Solar_model.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 math\n",
    "import matplotlib as mpl\n",
    "from matplotlib.image import imread\n",
    "from random import randint\n",
    "\n",
    "import keras\n",
    "import pandas\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.python.keras.models import Sequential\n",
    "from tensorflow.python.keras.layers import Input, Dense, GRU, Embedding\n",
    "from tensorflow.python.keras.optimizers import RMSprop\n",
    "from tensorflow.python.keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation, Flatten\n",
    "from keras.optimizers import SGD, adam\n",
    "from keras.layers import Embedding\n",
    "from keras.layers import LSTM\n",
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "from keras import optimizers\n",
    "import keras.utils\n",
    "import keras.layers\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "import copy\n",
    "import csv\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/data2/Datasets\n"
     ]
    }
   ],
   "source": [
    "mpl.use('Agg')\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "ROOT = os.getcwd()\n",
    "DATASET_PATH = os.path.join(ROOT, \"Datasets\")\n",
    "print(DATASET_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set y values of data to lie between 0 and 1\n",
    "def normalize_data(dataset, data_min, data_max):\n",
    "    data_std = (dataset - data_min) / (data_max - data_min)\n",
    "    test_scaled = data_std * (np.amax(data_std) - np.amin(data_std)) + np.amin(data_std)\n",
    "    return test_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import and pre-process data for future applications\n",
    "def import_data(train_dataframe, test_dataframe):\n",
    "    dataset = train_dataframe.values\n",
    "    dataset = dataset.astype('float32')\n",
    "\n",
    "    #Include all 12 initial factors (Year ; Month ; Hour ; Day ; Cloud Coverage ; Visibility ; Temperature ; Dew Point ;\n",
    "    #Relative Humidity ; Wind Speed ; Station Pressure ; Altimeter\n",
    "    max_test = np.max(dataset[:,6])\n",
    "    min_test = np.min(dataset[:,6])\n",
    "    scale_factor = max_test - min_test\n",
    "    max = np.empty(7)\n",
    "    min = np.empty(7)\n",
    "\n",
    "    #Create training dataset\n",
    "    for i in range(0,6):\n",
    "        min[i] = np.amin(dataset[:,i],axis = 0)\n",
    "        max[i] = np.amax(dataset[:,i],axis = 0)\n",
    "        dataset[:,i] = normalize_data(dataset[:, i], min[i], max[i])\n",
    "\n",
    "    train_data = dataset[:,0:7]\n",
    "    train_labels = dataset[:,6]\n",
    "\n",
    "\n",
    "    # Create test dataset\n",
    "    dataset = test_dataframe.values\n",
    "    dataset = dataset.astype('float32')\n",
    "\n",
    "    for i in range(0, 6):\n",
    "        dataset[:, i] = normalize_data(dataset[:, i], min[i], max[i])\n",
    "\n",
    "    test_data = dataset[:, 0:7]\n",
    "    test_labels = dataset[:, 6]\n",
    "\n",
    "    return train_data, train_labels, test_data, test_labels, scale_factor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Construt and return Keras RNN model\n",
    "def build_model(init_type = 'glorot_uniform', optimizer = 'adam', num_features = 7):\n",
    "    \n",
    "    model = Sequential()\n",
    "    layers = [num_features, 64, 64, 1, 1]\n",
    "    model.add(keras.layers.LSTM(\n",
    "        layers[0],\n",
    "        input_shape = (None, num_features),\n",
    "        return_sequences=True))\n",
    "    model.add(keras.layers.Dropout(0.2))\n",
    "\n",
    "    model.add(keras.layers.LSTM(\n",
    "        layers[1],\n",
    "        kernel_initializer = init_type,\n",
    "        return_sequences=True\n",
    "        #bias_initializer = 'zeros'\n",
    "    ))\n",
    "    model.add(keras.layers.Dropout(0.2))\n",
    "\n",
    "    model.add(Dense(\n",
    "        layers[2], activation='tanh',\n",
    "        kernel_initializer=init_type,\n",
    "        input_shape = (None, 1)\n",
    "        ))\n",
    "    model.add(Dense(\n",
    "        layers[3]))\n",
    "\n",
    "    model.add(Activation(\"softmax\"))\n",
    "    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    \n",
    "\n",
    "    #Alternative parameters:\n",
    "    #momentum = 0.8\n",
    "    #learning_rate = 0.1\n",
    "    #epochs = 100\n",
    "    #decay_rate = learning_rate / 100\n",
    "    #sgd = keras.optimizers.SGD(lr=learning_rate, momentum=momentum, decay=decay_rate, nesterov=False)\n",
    "    #model.compile(loss=\"binary_crossentropy\", optimizer=sgd)\n",
    "    rms = keras.optimizers.RMSprop(lr=0.002, rho=0.9, epsilon=1e-08, decay=0.01)\n",
    "    model.compile(loss=\"mean_squared_error\", optimizer=optimizer)\n",
