputs_val.reshape(-1, 28, 28), n_rows, n_cols)\n", - "save_fig(\"generated_digits_plot\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, - "outputs": [], - "source": [ - "n_rows = 6\n", - "n_cols = 10\n", - "n_digits = n_rows * n_cols\n", - "codings_rnd = np.random.normal(size=[n_digits, n_hidden3])\n", - "\n", - "with tf.Session() as sess:\n", - " saver.restore(sess, \"./my_model_variational.ckpt\")\n", - " outputs_val = outputs.eval(feed_dict={codings: codings_rnd})" + "fig = plt.figure(figsize=(8, 2.5 * n_digits))\n", + "for iteration in range(n_digits):\n", + " plt.subplot(n_digits, 2, 1 + 2 * iteration)\n", + " plot_image(X_test[iteration])\n", + " plt.subplot(n_digits, 2, 2 + 2 * iteration)\n", + " plot_image(outputs_val[iteration])" ] }, { @@ -2216,7 +2613,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 62, "metadata": { "collapsed": false, "deletable": true, @@ -2224,11 +2621,18 @@ "scrolled": true }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Restoring parameters from ./my_model_variational.ckpt\n" + ] + }, { "data": { - "image/png": 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