demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import numpy as np\n",
    "import math\n",
    "import tensorflow as tf\n",
    "import tensorflow_probability as tfp\n",
    "\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from jacques import kcqe\n",
    "\n",
    "# If in dark mode, uncomment the following line\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data generation adapted from https://matplotlib.org/stable/gallery/mplot3d/scatter3d.html\n",
    "# Fix random state for reproducibility\n",
    "np.random.seed(9731)\n",
    "\n",
    "np.random.rand(5)\n",
    "\n",
    "def randrange(n, vmin, vmax):\n",
    "    \"\"\"\n",
    "    Helper function to make an array of random numbers having shape (n, )\n",
    "    with each number distributed Uniform(vmin, vmax).\n",
    "    \"\"\"\n",
    "    return (vmax - vmin)*np.random.rand(n) + vmin\n",
    "\n",
    "n = 500\n",
    "\n",
    "#Create random predictors x0 and x1\n",
    "x0 = randrange(n, -2. * np.pi, 2. * np.pi)\n",
    "x1 = randrange(n, -2. * np.pi, 2. * np.pi)\n",
    "\n",
    "#Create response variable y\n",
    "y = 10. * np.cos(x0) + 0.5 * x1**2 + np.random.normal(loc=0., scale=5., size=n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Combine x0 and x1 into a single data array of predictors \n",
    "\n",
    "\n",
    "x = np.concatenate([x0[:, np.newaxis], x1[:, np.newaxis]], axis=1)\n",
    "\n",
    "grid_size = 120\n",
    "\n",
    "#Creates a grid of x values for test set\n",
    "x_test = np.concatenate(\n",
    "    [np.tile(np.linspace(-2. * np.pi, 2. * np.pi, num = grid_size), grid_size)[:, np.newaxis],\n",
    "        np.repeat(np.linspace(-2. * np.pi, 2. * np.pi, num = grid_size), grid_size)[:, np.newaxis]],\n",
    "    axis=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# x_train_val\n",
    "x = np.reshape(x, (1, 500, 2))\n",
    "# y_train_val\n",
    "y = np.reshape(y, (1, 500))\n",
    "\n",
    "x_test = np.reshape(x_test, (1, 14400, 2))\n",
    "\n",
    "x = tf.constant(x, dtype=tf.float32)\n",
    "y = tf.constant(y, dtype=tf.float32)\n",
    "x_test = tf.constant(x_test, dtype=tf.float32)\n",
    "    \n",
    "block_size = 50\n",
    "num_blocks = math.floor(y.shape[1]/block_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "kcqe_obj = kcqe.KCQE(p=2)\n",
    "generator = kcqe_obj.generator(x, y, batch_size = 10, block_size = block_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 50, 2)\n",
      "(10, 50)\n",
      "(10, 350, 2)\n",
      "(10, 350)\n"
     ]
    }
   ],
   "source": [
    "x_val, x_train, y_val, y_train = next(generator)\n",
    "print(x_val.shape)\n",
    "print(y_val.shape)\n",
    "print(x_train.shape)\n",
    "print(y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch idx = 0\n",
      "batch idx = 0\n",
      "loss idx = 1\n",
      "param estimates vec = \n",
      "[2.9000025 2.9000049 1.0999191]\n",
      "loss = \n",
      "1.842192\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.17958276,  0.07446898, -0.00394023], dtype=float32)>]\n",
      "epoch idx = 0\n",
      "batch idx = 1\n",
      "loss idx = 1loss idx = 1\n",
      "param estimates vec = \n",
      "[2.799888  2.8002446 1.1998558]\n",
      "loss = \n",
      "1.8236434\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.19167222,  0.06955977, -0.00393211], dtype=float32)>]\n",
      "epoch idx = 0\n",
      "batch idx = 2\n",
      "loss idx = 1loss idx = 1loss idx = 1\n",
      "param estimates vec = \n",
      "[2.6996505 2.7006023 1.2986425]\n",
      "loss = \n",
      "1.8083805\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.19662091,  0.06852128, -0.0031915 ], dtype=float32)>]\n",
      "epoch idx = 0\n",
      "batch idx = 3\n",
      "loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
      "param estimates vec = \n",
      "[2.599317  2.6033454 1.3970671]\n",
      "loss = \n",
      "1.7607116\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.19837826,  0.04842849, -0.00323941], dtype=float32)>]\n",
      "epoch idx = 0\n",
      "batch idx = 4\n",
      "loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
      "param estimates vec = \n",
      "[2.4987133 2.5062532 1.4957827]\n",
      "loss = \n",
      "1.729641\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.21125045,  0.05691139, -0.00352802], dtype=float32)>]\n",
      "epoch idx = 0\n",
      "batch idx = 5\n",
      "loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
      "param estimates vec = \n",
      "[2.3981245 2.4076552 1.5923631]\n",
      "loss = \n",
      "1.7086257\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.20089568,  0.08428677, -0.00242907], dtype=float32)>]\n",
      "epoch idx = 0\n",
      "batch idx = 6\n",
      "loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
      "param estimates vec = \n",
      "[2.2981992 2.307796  1.6861664]\n",
      "loss = \n",
      "1.6796335\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.17688504,  0.09221584, -0.00199667], dtype=float32)>]\n",
      "epoch idx = 0\n",
      "batch idx = 7\n",
      "loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
      "param estimates vec = \n",
      "[2.1989205 2.207567  1.7781724]\n",
      "loss = \n",
      "1.6723717\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.17165783,  0.07743218, -0.00206914], dtype=float32)>]\n",
      "epoch idx = 0\n",
      "batch idx = 8\n",
      "loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
      "param estimates vec = \n",
      "[2.1005316 2.1073053 1.8696551]\n",
      "loss = \n",
      "1.6561368\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.16000824,  0.07370939, -0.00234353], dtype=float32)>]\n",
      "epoch idx = 0\n",
      "batch idx = 9\n",
      "loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
      "param estimates vec = \n",
      "[2.0035422 2.006688  1.961708 ]\n",
      "loss = \n",
      "1.61352\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.14190692,  0.07871971, -0.00270886], dtype=float32)>]\n",
      "epoch idx = 1\n",
      "batch idx = 0\n",
      "loss idx = 2\n",
      "param estimates vec = \n",
      "[1.9083072 1.9061228 2.054336 ]\n",
      "loss = \n",
      "1.5810052\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.12730442,  0.07395212, -0.00274613], dtype=float32)>]\n",
      "epoch idx = 1\n",
      "batch idx = 1\n",
      "loss idx = 2loss idx = 2\n",
      "param estimates vec = \n",
      "[1.8153732 1.8066419 2.1458778]\n",
      "loss = \n",
      "1.5688558\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.1093511 ,  0.06160902, -0.00212804], dtype=float32)>]\n",
      "epoch idx = 1\n",
      "batch idx = 2\n",
      "loss idx = 2loss idx = 2loss idx = 2\n",
      "param estimates vec = \n",
      "[1.7236977 1.7076926 2.2364676]\n",
      "loss = \n",
      "1.5598991\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.12207814,  0.06540862, -0.00209805], dtype=float32)>]\n",
      "epoch idx = 1\n",
      "batch idx = 3\n",
      "loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
      "param estimates vec = \n",
      "[1.6337197 1.6084582 2.3253818]\n",
      "loss = \n",
      "1.5470736\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.10881519,  0.07389895, -0.00179646], dtype=float32)>]\n",
      "epoch idx = 1\n",
      "batch idx = 4\n",
      "loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
      "param estimates vec = \n",
      "[1.545393  1.5090497 2.4130933]\n",
      "loss = \n",
      "1.5377227\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.10437928,  0.07311903, -0.00185756], dtype=float32)>]\n",
      "epoch idx = 1\n",
      "batch idx = 5\n",
      "loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
      "param estimates vec = \n",
      "[1.4591417 1.4094998 2.4997888]\n",
      "loss = \n",
      "1.5218382\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.09200424,  0.07306314, -0.00185085], dtype=float32)>]\n",
      "epoch idx = 1\n",
      "batch idx = 6\n",
      "loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
      "param estimates vec = \n",
      "[1.3757746 1.3093805 2.5852606]\n",
      "loss = \n",
      "1.493098\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.07339107,  0.07868449, -0.00173159], dtype=float32)>]\n",
      "epoch idx = 1\n",
      "batch idx = 7\n",
      "loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
      "param estimates vec = \n",
      "[1.2970629 1.2114276 2.6696327]\n",
      "loss = \n",
      "1.4758615\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.04089103,  0.05057912, -0.00170977], dtype=float32)>]\n",
      "epoch idx = 1\n",
      "batch idx = 8\n",
      "loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
      "param estimates vec = \n",
      "[1.2237631 1.1143844 2.7522388]\n",
      "loss = \n",
      "1.4738764\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.02324354,  0.05958575, -0.00146456], dtype=float32)>]\n",
      "epoch idx = 1\n",
      "batch idx = 9\n",
      "loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
      "param estimates vec = \n",
      "[1.1557505 1.018653  2.832692 ]\n",
      "loss = \n",
      "1.4623235\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.01752051,  0.05452095, -0.0012908 ], dtype=float32)>]\n",
      "epoch idx = 2\n",
      "batch idx = 0\n",
      "loss idx = 3\n",
      "param estimates vec = \n",
      "[1.0925932 0.9246962 2.9104476]\n",
      "loss = \n",
      "1.4505153\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.01629195,  0.04881333, -0.00107566], dtype=float32)>]\n",
      "epoch idx = 2\n",
      "batch idx = 1\n",
      "loss idx = 3loss idx = 3\n",
      "param estimates vec = \n",
      "[1.0356227 0.8334024 2.9851775]\n",
      "loss = \n",
      "1.4470191\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00639804,  0.0396426 , -0.00091386], dtype=float32)>]\n",
      "epoch idx = 2\n",
      "batch idx = 2\n",
      "loss idx = 3loss idx = 3loss idx = 3\n",
      "param estimates vec = \n",
      "[0.9846718  0.74347967 3.0569613 ]\n",
      "loss = \n",
      "1.4486709\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01123504,  0.04742689, -0.00085114], dtype=float32)>]\n",
      "epoch idx = 2\n",
      "batch idx = 3\n",
      "loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
      "param estimates vec = \n",
      "[0.9397962  0.65534556 3.1259131 ]\n",
      "loss = \n",
      "1.4449323\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01833931,  0.04263168, -0.00080159], dtype=float32)>]\n",
      "epoch idx = 2\n",
      "batch idx = 4\n",
      "loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
      "param estimates vec = \n",
      "[0.9016608  0.57070667 3.19153   ]\n",
      "loss = \n",
      "1.4487878\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.03235657,  0.02838082, -0.00061536], dtype=float32)>]\n",
      "epoch idx = 2\n",
      "batch idx = 5\n",
      "loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
      "param estimates vec = \n",
      "[0.8698655  0.49141037 3.2539175 ]\n",
      "loss = \n",
      "1.445109\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.03447305,  0.01357921, -0.00056298], dtype=float32)>]\n",
      "epoch idx = 2\n",
      "batch idx = 6\n",
      "loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
      "param estimates vec = \n",
      "[0.8435799  0.41644228 3.3125002 ]\n",
      "loss = \n",
      "1.4470602\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.03117552,  0.01677602, -0.00036749], dtype=float32)>]\n",
      "epoch idx = 2\n",
      "batch idx = 7\n",
      "loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
      "param estimates vec = \n",
      "[0.82223934 0.34924883 3.3672285 ]\n",
      "loss = \n",
      "1.4425766\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.03006091, -0.00645812, -0.0002804 ], dtype=float32)>]\n",
      "epoch idx = 2\n",
      "batch idx = 8\n",
      "loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
      "param estimates vec = \n",
      "[0.8071481  0.29143378 3.4188473 ]\n",
      "loss = \n",
      "1.4469512\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.04953224, -0.01920903, -0.00036029], dtype=float32)>]\n",
      "epoch idx = 2\n",
      "batch idx = 9\n",
      "loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
      "param estimates vec = \n",
      "[0.7975068  0.24065925 3.4674718 ]\n",
      "loss = \n",
      "1.4483721\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.04651206, -0.01077145, -0.00032277], dtype=float32)>]\n",
      "epoch idx = 3\n",
      "batch idx = 0\n",
      "loss idx = 4\n",
      "param estimates vec = \n",
      "[0.7934272 0.1989816 3.5129714]\n",
      "loss = \n",
      "1.4583516\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.05291445, -0.02532014, -0.00023899], dtype=float32)>]\n",
      "epoch idx = 3\n",
      "batch idx = 1\n",
      "loss idx = 4loss idx = 4\n",
      "param estimates vec = \n",
      "[0.7951882 0.168268  3.555098 ]\n",
      "loss = \n",
      "1.450285\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.06133314, -0.03958578, -0.00013258], dtype=float32)>]\n",
      "epoch idx = 3\n",
      "batch idx = 2\n",
      "loss idx = 4loss idx = 4loss idx = 4\n",
      "param estimates vec = \n",
      "[0.802463   0.14781263 3.594179  ]\n",
      "loss = \n",
      "1.4521937\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.0633002 , -0.04125423, -0.00013674], dtype=float32)>]\n",
      "epoch idx = 3\n",
      "batch idx = 3\n",
      "loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
      "param estimates vec = \n",
      "[0.8148231  0.13747549 3.630944  ]\n",
      "loss = \n",