    "    \n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Save output predictions for graphing and inspection\n",
    "def write_to_csv(prediction, filename):\n",
    "    print(\"Writing to CSV...\")\n",
    "    with open(filename, 'w') as file:\n",
    "        for i in range(prediction.shape[0]):\n",
    "            file.write(\"%.5f\" % prediction[i][0][0])\n",
    "            file.write('\\n')\n",
    "    print(\"...finished!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Return MSE error values of all three data sets based on a single model\n",
    "def evaluate(model, X_train, Y_train, scale_factor):\n",
    "    scores = model.evaluate(X_train, Y_train, verbose = 0) \n",
    "    print(\"train: \", model.metrics_names, \": \", scores)\n",
    "\n",
    "#Calculate MSE between two arrays of values \n",
    "def mse(predicted, observed):\n",
    "    return np.sum(np.multiply((predicted - observed),(predicted - observed)))/predicted.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0829 19:43:16.968211 139879217338112 deprecation_wrapper.py:119] From /data/anaconda/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "W0829 19:43:16.984912 139879217338112 deprecation_wrapper.py:119] From /data/anaconda/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "W0829 19:43:16.987200 139879217338112 deprecation_wrapper.py:119] From /data/anaconda/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
      "\n",
      "W0829 19:43:17.167032 139879217338112 deprecation_wrapper.py:119] From /data/anaconda/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
      "\n",
      "W0829 19:43:17.173797 139879217338112 deprecation.py:506] From /data/anaconda/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
      "W0829 19:43:17.545043 139879217338112 deprecation_wrapper.py:119] From /data/anaconda/envs/py36/lib/python3.6/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n",
      "W0829 19:43:17.565146 139879217338112 deprecation_wrapper.py:119] From /data/anaconda/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3376: The name tf.log is deprecated. Please use tf.math.log instead.\n",
      "\n",
      "W0829 19:43:17.569605 139879217338112 deprecation.py:323] From /data/anaconda/envs/py36/lib/python3.6/site-packages/tensorflow/python/ops/nn_impl.py:180: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape:  (21899, 1, 7)  Y_train shape:  (21899, 1, 1)\n",
      "Epoch 1/100\n",
      " 3248/21899 [===>..........................] - ETA: 23s - loss: 38.5189"
     ]
    },
    {
     "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-8-dc1640ca36b5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     31\u001b[0m model.fit(\n\u001b[1;32m     32\u001b[0m     \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m     batch_size = 16, epochs = model_fit_epochs)\n\u001b[0m\u001b[1;32m     34\u001b[0m \u001b[0mtrainset_predicted\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\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[0m\n\u001b[1;32m     35\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/data/anaconda/envs/py36/lib/python3.6/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/data/anaconda/envs/py36/lib/python3.6/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    197\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    198\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 199\u001b[0;31m                 \u001b[0mouts\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    200\u001b[0m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    201\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mouts\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/data/anaconda/envs/py36/lib/python3.6/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/data/anaconda/envs/py36/lib/python3.6/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/data/anaconda/envs/py36/lib/python3.6/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   1456\u001b[0m         ret = tf_session.TF_SessionRunCallable(self._session._session,\n\u001b[1;32m   1457\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[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1458\u001b[0;31m                                                run_metadata_ptr)\n\u001b[0m\u001b[1;32m   1459\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   1460\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": [
    "    mpl.use('Agg')\n",
    "\n",
    "    #Import test data (6027, 13)\n",
    "    train_dataframe = pandas.read_csv(DATASET_PATH + '/SolarPrediction.csv', sep=\",\", engine='python', header = None)\n",
    "    traincsv_data, testcsv_data = train_test_split(train_dataframe, test_size=0.33, random_state = 0)\n",
    "    train_data, train_labels,test_data, test_labels, scale_factor = import_data(traincsv_data, testcsv_data)\n",
