testing the blog code - train and pred.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "from keras.callbacks import LambdaCallback\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 699, 1) (1, 699, 1)\n"
     ]
    }
   ],
   "source": [
    "#dummy training set\n",
    "training_set = np.array(range(700)).reshape((700,1))/700.\n",
    "\n",
    "x_train = training_set[0:len(training_set)-1]\n",
    "y_train = training_set[1:len(training_set)]\n",
    "\n",
    "#correct shapes - batch size = 1 sequence // sequence length = 699 // features = 1\n",
    "x_train = np.reshape(x_train, (1, len(x_train), 1))\n",
    "y_train = np.reshape(y_train, (1, len(y_train), 1))\n",
    "\n",
    "print(x_train.shape, y_train.shape)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 699, 1) (1, 699, 1)\n",
      "Epoch 1/100\n",
      "1/1 [==============================] - 0s 316ms/step - loss: 1.1681\n",
      "Epoch 2/100\n",
      "1/1 [==============================] - 0s 208ms/step - loss: 1.1630\n",
      "Epoch 3/100\n",
      "1/1 [==============================] - 0s 204ms/step - loss: 1.1579\n",
      "Epoch 4/100\n",
      "1/1 [==============================] - 0s 208ms/step - loss: 1.1528\n",
      "Epoch 5/100\n",
      "1/1 [==============================] - 0s 199ms/step - loss: 1.1477\n",
      "Epoch 6/100\n",
      "1/1 [==============================] - 0s 199ms/step - loss: 1.1426\n",
      "Epoch 7/100\n",
      "1/1 [==============================] - 0s 196ms/step - loss: 1.1376\n",
      "Epoch 8/100\n",
      "1/1 [==============================] - 0s 199ms/step - loss: 1.1326\n",
      "Epoch 9/100\n",
      "1/1 [==============================] - 0s 198ms/step - loss: 1.1276\n",
      "Epoch 10/100\n",
      "1/1 [==============================] - 0s 197ms/step - loss: 1.1226\n",
      "Epoch 11/100\n",
      "1/1 [==============================] - 0s 191ms/step - loss: 1.1177\n",
      "Epoch 12/100\n",
      "1/1 [==============================] - 0s 191ms/step - loss: 1.1127\n",
      "Epoch 13/100\n",
      "1/1 [==============================] - 0s 189ms/step - loss: 1.1078\n",
      "Epoch 14/100\n",
      "1/1 [==============================] - 0s 190ms/step - loss: 1.1029\n",
      "Epoch 15/100\n",
      "1/1 [==============================] - 0s 192ms/step - loss: 1.0981\n",
      "Epoch 16/100\n",
      "1/1 [==============================] - 0s 190ms/step - loss: 1.0932\n",
      "Epoch 17/100\n",
      "1/1 [==============================] - 0s 191ms/step - loss: 1.0884\n",
      "Epoch 18/100\n",
      "1/1 [==============================] - 0s 192ms/step - loss: 1.0836\n",
      "Epoch 19/100\n",
      "1/1 [==============================] - 0s 191ms/step - loss: 1.0789\n",
      "Epoch 20/100\n",
      "1/1 [==============================] - 0s 192ms/step - loss: 1.0741\n",
      "Epoch 21/100\n",
      "1/1 [==============================] - 0s 192ms/step - loss: 1.0694\n",
      "Epoch 22/100\n",
      "1/1 [==============================] - 0s 192ms/step - loss: 1.0647\n",
      "Epoch 23/100\n",
      "1/1 [==============================] - 0s 193ms/step - loss: 1.0600\n",
      "Epoch 24/100\n",
      "1/1 [==============================] - 0s 192ms/step - loss: 1.0554\n",
      "Epoch 25/100\n",
      "1/1 [==============================] - 0s 190ms/step - loss: 1.0508\n",
      "Epoch 26/100\n",
      "1/1 [==============================] - 0s 191ms/step - loss: 1.0461\n",
      "Epoch 27/100\n",
      "1/1 [==============================] - 0s 191ms/step - loss: 1.0416\n",
      "Epoch 28/100\n",
      "1/1 [==============================] - 0s 193ms/step - loss: 1.0370\n",