      "1.4536517\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.06380248, -0.04573262, -0.00022409], dtype=float32)>]\n",
      "epoch idx = 3\n",
      "batch idx = 4\n",
      "loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
      "param estimates vec = \n",
      "[0.83220524 0.1379679  3.665462  ]\n",
      "loss = \n",
      "1.4534432\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.06799801, -0.05517248, -0.00019579], dtype=float32)>]\n",
      "epoch idx = 3\n",
      "batch idx = 5\n",
      "loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
      "param estimates vec = \n",
      "[0.85327095 0.1471979  3.697747  ]\n",
      "loss = \n",
      "1.4548279\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.05777238, -0.04949598, -0.00015911], dtype=float32)>]\n",
      "epoch idx = 3\n",
      "batch idx = 6\n",
      "loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
      "param estimates vec = \n",
      "[0.8768133 0.1629495 3.7281516]\n",
      "loss = \n",
      "1.452676\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.04767168, -0.04155282, -0.00018634], dtype=float32)>]\n",
      "epoch idx = 3\n",
      "batch idx = 7\n",
      "loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
      "param estimates vec = \n",
      "[0.9023623  0.18509561 3.7569134 ]\n",
      "loss = \n",
      "1.4521537\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.04458722, -0.04428627, -0.00019758], dtype=float32)>]\n",
      "epoch idx = 3\n",
      "batch idx = 8\n",
      "loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
      "param estimates vec = \n",
      "[0.9285128  0.21183234 3.7841148 ]\n",
      "loss = \n",
      "1.4497753\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.03093106, -0.03719429, -0.00018419], dtype=float32)>]\n",
      "epoch idx = 3\n",
      "batch idx = 9\n",
      "loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
      "param estimates vec = \n",
      "[0.95514625 0.2389763  3.8103368 ]\n",
      "loss = \n",
      "1.4476804\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.02996248, -0.01558652, -0.0002619 ], dtype=float32)>]\n",
      "epoch idx = 4\n",
      "batch idx = 0\n",
      "loss idx = 5\n",
      "param estimates vec = \n",
      "[0.98284054 0.26766473 3.8354344 ]\n",
      "loss = \n",
      "1.440324\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.0362909 , -0.02199792, -0.00021823], dtype=float32)>]\n",
      "epoch idx = 4\n",
      "batch idx = 1\n",
      "loss idx = 5loss idx = 5\n",
      "param estimates vec = \n",
      "[1.0107948  0.29471922 3.8596227 ]\n",
      "loss = \n",
      "1.4389327\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.0285775 , -0.00514138, -0.00023646], dtype=float32)>]\n",
      "epoch idx = 4\n",
      "batch idx = 2\n",
      "loss idx = 5loss idx = 5loss idx = 5\n",
      "param estimates vec = \n",
      "[1.0375991  0.32115778 3.8833475 ]\n",
      "loss = \n",
      "1.4450333\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01402356, -0.00979149, -0.00029777], dtype=float32)>]\n",
      "epoch idx = 4\n",
      "batch idx = 3\n",
      "loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
      "param estimates vec = \n",
      "[1.0639434 0.3475969 3.9063044]\n",
      "loss = \n",
      "1.4459312\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01987683, -0.01270972, -0.00023561], dtype=float32)>]\n",
      "epoch idx = 4\n",
      "batch idx = 4\n",
      "loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
      "param estimates vec = \n",
      "[1.0880895  0.37248653 3.9287438 ]\n",
      "loss = \n",
      "1.4422848\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00185204, -0.0044181 , -0.00026452], dtype=float32)>]\n",
      "epoch idx = 4\n",
      "batch idx = 5\n",
      "loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
      "param estimates vec = \n",
      "[1.1112101  0.39394766 3.9504275 ]\n",
      "loss = \n",
      "1.4337175\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01150758,  0.00605297, -0.00021433], dtype=float32)>]\n",
      "epoch idx = 4\n",
      "batch idx = 6\n",
      "loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
      "param estimates vec = \n",
      "[1.1316968  0.41228497 3.971874  ]\n",
      "loss = \n",
      "1.4485946\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.0052239 ,  0.00601129, -0.00028764], dtype=float32)>]\n",
      "epoch idx = 4\n",
      "batch idx = 7\n",
      "loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
      "param estimates vec = \n",
      "[1.1492966 0.4286675 3.993079 ]\n",
      "loss = \n",
      "1.4374295\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00989177,  0.00146055, -0.00028105], dtype=float32)>]\n",
      "epoch idx = 4\n",
      "batch idx = 8\n",
      "loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
      "param estimates vec = \n",
      "[1.1637803  0.44111693 4.014187  ]\n",
      "loss = \n",
      "1.4491439\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.01446706,  0.01222664, -0.00029883], dtype=float32)>]\n",
      "epoch idx = 4\n",
      "batch idx = 9\n",
      "loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
      "param estimates vec = \n",
      "[1.1744717  0.44987336 4.035012  ]\n",
      "loss = \n",
      "1.4431133\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.02338649,  0.01270325, -0.0002649 ], dtype=float32)>]\n",
      "epoch idx = 5\n",
      "batch idx = 0\n",
      "loss idx = 6\n",
      "param estimates vec = \n",
      "[1.1833935  0.45439652 4.0555754 ]\n",
      "loss = \n",
      "1.4366727\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00745552,  0.016912  , -0.00026216], dtype=float32)>]\n",
      "epoch idx = 5\n",
      "batch idx = 1\n",
      "loss idx = 6loss idx = 6\n",
      "param estimates vec = \n",
      "[1.1888099  0.45567632 4.0760813 ]\n",
      "loss = \n",
      "1.4386952\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.02501199,  0.01383792, -0.00028872], dtype=float32)>]\n",
      "epoch idx = 5\n",
      "batch idx = 2\n",
      "loss idx = 6loss idx = 6loss idx = 6\n",
      "param estimates vec = \n",
      "[1.1923411  0.45134878 4.096587  ]\n",
      "loss = \n",
      "1.446478\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.01282045,  0.02668777, -0.00029474], dtype=float32)>]\n",
      "epoch idx = 5\n",
      "batch idx = 3\n",
      "loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
      "param estimates vec = \n",
      "[1.1932838  0.44618145 4.116813  ]\n",
      "loss = \n",
      "1.4452852\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.02071659,  0.00596254, -0.00024882], dtype=float32)>]\n",
      "epoch idx = 5\n",
      "batch idx = 4\n",
      "loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
      "param estimates vec = \n",
      "[1.1914343  0.43974406 4.1369996 ]\n",
      "loss = \n",
      "1.4334708\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.02455385,  0.00834037, -0.00028027], dtype=float32)>]\n",
      "epoch idx = 5\n",
      "batch idx = 5\n",
      "loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
      "param estimates vec = \n",
      "[1.1875178  0.43252608 4.156955  ]\n",
      "loss = \n",
      "1.4383688\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.0201391 ,  0.00650353, -0.0002482 ], dtype=float32)>]\n",
      "epoch idx = 5\n",
      "batch idx = 6\n",
      "loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
      "param estimates vec = \n",
      "[1.1832361  0.42348263 4.176452  ]\n",
      "loss = \n",
      "1.4382118\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00647444,  0.01171121, -0.00020874], dtype=float32)>]\n",
      "epoch idx = 5\n",
      "batch idx = 7\n",
      "loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
      "param estimates vec = \n",
      "[1.1765053  0.41288206 4.1957498 ]\n",
      "loss = \n",
      "1.4452323\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.0251914 ,  0.01115507, -0.00023986], dtype=float32)>]\n",
      "epoch idx = 5\n",
      "batch idx = 8\n",
      "loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
      "param estimates vec = \n",
      "[1.1679971 0.4008849 4.2147465]\n",
      "loss = \n",
      "1.4429992\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.02108202,  0.01099462, -0.00022027], dtype=float32)>]\n",
      "epoch idx = 5\n",
      "batch idx = 9\n",
      "loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
      "param estimates vec = \n",
      "[1.1584523  0.38807756 4.233438  ]\n",
      "loss = \n",
      "1.4429775\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.01588852,  0.00880811, -0.00021405], dtype=float32)>]\n",
      "epoch idx = 6\n",
      "batch idx = 0\n",
      "loss idx = 7\n",
      "param estimates vec = \n",
      "[1.1485958  0.37564033 4.2517805 ]\n",
      "loss = \n",
      "1.4357446\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.01032232,  0.00370391, -0.00020194], dtype=float32)>]\n",
      "epoch idx = 6\n",
      "batch idx = 1\n",
      "loss idx = 7loss idx = 7\n",
      "param estimates vec = \n",
      "[1.139312   0.35905427 4.27008   ]\n",
      "loss = \n",
      "1.4395021\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00291093,  0.02407301, -0.00024052], dtype=float32)>]\n",
      "epoch idx = 6\n",
      "batch idx = 2\n",
      "loss idx = 7loss idx = 7loss idx = 7\n",
      "param estimates vec = \n",
      "[1.1296252  0.34453222 4.28805   ]\n",
      "loss = \n",
      "1.4375937\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.01074547, -0.00236056, -0.00019692], dtype=float32)>]\n",
      "epoch idx = 6\n",
      "batch idx = 3\n",
      "loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
      "param estimates vec = \n",
      "[1.1207823 0.3318469 4.305574 ]\n",
      "loss = \n",
      "1.4405926\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00046233, -0.00217559, -0.00017456], dtype=float32)>]\n",
      "epoch idx = 6\n",
      "batch idx = 4\n",
      "loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
      "param estimates vec = \n",
      "[1.1126881  0.32245648 4.3226776 ]\n",
      "loss = \n",
      "1.4392704\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00060603, -0.00932529, -0.0001712 ], dtype=float32)>]\n",
      "epoch idx = 6\n",
      "batch idx = 5\n",
      "loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
      "param estimates vec = \n",
      "[1.1061152  0.31639445 4.339199  ]\n",
      "loss = \n",
      "1.4316002\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00637821, -0.01074126, -0.00014183], dtype=float32)>]\n",
      "epoch idx = 6\n",
      "batch idx = 6\n",
      "loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
      "param estimates vec = \n",
      "[1.1009691  0.31413436 4.355367  ]\n",
      "loss = \n",
      "1.4352622\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00670968, -0.0140708 , -0.00016537], dtype=float32)>]\n",
      "epoch idx = 6\n",
      "batch idx = 7\n",
      "loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
      "param estimates vec = \n",
      "[1.0963562  0.31563038 4.3712034 ]\n",
      "loss = \n",
      "1.4431243\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00044027, -0.01531911, -0.00016278], dtype=float32)>]\n",
      "epoch idx = 6\n",
      "batch idx = 8\n",
      "loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
      "param estimates vec = \n",
      "[1.0941831  0.31885317 4.3869133 ]\n",
      "loss = \n",
      "1.4383785\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01629994, -0.00801161, -0.00018586], dtype=float32)>]\n",
      "epoch idx = 6\n",
      "batch idx = 9\n",
      "loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
      "param estimates vec = \n",
      "[1.0921764 0.3247177 4.402421 ]\n",
      "loss = \n",
      "1.442446\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00029344, -0.01255248, -0.00017266], dtype=float32)>]\n",
      "epoch idx = 7\n",
      "batch idx = 0\n",
      "loss idx = 8\n",
      "param estimates vec = \n",
      "[1.0906609  0.33380148 4.417589  ]\n",
      "loss = \n",
      "1.439075\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00242848, -0.01598314, -0.00015042], dtype=float32)>]\n",
      "epoch idx = 7\n",
      "batch idx = 1\n",
      "loss idx = 8loss idx = 8\n",
      "param estimates vec = \n",
      "[1.0894346  0.34302503 4.4327664 ]\n",
      "loss = \n",
      "1.4470668\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00117162, -0.00416337, -0.00019224], dtype=float32)>]\n",
      "epoch idx = 7\n",
      "batch idx = 2\n",
      "loss idx = 8loss idx = 8loss idx = 8\n",
      "param estimates vec = \n",
      "[1.0869502 0.3525718 4.4478645]\n",
      "loss = \n",
      "1.4433887\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.01082762, -0.00495951, -0.00017937], dtype=float32)>]\n",
      "epoch idx = 7\n",
      "batch idx = 3\n",
      "loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
      "param estimates vec = \n",
      "[1.0843853  0.36318544 4.462837  ]\n",
      "loss = \n",
      "1.4384784\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00245179, -0.00814662, -0.00017114], dtype=float32)>]\n",
      "epoch idx = 7\n",
      "batch idx = 4\n",
      "loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
      "param estimates vec = \n",
      "[1.0835631  0.37384227 4.4773154 ]\n",
      "loss = \n",
      "1.4315205\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01168082, -0.00428121, -0.00011989], dtype=float32)>]\n",
      "epoch idx = 7\n",
      "batch idx = 5\n",
      "loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
