2020-08-04_BearingFatigue_GammaDistributedOutputV3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from collections import OrderedDict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What is different from V1:\n",
    "I realized, that I can define losses directly to the embeddings. However I will probably have to pass different pairs of datasets for the different loss function - otherwise I will be trying to learn similarity on dissimilar things. I'll start with the \"y\" loss: The embedding for the head predicting \"y\" should be the same regardless of the experiment Id. I'm going to figure out a way to impose exactly that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# PHM IEEE 2012 Data Challenge\r\n",
      "\r\n",
      "This is a dataset that was used for the PHM IEEE 2012 Data Challenge. It used to be online at http://www.femto-st.fr/en/Research-departments/AS2M/Research-groups/PHM/IEEE-PHM-2012-Data-challenge.php, but isn't anymore.\r\n",
      "\r\n",
      "The set contains a training set of 6 rolling bearings that were operated in three different conditions, and a testing set of 11 more. See the added PDF file for all the info of the challenge and the set.\r\n",
      "\r\n",
      "## License\r\n",
      "\r\n",
      "I don't know if there was any license to this, but always cite using below citation. There is a BibTeX entry in the PDF file.\r\n",
      "\r\n",
      "## Citation\r\n",
      "\r\n",
      "Nectoux, P., Gouriveau, R., Medjaher, K., Ramasso, E., Chebel-Morello, B., Zerhouni, N., … Varnier, C. (2012). PRONOSTIA : An experimental platform for bearings accelerated degradation tests. In IEEE International Conference on Prognostics and Health Management (pp. 1–8). Denver.\r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "!cat bearing_fatigue_dataset/phm-ieee-2012-data-challenge-dataset/README.md"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TODO:\n",
    "Read this: [Adversarial Discriminative Learning](http://openaccess.thecvf.com/content_cvpr_2017/papers/Tzeng_Adversarial_Discriminative_Domain_CVPR_2017_paper.pdf)\n",
    "![image](img/AdversarialDiscriminativeDomainAdaptation.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bearing1_1  Bearing1_2\tBearing2_1  Bearing2_2\tBearing3_1  Bearing3_2\n",
      "(172, 2559, 2)\n",
      "0\n",
      "(1502, 2559, 2)\n",
      "1\n",
      "(1637, 2559, 2)\n",
      "2\n",
      "(515, 2559, 2)\n",
      "3\n",
      "(871, 2559, 2)\n",
      "4\n",
      "(1802, 2559, 2)\n",
      "5\n",
      "(2302, 2559, 2)\n",
      "6\n",
      "(1202, 2559, 2)\n",
      "7\n",
      "(1139, 2559, 2)\n",
      "8\n",
      "(2002, 2559, 2)\n",
      "9\n",
      "(572, 2559, 2)\n",
      "10\n",
      "(352, 2559, 2)\n",
      "11\n",
      "(797, 2559, 2)\n",
      "12\n",
      "(911, 2559, 2)\n",
      "13\n",
      "(612, 2559, 2)\n",
      "14\n",
      "(2803, 2559, 2)\n",
      "15\n",
      "(2302, 2559, 2)\n",
      "16\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as pplot\n",
    "import numpy as np\n",
    "all_files = [f for f in os.listdir(os.path.join('bearing_fatigue_dataset')) if 'pickle' in f]\n",
    "!ls bearing_fatigue_dataset/phm-ieee-2012-data-challenge-dataset/Learning_set/\n",
    "\n",
    "load_from_disk = True;\n",
    "if load_from_disk:\n",
    "    def load_bearings_dataset(all_files = all_files):\n",
    "        fnames = all_files\n",
    "        exp_ids_tot = [];\n",
    "        yrem_tot = [];\n",
    "        sensor_values_tot = []\n",
    "\n",
    "        for kk,fname in enumerate(fnames):\n",
    "            p1 = pd.read_pickle(\"bearing_fatigue_dataset/%s\"%fname)\n",
    "            p1['abs_timestamp'] = (p1['h']*60+p1['m'])*60+p1['s']+p1['se-6']/1e6\n",
    "            \n",
    "            sensors = p1.groupby(\"block\").apply(lambda x : [x['abs_timestamp'].mean(),np.vstack([x['h_acc'].values, x['v_acc'].values]).T])\n",
    "            sens_val = np.dstack([v[1] for v in sensors.values])\n",
    "            sensor_values = np.swapaxes(np.swapaxes(sens_val,0,-1),1,2).astype('float32')\n",
    "            print(sensor_values.shape)\n",
    "\n",
    "            yrem = np.vstack([v[0] for v in sensors.values]).astype('float32')\n",
    "            yrem = np.abs(yrem - np.max(yrem))\n",
    "\n",
    "            exp_id = np.hstack([kk for v in range(yrem.shape[0])])\n",
    "\n",
    "            sensor_values_tot.append(sensor_values)\n",
    "            exp_ids_tot.append(exp_id);\n",
    "            yrem_tot.append(yrem)\n",
    "\n",
    "            print(kk)\n",
    "\n",
    "        yrem_tot = np.vstack(yrem_tot).flatten()\n",
    "        eid = np.hstack(exp_ids_tot).flatten()\n",
    "        X = np.vstack(sensor_values_tot)\n",
    "        return yrem_tot, eid, X\n",
    "    yrem_s_raw, eid, X = load_bearings_dataset()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# yrem = [];\n",
    "# for ee in np.unique(eid):\n",
    "#     yelapsed_ = np.cumsum(yrem_s_raw[eid == ee])\n",
    "#     yrem_ = np.max(yelapsed_) - yelapsed_;\n",
    "#     yrem.append(yrem_)\n",
    "    \n",
    "# yrem = np.hstack(yrem)\n",
    "# gm = gamma_model(X[0:1]).sample(300)\n",
    "# pplot.hist(gm[:,0,0],20)\n",
    "normalization_factor_time = 25000;\n",
    "yrem_norm = yrem_s_raw/normalization_factor_time;\n",
    "#y>10000\n",
    "#y[y>0.2] = 0.2\n",
    "yrem_norm[yrem_norm<0.0001] = 0.0001\n",
    "#pplot.plot(y,'-')\n",
    "#yrem = y\n",
    "\n",
    "eid_oh = np.zeros([eid.shape[0],np.max(eid) + 1])\n",
    "for k in np.unique(eid):\n",
    "    eid_oh[eid == k,k] = 1.;\n",
    "\n",
    "#yrem_norm = yrem / np.std(yrem);\n",
    "\n",
    "#training_set_mask = \n",
    "conds_flag_source = [0,0,0,0,   1,1, 1,1,  2,2]\n",
    "inds_exp_source =  [4,6,8,15,  9,10,0,7,  2,11]\n",
    "\n",
    "conds_flag_target = [0,0,0,   1, 1, 1,  2]\n",
    "inds_exp_target  = [1,5,16,  12,13,14,  3]\n",
    "#conds_flag_target = [0,0,0,   1,   2]\n",
    "#inds_exp_target  = [1,5,16,  12,  3]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "conds_flag = [*conds_flag_source, *conds_flag_target]\n",
    "inds_experiments = [*inds_exp_source, *inds_exp_target]\n",
    "exp_to_cond_dict = {k:v for k,v in zip(inds_experiments, conds_flag)}\n",
    "\n",
    "loading_cond = [exp_to_cond_dict[k] for i,k in enumerate(np.argmax(eid_oh,1))]\n",
    "loading_oh = np.zeros([eid_oh.shape[0],3])\n",
    "for k in np.unique(loading_cond):\n",
    "    loading_oh[loading_cond == k,k] = 1;\n",
    "\n",
    "\n",
    "conds_flag_source = [0,0,0,0,   1,1, 1,1,  2,2]\n",
    "inds_exp_source =  [4,6,8,15,  9,10,0,7,  2,11]\n",
    "\n",
    "conds_flag_target = [0,0,0,   1,  2]\n",
    "inds_exp_target  = [1,5,16,  12,  3]\n",
    "    \n",
    "\n",
    "\n",
    "# ###########################################################\n",
    "# conds_flag_source =[0,0,0,0 ,0,  1,1,1 ,  2]\n",
    "# inds_exp_source =  [4,6,8,15,16,  9,7,12,  11]\n",
    "\n",
    "# conds_flag_target = [0,0 ,   1,  2]\n",
    "# inds_exp_target   = [5,1,  13,  3]\n",
    "\n",
    "\n",
    "###########################################################\n",
    "# Less ambitious split:\n",
    "# conds_flag_source = [1,1, 1,1]  #2,2]\n",
    "# inds_exp_source =  [12,10,7,13]#  2,3]\n",
    "\n",
    "# conds_flag_target = [   1]#,   2]\n",
    "# inds_exp_target  = [9]#   11]\n",
    "\n",
    "\n",
    "inds_experiments = [*inds_exp_source, *inds_exp_target]\n",
    "\n",
    "#############################################################\n",
    "#Remove unused experiments:\n",
    "\n",
    "ids_filter = [i for i,k in enumerate(np.argmax(eid_oh,1)) if k in inds_experiments]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "###################################################\n",
    "# \n",
    "# for the moment I do not try to adapt domains with info pertaining to the loading conditions.\n",
    "dat_vals = [v_[ids_filter] for v_ in [X, yrem_norm,eid_oh, loading_oh]]\n",
    "eid_oh_subs = dat_vals[2];\n",
    "\n",
    "#dat_vals\n",
    "## Ballance classes/experiments by resampling:\n",
    "eid_vals = np.argmax(eid_oh_subs,1);\n",
    "exp_vals_unique = np.unique(eid_vals)\n",
    "\n",
    "exp_vals_counts = []\n",
    "for e in exp_vals_unique:\n",
    "    exp_vals_counts.append(np.sum(eid == e))\n",
    "\n",
    "exp_vals_counts = np.array(exp_vals_counts)\n",
    "resample_factors = [int(np.max(exp_vals_counts)/ee) for ee in exp_vals_counts]\n",
    "resampling_dict = OrderedDict([(k,v) for k,v in zip(exp_vals_unique,resample_factors)])\n",
    "\n",
    "#resampling_dict\n",
    "# Change dat_vals by resampling experiments:\n",
    "dat_vals_resampled = [[],[],[],[]];\n",
    "for k,v in zip(resampling_dict.keys(), resampling_dict.values()):\n",
    "    ids_this_exp = dat_vals[2]==k\n",
    "    dat_vals_resampled[0].append(np.repeat(dat_vals[0][np.argmax(eid_oh_subs,1)==k],v,0))\n",
    "    dat_vals_resampled[1].append(np.repeat(dat_vals[1][np.argmax(eid_oh_subs,1)==k],v,0))\n",
    "    dat_vals_resampled[2].append(np.repeat(dat_vals[2][np.argmax(eid_oh_subs,1)==k],v,0))\n",
    "    dat_vals_resampled[3].append(np.repeat(dat_vals[3][np.argmax(eid_oh_subs,1)==k],v,0))\n",
    "\n",
    "vv = dat_vals_resampled[0]\n",
    "dat_vals_resampled = [np.concatenate(vv, axis = 0) for vv in dat_vals_resampled]\n",
    "eid_oh_subs = dat_vals_resampled[2];\n",
    "\n",
    "def get_source_or_target(x_,f_):\n",
    "    inds = [i for i,k in enumerate(np.argmax(eid_oh_subs,1)) if k in f_]\n",
    "    return x_[inds]\n",
    "\n",
    "\n",
    "#get_source_or_target  = lambda x_,f_ : x_[[i for i,k in enumerate(np.argmax(eid_oh,1)) if k in f_]]\n",
    "X_source_experiments, yrem_norm_source, eid_oh_source, cond_oh_source  = [get_source_or_target(v_, inds_exp_source)\n",
    "                                                                          for v_ in dat_vals_resampled]\n",
    "\n",
    "X_target_experiments,yrem_norm_target, eid_oh_target, cond_oh_target  = [get_source_or_target(v_, inds_exp_target) \n",
    "                                                                          for v_ in  dat_vals_resampled]\n",
    "\n",
    "\n",
    "### Use the top \"source\" domain. I need to generalize to \"target\" and show that embeddings \n",
    "# are indeed similar without the network ever seeing the \"target\" domain.\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test, d_train, d_test, c_train, c_test = train_test_split(X_source_experiments, yrem_norm_source, eid_oh_source, cond_oh_source, stratify = eid_oh_source)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adversarial learning on the FEMTO accelerated fatigue bearing dataset\n",
    "The dataset is associated with a challenge for the PHM conference on 2012.\n",
    "The motivation of this work is to investigate if and when adversarial learning on raw time-series performs well. The hypotheses of this work are the following:\n",
    "\n",
    "* **Dilated DenseNets** are flexible enough to work with raw time-series (this is not an established fact in SHM applications)\n",
    "* Adversarial learning can be used to normalize implicitly for **domain shifts** without manual intervention. \n",
    "\n",
    "The following need to be performed:\n",
    "* Preprocessing (making the dataset appropriate for training with the adversarial densenet - domain labels etc)\n",
    "* Train/test splitting for the raw accelerometer time-series\n",
    "* write the optimization loop\n",
    "* run prelim. experiments - hopefully something will fit. \n",
    "* write code for performing 1-vs-rest experiments to test generalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "#tf.enable_eager_execution()\n",
    "\n",
    "import tensorflow.keras as keras\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as pplot\n",
    "from tensorflow.keras import Model\n",
    "from tensorflow.keras.layers import Input, Dense, Conv1D, AveragePooling1D, GlobalAveragePooling1D, Flatten, BatchNormalization, Dropout\n",
    "\n",
    "import os\n",
    "\n",
    "from tensorflow.compat.v1 import ConfigProto\n",
    "from tensorflow.compat.v1 import InteractiveSession\n",
    "import tensorflow_probability as tfp\n",
    "tfd  = tfp.distributions\n",
    "\n",
    "\n",
    "config = ConfigProto()\n",
    "config.gpu_options.allow_growth = True\n",
    "sess = InteractiveSession(config=config)\n",
    "#tf.keras.backend.set_session(sess)\n",
    "#from tensorflow.math import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.1.0'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from src.dilated_densenet_adversarial import make_model, make_densenet_parametrization_dictionary\n",
    "\n",
    "# def calc_receptive_field(Nd, Nf, conv_type = \"causal\"):\n",
    "#     \"returns the number of input taps each layer in a dilated caysal conv stack sees.\"\n",
    "#     if conv_type == \"causal\":\n",
    "#         corr = [0]+[1]*(len(Nd)-1)#correction for double counted neurons.\n",
    "    \n",
    "#     return np.cumsum([(nf_) * (nd_) - (nd_-1) - c for nf_, nd_ ,c in zip(Nf, Nd, corr)])  #- (len(Nd)) + 1\n",
    "\n",
    "\n",
    "# from src.util import plot_confusion_matrix\n",
    "# from src.experiments_utils import *\n",
    "\n",
    "# ndn = 4\n",
    "# nkernel = 4;\n",
    "# ndn2 = 6\n",
    "# ndomain = 17;\n",
    "\n",
    "# experiment_parametrization = {\n",
    "#     'dilation_rates_dn1': [1,4,8,16,32,64,128,256,512,8,16,32,64,128,512][0:ndn],#,4,8,16,32],\n",
    "#     'kernel_sizes_dn1' :  [nkernel] * ndn,#,5,5],\n",
    "#     'nfilters_dn1' :  [50]*ndn,\n",
    "#     'dilation_rates_dn2' : [2]*ndn2,\n",
    "#     'kernel_sizes_dn2' : [5]*ndn2,\n",
    "#     'nfilters_dn2': [10]*ndn2\n",
    "# }\n",
    "\n",
    "# make_network_description_experiments_A(**experiment_parametrization)\n",
    "# model_json = make_network_json_experiments_A(**experiment_parametrization)\n",
    "# #\n",
    "\n",
    "# model_json['classifier']['network_layers'][0]['params']['units'] = 640\n",
    "# model_json[\"densenet_1d\"][\"network_layers\"][0][\"params\"][\"Variational\"] = False\n",
    "# model_json[\"densenet_1d\"][\"network_layers\"][0]['params'][\"dropout_after_composite\"] = 0.1\n",
    "# model_json[\"densenet_1d\"][\"network_layers\"] = [\n",
    "#     model_json[\"densenet_1d\"][\"network_layers\"][0],model_json[\"densenet_1d\"][\"network_layers\"][-1]\n",
    "# ]\n",
    "# model_json['inputs'][0]['shape'] = (2559,2)\n",
    "# model_json['domain_classifier']['network_layers'][-1]['params']['units'] = ndomain\n",
    "# # Make the model act as a regressor:\n",
    "# model_json['classifier']['network_layers'][-1]['params']['units'] = 1\n",
    "# Nf = model_json['densenet_1d']['network_layers'][0]['params']['kernel_sizes']\n",
    "# Nd = model_json['densenet_1d']['network_layers'][0]['params']['dilation_rates']\n",
    "# #model_json['densenet_1d']['network_layers']\n",
    "# #single_DN_stack_rf = calc_receptive_field(Nd,Nf)\n",
    "# #single_DN_stack_rf * len(Nf)\n",
    "# calc_receptive_field(Nd,Nf) * len(Nf)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def make_conv_input_head(nfilts = 64, nfilts2 = 128, ksize = 2):\n",
    "#     nfilts = 64;\n",
    "#     nfilts2 = 128\n",
    "#     ksize = 2;\n",
    "\n",
    "    xin = tf.keras.Input(shape = (None, 2) , name = \"timeseries_input\"); \n",
    "\n",
    "    yout = Conv1D(kernel_size = 1 , filters = 100, strides = 1, use_bias = False)(xin)\n",
    "    yout = Conv1D(kernel_size=ksize,filters = nfilts, strides=2, use_bias= False)(yout)\n",
    "    yout = Conv1D(kernel_size=ksize,strides=2, filters = nfilts,use_bias= False)(yout)\n",
    "    yout = Dropout(rate = 0.2)(yout)\n",
    "    yout = Conv1D(kernel_size=ksize,strides=2, filters = nfilts,activation  = \"relu\",use_bias= True)(yout)\n",
    "    yout = keras.layers.AveragePooling1D(pool_size=2)(yout)\n",
    "\n",
    "    yout = Conv1D(kernel_size=ksize, strides=2, filters = nfilts2,use_bias= False)(yout)\n",
    "    yout = Dropout(rate = 0.2)(yout)\n",
    "    yout = Conv1D(kernel_size=ksize,strides=2, filters = nfilts2,use_bias= False)(yout)\n",
    "    yout = Conv1D(kernel_size=ksize,strides=2, filters = nfilts2,activation  = \"relu\",use_bias= False)(yout)\n",
    "    yout = keras.layers.AveragePooling1D(pool_size=2)(yout)\n",
    "\n",
    "    yout = keras.layers.GlobalAveragePooling1D()(yout)\n",
    "\n",
    "    mconv = keras.Model(inputs = xin, outputs = yout)\n",
    "    # mconv.summary()\n",
    "    return mconv\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# I need to transform the model to \n",
    "## Make the creation of discriminators between domains automatic:\n",
    "\n",
    "#base_model = make_model(model_json)\n",
    "base_model = make_conv_input_head();\n",
    "get_global_pool_layer = lambda mm : mm.get_layer([l.name for l in mm.layers if 'global_average' in l.name][0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "NParams = 1;\n",
    "NSamples = 10;\n",
    "\n",
    "y_distr_in = Dense(NParams*2, use_bias = False, activation = lambda x : tf.nn.softplus(x))(base_model.output);\n",
    "gamma_out = tfp.layers.DistributionLambda(make_distribution_fn = lambda t: tfd.Gamma(concentration = t[:,0:NParams], rate = t[:,NParams:2*NParams] ), convert_to_tensor_fn= lambda s : s.sample(NSamples))(y_distr_in);\n",
    "\n",
    "gamma_model = Model(inputs = base_model.input, outputs = gamma_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff1e8578438>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pplot.plot(yrem_norm,(1.2 - yrem_norm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 17657 samples, validate on 5886 samples\n",
      "Epoch 1/100\n",
      "17657/17657 [==============================] - 6s 347us/sample - loss: -0.8519 - val_loss: -0.9428\n",
      "Epoch 2/100\n",
      "17657/17657 [==============================] - 4s 213us/sample - loss: -1.0909 - val_loss: -0.9354\n",
      "Epoch 3/100\n",
      "17657/17657 [==============================] - 4s 215us/sample - loss: -1.2374 - val_loss: -1.3444\n",
      "Epoch 4/100\n",
      "17657/17657 [==============================] - 4s 215us/sample - loss: -1.3508 - val_loss: -1.1198\n",
      "Epoch 5/100\n",
      "17657/17657 [==============================] - 4s 217us/sample - loss: -1.3993 - val_loss: -1.4115\n",
      "Epoch 6/100\n",
      "17657/17657 [==============================] - 4s 218us/sample - loss: -1.4745 - val_loss: -1.5740\n",
      "Epoch 7/100\n",
      "17657/17657 [==============================] - 4s 216us/sample - loss: -1.5317 - val_loss: -1.5220\n",
      "Epoch 8/100\n",
      "17657/17657 [==============================] - 4s 217us/sample - loss: -1.5420 - val_loss: -1.6078\n",
      "Epoch 9/100\n",
      "17657/17657 [==============================] - 4s 220us/sample - loss: -1.5668 - val_loss: -1.5106\n",
      "Epoch 10/100\n",
      "17657/17657 [==============================] - 4s 217us/sample - loss: -1.6282 - val_loss: -1.5779\n",
      "Epoch 11/100\n",
      "17657/17657 [==============================] - 4s 215us/sample - loss: -1.5895 - val_loss: -1.7200\n",
      "Epoch 12/100\n",
      "17657/17657 [==============================] - 4s 214us/sample - loss: -1.6553 - val_loss: -1.7550\n",
      "Epoch 13/100\n",
      "17657/17657 [==============================] - 4s 217us/sample - loss: -1.6276 - val_loss: -1.5250\n",
      "Epoch 14/100\n",
      "17657/17657 [==============================] - 4s 215us/sample - loss: -1.7083 - val_loss: -1.2871\n",
      "Epoch 15/100\n",
      "17657/17657 [==============================] - 4s 215us/sample - loss: -1.7240 - val_loss: -1.8806\n",
      "Epoch 16/100\n",
      "17657/17657 [==============================] - 4s 220us/sample - loss: -1.7687 - val_loss: -1.7691\n",
      "Epoch 17/100\n",
      "17657/17657 [==============================] - 4s 217us/sample - loss: -1.7434 - val_loss: -1.8160\n",
      "Epoch 18/100\n",
      "17657/17657 [==============================] - 4s 219us/sample - loss: -1.7990 - val_loss: -1.8848\n",
      "Epoch 19/100\n",
      "17657/17657 [==============================] - 4s 218us/sample - loss: -1.8034 - val_loss: -1.9119\n",
      "Epoch 20/100\n",
      "17657/17657 [==============================] - 4s 222us/sample - loss: -1.8286 - val_loss: -1.8107\n",
      "Epoch 21/100\n",
      "17657/17657 [==============================] - 4s 219us/sample - loss: -1.8718 - val_loss: -1.8351\n",
      "Epoch 22/100\n",
      "17657/17657 [==============================] - 4s 218us/sample - loss: -1.8965 - val_loss: -1.8352\n",
      "Epoch 23/100\n",
      "17657/17657 [==============================] - 4s 222us/sample - loss: -1.9050 - val_loss: -1.5480\n",
      "Epoch 24/100\n",
      "17657/17657 [==============================] - 4s 220us/sample - loss: -1.9016 - val_loss: -1.9244\n",
      "Epoch 25/100\n",
      "17657/17657 [==============================] - 4s 218us/sample - loss: -1.8828 - val_loss: -1.8073\n",
      "Epoch 26/100\n",
      "17657/17657 [==============================] - 4s 215us/sample - loss: -1.9642 - val_loss: -1.9666\n",
      "Epoch 27/100\n",
      "17657/17657 [==============================] - 4s 221us/sample - loss: -1.9510 - val_loss: -1.9724\n",
      "Epoch 28/100\n",
      "17657/17657 [==============================] - 4s 217us/sample - loss: -1.9859 - val_loss: -1.9799\n",
      "Epoch 29/100\n",
      "17657/17657 [==============================] - 4s 215us/sample - loss: -1.9283 - val_loss: -1.6856\n",
      "Epoch 30/100\n",
      "17657/17657 [==============================] - 4s 216us/sample - loss: -1.9932 - val_loss: -1.9122\n",
      "Epoch 31/100\n",
      "17657/17657 [==============================] - 4s 217us/sample - loss: -2.0254 - val_loss: -1.9996\n",
      "Epoch 32/100\n",
      "17657/17657 [==============================] - 4s 217us/sample - loss: -2.0549 - val_loss: -1.9906\n",
      "Epoch 33/100\n",
      "17657/17657 [==============================] - 4s 217us/sample - loss: -2.0486 - val_loss: -1.9415\n",
      "Epoch 34/100\n",
      "17657/17657 [==============================] - 4s 218us/sample - loss: -2.0632 - val_loss: -2.1668\n",
      "Epoch 35/100\n",
      "17657/17657 [==============================] - 4s 215us/sample - loss: -2.0816 - val_loss: -2.0852\n",
      "Epoch 36/100\n",
      "17657/17657 [==============================] - 4s 217us/sample - loss: -2.0961 - val_loss: -2.0530\n",
      "Epoch 37/100\n",
