Activity_Density_Estimator_TF.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: CUDA_VISIBLE_DEVICES=1\n"
     ]
    }
   ],
   "source": [
    "%env CUDA_VISIBLE_DEVICES=1\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import tensorflow as tf\n",
    "import utils.plot_functions as pf\n",
    "import numpy as np\n",
    "from cae_model import cae\n",
    "import time\n",
    "import utils.entropy_funcs as ef "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "78608.2\n",
      "77410.2\n",
      "68334.9\n",
      "63541.8\n",
      "60632.6\n",
      "58648.8\n",
      "57109.5\n",
      "56014.5\n",
      "55070.2\n",
      "54309.5\n",
      "53410.1\n",
      "52924.0\n",
      "52419.9\n",
      "51956.6\n",
      "51710.7\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-555da72f853d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    105\u001b[0m     \u001b[0;31m#u_tri_evals = sess.run(ef.eval_triangle(u_vals, ef.weights(cae_model.mle_thetas), cae_model.triangle_centers), feed_dict)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    106\u001b[0m     \u001b[0;31m#tri_heights, tri_centers = sess.run([ef.weights(cae_model.mle_thetas), cae_model.triangle_centers], feed_dict)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 107\u001b[0;31m     \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ment_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcae_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_op\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcae_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0ment_loss\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    108\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ment_loss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    109\u001b[0m   \u001b[0mt1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/rzarcone/tensorflow/lib/python3.4/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    765\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    766\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 767\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    768\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    769\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/rzarcone/tensorflow/lib/python3.4/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    963\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    964\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 965\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    966\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    967\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/rzarcone/tensorflow/lib/python3.4/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1013\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1014\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1015\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m   1016\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1017\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
      "\u001b[0;32m/home/rzarcone/tensorflow/lib/python3.4/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1020\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1021\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1022\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1023\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1024\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/rzarcone/tensorflow/lib/python3.4/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1002\u001b[0m         return tf_session.TF_Run(session, options,\n\u001b[1;32m   1003\u001b[0m                                  \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1004\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m   1005\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1006\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Error reported to Coordinator: <class 'tensorflow.python.framework.errors_impl.CancelledError'>, Enqueue operation was cancelled\n",