      "param estimates vec = \n",
      "[1.0842719  0.38367778 4.491507  ]\n",
      "loss = \n",
      "1.4380674\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01123098, -0.00073078, -0.0001404 ], dtype=float32)>]\n",
      "epoch idx = 7\n",
      "batch idx = 6\n",
      "loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
      "param estimates vec = \n",
      "[1.085438   0.39336017 4.5056567 ]\n",
      "loss = \n",
      "1.433667\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00402009, -0.00313354, -0.00016791], dtype=float32)>]\n",
      "epoch idx = 7\n",
      "batch idx = 7\n",
      "loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
      "param estimates vec = \n",
      "[1.0857435  0.40166748 4.519708  ]\n",
      "loss = \n",
      "1.44164\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00572438,  0.00187825, -0.00015916], dtype=float32)>]\n",
      "epoch idx = 7\n",
      "batch idx = 8\n",
      "loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
      "param estimates vec = \n",
      "[1.0860194 0.4078472 4.5334926]\n",
      "loss = \n",
      "1.4473066\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 7.4390368e-06,  5.3982227e-03, -1.3540038e-04], dtype=float32)>]\n",
      "epoch idx = 7\n",
      "batch idx = 9\n",
      "loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
      "param estimates vec = \n",
      "[1.0861707  0.41147858 4.5473948 ]\n",
      "loss = \n",
      "1.443031\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00074206,  0.00783633, -0.00018063], dtype=float32)>]\n",
      "epoch idx = 8\n",
      "batch idx = 0\n",
      "loss idx = 9\n",
      "param estimates vec = \n",
      "[1.0865843 0.4135923 4.5610366]\n",
      "loss = \n",
      "1.4426723\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00206831,  0.0046567 , -0.00013298], dtype=float32)>]\n",
      "epoch idx = 8\n",
      "batch idx = 1\n",
      "loss idx = 9loss idx = 9\n",
      "param estimates vec = \n",
      "[1.086962   0.41516757 4.5744743 ]\n",
      "loss = \n",
      "1.4391497\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-2.2409678e-05,  1.3354786e-03, -1.3624398e-04], dtype=float32)>]\n",
      "epoch idx = 8\n",
      "batch idx = 2\n",
      "loss idx = 9loss idx = 9loss idx = 9\n",
      "param estimates vec = \n",
      "[1.089566   0.41517997 4.5874853 ]\n",
      "loss = \n",
      "1.4338611\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01670188,  0.00553294, -0.0001052 ], dtype=float32)>]\n",
      "epoch idx = 8\n",
      "batch idx = 3\n",
      "loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
      "param estimates vec = \n",
      "[1.0929747  0.41109115 4.600365  ]\n",
      "loss = \n",
      "1.4347651\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.0077092 ,  0.01595904, -0.00013646], dtype=float32)>]\n",
      "epoch idx = 8\n",
      "batch idx = 4\n",
      "loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
      "param estimates vec = \n",
      "[1.0958776  0.40398166 4.6131945 ]\n",
      "loss = \n",
      "1.4527373\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00133967,  0.01319903, -0.00014419], dtype=float32)>]\n",
      "epoch idx = 8\n",
      "batch idx = 5\n",
      "loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
      "param estimates vec = \n",
      "[1.0989645  0.39788467 4.625984  ]\n",
      "loss = \n",
      "1.433577\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00332123, -0.00131052, -0.00014414], dtype=float32)>]\n",
      "epoch idx = 8\n",
      "batch idx = 6\n",
      "loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
      "param estimates vec = \n",
      "[1.1025059  0.38908994 4.6386113 ]\n",
      "loss = \n",
      "1.4415176\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00538006,  0.01254487, -0.00012795], dtype=float32)>]\n",
      "epoch idx = 8\n",
      "batch idx = 7\n",
      "loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
      "param estimates vec = \n",
      "[1.1044495  0.38172662 4.651297  ]\n",
      "loss = \n",
      "1.4436734\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00904359, -0.00227912, -0.00015223], dtype=float32)>]\n",
      "epoch idx = 8\n",
      "batch idx = 8\n",
      "loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
      "param estimates vec = \n",
      "[1.1069456 0.3701836 4.664143 ]\n",
      "loss = \n",
      "1.4396455\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00524557,  0.01850639, -0.00016446], dtype=float32)>]\n",
      "epoch idx = 8\n",
      "batch idx = 9\n",
      "loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
      "param estimates vec = \n",
      "[1.108262   0.36057305 4.6769705 ]\n",
      "loss = \n",
      "1.4383945\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00667872, -0.00315757, -0.0001439 ], dtype=float32)>]\n",
      "epoch idx = 9\n",
      "batch idx = 0\n",
      "loss idx = 10\n",
      "param estimates vec = \n",
      "[1.1098832 0.3504471 4.6898117]\n",
      "loss = \n",
      "1.4454774\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00302575,  0.00534486, -0.00014687], dtype=float32)>]\n",
      "epoch idx = 9\n",
      "batch idx = 1\n",
      "loss idx = 10loss idx = 10\n",
      "param estimates vec = \n",
      "[1.1115258  0.34209138 4.7024536 ]\n",
      "loss = \n",
      "1.4405192\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00122528, -0.00301396, -0.00012089], dtype=float32)>]\n",
      "epoch idx = 9\n",
      "batch idx = 2\n",
      "loss idx = 10loss idx = 10loss idx = 10\n",
      "param estimates vec = \n",
      "[1.1119335  0.33700937 4.714936  ]\n",
      "loss = \n",
      "1.4491068\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00751026, -0.00917777, -0.00012279], dtype=float32)>]\n",
      "epoch idx = 9\n",
      "batch idx = 3\n",
      "loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
      "param estimates vec = \n",
      "[1.1127315  0.33444378 4.727139  ]\n",
      "loss = \n",
      "1.4446946\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00296978, -0.00748824, -0.00010637], dtype=float32)>]\n",
      "epoch idx = 9\n",
      "batch idx = 4\n",
      "loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
      "param estimates vec = \n",
      "[1.1133637 0.3352313 4.739198 ]\n",
      "loss = \n",
      "1.4422052\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00061911, -0.01139222, -0.00011853], dtype=float32)>]\n",
      "epoch idx = 9\n",
      "batch idx = 5\n",
      "loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
      "param estimates vec = \n",
      "[1.1135654  0.33966425 4.7511787 ]\n",
      "loss = \n",
      "1.438652\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.0025362 , -0.01357338, -0.00012398], dtype=float32)>]\n",
      "epoch idx = 9\n",
      "batch idx = 6\n",
      "loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
      "param estimates vec = \n",
      "[1.114643   0.34629375 4.7629676 ]\n",
      "loss = \n",
      "1.4348353\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.006095  , -0.00950801, -0.00010942], dtype=float32)>]\n",
      "epoch idx = 9\n",
      "batch idx = 7\n",
      "loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
      "param estimates vec = \n",
      "[1.1149516 0.3518664 4.7748837]\n",
      "loss = \n",
      "1.447805\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00451319,  0.00153804, -0.00014353], dtype=float32)>]\n",
      "epoch idx = 9\n",
      "batch idx = 8\n",
      "loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
      "param estimates vec = \n",
      "[1.1145345  0.35975656 4.7867503 ]\n",
      "loss = \n",
      "1.4338326\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00469216, -0.01023915, -0.00012396], dtype=float32)>]\n",
      "epoch idx = 9\n",
      "batch idx = 9\n",
      "loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
      "param estimates vec = \n",
      "[1.1138574  0.36365664 4.798547  ]\n",
      "loss = \n",
      "1.4366425\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00200888,  0.01156584, -0.00012051], dtype=float32)>]\n"
     ]
    }
   ],
   "source": [
    "# plug in generator instead of raw data to get away from calculating 7-day avg\n",
    "# initialize parameters at some bad values; otherwise, convergence is instantaneous\n",
    "param_vec = kcqe_obj.fit(xval_batch_gen = generator,\n",
    "    num_blocks = num_blocks, \n",
    "    tau=tf.constant(np.array([0.1, 0.5, 0.9]), dtype=tf.float32),\n",
    "    optim_method=\"adam\", \n",
    "    num_epochs=10, \n",
    "    learning_rate=0.1,\n",
    "    init_param_vec=tf.constant(np.array([3.0, 3.0, 1.0]), dtype=tf.float32),\n",
    "    verbose = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.arange(len(kcqe_obj.loss_trace)), kcqe_obj.loss_trace)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "q_hat = kcqe_obj.predict(param_vec,\n",
    "                         x_train=x,\n",
    "                         y_train=y,\n",
    "                         x_test=x_test,\n",
    "                         tau=tf.constant(np.array([0.1, 0.5, 0.9]), dtype=tf.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,8))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "#ax = Axes3D(fig)\n",
    "\n",
    "ax.scatter(x0, x1, y)\n",
    "\n",
    "x0_grid = x_test[..., 0].numpy().reshape((grid_size, grid_size))\n",
    "x1_grid = x_test[..., 1].numpy().reshape((grid_size, grid_size))\n",
    "q_grid_q10 = q_hat[..., 0].numpy().reshape((grid_size, grid_size))\n",
    "q_grid_median = q_hat[..., 1].numpy().reshape((grid_size, grid_size))\n",
    "q_grid_q90 = q_hat[..., 2].numpy().reshape((grid_size, grid_size))\n",
    "\n",
    "ax.plot_wireframe(x0_grid, x1_grid, q_grid_q10, rstride=10, cstride=10, color=\"red\")\n",
    "ax.plot_wireframe(x0_grid, x1_grid, q_grid_median, rstride=10, cstride=10)\n",
    "ax.plot_wireframe(x0_grid, x1_grid, q_grid_q90, rstride=10, cstride=10, color=\"orange\")\n",
    "\n",
    "ax.set_xlabel('X_0')\n",
    "ax.set_ylabel('X_1')\n",
    "ax.set_zlabel('Y')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "kcqe_obj = kcqe.KCQE(x_kernel='gaussian_full', p=2)\n",
    "generator = kcqe_obj.generator(x, y, batch_size = 10, block_size = block_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch idx = 0\n",
      "batch idx = 0\n",
      "loss idx = 1\n",
      "param estimates vec = \n",
      "[0.09999836 0.09999274 0.09998715 0.09953386]\n",
      "loss = \n",
      "1.6402513\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.3250575 , -0.04792129, -0.02594887, -0.00067621], dtype=float32)>]\n",
      "epoch idx = 0\n",
      "batch idx = 1\n",
      "loss idx = 1loss idx = 1\n",
      "param estimates vec = \n",
      "[0.19958878 0.19301751 0.11481524 0.18942   ]\n",
      "loss = \n",
      "1.6194605\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=\n",
      "array([-2.9279688e-01, -2.3657570e-02,  1.7156720e-02, -2.7801079e-04],\n",
      "      dtype=float32)>]\n",
      "epoch idx = 0\n",
      "batch idx = 2\n",
      "loss idx = 1loss idx = 1loss idx = 1\n",
      "param estimates vec = \n",
      "[0.29796183 0.2761684  0.08389858 0.27906877]\n",
      "loss = \n",
      "1.5841627\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.24453256, -0.01080618,  0.02483328, -0.00130381], dtype=float32)>]\n",
      "epoch idx = 0\n",
      "batch idx = 3\n",
      "loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
      "param estimates vec = \n",
      "[0.39443946 0.33660626 0.05513746 0.37005606]\n",
      "loss = \n",
      "1.5639019\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.20215715,  0.00675638,  0.00238688, -0.00191076], dtype=float32)>]\n",
      "epoch idx = 0\n",
      "batch idx = 4\n",
      "loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
      "param estimates vec = \n",
      "[0.48960364 0.3735551  0.03056316 0.4584576 ]\n",
      "loss = \n",
      "1.5361755\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.19330753,  0.01315719,  0.00019334, -0.00355138], dtype=float32)>]\n",
      "epoch idx = 0\n",
      "batch idx = 5\n",
      "loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
      "param estimates vec = \n",
      "[0.58307105 0.38692778 0.02550786 0.55002856]\n",
      "loss = \n",
      "1.5281783\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.16963328,  0.01913507, -0.01211669, -0.00385544], dtype=float32)>]\n",
      "epoch idx = 0\n",
      "batch idx = 6\n",
      "loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
      "param estimates vec = \n",
      "[0.6735619  0.38303286 0.05074482 0.6446692 ]\n",
      "loss = \n",
      "1.5114645\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.12928146,  0.01851572, -0.02884867, -0.0037433 ], dtype=float32)>]\n",
      "epoch idx = 0\n",
      "batch idx = 7\n",
      "loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
      "param estimates vec = \n",
      "[0.7607609  0.36849618 0.07802705 0.7416695 ]\n",
      "loss = \n",
      "1.4982831\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.10821951,  0.0144334 , -0.00528861, -0.00381252], dtype=float32)>]\n",
      "epoch idx = 0\n",
      "batch idx = 8\n",
      "loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
      "param estimates vec = \n",
      "[0.8439411  0.33779845 0.0971694  0.8406789 ]\n",
      "loss = \n",
      "1.4823743\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.08249655,  0.02660319,  0.00517765, -0.00444004], dtype=float32)>]\n",
      "epoch idx = 0\n",
      "batch idx = 9\n",
      "loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
      "param estimates vec = \n",