      "17657/17657 [==============================] - 4s 217us/sample - loss: -2.0537 - val_loss: -2.1977\n",
      "Epoch 38/100\n",
      "17657/17657 [==============================] - 4s 217us/sample - loss: -2.0974 - val_loss: -2.1247\n",
      "Epoch 39/100\n",
      "17657/17657 [==============================] - 4s 216us/sample - loss: -2.1330 - val_loss: -1.9281\n",
      "Epoch 40/100\n",
      "17657/17657 [==============================] - 4s 216us/sample - loss: -2.1349 - val_loss: -2.1408\n",
      "Epoch 41/100\n",
      "17657/17657 [==============================] - 4s 218us/sample - loss: -2.1403 - val_loss: -1.9647\n",
      "Epoch 42/100\n",
      "17657/17657 [==============================] - 4s 217us/sample - loss: -2.1568 - val_loss: -2.1731\n",
      "Epoch 43/100\n",
      "17657/17657 [==============================] - 4s 219us/sample - loss: -2.1418 - val_loss: -2.0130\n",
      "Epoch 44/100\n",
      "17657/17657 [==============================] - 4s 218us/sample - loss: -2.1728 - val_loss: -2.2688\n",
      "Epoch 45/100\n",
      "17657/17657 [==============================] - 4s 219us/sample - loss: -2.1633 - val_loss: -2.1989\n",
      "Epoch 46/100\n",
      "17657/17657 [==============================] - 4s 216us/sample - loss: -2.1661 - val_loss: -2.1901\n",
      "Epoch 47/100\n",
      "17657/17657 [==============================] - 4s 218us/sample - loss: -2.2010 - val_loss: -1.9264\n",
      "Epoch 48/100\n",
      "17657/17657 [==============================] - 4s 218us/sample - loss: -2.1734 - val_loss: -2.1477\n",
      "Epoch 49/100\n",
      "17657/17657 [==============================] - 4s 218us/sample - loss: -2.2193 - val_loss: -2.1050\n",
      "Epoch 50/100\n",
      "17657/17657 [==============================] - 4s 217us/sample - loss: -2.2182 - val_loss: -2.2616\n",
      "Epoch 51/100\n",
      "17657/17657 [==============================] - 4s 219us/sample - loss: -2.2251 - val_loss: -2.2257\n",
      "Epoch 52/100\n",
      "17657/17657 [==============================] - 4s 219us/sample - loss: -2.2324 - val_loss: -2.1622\n",
      "Epoch 53/100\n",
      "17657/17657 [==============================] - 4s 218us/sample - loss: -2.2358 - val_loss: -2.2627\n",
      "Epoch 54/100\n",
      "17657/17657 [==============================] - 4s 219us/sample - loss: -2.2486 - val_loss: -1.9843\n",
      "Epoch 55/100\n",
      "17657/17657 [==============================] - 4s 213us/sample - loss: -2.2571 - val_loss: -2.3226\n",
      "Epoch 56/100\n",
      "17657/17657 [==============================] - 4s 212us/sample - loss: -2.2458 - val_loss: -2.2290\n",
      "Epoch 57/100\n",
      "17657/17657 [==============================] - 4s 212us/sample - loss: -2.2723 - val_loss: -2.1275\n",
      "Epoch 58/100\n",
      "17657/17657 [==============================] - 4s 212us/sample - loss: -2.2604 - val_loss: -2.2336\n",
      "Epoch 59/100\n",
      "17657/17657 [==============================] - 4s 213us/sample - loss: -2.2927 - val_loss: -2.2411\n",
      "Epoch 60/100\n",
      "17657/17657 [==============================] - 4s 212us/sample - loss: -2.2754 - val_loss: -2.3191\n",
      "Epoch 61/100\n",
      "17657/17657 [==============================] - 4s 212us/sample - loss: -2.2488 - val_loss: -2.3433\n",
      "Epoch 62/100\n",
      "17657/17657 [==============================] - 4s 212us/sample - loss: -2.3067 - val_loss: -2.1884\n",
      "Epoch 63/100\n",
      "17657/17657 [==============================] - 4s 211us/sample - loss: -2.3197 - val_loss: -2.2845\n",
      "Epoch 64/100\n",
      "17657/17657 [==============================] - 4s 212us/sample - loss: -2.2867 - val_loss: -2.3099\n",
      "Epoch 65/100\n",
      "17657/17657 [==============================] - 4s 210us/sample - loss: -2.3097 - val_loss: -2.2512\n",
      "Epoch 66/100\n",
      "17657/17657 [==============================] - 4s 212us/sample - loss: -2.2635 - val_loss: -2.2234\n",
      "Epoch 67/100\n",
      "17657/17657 [==============================] - 4s 212us/sample - loss: -2.3117 - val_loss: -2.1908\n",
      "Epoch 68/100\n",
      "17657/17657 [==============================] - 4s 217us/sample - loss: -2.3322 - val_loss: -2.1999\n",
      "Epoch 69/100\n",
      "17657/17657 [==============================] - 4s 223us/sample - loss: -2.3245 - val_loss: -2.3621\n",
      "Epoch 70/100\n",
      "17657/17657 [==============================] - 4s 217us/sample - loss: -2.3279 - val_loss: -2.3221\n",
      "Epoch 71/100\n",
      "17657/17657 [==============================] - 4s 221us/sample - loss: -2.3445 - val_loss: -2.3710\n",
      "Epoch 72/100\n",
      "17657/17657 [==============================] - 4s 232us/sample - loss: -2.3574 - val_loss: -2.3901\n",
      "Epoch 73/100\n",
      "17657/17657 [==============================] - 4s 231us/sample - loss: -2.3549 - val_loss: -2.2562\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 74/100\n",
      "17657/17657 [==============================] - 4s 214us/sample - loss: -2.3536 - val_loss: -2.2272\n",
      "Epoch 75/100\n",
      "17657/17657 [==============================] - 4s 219us/sample - loss: -2.3373 - val_loss: -2.4083\n",
      "Epoch 76/100\n",
      "17657/17657 [==============================] - 4s 225us/sample - loss: -2.3657 - val_loss: -2.3619\n",
      "Epoch 77/100\n",
      "17657/17657 [==============================] - 4s 224us/sample - loss: -2.3719 - val_loss: -2.3421\n",
      "Epoch 78/100\n",
      "17657/17657 [==============================] - 4s 218us/sample - loss: -2.3581 - val_loss: -2.3656\n",
      "Epoch 79/100\n",
      "17657/17657 [==============================] - 4s 214us/sample - loss: -2.3837 - val_loss: -2.1025\n",
      "Epoch 80/100\n",
      "17657/17657 [==============================] - 4s 226us/sample - loss: -2.3750 - val_loss: -2.4420\n",
      "Epoch 81/100\n",
      "17657/17657 [==============================] - 4s 213us/sample - loss: -2.3976 - val_loss: -2.1632\n",
      "Epoch 82/100\n",
      "17657/17657 [==============================] - 4s 211us/sample - loss: -2.3699 - val_loss: -2.4268\n",
      "Epoch 83/100\n",
      "17657/17657 [==============================] - 4s 213us/sample - loss: -2.4024 - val_loss: -2.3596\n",
      "Epoch 84/100\n",
      "17657/17657 [==============================] - 4s 214us/sample - loss: -2.3885 - val_loss: -2.3449\n",
      "Epoch 85/100\n",
      "17657/17657 [==============================] - 4s 219us/sample - loss: -2.3957 - val_loss: -2.3321\n",
      "Epoch 86/100\n",
      "17657/17657 [==============================] - 4s 216us/sample - loss: -2.3931 - val_loss: -2.4470\n",
      "Epoch 87/100\n",
      "17657/17657 [==============================] - 4s 213us/sample - loss: -2.4073 - val_loss: -2.3563\n",
      "Epoch 88/100\n",
      "17657/17657 [==============================] - 4s 217us/sample - loss: -2.4239 - val_loss: -2.4044\n",
      "Epoch 89/100\n",
      "17657/17657 [==============================] - 4s 217us/sample - loss: -2.4165 - val_loss: -2.3897\n",
      "Epoch 90/100\n",
      "17657/17657 [==============================] - 4s 221us/sample - loss: -2.4126 - val_loss: -2.2953\n",
      "Epoch 91/100\n",
      "17657/17657 [==============================] - 4s 224us/sample - loss: -2.4479 - val_loss: -2.3289\n",
      "Epoch 92/100\n",
      "17657/17657 [==============================] - 4s 225us/sample - loss: -2.4179 - val_loss: -2.3211\n",
      "Epoch 93/100\n",
      "17657/17657 [==============================] - 4s 222us/sample - loss: -2.4301 - val_loss: -2.4726\n",
      "Epoch 94/100\n",
      "17657/17657 [==============================] - 4s 216us/sample - loss: -2.4343 - val_loss: -2.4257\n",
      "Epoch 95/100\n",
      "17657/17657 [==============================] - 4s 218us/sample - loss: -2.4384 - val_loss: -2.4515\n",
      "Epoch 96/100\n",
      "17657/17657 [==============================] - 4s 224us/sample - loss: -2.4057 - val_loss: -2.2605\n",
      "Epoch 97/100\n",
      "17657/17657 [==============================] - 4s 213us/sample - loss: -2.4269 - val_loss: -2.3924\n",
      "Epoch 98/100\n",
      "17657/17657 [==============================] - 4s 215us/sample - loss: -2.4547 - val_loss: -2.4518\n",
      "Epoch 99/100\n",
      "17657/17657 [==============================] - 4s 214us/sample - loss: -2.4407 - val_loss: -2.4540\n",
      "Epoch 100/100\n",
      "17657/17657 [==============================] - 4s 220us/sample - loss: -2.4018 - val_loss: -2.4360\n"
     ]
    }
   ],
   "source": [
    "opt = tf.keras.optimizers.Adam(learning_rate=0.001, clipnorm = 1)\n",
    "\n",
    "gamma_nll_loss = lambda y_, py_ : -py_.log_prob(y_) * (1.2 - y_)**2\n",
    "gamma_model.compile(loss = gamma_nll_loss, optimizer=opt)\n",
    "h = gamma_model.fit(x_train,y_train, epochs=100, validation_data= [x_test,y_test])\n",
    "# for kk in range(150, 199):\n",
    "#     Xt = tf.Variable(X[kk:kk+1])\n",
    "#     yt = tf.Variable(y[kk:kk+1])\n",
    "#     gamma_nll_loss = lambda y_, py_ : -py_.log_prob(y_)\n",
    "\n",
    "#     with tf.GradientTape() as tape:\n",
    "#         loss = gamma_nll_loss(yt,gamma_model(Xt))\n",
    "#         g = tape.gradient(loss, gamma_model.weights)\n",
    "\n",
    "#         #gamma_model.fit(X, y, batch_size=100, validation_split=0.5)\n",
    "#     print(kk, np.any([np.any(np.isnan(g_)) for g_ in g]))\n",
    "#     if np.any([np.any(np.isnan(g_)) for g_ in g]):\n",
    "\n",
    "#         break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff1e808dcf8>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pplot.plot(h.history['loss'])\n",
    "pplot.plot(h.history['val_loss'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f22d16764a8>]"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pplot.plot(h.history['loss'])\n",
    "pplot.plot(h.history['val_loss'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "times=np.linspace(0,1.5,50);\n",
    "res = []\n",
    "for kstart in range(0, X.shape[0],200):\n",
    "    \n",
    "    kend = kstart + 200\n",
    "    res.append(gamma_model(X_target_experiments[kstart:kend]).prob(times))\n",
    "res_tot = np.vstack(res)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.0000000e+00, 1.3012793e-22, 5.7877281e-10, ..., 0.0000000e+00,\n",
       "        0.0000000e+00, 0.0000000e+00],\n",
       "       [0.0000000e+00, 2.5311648e-09, 1.4644673e-02, ..., 0.0000000e+00,\n",
       "        0.0000000e+00, 0.0000000e+00],\n",
       "       [0.0000000e+00, 7.3145249e-12, 5.1325289e-05, ..., 0.0000000e+00,\n",
       "        0.0000000e+00, 0.0000000e+00],\n",
       "       ...,\n",
       "       [0.0000000e+00, 3.8150562e+01, 7.0374352e-01, ..., 0.0000000e+00,\n",
       "        0.0000000e+00, 0.0000000e+00],\n",
       "       [0.0000000e+00, 4.2840851e+01, 1.7284515e+00, ..., 0.0000000e+00,\n",
       "        0.0000000e+00, 0.0000000e+00],\n",
       "       [0.0000000e+00, 7.5380411e+00, 1.0280247e-04, ..., 0.0000000e+00,\n",
       "        0.0000000e+00, 0.0000000e+00]], dtype=float32)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def estimation_for_experiment(exp_number):\n",
    "    None\n",
    "#pplot.plot(res_tot[0:1000].T)\n",
    "#pplot.pcolor(res_tot[0:1400].T)\n",
    "res_tot[np.argmax(eid_oh_target,1) == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((10572, 2559, 2), (10572,), (10572, 50))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pplot.plot(y)\n",
    "#_.quantile([0.1,0.5,0.9])\n",
    "X_target_experiments.shape, yrem_norm_target.shape, res_tot.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 3000x1500 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "skip_vals = 1;\n",
    "pplot.figure(figsize = (10,5), dpi = 300)\n",
    "res_plt = res_tot[::skip_vals,...].T;\n",
    "yy_ = times * normalization_factor_time\n",
    "xx_ = np.array([kk for kk in range(res_plt.shape[1])])\n",
    "[xx__, yy__] = np.meshgrid(xx_, yy_);\n",
    "\n",
    "pplot.contourf(xx__,yy__,res_plt**0.4, cmap = \"gray\")\n",
    "pplot.xlabel(\"Segment index\")\n",
    "pplot.ylabel(\"Time to failure [s]\")\n",
    "\n",
    "pplot.plot(yrem_norm_target[::skip_vals]*normalization_factor_time,'C1-', label = \"actual remaining time\")\n",
    "pplot.legend()\n",
    "pplot.title(\"Single-segment probability\\nestimates and actual remaining time \\n Unseen experiments\", fontsize = 20)\n",
    "pplot.colorbar()\n",
    "#pplot.savefig(\"gamma_distr_out_predictions_noadv.png\")\n",
    "pplot.text(0.0,15000.0,\"E5(C1)\", fontdict={\"color\" : \"C1\", \"fontsize\" : 20})\n",
    "pplot.text(2000.0,5000.0,\"E1(C1)\", fontdict={\"color\" : \"C1\", \"fontsize\" : 20})\n",
    "pplot.text(4000,20000,\"E13(C2)\", fontdict={\"color\" : \"C1\", \"fontsize\" : 20})\n",
    "pplot.text(6000,10000.0,\"E3(C3)\", fontdict={\"color\" : \"C1\", \"fontsize\" : 20})\n",
    "pplot.savefig(\"gamma_distr_out_predictions_noadv_morex.png\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Adverarial regularization\n",
    "I'm adding heads to the network so that the predictive task is learned separately from the bias of the experiments. The bias should not be used for prediction directly but implicitly. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Automatic creation of discriminators between domains:\n",
    "mconv_adv = make_conv_input_head();\n",
    "base_model_adv = mconv_adv;\n",
    "get_global_pool_layer = lambda mm : mm.get_layer([l.name for l in mm.layers if 'global_average' in l.name][0])\n",
    "from src.dilated_densenet_adversarial import GradReverse\n",
    "\n",
    "# The definition of the domains:\n",
    "#d_y = {'name' : 'y' , 'output_head_size' : 1, \"loss\" : lambda y,yhat : tf.reduce_mean(tf.abs(y-yhat))}\n",
    "d_y = {\"name\" : \"y\" , \"output_params\" : {\"size\": 1, \"isprob\" : True ,\"prob_params\" : {\"distribution\" : \"gamma\" }}, \"loss\"  : lambda y_, py_ : gamma_nll_loss(y_, py_)}\n",
    "d_d = {'name' : 'd' , 'output_params' : {\"size\" : 17, \"isprob\" : False}, \"loss\" : lambda y,yhat : tf.compat.v1.losses.softmax_cross_entropy(y,yhat)}\n",
    "d_l = {'name' : 'l' , 'output_params' : {\"size\" : 3 , \"isprob\" : False} , \"loss\" : lambda y,yhat : tf.compat.v1.losses.softmax_cross_entropy(y,yhat)}\n",
    "\n",
    "#domains = [d_a,d_b,d_c]\n",
    "\n",
    "# Define domain relations:\n",
    "#a_i sub c\n",
    "#c sub b\n",
    "\n",
    "DDA_input_layer = get_global_pool_layer(base_model_adv)\n",
    "\n",
    "def make_sep_model_heads_fcn(features_input_layer, head_params):\n",
    "    \"\"\"\n",
    "    Returns 4 network heads, their embedding layers, \n",
    "    and their respective losses for a discriminative domain adversarial network.\n",
    "    \"\"\"\n",
    "    \n",
    "    d1_name, d1_out, d1_loss = [head_params[0][hval] for hval in ['name' , 'output_params' , 'loss']]\n",
    "    d2_name, d2_out, d2_loss = [head_params[1][hval] for hval in ['name' , 'output_params' , 'loss']]\n",
    "    \n",
    "    Ename_d1d2 = \"P_%s_C_%s\"%(d1_name,d2_name);\n",
    "    Ename_d2d1 = \"P_%s_C_%s\"%(d2_name,d1_name);\n",
    "    \n",
    "    # The embeddings that learn @ each domain separately:\n",
    "    Emb_P_d1_C_d2 = keras.Sequential([\n",
    "        keras.layers.Dense(100,activation = \"relu\") ,\n",
    "        keras.layers.Dense(100, use_bias = False),\n",
    "        Dropout(rate = 0.2),\n",
    "    ], name = Ename_d1d2)(features_input_layer.output)\n",
    "    \n",
    "    Emb_P_d2_C_d1 = keras.Sequential([\n",
    "        keras.layers.Dense(100,activation = \"relu\") ,\n",
    "        keras.layers.Dense(100, use_bias = False),\n",
    "        Dropout(rate = 0.2),\n",
    "    ],name =Ename_d2d1)(features_input_layer.output)\n",
    "    \n",
    "    def make_class_head(emb_in, out_params , name):\n",
    "        \"\"\"\n",
    "        emb_in   : the input tensor to the stack\n",
    "        out_size : the output of the stack (for classifier it is the number of classes. For regressor it is \"1\".)\n",
    "        name     : the name of the output. The name contains the input embedding, and whether it is a critic or a predictor.\n",
    "        \"\"\"\n",
    "        \n",
    "        domain_critic_or_predictor    = keras.Sequential(name = name)\n",
    "\n",
    "        # if this is a \"critic\" head a grad reverse layer is added:\n",
    "        if name[-6:] == \"critic\":\n",
    "            domain_critic_or_predictor.add(GradReverse());\n",
    "        \n",
    "        if not out_params['isprob']:\n",
    "            domain_critic_or_predictor.add(Dense(100, activation = 'relu'))\n",
    "            domain_critic_or_predictor.add(Dense(100, use_bias = False))\n",
    "            domain_critic_or_predictor.add(Dropout(rate = 0.2))\n",
    "            domain_critic_or_predictor.add(Dense(out_params['size']));\n",
    "            \n",
    "        if out_params['isprob']:\n",
    "            # Build a probabilistic layer for this output\n",
    "            NParams = 1;\n",
    "            NSamples = 10;\n",
    "            \n",
    "            domain_critic_or_predictor.add(\n",
    "                Dense(NParams*2, use_bias = False, activation = lambda x : tf.nn.softplus(x)+0.0001)\n",
    "            )\n",
    "            domain_critic_or_predictor.add(\n",
    "                tfp.layers.DistributionLambda(\n",
    "                    make_distribution_fn = lambda t: tfd.Gamma(concentration = t[:,0:NParams], rate = t[:,NParams:2*NParams] ),\n",
    "                    convert_to_tensor_fn= lambda s : s.sample(NSamples))\n",
    "            )\n",
    "\n",
    "            \n",
    "\n",
    "        return domain_critic_or_predictor(emb_in)\n",
    "    \n",
    "    # Construct the critic and the predictor for each domain:\n",
    "    Pd1Cd2_d2_critic , Pd1Cd2_d1_predictor = [make_class_head(Emb_P_d1_C_d2 , out_size ,\"%s_%s\"%(Ename_d1d2, head_)) \n",
    "                                              for out_size, head_ in zip([d2_out,d1_out], [\"critic\", \"predictor\"])];\n",
    "    \n",
    "        \n",
    "    Pd2Cd1_d1_critic , Pd1Cd2_d2_predictor = [make_class_head( Emb_P_d2_C_d1 ,out_size, \"%s_%s\"%(Ename_d2d1, head_)) \n",
    "                                              for out_size,head_ in zip([d1_out,d2_out], [\"critic\", \"predictor\"])];\n",
    "    \n",
    "    \n",
    "    \n",
    "    losses = OrderedDict()\n",
    "    \n",
    "    losses.update({\"%s_%s\"%(Ename_d1d2, \"critic\")    : d2_loss})\n",
    "    losses.update({ \"%s_%s\"%(Ename_d1d2, \"predictor\") : d1_loss})\n",
    "    losses.update({ \"%s_%s\"%(Ename_d2d1, \"critic\") : d1_loss})\n",
    "    losses.update({ \"%s_%s\"%(Ename_d2d1, \"predictor\") : d2_loss})\n",
    "    \n",
    "    outputs = [Pd1Cd2_d2_critic , Pd1Cd2_d1_predictor , Pd2Cd1_d1_critic , Pd1Cd2_d2_predictor]\n",
    "    \n",
    "    outputs_expected_label  = [d2_name, d1_name, d1_name, d2_name];\n",
    "    \n",
    "    return outputs, losses, outputs_expected_label\n",
    "\n",
    "outputs_ab = make_sep_model_heads_fcn(DDA_input_layer, [d_y, d_d])\n",
    "outputs_ac = [[],{},[]];outputs_bc = [[],{},[]];\n",
    "outputs_ac = make_sep_model_heads_fcn(DDA_input_layer, [d_y, d_l])\n",
    "#outputs_bc = make_sep_model_heads_fcn(DDA_input_layer, [d_d, d_l])\n",
    "\n",
    "all_output_domains = [*outputs_ab[2],          *outputs_ac[2]         ,*outputs_bc[2]]\n",
    "all_outputs        = [*outputs_ab[0],          *outputs_ac[0]         ,*outputs_bc[0]]\n",
    "all_loss_names     = [*outputs_ab[1].keys(),   *outputs_ac[1].keys(),  *outputs_bc[1].keys()]\n",
    "all_loss_functions = [*outputs_ab[1].values(), *outputs_ac[1].values(),*outputs_bc[1].values()]\n",
    "\n",
    "losses = OrderedDict({k:v for k,v in zip(all_loss_names, all_loss_functions)})\n",
    "\n",
    "# def loss_fcn(yreal,yhat):\n",
    "#     \"\"\"\n",
    "#     A discrepancy loss\n",
    "#     \"\"\"\n",
    "#     W= tf.matmul(yhat[:,0:-17], yhat[:,-17:], transpose_a=True, transpose_b=False)\n",
    "#     Vmeans = tf.transpose(tf.matmul(W , yhat[:,-17:], transpose_a = False, transpose_b = True))\n",
    "#     loss_mean = tf.reduce_sum(tf.square(yhat[:,0:-17] - Vmeans), axis=0)\n",
    "#     loss_norm = tf.reduce_sum(tf.abs(tf.reduce_mean(tf.square(yhat[:,0:-17]), axis = 1)-1), axis = 0) # approximately the squared norm of each row should be close to 1.\n",
    "#     return (loss_mean + loss_norm) * 0.0001\n",
    "\n",
    "# def loss_fcn_pred(y_, py_):\n",
    "#     # gamma_nll_loss = lambda y_, py_ : -py_.log_prob(y_)\n",
    "#     return -py_.log_prob(y_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training the adversarial model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "setting loss to 1 for P_y_C_d_predictor\n",
      "Train on 17657 samples, validate on 5886 samples\n",
      "Epoch 1/200\n",
      "custom grad\n",
      "custom grad\n",
      "custom grad\n",
      "custom grad\n",
      "custom grad\n",
      "custom grad\n",
      "custom grad\n",
      "custom grad\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -0.1947 - P_y_C_d_critic_loss: 2.8355 - P_y_C_d_predictor_loss: -0.1672 - P_d_C_y_critic_loss: -0.1173 - P_d_C_y_predictor_loss: 2.8294 - P_y_C_l_critic_loss: 1.0932 - P_y_C_l_predictor_loss: -0.1226 - P_l_C_y_critic_loss: -0.0978 - P_l_C_y_predictor_loss: 1.0976----------\n",
      "<tf.Variable 'Adam/learning_rate:0' shape=() dtype=float32, numpy=1e-04>\n",
      "----------\n",
      "supposedly plotting...\n",
      "17657/17657 [==============================] - 6s 368us/sample - loss: -0.1940 - P_y_C_d_critic_loss: 2.8355 - P_y_C_d_predictor_loss: -0.1662 - P_d_C_y_critic_loss: -0.1162 - P_d_C_y_predictor_loss: 2.8295 - P_y_C_l_critic_loss: 1.0932 - P_y_C_l_predictor_loss: -0.1215 - P_l_C_y_critic_loss: -0.0966 - P_l_C_y_predictor_loss: 1.0975 - val_loss: -0.1778 - val_P_y_C_d_critic_loss: 2.8353 - val_P_y_C_d_predictor_loss: -0.1541 - val_P_d_C_y_critic_loss: -0.1103 - val_P_d_C_y_predictor_loss: 2.8305 - val_P_y_C_l_critic_loss: 1.0941 - val_P_y_C_l_predictor_loss: -0.1161 - val_P_l_C_y_critic_loss: -0.0972 - val_P_l_C_y_predictor_loss: 1.0981\n",
      "Epoch 2/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -0.4329 - P_y_C_d_critic_loss: 2.8289 - P_y_C_d_predictor_loss: -0.3290 - P_d_C_y_critic_loss: -0.1797 - P_d_C_y_predictor_loss: 2.8251 - P_y_C_l_critic_loss: 1.0663 - P_y_C_l_predictor_loss: -0.2750 - P_l_C_y_critic_loss: -0.0523 - P_l_C_y_predictor_loss: 1.0964----------\n",