    "    #X_train, X_test, Y_train, Y_test = train_test_split(train_data, train_labels, test_size=0.33, random_state = 42)\n",
    "    #X_train, X_test, Y_train, Y_test = train_test_split(x, y, test_size = 0.2, random_state = 0)\n",
    "    #trainX_data = import_data(train)\n",
    "    #trainY_labels = train \n",
    "    #testX_data = test\n",
    "    #testY_labels = test \n",
    "\n",
    "\n",
    "    time_steps = 1    \n",
    "    assert(train_data.shape[0] % time_steps == 0)\n",
    "\n",
    "    X_train = np.reshape(train_data, (train_data.shape[0] // time_steps, time_steps, train_data.shape[1]))\n",
    "    X_test = np.reshape(test_data, (test_data.shape[0] // time_steps, time_steps, test_data.shape[1]))\n",
    "    Y_train = np.reshape(train_labels, (train_labels.shape[0] // time_steps, time_steps, 1))\n",
    "    Y_test = np.reshape(test_labels, (test_labels.shape[0] // time_steps, time_steps, 1)) \n",
    "\n",
    "\n",
    "    model = build_model('glorot_uniform', 'adam')\n",
    "\n",
    "    #Standard vanilla LSTM model\n",
    "\n",
    "    model_fit_epochs = 100\n",
    "    print(\"X_train shape: \",X_train.shape, \" Y_train shape: \",Y_train.shape)\n",
    "\n",
    "    model.fit(\n",
    "        X_train, Y_train,\n",
    "        batch_size = 16, epochs = model_fit_epochs)\n",
    "    trainset_predicted = model.predict(X_train)\n",
    "\n",
    "\n",
    "    print(\"Train MSE: \", mse(trainset_predicted, X_train) * scale_factor * scale_factor)\n",
    " \n",
    "\n",
    "    write_to_csv(trainset_predicted,'TrainNewData_prediction.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0827 21:09:14.954859 140304533006080 deprecation.py:323] From /data/anaconda/envs/py36/lib/python3.6/site-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:201: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'dict' object has no attribute 'dtype'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-29-619aa8414b4a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[0mK\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msess\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m \u001b[0mprediction_signature\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msaved_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature_def_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_signature_def\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"features\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mreceiver_tensors\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\"prediction\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput\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[1;32m     31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0mbuilder\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msaved_model_builder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSavedModelBuilder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Model4\"\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/data/anaconda/envs/py36/lib/python3.6/site-packages/tensorflow/python/saved_model/signature_def_utils_impl.py\u001b[0m in \u001b[0;36mpredict_signature_def\u001b[0;34m(inputs, outputs)\u001b[0m\n\u001b[1;32m    199\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    200\u001b[0m   signature_inputs = {key: utils.build_tensor_info(tensor)\n\u001b[0;32m--> 201\u001b[0;31m                       for key, tensor in inputs.items()}\n\u001b[0m\u001b[1;32m    202\u001b[0m   signature_outputs = {key: utils.build_tensor_info(tensor)\n\u001b[1;32m    203\u001b[0m                        for key, tensor in outputs.items()}\n",
      "\u001b[0;32m/data/anaconda/envs/py36/lib/python3.6/site-packages/tensorflow/python/saved_model/signature_def_utils_impl.py\u001b[0m in \u001b[0;36m<dictcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    199\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    200\u001b[0m   signature_inputs = {key: utils.build_tensor_info(tensor)\n\u001b[0;32m--> 201\u001b[0;31m                       for key, tensor in inputs.items()}\n\u001b[0m\u001b[1;32m    202\u001b[0m   signature_outputs = {key: utils.build_tensor_info(tensor)\n\u001b[1;32m    203\u001b[0m                        for key, tensor in outputs.items()}\n",
