integralnet-3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import functools\n",
    "import numpy as np\n",
    "import scipy\n",
    "import sympy\n",
    "import matplotlib.pyplot as plt\n",
    "import keras\n",
    "import keras.backend as K\n",
    "sympy.init_printing()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/latex": [
       "$$\\left ( e^{- \\left(a + b\\right)^{2}} \\tanh{\\left (c d \\right )}, \\quad \\frac{\\sqrt{\\pi}}{2} \\tanh{\\left (c d \\right )} \\operatorname{erf}{\\left (a + b \\right )}\\right )$$"
      ],
      "text/plain": [
       "⎛         2                                   ⎞\n",
       "⎜ -(a + b)             √π⋅tanh(c⋅d)⋅erf(a + b)⎟\n",
       "⎜ℯ         ⋅tanh(c⋅d), ───────────────────────⎟\n",
       "⎝                                 2           ⎠"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a,b,c,d = sympy.symbols('a,b,c,d', real=True)\n",
    "F = sympy.exp(-(a+b)**2) * sympy.tanh(c*d)\n",
    "iF = F.integrate(a)\n",
    "lambda_F = sympy.lambdify((a,b,c,d), F)\n",
    "lambda_iF = sympy.lambdify((a,b,c,d), iF, modules=['numpy', {'erf': scipy.special.erf}])\n",
    "display((F, iF))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb8b9ab4128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<sympy.plotting.plot.Plot at 0x7fb8b9ab4048>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sympy.plot(F.subs({b:1,c:1,d:1}), iF.subs({b:1,c:1,d:1}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "xtrain = (np.random.random((100000,4)) - 0.5) * 20\n",
    "xtest = (np.random.random((10000,4)) - 0.5) * 20\n",
    "xtest[:,1:] = np.round(xtest[:,1:]/3)*3\n",
    "xtest[:,2:] = xtest[:,1:2]\n",
    "ytrain, ytest = lambda_F(*np.hsplit(xtrain,4)), lambda_F(*np.hsplit(xtest,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def build_nn_pair():\n",
    "    X = X_input = keras.layers.Input((4,))\n",
    "    X = keras.layers.BatchNormalization()(X)\n",
    "    X = keras.layers.Dense(128, activation='tanh')(X)\n",
    "    for _ in range(3):\n",
    "        X_skip = X\n",
    "        X = keras.layers.Dense(128, activation='relu')(X)\n",
    "        X = keras.layers.Dense(128)(X)\n",
    "        X = keras.layers.Add()([X, X_skip])\n",
    "        X = keras.layers.Activation('tanh')(X)\n",
    "    X = keras.layers.Dense(1)(X)\n",
    "    M_Integral = keras.Model(X_input, X)\n",
    "    M_Integral.compile('nadam', 'mse', ['mae'])\n",
    "    X = M_Integral(X_input)\n",
    "    def compute_jacobian(x):\n",
    "        return K.reshape(K.stack([\n",
    "            K.gradients(x[1][:,ix], x[0])[0]\n",
    "            for ix in range(1)\n",
    "        ], axis=1), (-1,1,4))\n",
    "    def compute_grad(x):\n",
    "        j = compute_jacobian(x)\n",
    "        return j[:,0,:1]\n",
    "    X = keras.layers.Lambda(\n",
    "        lambda x: compute_grad(x) + K.sum(x[1])*0,\n",
    "        output_shape=(1,))([X_input, X])\n",
    "    M_DIntegral = keras.Model(X_input, X)\n",
    "    M_DIntegral.compile('nadam', 'mse', ['mae'])\n",
    "    return M_Integral, M_DIntegral"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_13 (InputLayer)           (None, 4)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "model_25 (Model)                (None, 1)            99857       input_13[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "lambda_13 (Lambda)              (None, 1)            0           input_13[0][0]                   \n",
      "                                                                 model_25[1][0]                   \n",
      "==================================================================================================\n",
      "Total params: 99,857\n",
      "Trainable params: 99,849\n",
      "Non-trainable params: 8\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "M_Integral, M_DIntegral = build_nn_pair()\n",
    "M = M_DIntegral\n",
    "M.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 100000 samples, validate on 10000 samples\n",
      "Epoch 1/200\n",
      "100000/100000 [==============================] - 4s 40us/step - loss: 0.0193 - mean_absolute_error: 0.0626 - val_loss: 0.0062 - val_mean_absolute_error: 0.0309\n",
      "Epoch 2/200\n",
      "100000/100000 [==============================] - 4s 40us/step - loss: 0.0182 - mean_absolute_error: 0.0601 - val_loss: 0.0034 - val_mean_absolute_error: 0.0267\n",
      "Epoch 3/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0177 - mean_absolute_error: 0.0592 - val_loss: 0.0036 - val_mean_absolute_error: 0.0293\n",
      "Epoch 4/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0182 - mean_absolute_error: 0.0602 - val_loss: 0.0051 - val_mean_absolute_error: 0.0306\n",
      "Epoch 5/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0186 - mean_absolute_error: 0.0613 - val_loss: 0.0050 - val_mean_absolute_error: 0.0303\n",
      "Epoch 6/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0183 - mean_absolute_error: 0.0605 - val_loss: 0.0071 - val_mean_absolute_error: 0.0372\n",
      "Epoch 7/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0172 - mean_absolute_error: 0.0583 - val_loss: 0.0058 - val_mean_absolute_error: 0.0314\n",
      "\n",
      "Epoch 00007: ReduceLROnPlateau reducing learning rate to 0.00020000000949949026.\n",
      "Epoch 8/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0154 - mean_absolute_error: 0.0551 - val_loss: 0.0029 - val_mean_absolute_error: 0.0213\n",