      "----- setting learning rate to 0.0010\n",
      "<tf.Variable 'Adam/learning_rate:0' shape=() dtype=float32, numpy=0.001>\n",
      "----------\n",
      "supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 152us/sample - loss: -0.4370 - P_y_C_d_critic_loss: 2.8282 - P_y_C_d_predictor_loss: -0.3347 - P_d_C_y_critic_loss: -0.1831 - P_d_C_y_predictor_loss: 2.8250 - P_y_C_l_critic_loss: 1.0651 - P_y_C_l_predictor_loss: -0.2825 - P_l_C_y_critic_loss: -0.0503 - P_l_C_y_predictor_loss: 1.0962 - val_loss: -0.6385 - val_P_y_C_d_critic_loss: 2.7860 - val_P_y_C_d_predictor_loss: -0.4471 - val_P_d_C_y_critic_loss: -0.2466 - val_P_d_C_y_predictor_loss: 2.8179 - val_P_y_C_l_critic_loss: 1.0175 - val_P_y_C_l_predictor_loss: -0.4501 - val_P_l_C_y_critic_loss: 0.0076 - val_P_l_C_y_predictor_loss: 1.1002\n",
      "Epoch 3/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -1.0074 - P_y_C_d_critic_loss: 2.4805 - P_y_C_d_predictor_loss: -0.6939 - P_d_C_y_critic_loss: -0.3501 - P_d_C_y_predictor_loss: 2.8079 - P_y_C_l_critic_loss: 1.0295 - P_y_C_l_predictor_loss: -0.6870 - P_l_C_y_critic_loss: 0.1474 - P_l_C_y_predictor_loss: 1.1419supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 148us/sample - loss: -1.0085 - P_y_C_d_critic_loss: 2.4758 - P_y_C_d_predictor_loss: -0.6974 - P_d_C_y_critic_loss: -0.3508 - P_d_C_y_predictor_loss: 2.8074 - P_y_C_l_critic_loss: 1.0291 - P_y_C_l_predictor_loss: -0.6846 - P_l_C_y_critic_loss: 0.1489 - P_l_C_y_predictor_loss: 1.1400 - val_loss: -1.1492 - val_P_y_C_d_critic_loss: 2.3099 - val_P_y_C_d_predictor_loss: -0.7874 - val_P_d_C_y_critic_loss: -0.3343 - val_P_d_C_y_predictor_loss: 2.8075 - val_P_y_C_l_critic_loss: 1.0203 - val_P_y_C_l_predictor_loss: -0.7807 - val_P_l_C_y_critic_loss: 0.0737 - val_P_l_C_y_predictor_loss: 1.1083\n",
      "Epoch 4/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -1.1686 - P_y_C_d_critic_loss: 2.3375 - P_y_C_d_predictor_loss: -0.7978 - P_d_C_y_critic_loss: -0.3506 - P_d_C_y_predictor_loss: 2.8012 - P_y_C_l_critic_loss: 1.0254 - P_y_C_l_predictor_loss: -0.7985 - P_l_C_y_critic_loss: 0.0964 - P_l_C_y_predictor_loss: 1.1236supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 147us/sample - loss: -1.1708 - P_y_C_d_critic_loss: 2.3362 - P_y_C_d_predictor_loss: -0.8015 - P_d_C_y_critic_loss: -0.3511 - P_d_C_y_predictor_loss: 2.8014 - P_y_C_l_critic_loss: 1.0256 - P_y_C_l_predictor_loss: -0.8015 - P_l_C_y_critic_loss: 0.0944 - P_l_C_y_predictor_loss: 1.1238 - val_loss: -1.2008 - val_P_y_C_d_critic_loss: 2.2908 - val_P_y_C_d_predictor_loss: -0.8239 - val_P_d_C_y_critic_loss: -0.3355 - val_P_d_C_y_predictor_loss: 2.7982 - val_P_y_C_l_critic_loss: 1.0393 - val_P_y_C_l_predictor_loss: -0.8113 - val_P_l_C_y_critic_loss: 0.0569 - val_P_l_C_y_predictor_loss: 1.1116\n",
      "Epoch 5/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -1.2710 - P_y_C_d_critic_loss: 2.3076 - P_y_C_d_predictor_loss: -0.8663 - P_d_C_y_critic_loss: -0.3332 - P_d_C_y_predictor_loss: 2.8006 - P_y_C_l_critic_loss: 1.0470 - P_y_C_l_predictor_loss: -0.8660 - P_l_C_y_critic_loss: 0.1037 - P_l_C_y_predictor_loss: 1.1266supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 147us/sample - loss: -1.2712 - P_y_C_d_critic_loss: 2.3063 - P_y_C_d_predictor_loss: -0.8666 - P_d_C_y_critic_loss: -0.3314 - P_d_C_y_predictor_loss: 2.8007 - P_y_C_l_critic_loss: 1.0478 - P_y_C_l_predictor_loss: -0.8664 - P_l_C_y_critic_loss: 0.1071 - P_l_C_y_predictor_loss: 1.1267 - val_loss: -1.3980 - val_P_y_C_d_critic_loss: 2.2145 - val_P_y_C_d_predictor_loss: -0.9516 - val_P_d_C_y_critic_loss: -0.3264 - val_P_d_C_y_predictor_loss: 2.8064 - val_P_y_C_l_critic_loss: 1.0128 - val_P_y_C_l_predictor_loss: -0.9492 - val_P_l_C_y_critic_loss: 0.1350 - val_P_l_C_y_predictor_loss: 1.1189\n",
      "Epoch 6/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -1.4260 - P_y_C_d_critic_loss: 2.1869 - P_y_C_d_predictor_loss: -0.9709 - P_d_C_y_critic_loss: -0.3389 - P_d_C_y_predictor_loss: 2.8118 - P_y_C_l_critic_loss: 0.9688 - P_y_C_l_predictor_loss: -0.9635 - P_l_C_y_critic_loss: 0.1688 - P_l_C_y_predictor_loss: 1.1388supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 153us/sample - loss: -1.4281 - P_y_C_d_critic_loss: 2.1837 - P_y_C_d_predictor_loss: -0.9740 - P_d_C_y_critic_loss: -0.3441 - P_d_C_y_predictor_loss: 2.8117 - P_y_C_l_critic_loss: 0.9712 - P_y_C_l_predictor_loss: -0.9670 - P_l_C_y_critic_loss: 0.1712 - P_l_C_y_predictor_loss: 1.1396 - val_loss: -1.4769 - val_P_y_C_d_critic_loss: 1.9976 - val_P_y_C_d_predictor_loss: -1.0019 - val_P_d_C_y_critic_loss: -0.3338 - val_P_d_C_y_predictor_loss: 2.8189 - val_P_y_C_l_critic_loss: 0.8566 - val_P_y_C_l_predictor_loss: -1.0019 - val_P_l_C_y_critic_loss: 0.0378 - val_P_l_C_y_predictor_loss: 1.1293\n",
      "Epoch 7/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -1.5408 - P_y_C_d_critic_loss: 2.0279 - P_y_C_d_predictor_loss: -1.0428 - P_d_C_y_critic_loss: -0.3754 - P_d_C_y_predictor_loss: 2.8168 - P_y_C_l_critic_loss: 0.9298 - P_y_C_l_predictor_loss: -1.0460 - P_l_C_y_critic_loss: 0.1362 - P_l_C_y_predictor_loss: 1.1571supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 148us/sample - loss: -1.5408 - P_y_C_d_critic_loss: 2.0245 - P_y_C_d_predictor_loss: -1.0423 - P_d_C_y_critic_loss: -0.3748 - P_d_C_y_predictor_loss: 2.8172 - P_y_C_l_critic_loss: 0.9274 - P_y_C_l_predictor_loss: -1.0466 - P_l_C_y_critic_loss: 0.1360 - P_l_C_y_predictor_loss: 1.1577 - val_loss: -1.4912 - val_P_y_C_d_critic_loss: 1.8901 - val_P_y_C_d_predictor_loss: -1.0122 - val_P_d_C_y_critic_loss: -0.3632 - val_P_d_C_y_predictor_loss: 2.8141 - val_P_y_C_l_critic_loss: 0.8499 - val_P_y_C_l_predictor_loss: -1.0080 - val_P_l_C_y_critic_loss: -0.0250 - val_P_l_C_y_predictor_loss: 1.1392\n",
      "Epoch 8/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -1.5164 - P_y_C_d_critic_loss: 2.0257 - P_y_C_d_predictor_loss: -1.0319 - P_d_C_y_critic_loss: -0.3621 - P_d_C_y_predictor_loss: 2.8158 - P_y_C_l_critic_loss: 0.9365 - P_y_C_l_predictor_loss: -1.0190 - P_l_C_y_critic_loss: 0.1218 - P_l_C_y_predictor_loss: 1.1499supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 147us/sample - loss: -1.5188 - P_y_C_d_critic_loss: 2.0263 - P_y_C_d_predictor_loss: -1.0353 - P_d_C_y_critic_loss: -0.3627 - P_d_C_y_predictor_loss: 2.8157 - P_y_C_l_critic_loss: 0.9355 - P_y_C_l_predictor_loss: -1.0232 - P_l_C_y_critic_loss: 0.1190 - P_l_C_y_predictor_loss: 1.1503 - val_loss: -1.6019 - val_P_y_C_d_critic_loss: 1.9291 - val_P_y_C_d_predictor_loss: -1.0899 - val_P_d_C_y_critic_loss: -0.3256 - val_P_d_C_y_predictor_loss: 2.8196 - val_P_y_C_l_critic_loss: 0.8784 - val_P_y_C_l_predictor_loss: -1.0750 - val_P_l_C_y_critic_loss: 0.0248 - val_P_l_C_y_predictor_loss: 1.1348\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -1.6541 - P_y_C_d_critic_loss: 1.9568 - P_y_C_d_predictor_loss: -1.1224 - P_d_C_y_critic_loss: -0.3785 - P_d_C_y_predictor_loss: 2.8154 - P_y_C_l_critic_loss: 0.9053 - P_y_C_l_predictor_loss: -1.1116 - P_l_C_y_critic_loss: 0.0846 - P_l_C_y_predictor_loss: 1.1804supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 149us/sample - loss: -1.6556 - P_y_C_d_critic_loss: 1.9601 - P_y_C_d_predictor_loss: -1.1248 - P_d_C_y_critic_loss: -0.3836 - P_d_C_y_predictor_loss: 2.8155 - P_y_C_l_critic_loss: 0.9070 - P_y_C_l_predictor_loss: -1.1138 - P_l_C_y_critic_loss: 0.0917 - P_l_C_y_predictor_loss: 1.1811 - val_loss: -1.7888 - val_P_y_C_d_critic_loss: 1.8497 - val_P_y_C_d_predictor_loss: -1.2159 - val_P_d_C_y_critic_loss: -0.4003 - val_P_d_C_y_predictor_loss: 2.8054 - val_P_y_C_l_critic_loss: 0.8318 - val_P_y_C_l_predictor_loss: -1.1954 - val_P_l_C_y_critic_loss: 0.0795 - val_P_l_C_y_predictor_loss: 1.1576\n",
      "Epoch 10/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -1.7358 - P_y_C_d_critic_loss: 1.9643 - P_y_C_d_predictor_loss: -1.1751 - P_d_C_y_critic_loss: -0.3844 - P_d_C_y_predictor_loss: 2.8110 - P_y_C_l_critic_loss: 0.8508 - P_y_C_l_predictor_loss: -1.1692 - P_l_C_y_critic_loss: 0.1214 - P_l_C_y_predictor_loss: 1.1697supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 151us/sample - loss: -1.7384 - P_y_C_d_critic_loss: 1.9652 - P_y_C_d_predictor_loss: -1.1788 - P_d_C_y_critic_loss: -0.3870 - P_d_C_y_predictor_loss: 2.8115 - P_y_C_l_critic_loss: 0.8506 - P_y_C_l_predictor_loss: -1.1741 - P_l_C_y_critic_loss: 0.1162 - P_l_C_y_predictor_loss: 1.1697 - val_loss: -1.8445 - val_P_y_C_d_critic_loss: 1.8091 - val_P_y_C_d_predictor_loss: -1.2536 - val_P_d_C_y_critic_loss: -0.3868 - val_P_d_C_y_predictor_loss: 2.8084 - val_P_y_C_l_critic_loss: 0.7176 - val_P_y_C_l_predictor_loss: -1.2294 - val_P_l_C_y_critic_loss: 0.1041 - val_P_l_C_y_predictor_loss: 1.1449\n",
      "Epoch 11/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -1.7798 - P_y_C_d_critic_loss: 1.8997 - P_y_C_d_predictor_loss: -1.2033 - P_d_C_y_critic_loss: -0.3756 - P_d_C_y_predictor_loss: 2.8098 - P_y_C_l_critic_loss: 0.7744 - P_y_C_l_predictor_loss: -1.1987 - P_l_C_y_critic_loss: 0.1227 - P_l_C_y_predictor_loss: 1.1615supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 148us/sample - loss: -1.7829 - P_y_C_d_critic_loss: 1.8967 - P_y_C_d_predictor_loss: -1.2079 - P_d_C_y_critic_loss: -0.3772 - P_d_C_y_predictor_loss: 2.8093 - P_y_C_l_critic_loss: 0.7758 - P_y_C_l_predictor_loss: -1.2037 - P_l_C_y_critic_loss: 0.1189 - P_l_C_y_predictor_loss: 1.1608 - val_loss: -1.8895 - val_P_y_C_d_critic_loss: 1.6333 - val_P_y_C_d_predictor_loss: -1.2835 - val_P_d_C_y_critic_loss: -0.3968 - val_P_d_C_y_predictor_loss: 2.7853 - val_P_y_C_l_critic_loss: 0.5806 - val_P_y_C_l_predictor_loss: -1.2541 - val_P_l_C_y_critic_loss: 0.1403 - val_P_l_C_y_predictor_loss: 1.1252\n",
      "Epoch 12/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -1.9090 - P_y_C_d_critic_loss: 1.7721 - P_y_C_d_predictor_loss: -1.2870 - P_d_C_y_critic_loss: -0.3897 - P_d_C_y_predictor_loss: 2.8066 - P_y_C_l_critic_loss: 0.7903 - P_y_C_l_predictor_loss: -1.2874 - P_l_C_y_critic_loss: 0.1755 - P_l_C_y_predictor_loss: 1.1692supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 147us/sample - loss: -1.9071 - P_y_C_d_critic_loss: 1.7733 - P_y_C_d_predictor_loss: -1.2842 - P_d_C_y_critic_loss: -0.3870 - P_d_C_y_predictor_loss: 2.8062 - P_y_C_l_critic_loss: 0.7903 - P_y_C_l_predictor_loss: -1.2843 - P_l_C_y_critic_loss: 0.1741 - P_l_C_y_predictor_loss: 1.1703 - val_loss: -2.0171 - val_P_y_C_d_critic_loss: 1.4742 - val_P_y_C_d_predictor_loss: -1.3650 - val_P_d_C_y_critic_loss: -0.3860 - val_P_d_C_y_predictor_loss: 2.7903 - val_P_y_C_l_critic_loss: 0.6691 - val_P_y_C_l_predictor_loss: -1.3445 - val_P_l_C_y_critic_loss: 0.0743 - val_P_l_C_y_predictor_loss: 1.1189\n",
      "Epoch 13/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -2.0087 - P_y_C_d_critic_loss: 1.7411 - P_y_C_d_predictor_loss: -1.3530 - P_d_C_y_critic_loss: -0.3933 - P_d_C_y_predictor_loss: 2.8055 - P_y_C_l_critic_loss: 0.8246 - P_y_C_l_predictor_loss: -1.3546 - P_l_C_y_critic_loss: 0.1959 - P_l_C_y_predictor_loss: 1.1724supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 148us/sample - loss: -2.0109 - P_y_C_d_critic_loss: 1.7397 - P_y_C_d_predictor_loss: -1.3563 - P_d_C_y_critic_loss: -0.3939 - P_d_C_y_predictor_loss: 2.8045 - P_y_C_l_critic_loss: 0.8235 - P_y_C_l_predictor_loss: -1.3579 - P_l_C_y_critic_loss: 0.1982 - P_l_C_y_predictor_loss: 1.1725 - val_loss: -2.1085 - val_P_y_C_d_critic_loss: 1.4728 - val_P_y_C_d_predictor_loss: -1.4131 - val_P_d_C_y_critic_loss: -0.3977 - val_P_d_C_y_predictor_loss: 2.7807 - val_P_y_C_l_critic_loss: 0.7548 - val_P_y_C_l_predictor_loss: -1.4320 - val_P_l_C_y_critic_loss: 0.1119 - val_P_l_C_y_predictor_loss: 1.1367\n",
      "Epoch 14/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -1.8965 - P_y_C_d_critic_loss: 1.7061 - P_y_C_d_predictor_loss: -1.2747 - P_d_C_y_critic_loss: -0.3698 - P_d_C_y_predictor_loss: 2.8045 - P_y_C_l_critic_loss: 0.8172 - P_y_C_l_predictor_loss: -1.2858 - P_l_C_y_critic_loss: 0.1925 - P_l_C_y_predictor_loss: 1.1678supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 148us/sample - loss: -1.8942 - P_y_C_d_critic_loss: 1.7085 - P_y_C_d_predictor_loss: -1.2717 - P_d_C_y_critic_loss: -0.3709 - P_d_C_y_predictor_loss: 2.8035 - P_y_C_l_critic_loss: 0.8212 - P_y_C_l_predictor_loss: -1.2814 - P_l_C_y_critic_loss: 0.1909 - P_l_C_y_predictor_loss: 1.1681 - val_loss: -1.9292 - val_P_y_C_d_critic_loss: 1.4405 - val_P_y_C_d_predictor_loss: -1.2883 - val_P_d_C_y_critic_loss: -0.3892 - val_P_d_C_y_predictor_loss: 2.7414 - val_P_y_C_l_critic_loss: 0.7045 - val_P_y_C_l_predictor_loss: -1.3206 - val_P_l_C_y_critic_loss: 0.0955 - val_P_l_C_y_predictor_loss: 1.0998\n",
      "Epoch 15/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -1.9558 - P_y_C_d_critic_loss: 1.7668 - P_y_C_d_predictor_loss: -1.3148 - P_d_C_y_critic_loss: -0.3727 - P_d_C_y_predictor_loss: 2.7806 - P_y_C_l_critic_loss: 0.8242 - P_y_C_l_predictor_loss: -1.3257 - P_l_C_y_critic_loss: 0.1901 - P_l_C_y_predictor_loss: 1.1541supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 149us/sample - loss: -1.9606 - P_y_C_d_critic_loss: 1.7618 - P_y_C_d_predictor_loss: -1.3220 - P_d_C_y_critic_loss: -0.3762 - P_d_C_y_predictor_loss: 2.7827 - P_y_C_l_critic_loss: 0.8239 - P_y_C_l_predictor_loss: -1.3328 - P_l_C_y_critic_loss: 0.1931 - P_l_C_y_predictor_loss: 1.1546 - val_loss: -2.1499 - val_P_y_C_d_critic_loss: 1.4130 - val_P_y_C_d_predictor_loss: -1.4398 - val_P_d_C_y_critic_loss: -0.3910 - val_P_d_C_y_predictor_loss: 2.7751 - val_P_y_C_l_critic_loss: 0.6840 - val_P_y_C_l_predictor_loss: -1.4591 - val_P_l_C_y_critic_loss: 0.1190 - val_P_l_C_y_predictor_loss: 1.1192\n",
      "Epoch 16/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -2.1155 - P_y_C_d_critic_loss: 1.5481 - P_y_C_d_predictor_loss: -1.4195 - P_d_C_y_critic_loss: -0.3678 - P_d_C_y_predictor_loss: 2.7988 - P_y_C_l_critic_loss: 0.7503 - P_y_C_l_predictor_loss: -1.4306 - P_l_C_y_critic_loss: 0.1814 - P_l_C_y_predictor_loss: 1.1731supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 144us/sample - loss: -2.1153 - P_y_C_d_critic_loss: 1.5439 - P_y_C_d_predictor_loss: -1.4192 - P_d_C_y_critic_loss: -0.3659 - P_d_C_y_predictor_loss: 2.7995 - P_y_C_l_critic_loss: 0.7476 - P_y_C_l_predictor_loss: -1.4302 - P_l_C_y_critic_loss: 0.1838 - P_l_C_y_predictor_loss: 1.1751 - val_loss: -2.0730 - val_P_y_C_d_critic_loss: 1.2771 - val_P_y_C_d_predictor_loss: -1.3787 - val_P_d_C_y_critic_loss: -0.3478 - val_P_d_C_y_predictor_loss: 2.7699 - val_P_y_C_l_critic_loss: 0.5297 - val_P_y_C_l_predictor_loss: -1.4230 - val_P_l_C_y_critic_loss: 0.0393 - val_P_l_C_y_predictor_loss: 1.1109\n",
      "Epoch 17/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -2.1340 - P_y_C_d_critic_loss: 1.4523 - P_y_C_d_predictor_loss: -1.4312 - P_d_C_y_critic_loss: -0.3526 - P_d_C_y_predictor_loss: 2.8028 - P_y_C_l_critic_loss: 0.6818 - P_y_C_l_predictor_loss: -1.4416 - P_l_C_y_critic_loss: 0.2355 - P_l_C_y_predictor_loss: 1.1953supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 155us/sample - loss: -2.1343 - P_y_C_d_critic_loss: 1.4510 - P_y_C_d_predictor_loss: -1.4321 - P_d_C_y_critic_loss: -0.3520 - P_d_C_y_predictor_loss: 2.8020 - P_y_C_l_critic_loss: 0.6828 - P_y_C_l_predictor_loss: -1.4408 - P_l_C_y_critic_loss: 0.2353 - P_l_C_y_predictor_loss: 1.1948 - val_loss: -2.2755 - val_P_y_C_d_critic_loss: 1.0716 - val_P_y_C_d_predictor_loss: -1.5142 - val_P_d_C_y_critic_loss: -0.3650 - val_P_d_C_y_predictor_loss: 2.7713 - val_P_y_C_l_critic_loss: 0.5324 - val_P_y_C_l_predictor_loss: -1.5516 - val_P_l_C_y_critic_loss: 0.1774 - val_P_l_C_y_predictor_loss: 1.1479\n",
      "Epoch 18/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -2.2543 - P_y_C_d_critic_loss: 1.3789 - P_y_C_d_predictor_loss: -1.5135 - P_d_C_y_critic_loss: -0.3380 - P_d_C_y_predictor_loss: 2.8074 - P_y_C_l_critic_loss: 0.7249 - P_y_C_l_predictor_loss: -1.5164 - P_l_C_y_critic_loss: 0.3017 - P_l_C_y_predictor_loss: 1.2284supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 148us/sample - loss: -2.2556 - P_y_C_d_critic_loss: 1.3807 - P_y_C_d_predictor_loss: -1.5146 - P_d_C_y_critic_loss: -0.3383 - P_d_C_y_predictor_loss: 2.8092 - P_y_C_l_critic_loss: 0.7273 - P_y_C_l_predictor_loss: -1.5203 - P_l_C_y_critic_loss: 0.3027 - P_l_C_y_predictor_loss: 1.2307 - val_loss: -2.2899 - val_P_y_C_d_critic_loss: 1.0721 - val_P_y_C_d_predictor_loss: -1.5261 - val_P_d_C_y_critic_loss: -0.3300 - val_P_d_C_y_predictor_loss: 2.7795 - val_P_y_C_l_critic_loss: 0.5545 - val_P_y_C_l_predictor_loss: -1.5578 - val_P_l_C_y_critic_loss: 0.1151 - val_P_l_C_y_predictor_loss: 1.1664\n",
      "Epoch 19/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -2.3129 - P_y_C_d_critic_loss: 1.3843 - P_y_C_d_predictor_loss: -1.5499 - P_d_C_y_critic_loss: -0.3234 - P_d_C_y_predictor_loss: 2.8207 - P_y_C_l_critic_loss: 0.7332 - P_y_C_l_predictor_loss: -1.5611 - P_l_C_y_critic_loss: 0.3465 - P_l_C_y_predictor_loss: 1.2719supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 156us/sample - loss: -2.3113 - P_y_C_d_critic_loss: 1.3867 - P_y_C_d_predictor_loss: -1.5473 - P_d_C_y_critic_loss: -0.3223 - P_d_C_y_predictor_loss: 2.8196 - P_y_C_l_critic_loss: 0.7312 - P_y_C_l_predictor_loss: -1.5588 - P_l_C_y_critic_loss: 0.3461 - P_l_C_y_predictor_loss: 1.2734 - val_loss: -2.3540 - val_P_y_C_d_critic_loss: 1.0650 - val_P_y_C_d_predictor_loss: -1.5797 - val_P_d_C_y_critic_loss: -0.3265 - val_P_d_C_y_predictor_loss: 2.7592 - val_P_y_C_l_critic_loss: 0.5703 - val_P_y_C_l_predictor_loss: -1.5795 - val_P_l_C_y_critic_loss: 0.1643 - val_P_l_C_y_predictor_loss: 1.1825\n",
      "Epoch 20/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -2.3466 - P_y_C_d_critic_loss: 1.3380 - P_y_C_d_predictor_loss: -1.5713 - P_d_C_y_critic_loss: -0.3214 - P_d_C_y_predictor_loss: 2.8153 - P_y_C_l_critic_loss: 0.6959 - P_y_C_l_predictor_loss: -1.5843 - P_l_C_y_critic_loss: 0.3587 - P_l_C_y_predictor_loss: 1.2887supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 143us/sample - loss: -2.3422 - P_y_C_d_critic_loss: 1.3374 - P_y_C_d_predictor_loss: -1.5643 - P_d_C_y_critic_loss: -0.3180 - P_d_C_y_predictor_loss: 2.8169 - P_y_C_l_critic_loss: 0.6944 - P_y_C_l_predictor_loss: -1.5779 - P_l_C_y_critic_loss: 0.3595 - P_l_C_y_predictor_loss: 1.2894 - val_loss: -2.3475 - val_P_y_C_d_critic_loss: 1.0682 - val_P_y_C_d_predictor_loss: -1.5610 - val_P_d_C_y_critic_loss: -0.3428 - val_P_d_C_y_predictor_loss: 2.7776 - val_P_y_C_l_critic_loss: 0.5171 - val_P_y_C_l_predictor_loss: -1.6023 - val_P_l_C_y_critic_loss: 0.1925 - val_P_l_C_y_predictor_loss: 1.2256\n",
      "Epoch 21/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -2.3849 - P_y_C_d_critic_loss: 1.2709 - P_y_C_d_predictor_loss: -1.5985 - P_d_C_y_critic_loss: -0.3150 - P_d_C_y_predictor_loss: 2.8313 - P_y_C_l_critic_loss: 0.6980 - P_y_C_l_predictor_loss: -1.6052 - P_l_C_y_critic_loss: 0.4174 - P_l_C_y_predictor_loss: 1.3213supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 149us/sample - loss: -2.3879 - P_y_C_d_critic_loss: 1.2704 - P_y_C_d_predictor_loss: -1.6033 - P_d_C_y_critic_loss: -0.3143 - P_d_C_y_predictor_loss: 2.8320 - P_y_C_l_critic_loss: 0.6982 - P_y_C_l_predictor_loss: -1.6092 - P_l_C_y_critic_loss: 0.4158 - P_l_C_y_predictor_loss: 1.3253 - val_loss: -2.4527 - val_P_y_C_d_critic_loss: 0.9313 - val_P_y_C_d_predictor_loss: -1.6271 - val_P_d_C_y_critic_loss: -0.3152 - val_P_d_C_y_predictor_loss: 2.7709 - val_P_y_C_l_critic_loss: 0.5024 - val_P_y_C_l_predictor_loss: -1.6788 - val_P_l_C_y_critic_loss: 0.2538 - val_P_l_C_y_predictor_loss: 1.2485\n",
      "Epoch 22/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -2.4041 - P_y_C_d_critic_loss: 1.3253 - P_y_C_d_predictor_loss: -1.6197 - P_d_C_y_critic_loss: -0.2966 - P_d_C_y_predictor_loss: 2.8384 - P_y_C_l_critic_loss: 0.6994 - P_y_C_l_predictor_loss: -1.6025 - P_l_C_y_critic_loss: 0.4417 - P_l_C_y_predictor_loss: 1.3487supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 148us/sample - loss: -2.4014 - P_y_C_d_critic_loss: 1.3269 - P_y_C_d_predictor_loss: -1.6153 - P_d_C_y_critic_loss: -0.2941 - P_d_C_y_predictor_loss: 2.8378 - P_y_C_l_critic_loss: 0.6992 - P_y_C_l_predictor_loss: -1.5986 - P_l_C_y_critic_loss: 0.4380 - P_l_C_y_predictor_loss: 1.3494 - val_loss: -2.5438 - val_P_y_C_d_critic_loss: 0.9223 - val_P_y_C_d_predictor_loss: -1.7038 - val_P_d_C_y_critic_loss: -0.3293 - val_P_d_C_y_predictor_loss: 2.7710 - val_P_y_C_l_critic_loss: 0.5394 - val_P_y_C_l_predictor_loss: -1.7064 - val_P_l_C_y_critic_loss: 0.3146 - val_P_l_C_y_predictor_loss: 1.2591\n",
      "Epoch 23/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -2.4592 - P_y_C_d_critic_loss: 1.2205 - P_y_C_d_predictor_loss: -1.6514 - P_d_C_y_critic_loss: -0.2733 - P_d_C_y_predictor_loss: 2.8450 - P_y_C_l_critic_loss: 0.6941 - P_y_C_l_predictor_loss: -1.6470 - P_l_C_y_critic_loss: 0.4825 - P_l_C_y_predictor_loss: 1.3655supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 148us/sample - loss: -2.4584 - P_y_C_d_critic_loss: 1.2182 - P_y_C_d_predictor_loss: -1.6497 - P_d_C_y_critic_loss: -0.2680 - P_d_C_y_predictor_loss: 2.8470 - P_y_C_l_critic_loss: 0.6936 - P_y_C_l_predictor_loss: -1.6464 - P_l_C_y_critic_loss: 0.4841 - P_l_C_y_predictor_loss: 1.3676 - val_loss: -2.5895 - val_P_y_C_d_critic_loss: 0.8820 - val_P_y_C_d_predictor_loss: -1.7356 - val_P_d_C_y_critic_loss: -0.2868 - val_P_d_C_y_predictor_loss: 2.7824 - val_P_y_C_l_critic_loss: 0.5315 - val_P_y_C_l_predictor_loss: -1.7341 - val_P_l_C_y_critic_loss: 0.3268 - val_P_l_C_y_predictor_loss: 1.2586\n",
      "Epoch 24/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -2.5345 - P_y_C_d_critic_loss: 1.1512 - P_y_C_d_predictor_loss: -1.6998 - P_d_C_y_critic_loss: -0.2383 - P_d_C_y_predictor_loss: 2.8568 - P_y_C_l_critic_loss: 0.6965 - P_y_C_l_predictor_loss: -1.6994 - P_l_C_y_critic_loss: 0.5966 - P_l_C_y_predictor_loss: 1.3963supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 143us/sample - loss: -2.5355 - P_y_C_d_critic_loss: 1.1474 - P_y_C_d_predictor_loss: -1.7010 - P_d_C_y_critic_loss: -0.2378 - P_d_C_y_predictor_loss: 2.8570 - P_y_C_l_critic_loss: 0.6966 - P_y_C_l_predictor_loss: -1.7017 - P_l_C_y_critic_loss: 0.5996 - P_l_C_y_predictor_loss: 1.3953 - val_loss: -2.4695 - val_P_y_C_d_critic_loss: 0.8775 - val_P_y_C_d_predictor_loss: -1.6569 - val_P_d_C_y_critic_loss: -0.2706 - val_P_d_C_y_predictor_loss: 2.8013 - val_P_y_C_l_critic_loss: 0.5664 - val_P_y_C_l_predictor_loss: -1.6530 - val_P_l_C_y_critic_loss: 0.2733 - val_P_l_C_y_predictor_loss: 1.2527\n",