      "\t [[Node: queue/input_producer/input_producer_EnqueueMany = QueueEnqueueManyV2[Tcomponents=[DT_STRING], timeout_ms=-1, _device=\"/job:localhost/replica:0/task:0/cpu:0\"](queue/input_producer, queue/input_producer/RandomShuffle)]]\n",
      "\n",
      "Caused by op 'queue/input_producer/input_producer_EnqueueMany', defined at:\n",
      "  File \"/usr/lib/python3.4/runpy.py\", line 170, in _run_module_as_main\n",
      "    \"__main__\", mod_spec)\n",
      "  File \"/usr/lib/python3.4/runpy.py\", line 85, in _run_code\n",
      "    exec(code, run_globals)\n",
      "  File \"/home/rzarcone/tensorflow/lib/python3.4/site-packages/ipykernel_launcher.py\", line 16, in <module>\n",
      "    app.launch_new_instance()\n",
      "  File \"/home/rzarcone/tensorflow/lib/python3.4/site-packages/traitlets/config/application.py\", line 658, in launch_instance\n",
      "    app.start()\n",
      "  File \"/home/rzarcone/tensorflow/lib/python3.4/site-packages/ipykernel/kernelapp.py\", line 477, in start\n",
      "    ioloop.IOLoop.instance().start()\n",
      "  File \"/home/rzarcone/tensorflow/lib/python3.4/site-packages/zmq/eventloop/ioloop.py\", line 177, in start\n",
      "    super(ZMQIOLoop, self).start()\n",
      "  File \"/home/rzarcone/tensorflow/lib/python3.4/site-packages/tornado/ioloop.py\", line 888, in start\n",
      "    handler_func(fd_obj, events)\n",
      "  File \"/home/rzarcone/tensorflow/lib/python3.4/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "  File \"/home/rzarcone/tensorflow/lib/python3.4/site-packages/zmq/eventloop/zmqstream.py\", line 440, in _handle_events\n",
      "    self._handle_recv()\n",
      "  File \"/home/rzarcone/tensorflow/lib/python3.4/site-packages/zmq/eventloop/zmqstream.py\", line 472, in _handle_recv\n",
      "    self._run_callback(callback, msg)\n",
      "  File \"/home/rzarcone/tensorflow/lib/python3.4/site-packages/zmq/eventloop/zmqstream.py\", line 414, in _run_callback\n",
      "    callback(*args, **kwargs)\n",
      "  File \"/home/rzarcone/tensorflow/lib/python3.4/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "  File \"/home/rzarcone/tensorflow/lib/python3.4/site-packages/ipykernel/kernelbase.py\", line 283, in dispatcher\n",
      "    return self.dispatch_shell(stream, msg)\n",
      "  File \"/home/rzarcone/tensorflow/lib/python3.4/site-packages/ipykernel/kernelbase.py\", line 235, in dispatch_shell\n",
      "    handler(stream, idents, msg)\n",
      "  File \"/home/rzarcone/tensorflow/lib/python3.4/site-packages/ipykernel/kernelbase.py\", line 399, in execute_request\n",
      "    user_expressions, allow_stdin)\n",
      "  File \"/home/rzarcone/tensorflow/lib/python3.4/site-packages/ipykernel/ipkernel.py\", line 196, in do_execute\n",
      "    res = shell.run_cell(code, store_history=store_history, silent=silent)\n",
      "  File \"/home/rzarcone/tensorflow/lib/python3.4/site-packages/ipykernel/zmqshell.py\", line 533, in run_cell\n",
      "    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n",
      "  File \"/home/rzarcone/tensorflow/lib/python3.4/site-packages/IPython/core/interactiveshell.py\", line 2717, in run_cell\n",
      "    interactivity=interactivity, compiler=compiler, result=result)\n",
      "  File \"/home/rzarcone/tensorflow/lib/python3.4/site-packages/IPython/core/interactiveshell.py\", line 2821, in run_ast_nodes\n",
      "    if self.run_code(code, result):\n",
      "  File \"/home/rzarcone/tensorflow/lib/python3.4/site-packages/IPython/core/interactiveshell.py\", line 2881, in run_code\n",
      "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "  File \"<ipython-input-7-555da72f853d>\", line 61, in <module>\n",
      "    cae_model = cae(params)\n",
      "  File \"/home/rzarcone/CAE_Project/CAEs/cae_model.py\", line 13, in __init__\n",
      "    self.construct_graph()\n",