      "Epoch 9/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0151 - mean_absolute_error: 0.0543 - val_loss: 0.0027 - val_mean_absolute_error: 0.0209\n",
      "Epoch 10/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0157 - mean_absolute_error: 0.0556 - val_loss: 0.0020 - val_mean_absolute_error: 0.0186\n",
      "Epoch 11/200\n",
      "100000/100000 [==============================] - 4s 39us/step - loss: 0.0158 - mean_absolute_error: 0.0552 - val_loss: 0.0028 - val_mean_absolute_error: 0.0228\n",
      "Epoch 12/200\n",
      "100000/100000 [==============================] - 4s 39us/step - loss: 0.0157 - mean_absolute_error: 0.0552 - val_loss: 0.0027 - val_mean_absolute_error: 0.0218\n",
      "Epoch 13/200\n",
      "100000/100000 [==============================] - 4s 39us/step - loss: 0.0152 - mean_absolute_error: 0.0547 - val_loss: 0.0029 - val_mean_absolute_error: 0.0210\n",
      "Epoch 14/200\n",
      "100000/100000 [==============================] - 4s 39us/step - loss: 0.0160 - mean_absolute_error: 0.0567 - val_loss: 0.0029 - val_mean_absolute_error: 0.0221\n",
      "Epoch 15/200\n",
      "100000/100000 [==============================] - 4s 39us/step - loss: 0.0158 - mean_absolute_error: 0.0558 - val_loss: 0.0025 - val_mean_absolute_error: 0.0213\n",
      "\n",
      "Epoch 00015: ReduceLROnPlateau reducing learning rate to 2.0000000949949027e-05.\n",
      "Epoch 16/200\n",
      "100000/100000 [==============================] - 4s 39us/step - loss: 0.0147 - mean_absolute_error: 0.0540 - val_loss: 0.0026 - val_mean_absolute_error: 0.0210\n",
      "Epoch 17/200\n",
      "100000/100000 [==============================] - 4s 40us/step - loss: 0.0151 - mean_absolute_error: 0.0541 - val_loss: 0.0024 - val_mean_absolute_error: 0.0200\n",
      "Epoch 18/200\n",
      "100000/100000 [==============================] - 4s 40us/step - loss: 0.0154 - mean_absolute_error: 0.0541 - val_loss: 0.0025 - val_mean_absolute_error: 0.0206\n",
      "Epoch 19/200\n",
      "100000/100000 [==============================] - 4s 40us/step - loss: 0.0156 - mean_absolute_error: 0.0555 - val_loss: 0.0028 - val_mean_absolute_error: 0.0221\n",
      "Epoch 20/200\n",
      "100000/100000 [==============================] - 4s 40us/step - loss: 0.0141 - mean_absolute_error: 0.0530 - val_loss: 0.0023 - val_mean_absolute_error: 0.0200\n",
      "\n",
      "Epoch 00020: ReduceLROnPlateau reducing learning rate to 2.0000001313746906e-06.\n",
      "Epoch 21/200\n",
      "100000/100000 [==============================] - 4s 40us/step - loss: 0.0142 - mean_absolute_error: 0.0528 - val_loss: 0.0024 - val_mean_absolute_error: 0.0203\n",
      "Epoch 22/200\n",
      "100000/100000 [==============================] - 4s 40us/step - loss: 0.0143 - mean_absolute_error: 0.0536 - val_loss: 0.0026 - val_mean_absolute_error: 0.0210\n",
      "Epoch 23/200\n",
      "100000/100000 [==============================] - 4s 40us/step - loss: 0.0150 - mean_absolute_error: 0.0534 - val_loss: 0.0028 - val_mean_absolute_error: 0.0218\n",
      "Epoch 24/200\n",
      "100000/100000 [==============================] - 4s 40us/step - loss: 0.0150 - mean_absolute_error: 0.0543 - val_loss: 0.0025 - val_mean_absolute_error: 0.0209\n",
      "Epoch 25/200\n",
      "100000/100000 [==============================] - 4s 40us/step - loss: 0.0154 - mean_absolute_error: 0.0541 - val_loss: 0.0024 - val_mean_absolute_error: 0.0201\n",
      "\n",
      "Epoch 00025: ReduceLROnPlateau reducing learning rate to 2.000000222324161e-07.\n",
      "Epoch 26/200\n",
      "100000/100000 [==============================] - 4s 40us/step - loss: 0.0151 - mean_absolute_error: 0.0550 - val_loss: 0.0025 - val_mean_absolute_error: 0.0208\n",
      "Epoch 27/200\n",
      "100000/100000 [==============================] - 4s 40us/step - loss: 0.0147 - mean_absolute_error: 0.0539 - val_loss: 0.0026 - val_mean_absolute_error: 0.0208\n",
      "Epoch 28/200\n",
      "100000/100000 [==============================] - 4s 40us/step - loss: 0.0151 - mean_absolute_error: 0.0539 - val_loss: 0.0025 - val_mean_absolute_error: 0.0204\n",
      "Epoch 29/200\n",
      "100000/100000 [==============================] - 4s 40us/step - loss: 0.0142 - mean_absolute_error: 0.0523 - val_loss: 0.0025 - val_mean_absolute_error: 0.0202\n",
      "Epoch 30/200\n",
      "100000/100000 [==============================] - 4s 40us/step - loss: 0.0150 - mean_absolute_error: 0.0538 - val_loss: 0.0024 - val_mean_absolute_error: 0.0202\n",
      "\n",
      "Epoch 00030: ReduceLROnPlateau reducing learning rate to 2.000000165480742e-08.\n",
      "Epoch 31/200\n",
      "100000/100000 [==============================] - 4s 40us/step - loss: 0.0148 - mean_absolute_error: 0.0537 - val_loss: 0.0024 - val_mean_absolute_error: 0.0202\n",
      "Epoch 32/200\n",
      "100000/100000 [==============================] - 4s 40us/step - loss: 0.0143 - mean_absolute_error: 0.0530 - val_loss: 0.0024 - val_mean_absolute_error: 0.0206\n",
      "Epoch 33/200\n",
      "100000/100000 [==============================] - 4s 40us/step - loss: 0.0160 - mean_absolute_error: 0.0559 - val_loss: 0.0024 - val_mean_absolute_error: 0.0203\n",
      "Epoch 34/200\n",
      "100000/100000 [==============================] - 4s 40us/step - loss: 0.0147 - mean_absolute_error: 0.0538 - val_loss: 0.0027 - val_mean_absolute_error: 0.0217\n",
      "Epoch 35/200\n",
      "100000/100000 [==============================] - 4s 40us/step - loss: 0.0150 - mean_absolute_error: 0.0539 - val_loss: 0.0024 - val_mean_absolute_error: 0.0202\n",