      "[0.9232868  0.3024898  0.12231627 0.9413978 ]\n",
      "loss = \n",
      "1.4897373\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.07286155,  0.01205962, -0.00890009, -0.00458791], dtype=float32)>]\n",
      "epoch idx = 1\n",
      "batch idx = 0\n",
      "loss idx = 2\n",
      "param estimates vec = \n",
      "[0.9970382  0.27022952 0.14061205 1.0429796 ]\n",
      "loss = \n",
      "1.481229\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.03605873,  0.00090227,  0.00448399, -0.00391939], dtype=float32)>]\n",
      "epoch idx = 1\n",
      "batch idx = 1\n",
      "loss idx = 2loss idx = 2\n",
      "param estimates vec = \n",
      "[1.0643114  0.24979137 0.15430433 1.1458089 ]\n",
      "loss = \n",
      "1.4754615\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.0127482 , -0.01205116,  0.0029543 , -0.00452619], dtype=float32)>]\n",
      "epoch idx = 1\n",
      "batch idx = 2\n",
      "loss idx = 2loss idx = 2loss idx = 2\n",
      "param estimates vec = \n",
      "[1.1249114  0.23880418 0.14114016 1.248789  ]\n",
      "loss = \n",
      "1.4779395\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.00061318, -0.01079481,  0.02969244, -0.00375957], dtype=float32)>]\n",
      "epoch idx = 1\n",
      "batch idx = 3\n",
      "loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
      "param estimates vec = \n",
      "[1.1780809  0.24374688 0.11576655 1.3528366 ]\n",
      "loss = \n",
      "1.4736712\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.02017299, -0.02246803,  0.01809866, -0.00457814], dtype=float32)>]\n",
      "epoch idx = 1\n",
      "batch idx = 4\n",
      "loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
      "param estimates vec = \n",
      "[1.2245026  0.25046742 0.12325899 1.4581219 ]\n",
      "loss = \n",
      "1.4834626\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.02160002, -0.00349446, -0.04107815, -0.00499831], dtype=float32)>]\n",
      "epoch idx = 1\n",
      "batch idx = 5\n",
      "loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
      "param estimates vec = \n",
      "[1.2654577  0.26792195 0.14452161 1.5625452 ]\n",
      "loss = \n",
      "1.4734219\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.01420503, -0.01829034, -0.02372216, -0.00351041], dtype=float32)>]\n",
      "epoch idx = 1\n",
      "batch idx = 6\n",
      "loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
      "param estimates vec = \n",
      "[1.3005276  0.2843766  0.14558935 1.6671828 ]\n",
      "loss = \n",
      "1.475789\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.0262715 , -0.00097435,  0.02888214, -0.00413799], dtype=float32)>]\n",
      "epoch idx = 1\n",
      "batch idx = 7\n",
      "loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
      "param estimates vec = \n",
      "[1.3297822  0.3000995  0.13145174 1.7710896 ]\n",
      "loss = \n",
      "1.4850681\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.03235551, -0.00121832,  0.02655624, -0.00355487], dtype=float32)>]\n",
      "epoch idx = 1\n",
      "batch idx = 8\n",
      "loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
      "param estimates vec = \n",
      "[1.3548157  0.3040187  0.11800842 1.8741741 ]\n",
      "loss = \n",
      "1.4821291\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.01936652,  0.01588403,  0.00103956, -0.00344409], dtype=float32)>]\n",
      "epoch idx = 1\n",
      "batch idx = 9\n",
      "loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
      "param estimates vec = \n",
      "[1.3759123  0.30664176 0.08676921 1.9773647 ]\n",
      "loss = \n",
      "1.4785596\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.02060813,  0.00146709,  0.03763166, -0.00392889], dtype=float32)>]\n",
      "epoch idx = 2\n",
      "batch idx = 0\n",
      "loss idx = 3\n",
      "param estimates vec = \n",
      "[1.3947673  0.31020707 0.05201839 2.0791633 ]\n",
      "loss = \n",
      "1.4767807\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.00417856, -0.00182016,  0.01230977, -0.00309878], dtype=float32)>]\n",
      "epoch idx = 2\n",
      "batch idx = 1\n",
      "loss idx = 3loss idx = 3\n",
      "param estimates vec = \n",
      "[1.4113834  0.3146126  0.02743187 2.1800866 ]\n",
      "loss = \n",
      "1.48352\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.00663341, -0.00177236, -0.01267152, -0.00327136], dtype=float32)>]\n",
      "epoch idx = 2\n",
      "batch idx = 2\n",
      "loss idx = 3loss idx = 3loss idx = 3\n",
      "param estimates vec = \n",
      "[1.4247768  0.31521884 0.015232   2.2792482 ]\n",
      "loss = \n",
      "1.4818169\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.0207851 ,  0.00513569, -0.01843324, -0.00277671], dtype=float32)>]\n",
      "epoch idx = 2\n",
      "batch idx = 3\n",
      "loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
      "param estimates vec = \n",
      "[1.4348984  0.3117367  0.01873958 2.3758717 ]\n",
      "loss = \n",
      "1.4846579\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.02459529,  0.0060431 , -0.02744554, -0.00232965], dtype=float32)>]\n",
      "epoch idx = 2\n",
      "batch idx = 4\n",
      "loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
      "param estimates vec = \n",
      "[1.4412855  0.31033218 0.04281929 2.4696493 ]\n",
      "loss = \n",
      "1.467225\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.03319721, -0.00261562, -0.04391843, -0.00207645], dtype=float32)>]\n",
      "epoch idx = 2\n",
      "batch idx = 5\n",
      "loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
      "param estimates vec = \n",
      "[1.4451069  0.29242945 0.07565408 2.5607624 ]\n",
      "loss = \n",
      "1.4715353\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.02320141,  0.02566747, -0.02390242, -0.00201919], dtype=float32)>]\n",
      "epoch idx = 2\n",
      "batch idx = 6\n",
      "loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
      "param estimates vec = \n",
      "[1.4456037  0.27885604 0.09251976 2.6492467 ]\n",
      "loss = \n",
      "1.47507\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.03430685, -0.00413443,  0.02614284, -0.00191484], dtype=float32)>]\n",
      "epoch idx = 2\n",
      "batch idx = 7\n",
      "loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
      "param estimates vec = \n",
      "[1.4437426  0.2697211  0.08769441 2.733957  ]\n",
      "loss = \n",
      "1.4700768\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.02633417, -0.00483444,  0.04310556, -0.00137942], dtype=float32)>]\n",
      "epoch idx = 2\n",
      "batch idx = 8\n",
      "loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
      "param estimates vec = \n",
      "[1.4382817  0.26897335 0.06894324 2.8155482 ]\n",
      "loss = \n",
      "1.4694979\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.04247834, -0.01125484,  0.03323054, -0.00147456], dtype=float32)>]\n",
      "epoch idx = 2\n",
      "batch idx = 9\n",
      "loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
      "param estimates vec = \n",
      "[1.4308465  0.28384215 0.06886585 2.8943603 ]\n",
      "loss = \n",
      "1.4802501\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.02745956, -0.02392134, -0.03848233, -0.00147867], dtype=float32)>]\n",
      "epoch idx = 3\n",
      "batch idx = 0\n",
      "loss idx = 4\n",
      "param estimates vec = \n",
      "[1.4215188  0.29722986 0.07678682 2.9703808 ]\n",
      "loss = \n",
      "1.4824563\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.02821134,  0.00024172, -0.01883159, -0.00137167], dtype=float32)>]\n",
      "epoch idx = 3\n",
      "batch idx = 1\n",
      "loss idx = 4loss idx = 4\n",
      "param estimates vec = \n",
      "[1.4112508  0.30473882 0.07895797 3.0438044 ]\n",
      "loss = \n",
      "1.4728497\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.01932339,  0.00701265,  0.0117266 , -0.00133829], dtype=float32)>]\n",
      "epoch idx = 3\n",
      "batch idx = 2\n",
      "loss idx = 4loss idx = 4loss idx = 4\n",
      "param estimates vec = \n",
      "[1.399996   0.31081176 0.08825455 3.11414   ]\n",
      "loss = \n",
      "1.4799534\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.02051906,  0.00114291, -0.01706656, -0.00108516], dtype=float32)>]\n",
      "epoch idx = 3\n",
      "batch idx = 3\n",
      "loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
      "param estimates vec = \n",
      "[1.3876789  0.30722994 0.08894125 3.1813455 ]\n",
      "loss = \n",
      "1.4712075\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.02205238,  0.01348474,  0.01787732, -0.00097166], dtype=float32)>]\n",
      "epoch idx = 3\n",
      "batch idx = 4\n",
      "loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
      "param estimates vec = \n",
      "[1.3752047  0.30474526 0.09225    3.245203  ]\n",
      "loss = \n",
      "1.478834\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.01324023, -0.00114463, -0.00615611, -0.00080575], dtype=float32)>]\n",
      "epoch idx = 3\n",
      "batch idx = 5\n",
      "loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
      "param estimates vec = \n",
      "[1.3629289  0.30484062 0.08799864 3.306882  ]\n",
      "loss = \n",
      "1.4748082\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.00953831, -0.00343216,  0.01653639, -0.00106805], dtype=float32)>]\n",
      "epoch idx = 3\n",
      "batch idx = 6\n",
      "loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
      "param estimates vec = \n",
      "[1.350866   0.30312118 0.09015243 3.3659344 ]\n",
      "loss = \n",
      "1.4746374\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.00911052,  0.00260153, -0.01361911, -0.00086059], dtype=float32)>]\n",
      "epoch idx = 3\n",
      "batch idx = 7\n",
      "loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
      "param estimates vec = \n",
      "[1.3384873  0.30389658 0.09882917 3.4227893 ]\n",
      "loss = \n",
      "1.4720842\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.01422016, -0.00333368, -0.01516823, -0.00091109], dtype=float32)>]\n",
      "epoch idx = 3\n",
      "batch idx = 8\n",
      "loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
      "param estimates vec = \n",
      "[1.3269209  0.29957598 0.11685781 3.4771311 ]\n",
      "loss = \n",
      "1.4648303\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.00299519,  0.00710023, -0.02312507, -0.0007506 ], dtype=float32)>]\n",
      "epoch idx = 3\n",
      "batch idx = 9\n",
      "loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
      "param estimates vec = \n",
      "[1.3148266  0.2988116  0.12277459 3.5285637 ]\n",
      "loss = \n",
      "1.4743724\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.01560555, -0.00442586,  0.02326446, -0.0005632 ], dtype=float32)>]\n",
      "epoch idx = 4\n",
      "batch idx = 0\n",
      "loss idx = 5\n",
      "param estimates vec = \n",
      "[1.3017737  0.30257574 0.12310667 3.5780244 ]\n",
      "loss = \n",
      "1.4797908\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.02018175, -0.00618352,  0.0113093 , -0.00075283], dtype=float32)>]\n",
      "epoch idx = 4\n",
      "batch idx = 1\n",
      "loss idx = 5loss idx = 5\n",
      "param estimates vec = \n",
      "[1.2872659  0.30286303 0.11277162 3.6253512 ]\n",
      "loss = \n",
      "1.475108\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.02570754,  0.00430433,  0.02394242, -0.00064994], dtype=float32)>]\n",
      "epoch idx = 4\n",
      "batch idx = 2\n",
      "loss idx = 5loss idx = 5loss idx = 5\n",
      "param estimates vec = \n",
      "[1.2731322  0.30696848 0.11337348 3.670641  ]\n",
      "loss = \n",
      "1.4722971\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.00901646, -0.00523284, -0.02230026, -0.00061789], dtype=float32)>]\n",
      "epoch idx = 4\n",
      "batch idx = 3\n",
      "loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
      "param estimates vec = \n",
      "[1.2589116  0.3109514  0.10928538 3.7139359 ]\n",
      "loss = \n",
      "1.46404\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.01302771, -0.0003346 ,  0.01035686, -0.00057372], dtype=float32)>]\n",
      "epoch idx = 4\n",
      "batch idx = 4\n",
      "loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
      "param estimates vec = \n",
      "[1.2453078  0.32435504 0.11366274 3.7549722 ]\n",
      "loss = \n",
      "1.4709655\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.00632606, -0.01322985, -0.01797429, -0.00044808], dtype=float32)>]\n",
      "epoch idx = 4\n",
      "batch idx = 5\n",
      "loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
      "param estimates vec = \n",
      "[1.2314651  0.3360994  0.12888493 3.7942252 ]\n",
      "loss = \n",
      "1.4835753\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.01376949,  0.00059237, -0.02526382, -0.00051789], dtype=float32)>]\n",
      "epoch idx = 4\n",
      "batch idx = 6\n",
      "loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
      "param estimates vec = \n",
      "[1.2174485  0.34236693 0.15313047 3.8317373 ]\n",
      "loss = \n",
      "1.460815\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.01324859,  0.00580429, -0.0236869 , -0.00048219], dtype=float32)>]\n",
      "epoch idx = 4\n",
      "batch idx = 7\n",
      "loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
      "param estimates vec = \n",
      "[1.201375   0.35483053 0.16433437 3.8676574 ]\n",
      "loss = \n",
      "1.4671414\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.0306579 , -0.0089381 ,  0.02374151, -0.00047621], dtype=float32)>]\n",
      "epoch idx = 4\n",
      "batch idx = 8\n",
