Series-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import keras as k\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dropout,Input, Dense,merge, Add\n",
    "from keras import optimizers\n",
    "from keras.models import Model\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_ = np.loadtxt(\"./asy/random.txt\")\n",
    "green_ = np.loadtxt(\"./asy/green.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.DataFrame(columns=[\"amu\",\"beta\"])\n",
    "X[\"amu\"] = random_[:,0]\n",
    "X[\"beta\"] = random_[:,1]\n",
    "Y = pd.DataFrame(columns=[\"G\",\"damu\",\"dbeta\"])\n",
    "Y[\"G\"] = green_[:,0]\n",
    "Y[\"damu\"] = green_[:,1]\n",
    "Y[\"dbeta\"] = green_[:,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "G       -0.000313\n",
       "damu    -0.001623\n",
       "dbeta    0.741350\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.mean(axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.30, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scaler = Pre()\n",
    "random_scale = preprocessing.MinMaxScaler([0,10])\n",
    "green_scale = preprocessing.MinMaxScaler([0,10])\n",
    "\n",
    "random_scale.fit(X_train)\n",
    "green_scale.fit(y_train)\n",
    "X_train = random_scale.transform(X_train)\n",
    "X_test = random_scale.transform(X_test)\n",
    "\n",
    "y_train = green_scale.transform(y_train)\n",
    "y_test = green_scale.transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y1,Y2,Y3 =y_train[:,0],y_train[:,1],y_train[:,2]\n",
    "Y1_,Y2_,Y3_ =y_test[:,0],y_test[:,1],y_test[:,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y1,Y2,Y3 = Y1.reshape(Y1.size,1),Y2.reshape(Y2.size,1),Y3.reshape(Y3.size,1)\n",
    "Y1_,Y2_,Y3_ = Y1_.reshape(Y1_.size,1),Y2_.reshape(Y2_.size,1),Y3_.reshape(Y3_.size,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model = Sequential()\n",
    "# model.add(Dense(100, input_dim=2, activation='relu'))\n",
    "# model.add(Dense(100, activation='relu'))\n",
    "# model.add(Dense(1000, activation='relu'))\n",
    "# model.add(Dense(1000, activation='relu'))\n",
    "# model.add(Dense(1000, activation='relu'))\n",
    "# model.add(Dense(500, activation='relu'))\n",
    "# model.add(Dense(50, activation='relu'))\n",
    "# model.add(Dense(10, activation='relu'))\n",
    "# model.add(Dropout(0.5))\n",
    "# model.add(Dense(3, activation=None))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = Input(shape=(2,))\n",
    "x = Dense(8, activation='tanh')(inputs)\n",
    "x = Dense(64, activation='tanh')(x)\n",
    "x = Dense(64, activation='tanh')(x)\n",
    "x = Dense(64, activation='tanh')(x)\n",
    "x_1 = Dense(64, activation='tanh')(x)\n",
    "x = Dense(64, activation='tanh')(x_1)\n",
    "# x = Dense(64, activation='relu')(x)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "x1_ = Dense(64, activation='tanh')(x)\n",
    "x1 = Dense(64, activation='tanh')(x1_)\n",
    "x1 = Dense(64, activation='tanh')(x1)\n",
    "x1 = Dense(64, activation='tanh')(x1)\n",
    "x1 = Dense(64, activation='tanh')(x1)\n",
    "# x = Dense(64, activation='relu')(x)\n",
    "x1 = Add()([x1, x1_])\n",
    "x1 = Dense(64, activation='tanh')(x)\n",
    "y1 = Dense(1, activation=None)(x1)\n",
    "\n",
    "x2_ = Dense(64, activation='tanh')(x)\n",
    "x2 = Dense(64, activation='tanh')(x2_)\n",
    "x2 = Dense(64, activation='tanh')(x2)\n",
    "x2 = Dense(64, activation='tanh')(x2)\n",
    "x2 = Dense(64, activation='tanh')(x2)\n",
    "x2 = Dense(64, activation='tanh')(x2)\n",
    "# x = Dense(64, activation='relu')(x)\n",
    "x2 = Add()([x2, x2_])\n",
    "y2 = Dense(1, activation=None)(x2)\n",
    "\n",
    "x3 = Dense(32, activation='tanh')(x)\n",
    "x3 = Dense(10, activation='tanh')(x3)\n",
    "y3 = Dense(1, activation=None)(x3)\n",
    "\n",
    "model = Model(inputs=inputs, outputs=[y1,y2,y3])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "sgd = optimizers.adam(lr=0.0001)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss=['mean_squared_error','mean_squared_error','mean_squared_error'], optimizer=sgd, metrics=['mae'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 596049 samples, validate on 255450 samples\n",
      "Epoch 1/40\n",
      "596049/596049 [==============================] - 148s 248us/step - loss: 0.3775 - dense_36_loss: 0.1204 - dense_43_loss: 0.0522 - dense_46_loss: 0.2049 - dense_36_mean_absolute_error: 0.0587 - dense_43_mean_absolute_error: 0.0438 - dense_46_mean_absolute_error: 0.1233 - val_loss: 0.0964 - val_dense_36_loss: 0.0483 - val_dense_43_loss: 0.0278 - val_dense_46_loss: 0.0203 - val_dense_36_mean_absolute_error: 0.0413 - val_dense_43_mean_absolute_error: 0.0274 - val_dense_46_mean_absolute_error: 0.0138\n",
      "Epoch 2/40\n",
      "596049/596049 [==============================] - 146s 246us/step - loss: 0.1039 - dense_36_loss: 0.0506 - dense_43_loss: 0.0311 - dense_46_loss: 0.0221 - dense_36_mean_absolute_error: 0.0334 - dense_43_mean_absolute_error: 0.0320 - dense_46_mean_absolute_error: 0.0128 - val_loss: 0.0964 - val_dense_36_loss: 0.0485 - val_dense_43_loss: 0.0276 - val_dense_46_loss: 0.0203 - val_dense_36_mean_absolute_error: 0.0447 - val_dense_43_mean_absolute_error: 0.0198 - val_dense_46_mean_absolute_error: 0.0141\n",
      "Epoch 3/40\n",
      "596049/596049 [==============================] - 148s 249us/step - loss: 0.1036 - dense_36_loss: 0.0505 - dense_43_loss: 0.0310 - dense_46_loss: 0.0221 - dense_36_mean_absolute_error: 0.0309 - dense_43_mean_absolute_error: 0.0294 - dense_46_mean_absolute_error: 0.0129 - val_loss: 0.0960 - val_dense_36_loss: 0.0481 - val_dense_43_loss: 0.0277 - val_dense_46_loss: 0.0203 - val_dense_36_mean_absolute_error: 0.0351 - val_dense_43_mean_absolute_error: 0.0242 - val_dense_46_mean_absolute_error: 0.0121\n",
      "Epoch 4/40\n",
      "596049/596049 [==============================] - 148s 248us/step - loss: 0.1034 - dense_36_loss: 0.0505 - dense_43_loss: 0.0308 - dense_46_loss: 0.0221 - dense_36_mean_absolute_error: 0.0295 - dense_43_mean_absolute_error: 0.0265 - dense_46_mean_absolute_error: 0.0128 - val_loss: 0.0959 - val_dense_36_loss: 0.0478 - val_dense_43_loss: 0.0277 - val_dense_46_loss: 0.0204 - val_dense_36_mean_absolute_error: 0.0232 - val_dense_43_mean_absolute_error: 0.0248 - val_dense_46_mean_absolute_error: 0.0174\n",