      "\n",
      "Epoch 00035: ReduceLROnPlateau reducing learning rate to 2.000000165480742e-09.\n",
      "Epoch 36/200\n",
      "100000/100000 [==============================] - 4s 41us/step - loss: 0.0151 - mean_absolute_error: 0.0552 - val_loss: 0.0025 - val_mean_absolute_error: 0.0207\n",
      "Epoch 37/200\n",
      "100000/100000 [==============================] - 4s 41us/step - loss: 0.0150 - mean_absolute_error: 0.0540 - val_loss: 0.0026 - val_mean_absolute_error: 0.0206\n",
      "Epoch 38/200\n",
      "100000/100000 [==============================] - 4s 40us/step - loss: 0.0144 - mean_absolute_error: 0.0529 - val_loss: 0.0025 - val_mean_absolute_error: 0.0206\n",
      "Epoch 39/200\n",
      "100000/100000 [==============================] - 4s 40us/step - loss: 0.0147 - mean_absolute_error: 0.0536 - val_loss: 0.0025 - val_mean_absolute_error: 0.0204\n",
      "Epoch 40/200\n",
      "100000/100000 [==============================] - 4s 40us/step - loss: 0.0151 - mean_absolute_error: 0.0542 - val_loss: 0.0025 - val_mean_absolute_error: 0.0207\n",
      "\n",
      "Epoch 00040: ReduceLROnPlateau reducing learning rate to 2.000000165480742e-10.\n",
      "Epoch 41/200\n",
      "100000/100000 [==============================] - 4s 41us/step - loss: 0.0141 - mean_absolute_error: 0.0526 - val_loss: 0.0024 - val_mean_absolute_error: 0.0203\n",
      "Epoch 42/200\n",
      "100000/100000 [==============================] - 4s 41us/step - loss: 0.0149 - mean_absolute_error: 0.0539 - val_loss: 0.0025 - val_mean_absolute_error: 0.0204\n",
      "Epoch 43/200\n",
      "100000/100000 [==============================] - 4s 41us/step - loss: 0.0145 - mean_absolute_error: 0.0535 - val_loss: 0.0024 - val_mean_absolute_error: 0.0204\n",
      "Epoch 44/200\n",
      "100000/100000 [==============================] - 4s 41us/step - loss: 0.0147 - mean_absolute_error: 0.0531 - val_loss: 0.0025 - val_mean_absolute_error: 0.0207\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 45/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0145 - mean_absolute_error: 0.0532 - val_loss: 0.0024 - val_mean_absolute_error: 0.0202\n",
      "\n",
      "Epoch 00045: ReduceLROnPlateau reducing learning rate to 2.000000165480742e-11.\n",
      "Epoch 46/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0155 - mean_absolute_error: 0.0549 - val_loss: 0.0024 - val_mean_absolute_error: 0.0204\n",
      "Epoch 47/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0147 - mean_absolute_error: 0.0537 - val_loss: 0.0025 - val_mean_absolute_error: 0.0206\n",
      "Epoch 48/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0145 - mean_absolute_error: 0.0531 - val_loss: 0.0024 - val_mean_absolute_error: 0.0201\n",
      "Epoch 49/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0138 - mean_absolute_error: 0.0519 - val_loss: 0.0024 - val_mean_absolute_error: 0.0203\n",
      "Epoch 50/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0141 - mean_absolute_error: 0.0525 - val_loss: 0.0025 - val_mean_absolute_error: 0.0209\n",
      "\n",
      "Epoch 00050: ReduceLROnPlateau reducing learning rate to 2.000000165480742e-12.\n",
      "Epoch 51/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0148 - mean_absolute_error: 0.0542 - val_loss: 0.0030 - val_mean_absolute_error: 0.0227\n",
      "Epoch 52/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0141 - mean_absolute_error: 0.0526 - val_loss: 0.0024 - val_mean_absolute_error: 0.0203\n",
      "Epoch 53/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0146 - mean_absolute_error: 0.0534 - val_loss: 0.0029 - val_mean_absolute_error: 0.0223\n",
      "Epoch 54/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0146 - mean_absolute_error: 0.0536 - val_loss: 0.0024 - val_mean_absolute_error: 0.0203\n",
      "Epoch 55/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0149 - mean_absolute_error: 0.0535 - val_loss: 0.0028 - val_mean_absolute_error: 0.0216\n",
      "\n",
      "Epoch 00055: ReduceLROnPlateau reducing learning rate to 2.000000208848829e-13.\n",
      "Epoch 56/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0149 - mean_absolute_error: 0.0542 - val_loss: 0.0024 - val_mean_absolute_error: 0.0204\n",
      "Epoch 57/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0146 - mean_absolute_error: 0.0540 - val_loss: 0.0025 - val_mean_absolute_error: 0.0207\n",
      "Epoch 58/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0147 - mean_absolute_error: 0.0538 - val_loss: 0.0024 - val_mean_absolute_error: 0.0204\n",
      "Epoch 59/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0151 - mean_absolute_error: 0.0537 - val_loss: 0.0025 - val_mean_absolute_error: 0.0202\n",
      "Epoch 60/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0141 - mean_absolute_error: 0.0525 - val_loss: 0.0032 - val_mean_absolute_error: 0.0233\n",
      "\n",
      "Epoch 00060: ReduceLROnPlateau reducing learning rate to 2.0000002359538835e-14.\n",
      "Epoch 61/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0145 - mean_absolute_error: 0.0535 - val_loss: 0.0027 - val_mean_absolute_error: 0.0211\n",
      "Epoch 62/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0153 - mean_absolute_error: 0.0551 - val_loss: 0.0026 - val_mean_absolute_error: 0.0209\n",
      "Epoch 63/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0151 - mean_absolute_error: 0.0542 - val_loss: 0.0024 - val_mean_absolute_error: 0.0204\n",