      "loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
      "param estimates vec = \n",
      "[1.1845106  0.36888134 0.15849812 3.9020236 ]\n",
      "loss = \n",
      "1.4740248\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.02053007, -0.00355967,  0.0359517 , -0.00044492], dtype=float32)>]\n",
      "epoch idx = 4\n",
      "batch idx = 9\n",
      "loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
      "param estimates vec = \n",
      "[1.1652558  0.39058974 0.14442721 3.9346256 ]\n",
      "loss = \n",
      "1.4795599\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.03558068, -0.01177509,  0.02006264, -0.00035094], dtype=float32)>]\n",
      "epoch idx = 5\n",
      "batch idx = 0\n",
      "loss idx = 6\n",
      "param estimates vec = \n",
      "[1.1454283  0.4163618  0.13259892 3.9657214 ]\n",
      "loss = \n",
      "1.464806\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.02087525, -0.00791931, -0.00215895, -0.00037343], dtype=float32)>]\n",
      "epoch idx = 5\n",
      "batch idx = 1\n",
      "loss idx = 6loss idx = 6\n",
      "param estimates vec = \n",
      "[1.1250712  0.44121438 0.13246994 3.9956145 ]\n",
      "loss = \n",
      "1.4808171\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.0207957 , -0.00184795, -0.02372461, -0.00041286], dtype=float32)>]\n",
      "epoch idx = 5\n",
      "batch idx = 2\n",
      "loss idx = 6loss idx = 6loss idx = 6\n",
      "param estimates vec = \n",
      "[1.1047906  0.44693622 0.13493575 4.0245857 ]\n",
      "loss = \n",
      "1.46139\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.01570843,  0.0212195 , -0.00578459, -0.00045279], dtype=float32)>]\n",
      "epoch idx = 5\n",
      "batch idx = 3\n",
      "loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
      "param estimates vec = \n",
      "[1.0855312  0.4178543  0.15262885 4.0525603 ]\n",
      "loss = \n",
      "1.4766698\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.0072835 ,  0.04910354, -0.03521376, -0.00041004], dtype=float32)>]\n",
      "epoch idx = 5\n",
      "batch idx = 4\n",
      "loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
      "param estimates vec = \n",
      "[1.0672566  0.3922618  0.16913089 4.0793614 ]\n",
      "loss = \n",
      "1.4692125\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.00674693, -0.0011823 , -0.0009749 , -0.00034125], dtype=float32)>]\n",
      "epoch idx = 5\n",
      "batch idx = 5\n",
      "loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
      "param estimates vec = \n",
      "[1.0512686  0.3707772  0.17619345 4.1050196 ]\n",
      "loss = \n",
      "1.4674927\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.0051732 , -0.00250509,  0.0175836 , -0.00032015], dtype=float32)>]\n",
      "epoch idx = 5\n",
      "batch idx = 6\n",
      "loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
      "param estimates vec = \n",
      "[1.0382329  0.36414465 0.16725788 4.129839  ]\n",
      "loss = \n",
      "1.4656051\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.01249879, -0.01813798,  0.03455264, -0.00036616], dtype=float32)>]\n",
      "epoch idx = 5\n",
      "batch idx = 7\n",
      "loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
      "param estimates vec = \n",
      "[1.0275866  0.3732813  0.14463386 4.153872  ]\n",
      "loss = \n",
      "1.4672718\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.00994853, -0.02190788,  0.03366908, -0.00035772], dtype=float32)>]\n",
      "epoch idx = 5\n",
      "batch idx = 8\n",
      "loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
      "param estimates vec = \n",
      "[1.0182545  0.3914153  0.11069259 4.1772327 ]\n",
      "loss = \n",
      "1.4684888\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.00276058, -0.01444083,  0.03180678, -0.00036525], dtype=float32)>]\n",
      "epoch idx = 5\n",
      "batch idx = 9\n",
      "loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
      "param estimates vec = \n",
      "[1.0115402  0.4006161  0.09369537 4.1998343 ]\n",
      "loss = \n",
      "1.4765929\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.01445541,  0.01042739, -0.03108143, -0.0003278 ], dtype=float32)>]\n",
      "epoch idx = 6\n",
      "batch idx = 0\n",
      "loss idx = 7\n",
      "param estimates vec = \n",
      "[1.0083554  0.4019139  0.08855046 4.2216873 ]\n",
      "loss = \n",
      "1.4678197\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.02375967,  0.01014121, -0.02365518, -0.0003117 ], dtype=float32)>]\n",
      "epoch idx = 6\n",
      "batch idx = 1\n",
      "loss idx = 7loss idx = 7\n",
      "param estimates vec = \n",
      "[1.0073117  0.4064316  0.09625442 4.242969  ]\n",
      "loss = \n",
      "1.4707022\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.01497774, -0.00479318, -0.02883741, -0.00033468], dtype=float32)>]\n",
      "epoch idx = 6\n",
      "batch idx = 2\n",
      "loss idx = 7loss idx = 7loss idx = 7\n",
      "param estimates vec = \n",
      "[1.0085763  0.41015673 0.11021784 4.2636433 ]\n",
      "loss = \n",
      "1.4702549\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.01780765,  0.00053196, -0.01632995, -0.00031218], dtype=float32)>]\n",
      "epoch idx = 6\n",
      "batch idx = 3\n",
      "loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
      "param estimates vec = \n",
      "[1.0121263  0.4144175  0.12869242 4.2840033 ]\n",
      "loss = \n",
      "1.4649929\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.01921035, -0.00124442, -0.01355308, -0.00036409], dtype=float32)>]\n",
      "epoch idx = 6\n",
      "batch idx = 4\n",
      "loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
      "param estimates vec = \n",
      "[1.0179691  0.42057192 0.15119274 4.303747  ]\n",
      "loss = \n",
      "1.4653531\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.02082286, -0.00321031, -0.01335466, -0.00028697], dtype=float32)>]\n",
      "epoch idx = 6\n",
      "batch idx = 5\n",
      "loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
      "param estimates vec = \n",
      "[1.0260326  0.4283504  0.16142815 4.3229856 ]\n",
      "loss = \n",
      "1.4643946\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.02179791, -0.00305782,  0.02311244, -0.00029708], dtype=float32)>]\n",
      "epoch idx = 6\n",
      "batch idx = 6\n",
      "loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
      "param estimates vec = \n",
      "[1.0354291  0.43393147 0.15222403 4.34191   ]\n",
      "loss = \n",
      "1.468788\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.01631366,  0.00203263,  0.04290977, -0.00032736], dtype=float32)>]\n",
      "epoch idx = 6\n",
      "batch idx = 7\n",
      "loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
      "param estimates vec = \n",
      "[1.0454726  0.43487373 0.13510002 4.360373  ]\n",
      "loss = \n",
      "1.4682231\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.01182866,  0.0056418 ,  0.02072476, -0.0002865 ], dtype=float32)>]\n",
      "epoch idx = 6\n",
      "batch idx = 8\n",
      "loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
      "param estimates vec = \n",
      "[1.0549059  0.43380138 0.12272466 4.3784437 ]\n",
      "loss = \n",
      "1.474194\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.00251481,  0.00262401, -0.00732394, -0.00029027], dtype=float32)>]\n",
      "epoch idx = 6\n",
      "batch idx = 9\n",
      "loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
      "param estimates vec = \n",
      "[1.0630629  0.43213743 0.1217523  4.396091  ]\n",
      "loss = \n",
      "1.4801712\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.00302672,  0.00093529, -0.02370686, -0.00027388], dtype=float32)>]\n",
      "epoch idx = 7\n",
      "batch idx = 0\n",
      "loss idx = 8\n",
      "param estimates vec = \n",
      "[1.0706397  0.42809755 0.12982714 4.413541  ]\n",
      "loss = \n",
      "1.4746027\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.00138136,  0.00340035, -0.02085257, -0.00031249], dtype=float32)>]\n",
      "epoch idx = 7\n",
      "batch idx = 1\n",
      "loss idx = 8loss idx = 8\n",
      "param estimates vec = \n",
      "[1.0756589  0.41774595 0.14136603 4.4307494 ]\n",
      "loss = \n",
      "1.460237\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.01393123,  0.00896378, -0.0097927 , -0.00029718], dtype=float32)>]\n",
      "epoch idx = 7\n",
      "batch idx = 2\n",
      "loss idx = 8loss idx = 8loss idx = 8\n",
      "param estimates vec = \n",
      "[1.0795549  0.40373513 0.16072679 4.447423  ]\n",
      "loss = \n",
      "1.4726218\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.00489151,  0.00617523, -0.02072528, -0.00022828], dtype=float32)>]\n",
      "epoch idx = 7\n",
      "batch idx = 3\n",
      "loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
      "param estimates vec = \n",
      "[1.0835911  0.39476585 0.17351837 4.4636073 ]\n",
      "loss = \n",
      "1.47971\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.00375645, -0.00491437,  0.01087551, -0.00022596], dtype=float32)>]\n",
      "epoch idx = 7\n",
      "batch idx = 4\n",
      "loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
      "param estimates vec = \n",
      "[1.0863116  0.39466655 0.16991676 4.479358  ]\n",
      "loss = \n",
      "1.4747012\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.00691975, -0.01054209,  0.0348386 , -0.00022687], dtype=float32)>]\n",
      "epoch idx = 7\n",
      "batch idx = 5\n",
      "loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
      "param estimates vec = \n",
      "[1.0886346  0.40699264 0.15502921 4.4947195 ]\n",
      "loss = \n",
      "1.4702027\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.00104284, -0.01643643,  0.02711433, -0.0002262 ], dtype=float32)>]\n",
      "epoch idx = 7\n",
      "batch idx = 6\n",
      "loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
      "param estimates vec = \n",
      "[1.0901313  0.42093527 0.13920946 4.5097065 ]\n",
      "loss = \n",
      "1.479689\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.00443028, -0.00365726,  0.00539808, -0.00022048], dtype=float32)>]\n",
      "epoch idx = 7\n",
      "batch idx = 7\n",
      "loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
      "param estimates vec = \n",
      "[1.0906941  0.43547127 0.1346438  4.5245047 ]\n",
      "loss = \n",
      "1.46162\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.0057403 , -0.00249244, -0.02236363, -0.00025012], dtype=float32)>]\n",
      "epoch idx = 7\n",
      "batch idx = 8\n",
      "loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
      "param estimates vec = \n",
      "[1.0897138  0.44123152 0.13021454 4.5391827 ]\n",
      "loss = \n",
      "1.466509\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.01071349,  0.00959449,  0.00067255, -0.00025918], dtype=float32)>]\n",
      "epoch idx = 7\n",
      "batch idx = 9\n",
      "loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
      "param estimates vec = \n",
      "[1.0881763  0.4245595  0.13593873 4.5537376 ]\n",
      "loss = \n",
      "1.4616185\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.00464047,  0.02903827, -0.0222872 , -0.00025485], dtype=float32)>]\n",
      "epoch idx = 8\n",
      "batch idx = 0\n",
      "loss idx = 9\n",
      "param estimates vec = \n",
      "[1.0853024  0.4148813  0.15062658 4.568049  ]\n",
      "loss = \n",
      "1.4716642\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.01051533, -0.00717785, -0.02186439, -0.00022677], dtype=float32)>]\n",
      "epoch idx = 8\n",
      "batch idx = 1\n",
      "loss idx = 9loss idx = 9\n",
      "param estimates vec = \n",
      "[1.0815704  0.40993923 0.16589272 4.582159  ]\n",
      "loss = \n",
      "1.4710623\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.00796487, -0.00504899, -0.00448124, -0.00022943], dtype=float32)>]\n",
      "epoch idx = 8\n",
      "batch idx = 2\n",
      "loss idx = 9loss idx = 9loss idx = 9\n",
      "param estimates vec = \n",
      "[1.079315   0.40544975 0.16712639 4.595846  ]\n",
      "loss = \n",
      "1.4750339\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=\n",
      "array([-7.8810425e-03,  1.7790733e-05,  2.8497342e-02, -1.8029766e-04],\n",
      "      dtype=float32)>]\n",
      "epoch idx = 8\n",
      "batch idx = 3\n",
      "loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
      "param estimates vec = \n",
      "[1.0776144  0.4075643  0.15032841 4.609428  ]\n",
      "loss = \n",
      "1.4630368\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.00238332, -0.0080823 ,  0.04150968, -0.00023423], dtype=float32)>]\n",
      "epoch idx = 8\n",
      "batch idx = 4\n",
      "loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
      "param estimates vec = \n",
      "[1.0749834  0.4001448  0.13884933 4.6228027 ]\n",
      "loss = \n",
      "1.4632336\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.00755697,  0.0122029 , -0.00858332, -0.0002111 ], dtype=float32)>]\n",
      "epoch idx = 8\n",
      "batch idx = 5\n",
      "loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
      "param estimates vec = \n",
      "[1.0715575  0.38285673 0.13337135 4.63603   ]\n",
      "loss = \n",
      "1.4632282\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.00718257,  0.0139043 , -0.01125779, -0.00021783], dtype=float32)>]\n",
      "epoch idx = 8\n",
      "batch idx = 6\n",
      "loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
      "param estimates vec = \n",
      "[1.0669883  0.36676878 0.12793133 4.649174  ]\n",
      "loss = \n",
      "1.4598929\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.01004308,  0.0005662 ,  0.00109625, -0.00022641], dtype=float32)>]\n",