      "  File \"/home/rzarcone/CAE_Project/CAEs/cae_model.py\", line 265, in construct_graph\n",
      "    capacity=self.params[\"capacity\"])\n",
      "  File \"/home/rzarcone/tensorflow/lib/python3.4/site-packages/tensorflow/python/training/input.py\", line 230, in string_input_producer\n",
      "    cancel_op=cancel_op)\n",
      "  File \"/home/rzarcone/tensorflow/lib/python3.4/site-packages/tensorflow/python/training/input.py\", line 162, in input_producer\n",
      "    enq = q.enqueue_many([input_tensor])\n",
      "  File \"/home/rzarcone/tensorflow/lib/python3.4/site-packages/tensorflow/python/ops/data_flow_ops.py\", line 367, in enqueue_many\n",
      "    self._queue_ref, vals, name=scope)\n",
      "  File \"/home/rzarcone/tensorflow/lib/python3.4/site-packages/tensorflow/python/ops/gen_data_flow_ops.py\", line 1538, in _queue_enqueue_many_v2\n",
      "    name=name)\n",
      "  File \"/home/rzarcone/tensorflow/lib/python3.4/site-packages/tensorflow/python/framework/op_def_library.py\", line 763, in apply_op\n",
      "    op_def=op_def)\n",
      "  File \"/home/rzarcone/tensorflow/lib/python3.4/site-packages/tensorflow/python/framework/ops.py\", line 2327, in create_op\n",
      "    original_op=self._default_original_op, op_def=op_def)\n",
      "  File \"/home/rzarcone/tensorflow/lib/python3.4/site-packages/tensorflow/python/framework/ops.py\", line 1226, in __init__\n",
      "    self._traceback = _extract_stack()\n",
      "\n",
      "CancelledError (see above for traceback): Enqueue operation was cancelled\n",
      "\t [[Node: queue/input_producer/input_producer_EnqueueMany = QueueEnqueueManyV2[Tcomponents=[DT_STRING], timeout_ms=-1, _device=\"/job:localhost/replica:0/task:0/cpu:0\"](queue/input_producer, queue/input_producer/RandomShuffle)]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "params = {}\n",
    "#shitty hard coding\n",
    "params[\"n_mem\"] = 32768  #32768 #49152 for color, 32768 for grayscale\n",
    "\n",
    "#general params\n",
    "params[\"run_name\"] = \"test_run\"\n",
    "# params[\"file_location\"] = \"/media/tbell/datasets/natural_images.txt\"\n",
    "params[\"file_location\"] = \"/media/tbell/datasets/test_images.txt\"\n",
    "params[\"gpu_ids\"] = [\"0\"]\n",
    "params[\"output_location\"] = os.path.expanduser(\"~\")+\"/CAE_Project/CAEs/model_outputs/\"+params[\"run_name\"]\n",
    "params[\"num_threads\"] = 6\n",
    "params[\"num_epochs\"] = 40\n",
    "params[\"epoch_size\"] = 49000\n",
    "params[\"eval_interval\"] = 100\n",
    "params[\"seed\"] = 1234567890\n",
    "\n",
    "#checkpoint params\n",
    "params[\"run_from_check\"] = False\n",
    "params[\"check_load_run_name\"] = \"train\"\n",
    "params[\"check_load_path\"] = \"/home/dpaiton/CAE_Project/CAEs/model_outputs/\"+params[\"check_load_run_name\"]+\"/checkpoints/chkpt_-22800\"\n",
    "\n",
    "#image params\n",
    "params[\"shuffle_inputs\"] = True\n",
    "params[\"batch_size\"] = 100\n",
    "params[\"img_shape_y\"] = 256\n",
    "params[\"num_colors\"] = 1\n",
    "params[\"downsample_images\"] = True\n",
    "params[\"downsample_method\"] = \"resize\" # can be \"crop\" or \"resize\"\n",
    "\n",
    "#learning rates\n",
    "params[\"init_learning_rate\"] = 5.0e-4\n",
    "params[\"decay_steps\"] = 10000#epoch_size*0.5*num_epochs #0.5*epoch_size\n",
    "params[\"staircase\"] = True\n",
    "params[\"decay_rate\"] = 0.9\n",
    "\n",
    "#layer params\n",
    "params[\"memristorify\"] = False\n",
    "params[\"god_damn_network\"] = True\n",
    "params[\"relu\"] = False\n",
    "\n",
    "#layer dimensions\n",
    "params[\"input_channels\"] = [params[\"num_colors\"], 128, 128]\n",
    "params[\"output_channels\"] = [128, 128, 128]\n",
    "params[\"patch_size_y\"] = [9, 5, 5]\n",
    "params[\"strides\"] = [4, 2, 2]\n",
    "\n",
    "#memristor params\n",
    "params[\"GAMMA\"] = 1.0  # slope of the out of bounds cost\n",