      "Epoch 25/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17400/17657 [============================>.] - ETA: 0s - loss: -2.5250 - P_y_C_d_critic_loss: 1.1208 - P_y_C_d_predictor_loss: -1.6951 - P_d_C_y_critic_loss: -0.2125 - P_d_C_y_predictor_loss: 2.8760 - P_y_C_l_critic_loss: 0.6859 - P_y_C_l_predictor_loss: -1.6891 - P_l_C_y_critic_loss: 0.6037 - P_l_C_y_predictor_loss: 1.4122supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 141us/sample - loss: -2.5274 - P_y_C_d_critic_loss: 1.1188 - P_y_C_d_predictor_loss: -1.6987 - P_d_C_y_critic_loss: -0.2120 - P_d_C_y_predictor_loss: 2.8749 - P_y_C_l_critic_loss: 0.6861 - P_y_C_l_predictor_loss: -1.6929 - P_l_C_y_critic_loss: 0.6028 - P_l_C_y_predictor_loss: 1.4142 - val_loss: -2.3186 - val_P_y_C_d_critic_loss: 0.9354 - val_P_y_C_d_predictor_loss: -1.5589 - val_P_d_C_y_critic_loss: -0.2329 - val_P_d_C_y_predictor_loss: 2.7891 - val_P_y_C_l_critic_loss: 0.5296 - val_P_y_C_l_predictor_loss: -1.5499 - val_P_l_C_y_critic_loss: 0.1794 - val_P_l_C_y_predictor_loss: 1.2339\n",
      "Epoch 26/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -2.5746 - P_y_C_d_critic_loss: 1.0599 - P_y_C_d_predictor_loss: -1.7277 - P_d_C_y_critic_loss: -0.1840 - P_d_C_y_predictor_loss: 2.8774 - P_y_C_l_critic_loss: 0.6550 - P_y_C_l_predictor_loss: -1.7216 - P_l_C_y_critic_loss: 0.6086 - P_l_C_y_predictor_loss: 1.4199supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 142us/sample - loss: -2.5729 - P_y_C_d_critic_loss: 1.0546 - P_y_C_d_predictor_loss: -1.7253 - P_d_C_y_critic_loss: -0.1823 - P_d_C_y_predictor_loss: 2.8778 - P_y_C_l_critic_loss: 0.6545 - P_y_C_l_predictor_loss: -1.7182 - P_l_C_y_critic_loss: 0.6021 - P_l_C_y_predictor_loss: 1.4192 - val_loss: -2.5528 - val_P_y_C_d_critic_loss: 0.7876 - val_P_y_C_d_predictor_loss: -1.7092 - val_P_d_C_y_critic_loss: -0.2209 - val_P_d_C_y_predictor_loss: 2.7987 - val_P_y_C_l_critic_loss: 0.4958 - val_P_y_C_l_predictor_loss: -1.7112 - val_P_l_C_y_critic_loss: 0.2826 - val_P_l_C_y_predictor_loss: 1.2636\n",
      "Epoch 27/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -2.4354 - P_y_C_d_critic_loss: 1.1859 - P_y_C_d_predictor_loss: -1.6352 - P_d_C_y_critic_loss: -0.1979 - P_d_C_y_predictor_loss: 2.8633 - P_y_C_l_critic_loss: 0.7145 - P_y_C_l_predictor_loss: -1.6312 - P_l_C_y_critic_loss: 0.5123 - P_l_C_y_predictor_loss: 1.3931supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 142us/sample - loss: -2.4374 - P_y_C_d_critic_loss: 1.1849 - P_y_C_d_predictor_loss: -1.6380 - P_d_C_y_critic_loss: -0.1984 - P_d_C_y_predictor_loss: 2.8596 - P_y_C_l_critic_loss: 0.7140 - P_y_C_l_predictor_loss: -1.6347 - P_l_C_y_critic_loss: 0.5046 - P_l_C_y_predictor_loss: 1.3928 - val_loss: -2.4096 - val_P_y_C_d_critic_loss: 0.9969 - val_P_y_C_d_predictor_loss: -1.6259 - val_P_d_C_y_critic_loss: -0.2408 - val_P_d_C_y_predictor_loss: 2.7838 - val_P_y_C_l_critic_loss: 0.5730 - val_P_y_C_l_predictor_loss: -1.5982 - val_P_l_C_y_critic_loss: 0.1776 - val_P_l_C_y_predictor_loss: 1.2565\n",
      "Epoch 28/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -2.5154 - P_y_C_d_critic_loss: 1.0819 - P_y_C_d_predictor_loss: -1.6869 - P_d_C_y_critic_loss: -0.2144 - P_d_C_y_predictor_loss: 2.8630 - P_y_C_l_critic_loss: 0.7116 - P_y_C_l_predictor_loss: -1.6857 - P_l_C_y_critic_loss: 0.4876 - P_l_C_y_predictor_loss: 1.3806supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 147us/sample - loss: -2.5186 - P_y_C_d_critic_loss: 1.0807 - P_y_C_d_predictor_loss: -1.6919 - P_d_C_y_critic_loss: -0.2148 - P_d_C_y_predictor_loss: 2.8610 - P_y_C_l_critic_loss: 0.7165 - P_y_C_l_predictor_loss: -1.6902 - P_l_C_y_critic_loss: 0.4928 - P_l_C_y_predictor_loss: 1.3856 - val_loss: -2.3429 - val_P_y_C_d_critic_loss: 0.7929 - val_P_y_C_d_predictor_loss: -1.5688 - val_P_d_C_y_critic_loss: -0.2164 - val_P_d_C_y_predictor_loss: 2.8023 - val_P_y_C_l_critic_loss: 0.6119 - val_P_y_C_l_predictor_loss: -1.5773 - val_P_l_C_y_critic_loss: 0.2574 - val_P_l_C_y_predictor_loss: 1.3024\n",
      "Epoch 29/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -2.5999 - P_y_C_d_critic_loss: 0.9677 - P_y_C_d_predictor_loss: -1.7453 - P_d_C_y_critic_loss: -0.1743 - P_d_C_y_predictor_loss: 2.8837 - P_y_C_l_critic_loss: 0.7028 - P_y_C_l_predictor_loss: -1.7356 - P_l_C_y_critic_loss: 0.5586 - P_l_C_y_predictor_loss: 1.4144supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 150us/sample - loss: -2.6000 - P_y_C_d_critic_loss: 0.9642 - P_y_C_d_predictor_loss: -1.7457 - P_d_C_y_critic_loss: -0.1713 - P_d_C_y_predictor_loss: 2.8825 - P_y_C_l_critic_loss: 0.7030 - P_y_C_l_predictor_loss: -1.7354 - P_l_C_y_critic_loss: 0.5581 - P_l_C_y_predictor_loss: 1.4127 - val_loss: -2.7298 - val_P_y_C_d_critic_loss: 0.5746 - val_P_y_C_d_predictor_loss: -1.8272 - val_P_d_C_y_critic_loss: -0.1918 - val_P_d_C_y_predictor_loss: 2.8135 - val_P_y_C_l_critic_loss: 0.5341 - val_P_y_C_l_predictor_loss: -1.8252 - val_P_l_C_y_critic_loss: 0.3853 - val_P_l_C_y_predictor_loss: 1.3217\n",
      "Epoch 30/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -2.6796 - P_y_C_d_critic_loss: 0.9216 - P_y_C_d_predictor_loss: -1.7975 - P_d_C_y_critic_loss: -0.1296 - P_d_C_y_predictor_loss: 2.9054 - P_y_C_l_critic_loss: 0.6942 - P_y_C_l_predictor_loss: -1.7896 - P_l_C_y_critic_loss: 0.6997 - P_l_C_y_predictor_loss: 1.4526supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 143us/sample - loss: -2.6774 - P_y_C_d_critic_loss: 0.9187 - P_y_C_d_predictor_loss: -1.7939 - P_d_C_y_critic_loss: -0.1244 - P_d_C_y_predictor_loss: 2.9084 - P_y_C_l_critic_loss: 0.6920 - P_y_C_l_predictor_loss: -1.7865 - P_l_C_y_critic_loss: 0.7015 - P_l_C_y_predictor_loss: 1.4524 - val_loss: -2.4812 - val_P_y_C_d_critic_loss: 0.6933 - val_P_y_C_d_predictor_loss: -1.6439 - val_P_d_C_y_critic_loss: -0.1190 - val_P_d_C_y_predictor_loss: 2.8252 - val_P_y_C_l_critic_loss: 0.5505 - val_P_y_C_l_predictor_loss: -1.6993 - val_P_l_C_y_critic_loss: 0.3876 - val_P_l_C_y_predictor_loss: 1.3208\n",
      "Epoch 31/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -2.6613 - P_y_C_d_critic_loss: 0.9090 - P_y_C_d_predictor_loss: -1.7850 - P_d_C_y_critic_loss: -0.1000 - P_d_C_y_predictor_loss: 2.9179 - P_y_C_l_critic_loss: 0.6687 - P_y_C_l_predictor_loss: -1.7775 - P_l_C_y_critic_loss: 0.7100 - P_l_C_y_predictor_loss: 1.4671supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 147us/sample - loss: -2.6625 - P_y_C_d_critic_loss: 0.9049 - P_y_C_d_predictor_loss: -1.7868 - P_d_C_y_critic_loss: -0.0990 - P_d_C_y_predictor_loss: 2.9152 - P_y_C_l_critic_loss: 0.6689 - P_y_C_l_predictor_loss: -1.7791 - P_l_C_y_critic_loss: 0.7141 - P_l_C_y_predictor_loss: 1.4701 - val_loss: -2.7977 - val_P_y_C_d_critic_loss: 0.6139 - val_P_y_C_d_predictor_loss: -1.8653 - val_P_d_C_y_critic_loss: -0.0867 - val_P_d_C_y_predictor_loss: 2.8312 - val_P_y_C_l_critic_loss: 0.4964 - val_P_y_C_l_predictor_loss: -1.8863 - val_P_l_C_y_critic_loss: 0.4617 - val_P_l_C_y_predictor_loss: 1.3567\n",
      "Epoch 32/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -2.7062 - P_y_C_d_critic_loss: 0.8578 - P_y_C_d_predictor_loss: -1.8141 - P_d_C_y_critic_loss: -0.0351 - P_d_C_y_predictor_loss: 2.9299 - P_y_C_l_critic_loss: 0.6399 - P_y_C_l_predictor_loss: -1.8078 - P_l_C_y_critic_loss: 0.7423 - P_l_C_y_predictor_loss: 1.4963supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 145us/sample - loss: -2.7065 - P_y_C_d_critic_loss: 0.8575 - P_y_C_d_predictor_loss: -1.8142 - P_d_C_y_critic_loss: -0.0322 - P_d_C_y_predictor_loss: 2.9305 - P_y_C_l_critic_loss: 0.6383 - P_y_C_l_predictor_loss: -1.8088 - P_l_C_y_critic_loss: 0.7416 - P_l_C_y_predictor_loss: 1.4962 - val_loss: -2.6773 - val_P_y_C_d_critic_loss: 0.5857 - val_P_y_C_d_predictor_loss: -1.7999 - val_P_d_C_y_critic_loss: -0.0590 - val_P_d_C_y_predictor_loss: 2.8336 - val_P_y_C_l_critic_loss: 0.5432 - val_P_y_C_l_predictor_loss: -1.7763 - val_P_l_C_y_critic_loss: 0.4149 - val_P_l_C_y_predictor_loss: 1.3331\n",
      "Epoch 33/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -2.7479 - P_y_C_d_critic_loss: 0.8546 - P_y_C_d_predictor_loss: -1.8426 - P_d_C_y_critic_loss: -0.0273 - P_d_C_y_predictor_loss: 2.9393 - P_y_C_l_critic_loss: 0.6413 - P_y_C_l_predictor_loss: -1.8342 - P_l_C_y_critic_loss: 0.7386 - P_l_C_y_predictor_loss: 1.4960supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 142us/sample - loss: -2.7517 - P_y_C_d_critic_loss: 0.8533 - P_y_C_d_predictor_loss: -1.8476 - P_d_C_y_critic_loss: -0.0266 - P_d_C_y_predictor_loss: 2.9468 - P_y_C_l_critic_loss: 0.6405 - P_y_C_l_predictor_loss: -1.8416 - P_l_C_y_critic_loss: 0.7406 - P_l_C_y_predictor_loss: 1.4950 - val_loss: -2.8154 - val_P_y_C_d_critic_loss: 0.5236 - val_P_y_C_d_predictor_loss: -1.8807 - val_P_d_C_y_critic_loss: -0.0789 - val_P_d_C_y_predictor_loss: 2.8326 - val_P_y_C_l_critic_loss: 0.4477 - val_P_y_C_l_predictor_loss: -1.8877 - val_P_l_C_y_critic_loss: 0.3627 - val_P_l_C_y_predictor_loss: 1.3151\n",
      "Epoch 34/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -2.7797 - P_y_C_d_critic_loss: 0.8474 - P_y_C_d_predictor_loss: -1.8617 - P_d_C_y_critic_loss: 0.0062 - P_d_C_y_predictor_loss: 2.9607 - P_y_C_l_critic_loss: 0.6207 - P_y_C_l_predictor_loss: -1.8591 - P_l_C_y_critic_loss: 0.8520 - P_l_C_y_predictor_loss: 1.5565supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 147us/sample - loss: -2.7791 - P_y_C_d_critic_loss: 0.8469 - P_y_C_d_predictor_loss: -1.8609 - P_d_C_y_critic_loss: 0.0033 - P_d_C_y_predictor_loss: 2.9609 - P_y_C_l_critic_loss: 0.6229 - P_y_C_l_predictor_loss: -1.8579 - P_l_C_y_critic_loss: 0.8472 - P_l_C_y_predictor_loss: 1.5549 - val_loss: -2.8582 - val_P_y_C_d_critic_loss: 0.5791 - val_P_y_C_d_predictor_loss: -1.9041 - val_P_d_C_y_critic_loss: -0.0033 - val_P_d_C_y_predictor_loss: 2.8495 - val_P_y_C_l_critic_loss: 0.4886 - val_P_y_C_l_predictor_loss: -1.9279 - val_P_l_C_y_critic_loss: 0.4787 - val_P_l_C_y_predictor_loss: 1.3704\n",
      "Epoch 35/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -2.8138 - P_y_C_d_critic_loss: 0.8215 - P_y_C_d_predictor_loss: -1.8833 - P_d_C_y_critic_loss: 0.0216 - P_d_C_y_predictor_loss: 2.9708 - P_y_C_l_critic_loss: 0.6358 - P_y_C_l_predictor_loss: -1.8839 - P_l_C_y_critic_loss: 0.8543 - P_l_C_y_predictor_loss: 1.5846supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 150us/sample - loss: -2.8129 - P_y_C_d_critic_loss: 0.8222 - P_y_C_d_predictor_loss: -1.8817 - P_d_C_y_critic_loss: 0.0241 - P_d_C_y_predictor_loss: 2.9730 - P_y_C_l_critic_loss: 0.6341 - P_y_C_l_predictor_loss: -1.8828 - P_l_C_y_critic_loss: 0.8504 - P_l_C_y_predictor_loss: 1.5877 - val_loss: -2.8966 - val_P_y_C_d_critic_loss: 0.5199 - val_P_y_C_d_predictor_loss: -1.9400 - val_P_d_C_y_critic_loss: -0.0725 - val_P_d_C_y_predictor_loss: 2.8346 - val_P_y_C_l_critic_loss: 0.4456 - val_P_y_C_l_predictor_loss: -1.9306 - val_P_l_C_y_critic_loss: 0.4204 - val_P_l_C_y_predictor_loss: 1.3536\n",
      "Epoch 36/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -2.8286 - P_y_C_d_critic_loss: 0.8116 - P_y_C_d_predictor_loss: -1.8959 - P_d_C_y_critic_loss: 0.0527 - P_d_C_y_predictor_loss: 2.9904 - P_y_C_l_critic_loss: 0.6162 - P_y_C_l_predictor_loss: -1.8878 - P_l_C_y_critic_loss: 0.9303 - P_l_C_y_predictor_loss: 1.6229supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 144us/sample - loss: -2.8310 - P_y_C_d_critic_loss: 0.8105 - P_y_C_d_predictor_loss: -1.8994 - P_d_C_y_critic_loss: 0.0469 - P_d_C_y_predictor_loss: 2.9856 - P_y_C_l_critic_loss: 0.6158 - P_y_C_l_predictor_loss: -1.8918 - P_l_C_y_critic_loss: 0.9359 - P_l_C_y_predictor_loss: 1.6239 - val_loss: -2.7244 - val_P_y_C_d_critic_loss: 0.6152 - val_P_y_C_d_predictor_loss: -1.8164 - val_P_d_C_y_critic_loss: -0.0556 - val_P_d_C_y_predictor_loss: 2.8355 - val_P_y_C_l_critic_loss: 0.4356 - val_P_y_C_l_predictor_loss: -1.8357 - val_P_l_C_y_critic_loss: 0.3188 - val_P_l_C_y_predictor_loss: 1.3389\n",
      "Epoch 37/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -2.7899 - P_y_C_d_critic_loss: 0.8163 - P_y_C_d_predictor_loss: -1.8706 - P_d_C_y_critic_loss: 0.0396 - P_d_C_y_predictor_loss: 2.9786 - P_y_C_l_critic_loss: 0.6220 - P_y_C_l_predictor_loss: -1.8612 - P_l_C_y_critic_loss: 0.8163 - P_l_C_y_predictor_loss: 1.6020supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 153us/sample - loss: -2.7886 - P_y_C_d_critic_loss: 0.8126 - P_y_C_d_predictor_loss: -1.8683 - P_d_C_y_critic_loss: 0.0379 - P_d_C_y_predictor_loss: 2.9755 - P_y_C_l_critic_loss: 0.6191 - P_y_C_l_predictor_loss: -1.8599 - P_l_C_y_critic_loss: 0.8114 - P_l_C_y_predictor_loss: 1.6045 - val_loss: -2.9188 - val_P_y_C_d_critic_loss: 0.5194 - val_P_y_C_d_predictor_loss: -1.9523 - val_P_d_C_y_critic_loss: -0.0111 - val_P_d_C_y_predictor_loss: 2.8365 - val_P_y_C_l_critic_loss: 0.4782 - val_P_y_C_l_predictor_loss: -1.9516 - val_P_l_C_y_critic_loss: 0.5003 - val_P_l_C_y_predictor_loss: 1.4217\n",
      "Epoch 38/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -2.8319 - P_y_C_d_critic_loss: 0.8430 - P_y_C_d_predictor_loss: -1.8988 - P_d_C_y_critic_loss: 0.0610 - P_d_C_y_predictor_loss: 2.9912 - P_y_C_l_critic_loss: 0.6266 - P_y_C_l_predictor_loss: -1.8892 - P_l_C_y_critic_loss: 0.8874 - P_l_C_y_predictor_loss: 1.6438supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 153us/sample - loss: -2.8328 - P_y_C_d_critic_loss: 0.8467 - P_y_C_d_predictor_loss: -1.8998 - P_d_C_y_critic_loss: 0.0624 - P_d_C_y_predictor_loss: 2.9949 - P_y_C_l_critic_loss: 0.6287 - P_y_C_l_predictor_loss: -1.8915 - P_l_C_y_critic_loss: 0.9086 - P_l_C_y_predictor_loss: 1.6478 - val_loss: -2.9622 - val_P_y_C_d_critic_loss: 0.5262 - val_P_y_C_d_predictor_loss: -1.9811 - val_P_d_C_y_critic_loss: 0.0469 - val_P_d_C_y_predictor_loss: 2.8424 - val_P_y_C_l_critic_loss: 0.4531 - val_P_y_C_l_predictor_loss: -1.9810 - val_P_l_C_y_critic_loss: 0.4522 - val_P_l_C_y_predictor_loss: 1.4322\n",
      "Epoch 39/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -2.8711 - P_y_C_d_critic_loss: 0.8332 - P_y_C_d_predictor_loss: -1.9255 - P_d_C_y_critic_loss: 0.1085 - P_d_C_y_predictor_loss: 3.0115 - P_y_C_l_critic_loss: 0.5906 - P_y_C_l_predictor_loss: -1.9137 - P_l_C_y_critic_loss: 0.8706 - P_l_C_y_predictor_loss: 1.6805supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 146us/sample - loss: -2.8694 - P_y_C_d_critic_loss: 0.8319 - P_y_C_d_predictor_loss: -1.9227 - P_d_C_y_critic_loss: 0.1080 - P_d_C_y_predictor_loss: 3.0087 - P_y_C_l_critic_loss: 0.5913 - P_y_C_l_predictor_loss: -1.9118 - P_l_C_y_critic_loss: 0.8775 - P_l_C_y_predictor_loss: 1.6837 - val_loss: -2.8509 - val_P_y_C_d_critic_loss: 0.5515 - val_P_y_C_d_predictor_loss: -1.9088 - val_P_d_C_y_critic_loss: 0.0624 - val_P_d_C_y_predictor_loss: 2.8633 - val_P_y_C_l_critic_loss: 0.4479 - val_P_y_C_l_predictor_loss: -1.9043 - val_P_l_C_y_critic_loss: 0.3747 - val_P_l_C_y_predictor_loss: 1.3838\n",
      "Epoch 40/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -2.8611 - P_y_C_d_critic_loss: 0.8154 - P_y_C_d_predictor_loss: -1.9156 - P_d_C_y_critic_loss: 0.1203 - P_d_C_y_predictor_loss: 3.0112 - P_y_C_l_critic_loss: 0.5753 - P_y_C_l_predictor_loss: -1.9130 - P_l_C_y_critic_loss: 0.9627 - P_l_C_y_predictor_loss: 1.7118supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 143us/sample - loss: -2.8631 - P_y_C_d_critic_loss: 0.8138 - P_y_C_d_predictor_loss: -1.9182 - P_d_C_y_critic_loss: 0.1219 - P_d_C_y_predictor_loss: 3.0093 - P_y_C_l_critic_loss: 0.5726 - P_y_C_l_predictor_loss: -1.9172 - P_l_C_y_critic_loss: 0.9605 - P_l_C_y_predictor_loss: 1.7119 - val_loss: -2.9411 - val_P_y_C_d_critic_loss: 0.4754 - val_P_y_C_d_predictor_loss: -1.9651 - val_P_d_C_y_critic_loss: 0.0498 - val_P_d_C_y_predictor_loss: 2.8409 - val_P_y_C_l_critic_loss: 0.4027 - val_P_y_C_l_predictor_loss: -1.9695 - val_P_l_C_y_critic_loss: 0.4392 - val_P_l_C_y_predictor_loss: 1.4567\n",
      "Epoch 41/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17400/17657 [============================>.] - ETA: 0s - loss: -2.8588 - P_y_C_d_critic_loss: 0.8010 - P_y_C_d_predictor_loss: -1.9106 - P_d_C_y_critic_loss: 0.1251 - P_d_C_y_predictor_loss: 3.0132 - P_y_C_l_critic_loss: 0.5654 - P_y_C_l_predictor_loss: -1.9180 - P_l_C_y_critic_loss: 0.9196 - P_l_C_y_predictor_loss: 1.7344supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 146us/sample - loss: -2.8598 - P_y_C_d_critic_loss: 0.8021 - P_y_C_d_predictor_loss: -1.9121 - P_d_C_y_critic_loss: 0.1249 - P_d_C_y_predictor_loss: 3.0133 - P_y_C_l_critic_loss: 0.5668 - P_y_C_l_predictor_loss: -1.9196 - P_l_C_y_critic_loss: 0.9140 - P_l_C_y_predictor_loss: 1.7338 - val_loss: -2.8227 - val_P_y_C_d_critic_loss: 0.5053 - val_P_y_C_d_predictor_loss: -1.8830 - val_P_d_C_y_critic_loss: 0.0521 - val_P_d_C_y_predictor_loss: 2.8107 - val_P_y_C_l_critic_loss: 0.3402 - val_P_y_C_l_predictor_loss: -1.8959 - val_P_l_C_y_critic_loss: 0.5086 - val_P_l_C_y_predictor_loss: 1.4843\n",
      "Epoch 42/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -2.9373 - P_y_C_d_critic_loss: 0.7552 - P_y_C_d_predictor_loss: -1.9659 - P_d_C_y_critic_loss: 0.1458 - P_d_C_y_predictor_loss: 3.0166 - P_y_C_l_critic_loss: 0.5320 - P_y_C_l_predictor_loss: -1.9632 - P_l_C_y_critic_loss: 0.9687 - P_l_C_y_predictor_loss: 1.7710supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 142us/sample - loss: -2.9319 - P_y_C_d_critic_loss: 0.7612 - P_y_C_d_predictor_loss: -1.9576 - P_d_C_y_critic_loss: 0.1503 - P_d_C_y_predictor_loss: 3.0138 - P_y_C_l_critic_loss: 0.5319 - P_y_C_l_predictor_loss: -1.9553 - P_l_C_y_critic_loss: 0.9610 - P_l_C_y_predictor_loss: 1.7745 - val_loss: -2.9230 - val_P_y_C_d_critic_loss: 0.5528 - val_P_y_C_d_predictor_loss: -1.9568 - val_P_d_C_y_critic_loss: 0.1441 - val_P_d_C_y_predictor_loss: 2.8683 - val_P_y_C_l_critic_loss: 0.4210 - val_P_y_C_l_predictor_loss: -1.9523 - val_P_l_C_y_critic_loss: 0.4456 - val_P_l_C_y_predictor_loss: 1.4639\n",
      "Epoch 43/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -2.9807 - P_y_C_d_critic_loss: 0.8088 - P_y_C_d_predictor_loss: -1.9951 - P_d_C_y_critic_loss: 0.2410 - P_d_C_y_predictor_loss: 3.0666 - P_y_C_l_critic_loss: 0.5390 - P_y_C_l_predictor_loss: -1.9928 - P_l_C_y_critic_loss: 1.0579 - P_l_C_y_predictor_loss: 1.8444supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 150us/sample - loss: -2.9803 - P_y_C_d_critic_loss: 0.8101 - P_y_C_d_predictor_loss: -1.9950 - P_d_C_y_critic_loss: 0.2418 - P_d_C_y_predictor_loss: 3.0639 - P_y_C_l_critic_loss: 0.5378 - P_y_C_l_predictor_loss: -1.9912 - P_l_C_y_critic_loss: 1.0693 - P_l_C_y_predictor_loss: 1.8440 - val_loss: -3.0087 - val_P_y_C_d_critic_loss: 0.5603 - val_P_y_C_d_predictor_loss: -2.0071 - val_P_d_C_y_critic_loss: 0.1876 - val_P_d_C_y_predictor_loss: 2.8937 - val_P_y_C_l_critic_loss: 0.4381 - val_P_y_C_l_predictor_loss: -2.0224 - val_P_l_C_y_critic_loss: 0.4967 - val_P_l_C_y_predictor_loss: 1.5425\n",
      "Epoch 44/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -2.9068 - P_y_C_d_critic_loss: 0.7812 - P_y_C_d_predictor_loss: -1.9496 - P_d_C_y_critic_loss: 0.2307 - P_d_C_y_predictor_loss: 3.0711 - P_y_C_l_critic_loss: 0.5552 - P_y_C_l_predictor_loss: -1.9355 - P_l_C_y_critic_loss: 1.0538 - P_l_C_y_predictor_loss: 1.8654supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 153us/sample - loss: -2.9045 - P_y_C_d_critic_loss: 0.7773 - P_y_C_d_predictor_loss: -1.9460 - P_d_C_y_critic_loss: 0.2344 - P_d_C_y_predictor_loss: 3.0720 - P_y_C_l_critic_loss: 0.5576 - P_y_C_l_predictor_loss: -1.9321 - P_l_C_y_critic_loss: 1.0537 - P_l_C_y_predictor_loss: 1.8661 - val_loss: -3.0493 - val_P_y_C_d_critic_loss: 0.4035 - val_P_y_C_d_predictor_loss: -2.0359 - val_P_d_C_y_critic_loss: 0.1811 - val_P_d_C_y_predictor_loss: 2.8762 - val_P_y_C_l_critic_loss: 0.3447 - val_P_y_C_l_predictor_loss: -2.0415 - val_P_l_C_y_critic_loss: 0.5580 - val_P_l_C_y_predictor_loss: 1.5912\n",
      "Epoch 45/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -2.9736 - P_y_C_d_critic_loss: 0.7709 - P_y_C_d_predictor_loss: -1.9949 - P_d_C_y_critic_loss: 0.2247 - P_d_C_y_predictor_loss: 3.0633 - P_y_C_l_critic_loss: 0.5603 - P_y_C_l_predictor_loss: -1.9784 - P_l_C_y_critic_loss: 1.1818 - P_l_C_y_predictor_loss: 1.8791supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 152us/sample - loss: -2.9735 - P_y_C_d_critic_loss: 0.7723 - P_y_C_d_predictor_loss: -1.9938 - P_d_C_y_critic_loss: 0.2287 - P_d_C_y_predictor_loss: 3.0639 - P_y_C_l_critic_loss: 0.5632 - P_y_C_l_predictor_loss: -1.9802 - P_l_C_y_critic_loss: 1.1824 - P_l_C_y_predictor_loss: 1.8841 - val_loss: -3.0862 - val_P_y_C_d_critic_loss: 0.4296 - val_P_y_C_d_predictor_loss: -2.0714 - val_P_d_C_y_critic_loss: 0.2715 - val_P_d_C_y_predictor_loss: 2.8935 - val_P_y_C_l_critic_loss: 0.4102 - val_P_y_C_l_predictor_loss: -2.0476 - val_P_l_C_y_critic_loss: 0.7852 - val_P_l_C_y_predictor_loss: 1.6394\n",
      "Epoch 46/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.0161 - P_y_C_d_critic_loss: 0.7365 - P_y_C_d_predictor_loss: -2.0205 - P_d_C_y_critic_loss: 0.3419 - P_d_C_y_predictor_loss: 3.0815 - P_y_C_l_critic_loss: 0.5414 - P_y_C_l_predictor_loss: -2.0113 - P_l_C_y_critic_loss: 1.2739 - P_l_C_y_predictor_loss: 1.9347supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 147us/sample - loss: -3.0179 - P_y_C_d_critic_loss: 0.7403 - P_y_C_d_predictor_loss: -2.0230 - P_d_C_y_critic_loss: 0.3376 - P_d_C_y_predictor_loss: 3.0841 - P_y_C_l_critic_loss: 0.5449 - P_y_C_l_predictor_loss: -2.0149 - P_l_C_y_critic_loss: 1.2743 - P_l_C_y_predictor_loss: 1.9355 - val_loss: -3.0919 - val_P_y_C_d_critic_loss: 0.3908 - val_P_y_C_d_predictor_loss: -2.0795 - val_P_d_C_y_critic_loss: 0.2548 - val_P_d_C_y_predictor_loss: 2.8762 - val_P_y_C_l_critic_loss: 0.3605 - val_P_y_C_l_predictor_loss: -2.0402 - val_P_l_C_y_critic_loss: 0.5734 - val_P_l_C_y_predictor_loss: 1.5860\n",
      "Epoch 47/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.0254 - P_y_C_d_critic_loss: 0.7367 - P_y_C_d_predictor_loss: -2.0272 - P_d_C_y_critic_loss: 0.3345 - P_d_C_y_predictor_loss: 3.1089 - P_y_C_l_critic_loss: 0.5431 - P_y_C_l_predictor_loss: -2.0166 - P_l_C_y_critic_loss: 1.2194 - P_l_C_y_predictor_loss: 1.9716supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 147us/sample - loss: -3.0224 - P_y_C_d_critic_loss: 0.7358 - P_y_C_d_predictor_loss: -2.0223 - P_d_C_y_critic_loss: 0.3426 - P_d_C_y_predictor_loss: 3.1115 - P_y_C_l_critic_loss: 0.5487 - P_y_C_l_predictor_loss: -2.0129 - P_l_C_y_critic_loss: 1.2120 - P_l_C_y_predictor_loss: 1.9675 - val_loss: -3.0292 - val_P_y_C_d_critic_loss: 0.3511 - val_P_y_C_d_predictor_loss: -2.0313 - val_P_d_C_y_critic_loss: 0.2724 - val_P_d_C_y_predictor_loss: 2.9003 - val_P_y_C_l_critic_loss: 0.3536 - val_P_y_C_l_predictor_loss: -2.0099 - val_P_l_C_y_critic_loss: 0.6557 - val_P_l_C_y_predictor_loss: 1.6383\n",