      "Epoch 5/40\n",
      "596000/596049 [============================>.] - ETA: 0s - loss: 0.1033 - dense_36_loss: 0.0504 - dense_43_loss: 0.0307 - dense_46_loss: 0.0221 - dense_36_mean_absolute_error: 0.0282 - dense_43_mean_absolute_error: 0.0242 - dense_46_mean_absolute_error: 0.0128"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-25-5f599ca0724e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhistory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mY1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mY2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mY3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m40\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mY1_\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mY2_\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mY3_\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/.conda/envs/crystal/lib/python3.7/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m   1037\u001b[0m                                         \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1038\u001b[0m                                         \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1039\u001b[0;31m                                         validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m   1040\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1041\u001b[0m     def evaluate(self, x=None, y=None,\n",
      "\u001b[0;32m~/.conda/envs/crystal/lib/python3.7/site-packages/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mfit_loop\u001b[0;34m(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[1;32m    210\u001b[0m                         val_outs = test_loop(model, val_f, val_ins,\n\u001b[1;32m    211\u001b[0m                                              \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 212\u001b[0;31m                                              verbose=0)\n\u001b[0m\u001b[1;32m    213\u001b[0m                         \u001b[0mval_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval_outs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m                         \u001b[0;31m# Same labels assumed.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/crystal/lib/python3.7/site-packages/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mtest_loop\u001b[0;34m(model, f, ins, batch_size, verbose, steps)\u001b[0m\n\u001b[1;32m    390\u001b[0m                 \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    391\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 392\u001b[0;31m             \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    393\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_outs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    394\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mbatch_index\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/crystal/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2713\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_legacy_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2714\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2715\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2716\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2717\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mpy_any\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mis_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/crystal/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2673\u001b[0m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_metadata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2674\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2675\u001b[0;31m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2676\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfetched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/crystal/lib/python3.7/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1437\u001b[0m           ret = tf_session.TF_SessionRunCallable(\n\u001b[1;32m   1438\u001b[0m               \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1439\u001b[0;31m               run_metadata_ptr)\n\u001b[0m\u001b[1;32m   1440\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1441\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "history = model.fit([X_train], [Y1,Y2,Y3], epochs=40, batch_size=32,validation_data=([X_test],[Y1_,Y2_,Y3_]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_ = random_scale.transform(X)\n",
    "output_ = model.predict(input_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_ = np.transpose(np.array(output_))\n",
    "output_ = green_scale.inverse_transform(output_.reshape(output_.shape[1],3))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f527c792278>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(output_[:,2],green_[:,2],alpha=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "G         12.772139\n",
       "damu      69.942667\n",
       "dbeta    325.809673\n",
       "dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.max(axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.plot(y_test[:,0][200:1000],predict[0][200:1000])\n",
    "plt.scatter(y_test[:,0][0:10],predict[:][0][0:10],alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.73858659,  1.22535408, -0.05017406, ..., -0.10227372,\n",
       "        0.3537558 , -0.12147191])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.09      , 9.90999985]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_scale.transform([[0.09  , 9.90999985]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12.7721387584226, 69.942667009624, 325.80967280782)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(green_[:,0]),max(green_[:,1]),max(green_[:,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "ran = random_scale.transform([[1,2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.07142851, 0.14224446]])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ran"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.7068106354022103"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03998879980190932"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "green_[1][2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}