build_the_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "26538/26538 [==============================] - 106s 4ms/step - loss: 0.0326\n",
      "Epoch 2/10\n",
      "26538/26538 [==============================] - 96s 4ms/step - loss: 0.0250\n",
      "Epoch 3/10\n",
      "26538/26538 [==============================] - 97s 4ms/step - loss: 0.0229\n",
      "Epoch 4/10\n",
      "26538/26538 [==============================] - 97s 4ms/step - loss: 0.0223\n",
      "Epoch 5/10\n",
      "26538/26538 [==============================] - 97s 4ms/step - loss: 0.0221\n",
      "Epoch 6/10\n",
      "26538/26538 [==============================] - 97s 4ms/step - loss: 0.0219\n",
      "Epoch 7/10\n",
      "26538/26538 [==============================] - 96s 4ms/step - loss: 0.0217\n",
      "Epoch 8/10\n",
      "26538/26538 [==============================] - 98s 4ms/step - loss: 0.0217\n",
      "Epoch 9/10\n",
      "26538/26538 [==============================] - 98s 4ms/step - loss: 0.0216\n",
      "Epoch 10/10\n",
      "26538/26538 [==============================] - 100s 4ms/step - loss: 0.0216\n",
      "Saved model to disk\n"
     ]
    }
   ],
   "source": [
    "epochs = 10\n",
    "\n",
    "model.add(LSTM(units=50, return_sequences = True, input_shape = (x_train.shape[1],9)))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(LSTM(units=50, return_sequences = True))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(LSTM(units=50, return_sequences = True))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(LSTM(units=50))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(units=9))\n",
    "model.compile(loss=\"mse\", optimizer=\"adam\")\n",
    "model.fit(x_train, y_train, batch_size = 32, epochs = epochs)\n",
    "model.summary\n",
    "\n",
    "model.save(\"multiple_features_\"+str(steps)+\"_steps_\"+str(epochs)+\"_epochs.h5\")\n",
    "print(\"Saved model to disk\")"
   ]
  }
 ],
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