      "Epoch 48/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.0741 - P_y_C_d_critic_loss: 0.7049 - P_y_C_d_predictor_loss: -2.0581 - P_d_C_y_critic_loss: 0.3998 - P_d_C_y_predictor_loss: 3.1319 - P_y_C_l_critic_loss: 0.5560 - P_y_C_l_predictor_loss: -2.0516 - P_l_C_y_critic_loss: 1.2998 - P_l_C_y_predictor_loss: 2.0138supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 150us/sample - loss: -3.0750 - P_y_C_d_critic_loss: 0.7024 - P_y_C_d_predictor_loss: -2.0598 - P_d_C_y_critic_loss: 0.3987 - P_d_C_y_predictor_loss: 3.1428 - P_y_C_l_critic_loss: 0.5539 - P_y_C_l_predictor_loss: -2.0521 - P_l_C_y_critic_loss: 1.3283 - P_l_C_y_predictor_loss: 2.0141 - val_loss: -3.1840 - val_P_y_C_d_critic_loss: 0.3713 - val_P_y_C_d_predictor_loss: -2.1412 - val_P_d_C_y_critic_loss: 0.3527 - val_P_d_C_y_predictor_loss: 2.9248 - val_P_y_C_l_critic_loss: 0.3808 - val_P_y_C_l_predictor_loss: -2.1012 - val_P_l_C_y_critic_loss: 0.7148 - val_P_l_C_y_predictor_loss: 1.6481\n",
      "Epoch 49/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.0365 - P_y_C_d_critic_loss: 0.6889 - P_y_C_d_predictor_loss: -2.0358 - P_d_C_y_critic_loss: 0.4129 - P_d_C_y_predictor_loss: 3.1643 - P_y_C_l_critic_loss: 0.5439 - P_y_C_l_predictor_loss: -2.0206 - P_l_C_y_critic_loss: 1.3372 - P_l_C_y_predictor_loss: 2.0089supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 156us/sample - loss: -3.0431 - P_y_C_d_critic_loss: 0.6914 - P_y_C_d_predictor_loss: -2.0456 - P_d_C_y_critic_loss: 0.4079 - P_d_C_y_predictor_loss: 3.1671 - P_y_C_l_critic_loss: 0.5431 - P_y_C_l_predictor_loss: -2.0312 - P_l_C_y_critic_loss: 1.3439 - P_l_C_y_predictor_loss: 2.0119 - val_loss: -3.2055 - val_P_y_C_d_critic_loss: 0.3291 - val_P_y_C_d_predictor_loss: -2.1416 - val_P_d_C_y_critic_loss: 0.3610 - val_P_d_C_y_predictor_loss: 2.9164 - val_P_y_C_l_critic_loss: 0.3548 - val_P_y_C_l_predictor_loss: -2.1418 - val_P_l_C_y_critic_loss: 0.7224 - val_P_l_C_y_predictor_loss: 1.6389\n",
      "Epoch 50/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.1471 - P_y_C_d_critic_loss: 0.6648 - P_y_C_d_predictor_loss: -2.1059 - P_d_C_y_critic_loss: 0.5116 - P_d_C_y_predictor_loss: 3.1806 - P_y_C_l_critic_loss: 0.5216 - P_y_C_l_predictor_loss: -2.1008 - P_l_C_y_critic_loss: 1.4272 - P_l_C_y_predictor_loss: 2.0791supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 142us/sample - loss: -3.1471 - P_y_C_d_critic_loss: 0.6631 - P_y_C_d_predictor_loss: -2.1058 - P_d_C_y_critic_loss: 0.5133 - P_d_C_y_predictor_loss: 3.1788 - P_y_C_l_critic_loss: 0.5203 - P_y_C_l_predictor_loss: -2.1013 - P_l_C_y_critic_loss: 1.4176 - P_l_C_y_predictor_loss: 2.0783 - val_loss: -3.0079 - val_P_y_C_d_critic_loss: 0.3701 - val_P_y_C_d_predictor_loss: -2.0261 - val_P_d_C_y_critic_loss: 0.5051 - val_P_d_C_y_predictor_loss: 2.9236 - val_P_y_C_l_critic_loss: 0.3453 - val_P_y_C_l_predictor_loss: -1.9793 - val_P_l_C_y_critic_loss: 0.8080 - val_P_l_C_y_predictor_loss: 1.7031\n",
      "Epoch 51/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.0824 - P_y_C_d_critic_loss: 0.6857 - P_y_C_d_predictor_loss: -2.0649 - P_d_C_y_critic_loss: 0.5044 - P_d_C_y_predictor_loss: 3.2154 - P_y_C_l_critic_loss: 0.5442 - P_y_C_l_predictor_loss: -2.0542 - P_l_C_y_critic_loss: 1.4328 - P_l_C_y_predictor_loss: 2.0923supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 153us/sample - loss: -3.0837 - P_y_C_d_critic_loss: 0.6842 - P_y_C_d_predictor_loss: -2.0674 - P_d_C_y_critic_loss: 0.5062 - P_d_C_y_predictor_loss: 3.2126 - P_y_C_l_critic_loss: 0.5410 - P_y_C_l_predictor_loss: -2.0549 - P_l_C_y_critic_loss: 1.4359 - P_l_C_y_predictor_loss: 2.0916 - val_loss: -3.2202 - val_P_y_C_d_critic_loss: 0.3460 - val_P_y_C_d_predictor_loss: -2.1545 - val_P_d_C_y_critic_loss: 0.6052 - val_P_d_C_y_predictor_loss: 2.9667 - val_P_y_C_l_critic_loss: 0.3507 - val_P_y_C_l_predictor_loss: -2.1459 - val_P_l_C_y_critic_loss: 1.0819 - val_P_l_C_y_predictor_loss: 1.7849\n",
      "Epoch 52/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.0470 - P_y_C_d_critic_loss: 0.7302 - P_y_C_d_predictor_loss: -2.0392 - P_d_C_y_critic_loss: 0.5278 - P_d_C_y_predictor_loss: 3.2324 - P_y_C_l_critic_loss: 0.5115 - P_y_C_l_predictor_loss: -2.0352 - P_l_C_y_critic_loss: 1.5237 - P_l_C_y_predictor_loss: 2.1009supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 146us/sample - loss: -3.0482 - P_y_C_d_critic_loss: 0.7365 - P_y_C_d_predictor_loss: -2.0412 - P_d_C_y_critic_loss: 0.5293 - P_d_C_y_predictor_loss: 3.2330 - P_y_C_l_critic_loss: 0.5132 - P_y_C_l_predictor_loss: -2.0371 - P_l_C_y_critic_loss: 1.5259 - P_l_C_y_predictor_loss: 2.1018 - val_loss: -3.1906 - val_P_y_C_d_critic_loss: 0.3318 - val_P_y_C_d_predictor_loss: -2.1279 - val_P_d_C_y_critic_loss: 0.5187 - val_P_d_C_y_predictor_loss: 2.9454 - val_P_y_C_l_critic_loss: 0.2916 - val_P_y_C_l_predictor_loss: -2.1389 - val_P_l_C_y_critic_loss: 0.8506 - val_P_l_C_y_predictor_loss: 1.7734\n",
      "Epoch 53/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.1634 - P_y_C_d_critic_loss: 0.6664 - P_y_C_d_predictor_loss: -2.1167 - P_d_C_y_critic_loss: 0.5839 - P_d_C_y_predictor_loss: 3.2311 - P_y_C_l_critic_loss: 0.4718 - P_y_C_l_predictor_loss: -2.1113 - P_l_C_y_critic_loss: 1.5775 - P_l_C_y_predictor_loss: 2.1726supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 142us/sample - loss: -3.1641 - P_y_C_d_critic_loss: 0.6677 - P_y_C_d_predictor_loss: -2.1169 - P_d_C_y_critic_loss: 0.5795 - P_d_C_y_predictor_loss: 3.2286 - P_y_C_l_critic_loss: 0.4713 - P_y_C_l_predictor_loss: -2.1143 - P_l_C_y_critic_loss: 1.5989 - P_l_C_y_predictor_loss: 2.1721 - val_loss: -3.0395 - val_P_y_C_d_critic_loss: 0.4266 - val_P_y_C_d_predictor_loss: -2.0274 - val_P_d_C_y_critic_loss: 0.4984 - val_P_d_C_y_predictor_loss: 2.9462 - val_P_y_C_l_critic_loss: 0.3428 - val_P_y_C_l_predictor_loss: -2.0406 - val_P_l_C_y_critic_loss: 0.7359 - val_P_l_C_y_predictor_loss: 1.6750\n",
      "Epoch 54/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.1609 - P_y_C_d_critic_loss: 0.7001 - P_y_C_d_predictor_loss: -2.1164 - P_d_C_y_critic_loss: 0.6177 - P_d_C_y_predictor_loss: 3.2346 - P_y_C_l_critic_loss: 0.5061 - P_y_C_l_predictor_loss: -2.1081 - P_l_C_y_critic_loss: 1.6932 - P_l_C_y_predictor_loss: 2.2094supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 148us/sample - loss: -3.1622 - P_y_C_d_critic_loss: 0.7002 - P_y_C_d_predictor_loss: -2.1183 - P_d_C_y_critic_loss: 0.6195 - P_d_C_y_predictor_loss: 3.2408 - P_y_C_l_critic_loss: 0.5074 - P_y_C_l_predictor_loss: -2.1102 - P_l_C_y_critic_loss: 1.7108 - P_l_C_y_predictor_loss: 2.2146 - val_loss: -3.3263 - val_P_y_C_d_critic_loss: 0.3183 - val_P_y_C_d_predictor_loss: -2.2276 - val_P_d_C_y_critic_loss: 0.5140 - val_P_d_C_y_predictor_loss: 2.9627 - val_P_y_C_l_critic_loss: 0.3010 - val_P_y_C_l_predictor_loss: -2.2112 - val_P_l_C_y_critic_loss: 0.9297 - val_P_l_C_y_predictor_loss: 1.7689\n",
      "Epoch 55/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.1927 - P_y_C_d_critic_loss: 0.6650 - P_y_C_d_predictor_loss: -2.1366 - P_d_C_y_critic_loss: 0.6963 - P_d_C_y_predictor_loss: 3.2752 - P_y_C_l_critic_loss: 0.5108 - P_y_C_l_predictor_loss: -2.1307 - P_l_C_y_critic_loss: 1.7104 - P_l_C_y_predictor_loss: 2.2258supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 142us/sample - loss: -3.1911 - P_y_C_d_critic_loss: 0.6619 - P_y_C_d_predictor_loss: -2.1348 - P_d_C_y_critic_loss: 0.6909 - P_d_C_y_predictor_loss: 3.2664 - P_y_C_l_critic_loss: 0.5120 - P_y_C_l_predictor_loss: -2.1269 - P_l_C_y_critic_loss: 1.6991 - P_l_C_y_predictor_loss: 2.2266 - val_loss: -3.2861 - val_P_y_C_d_critic_loss: 0.3130 - val_P_y_C_d_predictor_loss: -2.2007 - val_P_d_C_y_critic_loss: 0.6501 - val_P_d_C_y_predictor_loss: 2.9972 - val_P_y_C_l_critic_loss: 0.3260 - val_P_y_C_l_predictor_loss: -2.1847 - val_P_l_C_y_critic_loss: 0.9428 - val_P_l_C_y_predictor_loss: 1.8243\n",
      "Epoch 56/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.2538 - P_y_C_d_critic_loss: 0.6401 - P_y_C_d_predictor_loss: -2.1786 - P_d_C_y_critic_loss: 0.7468 - P_d_C_y_predictor_loss: 3.3054 - P_y_C_l_critic_loss: 0.4785 - P_y_C_l_predictor_loss: -2.1680 - P_l_C_y_critic_loss: 1.8151 - P_l_C_y_predictor_loss: 2.2905supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 150us/sample - loss: -3.2550 - P_y_C_d_critic_loss: 0.6424 - P_y_C_d_predictor_loss: -2.1795 - P_d_C_y_critic_loss: 0.7473 - P_d_C_y_predictor_loss: 3.3115 - P_y_C_l_critic_loss: 0.4757 - P_y_C_l_predictor_loss: -2.1716 - P_l_C_y_critic_loss: 1.8129 - P_l_C_y_predictor_loss: 2.2888 - val_loss: -3.3778 - val_P_y_C_d_critic_loss: 0.2969 - val_P_y_C_d_predictor_loss: -2.2546 - val_P_d_C_y_critic_loss: 0.6728 - val_P_d_C_y_predictor_loss: 3.0129 - val_P_y_C_l_critic_loss: 0.2777 - val_P_y_C_l_predictor_loss: -2.2601 - val_P_l_C_y_critic_loss: 1.0267 - val_P_l_C_y_predictor_loss: 1.8557\n",
      "Epoch 57/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.2338 - P_y_C_d_critic_loss: 0.6429 - P_y_C_d_predictor_loss: -2.1634 - P_d_C_y_critic_loss: 0.7818 - P_d_C_y_predictor_loss: 3.3348 - P_y_C_l_critic_loss: 0.4787 - P_y_C_l_predictor_loss: -2.1584 - P_l_C_y_critic_loss: 1.7783 - P_l_C_y_predictor_loss: 2.3270supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 146us/sample - loss: -3.2305 - P_y_C_d_critic_loss: 0.6457 - P_y_C_d_predictor_loss: -2.1578 - P_d_C_y_critic_loss: 0.7830 - P_d_C_y_predictor_loss: 3.3339 - P_y_C_l_critic_loss: 0.4787 - P_y_C_l_predictor_loss: -2.1546 - P_l_C_y_critic_loss: 1.7811 - P_l_C_y_predictor_loss: 2.3257 - val_loss: -3.2938 - val_P_y_C_d_critic_loss: 0.3239 - val_P_y_C_d_predictor_loss: -2.2125 - val_P_d_C_y_critic_loss: 0.8943 - val_P_d_C_y_predictor_loss: 3.0269 - val_P_y_C_l_critic_loss: 0.3226 - val_P_y_C_l_predictor_loss: -2.1765 - val_P_l_C_y_critic_loss: 1.2109 - val_P_l_C_y_predictor_loss: 1.9227\n",
      "Epoch 58/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.2336 - P_y_C_d_critic_loss: 0.6818 - P_y_C_d_predictor_loss: -2.1629 - P_d_C_y_critic_loss: 0.8347 - P_d_C_y_predictor_loss: 3.3564 - P_y_C_l_critic_loss: 0.4932 - P_y_C_l_predictor_loss: -2.1598 - P_l_C_y_critic_loss: 1.8186 - P_l_C_y_predictor_loss: 2.3514supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 142us/sample - loss: -3.2358 - P_y_C_d_critic_loss: 0.6865 - P_y_C_d_predictor_loss: -2.1669 - P_d_C_y_critic_loss: 0.8345 - P_d_C_y_predictor_loss: 3.3519 - P_y_C_l_critic_loss: 0.4925 - P_y_C_l_predictor_loss: -2.1624 - P_l_C_y_critic_loss: 1.8311 - P_l_C_y_predictor_loss: 2.3490 - val_loss: -3.3477 - val_P_y_C_d_critic_loss: 0.3114 - val_P_y_C_d_predictor_loss: -2.2482 - val_P_d_C_y_critic_loss: 0.7835 - val_P_d_C_y_predictor_loss: 3.0165 - val_P_y_C_l_critic_loss: 0.3098 - val_P_y_C_l_predictor_loss: -2.2127 - val_P_l_C_y_critic_loss: 1.1378 - val_P_l_C_y_predictor_loss: 1.8886\n",
      "Epoch 59/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.2539 - P_y_C_d_critic_loss: 0.6969 - P_y_C_d_predictor_loss: -2.1802 - P_d_C_y_critic_loss: 0.8441 - P_d_C_y_predictor_loss: 3.3740 - P_y_C_l_critic_loss: 0.4606 - P_y_C_l_predictor_loss: -2.1660 - P_l_C_y_critic_loss: 1.9911 - P_l_C_y_predictor_loss: 2.4192supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 150us/sample - loss: -3.2520 - P_y_C_d_critic_loss: 0.6928 - P_y_C_d_predictor_loss: -2.1779 - P_d_C_y_critic_loss: 0.8433 - P_d_C_y_predictor_loss: 3.3724 - P_y_C_l_critic_loss: 0.4580 - P_y_C_l_predictor_loss: -2.1618 - P_l_C_y_critic_loss: 1.9905 - P_l_C_y_predictor_loss: 2.4228 - val_loss: -3.2998 - val_P_y_C_d_critic_loss: 0.3774 - val_P_y_C_d_predictor_loss: -2.1968 - val_P_d_C_y_critic_loss: 0.8222 - val_P_d_C_y_predictor_loss: 3.0322 - val_P_y_C_l_critic_loss: 0.2829 - val_P_y_C_l_predictor_loss: -2.2211 - val_P_l_C_y_critic_loss: 1.0697 - val_P_l_C_y_predictor_loss: 1.9023\n",
      "Epoch 60/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.2780 - P_y_C_d_critic_loss: 0.6835 - P_y_C_d_predictor_loss: -2.1918 - P_d_C_y_critic_loss: 0.9001 - P_d_C_y_predictor_loss: 3.3959 - P_y_C_l_critic_loss: 0.4701 - P_y_C_l_predictor_loss: -2.1907 - P_l_C_y_critic_loss: 1.9808 - P_l_C_y_predictor_loss: 2.4402supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 142us/sample - loss: -3.2810 - P_y_C_d_critic_loss: 0.6802 - P_y_C_d_predictor_loss: -2.1967 - P_d_C_y_critic_loss: 0.9000 - P_d_C_y_predictor_loss: 3.3973 - P_y_C_l_critic_loss: 0.4677 - P_y_C_l_predictor_loss: -2.1945 - P_l_C_y_critic_loss: 2.0004 - P_l_C_y_predictor_loss: 2.4376 - val_loss: -3.3504 - val_P_y_C_d_critic_loss: 0.4087 - val_P_y_C_d_predictor_loss: -2.2394 - val_P_d_C_y_critic_loss: 0.8075 - val_P_d_C_y_predictor_loss: 3.0878 - val_P_y_C_l_critic_loss: 0.2645 - val_P_y_C_l_predictor_loss: -2.2377 - val_P_l_C_y_critic_loss: 1.2382 - val_P_l_C_y_predictor_loss: 1.9385\n",
      "Epoch 61/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.0023 - P_y_C_d_critic_loss: 0.7610 - P_y_C_d_predictor_loss: -2.0085 - P_d_C_y_critic_loss: 0.8561 - P_d_C_y_predictor_loss: 3.4107 - P_y_C_l_critic_loss: 0.4786 - P_y_C_l_predictor_loss: -2.0076 - P_l_C_y_critic_loss: 1.8446 - P_l_C_y_predictor_loss: 2.3897supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 142us/sample - loss: -2.9942 - P_y_C_d_critic_loss: 0.7560 - P_y_C_d_predictor_loss: -1.9960 - P_d_C_y_critic_loss: 0.8510 - P_d_C_y_predictor_loss: 3.4075 - P_y_C_l_critic_loss: 0.4804 - P_y_C_l_predictor_loss: -1.9953 - P_l_C_y_critic_loss: 1.8127 - P_l_C_y_predictor_loss: 2.3846 - val_loss: -2.6770 - val_P_y_C_d_critic_loss: 0.5487 - val_P_y_C_d_predictor_loss: -1.7638 - val_P_d_C_y_critic_loss: 0.6612 - val_P_d_C_y_predictor_loss: 3.0189 - val_P_y_C_l_critic_loss: 0.3593 - val_P_y_C_l_predictor_loss: -1.8441 - val_P_l_C_y_critic_loss: 0.8897 - val_P_l_C_y_predictor_loss: 1.8509\n",
      "Epoch 62/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.0066 - P_y_C_d_critic_loss: 0.8321 - P_y_C_d_predictor_loss: -2.0171 - P_d_C_y_critic_loss: 0.6651 - P_d_C_y_predictor_loss: 3.2805 - P_y_C_l_critic_loss: 0.4955 - P_y_C_l_predictor_loss: -2.0006 - P_l_C_y_critic_loss: 1.6484 - P_l_C_y_predictor_loss: 2.2363supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 141us/sample - loss: -3.0138 - P_y_C_d_critic_loss: 0.8300 - P_y_C_d_predictor_loss: -2.0285 - P_d_C_y_critic_loss: 0.6630 - P_d_C_y_predictor_loss: 3.2802 - P_y_C_l_critic_loss: 0.4952 - P_y_C_l_predictor_loss: -2.0109 - P_l_C_y_critic_loss: 1.6485 - P_l_C_y_predictor_loss: 2.2385 - val_loss: -3.3091 - val_P_y_C_d_critic_loss: 0.4736 - val_P_y_C_d_predictor_loss: -2.2097 - val_P_d_C_y_critic_loss: 0.7004 - val_P_d_C_y_predictor_loss: 2.9697 - val_P_y_C_l_critic_loss: 0.2943 - val_P_y_C_l_predictor_loss: -2.2157 - val_P_l_C_y_critic_loss: 0.9129 - val_P_l_C_y_predictor_loss: 1.8409\n",
      "Epoch 63/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.2636 - P_y_C_d_critic_loss: 0.6742 - P_y_C_d_predictor_loss: -2.1847 - P_d_C_y_critic_loss: 0.7686 - P_d_C_y_predictor_loss: 3.2811 - P_y_C_l_critic_loss: 0.4455 - P_y_C_l_predictor_loss: -2.1756 - P_l_C_y_critic_loss: 1.6735 - P_l_C_y_predictor_loss: 2.2791supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 148us/sample - loss: -3.2679 - P_y_C_d_critic_loss: 0.6713 - P_y_C_d_predictor_loss: -2.1911 - P_d_C_y_critic_loss: 0.7677 - P_d_C_y_predictor_loss: 3.2806 - P_y_C_l_critic_loss: 0.4436 - P_y_C_l_predictor_loss: -2.1827 - P_l_C_y_critic_loss: 1.6646 - P_l_C_y_predictor_loss: 2.2831 - val_loss: -3.4438 - val_P_y_C_d_critic_loss: 0.3200 - val_P_y_C_d_predictor_loss: -2.3043 - val_P_d_C_y_critic_loss: 0.8254 - val_P_d_C_y_predictor_loss: 3.0126 - val_P_y_C_l_critic_loss: 0.2517 - val_P_y_C_l_predictor_loss: -2.2930 - val_P_l_C_y_critic_loss: 1.0885 - val_P_l_C_y_predictor_loss: 1.9513\n",
      "Epoch 64/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.3597 - P_y_C_d_critic_loss: 0.6244 - P_y_C_d_predictor_loss: -2.2506 - P_d_C_y_critic_loss: 0.9385 - P_d_C_y_predictor_loss: 3.3613 - P_y_C_l_critic_loss: 0.4375 - P_y_C_l_predictor_loss: -2.2350 - P_l_C_y_critic_loss: 1.8874 - P_l_C_y_predictor_loss: 2.4181supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 148us/sample - loss: -3.3579 - P_y_C_d_critic_loss: 0.6249 - P_y_C_d_predictor_loss: -2.2476 - P_d_C_y_critic_loss: 0.9494 - P_d_C_y_predictor_loss: 3.3764 - P_y_C_l_critic_loss: 0.4367 - P_y_C_l_predictor_loss: -2.2330 - P_l_C_y_critic_loss: 1.8972 - P_l_C_y_predictor_loss: 2.4139 - val_loss: -3.4804 - val_P_y_C_d_critic_loss: 0.2986 - val_P_y_C_d_predictor_loss: -2.3307 - val_P_d_C_y_critic_loss: 0.9079 - val_P_d_C_y_predictor_loss: 3.0692 - val_P_y_C_l_critic_loss: 0.2515 - val_P_y_C_l_predictor_loss: -2.3130 - val_P_l_C_y_critic_loss: 1.2742 - val_P_l_C_y_predictor_loss: 2.0099\n",
      "Epoch 65/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.3268 - P_y_C_d_critic_loss: 0.6363 - P_y_C_d_predictor_loss: -2.2254 - P_d_C_y_critic_loss: 1.0105 - P_d_C_y_predictor_loss: 3.4362 - P_y_C_l_critic_loss: 0.4268 - P_y_C_l_predictor_loss: -2.2199 - P_l_C_y_critic_loss: 2.1667 - P_l_C_y_predictor_loss: 2.4971supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 146us/sample - loss: -3.3286 - P_y_C_d_critic_loss: 0.6350 - P_y_C_d_predictor_loss: -2.2282 - P_d_C_y_critic_loss: 1.0130 - P_d_C_y_predictor_loss: 3.4386 - P_y_C_l_critic_loss: 0.4256 - P_y_C_l_predictor_loss: -2.2225 - P_l_C_y_critic_loss: 2.1501 - P_l_C_y_predictor_loss: 2.4980 - val_loss: -3.3545 - val_P_y_C_d_critic_loss: 0.3281 - val_P_y_C_d_predictor_loss: -2.2457 - val_P_d_C_y_critic_loss: 1.1125 - val_P_d_C_y_predictor_loss: 3.0832 - val_P_y_C_l_critic_loss: 0.2812 - val_P_y_C_l_predictor_loss: -2.2319 - val_P_l_C_y_critic_loss: 1.4322 - val_P_l_C_y_predictor_loss: 2.0840\n",
      "Epoch 66/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.3424 - P_y_C_d_critic_loss: 0.6381 - P_y_C_d_predictor_loss: -2.2388 - P_d_C_y_critic_loss: 1.0439 - P_d_C_y_predictor_loss: 3.4641 - P_y_C_l_critic_loss: 0.4157 - P_y_C_l_predictor_loss: -2.2241 - P_l_C_y_critic_loss: 2.1781 - P_l_C_y_predictor_loss: 2.5584supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 143us/sample - loss: -3.3423 - P_y_C_d_critic_loss: 0.6385 - P_y_C_d_predictor_loss: -2.2390 - P_d_C_y_critic_loss: 1.0490 - P_d_C_y_predictor_loss: 3.4664 - P_y_C_l_critic_loss: 0.4169 - P_y_C_l_predictor_loss: -2.2232 - P_l_C_y_critic_loss: 2.1682 - P_l_C_y_predictor_loss: 2.5606 - val_loss: -3.4832 - val_P_y_C_d_critic_loss: 0.3160 - val_P_y_C_d_predictor_loss: -2.3297 - val_P_d_C_y_critic_loss: 1.1245 - val_P_d_C_y_predictor_loss: 3.1064 - val_P_y_C_l_critic_loss: 0.2456 - val_P_y_C_l_predictor_loss: -2.3212 - val_P_l_C_y_critic_loss: 1.4603 - val_P_l_C_y_predictor_loss: 2.1537\n",
      "Epoch 67/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.4085 - P_y_C_d_critic_loss: 0.6149 - P_y_C_d_predictor_loss: -2.2854 - P_d_C_y_critic_loss: 1.1512 - P_d_C_y_predictor_loss: 3.5349 - P_y_C_l_critic_loss: 0.4161 - P_y_C_l_predictor_loss: -2.2626 - P_l_C_y_critic_loss: 2.3489 - P_l_C_y_predictor_loss: 2.6557supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 143us/sample - loss: -3.4134 - P_y_C_d_critic_loss: 0.6120 - P_y_C_d_predictor_loss: -2.2928 - P_d_C_y_critic_loss: 1.1671 - P_d_C_y_predictor_loss: 3.5393 - P_y_C_l_critic_loss: 0.4182 - P_y_C_l_predictor_loss: -2.2701 - P_l_C_y_critic_loss: 2.3699 - P_l_C_y_predictor_loss: 2.6633 - val_loss: -3.3610 - val_P_y_C_d_critic_loss: 0.3041 - val_P_y_C_d_predictor_loss: -2.2402 - val_P_d_C_y_critic_loss: 1.3226 - val_P_d_C_y_predictor_loss: 3.1659 - val_P_y_C_l_critic_loss: 0.2213 - val_P_y_C_l_predictor_loss: -2.2542 - val_P_l_C_y_critic_loss: 1.7878 - val_P_l_C_y_predictor_loss: 2.2808\n",
      "Epoch 68/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.3731 - P_y_C_d_critic_loss: 0.6349 - P_y_C_d_predictor_loss: -2.2613 - P_d_C_y_critic_loss: 1.2415 - P_d_C_y_predictor_loss: 3.5650 - P_y_C_l_critic_loss: 0.4253 - P_y_C_l_predictor_loss: -2.2405 - P_l_C_y_critic_loss: 2.4858 - P_l_C_y_predictor_loss: 2.7261supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 144us/sample - loss: -3.3723 - P_y_C_d_critic_loss: 0.6321 - P_y_C_d_predictor_loss: -2.2597 - P_d_C_y_critic_loss: 1.2431 - P_d_C_y_predictor_loss: 3.5665 - P_y_C_l_critic_loss: 0.4245 - P_y_C_l_predictor_loss: -2.2402 - P_l_C_y_critic_loss: 2.4986 - P_l_C_y_predictor_loss: 2.7307 - val_loss: -3.4046 - val_P_y_C_d_critic_loss: 0.3406 - val_P_y_C_d_predictor_loss: -2.2822 - val_P_d_C_y_critic_loss: 1.1160 - val_P_d_C_y_predictor_loss: 3.1595 - val_P_y_C_l_critic_loss: 0.2623 - val_P_y_C_l_predictor_loss: -2.2589 - val_P_l_C_y_critic_loss: 1.3643 - val_P_l_C_y_predictor_loss: 2.1411\n",
      "Epoch 69/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.3378 - P_y_C_d_critic_loss: 0.6267 - P_y_C_d_predictor_loss: -2.2369 - P_d_C_y_critic_loss: 1.2121 - P_d_C_y_predictor_loss: 3.5570 - P_y_C_l_critic_loss: 0.4010 - P_y_C_l_predictor_loss: -2.2183 - P_l_C_y_critic_loss: 2.4381 - P_l_C_y_predictor_loss: 2.7103supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 149us/sample - loss: -3.3407 - P_y_C_d_critic_loss: 0.6271 - P_y_C_d_predictor_loss: -2.2414 - P_d_C_y_critic_loss: 1.2210 - P_d_C_y_predictor_loss: 3.5623 - P_y_C_l_critic_loss: 0.4021 - P_y_C_l_predictor_loss: -2.2226 - P_l_C_y_critic_loss: 2.4483 - P_l_C_y_predictor_loss: 2.7095 - val_loss: -3.5364 - val_P_y_C_d_critic_loss: 0.2789 - val_P_y_C_d_predictor_loss: -2.3674 - val_P_d_C_y_critic_loss: 1.2941 - val_P_d_C_y_predictor_loss: 3.1774 - val_P_y_C_l_critic_loss: 0.2229 - val_P_y_C_l_predictor_loss: -2.3510 - val_P_l_C_y_critic_loss: 1.8582 - val_P_l_C_y_predictor_loss: 2.2723\n",
      "Epoch 70/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.3889 - P_y_C_d_critic_loss: 0.6161 - P_y_C_d_predictor_loss: -2.2682 - P_d_C_y_critic_loss: 1.2505 - P_d_C_y_predictor_loss: 3.5813 - P_y_C_l_critic_loss: 0.4132 - P_y_C_l_predictor_loss: -2.2578 - P_l_C_y_critic_loss: 2.5896 - P_l_C_y_predictor_loss: 2.7116supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 142us/sample - loss: -3.3923 - P_y_C_d_critic_loss: 0.6181 - P_y_C_d_predictor_loss: -2.2730 - P_d_C_y_critic_loss: 1.2483 - P_d_C_y_predictor_loss: 3.5826 - P_y_C_l_critic_loss: 0.4104 - P_y_C_l_predictor_loss: -2.2638 - P_l_C_y_critic_loss: 2.5724 - P_l_C_y_predictor_loss: 2.7093 - val_loss: -3.5055 - val_P_y_C_d_critic_loss: 0.2784 - val_P_y_C_d_predictor_loss: -2.3428 - val_P_d_C_y_critic_loss: 1.1959 - val_P_d_C_y_predictor_loss: 3.1612 - val_P_y_C_l_critic_loss: 0.2101 - val_P_y_C_l_predictor_loss: -2.3384 - val_P_l_C_y_critic_loss: 1.4331 - val_P_l_C_y_predictor_loss: 2.1853\n",