      "Epoch 29/100\n",
      "1/1 [==============================] - 0s 192ms/step - loss: 1.0325\n",
      "Epoch 30/100\n",
      "1/1 [==============================] - 0s 196ms/step - loss: 1.0280\n",
      "Epoch 31/100\n",
      "1/1 [==============================] - 0s 192ms/step - loss: 1.0235\n",
      "Epoch 32/100\n",
      "1/1 [==============================] - 0s 190ms/step - loss: 1.0190\n",
      "Epoch 33/100\n",
      "1/1 [==============================] - 0s 191ms/step - loss: 1.0146\n",
      "Epoch 34/100\n",
      "1/1 [==============================] - 0s 190ms/step - loss: 1.0102\n",
      "Epoch 35/100\n",
      "1/1 [==============================] - 0s 193ms/step - loss: 1.0058\n",
      "Epoch 36/100\n",
      "1/1 [==============================] - 0s 193ms/step - loss: 1.0014\n",
      "Epoch 37/100\n",
      "1/1 [==============================] - 0s 191ms/step - loss: 0.9971\n",
      "Epoch 38/100\n",
      "1/1 [==============================] - 0s 191ms/step - loss: 0.9927\n",
      "Epoch 39/100\n",
      "1/1 [==============================] - 0s 189ms/step - loss: 0.9884\n",
      "Epoch 40/100\n",
      "1/1 [==============================] - 0s 189ms/step - loss: 0.9842\n",
      "Epoch 41/100\n",
      "1/1 [==============================] - 0s 187ms/step - loss: 0.9799\n",
      "Epoch 42/100\n",
      "1/1 [==============================] - 0s 190ms/step - loss: 0.9757\n",
      "Epoch 43/100\n",
      "1/1 [==============================] - 0s 188ms/step - loss: 0.9715\n",
      "Epoch 44/100\n",
      "1/1 [==============================] - 0s 191ms/step - loss: 0.9673\n",
      "Epoch 45/100\n",
      "1/1 [==============================] - 0s 188ms/step - loss: 0.9631\n",
      "Epoch 46/100\n",
      "1/1 [==============================] - 0s 189ms/step - loss: 0.9590\n",
      "Epoch 47/100\n",
      "1/1 [==============================] - 0s 188ms/step - loss: 0.9548\n",
      "Epoch 48/100\n",
      "1/1 [==============================] - 0s 188ms/step - loss: 0.9507\n",
      "Epoch 49/100\n",
      "1/1 [==============================] - 0s 187ms/step - loss: 0.9467\n",
      "Epoch 50/100\n",
      "1/1 [==============================] - 0s 189ms/step - loss: 0.9426\n",
      "Epoch 51/100\n",
      "1/1 [==============================] - 0s 189ms/step - loss: 0.9386\n",
      "Epoch 52/100\n",
      "1/1 [==============================] - 0s 187ms/step - loss: 0.9346\n",
      "Epoch 53/100\n",
      "1/1 [==============================] - 0s 187ms/step - loss: 0.9306\n",
      "Epoch 54/100\n",
      "1/1 [==============================] - 0s 189ms/step - loss: 0.9266\n",
      "Epoch 55/100\n",
      "1/1 [==============================] - 0s 187ms/step - loss: 0.9226\n",
      "Epoch 56/100\n",
      "1/1 [==============================] - 0s 185ms/step - loss: 0.9187\n",
      "Epoch 57/100\n",
      "1/1 [==============================] - 0s 187ms/step - loss: 0.9148\n",
      "Epoch 58/100\n",
      "1/1 [==============================] - 0s 187ms/step - loss: 0.9109\n",
      "Epoch 59/100\n",
      "1/1 [==============================] - 0s 188ms/step - loss: 0.9071\n",
      "Epoch 60/100\n",
      "1/1 [==============================] - 0s 188ms/step - loss: 0.9032\n",
      "Epoch 61/100\n",
      "1/1 [==============================] - 0s 191ms/step - loss: 0.8994\n",
      "Epoch 62/100\n",
      "1/1 [==============================] - 0s 188ms/step - loss: 0.8956\n",
      "Epoch 63/100\n",
      "1/1 [==============================] - 0s 187ms/step - loss: 0.8918\n",