      "epoch idx = 8\n",
      "batch idx = 7\n",
      "loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
      "param estimates vec = \n",
      "[1.0617608  0.35665703 0.12307126 4.662327  ]\n",
      "loss = \n",
      "1.4682754\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.00742208, -0.00574896, -0.00014802, -0.00024139], dtype=float32)>]\n",
      "epoch idx = 8\n",
      "batch idx = 8\n",
      "loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
      "param estimates vec = \n",
      "[1.056427   0.35642242 0.1249149  4.675348  ]\n",
      "loss = \n",
      "1.4614521\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.00406603, -0.0114881 , -0.01409691, -0.00021341], dtype=float32)>]\n",
      "epoch idx = 8\n",
      "batch idx = 9\n",
      "loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
      "param estimates vec = \n",
      "[1.0504242  0.3554722  0.13263598 4.688197  ]\n",
      "loss = \n",
      "1.4692975\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0.00789841,  0.00094934, -0.01364415, -0.00020239], dtype=float32)>]\n",
      "epoch idx = 9\n",
      "batch idx = 0\n",
      "loss idx = 10\n",
      "param estimates vec = \n",
      "[1.045026   0.36219493 0.14625551 4.700847  ]\n",
      "loss = \n",
      "1.4746419\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.00023705, -0.00972859, -0.01493078, -0.00019297], dtype=float32)>]\n",
      "epoch idx = 9\n",
      "batch idx = 1\n",
      "loss idx = 10loss idx = 10\n",
      "param estimates vec = \n",
      "[1.0409452  0.37571186 0.14453378 4.713335  ]\n",
      "loss = \n",
      "1.460904\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.00535356, -0.00953875,  0.03141667, -0.00019546], dtype=float32)>]\n",
      "epoch idx = 9\n",
      "batch idx = 2\n",
      "loss idx = 10loss idx = 10loss idx = 10\n",
      "param estimates vec = \n",
      "[1.0382669  0.38755155 0.13319385 4.7256546 ]\n",
      "loss = \n",
      "1.4629792\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.0067099 ,  0.0005021 ,  0.02210142, -0.00019048], dtype=float32)>]\n",
      "epoch idx = 9\n",
      "batch idx = 3\n",
      "loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
      "param estimates vec = \n",
      "[1.0371244  0.40315384 0.1294283  4.7377534 ]\n",
      "loss = \n",
      "1.4649252\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.008423  , -0.00621256, -0.0145671 , -0.00017676], dtype=float32)>]\n",
      "epoch idx = 9\n",
      "batch idx = 4\n",
      "loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
      "param estimates vec = \n",
      "[1.0367367  0.42129514 0.13064253 4.749678  ]\n",
      "loss = \n",
      "1.4693725\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.0042246 , -0.00508958, -0.01034631, -0.00018064], dtype=float32)>]\n",
      "epoch idx = 9\n",
      "batch idx = 5\n",
      "loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
      "param estimates vec = \n",
      "[1.0377816  0.43698412 0.13744737 4.7615213 ]\n",
      "loss = \n",
      "1.4725173\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.00907575,  0.00091292, -0.01274093, -0.00019382], dtype=float32)>]\n",
      "epoch idx = 9\n",
      "batch idx = 6\n",
      "loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
      "param estimates vec = \n",
      "[1.0403603  0.43879262 0.14802673 4.7734957 ]\n",
      "loss = \n",
      "1.4749101\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.01056583,  0.01544275, -0.00984116, -0.00023053], dtype=float32)>]\n",
      "epoch idx = 9\n",
      "batch idx = 7\n",
      "loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
      "param estimates vec = \n",
      "[1.0439966  0.42591837 0.15521029 4.7854886 ]\n",
      "loss = \n",
      "1.4699024\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.00838448,  0.01834553,  0.00527646, -0.00021106], dtype=float32)>]\n",
      "epoch idx = 9\n",
      "batch idx = 8\n",
      "loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
      "param estimates vec = \n",
      "[1.0480862  0.41545707 0.15408394 4.7973495 ]\n",
      "loss = \n",
      "1.4710892\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.00511762, -0.00149416,  0.01678044, -0.00018308], dtype=float32)>]\n",
      "epoch idx = 9\n",
      "batch idx = 9\n",
      "loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
      "param estimates vec = \n",
      "[1.0534236  0.40310916 0.15142182 4.808904  ]\n",
      "loss = \n",
      "1.4663296\n",
      "grads = \n",
      "[<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-0.01042908,  0.00361801,  0.00360659, -0.00014853], dtype=float32)>]\n"
     ]
    }
   ],
   "source": [
    "# plug in generator instead of raw data to get away from calculating 7-day avg\n",
    "# initialize parameters at some bad values; otherwise, convergence is instantaneous\n",
    "param_vec = kcqe_obj.fit(xval_batch_gen = generator,\n",
    "    num_blocks = num_blocks, \n",
    "    tau=tf.constant(np.array([0.1, 0.5, 0.9]), dtype=tf.float32),\n",
    "    optim_method=\"adam\", \n",
    "    num_epochs=10, \n",
    "    learning_rate=0.1,\n",
    "    verbose = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "q_hat = kcqe_obj.predict(param_vec,\n",
    "                         x_train=x,\n",
    "                         y_train=y,\n",
    "                         x_test=x_test,\n",
    "                         tau=tf.constant(np.array([0.1, 0.5, 0.9]), dtype=tf.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,8))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "#ax = Axes3D(fig)\n",
    "\n",
    "ax.scatter(x0, x1, y)\n",
    "\n",
    "x0_grid = x_test[..., 0].numpy().reshape((grid_size, grid_size))\n",
    "x1_grid = x_test[..., 1].numpy().reshape((grid_size, grid_size))\n",
    "q_grid_q10 = q_hat[..., 0].numpy().reshape((grid_size, grid_size))\n",
    "q_grid_median = q_hat[..., 1].numpy().reshape((grid_size, grid_size))\n",
    "q_grid_q90 = q_hat[..., 2].numpy().reshape((grid_size, grid_size))\n",
    "\n",
    "ax.plot_wireframe(x0_grid, x1_grid, q_grid_q10, rstride=10, cstride=10, color=\"red\")\n",
    "ax.plot_wireframe(x0_grid, x1_grid, q_grid_median, rstride=10, cstride=10)\n",
    "ax.plot_wireframe(x0_grid, x1_grid, q_grid_q90, rstride=10, cstride=10, color=\"orange\")\n",
    "\n",
    "ax.set_xlabel('X_0')\n",
    "ax.set_ylabel('X_1')\n",
    "ax.set_zlabel('Y')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Missing Data\n",
    "\n",
    "Testing to see if method still works with missing values added."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "missing = random.sample(range(1, 501), 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate 50 random indices\n",
    "random_indices = tf.random.shuffle(tf.range(500))[:50]\n",
    "\n",
    "# scatter NaN values at the random indices\n",
    "y_miss = 10. * np.cos(x0) + 0.5 * x1**2 + np.random.normal(loc=0., scale=5., size=n)\n",
    "\n",
    "y_miss [random_indices] = np.nan\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# y_train_val\n",
    "y_miss = np.reshape(y, (1, 500))\n",
    "\n",
    "y_miss = tf.constant(y_miss, dtype=tf.float32)\n",
    "\n",
    "    \n",
    "block_size = 50\n",
    "num_blocks = math.floor(y.shape[1]/block_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "kcqe_obj = kcqe.KCQE(p=2)\n",
    "generator = kcqe_obj.generator(x, y_miss, batch_size = 10, block_size = block_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch idx = 0\n",
      "batch idx = 0\n",
      "loss idx = 1\n",
      "param estimates vec = \n",
      "[2.9000022 2.9000053 1.0999295]\n",
      "loss = \n",
      "1.8559208\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.19440739,  0.06930105, -0.00452088], dtype=float32)>]\n",
      "epoch idx = 0\n",
      "batch idx = 1\n",
      "loss idx = 1loss idx = 1\n",
      "param estimates vec = \n",
      "[2.8000438 2.8       1.199272 ]\n",
      "loss = \n",
      "1.8236434\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.19167238,  0.06955975, -0.00393209], dtype=float32)>]\n",
      "epoch idx = 0\n",
      "batch idx = 2\n",
      "loss idx = 1loss idx = 1loss idx = 1\n",
      "param estimates vec = \n",
      "[2.7000172 2.700042  1.2976269]\n",
      "loss = \n",
      "1.7839864\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.19608937,  0.06858149, -0.00341937], dtype=float32)>]\n",
      "epoch idx = 0\n",
      "batch idx = 3\n",
      "loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
      "param estimates vec = \n",
      "[2.5998323 2.602054  1.396668 ]\n",
      "loss = \n",
      "1.7598867\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.20315905,  0.05021626, -0.00421176], dtype=float32)>]\n",
      "epoch idx = 0\n",
      "batch idx = 4\n",
      "loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
      "param estimates vec = \n",
      "[2.4995885 2.5045018 1.4941242]\n",
      "loss = \n",
      "1.7497969\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.20135477,  0.05505765, -0.00299582], dtype=float32)>]\n",
      "epoch idx = 0\n",
      "batch idx = 5\n",
      "loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
      "param estimates vec = \n",
      "[2.3989813 2.4076326 1.59027  ]\n",
      "loss = \n",
      "1.7066048\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.21908772,  0.05129457, -0.00284991], dtype=float32)>]\n",
      "epoch idx = 0\n",
      "batch idx = 6\n",
      "loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
      "param estimates vec = \n",
      "[2.2990868 2.3090844 1.6828065]\n",
      "loss = \n",
      "1.6970944\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.17948543,  0.08364669, -0.00189128], dtype=float32)>]\n",
      "epoch idx = 0\n",
      "batch idx = 7\n",
      "loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
      "param estimates vec = \n",
      "[2.2001014 2.2090614 1.772595 ]\n",
      "loss = \n",
      "1.6672314\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.1675583 ,  0.09621327, -0.00187299], dtype=float32)>]\n",
      "epoch idx = 0\n",
      "batch idx = 8\n",
      "loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
      "param estimates vec = \n",
      "[2.1019576 2.1083338 1.8623364]\n",
      "loss = \n",
      "1.6460686\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.16293514,  0.08118437, -0.00256723], dtype=float32)>]\n",
      "epoch idx = 0\n",
      "batch idx = 9\n",
      "loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1loss idx = 1\n",
      "param estimates vec = \n",
      "[2.004904  2.00799   1.9519942]\n",
      "loss = \n",
      "1.6058226\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.15114632,  0.06751331, -0.00252381], dtype=float32)>]\n",
      "epoch idx = 1\n",
      "batch idx = 0\n",
      "loss idx = 2\n",
      "param estimates vec = \n",
      "[1.9096098 1.9064658 2.0420508]\n",
      "loss = \n",
      "1.6036257\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.12997659,  0.09212349, -0.00269014], dtype=float32)>]\n",
      "epoch idx = 1\n",
      "batch idx = 1\n",
      "loss idx = 2loss idx = 2\n",
      "param estimates vec = \n",
      "[1.8165389 1.8050964 2.1315641]\n",
      "loss = \n",
      "1.5859874\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.11326034,  0.07368275, -0.00232928], dtype=float32)>]\n",
      "epoch idx = 1\n",
      "batch idx = 2\n",
      "loss idx = 2loss idx = 2loss idx = 2\n",
      "param estimates vec = \n",
      "[1.724881  1.7045035 2.2200985]\n",
      "loss = \n",
      "1.55997\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.12176181,  0.06573528, -0.00212138], dtype=float32)>]\n",
      "epoch idx = 1\n",
      "batch idx = 3\n",
      "loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
      "param estimates vec = \n",
      "[1.6354887 1.6036618 2.3074188]\n",
      "loss = \n",
      "1.53492\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.10016322,  0.07671296, -0.00197278], dtype=float32)>]\n",
      "epoch idx = 1\n",
      "batch idx = 4\n",
      "loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
      "param estimates vec = \n",
      "[1.548937  1.5031002 2.392799 ]\n",
      "loss = \n",
      "1.5166188\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.08365919,  0.07061405, -0.00166669], dtype=float32)>]\n",
      "epoch idx = 1\n",
      "batch idx = 5\n",
      "loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
      "param estimates vec = \n",
      "[1.4654448 1.4024062 2.4759827]\n",
      "loss = \n",
      "1.502093\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.07330681,  0.07511549, -0.00149781], dtype=float32)>]\n",
      "epoch idx = 1\n",
      "batch idx = 6\n",
      "loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
      "param estimates vec = \n",
      "[1.3851527 1.301945  2.5574634]\n",
      "loss = \n",
      "1.4913731\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.06460683,  0.07098984, -0.00155042], dtype=float32)>]\n",
      "epoch idx = 1\n",
      "batch idx = 7\n",
      "loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
      "param estimates vec = \n",
      "[1.3089576 1.2023017 2.6381624]\n",
      "loss = \n",
      "1.4874972\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.0450785 ,  0.06412175, -0.00175205], dtype=float32)>]\n",
      "epoch idx = 1\n",
      "batch idx = 8\n",
      "loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
      "param estimates vec = \n",
      "[1.237444  1.1043955 2.7170825]\n",
      "loss = \n",
      "1.4676135\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.03026615,  0.053858  , -0.00141577], dtype=float32)>]\n",