      "Epoch 71/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.4053 - P_y_C_d_critic_loss: 0.6444 - P_y_C_d_predictor_loss: -2.2831 - P_d_C_y_critic_loss: 1.2270 - P_d_C_y_predictor_loss: 3.6059 - P_y_C_l_critic_loss: 0.4045 - P_y_C_l_predictor_loss: -2.2614 - P_l_C_y_critic_loss: 2.4276 - P_l_C_y_predictor_loss: 2.7330supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 134us/sample - loss: -3.4068 - P_y_C_d_critic_loss: 0.6407 - P_y_C_d_predictor_loss: -2.2851 - P_d_C_y_critic_loss: 1.2306 - P_d_C_y_predictor_loss: 3.6036 - P_y_C_l_critic_loss: 0.4040 - P_y_C_l_predictor_loss: -2.2639 - P_l_C_y_critic_loss: 2.4151 - P_l_C_y_predictor_loss: 2.7249 - val_loss: -3.5129 - val_P_y_C_d_critic_loss: 0.3105 - val_P_y_C_d_predictor_loss: -2.3450 - val_P_d_C_y_critic_loss: 1.2473 - val_P_d_C_y_predictor_loss: 3.1942 - val_P_y_C_l_critic_loss: 0.2220 - val_P_y_C_l_predictor_loss: -2.3494 - val_P_l_C_y_critic_loss: 1.5831 - val_P_l_C_y_predictor_loss: 2.2240\n",
      "Epoch 72/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.4425 - P_y_C_d_critic_loss: 0.5962 - P_y_C_d_predictor_loss: -2.3067 - P_d_C_y_critic_loss: 1.3075 - P_d_C_y_predictor_loss: 3.6476 - P_y_C_l_critic_loss: 0.3909 - P_y_C_l_predictor_loss: -2.2875 - P_l_C_y_critic_loss: 2.4366 - P_l_C_y_predictor_loss: 2.8502supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 134us/sample - loss: -3.4367 - P_y_C_d_critic_loss: 0.5969 - P_y_C_d_predictor_loss: -2.2984 - P_d_C_y_critic_loss: 1.3146 - P_d_C_y_predictor_loss: 3.6492 - P_y_C_l_critic_loss: 0.3915 - P_y_C_l_predictor_loss: -2.2775 - P_l_C_y_critic_loss: 2.4288 - P_l_C_y_predictor_loss: 2.8454 - val_loss: -3.5090 - val_P_y_C_d_critic_loss: 0.4038 - val_P_y_C_d_predictor_loss: -2.3428 - val_P_d_C_y_critic_loss: 1.2864 - val_P_d_C_y_predictor_loss: 3.2197 - val_P_y_C_l_critic_loss: 0.3013 - val_P_y_C_l_predictor_loss: -2.3494 - val_P_l_C_y_critic_loss: 1.5181 - val_P_l_C_y_predictor_loss: 2.2517\n",
      "Epoch 73/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.4349 - P_y_C_d_critic_loss: 0.6236 - P_y_C_d_predictor_loss: -2.2972 - P_d_C_y_critic_loss: 1.3439 - P_d_C_y_predictor_loss: 3.6800 - P_y_C_l_critic_loss: 0.4065 - P_y_C_l_predictor_loss: -2.2919 - P_l_C_y_critic_loss: 2.5464 - P_l_C_y_predictor_loss: 2.8621supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 141us/sample - loss: -3.4351 - P_y_C_d_critic_loss: 0.6278 - P_y_C_d_predictor_loss: -2.2972 - P_d_C_y_critic_loss: 1.3521 - P_d_C_y_predictor_loss: 3.6795 - P_y_C_l_critic_loss: 0.4092 - P_y_C_l_predictor_loss: -2.2929 - P_l_C_y_critic_loss: 2.5570 - P_l_C_y_predictor_loss: 2.8589 - val_loss: -3.5777 - val_P_y_C_d_critic_loss: 0.3026 - val_P_y_C_d_predictor_loss: -2.3962 - val_P_d_C_y_critic_loss: 1.4726 - val_P_d_C_y_predictor_loss: 3.2276 - val_P_y_C_l_critic_loss: 0.2776 - val_P_y_C_l_predictor_loss: -2.3769 - val_P_l_C_y_critic_loss: 1.7003 - val_P_l_C_y_predictor_loss: 2.3749\n",
      "Epoch 74/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.4377 - P_y_C_d_critic_loss: 0.6387 - P_y_C_d_predictor_loss: -2.3005 - P_d_C_y_critic_loss: 1.3717 - P_d_C_y_predictor_loss: 3.7003 - P_y_C_l_critic_loss: 0.4129 - P_y_C_l_predictor_loss: -2.2913 - P_l_C_y_critic_loss: 2.4585 - P_l_C_y_predictor_loss: 2.8853supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 132us/sample - loss: -3.4437 - P_y_C_d_critic_loss: 0.6365 - P_y_C_d_predictor_loss: -2.3091 - P_d_C_y_critic_loss: 1.3736 - P_d_C_y_predictor_loss: 3.7032 - P_y_C_l_critic_loss: 0.4144 - P_y_C_l_predictor_loss: -2.3014 - P_l_C_y_critic_loss: 2.4827 - P_l_C_y_predictor_loss: 2.8875 - val_loss: -3.4561 - val_P_y_C_d_critic_loss: 0.3885 - val_P_y_C_d_predictor_loss: -2.3222 - val_P_d_C_y_critic_loss: 1.3147 - val_P_d_C_y_predictor_loss: 3.2053 - val_P_y_C_l_critic_loss: 0.3143 - val_P_y_C_l_predictor_loss: -2.2835 - val_P_l_C_y_critic_loss: 1.6350 - val_P_l_C_y_predictor_loss: 2.3252\n",
      "Epoch 75/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.5159 - P_y_C_d_critic_loss: 0.6519 - P_y_C_d_predictor_loss: -2.3568 - P_d_C_y_critic_loss: 1.4687 - P_d_C_y_predictor_loss: 3.7093 - P_y_C_l_critic_loss: 0.4520 - P_y_C_l_predictor_loss: -2.3357 - P_l_C_y_critic_loss: 2.6304 - P_l_C_y_predictor_loss: 2.9770supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 137us/sample - loss: -3.5138 - P_y_C_d_critic_loss: 0.6525 - P_y_C_d_predictor_loss: -2.3531 - P_d_C_y_critic_loss: 1.4743 - P_d_C_y_predictor_loss: 3.7121 - P_y_C_l_critic_loss: 0.4511 - P_y_C_l_predictor_loss: -2.3336 - P_l_C_y_critic_loss: 2.6235 - P_l_C_y_predictor_loss: 2.9701 - val_loss: -3.6480 - val_P_y_C_d_critic_loss: 0.2803 - val_P_y_C_d_predictor_loss: -2.4379 - val_P_d_C_y_critic_loss: 1.5116 - val_P_d_C_y_predictor_loss: 3.2821 - val_P_y_C_l_critic_loss: 0.2593 - val_P_y_C_l_predictor_loss: -2.4338 - val_P_l_C_y_critic_loss: 1.7445 - val_P_l_C_y_predictor_loss: 2.4732\n",
      "Epoch 76/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.5078 - P_y_C_d_critic_loss: 0.6401 - P_y_C_d_predictor_loss: -2.3506 - P_d_C_y_critic_loss: 1.5705 - P_d_C_y_predictor_loss: 3.7887 - P_y_C_l_critic_loss: 0.4067 - P_y_C_l_predictor_loss: -2.3314 - P_l_C_y_critic_loss: 2.8593 - P_l_C_y_predictor_loss: 3.0399supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 140us/sample - loss: -3.5044 - P_y_C_d_critic_loss: 0.6421 - P_y_C_d_predictor_loss: -2.3450 - P_d_C_y_critic_loss: 1.5658 - P_d_C_y_predictor_loss: 3.7822 - P_y_C_l_critic_loss: 0.4065 - P_y_C_l_predictor_loss: -2.3271 - P_l_C_y_critic_loss: 2.8575 - P_l_C_y_predictor_loss: 3.0250 - val_loss: -3.3299 - val_P_y_C_d_critic_loss: 0.5137 - val_P_y_C_d_predictor_loss: -2.2246 - val_P_d_C_y_critic_loss: 1.2640 - val_P_d_C_y_predictor_loss: 3.2544 - val_P_y_C_l_critic_loss: 0.3207 - val_P_y_C_l_predictor_loss: -2.2311 - val_P_l_C_y_critic_loss: 1.2335 - val_P_l_C_y_predictor_loss: 2.1245\n",
      "Epoch 77/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.3414 - P_y_C_d_critic_loss: 0.7077 - P_y_C_d_predictor_loss: -2.2396 - P_d_C_y_critic_loss: 1.5117 - P_d_C_y_predictor_loss: 3.7848 - P_y_C_l_critic_loss: 0.4358 - P_y_C_l_predictor_loss: -2.2220 - P_l_C_y_critic_loss: 2.8376 - P_l_C_y_predictor_loss: 2.9791supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 134us/sample - loss: -3.3381 - P_y_C_d_critic_loss: 0.7073 - P_y_C_d_predictor_loss: -2.2352 - P_d_C_y_critic_loss: 1.5034 - P_d_C_y_predictor_loss: 3.7804 - P_y_C_l_critic_loss: 0.4339 - P_y_C_l_predictor_loss: -2.2159 - P_l_C_y_critic_loss: 2.8111 - P_l_C_y_predictor_loss: 2.9663 - val_loss: -3.4695 - val_P_y_C_d_critic_loss: 0.3677 - val_P_y_C_d_predictor_loss: -2.3353 - val_P_d_C_y_critic_loss: 1.1980 - val_P_d_C_y_predictor_loss: 3.2577 - val_P_y_C_l_critic_loss: 0.2805 - val_P_y_C_l_predictor_loss: -2.2841 - val_P_l_C_y_critic_loss: 1.3029 - val_P_l_C_y_predictor_loss: 2.3122\n",
      "Epoch 78/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.4793 - P_y_C_d_critic_loss: 0.6315 - P_y_C_d_predictor_loss: -2.3320 - P_d_C_y_critic_loss: 1.4718 - P_d_C_y_predictor_loss: 3.7099 - P_y_C_l_critic_loss: 0.3946 - P_y_C_l_predictor_loss: -2.3112 - P_l_C_y_critic_loss: 2.7132 - P_l_C_y_predictor_loss: 2.9925supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 134us/sample - loss: -3.4799 - P_y_C_d_critic_loss: 0.6346 - P_y_C_d_predictor_loss: -2.3327 - P_d_C_y_critic_loss: 1.4655 - P_d_C_y_predictor_loss: 3.7023 - P_y_C_l_critic_loss: 0.3937 - P_y_C_l_predictor_loss: -2.3125 - P_l_C_y_critic_loss: 2.6964 - P_l_C_y_predictor_loss: 2.9966 - val_loss: -3.6082 - val_P_y_C_d_critic_loss: 0.3131 - val_P_y_C_d_predictor_loss: -2.4261 - val_P_d_C_y_critic_loss: 1.4004 - val_P_d_C_y_predictor_loss: 3.2680 - val_P_y_C_l_critic_loss: 0.2201 - val_P_y_C_l_predictor_loss: -2.3778 - val_P_l_C_y_critic_loss: 1.6275 - val_P_l_C_y_predictor_loss: 2.4459\n",
      "Epoch 79/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.5226 - P_y_C_d_critic_loss: 0.6575 - P_y_C_d_predictor_loss: -2.3601 - P_d_C_y_critic_loss: 1.5298 - P_d_C_y_predictor_loss: 3.7596 - P_y_C_l_critic_loss: 0.4138 - P_y_C_l_predictor_loss: -2.3423 - P_l_C_y_critic_loss: 2.7389 - P_l_C_y_predictor_loss: 3.0416supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 137us/sample - loss: -3.5271 - P_y_C_d_critic_loss: 0.6548 - P_y_C_d_predictor_loss: -2.3673 - P_d_C_y_critic_loss: 1.5288 - P_d_C_y_predictor_loss: 3.7626 - P_y_C_l_critic_loss: 0.4127 - P_y_C_l_predictor_loss: -2.3486 - P_l_C_y_critic_loss: 2.7586 - P_l_C_y_predictor_loss: 3.0466 - val_loss: -3.6195 - val_P_y_C_d_critic_loss: 0.2880 - val_P_y_C_d_predictor_loss: -2.4132 - val_P_d_C_y_critic_loss: 1.6132 - val_P_d_C_y_predictor_loss: 3.2932 - val_P_y_C_l_critic_loss: 0.2156 - val_P_y_C_l_predictor_loss: -2.4263 - val_P_l_C_y_critic_loss: 1.8444 - val_P_l_C_y_predictor_loss: 2.5610\n",
      "Epoch 80/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.5337 - P_y_C_d_critic_loss: 0.6771 - P_y_C_d_predictor_loss: -2.3679 - P_d_C_y_critic_loss: 1.6534 - P_d_C_y_predictor_loss: 3.8201 - P_y_C_l_critic_loss: 0.4285 - P_y_C_l_predictor_loss: -2.3494 - P_l_C_y_critic_loss: 2.9508 - P_l_C_y_predictor_loss: 3.1596supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 132us/sample - loss: -3.5282 - P_y_C_d_critic_loss: 0.6761 - P_y_C_d_predictor_loss: -2.3602 - P_d_C_y_critic_loss: 1.6515 - P_d_C_y_predictor_loss: 3.8245 - P_y_C_l_critic_loss: 0.4292 - P_y_C_l_predictor_loss: -2.3399 - P_l_C_y_critic_loss: 2.9186 - P_l_C_y_predictor_loss: 3.1544 - val_loss: -3.6146 - val_P_y_C_d_critic_loss: 0.3336 - val_P_y_C_d_predictor_loss: -2.4159 - val_P_d_C_y_critic_loss: 1.6539 - val_P_d_C_y_predictor_loss: 3.3304 - val_P_y_C_l_critic_loss: 0.2235 - val_P_y_C_l_predictor_loss: -2.4106 - val_P_l_C_y_critic_loss: 1.8615 - val_P_l_C_y_predictor_loss: 2.5430\n",
      "Epoch 81/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.4961 - P_y_C_d_critic_loss: 0.6611 - P_y_C_d_predictor_loss: -2.3443 - P_d_C_y_critic_loss: 1.6543 - P_d_C_y_predictor_loss: 3.8425 - P_y_C_l_critic_loss: 0.4254 - P_y_C_l_predictor_loss: -2.3210 - P_l_C_y_critic_loss: 2.8691 - P_l_C_y_predictor_loss: 3.1263supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 137us/sample - loss: -3.4940 - P_y_C_d_critic_loss: 0.6666 - P_y_C_d_predictor_loss: -2.3402 - P_d_C_y_critic_loss: 1.6644 - P_d_C_y_predictor_loss: 3.8545 - P_y_C_l_critic_loss: 0.4263 - P_y_C_l_predictor_loss: -2.3199 - P_l_C_y_critic_loss: 2.9057 - P_l_C_y_predictor_loss: 3.1239 - val_loss: -3.4930 - val_P_y_C_d_critic_loss: 0.3105 - val_P_y_C_d_predictor_loss: -2.3262 - val_P_d_C_y_critic_loss: 1.5986 - val_P_d_C_y_predictor_loss: 3.3156 - val_P_y_C_l_critic_loss: 0.2439 - val_P_y_C_l_predictor_loss: -2.3475 - val_P_l_C_y_critic_loss: 1.6888 - val_P_l_C_y_predictor_loss: 2.4634\n",
      "Epoch 82/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.5247 - P_y_C_d_critic_loss: 0.6590 - P_y_C_d_predictor_loss: -2.3617 - P_d_C_y_critic_loss: 1.6506 - P_d_C_y_predictor_loss: 3.8435 - P_y_C_l_critic_loss: 0.4354 - P_y_C_l_predictor_loss: -2.3435 - P_l_C_y_critic_loss: 2.9434 - P_l_C_y_predictor_loss: 3.1226supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 146us/sample - loss: -3.5270 - P_y_C_d_critic_loss: 0.6532 - P_y_C_d_predictor_loss: -2.3648 - P_d_C_y_critic_loss: 1.6484 - P_d_C_y_predictor_loss: 3.8487 - P_y_C_l_critic_loss: 0.4323 - P_y_C_l_predictor_loss: -2.3475 - P_l_C_y_critic_loss: 2.9604 - P_l_C_y_predictor_loss: 3.1186 - val_loss: -3.6807 - val_P_y_C_d_critic_loss: 0.3152 - val_P_y_C_d_predictor_loss: -2.4576 - val_P_d_C_y_critic_loss: 1.7129 - val_P_d_C_y_predictor_loss: 3.3360 - val_P_y_C_l_critic_loss: 0.2817 - val_P_y_C_l_predictor_loss: -2.4600 - val_P_l_C_y_critic_loss: 1.8839 - val_P_l_C_y_predictor_loss: 2.4976\n",
      "Epoch 83/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.5806 - P_y_C_d_critic_loss: 0.6411 - P_y_C_d_predictor_loss: -2.4004 - P_d_C_y_critic_loss: 1.7579 - P_d_C_y_predictor_loss: 3.9101 - P_y_C_l_critic_loss: 0.4426 - P_y_C_l_predictor_loss: -2.3777 - P_l_C_y_critic_loss: 3.1003 - P_l_C_y_predictor_loss: 3.2185supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 143us/sample - loss: -3.5868 - P_y_C_d_critic_loss: 0.6399 - P_y_C_d_predictor_loss: -2.4100 - P_d_C_y_critic_loss: 1.7528 - P_d_C_y_predictor_loss: 3.9052 - P_y_C_l_critic_loss: 0.4410 - P_y_C_l_predictor_loss: -2.3868 - P_l_C_y_critic_loss: 3.1010 - P_l_C_y_predictor_loss: 3.2159 - val_loss: -3.7186 - val_P_y_C_d_critic_loss: 0.3178 - val_P_y_C_d_predictor_loss: -2.4996 - val_P_d_C_y_critic_loss: 1.7023 - val_P_d_C_y_predictor_loss: 3.3644 - val_P_y_C_l_critic_loss: 0.2688 - val_P_y_C_l_predictor_loss: -2.4524 - val_P_l_C_y_critic_loss: 1.7984 - val_P_l_C_y_predictor_loss: 2.6046\n",
      "Epoch 84/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.6017 - P_y_C_d_critic_loss: 0.6679 - P_y_C_d_predictor_loss: -2.4147 - P_d_C_y_critic_loss: 1.8571 - P_d_C_y_predictor_loss: 3.9369 - P_y_C_l_critic_loss: 0.4501 - P_y_C_l_predictor_loss: -2.3919 - P_l_C_y_critic_loss: 3.2069 - P_l_C_y_predictor_loss: 3.3044supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 132us/sample - loss: -3.6009 - P_y_C_d_critic_loss: 0.6653 - P_y_C_d_predictor_loss: -2.4150 - P_d_C_y_critic_loss: 1.8556 - P_d_C_y_predictor_loss: 3.9324 - P_y_C_l_critic_loss: 0.4512 - P_y_C_l_predictor_loss: -2.3875 - P_l_C_y_critic_loss: 3.1902 - P_l_C_y_predictor_loss: 3.3071 - val_loss: -3.6826 - val_P_y_C_d_critic_loss: 0.3269 - val_P_y_C_d_predictor_loss: -2.4663 - val_P_d_C_y_critic_loss: 1.8528 - val_P_d_C_y_predictor_loss: 3.4008 - val_P_y_C_l_critic_loss: 0.2951 - val_P_y_C_l_predictor_loss: -2.4473 - val_P_l_C_y_critic_loss: 1.9246 - val_P_l_C_y_predictor_loss: 2.6134\n",
      "Epoch 85/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.5411 - P_y_C_d_critic_loss: 0.7108 - P_y_C_d_predictor_loss: -2.3739 - P_d_C_y_critic_loss: 1.8296 - P_d_C_y_predictor_loss: 3.9627 - P_y_C_l_critic_loss: 0.4630 - P_y_C_l_predictor_loss: -2.3531 - P_l_C_y_critic_loss: 3.1510 - P_l_C_y_predictor_loss: 3.3083supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 136us/sample - loss: -3.5412 - P_y_C_d_critic_loss: 0.7075 - P_y_C_d_predictor_loss: -2.3742 - P_d_C_y_critic_loss: 1.8285 - P_d_C_y_predictor_loss: 3.9550 - P_y_C_l_critic_loss: 0.4617 - P_y_C_l_predictor_loss: -2.3530 - P_l_C_y_critic_loss: 3.1215 - P_l_C_y_predictor_loss: 3.2961 - val_loss: -3.4990 - val_P_y_C_d_critic_loss: 0.4176 - val_P_y_C_d_predictor_loss: -2.3631 - val_P_d_C_y_critic_loss: 1.6064 - val_P_d_C_y_predictor_loss: 3.3661 - val_P_y_C_l_critic_loss: 0.3429 - val_P_y_C_l_predictor_loss: -2.2891 - val_P_l_C_y_critic_loss: 1.5589 - val_P_l_C_y_predictor_loss: 2.4275\n",
      "Epoch 86/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.6108 - P_y_C_d_critic_loss: 0.6922 - P_y_C_d_predictor_loss: -2.4213 - P_d_C_y_critic_loss: 1.9248 - P_d_C_y_predictor_loss: 4.0039 - P_y_C_l_critic_loss: 0.4352 - P_y_C_l_predictor_loss: -2.3973 - P_l_C_y_critic_loss: 3.2337 - P_l_C_y_predictor_loss: 3.3394supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 136us/sample - loss: -3.6058 - P_y_C_d_critic_loss: 0.6917 - P_y_C_d_predictor_loss: -2.4131 - P_d_C_y_critic_loss: 1.9226 - P_d_C_y_predictor_loss: 3.9932 - P_y_C_l_critic_loss: 0.4352 - P_y_C_l_predictor_loss: -2.3906 - P_l_C_y_critic_loss: 3.2428 - P_l_C_y_predictor_loss: 3.3361 - val_loss: -3.6992 - val_P_y_C_d_critic_loss: 0.3346 - val_P_y_C_d_predictor_loss: -2.4832 - val_P_d_C_y_critic_loss: 1.9613 - val_P_d_C_y_predictor_loss: 3.4077 - val_P_y_C_l_critic_loss: 0.2651 - val_P_y_C_l_predictor_loss: -2.4461 - val_P_l_C_y_critic_loss: 2.0339 - val_P_l_C_y_predictor_loss: 2.6850\n",
      "Epoch 87/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.5688 - P_y_C_d_critic_loss: 0.6917 - P_y_C_d_predictor_loss: -2.3954 - P_d_C_y_critic_loss: 1.9685 - P_d_C_y_predictor_loss: 4.0031 - P_y_C_l_critic_loss: 0.4510 - P_y_C_l_predictor_loss: -2.3653 - P_l_C_y_critic_loss: 3.2621 - P_l_C_y_predictor_loss: 3.3974supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 142us/sample - loss: -3.5741 - P_y_C_d_critic_loss: 0.6898 - P_y_C_d_predictor_loss: -2.4031 - P_d_C_y_critic_loss: 1.9622 - P_d_C_y_predictor_loss: 3.9990 - P_y_C_l_critic_loss: 0.4499 - P_y_C_l_predictor_loss: -2.3740 - P_l_C_y_critic_loss: 3.2650 - P_l_C_y_predictor_loss: 3.4099 - val_loss: -3.6098 - val_P_y_C_d_critic_loss: 0.3837 - val_P_y_C_d_predictor_loss: -2.4193 - val_P_d_C_y_critic_loss: 2.0647 - val_P_d_C_y_predictor_loss: 3.4178 - val_P_y_C_l_critic_loss: 0.3397 - val_P_y_C_l_predictor_loss: -2.3972 - val_P_l_C_y_critic_loss: 2.1647 - val_P_l_C_y_predictor_loss: 2.7814\n",
      "Epoch 88/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.5901 - P_y_C_d_critic_loss: 0.6894 - P_y_C_d_predictor_loss: -2.4091 - P_d_C_y_critic_loss: 1.9511 - P_d_C_y_predictor_loss: 4.0114 - P_y_C_l_critic_loss: 0.4672 - P_y_C_l_predictor_loss: -2.3805 - P_l_C_y_critic_loss: 3.2297 - P_l_C_y_predictor_loss: 3.3679supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 131us/sample - loss: -3.5932 - P_y_C_d_critic_loss: 0.6950 - P_y_C_d_predictor_loss: -2.4133 - P_d_C_y_critic_loss: 1.9409 - P_d_C_y_predictor_loss: 4.0178 - P_y_C_l_critic_loss: 0.4651 - P_y_C_l_predictor_loss: -2.3863 - P_l_C_y_critic_loss: 3.2219 - P_l_C_y_predictor_loss: 3.3682 - val_loss: -3.7180 - val_P_y_C_d_critic_loss: 0.3644 - val_P_y_C_d_predictor_loss: -2.4877 - val_P_d_C_y_critic_loss: 1.7772 - val_P_d_C_y_predictor_loss: 3.4278 - val_P_y_C_l_critic_loss: 0.2786 - val_P_y_C_l_predictor_loss: -2.4765 - val_P_l_C_y_critic_loss: 1.5605 - val_P_l_C_y_predictor_loss: 2.6455\n",
      "Epoch 89/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.5489 - P_y_C_d_critic_loss: 0.7233 - P_y_C_d_predictor_loss: -2.3773 - P_d_C_y_critic_loss: 1.9057 - P_d_C_y_predictor_loss: 3.9780 - P_y_C_l_critic_loss: 0.4513 - P_y_C_l_predictor_loss: -2.3623 - P_l_C_y_critic_loss: 3.1445 - P_l_C_y_predictor_loss: 3.3543supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 140us/sample - loss: -3.5532 - P_y_C_d_critic_loss: 0.7222 - P_y_C_d_predictor_loss: -2.3832 - P_d_C_y_critic_loss: 1.9134 - P_d_C_y_predictor_loss: 3.9879 - P_y_C_l_critic_loss: 0.4534 - P_y_C_l_predictor_loss: -2.3699 - P_l_C_y_critic_loss: 3.1957 - P_l_C_y_predictor_loss: 3.3597 - val_loss: -3.7355 - val_P_y_C_d_critic_loss: 0.3657 - val_P_y_C_d_predictor_loss: -2.5065 - val_P_d_C_y_critic_loss: 1.8364 - val_P_d_C_y_predictor_loss: 3.4158 - val_P_y_C_l_critic_loss: 0.2631 - val_P_y_C_l_predictor_loss: -2.4742 - val_P_l_C_y_critic_loss: 1.9081 - val_P_l_C_y_predictor_loss: 2.6904\n",
      "Epoch 90/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.5738 - P_y_C_d_critic_loss: 0.6889 - P_y_C_d_predictor_loss: -2.3948 - P_d_C_y_critic_loss: 1.9074 - P_d_C_y_predictor_loss: 4.0070 - P_y_C_l_critic_loss: 0.4765 - P_y_C_l_predictor_loss: -2.3765 - P_l_C_y_critic_loss: 3.2807 - P_l_C_y_predictor_loss: 3.3864supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 131us/sample - loss: -3.5762 - P_y_C_d_critic_loss: 0.6857 - P_y_C_d_predictor_loss: -2.3991 - P_d_C_y_critic_loss: 1.8910 - P_d_C_y_predictor_loss: 3.9923 - P_y_C_l_critic_loss: 0.4759 - P_y_C_l_predictor_loss: -2.3787 - P_l_C_y_critic_loss: 3.2600 - P_l_C_y_predictor_loss: 3.3755 - val_loss: -3.7370 - val_P_y_C_d_critic_loss: 0.3665 - val_P_y_C_d_predictor_loss: -2.5051 - val_P_d_C_y_critic_loss: 1.7737 - val_P_d_C_y_predictor_loss: 3.4384 - val_P_y_C_l_critic_loss: 0.3246 - val_P_y_C_l_predictor_loss: -2.4799 - val_P_l_C_y_critic_loss: 1.7214 - val_P_l_C_y_predictor_loss: 2.6782\n",
      "Epoch 91/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.6357 - P_y_C_d_critic_loss: 0.7037 - P_y_C_d_predictor_loss: -2.4366 - P_d_C_y_critic_loss: 1.9686 - P_d_C_y_predictor_loss: 4.0201 - P_y_C_l_critic_loss: 0.4749 - P_y_C_l_predictor_loss: -2.4170 - P_l_C_y_critic_loss: 3.3435 - P_l_C_y_predictor_loss: 3.4757supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 143us/sample - loss: -3.6381 - P_y_C_d_critic_loss: 0.7014 - P_y_C_d_predictor_loss: -2.4398 - P_d_C_y_critic_loss: 1.9759 - P_d_C_y_predictor_loss: 4.0214 - P_y_C_l_critic_loss: 0.4733 - P_y_C_l_predictor_loss: -2.4213 - P_l_C_y_critic_loss: 3.3717 - P_l_C_y_predictor_loss: 3.4940 - val_loss: -3.7647 - val_P_y_C_d_critic_loss: 0.3388 - val_P_y_C_d_predictor_loss: -2.5264 - val_P_d_C_y_critic_loss: 2.0226 - val_P_d_C_y_predictor_loss: 3.4750 - val_P_y_C_l_critic_loss: 0.2943 - val_P_y_C_l_predictor_loss: -2.4926 - val_P_l_C_y_critic_loss: 2.0915 - val_P_l_C_y_predictor_loss: 2.7748\n",
      "Epoch 92/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.6380 - P_y_C_d_critic_loss: 0.7071 - P_y_C_d_predictor_loss: -2.4399 - P_d_C_y_critic_loss: 2.0552 - P_d_C_y_predictor_loss: 4.0781 - P_y_C_l_critic_loss: 0.4802 - P_y_C_l_predictor_loss: -2.4151 - P_l_C_y_critic_loss: 3.3063 - P_l_C_y_predictor_loss: 3.4865supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 137us/sample - loss: -3.6356 - P_y_C_d_critic_loss: 0.7101 - P_y_C_d_predictor_loss: -2.4364 - P_d_C_y_critic_loss: 2.0600 - P_d_C_y_predictor_loss: 4.0798 - P_y_C_l_critic_loss: 0.4832 - P_y_C_l_predictor_loss: -2.4113 - P_l_C_y_critic_loss: 3.3058 - P_l_C_y_predictor_loss: 3.4974 - val_loss: -3.7869 - val_P_y_C_d_critic_loss: 0.4018 - val_P_y_C_d_predictor_loss: -2.5365 - val_P_d_C_y_critic_loss: 2.0547 - val_P_d_C_y_predictor_loss: 3.5094 - val_P_y_C_l_critic_loss: 0.2793 - val_P_y_C_l_predictor_loss: -2.5183 - val_P_l_C_y_critic_loss: 2.1276 - val_P_l_C_y_predictor_loss: 2.8503\n",
      "Epoch 93/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.6771 - P_y_C_d_critic_loss: 0.7114 - P_y_C_d_predictor_loss: -2.4633 - P_d_C_y_critic_loss: 2.1036 - P_d_C_y_predictor_loss: 4.1395 - P_y_C_l_critic_loss: 0.4803 - P_y_C_l_predictor_loss: -2.4467 - P_l_C_y_critic_loss: 3.3674 - P_l_C_y_predictor_loss: 3.5613supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 136us/sample - loss: -3.6781 - P_y_C_d_critic_loss: 0.7138 - P_y_C_d_predictor_loss: -2.4640 - P_d_C_y_critic_loss: 2.0966 - P_d_C_y_predictor_loss: 4.1417 - P_y_C_l_critic_loss: 0.4815 - P_y_C_l_predictor_loss: -2.4499 - P_l_C_y_critic_loss: 3.3831 - P_l_C_y_predictor_loss: 3.5717 - val_loss: -3.7982 - val_P_y_C_d_critic_loss: 0.4071 - val_P_y_C_d_predictor_loss: -2.5455 - val_P_d_C_y_critic_loss: 2.0293 - val_P_d_C_y_predictor_loss: 3.5581 - val_P_y_C_l_critic_loss: 0.3050 - val_P_y_C_l_predictor_loss: -2.5227 - val_P_l_C_y_critic_loss: 2.0162 - val_P_l_C_y_predictor_loss: 2.9297\n",