      "Epoch 64/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0145 - mean_absolute_error: 0.0534 - val_loss: 0.0027 - val_mean_absolute_error: 0.0217\n",
      "Epoch 65/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0154 - mean_absolute_error: 0.0551 - val_loss: 0.0028 - val_mean_absolute_error: 0.0214\n",
      "\n",
      "Epoch 00065: ReduceLROnPlateau reducing learning rate to 2.000000303716519e-15.\n",
      "Epoch 66/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0151 - mean_absolute_error: 0.0541 - val_loss: 0.0026 - val_mean_absolute_error: 0.0208\n",
      "Epoch 67/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0149 - mean_absolute_error: 0.0538 - val_loss: 0.0026 - val_mean_absolute_error: 0.0210\n",
      "Epoch 68/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0144 - mean_absolute_error: 0.0529 - val_loss: 0.0025 - val_mean_absolute_error: 0.0204\n",
      "Epoch 69/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0149 - mean_absolute_error: 0.0537 - val_loss: 0.0025 - val_mean_absolute_error: 0.0205\n",
      "Epoch 70/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0148 - mean_absolute_error: 0.0538 - val_loss: 0.0025 - val_mean_absolute_error: 0.0207\n",
      "\n",
      "Epoch 00070: ReduceLROnPlateau reducing learning rate to 2.0000002190132243e-16.\n",
      "Epoch 71/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0150 - mean_absolute_error: 0.0549 - val_loss: 0.0025 - val_mean_absolute_error: 0.0208\n",
      "Epoch 72/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0143 - mean_absolute_error: 0.0524 - val_loss: 0.0024 - val_mean_absolute_error: 0.0203\n",
      "Epoch 73/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0147 - mean_absolute_error: 0.0533 - val_loss: 0.0027 - val_mean_absolute_error: 0.0214\n",
      "Epoch 74/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0153 - mean_absolute_error: 0.0539 - val_loss: 0.0025 - val_mean_absolute_error: 0.0208\n",
      "Epoch 75/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0147 - mean_absolute_error: 0.0539 - val_loss: 0.0025 - val_mean_absolute_error: 0.0208\n",
      "\n",
      "Epoch 00075: ReduceLROnPlateau reducing learning rate to 2.0000001660736652e-17.\n",
      "Epoch 76/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0147 - mean_absolute_error: 0.0535 - val_loss: 0.0025 - val_mean_absolute_error: 0.0206\n",
      "Epoch 77/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0144 - mean_absolute_error: 0.0536 - val_loss: 0.0024 - val_mean_absolute_error: 0.0201\n",
      "Epoch 78/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0152 - mean_absolute_error: 0.0541 - val_loss: 0.0025 - val_mean_absolute_error: 0.0205\n",
      "Epoch 79/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0150 - mean_absolute_error: 0.0540 - val_loss: 0.0025 - val_mean_absolute_error: 0.0209\n",
      "Epoch 80/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0156 - mean_absolute_error: 0.0547 - val_loss: 0.0024 - val_mean_absolute_error: 0.0202\n",
      "\n",
      "Epoch 00080: ReduceLROnPlateau reducing learning rate to 2.0000001329864408e-18.\n",
      "Epoch 81/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0140 - mean_absolute_error: 0.0527 - val_loss: 0.0025 - val_mean_absolute_error: 0.0209\n",
      "Epoch 82/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0152 - mean_absolute_error: 0.0546 - val_loss: 0.0026 - val_mean_absolute_error: 0.0206\n",
      "Epoch 83/200\n",
      "100000/100000 [==============================] - 4s 39us/step - loss: 0.0147 - mean_absolute_error: 0.0534 - val_loss: 0.0025 - val_mean_absolute_error: 0.0201\n",
      "Epoch 84/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0149 - mean_absolute_error: 0.0543 - val_loss: 0.0026 - val_mean_absolute_error: 0.0207\n",
      "Epoch 85/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0148 - mean_absolute_error: 0.0538 - val_loss: 0.0025 - val_mean_absolute_error: 0.0205\n",
      "\n",
      "Epoch 00085: ReduceLROnPlateau reducing learning rate to 2.00000009162741e-19.\n",
      "Epoch 86/200\n",
      "100000/100000 [==============================] - 4s 39us/step - loss: 0.0142 - mean_absolute_error: 0.0529 - val_loss: 0.0025 - val_mean_absolute_error: 0.0204\n",
      "Epoch 87/200\n",
      "100000/100000 [==============================] - 4s 39us/step - loss: 0.0148 - mean_absolute_error: 0.0533 - val_loss: 0.0029 - val_mean_absolute_error: 0.0223\n",
      "Epoch 88/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0142 - mean_absolute_error: 0.0529 - val_loss: 0.0026 - val_mean_absolute_error: 0.0210\n",
      "Epoch 89/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0144 - mean_absolute_error: 0.0526 - val_loss: 0.0026 - val_mean_absolute_error: 0.0212\n",
      "Epoch 90/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0144 - mean_absolute_error: 0.0528 - val_loss: 0.0025 - val_mean_absolute_error: 0.0207\n",
      "\n",
      "Epoch 00090: ReduceLROnPlateau reducing learning rate to 2.000000065778016e-20.\n",
      "Epoch 91/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0144 - mean_absolute_error: 0.0525 - val_loss: 0.0025 - val_mean_absolute_error: 0.0203\n",
      "Epoch 92/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0137 - mean_absolute_error: 0.0520 - val_loss: 0.0027 - val_mean_absolute_error: 0.0217\n",