      "epoch idx = 1\n",
      "batch idx = 9\n",
      "loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2loss idx = 2\n",
      "param estimates vec = \n",
      "[1.1706498 1.0075078 2.7931123]\n",
      "loss = \n",
      "1.4639068\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.02306372,  0.05839595, -0.0010446 ], dtype=float32)>]\n",
      "epoch idx = 2\n",
      "batch idx = 0\n",
      "loss idx = 3\n",
      "param estimates vec = \n",
      "[1.1084764  0.91276044 2.8658946 ]\n",
      "loss = \n",
      "1.4530964\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.01804207,  0.04698691, -0.00086463], dtype=float32)>]\n",
      "epoch idx = 2\n",
      "batch idx = 1\n",
      "loss idx = 3loss idx = 3\n",
      "param estimates vec = \n",
      "[1.0517025 0.8210188 2.935904 ]\n",
      "loss = \n",
      "1.464254\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00228592,  0.03776612, -0.00089324], dtype=float32)>]\n",
      "epoch idx = 2\n",
      "batch idx = 2\n",
      "loss idx = 3loss idx = 3loss idx = 3\n",
      "param estimates vec = \n",
      "[1.0001569 0.7318968 3.0038497]\n",
      "loss = \n",
      "1.450712\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00182817,  0.03803513, -0.00099266], dtype=float32)>]\n",
      "epoch idx = 2\n",
      "batch idx = 3\n",
      "loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
      "param estimates vec = \n",
      "[0.95418423 0.6437734  3.0689113 ]\n",
      "loss = \n",
      "1.444152\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01180253,  0.04837034, -0.00073255], dtype=float32)>]\n",
      "epoch idx = 2\n",
      "batch idx = 4\n",
      "loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
      "param estimates vec = \n",
      "[0.9155319 0.5612134 3.130532 ]\n",
      "loss = \n",
      "1.4371657\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.03819613,  0.0144155 , -0.00053033], dtype=float32)>]\n",
      "epoch idx = 2\n",
      "batch idx = 5\n",
      "loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
      "param estimates vec = \n",
      "[0.8822253  0.48449677 3.1895068 ]\n",
      "loss = \n",
      "1.4463148\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.02258179,  0.00923126, -0.00063375], dtype=float32)>]\n",
      "epoch idx = 2\n",
      "batch idx = 6\n",
      "loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
      "param estimates vec = \n",
      "[0.8543499 0.4119088 3.2454674]\n",
      "loss = \n",
      "1.4519398\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.0289036 ,  0.01656612, -0.00048858], dtype=float32)>]\n",
      "epoch idx = 2\n",
      "batch idx = 7\n",
      "loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
      "param estimates vec = \n",
      "[0.8321453  0.34784347 3.2979848 ]\n",
      "loss = \n",
      "1.4470874\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.03694937, -0.01192567, -0.00032886], dtype=float32)>]\n",
      "epoch idx = 2\n",
      "batch idx = 8\n",
      "loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
      "param estimates vec = \n",
      "[0.81688684 0.29154918 3.3471909 ]\n",
      "loss = \n",
      "1.4471983\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.05690408, -0.01185824, -0.00028712], dtype=float32)>]\n",
      "epoch idx = 2\n",
      "batch idx = 9\n",
      "loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3loss idx = 3\n",
      "param estimates vec = \n",
      "[0.80550313 0.24211183 3.3933575 ]\n",
      "loss = \n",
      "1.4535428\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.02878779, -0.01049848, -0.00027894], dtype=float32)>]\n",
      "epoch idx = 3\n",
      "batch idx = 0\n",
      "loss idx = 4\n",
      "param estimates vec = \n",
      "[0.79996103 0.20118912 3.4366474 ]\n",
      "loss = \n",
      "1.4492233\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.05460704, -0.02281621, -0.00025339], dtype=float32)>]\n",
      "epoch idx = 3\n",
      "batch idx = 1\n",
      "loss idx = 4loss idx = 4\n",
      "param estimates vec = \n",
      "[0.8004906  0.16852792 3.476867  ]\n",
      "loss = \n",
      "1.4492558\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.06281266, -0.02542387, -0.00015878], dtype=float32)>]\n",
      "epoch idx = 3\n",
      "batch idx = 2\n",
      "loss idx = 4loss idx = 4loss idx = 4\n",
      "param estimates vec = \n",
      "[0.8070037  0.14676112 3.5141025 ]\n",
      "loss = \n",
      "1.4506102\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.06775291, -0.04343731, -0.00011977], dtype=float32)>]\n",
      "epoch idx = 3\n",
      "batch idx = 3\n",
      "loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
      "param estimates vec = \n",
      "[0.81906927 0.13645472 3.5488958 ]\n",
      "loss = \n",
      "1.4559287\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.06877872, -0.0521648 , -0.00017384], dtype=float32)>]\n",
      "epoch idx = 3\n",
      "batch idx = 4\n",
      "loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
      "param estimates vec = \n",
      "[0.83569235 0.1358833  3.581444  ]\n",
      "loss = \n",
      "1.4593924\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.06293649, -0.04894633, -0.00016829], dtype=float32)>]\n",
      "epoch idx = 3\n",
      "batch idx = 5\n",
      "loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
      "param estimates vec = \n",
      "[0.85626286 0.14438191 3.611901  ]\n",
      "loss = \n",
      "1.4534637\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.06037865, -0.05057959, -0.00015802], dtype=float32)>]\n",
      "epoch idx = 3\n",
      "batch idx = 6\n",
      "loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
      "param estimates vec = \n",
      "[0.87988895 0.15880553 3.6405165 ]\n",
      "loss = \n",
      "1.4477147\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.05401319, -0.03756427, -0.00016882], dtype=float32)>]\n",
      "epoch idx = 3\n",
      "batch idx = 7\n",
      "loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
      "param estimates vec = \n",
      "[0.9050867  0.17941949 3.6678402 ]\n",
      "loss = \n",
      "1.453794\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.04021679, -0.04212832, -0.00024062], dtype=float32)>]\n",
      "epoch idx = 3\n",
      "batch idx = 8\n",
      "loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
      "param estimates vec = \n",
      "[0.9316176  0.20455824 3.6940472 ]\n",
      "loss = \n",
      "1.4419829\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.03881332, -0.03576044, -0.00024964], dtype=float32)>]\n",
      "epoch idx = 3\n",
      "batch idx = 9\n",
      "loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4loss idx = 4\n",
      "param estimates vec = \n",
      "[0.95852613 0.2316449  3.7192802 ]\n",
      "loss = \n",
      "1.4485679\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.02944825, -0.02330769, -0.00025542], dtype=float32)>]\n",
      "epoch idx = 4\n",
      "batch idx = 0\n",
      "loss idx = 5\n",
      "param estimates vec = \n",
      "[0.9863718  0.25973713 3.7435422 ]\n",
      "loss = \n",
      "1.444761\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.03553405, -0.01886015, -0.00023764], dtype=float32)>]\n",
      "epoch idx = 4\n",
      "batch idx = 1\n",
      "loss idx = 5loss idx = 5\n",
      "param estimates vec = \n",
      "[1.0140066 0.2859389 3.7672126]\n",
      "loss = \n",
      "1.4425075\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.02399032, -0.00344075, -0.00028875], dtype=float32)>]\n",
      "epoch idx = 4\n",
      "batch idx = 2\n",
      "loss idx = 5loss idx = 5loss idx = 5\n",
      "param estimates vec = \n",
      "[1.0401615  0.31041712 3.7905743 ]\n",
      "loss = \n",
      "1.4420202\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01042622, -0.00340413, -0.00032761], dtype=float32)>]\n",
      "epoch idx = 4\n",
      "batch idx = 3\n",
      "loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
      "param estimates vec = \n",
      "[1.0661138  0.33569008 3.81343   ]\n",
      "loss = \n",
      "1.43649\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.02207221, -0.01592234, -0.00028469], dtype=float32)>]\n",
      "epoch idx = 4\n",
      "batch idx = 4\n",
      "loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
      "param estimates vec = \n",
      "[1.0915865 0.3600539 3.8359523]\n",
      "loss = \n",
      "1.438874\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01887969, -0.00719224, -0.0003046 ], dtype=float32)>]\n",
      "epoch idx = 4\n",
      "batch idx = 5\n",
      "loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
      "param estimates vec = \n",
      "[1.1149976  0.38278204 3.858116  ]\n",
      "loss = \n",
      "1.4407051\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00245952, -0.00295044, -0.0002924 ], dtype=float32)>]\n",
      "epoch idx = 4\n",
      "batch idx = 6\n",
      "loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
      "param estimates vec = \n",
      "[1.1353183 0.4014608 3.8804445]\n",
      "loss = \n",
      "1.442847\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00949064,  0.01008283, -0.00037498], dtype=float32)>]\n",
      "epoch idx = 4\n",
      "batch idx = 7\n",
      "loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
      "param estimates vec = \n",
      "[1.1524001  0.41801113 3.9024217 ]\n",
      "loss = \n",
      "1.442622\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.01353705,  0.00217182, -0.00028598], dtype=float32)>]\n",
      "epoch idx = 4\n",
      "batch idx = 8\n",
      "loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
      "param estimates vec = \n",
      "[1.1671234 0.4315588 3.924501 ]\n",
      "loss = \n",
      "1.4386582\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00775092,  0.00746074, -0.00035518], dtype=float32)>]\n",
      "epoch idx = 4\n",
      "batch idx = 9\n",
      "loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5loss idx = 5\n",
      "param estimates vec = \n",
      "[1.1798738  0.43836367 3.9465914 ]\n",
      "loss = \n",
      "1.441081\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.0060271 ,  0.02715849, -0.00033863], dtype=float32)>]\n",
      "epoch idx = 5\n",
      "batch idx = 0\n",
      "loss idx = 6\n",
      "param estimates vec = \n",
      "[1.1889488  0.44380313 3.9686806 ]\n",
      "loss = \n",
      "1.4426184\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.02373386,  0.00365785, -0.00033409], dtype=float32)>]\n",
      "epoch idx = 5\n",
      "batch idx = 1\n",
      "loss idx = 6loss idx = 6\n",
      "param estimates vec = \n",
      "[1.1949495  0.44726977 3.9905963 ]\n",
      "loss = \n",
      "1.4333929\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.02104502,  0.00718385, -0.00030305], dtype=float32)>]\n",
      "epoch idx = 5\n",
      "batch idx = 2\n",
      "loss idx = 6loss idx = 6loss idx = 6\n",
      "param estimates vec = \n",
      "[1.1985656  0.44524533 4.012247  ]\n",
      "loss = \n",
      "1.441034\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.0170673 ,  0.02499608, -0.00028323], dtype=float32)>]\n",
      "epoch idx = 5\n",
      "batch idx = 3\n",
      "loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
      "param estimates vec = \n",
      "[1.1992948  0.44096854 4.0336485 ]\n",
      "loss = \n",
      "1.4439518\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.02356364,  0.01170017, -0.00027971], dtype=float32)>]\n",
      "epoch idx = 5\n",
      "batch idx = 4\n",
      "loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
      "param estimates vec = \n",
      "[1.1966887 0.434133  4.054998 ]\n",
      "loss = \n",
      "1.4389583\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.02991313,  0.01405628, -0.00030563], dtype=float32)>]\n",
      "epoch idx = 5\n",
      "batch idx = 5\n",
      "loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
      "param estimates vec = \n",
      "[1.1927644  0.42415828 4.076297  ]\n",
      "loss = \n",
      "1.4397972\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.01413841,  0.01782531, -0.00030294], dtype=float32)>]\n",
      "epoch idx = 5\n",
      "batch idx = 6\n",
      "loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
      "param estimates vec = \n",
      "[1.1863697  0.41325137 4.0972033 ]\n",
      "loss = \n",
      "1.4446416\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.02551033,  0.00867383, -0.00024604], dtype=float32)>]\n",
      "epoch idx = 5\n",
      "batch idx = 7\n",
      "loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
      "param estimates vec = \n",
      "[1.1776865  0.40128598 4.117822  ]\n",
      "loss = \n",
      "1.4463412\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.02573439,  0.00959656, -0.00025509], dtype=float32)>]\n",
      "epoch idx = 5\n",
      "batch idx = 8\n",
      "loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
      "param estimates vec = \n",
      "[1.167838  0.3901589 4.138179 ]\n",
      "loss = \n",
      "1.4399837\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.01743715,  0.00122226, -0.00025315], dtype=float32)>]\n",
      "epoch idx = 5\n",
      "batch idx = 9\n",
      "loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6loss idx = 6\n",
      "param estimates vec = \n",
      "[1.1577613  0.37860078 4.1578574 ]\n",
      "loss = \n",
      "1.4366187\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.01000121,  0.00666728, -0.00018382], dtype=float32)>]\n",
      "epoch idx = 6\n",
      "batch idx = 0\n",
      "loss idx = 7\n",
      "param estimates vec = \n",