      "Epoch 94/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.6282 - P_y_C_d_critic_loss: 0.7125 - P_y_C_d_predictor_loss: -2.4338 - P_d_C_y_critic_loss: 2.1167 - P_d_C_y_predictor_loss: 4.1616 - P_y_C_l_critic_loss: 0.4921 - P_y_C_l_predictor_loss: -2.4080 - P_l_C_y_critic_loss: 3.3460 - P_l_C_y_predictor_loss: 3.5832supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 131us/sample - loss: -3.6251 - P_y_C_d_critic_loss: 0.7140 - P_y_C_d_predictor_loss: -2.4292 - P_d_C_y_critic_loss: 2.1277 - P_d_C_y_predictor_loss: 4.1550 - P_y_C_l_critic_loss: 0.4932 - P_y_C_l_predictor_loss: -2.4030 - P_l_C_y_critic_loss: 3.3640 - P_l_C_y_predictor_loss: 3.5954 - val_loss: -3.7288 - val_P_y_C_d_critic_loss: 0.3774 - val_P_y_C_d_predictor_loss: -2.5064 - val_P_d_C_y_critic_loss: 1.9179 - val_P_d_C_y_predictor_loss: 3.5515 - val_P_y_C_l_critic_loss: 0.3269 - val_P_y_C_l_predictor_loss: -2.4619 - val_P_l_C_y_critic_loss: 1.8296 - val_P_l_C_y_predictor_loss: 2.8324\n",
      "Epoch 95/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.6806 - P_y_C_d_critic_loss: 0.7100 - P_y_C_d_predictor_loss: -2.4687 - P_d_C_y_critic_loss: 2.1512 - P_d_C_y_predictor_loss: 4.1792 - P_y_C_l_critic_loss: 0.4922 - P_y_C_l_predictor_loss: -2.4429 - P_l_C_y_critic_loss: 3.4649 - P_l_C_y_predictor_loss: 3.6434supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 132us/sample - loss: -3.6802 - P_y_C_d_critic_loss: 0.7088 - P_y_C_d_predictor_loss: -2.4679 - P_d_C_y_critic_loss: 2.1518 - P_d_C_y_predictor_loss: 4.1765 - P_y_C_l_critic_loss: 0.4907 - P_y_C_l_predictor_loss: -2.4428 - P_l_C_y_critic_loss: 3.4672 - P_l_C_y_predictor_loss: 3.6412 - val_loss: -3.7653 - val_P_y_C_d_critic_loss: 0.3647 - val_P_y_C_d_predictor_loss: -2.5322 - val_P_d_C_y_critic_loss: 2.0423 - val_P_d_C_y_predictor_loss: 3.5379 - val_P_y_C_l_critic_loss: 0.3096 - val_P_y_C_l_predictor_loss: -2.4816 - val_P_l_C_y_critic_loss: 1.9035 - val_P_l_C_y_predictor_loss: 2.8519\n",
      "Epoch 96/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.7233 - P_y_C_d_critic_loss: 0.6887 - P_y_C_d_predictor_loss: -2.4940 - P_d_C_y_critic_loss: 2.2713 - P_d_C_y_predictor_loss: 4.2248 - P_y_C_l_critic_loss: 0.5035 - P_y_C_l_predictor_loss: -2.4775 - P_l_C_y_critic_loss: 3.5776 - P_l_C_y_predictor_loss: 3.7101supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 141us/sample - loss: -3.7293 - P_y_C_d_critic_loss: 0.6884 - P_y_C_d_predictor_loss: -2.5032 - P_d_C_y_critic_loss: 2.2626 - P_d_C_y_predictor_loss: 4.2286 - P_y_C_l_critic_loss: 0.5035 - P_y_C_l_predictor_loss: -2.4863 - P_l_C_y_critic_loss: 3.6349 - P_l_C_y_predictor_loss: 3.6990 - val_loss: -3.8248 - val_P_y_C_d_critic_loss: 0.3590 - val_P_y_C_d_predictor_loss: -2.5633 - val_P_d_C_y_critic_loss: 2.2010 - val_P_d_C_y_predictor_loss: 3.6384 - val_P_y_C_l_critic_loss: 0.3296 - val_P_y_C_l_predictor_loss: -2.5396 - val_P_l_C_y_critic_loss: 2.1973 - val_P_l_C_y_predictor_loss: 3.0153\n",
      "Epoch 97/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.6984 - P_y_C_d_critic_loss: 0.6870 - P_y_C_d_predictor_loss: -2.4791 - P_d_C_y_critic_loss: 2.3085 - P_d_C_y_predictor_loss: 4.2969 - P_y_C_l_critic_loss: 0.4933 - P_y_C_l_predictor_loss: -2.4573 - P_l_C_y_critic_loss: 3.6102 - P_l_C_y_predictor_loss: 3.7381supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 131us/sample - loss: -3.6973 - P_y_C_d_critic_loss: 0.6857 - P_y_C_d_predictor_loss: -2.4776 - P_d_C_y_critic_loss: 2.3261 - P_d_C_y_predictor_loss: 4.3008 - P_y_C_l_critic_loss: 0.4961 - P_y_C_l_predictor_loss: -2.4551 - P_l_C_y_critic_loss: 3.6114 - P_l_C_y_predictor_loss: 3.7424 - val_loss: -3.6729 - val_P_y_C_d_critic_loss: 0.3902 - val_P_y_C_d_predictor_loss: -2.4654 - val_P_d_C_y_critic_loss: 2.5573 - val_P_d_C_y_predictor_loss: 3.6950 - val_P_y_C_l_critic_loss: 0.3254 - val_P_y_C_l_predictor_loss: -2.4313 - val_P_l_C_y_critic_loss: 2.6401 - val_P_l_C_y_predictor_loss: 3.1550\n",
      "Epoch 98/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.7056 - P_y_C_d_critic_loss: 0.7269 - P_y_C_d_predictor_loss: -2.4839 - P_d_C_y_critic_loss: 2.3368 - P_d_C_y_predictor_loss: 4.3299 - P_y_C_l_critic_loss: 0.4892 - P_y_C_l_predictor_loss: -2.4628 - P_l_C_y_critic_loss: 3.7107 - P_l_C_y_predictor_loss: 3.7973supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 131us/sample - loss: -3.7081 - P_y_C_d_critic_loss: 0.7306 - P_y_C_d_predictor_loss: -2.4875 - P_d_C_y_critic_loss: 2.3302 - P_d_C_y_predictor_loss: 4.3256 - P_y_C_l_critic_loss: 0.4893 - P_y_C_l_predictor_loss: -2.4673 - P_l_C_y_critic_loss: 3.7631 - P_l_C_y_predictor_loss: 3.8009 - val_loss: -3.7563 - val_P_y_C_d_critic_loss: 0.4573 - val_P_y_C_d_predictor_loss: -2.5169 - val_P_d_C_y_critic_loss: 2.4648 - val_P_d_C_y_predictor_loss: 3.6719 - val_P_y_C_l_critic_loss: 0.3194 - val_P_y_C_l_predictor_loss: -2.4943 - val_P_l_C_y_critic_loss: 2.6880 - val_P_l_C_y_predictor_loss: 3.1818\n",
      "Epoch 99/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.6476 - P_y_C_d_critic_loss: 0.7211 - P_y_C_d_predictor_loss: -2.4471 - P_d_C_y_critic_loss: 2.3073 - P_d_C_y_predictor_loss: 4.3098 - P_y_C_l_critic_loss: 0.5046 - P_y_C_l_predictor_loss: -2.4205 - P_l_C_y_critic_loss: 3.7614 - P_l_C_y_predictor_loss: 3.7686supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 132us/sample - loss: -3.6515 - P_y_C_d_critic_loss: 0.7168 - P_y_C_d_predictor_loss: -2.4529 - P_d_C_y_critic_loss: 2.3160 - P_d_C_y_predictor_loss: 4.3179 - P_y_C_l_critic_loss: 0.5045 - P_y_C_l_predictor_loss: -2.4268 - P_l_C_y_critic_loss: 3.7591 - P_l_C_y_predictor_loss: 3.7763 - val_loss: -3.7006 - val_P_y_C_d_critic_loss: 0.3548 - val_P_y_C_d_predictor_loss: -2.5143 - val_P_d_C_y_critic_loss: 2.4326 - val_P_d_C_y_predictor_loss: 3.6698 - val_P_y_C_l_critic_loss: 0.3286 - val_P_y_C_l_predictor_loss: -2.3889 - val_P_l_C_y_critic_loss: 2.4167 - val_P_l_C_y_predictor_loss: 3.1602\n",
      "Epoch 100/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.6555 - P_y_C_d_critic_loss: 0.7093 - P_y_C_d_predictor_loss: -2.4505 - P_d_C_y_critic_loss: 2.3266 - P_d_C_y_predictor_loss: 4.3242 - P_y_C_l_critic_loss: 0.5067 - P_y_C_l_predictor_loss: -2.4292 - P_l_C_y_critic_loss: 3.8514 - P_l_C_y_predictor_loss: 3.7833supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 135us/sample - loss: -3.6517 - P_y_C_d_critic_loss: 0.7121 - P_y_C_d_predictor_loss: -2.4488 - P_d_C_y_critic_loss: 2.3302 - P_d_C_y_predictor_loss: 4.3262 - P_y_C_l_critic_loss: 0.5047 - P_y_C_l_predictor_loss: -2.4154 - P_l_C_y_critic_loss: 3.8235 - P_l_C_y_predictor_loss: 3.7822 - val_loss: -3.5459 - val_P_y_C_d_critic_loss: 0.4089 - val_P_y_C_d_predictor_loss: -2.3960 - val_P_d_C_y_critic_loss: 2.0934 - val_P_d_C_y_predictor_loss: 3.6843 - val_P_y_C_l_critic_loss: 0.3216 - val_P_y_C_l_predictor_loss: -2.3180 - val_P_l_C_y_critic_loss: 2.0811 - val_P_l_C_y_predictor_loss: 2.8734\n",
      "Epoch 101/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.5742 - P_y_C_d_critic_loss: 0.7591 - P_y_C_d_predictor_loss: -2.4005 - P_d_C_y_critic_loss: 2.1730 - P_d_C_y_predictor_loss: 4.3142 - P_y_C_l_critic_loss: 0.4879 - P_y_C_l_predictor_loss: -2.3675 - P_l_C_y_critic_loss: 3.6439 - P_l_C_y_predictor_loss: 3.6684supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 132us/sample - loss: -3.5758 - P_y_C_d_critic_loss: 0.7559 - P_y_C_d_predictor_loss: -2.4025 - P_d_C_y_critic_loss: 2.1616 - P_d_C_y_predictor_loss: 4.3116 - P_y_C_l_critic_loss: 0.4869 - P_y_C_l_predictor_loss: -2.3708 - P_l_C_y_critic_loss: 3.6326 - P_l_C_y_predictor_loss: 3.6721 - val_loss: -3.7101 - val_P_y_C_d_critic_loss: 0.4255 - val_P_y_C_d_predictor_loss: -2.5008 - val_P_d_C_y_critic_loss: 1.8502 - val_P_d_C_y_predictor_loss: 3.5911 - val_P_y_C_l_critic_loss: 0.2898 - val_P_y_C_l_predictor_loss: -2.4349 - val_P_l_C_y_critic_loss: 1.6017 - val_P_l_C_y_predictor_loss: 2.8092\n",
      "Epoch 102/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.7102 - P_y_C_d_critic_loss: 0.7379 - P_y_C_d_predictor_loss: -2.4963 - P_d_C_y_critic_loss: 2.1592 - P_d_C_y_predictor_loss: 4.2488 - P_y_C_l_critic_loss: 0.4707 - P_y_C_l_predictor_loss: -2.4472 - P_l_C_y_critic_loss: 3.5286 - P_l_C_y_predictor_loss: 3.6888supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 136us/sample - loss: -3.7072 - P_y_C_d_critic_loss: 0.7362 - P_y_C_d_predictor_loss: -2.4911 - P_d_C_y_critic_loss: 2.1657 - P_d_C_y_predictor_loss: 4.2495 - P_y_C_l_critic_loss: 0.4712 - P_y_C_l_predictor_loss: -2.4437 - P_l_C_y_critic_loss: 3.5098 - P_l_C_y_predictor_loss: 3.6889 - val_loss: -3.8018 - val_P_y_C_d_critic_loss: 0.3765 - val_P_y_C_d_predictor_loss: -2.5631 - val_P_d_C_y_critic_loss: 2.2140 - val_P_d_C_y_predictor_loss: 3.6574 - val_P_y_C_l_critic_loss: 0.3017 - val_P_y_C_l_predictor_loss: -2.4922 - val_P_l_C_y_critic_loss: 2.2103 - val_P_l_C_y_predictor_loss: 2.9002\n",
      "Epoch 103/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.6723 - P_y_C_d_critic_loss: 0.7114 - P_y_C_d_predictor_loss: -2.4660 - P_d_C_y_critic_loss: 2.2480 - P_d_C_y_predictor_loss: 4.3199 - P_y_C_l_critic_loss: 0.4928 - P_y_C_l_predictor_loss: -2.4318 - P_l_C_y_critic_loss: 3.6131 - P_l_C_y_predictor_loss: 3.7300supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 131us/sample - loss: -3.6718 - P_y_C_d_critic_loss: 0.7123 - P_y_C_d_predictor_loss: -2.4644 - P_d_C_y_critic_loss: 2.2526 - P_d_C_y_predictor_loss: 4.3217 - P_y_C_l_critic_loss: 0.4922 - P_y_C_l_predictor_loss: -2.4325 - P_l_C_y_critic_loss: 3.5851 - P_l_C_y_predictor_loss: 3.7236 - val_loss: -3.7967 - val_P_y_C_d_critic_loss: 0.4055 - val_P_y_C_d_predictor_loss: -2.5492 - val_P_d_C_y_critic_loss: 2.4411 - val_P_d_C_y_predictor_loss: 3.6691 - val_P_y_C_l_critic_loss: 0.3036 - val_P_y_C_l_predictor_loss: -2.5115 - val_P_l_C_y_critic_loss: 2.2821 - val_P_l_C_y_predictor_loss: 3.0066\n",
      "Epoch 104/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.7487 - P_y_C_d_critic_loss: 0.7319 - P_y_C_d_predictor_loss: -2.5171 - P_d_C_y_critic_loss: 2.3707 - P_d_C_y_predictor_loss: 4.3558 - P_y_C_l_critic_loss: 0.4796 - P_y_C_l_predictor_loss: -2.4827 - P_l_C_y_critic_loss: 3.7674 - P_l_C_y_predictor_loss: 3.7750supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 147us/sample - loss: -3.7546 - P_y_C_d_critic_loss: 0.7324 - P_y_C_d_predictor_loss: -2.5265 - P_d_C_y_critic_loss: 2.3732 - P_d_C_y_predictor_loss: 4.3595 - P_y_C_l_critic_loss: 0.4801 - P_y_C_l_predictor_loss: -2.4909 - P_l_C_y_critic_loss: 3.8024 - P_l_C_y_predictor_loss: 3.7614 - val_loss: -3.8566 - val_P_y_C_d_critic_loss: 0.4588 - val_P_y_C_d_predictor_loss: -2.5911 - val_P_d_C_y_critic_loss: 2.4236 - val_P_d_C_y_predictor_loss: 3.7425 - val_P_y_C_l_critic_loss: 0.3500 - val_P_y_C_l_predictor_loss: -2.5502 - val_P_l_C_y_critic_loss: 2.3877 - val_P_l_C_y_predictor_loss: 3.0610\n",
      "Epoch 105/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.7521 - P_y_C_d_critic_loss: 0.7289 - P_y_C_d_predictor_loss: -2.5179 - P_d_C_y_critic_loss: 2.4317 - P_d_C_y_predictor_loss: 4.4052 - P_y_C_l_critic_loss: 0.4977 - P_y_C_l_predictor_loss: -2.4879 - P_l_C_y_critic_loss: 3.7777 - P_l_C_y_predictor_loss: 3.8568supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 131us/sample - loss: -3.7532 - P_y_C_d_critic_loss: 0.7277 - P_y_C_d_predictor_loss: -2.5201 - P_d_C_y_critic_loss: 2.4404 - P_d_C_y_predictor_loss: 4.4117 - P_y_C_l_critic_loss: 0.4959 - P_y_C_l_predictor_loss: -2.4887 - P_l_C_y_critic_loss: 3.7612 - P_l_C_y_predictor_loss: 3.8621 - val_loss: -3.6846 - val_P_y_C_d_critic_loss: 0.4917 - val_P_y_C_d_predictor_loss: -2.4709 - val_P_d_C_y_critic_loss: 1.9778 - val_P_d_C_y_predictor_loss: 3.7337 - val_P_y_C_l_critic_loss: 0.3286 - val_P_y_C_l_predictor_loss: -2.4466 - val_P_l_C_y_critic_loss: 1.8596 - val_P_l_C_y_predictor_loss: 2.8647\n",
      "Epoch 106/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.6883 - P_y_C_d_critic_loss: 0.7149 - P_y_C_d_predictor_loss: -2.4698 - P_d_C_y_critic_loss: 2.3175 - P_d_C_y_predictor_loss: 4.3944 - P_y_C_l_critic_loss: 0.4827 - P_y_C_l_predictor_loss: -2.4561 - P_l_C_y_critic_loss: 3.6819 - P_l_C_y_predictor_loss: 3.7811supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 145us/sample - loss: -3.6875 - P_y_C_d_critic_loss: 0.7171 - P_y_C_d_predictor_loss: -2.4692 - P_d_C_y_critic_loss: 2.3188 - P_d_C_y_predictor_loss: 4.3964 - P_y_C_l_critic_loss: 0.4851 - P_y_C_l_predictor_loss: -2.4537 - P_l_C_y_critic_loss: 3.6655 - P_l_C_y_predictor_loss: 3.7869 - val_loss: -3.8069 - val_P_y_C_d_critic_loss: 0.4775 - val_P_y_C_d_predictor_loss: -2.5497 - val_P_d_C_y_critic_loss: 2.2996 - val_P_d_C_y_predictor_loss: 3.7304 - val_P_y_C_l_critic_loss: 0.3472 - val_P_y_C_l_predictor_loss: -2.5329 - val_P_l_C_y_critic_loss: 2.2643 - val_P_l_C_y_predictor_loss: 3.1021\n",
      "Epoch 107/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.7943 - P_y_C_d_critic_loss: 0.7251 - P_y_C_d_predictor_loss: -2.5436 - P_d_C_y_critic_loss: 2.5032 - P_d_C_y_predictor_loss: 4.4101 - P_y_C_l_critic_loss: 0.4893 - P_y_C_l_predictor_loss: -2.5208 - P_l_C_y_critic_loss: 3.8217 - P_l_C_y_predictor_loss: 3.9045supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 154us/sample - loss: -3.7899 - P_y_C_d_critic_loss: 0.7293 - P_y_C_d_predictor_loss: -2.5368 - P_d_C_y_critic_loss: 2.5064 - P_d_C_y_predictor_loss: 4.4087 - P_y_C_l_critic_loss: 0.4908 - P_y_C_l_predictor_loss: -2.5143 - P_l_C_y_critic_loss: 3.8310 - P_l_C_y_predictor_loss: 3.9048 - val_loss: -3.8820 - val_P_y_C_d_critic_loss: 0.3599 - val_P_y_C_d_predictor_loss: -2.6047 - val_P_d_C_y_critic_loss: 2.5350 - val_P_d_C_y_predictor_loss: 3.7491 - val_P_y_C_l_critic_loss: 0.3102 - val_P_y_C_l_predictor_loss: -2.5709 - val_P_l_C_y_critic_loss: 2.5178 - val_P_l_C_y_predictor_loss: 3.1630\n",
      "Epoch 108/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.7772 - P_y_C_d_critic_loss: 0.7313 - P_y_C_d_predictor_loss: -2.5328 - P_d_C_y_critic_loss: 2.5773 - P_d_C_y_predictor_loss: 4.5058 - P_y_C_l_critic_loss: 0.5095 - P_y_C_l_predictor_loss: -2.5085 - P_l_C_y_critic_loss: 3.9753 - P_l_C_y_predictor_loss: 3.9975supposedly plotting...\n",
      "17657/17657 [==============================] - 3s 144us/sample - loss: -3.7748 - P_y_C_d_critic_loss: 0.7302 - P_y_C_d_predictor_loss: -2.5289 - P_d_C_y_critic_loss: 2.5749 - P_d_C_y_predictor_loss: 4.5041 - P_y_C_l_critic_loss: 0.5083 - P_y_C_l_predictor_loss: -2.5050 - P_l_C_y_critic_loss: 3.9295 - P_l_C_y_predictor_loss: 3.9993 - val_loss: -3.7462 - val_P_y_C_d_critic_loss: 0.4705 - val_P_y_C_d_predictor_loss: -2.5103 - val_P_d_C_y_critic_loss: 2.3601 - val_P_d_C_y_predictor_loss: 3.8096 - val_P_y_C_l_critic_loss: 0.3344 - val_P_y_C_l_predictor_loss: -2.4901 - val_P_l_C_y_critic_loss: 2.0423 - val_P_l_C_y_predictor_loss: 3.1390\n",
      "Epoch 109/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.6814 - P_y_C_d_critic_loss: 0.7360 - P_y_C_d_predictor_loss: -2.4670 - P_d_C_y_critic_loss: 2.4822 - P_d_C_y_predictor_loss: 4.4937 - P_y_C_l_critic_loss: 0.5044 - P_y_C_l_predictor_loss: -2.4485 - P_l_C_y_critic_loss: 3.6574 - P_l_C_y_predictor_loss: 3.9679supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 133us/sample - loss: -3.6782 - P_y_C_d_critic_loss: 0.7356 - P_y_C_d_predictor_loss: -2.4621 - P_d_C_y_critic_loss: 2.4860 - P_d_C_y_predictor_loss: 4.4983 - P_y_C_l_critic_loss: 0.5046 - P_y_C_l_predictor_loss: -2.4438 - P_l_C_y_critic_loss: 3.6546 - P_l_C_y_predictor_loss: 3.9722 - val_loss: -3.7825 - val_P_y_C_d_critic_loss: 0.3832 - val_P_y_C_d_predictor_loss: -2.5394 - val_P_d_C_y_critic_loss: 2.4027 - val_P_d_C_y_predictor_loss: 3.8156 - val_P_y_C_l_critic_loss: 0.2799 - val_P_y_C_l_predictor_loss: -2.5019 - val_P_l_C_y_critic_loss: 2.1928 - val_P_l_C_y_predictor_loss: 3.1234\n",
      "Epoch 110/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.7994 - P_y_C_d_critic_loss: 0.7019 - P_y_C_d_predictor_loss: -2.5478 - P_d_C_y_critic_loss: 2.4727 - P_d_C_y_predictor_loss: 4.4717 - P_y_C_l_critic_loss: 0.5020 - P_y_C_l_predictor_loss: -2.5222 - P_l_C_y_critic_loss: 3.8607 - P_l_C_y_predictor_loss: 3.9769supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 136us/sample - loss: -3.8017 - P_y_C_d_critic_loss: 0.7028 - P_y_C_d_predictor_loss: -2.5505 - P_d_C_y_critic_loss: 2.4808 - P_d_C_y_predictor_loss: 4.4741 - P_y_C_l_critic_loss: 0.5022 - P_y_C_l_predictor_loss: -2.5276 - P_l_C_y_critic_loss: 3.8454 - P_l_C_y_predictor_loss: 3.9822 - val_loss: -3.9594 - val_P_y_C_d_critic_loss: 0.3578 - val_P_y_C_d_predictor_loss: -2.6522 - val_P_d_C_y_critic_loss: 2.4499 - val_P_d_C_y_predictor_loss: 3.8415 - val_P_y_C_l_critic_loss: 0.3272 - val_P_y_C_l_predictor_loss: -2.6301 - val_P_l_C_y_critic_loss: 2.5184 - val_P_l_C_y_predictor_loss: 3.2647\n",
      "Epoch 111/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.8244 - P_y_C_d_critic_loss: 0.7276 - P_y_C_d_predictor_loss: -2.5646 - P_d_C_y_critic_loss: 2.6867 - P_d_C_y_predictor_loss: 4.5813 - P_y_C_l_critic_loss: 0.5017 - P_y_C_l_predictor_loss: -2.5392 - P_l_C_y_critic_loss: 4.3231 - P_l_C_y_predictor_loss: 4.1533supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 132us/sample - loss: -3.8226 - P_y_C_d_critic_loss: 0.7279 - P_y_C_d_predictor_loss: -2.5615 - P_d_C_y_critic_loss: 2.6898 - P_d_C_y_predictor_loss: 4.5774 - P_y_C_l_critic_loss: 0.4995 - P_y_C_l_predictor_loss: -2.5370 - P_l_C_y_critic_loss: 4.3004 - P_l_C_y_predictor_loss: 4.1660 - val_loss: -3.9485 - val_P_y_C_d_critic_loss: 0.3555 - val_P_y_C_d_predictor_loss: -2.6493 - val_P_d_C_y_critic_loss: 2.6818 - val_P_d_C_y_predictor_loss: 3.8593 - val_P_y_C_l_critic_loss: 0.2799 - val_P_y_C_l_predictor_loss: -2.6136 - val_P_l_C_y_critic_loss: 2.6262 - val_P_l_C_y_predictor_loss: 3.2750\n",
      "Epoch 112/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.8152 - P_y_C_d_critic_loss: 0.7329 - P_y_C_d_predictor_loss: -2.5550 - P_d_C_y_critic_loss: 2.6779 - P_d_C_y_predictor_loss: 4.6269 - P_y_C_l_critic_loss: 0.4913 - P_y_C_l_predictor_loss: -2.5399 - P_l_C_y_critic_loss: 4.2407 - P_l_C_y_predictor_loss: 4.1093supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 131us/sample - loss: -3.8109 - P_y_C_d_critic_loss: 0.7343 - P_y_C_d_predictor_loss: -2.5481 - P_d_C_y_critic_loss: 2.6646 - P_d_C_y_predictor_loss: 4.6419 - P_y_C_l_critic_loss: 0.4935 - P_y_C_l_predictor_loss: -2.5344 - P_l_C_y_critic_loss: 4.2500 - P_l_C_y_predictor_loss: 4.1025 - val_loss: -3.8308 - val_P_y_C_d_critic_loss: 0.4609 - val_P_y_C_d_predictor_loss: -2.5636 - val_P_d_C_y_critic_loss: 2.7312 - val_P_d_C_y_predictor_loss: 3.9444 - val_P_y_C_l_critic_loss: 0.2976 - val_P_y_C_l_predictor_loss: -2.5518 - val_P_l_C_y_critic_loss: 2.8103 - val_P_l_C_y_predictor_loss: 3.1446\n",
      "Epoch 113/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.7800 - P_y_C_d_critic_loss: 0.7410 - P_y_C_d_predictor_loss: -2.5343 - P_d_C_y_critic_loss: 2.7504 - P_d_C_y_predictor_loss: 4.6547 - P_y_C_l_critic_loss: 0.5250 - P_y_C_l_predictor_loss: -2.5114 - P_l_C_y_critic_loss: 4.5249 - P_l_C_y_predictor_loss: 4.1362supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 133us/sample - loss: -3.7775 - P_y_C_d_critic_loss: 0.7434 - P_y_C_d_predictor_loss: -2.5321 - P_d_C_y_critic_loss: 2.7477 - P_d_C_y_predictor_loss: 4.6432 - P_y_C_l_critic_loss: 0.5226 - P_y_C_l_predictor_loss: -2.5045 - P_l_C_y_critic_loss: 4.5065 - P_l_C_y_predictor_loss: 4.1413 - val_loss: -3.8080 - val_P_y_C_d_critic_loss: 0.4244 - val_P_y_C_d_predictor_loss: -2.5668 - val_P_d_C_y_critic_loss: 2.6172 - val_P_d_C_y_predictor_loss: 3.8566 - val_P_y_C_l_critic_loss: 0.3086 - val_P_y_C_l_predictor_loss: -2.4991 - val_P_l_C_y_critic_loss: 2.6695 - val_P_l_C_y_predictor_loss: 3.2072\n",
      "Epoch 114/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.7050 - P_y_C_d_critic_loss: 0.7502 - P_y_C_d_predictor_loss: -2.4856 - P_d_C_y_critic_loss: 2.6063 - P_d_C_y_predictor_loss: 4.6117 - P_y_C_l_critic_loss: 0.4816 - P_y_C_l_predictor_loss: -2.4587 - P_l_C_y_critic_loss: 3.9003 - P_l_C_y_predictor_loss: 4.1225supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 131us/sample - loss: -3.7060 - P_y_C_d_critic_loss: 0.7479 - P_y_C_d_predictor_loss: -2.4871 - P_d_C_y_critic_loss: 2.6143 - P_d_C_y_predictor_loss: 4.6036 - P_y_C_l_critic_loss: 0.4831 - P_y_C_l_predictor_loss: -2.4602 - P_l_C_y_critic_loss: 3.8955 - P_l_C_y_predictor_loss: 4.1208 - val_loss: -3.9299 - val_P_y_C_d_critic_loss: 0.4020 - val_P_y_C_d_predictor_loss: -2.6339 - val_P_d_C_y_critic_loss: 2.6968 - val_P_d_C_y_predictor_loss: 3.8156 - val_P_y_C_l_critic_loss: 0.2563 - val_P_y_C_l_predictor_loss: -2.6088 - val_P_l_C_y_critic_loss: 2.7339 - val_P_l_C_y_predictor_loss: 3.2610\n",
      "Epoch 115/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.8455 - P_y_C_d_critic_loss: 0.7200 - P_y_C_d_predictor_loss: -2.5747 - P_d_C_y_critic_loss: 2.7535 - P_d_C_y_predictor_loss: 4.6293 - P_y_C_l_critic_loss: 0.4685 - P_y_C_l_predictor_loss: -2.5607 - P_l_C_y_critic_loss: 4.2813 - P_l_C_y_predictor_loss: 4.2051supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 132us/sample - loss: -3.8450 - P_y_C_d_critic_loss: 0.7272 - P_y_C_d_predictor_loss: -2.5740 - P_d_C_y_critic_loss: 2.7563 - P_d_C_y_predictor_loss: 4.6233 - P_y_C_l_critic_loss: 0.4702 - P_y_C_l_predictor_loss: -2.5596 - P_l_C_y_critic_loss: 4.2693 - P_l_C_y_predictor_loss: 4.2110 - val_loss: -3.9099 - val_P_y_C_d_critic_loss: 0.3944 - val_P_y_C_d_predictor_loss: -2.6280 - val_P_d_C_y_critic_loss: 2.9467 - val_P_d_C_y_predictor_loss: 3.9220 - val_P_y_C_l_critic_loss: 0.2605 - val_P_y_C_l_predictor_loss: -2.5805 - val_P_l_C_y_critic_loss: 2.8558 - val_P_l_C_y_predictor_loss: 3.4279\n",
      "Epoch 116/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.8779 - P_y_C_d_critic_loss: 0.7335 - P_y_C_d_predictor_loss: -2.5976 - P_d_C_y_critic_loss: 2.7939 - P_d_C_y_predictor_loss: 4.7211 - P_y_C_l_critic_loss: 0.5010 - P_y_C_l_predictor_loss: -2.5802 - P_l_C_y_critic_loss: 4.3881 - P_l_C_y_predictor_loss: 4.2339supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 131us/sample - loss: -3.8723 - P_y_C_d_critic_loss: 0.7324 - P_y_C_d_predictor_loss: -2.5896 - P_d_C_y_critic_loss: 2.7935 - P_d_C_y_predictor_loss: 4.7166 - P_y_C_l_critic_loss: 0.5025 - P_y_C_l_predictor_loss: -2.5708 - P_l_C_y_critic_loss: 4.3606 - P_l_C_y_predictor_loss: 4.2169 - val_loss: -3.7931 - val_P_y_C_d_critic_loss: 0.4472 - val_P_y_C_d_predictor_loss: -2.5452 - val_P_d_C_y_critic_loss: 2.5664 - val_P_d_C_y_predictor_loss: 3.9199 - val_P_y_C_l_critic_loss: 0.3168 - val_P_y_C_l_predictor_loss: -2.5143 - val_P_l_C_y_critic_loss: 2.2527 - val_P_l_C_y_predictor_loss: 3.1790\n",
      "Epoch 117/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.8072 - P_y_C_d_critic_loss: 0.7539 - P_y_C_d_predictor_loss: -2.5518 - P_d_C_y_critic_loss: 2.8203 - P_d_C_y_predictor_loss: 4.7679 - P_y_C_l_critic_loss: 0.5233 - P_y_C_l_predictor_loss: -2.5311 - P_l_C_y_critic_loss: 4.2729 - P_l_C_y_predictor_loss: 4.2625supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 132us/sample - loss: -3.8062 - P_y_C_d_critic_loss: 0.7504 - P_y_C_d_predictor_loss: -2.5509 - P_d_C_y_critic_loss: 2.8155 - P_d_C_y_predictor_loss: 4.7680 - P_y_C_l_critic_loss: 0.5251 - P_y_C_l_predictor_loss: -2.5282 - P_l_C_y_critic_loss: 4.2609 - P_l_C_y_predictor_loss: 4.2529 - val_loss: -3.7409 - val_P_y_C_d_critic_loss: 0.4286 - val_P_y_C_d_predictor_loss: -2.5128 - val_P_d_C_y_critic_loss: 2.7912 - val_P_d_C_y_predictor_loss: 4.0489 - val_P_y_C_l_critic_loss: 0.3037 - val_P_y_C_l_predictor_loss: -2.4744 - val_P_l_C_y_critic_loss: 2.7316 - val_P_l_C_y_predictor_loss: 3.3340\n",