      "Epoch 64/100\n",
      "1/1 [==============================] - 0s 185ms/step - loss: 0.8880\n",
      "Epoch 65/100\n",
      "1/1 [==============================] - 0s 185ms/step - loss: 0.8843\n",
      "Epoch 66/100\n",
      "1/1 [==============================] - 0s 185ms/step - loss: 0.8806\n",
      "Epoch 67/100\n",
      "1/1 [==============================] - 0s 186ms/step - loss: 0.8769\n",
      "Epoch 68/100\n",
      "1/1 [==============================] - 0s 185ms/step - loss: 0.8732\n",
      "Epoch 69/100\n",
      "1/1 [==============================] - 0s 186ms/step - loss: 0.8695\n",
      "Epoch 70/100\n",
      "1/1 [==============================] - 0s 186ms/step - loss: 0.8658\n",
      "Epoch 71/100\n",
      "1/1 [==============================] - 0s 185ms/step - loss: 0.8622\n",
      "Epoch 72/100\n",
      "1/1 [==============================] - 0s 186ms/step - loss: 0.8586\n",
      "Epoch 73/100\n",
      "1/1 [==============================] - 0s 185ms/step - loss: 0.8550\n",
      "Epoch 74/100\n",
      "1/1 [==============================] - 0s 185ms/step - loss: 0.8514\n",
      "Epoch 75/100\n",
      "1/1 [==============================] - 0s 185ms/step - loss: 0.8479\n",
      "Epoch 76/100\n",
      "1/1 [==============================] - 0s 185ms/step - loss: 0.8443\n",
      "Epoch 77/100\n",
      "1/1 [==============================] - 0s 185ms/step - loss: 0.8408\n",
      "Epoch 78/100\n",
      "1/1 [==============================] - 0s 189ms/step - loss: 0.8373\n",
      "Epoch 79/100\n",
      "1/1 [==============================] - 0s 187ms/step - loss: 0.8338\n",
      "Epoch 80/100\n",
      "1/1 [==============================] - 0s 188ms/step - loss: 0.8304\n",
      "Epoch 81/100\n",
      "1/1 [==============================] - 0s 188ms/step - loss: 0.8269\n",
      "Epoch 82/100\n",
      "1/1 [==============================] - 0s 188ms/step - loss: 0.8235\n",
      "Epoch 83/100\n",
      "1/1 [==============================] - 0s 186ms/step - loss: 0.8201\n",
      "Epoch 84/100\n",
      "1/1 [==============================] - 0s 191ms/step - loss: 0.8167\n",
      "Epoch 85/100\n",
      "1/1 [==============================] - 0s 199ms/step - loss: 0.8133\n",
      "Epoch 86/100\n",
      "1/1 [==============================] - 0s 193ms/step - loss: 0.8099\n",
      "Epoch 87/100\n",
      "1/1 [==============================] - 0s 189ms/step - loss: 0.8066\n",
      "Epoch 88/100\n",
      "1/1 [==============================] - 0s 190ms/step - loss: 0.8033\n",
      "Epoch 89/100\n",
      "1/1 [==============================] - 0s 186ms/step - loss: 0.8000\n",
      "Epoch 90/100\n",
      "1/1 [==============================] - 0s 188ms/step - loss: 0.7967\n",
      "Epoch 91/100\n",
      "1/1 [==============================] - 0s 189ms/step - loss: 0.7934\n",
      "Epoch 92/100\n",
      "1/1 [==============================] - 0s 187ms/step - loss: 0.7901\n",
      "Epoch 93/100\n",
      "1/1 [==============================] - 0s 188ms/step - loss: 0.7869\n",
      "Epoch 94/100\n",
      "1/1 [==============================] - 0s 189ms/step - loss: 0.7837\n",
      "Epoch 95/100\n",
      "1/1 [==============================] - 0s 189ms/step - loss: 0.7804\n",
      "Epoch 96/100\n",
      "1/1 [==============================] - 0s 187ms/step - loss: 0.7772\n",
      "Epoch 97/100\n",
      "1/1 [==============================] - 0s 191ms/step - loss: 0.7741\n",
      "Epoch 98/100\n",
      "1/1 [==============================] - 0s 190ms/step - loss: 0.7709\n",
      "Epoch 99/100\n",