      "[1.1465868  0.36812165 4.177256  ]\n",
      "loss = \n",
      "1.4445062\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 1.7711593e-02, -4.6406407e-05, -2.3371859e-04], dtype=float32)>]\n",
      "epoch idx = 6\n",
      "batch idx = 1\n",
      "loss idx = 7loss idx = 7\n",
      "param estimates vec = \n",
      "[1.1351373 0.3582987 4.1962585]\n",
      "loss = \n",
      "1.442611\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.01132894,  0.00140971, -0.00021067], dtype=float32)>]\n",
      "epoch idx = 6\n",
      "batch idx = 2\n",
      "loss idx = 7loss idx = 7loss idx = 7\n",
      "param estimates vec = \n",
      "[1.1251526 0.349082  4.214697 ]\n",
      "loss = \n",
      "1.4378071\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00345947,  0.00135463, -0.00017868], dtype=float32)>]\n",
      "epoch idx = 6\n",
      "batch idx = 3\n",
      "loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
      "param estimates vec = \n",
      "[1.1152713  0.34203127 4.232848  ]\n",
      "loss = \n",
      "1.4372504\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00700283, -0.00578776, -0.00021087], dtype=float32)>]\n",
      "epoch idx = 6\n",
      "batch idx = 4\n",
      "loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
      "param estimates vec = \n",
      "[1.1057912  0.33778358 4.2509656 ]\n",
      "loss = \n",
      "1.4413385\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00436127, -0.00941142, -0.00024297], dtype=float32)>]\n",
      "epoch idx = 6\n",
      "batch idx = 5\n",
      "loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
      "param estimates vec = \n",
      "[1.0972017 0.3347281 4.268706 ]\n",
      "loss = \n",
      "1.439222\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-7.7326884e-05, -3.4690390e-03, -1.9064292e-04], dtype=float32)>]\n",
      "epoch idx = 6\n",
      "batch idx = 6\n",
      "loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
      "param estimates vec = \n",
      "[1.0893782  0.33556113 4.2863555 ]\n",
      "loss = \n",
      "1.4413978\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00027638, -0.01556928, -0.00022595], dtype=float32)>]\n",
      "epoch idx = 6\n",
      "batch idx = 7\n",
      "loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
      "param estimates vec = \n",
      "[1.083191  0.3410916 4.3036447]\n",
      "loss = \n",
      "1.4431176\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00747986, -0.02052842, -0.00018442], dtype=float32)>]\n",
      "epoch idx = 6\n",
      "batch idx = 8\n",
      "loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
      "param estimates vec = \n",
      "[1.0785092 0.3492683 4.320729 ]\n",
      "loss = \n",
      "1.4393232\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00759681, -0.01351472, -0.00020077], dtype=float32)>]\n",
      "epoch idx = 6\n",
      "batch idx = 9\n",
      "loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7loss idx = 7\n",
      "param estimates vec = \n",
      "[1.0753169  0.35477516 4.337476  ]\n",
      "loss = \n",
      "1.436825\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00855085,  0.00807359, -0.00017798], dtype=float32)>]\n",
      "epoch idx = 7\n",
      "batch idx = 0\n",
      "loss idx = 8\n",
      "param estimates vec = \n",
      "[1.0737627  0.35662022 4.3541045 ]\n",
      "loss = \n",
      "1.4395348\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01080782,  0.01323894, -0.00020335], dtype=float32)>]\n",
      "epoch idx = 7\n",
      "batch idx = 1\n",
      "loss idx = 8loss idx = 8\n",
      "param estimates vec = \n",
      "[1.0722647 0.3605856 4.370283 ]\n",
      "loss = \n",
      "1.4396706\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.0007131 , -0.00959263, -0.0001542 ], dtype=float32)>]\n",
      "epoch idx = 7\n",
      "batch idx = 2\n",
      "loss idx = 8loss idx = 8loss idx = 8\n",
      "param estimates vec = \n",
      "[1.0712063  0.36567798 4.3861065 ]\n",
      "loss = \n",
      "1.4449698\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00238191, -0.00622507, -0.0001606 ], dtype=float32)>]\n",
      "epoch idx = 7\n",
      "batch idx = 3\n",
      "loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
      "param estimates vec = \n",
      "[1.0713689 0.3715807 4.4014378]\n",
      "loss = \n",
      "1.440836\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.0088608 , -0.00531256, -0.00013619], dtype=float32)>]\n",
      "epoch idx = 7\n",
      "batch idx = 4\n",
      "loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
      "param estimates vec = \n",
      "[1.0714346 0.3774383 4.416542 ]\n",
      "loss = \n",
      "1.4369131\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00064116, -0.00208263, -0.00016539], dtype=float32)>]\n",
      "epoch idx = 7\n",
      "batch idx = 5\n",
      "loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
      "param estimates vec = \n",
      "[1.0733511 0.3803406 4.4314203]\n",
      "loss = \n",
      "1.4371399\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.01447204,  0.00978383, -0.00016172], dtype=float32)>]\n",
      "epoch idx = 7\n",
      "batch idx = 6\n",
      "loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
      "param estimates vec = \n",
      "[1.0760314 0.3829049 4.4460254]\n",
      "loss = \n",
      "1.4395671\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00730673,  0.00026478, -0.00015152], dtype=float32)>]\n",
      "epoch idx = 7\n",
      "batch idx = 7\n",
      "loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
      "param estimates vec = \n",
      "[1.0789249  0.38234797 4.4606657 ]\n",
      "loss = \n",
      "1.4414686\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00357632,  0.01156854, -0.00018862], dtype=float32)>]\n",
      "epoch idx = 7\n",
      "batch idx = 8\n",
      "loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
      "param estimates vec = \n",
      "[1.0824571 0.3834414 4.475298 ]\n",
      "loss = \n",
      "1.4413307\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00695959, -0.00637969, -0.00018219], dtype=float32)>]\n",
      "epoch idx = 7\n",
      "batch idx = 9\n",
      "loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8loss idx = 8\n",
      "param estimates vec = \n",
      "[1.0851073  0.38370383 4.4898157 ]\n",
      "loss = \n",
      "1.4369284\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00417044,  0.00288797, -0.000167  ], dtype=float32)>]\n",
      "epoch idx = 8\n",
      "batch idx = 0\n",
      "loss idx = 9\n",
      "param estimates vec = \n",
      "[1.0880408  0.38543057 4.5043797 ]\n",
      "loss = \n",
      "1.4412868\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00401904, -0.00586963, -0.0001858 ], dtype=float32)>]\n",
      "epoch idx = 8\n",
      "batch idx = 1\n",
      "loss idx = 9loss idx = 9\n",
      "param estimates vec = \n",
      "[1.0908641  0.38741627 4.518667  ]\n",
      "loss = \n",
      "1.4384803\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00124609, -0.00165183, -0.00014314], dtype=float32)>]\n",
      "epoch idx = 8\n",
      "batch idx = 2\n",
      "loss idx = 9loss idx = 9loss idx = 9\n",
      "param estimates vec = \n",
      "[1.0939311  0.38878882 4.5328    ]\n",
      "loss = \n",
      "1.4422288\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00379818,  0.00165761, -0.00015478], dtype=float32)>]\n",
      "epoch idx = 8\n",
      "batch idx = 3\n",
      "loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
      "param estimates vec = \n",
      "[1.0971068  0.39152417 4.546935  ]\n",
      "loss = \n",
      "1.4412123\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00294688, -0.0057741 , -0.00017231], dtype=float32)>]\n",
      "epoch idx = 8\n",
      "batch idx = 4\n",
      "loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
      "param estimates vec = \n",
      "[1.100317   0.39493263 4.561067  ]\n",
      "loss = \n",
      "1.4411871\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00246   , -0.00358211, -0.00017078], dtype=float32)>]\n",
      "epoch idx = 8\n",
      "batch idx = 5\n",
      "loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
      "param estimates vec = \n",
      "[1.1036817  0.39168406 4.5751867 ]\n",
      "loss = \n",
      "1.4443262\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.0033477 ,  0.02424129, -0.00016864], dtype=float32)>]\n",
      "epoch idx = 8\n",
      "batch idx = 6\n",
      "loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
      "param estimates vec = \n",
      "[1.1055071  0.38627672 4.5893316 ]\n",
      "loss = \n",
      "1.4469988\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00887258,  0.00939817, -0.00017234], dtype=float32)>]\n",
      "epoch idx = 8\n",
      "batch idx = 7\n",
      "loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
      "param estimates vec = \n",
      "[1.106778   0.37997663 4.6032124 ]\n",
      "loss = \n",
      "1.4472324\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00275847,  0.00532113, -0.00013531], dtype=float32)>]\n",
      "epoch idx = 8\n",
      "batch idx = 8\n",
      "loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
      "param estimates vec = \n",
      "[1.106654  0.3746618 4.617041 ]\n",
      "loss = \n",
      "1.443735\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00915337, -0.0014635 , -0.000158  ], dtype=float32)>]\n",
      "epoch idx = 8\n",
      "batch idx = 9\n",
      "loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9loss idx = 9\n",
      "param estimates vec = \n",
      "[1.1066027 0.3720279 4.630771 ]\n",
      "loss = \n",
      "1.4424518\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00043422, -0.00814789, -0.00015081], dtype=float32)>]\n",
      "epoch idx = 9\n",
      "batch idx = 0\n",
      "loss idx = 10\n",
      "param estimates vec = \n",
      "[1.1044961 0.3693011 4.64445  ]\n",
      "loss = \n",
      "1.4434372\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.01462935,  0.00127526, -0.0001548 ], dtype=float32)>]\n",
      "epoch idx = 9\n",
      "batch idx = 1\n",
      "loss idx = 10loss idx = 10\n",
      "param estimates vec = \n",
      "[1.1031853  0.36209315 4.6580863 ]\n",
      "loss = \n",
      "1.4437724\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00420734,  0.01757874, -0.00015438], dtype=float32)>]\n",
      "epoch idx = 9\n",
      "batch idx = 2\n",
      "loss idx = 10loss idx = 10loss idx = 10\n",
      "param estimates vec = \n",
      "[1.1024231 0.3595044 4.6716294]\n",
      "loss = \n",
      "1.4425727\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00297887, -0.0144944 , -0.000147  ], dtype=float32)>]\n",
      "epoch idx = 9\n",
      "batch idx = 3\n",
      "loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
      "param estimates vec = \n",
      "[1.1021637  0.36038035 4.6852064 ]\n",
      "loss = \n",
      "1.441389\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00300559, -0.01180881, -0.00016066], dtype=float32)>]\n",
      "epoch idx = 9\n",
      "batch idx = 4\n",
      "loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
      "param estimates vec = \n",
      "[1.1019517 0.363261  4.6985316]\n",
      "loss = \n",
      "1.4385345\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00015816, -0.00762316, -0.00012563], dtype=float32)>]\n",
      "epoch idx = 9\n",
      "batch idx = 5\n",
      "loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
      "param estimates vec = \n",
      "[1.102833   0.36467758 4.711683  ]\n",
      "loss = \n",
      "1.4386714\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00741098,  0.00432125, -0.00013156], dtype=float32)>]\n",
      "epoch idx = 9\n",
      "batch idx = 6\n",
      "loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
      "param estimates vec = \n",
      "[1.1031005  0.36458832 4.724749  ]\n",
      "loss = \n",
      "1.448237\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.00364006,  0.00495168, -0.00013962], dtype=float32)>]\n",
      "epoch idx = 9\n",
      "batch idx = 7\n",
      "loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
      "param estimates vec = \n",
      "[1.1026801  0.36609784 4.73786   ]\n",
      "loss = \n",
      "1.4409873\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.0045265 , -0.00571362, -0.00015343], dtype=float32)>]\n",
      "epoch idx = 9\n",
      "batch idx = 8\n",
      "loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
      "param estimates vec = \n",
      "[1.10248    0.36436862 4.750865  ]\n",
      "loss = \n",
      "1.4367217\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00122608,  0.01106403, -0.00013526], dtype=float32)>]\n",
      "epoch idx = 9\n",
      "batch idx = 9\n",
      "loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10loss idx = 10\n",
      "param estimates vec = \n",
      "[1.102398   0.36562392 4.7637753 ]\n",
      "loss = \n",
      "1.442529\n",
      "grads = \n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.00066888, -0.01003128, -0.00013487], dtype=float32)>]\n"
     ]
    }
   ],
   "source": [
    "param_vec = kcqe_obj.fit(xval_batch_gen = generator,\n",
    "    num_blocks = num_blocks, \n",
    "    tau=tf.constant(np.array([0.1, 0.5, 0.9]), dtype=tf.float32),\n",
    "    optim_method=\"adam\", \n",
    "    num_epochs=10, \n",
    "    learning_rate=0.1,\n",
    "    init_param_vec=tf.constant(np.array([3.0, 3.0, 1.0]), dtype=tf.float32),\n",
    "    verbose = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.arange(len(kcqe_obj.loss_trace)), kcqe_obj.loss_trace)\n",
    "plt.show()"
   ]
  }
 ],
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