    "params[\"mem_v_min\"] = -1.0\n",
    "params[\"mem_v_max\"] = 1.0\n",
    "params[\"gauss_chan\"] = False\n",
    "\n",
    "#entropy params\n",
    "params[\"LAMBDA\"] = 0.1\n",
    "params[\"num_triangles\"] = 20\n",
    "params[\"mle_lr\"] = 0.1\n",
    "params[\"num_mle_steps\"] = 5\n",
    "params[\"quant_noise_scale\"] = 1.0/128.0 # simulating quantizing u in {-1.0, 1.0} to uint8 (256 values)\n",
    "mle_triangle_centers = np.linspace(params[\"mem_v_min\"], params[\"mem_v_max\"], params[\"num_triangles\"])\n",
    "\n",
    "cae_model = cae(params)\n",
    "\n",
    "config = tf.ConfigProto()\n",
    "config.gpu_options.allow_growth = True\n",
    "config.allow_soft_placement = True\n",
    "config.log_device_placement = False # for debugging - log devices used by each variable\n",
    "\n",
    "with tf.Session(config=config, graph=cae_model.graph) as sess:\n",
    "  sess.run(cae_model.init_op)\n",
    "  if cae_model.params[\"run_from_check\"] == True:\n",
    "    cae_model.full_saver.restore(sess, cae_model.params[\"check_load_path\"])\n",
    "  # Coordinator manages threads, checks for stopping requests\n",
    "  coord = tf.train.Coordinator()\n",
    "  # queue_runners are created by helper functions tf.train.string_input_producer() and tf.train.batch_join()\n",
    "  enqueue_threads = tf.train.start_queue_runners(sess, coord=coord, start=True)\n",
    "\n",
    "  mem_std_eps = np.random.standard_normal((cae_model.params[\"effective_batch_size\"], cae_model.params[\"n_mem\"])).astype(np.float32)\n",
    "  feed_dict={cae_model.memristor_std_eps:mem_std_eps, cae_model.triangle_centers:mle_triangle_centers}\n",
    "  if not params[\"memristorify\"] and not params[\"gauss_chan\"]:\n",
    "    quant_noise = np.random.uniform(-params[\"quant_noise_scale\"], params[\"quant_noise_scale\"],\n",
    "      size=(cae_model.params[\"effective_batch_size\"], cae_model.params[\"n_mem\"]))\n",
    "    feed_dict[cae_model.quantization_noise] = quant_noise\n",
    "  t0 = time.time()\n",
    "  for batch_id in range(100):\n",
    "    # Update MLE estimate\n",
    "    sess.run(cae_model.reset_mle_thetas, feed_dict)\n",
    "    #old_thetas = sess.run(cae_model.mle_thetas, feed_dict)\n",
    "    for mle_step in range(params[\"num_mle_steps\"]):\n",
    "      _, thetas = sess.run([cae_model.mle_update, cae_model.mle_thetas], feed_dict)\n",
    "      #_, thetas, grads = sess.run([cae_model.mle_update, cae_model.mle_thetas, cae_model.mle_grads], feed_dict)\n",
    "        #print(\"\\n\", mle_step, \" - grads: \", grads.shape, \"\\t\", np.mean(grads[0,:]), \"\\t\", np.var(grads[0,:]))\n",
    "        #mean_diff = np.mean(np.mean(np.square(old_thetas-thetas), axis=1))\n",
    "        #mean_var = np.mean(np.var(np.square(old_thetas-thetas), axis=1))\n",
    "        #print(mle_step, \" - weight diffs: \", mean_diff, \"\\t\", mean_var)\n",
    "        #old_thetas = thetas\n",
    "\n",
    "    #_, step, u_list = sess.run([cae_model.train_op, cae_model.global_step, cae_model.u_list])\n",
    "    #latent_ent_list, u_list, ent_loss = sess.run([cae_model.latent_entropies, cae_model.u_list, cae_model.ent_loss], feed_dict)\n",
    "\n",
    "    #tri_linspace = tf.stack([tf.linspace(tf.reduce_min(cae_model.u_list[int(params[\"num_layers\"]/2)]),\n",
    "    #  tf.reduce_max(cae_model.u_list[int(params[\"num_layers\"]/2)]), num=1000),]*params[\"n_mem\"], axis=1)\n",
    "    #tri_evals, tri_lin_eval = sess.run([ef.eval_triangle(tri_linspace, ef.weights(cae_model.mle_thetas), cae_model.triangle_centers),\n",
    "    #  tri_linspace], feed_dict)\n",
    "    #u_vals = tf.reshape(u_list[int(params[\"num_layers\"]/2)], (params[\"effective_batch_size\"], params[\"n_mem\"]))\n",
    "    #u_tri_evals = sess.run(ef.eval_triangle(u_vals, ef.weights(cae_model.mle_thetas), cae_model.triangle_centers), feed_dict)\n",