      "1/1 [==============================] - 0s 189ms/step - loss: 0.7677\n",
      "Epoch 100/100\n",
      "1/1 [==============================] - 0s 192ms/step - loss: 0.7646\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x40138828>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_units = 2\n",
    "activation_function = 'sigmoid'\n",
    "optimizer = 'adam'\n",
    "loss_function = 'mean_squared_error'\n",
    "\n",
    "#batch_size = 1 -- we only have one sequence, and we used batch_input_shape = (1, None, 1)\n",
    "batch_size = 1\n",
    "num_epochs = 100\n",
    "\n",
    "# Initialize the RNN\n",
    "regressor = Sequential()\n",
    "\n",
    "#Use only batch_input_shape\n",
    "# Adding the input layer and the LSTM layer\n",
    "regressor.add(LSTM(units = num_units, activation = activation_function,  \n",
    "                   stateful=True,return_sequences=True, batch_input_shape=(1, None, 1)))\n",
    "\n",
    "# Adding the output layer\n",
    "regressor.add(Dense(units = 1))\n",
    "\n",
    "# Compiling the RNN\n",
    "regressor.compile(optimizer = optimizer, loss = loss_function)\n",
    "\n",
    "#important - we need to reset states because we're using a stateful=True model\n",
    "resetCallback = LambdaCallback(on_epoch_begin=lambda epoch,logs: regressor.reset_states())\n",
    "\n",
    "# Using the training set to train the model\n",
    "print(x_train.shape, y_train.shape)\n",
    "regressor.fit(x_train, y_train, batch_size=batch_size, epochs = num_epochs, callbacks=[resetCallback])\n",
    "#regressor.save('model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#predictions from a test set:\n",
    "\n",
    "test_set = np.array(range(100)).reshape((100,1))/100.\n",
    "\n",
    "previous_inputs = training_set.reshape((1,len(training_set),1))\n",
    "inputs = np.reshape(test_set, (1,len(test_set), 1))\n",
    "\n",
    "regressor.reset_states()\n",
    "\n",
    "#first, let set the model's states (it's important for it to know the previous trends)\n",
    "previous_predictions = regressor.predict(previous_inputs) #this creates states\n",
    "future_predicitons = regressor.predict(inputs)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#future unknown predictions: in this case, test_set doesn't exist\n",
    "\n",
    "future_pred_count = 100 #let's predict 100 new steps\n",
    "\n",
    "regressor.reset_states() #always reset states when inputting a new sequence\n",
    "\n",
    "#first, let set the model's states (it's important for it to know the previous trends)\n",
    "predictions = regressor.predict(previous_inputs) #this creates states\n",
    "\n",
    "#future predictions\n",
    "future = []\n",
    "currentStep = predictions[:,-1:,:] #last step from the previous prediction\n",
    "\n",
    "for i in range(future_pred_count):\n",
    "    currentStep = regressor.predict(currentStep) #get the next step\n",
    "    future.append(currentStep) #store the future steps    \n",
    "\n",
    "#after processing a sequence, reset the states for safety\n",
    "regressor.reset_states()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f462c4b0450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10), dpi=80, facecolor = 'w', edgecolor = 'k')\n",
    "\n",
    "plt.plot(test_set[:, 0], color='red', label='Real BTC Price')\n",
    "plt.plot(predicted_price[:, 0], color = 'blue', label = 'Predicted BTC Price')\n",
    "\n",
    "plt.title('BTC Price Prediction', fontsize = 40)\n",
    "plt.xlabel('Time', fontsize=40)\n",
    "plt.ylabel('BTC Price(USD)', fontsize = 40)\n",
    "plt.legend(loc = 'best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}