      "\u001b[0;32m/data/anaconda/envs/py36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\u001b[0m in \u001b[0;36mnew_func\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    322\u001b[0m               \u001b[0;34m'in a future version'\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdate\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'after %s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mdate\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    323\u001b[0m               instructions)\n\u001b[0;32m--> 324\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    325\u001b[0m     return tf_decorator.make_decorator(\n\u001b[1;32m    326\u001b[0m         \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'deprecated'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/data/anaconda/envs/py36/lib/python3.6/site-packages/tensorflow/python/saved_model/utils_impl.py\u001b[0m in \u001b[0;36mbuild_tensor_info\u001b[0;34m(tensor)\u001b[0m\n\u001b[1;32m     61\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecuting_eagerly\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[1;32m     62\u001b[0m     \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"build_tensor_info is not supported in Eager mode.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mbuild_tensor_info_internal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor\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     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/data/anaconda/envs/py36/lib/python3.6/site-packages/tensorflow/python/saved_model/utils_impl.py\u001b[0m in \u001b[0;36mbuild_tensor_info_internal\u001b[0;34m(tensor)\u001b[0m\n\u001b[1;32m     67\u001b[0m   \u001b[0;34m\"\"\"Utility function to build TensorInfo proto from a Tensor.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m   tensor_info = meta_graph_pb2.TensorInfo(\n\u001b[0;32m---> 69\u001b[0;31m       \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_datatype_enum\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     70\u001b[0m       tensor_shape=tensor.get_shape().as_proto())\n\u001b[1;32m     71\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msparse_tensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSparseTensor\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;31mAttributeError\u001b[0m: 'dict' object has no attribute 'dtype'"
     ]
    }
   ],
   "source": [
    "# Export the model\n",
    "\n",
    "import os\n",
    "import sys\n",
    "\n",
    "ROOT = os.getcwd()\n",
    "OUT = os.path.join(ROOT, 'out')\n",
    "\n",
    "receiver_tensors = {\n",
    "    'radiation_ids': tf.placeholder(dtype=tf.float32, shape=[None, 256], name='radiation_ids'),\n",
    "    'temperature_ids': tf.placeholder(dtype=tf.float32, shape=[None, 256], name='temperature_ids'),\n",
    "    'pressure_mask': tf.placeholder(dtype=tf.float32, shape=[None, 256], name='pressure_mask'),\n",
    "    'humidity_ids': tf.placeholder(dtype=tf.float32, shape=[None, 256], name='humidity_ids'),\n",
    "    'winddirec_mask': tf.placeholder(dtype=tf.float32, shape=[None, 256], name='winddirc_mask'),\n",
    "    'speed_mask': tf.placeholder(dtype=tf.float32, shape=[None, 256], name='speed_mask')\n",
    "}\n",
    "\n",
    "from keras.models import Model\n",
    "from tensorflow.python.saved_model import builder as saved_model_builder\n",
    "from tensorflow.python.saved_model import tag_constants, signature_constants, signature_def_utils_impl\n",
    "import keras as k\n",
    "import numpy as np\n",
    "from keras import backend as K\n",
    "\n",
    "gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.8, allow_growth=False)\n",
    "sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))\n",
    "\n",
    "K.set_session(sess)\n",
    "\n",
    "prediction_signature = tf.saved_model.signature_def_utils.predict_signature_def({\"features\": receiver_tensors}, {\"prediction\":model.output})\n",
    "\n",
    "builder = saved_model_builder.SavedModelBuilder(os.path.join(\"Model4\"))\n",
    "legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op')\n",
    "\n",
    "init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())\n",
    "sess.run(init_op)\n",
    "\n",
    "# Add the meta_graph and the variables to the builder\n",
    "builder.add_meta_graph_and_variables(\n",
    "      sess, [tag_constants.SERVING],\n",
    "      signature_def_map={\n",
    "           signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:\n",
    "               prediction_signature,\n",
    "      },\n",
    "      )\n",
    "# save the graph      \n",
    "builder.save()     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10787/10787 [==============================] - 1s 137us/step\n",
      "Test Score: -3401.497068211711\n",
      "Test accuracy: 0.0\n"
     ]
    }
   ],
   "source": [
    "from keras import metrics\n",
    "\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer='adam',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "score, acc = model.evaluate(X_test, Y_test)\n",
    "print(\"Test Score:\", score * scale_factor)\n",
    "print(\"Test accuracy:\", acc * scale_factor * scale_factor)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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