      "Epoch 93/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0147 - mean_absolute_error: 0.0538 - val_loss: 0.0027 - val_mean_absolute_error: 0.0213\n",
      "Epoch 94/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0148 - mean_absolute_error: 0.0535 - val_loss: 0.0024 - val_mean_absolute_error: 0.0205\n",
      "Epoch 95/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0151 - mean_absolute_error: 0.0535 - val_loss: 0.0027 - val_mean_absolute_error: 0.0212\n",
      "\n",
      "Epoch 00095: ReduceLROnPlateau reducing learning rate to 2.0000000980897586e-21.\n",
      "Epoch 96/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0135 - mean_absolute_error: 0.0515 - val_loss: 0.0025 - val_mean_absolute_error: 0.0206\n",
      "Epoch 97/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0152 - mean_absolute_error: 0.0546 - val_loss: 0.0024 - val_mean_absolute_error: 0.0201\n",
      "Epoch 98/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0147 - mean_absolute_error: 0.0536 - val_loss: 0.0031 - val_mean_absolute_error: 0.0225\n",
      "Epoch 99/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0158 - mean_absolute_error: 0.0550 - val_loss: 0.0025 - val_mean_absolute_error: 0.0203\n",
      "Epoch 100/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0156 - mean_absolute_error: 0.0549 - val_loss: 0.0024 - val_mean_absolute_error: 0.0203\n",
      "\n",
      "Epoch 00100: ReduceLROnPlateau reducing learning rate to 2.000000138479437e-22.\n",
      "Epoch 101/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0145 - mean_absolute_error: 0.0538 - val_loss: 0.0024 - val_mean_absolute_error: 0.0206\n",
      "Epoch 102/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0159 - mean_absolute_error: 0.0554 - val_loss: 0.0025 - val_mean_absolute_error: 0.0202\n",
      "Epoch 103/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0141 - mean_absolute_error: 0.0523 - val_loss: 0.0024 - val_mean_absolute_error: 0.0201\n",
      "Epoch 104/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0154 - mean_absolute_error: 0.0552 - val_loss: 0.0030 - val_mean_absolute_error: 0.0226\n",
      "Epoch 105/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0144 - mean_absolute_error: 0.0532 - val_loss: 0.0024 - val_mean_absolute_error: 0.0202\n",
      "\n",
      "Epoch 00105: ReduceLROnPlateau reducing learning rate to 2.000000188966535e-23.\n",
      "Epoch 106/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0151 - mean_absolute_error: 0.0544 - val_loss: 0.0025 - val_mean_absolute_error: 0.0205\n",
      "Epoch 107/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0143 - mean_absolute_error: 0.0532 - val_loss: 0.0025 - val_mean_absolute_error: 0.0206\n",
      "Epoch 108/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0149 - mean_absolute_error: 0.0534 - val_loss: 0.0023 - val_mean_absolute_error: 0.0200\n",
      "Epoch 109/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0155 - mean_absolute_error: 0.0543 - val_loss: 0.0023 - val_mean_absolute_error: 0.0201\n",
      "Epoch 110/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0139 - mean_absolute_error: 0.0524 - val_loss: 0.0024 - val_mean_absolute_error: 0.0203\n",
      "\n",
      "Epoch 00110: ReduceLROnPlateau reducing learning rate to 2.000000157412099e-24.\n",
      "Epoch 111/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0144 - mean_absolute_error: 0.0537 - val_loss: 0.0025 - val_mean_absolute_error: 0.0204\n",
      "Epoch 112/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0166 - mean_absolute_error: 0.0569 - val_loss: 0.0026 - val_mean_absolute_error: 0.0210\n",
      "Epoch 113/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0156 - mean_absolute_error: 0.0554 - val_loss: 0.0024 - val_mean_absolute_error: 0.0201\n",
      "Epoch 114/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0151 - mean_absolute_error: 0.0538 - val_loss: 0.0025 - val_mean_absolute_error: 0.0205\n",
      "Epoch 115/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0147 - mean_absolute_error: 0.0535 - val_loss: 0.0024 - val_mean_absolute_error: 0.0204\n",
      "\n",
      "Epoch 00115: ReduceLROnPlateau reducing learning rate to 2.0000002362981892e-25.\n",
      "Epoch 116/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0150 - mean_absolute_error: 0.0539 - val_loss: 0.0024 - val_mean_absolute_error: 0.0201\n",
      "Epoch 117/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0146 - mean_absolute_error: 0.0538 - val_loss: 0.0024 - val_mean_absolute_error: 0.0202\n",
      "Epoch 118/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0152 - mean_absolute_error: 0.0546 - val_loss: 0.0024 - val_mean_absolute_error: 0.0203\n",
      "Epoch 119/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0144 - mean_absolute_error: 0.0525 - val_loss: 0.0025 - val_mean_absolute_error: 0.0204\n",
      "Epoch 120/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0138 - mean_absolute_error: 0.0521 - val_loss: 0.0024 - val_mean_absolute_error: 0.0200\n",
      "\n",
      "Epoch 00120: ReduceLROnPlateau reducing learning rate to 2.0000002856019956e-26.\n",
      "Epoch 121/200\n",
      "100000/100000 [==============================] - 4s 39us/step - loss: 0.0149 - mean_absolute_error: 0.0538 - val_loss: 0.0024 - val_mean_absolute_error: 0.0203\n",
      "Epoch 122/200\n",
      "100000/100000 [==============================] - 4s 39us/step - loss: 0.0149 - mean_absolute_error: 0.0542 - val_loss: 0.0025 - val_mean_absolute_error: 0.0208\n",