      "Epoch 118/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.8266 - P_y_C_d_critic_loss: 0.7316 - P_y_C_d_predictor_loss: -2.5664 - P_d_C_y_critic_loss: 2.8352 - P_d_C_y_predictor_loss: 4.7430 - P_y_C_l_critic_loss: 0.4976 - P_y_C_l_predictor_loss: -2.5400 - P_l_C_y_critic_loss: 4.3064 - P_l_C_y_predictor_loss: 4.3185supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 133us/sample - loss: -3.8319 - P_y_C_d_critic_loss: 0.7291 - P_y_C_d_predictor_loss: -2.5760 - P_d_C_y_critic_loss: 2.8385 - P_d_C_y_predictor_loss: 4.7508 - P_y_C_l_critic_loss: 0.4976 - P_y_C_l_predictor_loss: -2.5453 - P_l_C_y_critic_loss: 4.3832 - P_l_C_y_predictor_loss: 4.2963 - val_loss: -3.8944 - val_P_y_C_d_critic_loss: 0.4393 - val_P_y_C_d_predictor_loss: -2.6178 - val_P_d_C_y_critic_loss: 2.7093 - val_P_d_C_y_predictor_loss: 3.9981 - val_P_y_C_l_critic_loss: 0.2966 - val_P_y_C_l_predictor_loss: -2.5710 - val_P_l_C_y_critic_loss: 2.6129 - val_P_l_C_y_predictor_loss: 3.2671\n",
      "Epoch 119/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.8615 - P_y_C_d_critic_loss: 0.7431 - P_y_C_d_predictor_loss: -2.5895 - P_d_C_y_critic_loss: 2.8260 - P_d_C_y_predictor_loss: 4.7623 - P_y_C_l_critic_loss: 0.4962 - P_y_C_l_predictor_loss: -2.5638 - P_l_C_y_critic_loss: 4.4471 - P_l_C_y_predictor_loss: 4.2935supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 131us/sample - loss: -3.8594 - P_y_C_d_critic_loss: 0.7465 - P_y_C_d_predictor_loss: -2.5881 - P_d_C_y_critic_loss: 2.8358 - P_d_C_y_predictor_loss: 4.7822 - P_y_C_l_critic_loss: 0.4960 - P_y_C_l_predictor_loss: -2.5571 - P_l_C_y_critic_loss: 4.4510 - P_l_C_y_predictor_loss: 4.3084 - val_loss: -3.9410 - val_P_y_C_d_critic_loss: 0.4200 - val_P_y_C_d_predictor_loss: -2.6477 - val_P_d_C_y_critic_loss: 2.9024 - val_P_d_C_y_predictor_loss: 4.0306 - val_P_y_C_l_critic_loss: 0.2590 - val_P_y_C_l_predictor_loss: -2.6029 - val_P_l_C_y_critic_loss: 2.8563 - val_P_l_C_y_predictor_loss: 3.4775\n",
      "Epoch 120/200\n",
      "17400/17657 [============================>.] - ETA: 0s - loss: -3.7807 - P_y_C_d_critic_loss: 0.7868 - P_y_C_d_predictor_loss: -2.5377 - P_d_C_y_critic_loss: 2.8587 - P_d_C_y_predictor_loss: 4.7928 - P_y_C_l_critic_loss: 0.4983 - P_y_C_l_predictor_loss: -2.5067 - P_l_C_y_critic_loss: 4.3682 - P_l_C_y_predictor_loss: 4.2965Restoring model weights from the end of the best epoch.\n",
      "supposedly plotting...\n",
      "17657/17657 [==============================] - 2s 133us/sample - loss: -3.7854 - P_y_C_d_critic_loss: 0.7869 - P_y_C_d_predictor_loss: -2.5450 - P_d_C_y_critic_loss: 2.8535 - P_d_C_y_predictor_loss: 4.7871 - P_y_C_l_critic_loss: 0.4974 - P_y_C_l_predictor_loss: -2.5139 - P_l_C_y_critic_loss: 4.3504 - P_l_C_y_predictor_loss: 4.2950 - val_loss: -3.8937 - val_P_y_C_d_critic_loss: 0.4414 - val_P_y_C_d_predictor_loss: -2.6137 - val_P_d_C_y_critic_loss: 2.7918 - val_P_d_C_y_predictor_loss: 4.0156 - val_P_y_C_l_critic_loss: 0.2768 - val_P_y_C_l_predictor_loss: -2.5778 - val_P_l_C_y_critic_loss: 2.6844 - val_P_l_C_y_predictor_loss: 3.4701\n",
      "Epoch 00120: early stopping\n"
     ]
    }
   ],
   "source": [
    "epochs = 200; #training_parameters[\"nepochs\"]\n",
    "\n",
    "batchsize =600;\n",
    "if (tf.__version__ == '2.1.0'):\n",
    "    confusion_matrix = tf.compat.v1.math.confusion_matrix\n",
    "else:\n",
    "    from tensorflow.math import confusion_matrix\n",
    "    \n",
    "import tensorflow.keras as keras\n",
    "from tensorflow.keras import layers \n",
    "\n",
    "LRATE_MIN = 0.0001;\n",
    "LRATE_MAX = 0.001;\n",
    "BURNIN_EPOCHS = 1;\n",
    "\n",
    "model_filepath = \"discadv_models/model_test_closs0.1.h5\"\n",
    "\n",
    "closs = 0.01;\n",
    "\n",
    "aux_loss_factor = 0.5;\n",
    "\n",
    "def _loss_from_name(n_):\n",
    "    if 'P_y_C_d_predictor' in n_:\n",
    "        print(\"setting loss to 1 for %s\"%n_)\n",
    "        return 1.;\n",
    "    if 'P_y_C_d_critic' in n_:\n",
    "        return closs;\n",
    "    \n",
    "    if 'P_y_C_l_predictor' in n_:\n",
    "        return 1. * aux_loss_factor;\n",
    "    if 'P_y_C_l_critic' in n_:\n",
    "        return closs * aux_loss_factor\n",
    "    \n",
    "#     if 'P_d_C_y_predictor' in n_:\n",
    "#         return 1. * aux_loss_factor;\n",
    "#     if 'P_d_C_y_critic' in n_:\n",
    "#         return closs * aux_loss_factor;\n",
    "    \n",
    "   \n",
    "#     if 'P_l_C_y_predictor' in n_:\n",
    "#         return aux_loss_factor;\n",
    "#     if 'P_l_C_y_critic' in n_:\n",
    "#         return closs * aux_loss_factor;\n",
    "    \n",
    "    \n",
    "#     if 'P_l_C_d_predictor' in n_:\n",
    "#         return 0\n",
    "#     if 'P_l_C_d_critic' in n_:\n",
    "#         return 0\n",
    "\n",
    "#     if 'P_d_C_l_predictor' in n_:\n",
    "#         return 0\n",
    "#     if 'P_d_C_l_critic' in n_:\n",
    "#         return 0\n",
    "\n",
    "    return 0;\n",
    "    print(n_)\n",
    "\n",
    "def _loss_noadv(n_):\n",
    "    if 'P_y_C_d_predictor' != n_:\n",
    "        return closs;\n",
    "    else:\n",
    "        return 1.\n",
    "    \n",
    "\n",
    "    \n",
    "#dict_output_domain_name_to_label = {\"d\" :  ,\"y\" : \"y\", \"\"}\n",
    "def data_to_input_dict(X = None,y = None ,d = None,l = None):\n",
    "    \"\"\"\n",
    "    X: the input time-series\n",
    "    y: the prediction of time to failure\n",
    "    d: the experiment ID\n",
    "    l: the loading conditions \"domain\"\n",
    "    \"\"\"\n",
    "    kwargs = {\"X\" : X, \"y\" : y, \"d\":d,\"l\" : l};\n",
    "    \n",
    "    data_in_dict  = {\"timeseries_input\": X}\n",
    "    \n",
    "    # The ordering must be correct (it is, but could it break down?). \n",
    "    # Since domains don't have the same losses or values errors will be thrown otherwise.\n",
    "    #output_layers_names = [l.name for l in disc_adv.layers if \"critic\" in l.name or \"predictor\" in l.name];\n",
    "    data_out_dict = {k:kwargs[v] for k,v in zip(all_loss_names ,all_output_domains)}\n",
    "    return data_in_dict, data_out_dict\n",
    "    \n",
    "loss_weight_dict = {k: _loss_from_name(k) for k in losses.keys() }\n",
    "#loss_weight_dict = {k: _loss_noadv(k) for k in losses.keys() }\n",
    "\n",
    "monitor_loss = 'val_P_y_C_d_predictor_loss'\n",
    "\n",
    "es = keras.callbacks.EarlyStopping(monitor=monitor_loss , mode='min', verbose=1, patience=10, restore_best_weights = True)\n",
    "checkpointing = keras.callbacks.ModelCheckpoint(model_filepath , monitor = monitor_loss, mode = 'min', save_best_only = True)\n",
    "model_opt = keras.optimizers.Adam(learning_rate=0.0);\n",
    "#model_opt = tf.keras.optimizers.Adam(learning_rate=0.0, clipnorm = 1)\n",
    "\n",
    "target_predictions = [];\n",
    "        \n",
    "class plot_fit(tf.keras.callbacks.Callback):\n",
    "    def __init__(self):\n",
    "        self.y_output_index = [i for i,l in enumerate(disc_adv.outputs) if 'P_y_C_d_predictor' in l.name][0]\n",
    "        None\n",
    "        \n",
    "    def on_epoch_end(self,epoch, logs = None):\n",
    "        print(\"supposedly plotting...\")\n",
    "        return None\n",
    "        if (epoch+1) % 2 == 0:\n",
    "            \n",
    "            yhat = disc_adv.predict(x_train, batch_size = batchsize)\n",
    "            pplot.cla()\n",
    "            \n",
    "            pplot.plot(yhat[self.y_output_index], y_train,'.')\n",
    "            pplot.show()\n",
    "            \n",
    "        yhat_target = disc_adv.predict(X_target_experiments, batch_size = batchsize )\n",
    "        target_predictions.append(yhat_target[2])\n",
    "        \n",
    "class BurnIn(tf.keras.callbacks.Callback):\n",
    "    def __init__(self, burnin_epochs = None, lrate_max = LRATE_MAX):\n",
    "        self.learning_rate = lrate_max\n",
    "        self.burnin_epochs = burnin_epochs\n",
    "        \n",
    "    def on_epoch_end(self,epoch, logs = None):\n",
    "        if epoch <= self.burnin_epochs:\n",
    "            de = epoch/self.burnin_epochs\n",
    "            lrate = de * self.learning_rate\n",
    "            print(\"-\"*10)\n",
    "            if lrate < LRATE_MIN:\n",
    "                model_opt.learning_rate = LRATE_MIN\n",
    "            else:\n",
    "                model_opt.learning_rate = de * self.learning_rate\n",
    "                print(\"----- setting learning rate to %2.4f\"%(de * self.learning_rate))\n",
    "            print(model_opt.learning_rate)\n",
    "            print(\"-\"*10)\n",
    "\n",
    "\n",
    "loss_fcn = losses\n",
    "\n",
    "disc_adv = Model(inputs = mconv_adv.input, outputs = all_outputs)\n",
    "# GOAL: Maximize the loss of the domain critic while maximizing the actual predictor.\n",
    "# useful discriminator:\n",
    "disc_adv.compile(optimizer =  model_opt,\n",
    "              loss =loss_fcn,\n",
    "              loss_weights = loss_weight_dict)\n",
    "\n",
    "train_in, train_out = data_to_input_dict(X = x_train, y = y_train, d = d_train, l = c_train)\n",
    "test_in, test_out = data_to_input_dict(X = x_test, y = y_test, d = d_test, l = c_test)\n",
    "\n",
    "burnin = BurnIn(burnin_epochs = BURNIN_EPOCHS, lrate_max = LRATE_MAX)\n",
    "history_d = disc_adv.fit(train_in, train_out, epochs=epochs,\n",
    "                     batch_size=batchsize,\n",
    "                      validation_data = [test_in, test_out],# \"Rc_output\" :  RemCTest}],\n",
    "                     callbacks=[es, burnin, plot_fit(), checkpointing])#, Top3AccStrong()])#, InspectActivations()]);#, Top3AccStrong()])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f22d12ae908>]"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pplot.plot(history_d.history['loss'])\n",
    "pplot.plot(history_d.history['val_loss'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "adv_model = Model(inputs = mconv_adv.input, outputs=  all_outputs[1])\n",
    "t = np.linspace(0,1.5,75)\n",
    "def eval_model_batched(model_, data_in = X_target_experiments, data_out = yrem_norm_target):\n",
    "    \n",
    "    batch_size = 100;\n",
    "    r = []\n",
    "    nll_target_vals = []\n",
    "\n",
    "    for batch_start in range(0, data_in.shape[0], batch_size):\n",
    "        batch_end = batch_start + batch_size\n",
    "        actual_vals = data_out[batch_start:batch_end][:,np.newaxis].T;\n",
    "\n",
    "        p = model_(data_in[batch_start:batch_end]);\n",
    "        r.append(p.prob(t).numpy())\n",
    "        nll_target_vals.append(-p.log_prob(actual_vals.T).numpy())\n",
    "        #nll_target_vals.append(p.prob(actual_vals.T).numpy())\n",
    "\n",
    "    r = np.vstack(r).T\n",
    "    nll_target_vals = np.vstack(nll_target_vals);\n",
    "    return r, nll_target_vals\n",
    "\n",
    "adv_results = eval_model_batched(adv_model)\n",
    "noadv_results = eval_model_batched(gamma_model)\n",
    "\n",
    "#x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "adv_results_source = eval_model_batched(adv_model, data_in = X_source_experiments,data_out= yrem_norm_source)\n",
    "noadv_results_source = eval_model_batched(gamma_model, data_in = X_source_experiments,data_out= yrem_norm_source)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Expected NLL Results with adv: 14.642763, 14.831947\n"
     ]
    }
   ],
   "source": [
    "#pplot.plot(loglik_vals)\n",
    "print(\"Expected NLL Results with adv: %f, %f\"%(np.mean(adv_results[1]),np.mean(noadv_results[1])))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 3000x1500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f21a8581a58>]"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 3000x1500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pplot.figure(figsize = (10,5), dpi = 300);\n",
    "pplot.contourf(range(adv_results[0].shape[1]), t*normalization_factor_time, adv_results[0]**0.4, cmap = \"gray\")\n",
    "pplot.plot(range(adv_results[0].shape[1]), yrem_norm_target*normalization_factor_time,'C1')\n",
    "pplot.show()\n",
    "pplot.figure(figsize = (10,5), dpi = 300);\n",
    "pplot.contourf(range(adv_results[0].shape[1]), t*normalization_factor_time, noadv_results[0]**0.4, cmap = \"gray\")\n",
    "pplot.plot(range(adv_results[0].shape[1]), yrem_norm_target*normalization_factor_time,'C1')\n",
    "\n",
    "#pplot.plot(y)\n",
    "#batch_start,batch_end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function __main__.<lambda>(y_, py_)>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compute expected likelihood on output dataset:\n",
    "all_loss_functions[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![alt-text](gamma_distr_out_predictions_noadv_morex.png)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(array([9223.,    0.,    0.,    0.,    0., 5098.,    0.,    0.,    0.,\n",
       "        1848.]),\n",
       " array([0. , 0.2, 0.4, 0.6, 0.8, 1. , 1.2, 1.4, 1.6, 1.8, 2. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pplot.hist(np.argmax(d_train,1))\n",
    "pplot.show()\n",
    "pplot.hist(np.argmax(c_train,1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5390, 2559, 2)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_in['timeseries_input'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['P_y_C_d_critic', 'P_y_C_d_predictor', 'P_d_C_y_critic', 'P_d_C_y_predictor', 'P_y_C_l_critic', 'P_y_C_l_predictor', 'P_l_C_y_critic', 'P_l_C_y_predictor', 'P_d_C_l_critic', 'P_d_C_l_predictor', 'P_l_C_d_critic', 'P_l_C_d_predictor'])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_out.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('P_y_C_d_critic', <function __main__.<lambda>(y, yhat)>),\n",
       "             ('P_y_C_d_predictor', <function __main__.<lambda>(y_, py_)>),\n",
       "             ('P_d_C_y_critic', <function __main__.<lambda>(y_, py_)>),\n",
       "             ('P_d_C_y_predictor', <function __main__.<lambda>(y, yhat)>),\n",
       "             ('P_y_C_l_critic', <function __main__.<lambda>(y, yhat)>),\n",
       "             ('P_y_C_l_predictor', <function __main__.<lambda>(y_, py_)>),\n",
       "             ('P_l_C_y_critic', <function __main__.<lambda>(y_, py_)>),\n",
       "             ('P_l_C_y_predictor', <function __main__.<lambda>(y, yhat)>),\n",
       "             ('P_d_C_l_critic', <function __main__.<lambda>(y, yhat)>),\n",
       "             ('P_d_C_l_predictor', <function __main__.<lambda>(y, yhat)>),\n",
       "             ('P_l_C_d_critic', <function __main__.<lambda>(y, yhat)>),\n",
       "             ('P_l_C_d_predictor', <function __main__.<lambda>(y, yhat)>)])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss_fcn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([2, 10, 3])"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#def eval_sequential_gamma_model(nstates):\n",
    "#gamma_model(X_source_experiments[0:10])\n",
    "#gamma_model.summary()\n",
    "\n",
    "# A simple GraphNet for prediction using sequential data:\n",
    "# Prediction from sequences can be modeled as a graph where only the last edge has a loss function.\n",
    "# An arbitrary number of edges can be used \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mini-GraphNets\n",
    "A simple implementation of GraphNets in tensorflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_mlp = 10;\n",
    "n_edge_state = 15;\n",
    "n_node_state = 17;\n",
    "\n",
    "def make_edge_function():\n",
    "    edge_state_in = Input(shape = (n_edge_state), name = \"edge_state\");\n",
    "    node_state_sender_in = Input(shape = (n_node_state), name = \"node_sender\");\n",
    "    node_state_receiver_in = Input(shape = (n_node_state), name = \"node_receiver\");\n",
    "\n",
    "    ## Building the edge MLP:\n",
    "    edge_out = keras.layers.concatenate([edge_state_in, node_state_sender_in, node_state_receiver_in])\n",
    "    \n",
    "    edge_out = Dense(100, activation = \"sigmoid\")(edge_out)\n",
    "    edge_out = Dense(n_edge_state, activation = None)(edge_out)\n",
    "    \n",
    "    edge_mlp = Model(inputs = [edge_state_in, node_state_sender_in, node_state_receiver_in ] ,outputs = edge_out)\n",
    "    \n",
    "    return edge_mlp\n",
    "\n",
    "def per_node_edge_aggregator(node_incoming_edges):\n",
    "    \"\"\"\n",
    "    This seems to get the pure edge state.\n",
    "    I can pass the node state in the edge \n",
    "    state if I want to have this as well.\n",
    "    \"\"\"\n",
    "    val  = tf.reduce_mean(tf.stack(node_incoming_edges),0)\n",
    "    return val\n",
    "\n",
    "def make_node_function():\n",
    "    agg_edge_state_in = Input(shape = (n_edge_state), name = \"edge_state_agg\");\n",
    "    node_prev_state = Input(shape = (n_node_state), name = \"node_sender\");\n",
    "\n",
    "    ## Building the edge MLP:\n",
    "    node_out = keras.layers.concatenate([agg_edge_state_in, node_prev_state]);\n",
    "    \n",
    "    node_out = Dense(100, activation = \"sigmoid\")(node_out)\n",
    "    node_out = Dense(n_node_state, activation = None)(node_out)\n",
    "    \n",
    "    node_out_model = Model(inputs = [agg_edge_state_in, node_prev_state] ,outputs = node_out)\n",
    "    return node_out_model\n",
    "\n",
    "edge_mlp = make_edge_function()\n",
    "edge_mlp([vv.astype(\"float32\") for vv in [np.random.randn(10,n_edge_state), np.random.randn(10,n_node_state), np.random.randn(10,n_node_state)]])\n",
    "\n",
    "node_mlp = make_node_function()\n",
    "node_mlp([vv.astype(\"float32\") for vv in [np.random.randn(10,n_edge_state), np.random.randn(10,n_node_state)]])\n",
    "\n",
    "class Node:\n",
    "    def __init__(self, node_attr_tensor):\n",
    "        self.node_attr_tensor = node_attr_tensor\n",
    "        self.incoming_edges = [];\n",
    "        \n",
    "    def get_state(self):\n",
    "        return self.node_attr_tensor\n",
    "\n",
    "class Edge:\n",
    "    def __init__(self, edge_attr_tensor, node_from, node_to):\n",
    "        self.edge_tensor = edge_attr_tensor\n",
    "        self.node_from = node_from\n",
    "        self.node_to = node_to\n",
    "        \n",
    "        # Keep a reference to this edge since it is needed for aggregation afterwards.\n",
    "        node_to.incoming_edges.append(self)\n",
    "        \n",
    "class Graph:\n",
    "    def __init__(self, nodes, edges):\n",
    "        self.nodes = nodes\n",
    "        self.edges = edges\n",
    "        \n",
    "class GraphNet:\n",
    "    \"\"\"\n",
    "    Input is a graph and output is a graph.\n",
    "    \"\"\"\n",
    "    def __init__(self, edge_function, node_function, edge_aggregation_function):\n",
    "        self.edge_function             = edge_function\n",
    "        self.node_function             = node_function\n",
    "        self.edge_aggregation_function = edge_aggregation_function\n",
    "        \n",
    "    def graph_eval(self, graph):\n",
    "        # Evaluate all edge functions:\n",
    "        self.eval_edge_functions(graph)\n",
    "        \n",
    "        # Aggregate edges per node:\n",
    "        for n in graph.nodes:\n",
    "            if len(n.incoming_edges) is not 0:\n",
    "                edge_to_node_agg = self.edge_aggregation_function([e.edge_tensor for e in n.incoming_edges])\n",
    "                n.node_attr_tensor = self.node_function([edge_to_node_agg, n.node_attr_tensor])\n",
    "        \n",
    "        return \n",
    "                \n",
    "    def eval_edge_functions(self,graph):\n",
    "        \"\"\"\n",
    "        Evaluate all edge functions\n",
    "        \"\"\"\n",
    "        for edge in graph.edges:\n",
    "            edge.edge_tensor = self.edge_function([edge.edge_tensor, edge.node_from.node_attr_tensor, edge.node_to.node_attr_tensor])\n",
    "            \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(\n",
      "[ 0.41832855  0.18835607  0.3912712  -0.06191619 -0.7263874   0.38435724\n",
      " -0.3455946  -0.41121125  0.86421436 -0.8500655   0.48130178  0.27495554\n",
      "  0.38713956  0.38639674  0.34935838 -0.06566793  0.5967278 ], shape=(17,), dtype=float32)\n",
      "tf.Tensor(\n",
      "[ 0.5468834   0.2990827   0.48607638  0.16257733 -0.54706067  0.33029777\n",
      " -0.29881042 -0.2342881   0.754018   -0.8874005   0.35750324 -0.02003047\n",
      "  0.4953017   0.40786168  0.3928435  -0.1948385   0.7901766 ], shape=(17,), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "n1 = Node(tf.Variable(np.random.randn(10,n_node_state).astype(\"float32\")))\n",
    "n2 = Node(tf.Variable(np.random.randn(10,n_node_state).astype(\"float32\")))\n",
    "e1 = Edge(np.zeros([10,n_edge_state]).astype(\"float32\"), n1,n2)\n",
    "        \n",
    "g = Graph(nodes = [n1,n2], edges = [e1])\n",
    "gn = GraphNet(edge_function = edge_mlp, node_function = node_mlp, edge_aggregation_function = per_node_edge_aggregator)\n",
    "gn.graph_eval(g)\n",
    "print(n2.node_attr_tensor[0])\n",
    "gn.graph_eval(g)\n",
    "print(n2.node_attr_tensor[0])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<__main__.Edge object at 0x7f21a5b71748>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(1, 15), dtype=float32, numpy=\n",
       "array([[-0.95893955,  0.5616526 , -0.2530312 ,  0.06968968,  0.15343323,\n",
       "         0.9750211 ,  0.58112806,  0.33133668,  0.33467057,  0.3010056 ,\n",
       "         0.10168518, -0.5706796 , -0.72425723,  0.66489375,  0.42992464]],\n",
       "      dtype=float32)>"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#gn.edge_function([np.random.randn(10,n_edge_state), np.random.randn(10,n_node_state), np.random.randn(10,n_node_state)])\n",
    "print(e1)\n",
    "e1.edge_tensor.shape\n",
    "e1.node_from.node_attr_tensor.shape, n_node_state\n",
    "gn.edge_function([e1.edge_tensor, e1.node_from.node_attr_tensor, e1.node_from.node_attr_tensor])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23543, 2559, 2)"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#def experiment_to_graph_data(time_differences, eid):\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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