    "    #tri_heights, tri_centers = sess.run([ef.weights(cae_model.mle_thetas), cae_model.triangle_centers], feed_dict)\n",
    "    _, ent_loss = sess.run([cae_model.train_op, cae_model.ent_loss], feed_dict)\n",
    "    print(ent_loss)\n",
    "  t1 = time.time()\n",
    "  t_tot = t1-t0\n",
    "  print(\"\\n\\ntotal_time: \",t_tot)\n",
    "  print (ent_loss)\n",
    "  \n",
    "  coord.request_stop()\n",
    "  coord.join(enqueue_threads)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def calc_hist(u_val, num_bins):\n",
    "    value_range = (np.min(u_val), np.max(u_val))\n",
    "    hist, bin_edges = np.histogram(u_val, range=value_range, bins=num_bins)\n",
    "    hist = hist/np.sum(hist)\n",
    "    return bin_edges, hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_hists(latent_vals, u_val, num_plots, tri_x, tri_eval, tri_centers, tri_heights):\n",
    "    fig, ax = plt.subplots(num_plots, 3, figsize=(20, 5*num_plots))\n",
    "    for plot in range(num_plots):\n",
    "        entropy = latent_vals[plot]\n",
    "        bin_edges, hist = calc_hist(u_val[plot], 20)\n",
    "        print (\"gauss_ent\", plot, \" \", 0.5*np.log(2*np.pi*np.e*np.var(u_val[plot])))\n",
    "        print(\"u_var\", plot, \" \", np.var(u_val[plot]))\n",
    "        ax[plot, 0].scatter(bin_edges[:-1], hist)\n",
    "        for index in np.arange(len(hist[:-1])):\n",
    "          x_points = [bin_edges[index], bin_edges[index+1]]\n",
    "          y_points = [hist[index], hist[index+1]]\n",
    "          ax[plot, 0].plot(x_points, y_points, linewidth=2, color=\"k\")\n",
    "        ax[plot, 0].set_title(\"Entropy=\"+str(round(entropy,4)), fontsize=18)\n",
    "        ylim = ax[plot, 0].get_ylim()\n",
    "        ax[plot, 0].set_ylim((0, ylim[1]))\n",
    "        ax[plot, 1].bar(tri_x[plot], np.sum(tri_eval[plot], axis=1), width=0.01)\n",
    "        ax[plot, 2].bar(tri_centers, tri_heights[plot], width=0.01)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gauss_ent 0   1.69027545047\n",
      "u_var 0   1.7206\n",
      "gauss_ent 1   1.77129901853\n",
      "u_var 1   2.02328\n",
      "gauss_ent 2   1.83603598634\n",
      "u_var 2   2.30296\n",
      "gauss_ent 3   2.01878301497\n",
      "u_var 3   3.31908\n",
      "gauss_ent 4   2.0423505694\n",
      "u_var 4   3.47928\n"
     ]
    },
    {
     "data": {
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iekRMX7FiRVuEK7WKcinb8OHDiYhWf17v3r0ZMmQImzZtYu7cua3+PEmSpLYU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v16pk+fHpNcGzduZOussez54hPSmh9M+kE5lH61iU2P/o5We7bGpA8RERERkVSi+ZTqt2iKSEuA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\n5ZRTenWPOXPmALBx40ba2tpoaKjsr8H27dtpa2tj8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b+YRFKPDPck0lBrZwNb2iRJkjLz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X5LOl31C8685kHyTm2vY55hDb2lzd159NFHGTVqFMe/3scp5/wpXW54gtbd+gH6roqIiIiI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\nERERERERERFJ6f8BbNIULnL2LGQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff7501247b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_plots = 5\n",
    "rand_val_index = np.random.choice(len(latent_ent_list), num_plots, replace=False)\n",
    "latent_vals = [latent_ent_list[idx] for idx in rand_val_index]\n",
    "u_vals = np.reshape(u_list[int(params[\"num_layers\"]/2)], newshape=(params[\"batch_size\"], params[\"n_mem\"]))\n",
    "u_val = [u_vals[:,idx] for idx in rand_val_index]\n",
    "tri_eval = [u_tri_evals[:, idx, :] for idx in rand_val_index]\n",
    "tri_x = u_val\n",
    "#tri_eval = [tri_evals[:, idx, :] for idx in rand_val_index]\n",
    "#tri_x = [tri_lin_eval[:, idx] for idx in rand_val_index]\n",
    "tri_height = [tri_heights[idx,:] for idx in rand_val_index]\n",
    "plot_hists(latent_vals, u_val, num_plots, tri_x, tri_eval, tri_centers, tri_height)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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