      "Epoch 123/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0157 - mean_absolute_error: 0.0552 - val_loss: 0.0024 - val_mean_absolute_error: 0.0203\n",
      "Epoch 124/200\n",
      "100000/100000 [==============================] - 4s 39us/step - loss: 0.0146 - mean_absolute_error: 0.0534 - val_loss: 0.0024 - val_mean_absolute_error: 0.0204\n",
      "Epoch 125/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0145 - mean_absolute_error: 0.0529 - val_loss: 0.0024 - val_mean_absolute_error: 0.0204\n",
      "\n",
      "Epoch 00125: ReduceLROnPlateau reducing learning rate to 2.000000285601996e-27.\n",
      "Epoch 126/200\n",
      "100000/100000 [==============================] - 4s 39us/step - loss: 0.0159 - mean_absolute_error: 0.0557 - val_loss: 0.0027 - val_mean_absolute_error: 0.0214\n",
      "Epoch 127/200\n",
      "100000/100000 [==============================] - 4s 39us/step - loss: 0.0152 - mean_absolute_error: 0.0540 - val_loss: 0.0030 - val_mean_absolute_error: 0.0226\n",
      "Epoch 128/200\n",
      "100000/100000 [==============================] - 4s 39us/step - loss: 0.0149 - mean_absolute_error: 0.0541 - val_loss: 0.0026 - val_mean_absolute_error: 0.0207\n",
      "Epoch 129/200\n",
      "100000/100000 [==============================] - 4s 39us/step - loss: 0.0148 - mean_absolute_error: 0.0538 - val_loss: 0.0024 - val_mean_absolute_error: 0.0203\n",
      "Epoch 130/200\n",
      "100000/100000 [==============================] - 4s 39us/step - loss: 0.0145 - mean_absolute_error: 0.0540 - val_loss: 0.0025 - val_mean_absolute_error: 0.0207\n",
      "\n",
      "Epoch 00130: ReduceLROnPlateau reducing learning rate to 2.0000002470833968e-28.\n",
      "Epoch 131/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0153 - mean_absolute_error: 0.0547 - val_loss: 0.0024 - val_mean_absolute_error: 0.0203\n",
      "Epoch 132/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0150 - mean_absolute_error: 0.0537 - val_loss: 0.0026 - val_mean_absolute_error: 0.0212\n",
      "Epoch 133/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0155 - mean_absolute_error: 0.0549 - val_loss: 0.0025 - val_mean_absolute_error: 0.0208\n",
      "Epoch 134/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0146 - mean_absolute_error: 0.0534 - val_loss: 0.0026 - val_mean_absolute_error: 0.0211\n",
      "Epoch 135/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0150 - mean_absolute_error: 0.0534 - val_loss: 0.0022 - val_mean_absolute_error: 0.0196\n",
      "\n",
      "Epoch 00135: ReduceLROnPlateau reducing learning rate to 2.000000247083397e-29.\n",
      "Epoch 136/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0148 - mean_absolute_error: 0.0536 - val_loss: 0.0025 - val_mean_absolute_error: 0.0209\n",
      "Epoch 137/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0141 - mean_absolute_error: 0.0526 - val_loss: 0.0023 - val_mean_absolute_error: 0.0200\n",
      "Epoch 138/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0149 - mean_absolute_error: 0.0534 - val_loss: 0.0023 - val_mean_absolute_error: 0.0200\n",
      "Epoch 139/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0141 - mean_absolute_error: 0.0526 - val_loss: 0.0024 - val_mean_absolute_error: 0.0201\n",
      "Epoch 140/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0141 - mean_absolute_error: 0.0524 - val_loss: 0.0025 - val_mean_absolute_error: 0.0203\n",
      "\n",
      "Epoch 00140: ReduceLROnPlateau reducing learning rate to 2.0000003072687078e-30.\n",
      "Epoch 141/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0145 - mean_absolute_error: 0.0525 - val_loss: 0.0023 - val_mean_absolute_error: 0.0201\n",
      "Epoch 142/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0140 - mean_absolute_error: 0.0524 - val_loss: 0.0024 - val_mean_absolute_error: 0.0204\n",
      "Epoch 143/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0142 - mean_absolute_error: 0.0527 - val_loss: 0.0024 - val_mean_absolute_error: 0.0204\n",
      "Epoch 144/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0154 - mean_absolute_error: 0.0540 - val_loss: 0.0024 - val_mean_absolute_error: 0.0201\n",
      "Epoch 145/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0143 - mean_absolute_error: 0.0532 - val_loss: 0.0025 - val_mean_absolute_error: 0.0208\n",
      "\n",
      "Epoch 00145: ReduceLROnPlateau reducing learning rate to 2.000000382500346e-31.\n",
      "Epoch 146/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0144 - mean_absolute_error: 0.0527 - val_loss: 0.0026 - val_mean_absolute_error: 0.0211\n",
      "Epoch 147/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0146 - mean_absolute_error: 0.0532 - val_loss: 0.0026 - val_mean_absolute_error: 0.0208\n",
      "Epoch 148/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0142 - mean_absolute_error: 0.0526 - val_loss: 0.0024 - val_mean_absolute_error: 0.0204\n",
      "Epoch 149/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0151 - mean_absolute_error: 0.0546 - val_loss: 0.0024 - val_mean_absolute_error: 0.0205\n",
      "Epoch 150/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0153 - mean_absolute_error: 0.0546 - val_loss: 0.0025 - val_mean_absolute_error: 0.0210\n",
      "\n",
      "Epoch 00150: ReduceLROnPlateau reducing learning rate to 2.0000004295201202e-32.\n",
      "Epoch 151/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0138 - mean_absolute_error: 0.0520 - val_loss: 0.0024 - val_mean_absolute_error: 0.0204\n",
      "Epoch 152/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0148 - mean_absolute_error: 0.0537 - val_loss: 0.0027 - val_mean_absolute_error: 0.0214\n",
      "Epoch 153/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0152 - mean_absolute_error: 0.0542 - val_loss: 0.0024 - val_mean_absolute_error: 0.0204\n",
      "Epoch 154/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0144 - mean_absolute_error: 0.0530 - val_loss: 0.0024 - val_mean_absolute_error: 0.0201\n",
      "Epoch 155/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0151 - mean_absolute_error: 0.0542 - val_loss: 0.0029 - val_mean_absolute_error: 0.0224\n",
      "\n",
      "Epoch 00155: ReduceLROnPlateau reducing learning rate to 2.0000004882948376e-33.\n",
      "Epoch 156/200\n",
      "100000/100000 [==============================] - 4s 39us/step - loss: 0.0156 - mean_absolute_error: 0.0547 - val_loss: 0.0023 - val_mean_absolute_error: 0.0199\n",
      "Epoch 157/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0148 - mean_absolute_error: 0.0535 - val_loss: 0.0025 - val_mean_absolute_error: 0.0205\n",
      "Epoch 158/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0141 - mean_absolute_error: 0.0527 - val_loss: 0.0025 - val_mean_absolute_error: 0.0207\n",
      "Epoch 159/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0159 - mean_absolute_error: 0.0552 - val_loss: 0.0025 - val_mean_absolute_error: 0.0204\n",
      "Epoch 160/200\n",
      "100000/100000 [==============================] - 4s 39us/step - loss: 0.0141 - mean_absolute_error: 0.0523 - val_loss: 0.0023 - val_mean_absolute_error: 0.0201\n",
      "\n",
      "Epoch 00160: ReduceLROnPlateau reducing learning rate to 2.0000004148264407e-34.\n",
      "Epoch 161/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0151 - mean_absolute_error: 0.0542 - val_loss: 0.0025 - val_mean_absolute_error: 0.0205\n",
      "Epoch 162/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0154 - mean_absolute_error: 0.0545 - val_loss: 0.0024 - val_mean_absolute_error: 0.0203\n",
      "Epoch 163/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0140 - mean_absolute_error: 0.0521 - val_loss: 0.0027 - val_mean_absolute_error: 0.0217\n",
      "Epoch 164/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0147 - mean_absolute_error: 0.0534 - val_loss: 0.0024 - val_mean_absolute_error: 0.0204\n",
      "Epoch 165/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0148 - mean_absolute_error: 0.0540 - val_loss: 0.0024 - val_mean_absolute_error: 0.0202\n",
      "\n",
      "Epoch 00165: ReduceLROnPlateau reducing learning rate to 2.0000003229909445e-35.\n",
      "Epoch 166/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0148 - mean_absolute_error: 0.0540 - val_loss: 0.0025 - val_mean_absolute_error: 0.0209\n",
      "Epoch 167/200\n",
      "100000/100000 [==============================] - 4s 39us/step - loss: 0.0142 - mean_absolute_error: 0.0529 - val_loss: 0.0028 - val_mean_absolute_error: 0.0217\n",
      "Epoch 168/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0146 - mean_absolute_error: 0.0532 - val_loss: 0.0024 - val_mean_absolute_error: 0.0204\n",
      "Epoch 169/200\n",
      "100000/100000 [==============================] - 4s 39us/step - loss: 0.0149 - mean_absolute_error: 0.0541 - val_loss: 0.0027 - val_mean_absolute_error: 0.0215\n",
      "Epoch 170/200\n",
      "100000/100000 [==============================] - 4s 39us/step - loss: 0.0151 - mean_absolute_error: 0.0544 - val_loss: 0.0026 - val_mean_absolute_error: 0.0212\n",
      "\n",
      "Epoch 00170: ReduceLROnPlateau reducing learning rate to 2.0000003229909445e-36.\n",
      "Epoch 171/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0152 - mean_absolute_error: 0.0542 - val_loss: 0.0025 - val_mean_absolute_error: 0.0205\n",
      "Epoch 172/200\n",
      "100000/100000 [==============================] - 4s 39us/step - loss: 0.0154 - mean_absolute_error: 0.0544 - val_loss: 0.0024 - val_mean_absolute_error: 0.0207\n",
      "Epoch 173/200\n",
      "100000/100000 [==============================] - 4s 39us/step - loss: 0.0147 - mean_absolute_error: 0.0539 - val_loss: 0.0023 - val_mean_absolute_error: 0.0201\n",
      "Epoch 174/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0158 - mean_absolute_error: 0.0551 - val_loss: 0.0024 - val_mean_absolute_error: 0.0203\n",
      "Epoch 175/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0144 - mean_absolute_error: 0.0528 - val_loss: 0.0025 - val_mean_absolute_error: 0.0208\n",
      "\n",
      "Epoch 00175: ReduceLROnPlateau reducing learning rate to 2.0000002512444633e-37.\n",
      "Epoch 176/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0148 - mean_absolute_error: 0.0539 - val_loss: 0.0027 - val_mean_absolute_error: 0.0214\n",
      "Epoch 177/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0156 - mean_absolute_error: 0.0550 - val_loss: 0.0025 - val_mean_absolute_error: 0.0206\n",
      "Epoch 178/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0145 - mean_absolute_error: 0.0531 - val_loss: 0.0023 - val_mean_absolute_error: 0.0200\n",
      "Epoch 179/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0148 - mean_absolute_error: 0.0538 - val_loss: 0.0025 - val_mean_absolute_error: 0.0203\n",
      "Epoch 180/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0150 - mean_absolute_error: 0.0546 - val_loss: 0.0025 - val_mean_absolute_error: 0.0208\n",
      "\n",
      "Epoch 00180: ReduceLROnPlateau reducing learning rate to 2.0000002064029123e-38.\n",
      "Epoch 181/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0152 - mean_absolute_error: 0.0544 - val_loss: 0.0024 - val_mean_absolute_error: 0.0202\n",
      "Epoch 182/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0146 - mean_absolute_error: 0.0535 - val_loss: 0.0024 - val_mean_absolute_error: 0.0202\n",
      "Epoch 183/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0149 - mean_absolute_error: 0.0539 - val_loss: 0.0025 - val_mean_absolute_error: 0.0205\n",
      "Epoch 184/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0142 - mean_absolute_error: 0.0526 - val_loss: 0.0025 - val_mean_absolute_error: 0.0208\n",
      "Epoch 185/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0147 - mean_absolute_error: 0.0536 - val_loss: 0.0024 - val_mean_absolute_error: 0.0206\n",
      "\n",
      "Epoch 00185: ReduceLROnPlateau reducing learning rate to 2.0000001503509737e-39.\n",
      "Epoch 186/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0149 - mean_absolute_error: 0.0541 - val_loss: 0.0025 - val_mean_absolute_error: 0.0207\n",
      "Epoch 187/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0146 - mean_absolute_error: 0.0532 - val_loss: 0.0024 - val_mean_absolute_error: 0.0203\n",
      "Epoch 188/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0157 - mean_absolute_error: 0.0555 - val_loss: 0.0024 - val_mean_absolute_error: 0.0202\n",
      "Epoch 189/200\n",
      "100000/100000 [==============================] - 4s 37us/step - loss: 0.0147 - mean_absolute_error: 0.0534 - val_loss: 0.0025 - val_mean_absolute_error: 0.0204\n",
      "Epoch 190/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0151 - mean_absolute_error: 0.0539 - val_loss: 0.0025 - val_mean_absolute_error: 0.0206\n",
      "\n",
      "Epoch 00190: ReduceLROnPlateau reducing learning rate to 2.0000004306106668e-40.\n",
      "Epoch 191/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0150 - mean_absolute_error: 0.0540 - val_loss: 0.0025 - val_mean_absolute_error: 0.0204\n",
      "Epoch 192/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0153 - mean_absolute_error: 0.0546 - val_loss: 0.0025 - val_mean_absolute_error: 0.0204\n",
      "Epoch 193/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0152 - mean_absolute_error: 0.0546 - val_loss: 0.0027 - val_mean_absolute_error: 0.0214\n",
      "Epoch 194/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0151 - mean_absolute_error: 0.0540 - val_loss: 0.0026 - val_mean_absolute_error: 0.0213\n",
      "Epoch 195/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0147 - mean_absolute_error: 0.0534 - val_loss: 0.0025 - val_mean_absolute_error: 0.0207\n",
      "\n",
      "Epoch 00195: ReduceLROnPlateau reducing learning rate to 2.0000032332075952e-41.\n",
      "Epoch 196/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0154 - mean_absolute_error: 0.0549 - val_loss: 0.0025 - val_mean_absolute_error: 0.0206\n",
      "Epoch 197/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0153 - mean_absolute_error: 0.0547 - val_loss: 0.0024 - val_mean_absolute_error: 0.0202\n",
      "Epoch 198/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0150 - mean_absolute_error: 0.0536 - val_loss: 0.0026 - val_mean_absolute_error: 0.0210\n",
      "Epoch 199/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0146 - mean_absolute_error: 0.0531 - val_loss: 0.0025 - val_mean_absolute_error: 0.0202\n",
      "Epoch 200/200\n",
      "100000/100000 [==============================] - 4s 38us/step - loss: 0.0149 - mean_absolute_error: 0.0537 - val_loss: 0.0026 - val_mean_absolute_error: 0.0212\n",
      "\n",
      "Epoch 00200: ReduceLROnPlateau reducing learning rate to 1.999933168284379e-42.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fb8b897b898>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M.fit(xtrain, ytrain, validation_data=(xtest, ytest), batch_size=256, epochs=200, callbacks=[\n",
    "    keras.callbacks.ReduceLROnPlateau(patience=5, verbose=1),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb8bdf4f6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "plt.subplot(3,3,1)\n",
    "plt.title('learnt d/dx(integral)')\n",
    "plt.scatter(xtest[:,0], M_DIntegral.predict(xtest), label='prediction')\n",
    "plt.scatter(xtest[:,0], lambda_F(*np.hsplit(xtest, 4)), label='truth')\n",
    "plt.legend()\n",
    "plt.subplot(3,3,2)\n",
    "plt.title('indeterminate integral')\n",
    "plt.scatter(xtest[:,0], M_Integral.predict(xtest), label='prediction')\n",
    "plt.scatter(xtest[:,0], lambda_iF(*np.hsplit(xtest, 4)), label='truth')\n",
    "plt.legend()\n",
    "for i in range(7):\n",
    "    D = (i+1)\n",
    "    plt.subplot(3,3,i+3)\n",
    "    plt.title(f'integrate from x-{D} to x+{D}')\n",
    "    plt.scatter(xtest[:,0],\n",
    "                M_Integral.predict(np.hstack([xtest[:,:1] + D, *np.hsplit(xtest[:,1:], 3)])) - \\\n",
    "                M_Integral.predict(np.hstack([xtest[:,:1] - D, *np.hsplit(xtest[:,1:], 3)])),\n",
    "                label='prediction')\n",
    "    plt.scatter(xtest[:,0],\n",
    "                lambda_iF(xtest[:,:1] + D, *np.hsplit(xtest[:,1:], 3)) - \\\n",
    "                lambda_iF(xtest[:,:1] - D, *np.hsplit(xtest[:,1:], 3)),\n",
    "                label='truth')\n",
    "    plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ml",
   "language": "python",
   "name": "ml"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}