Notebook 6_ More predictions(using accuracy as a metric measure) with LSTM, GRU .ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"kernelspec":{"name":"python2","display_name":"Python 2"},"colab":{"name":" More predictions(using accuracy as a metric measure) with LSTM, GRU .ipynb","version":"0.3.2","provenance":[],"collapsed_sections":[]},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","metadata":{"id":"KVmQxEXpKz1g","colab_type":"text"},"source":["#**WHAT I DID IN THIS NOTEBOOK:**\n","\n","**In previous notebooks I used MAE as a metric of measure. However I then changed to using accuracy as a measure by changing the regression problem into classification where I found how I could predict datapoints of the closing price by creating EMA, normalizing the dataset and stacking X and finding Y based on X**\n","\n","**I then used this dataset to split into test, train and validate sets.** \n","\n","**I stacked the OHLCV data and used that as X and closing prices as Y hence genetrated  the classification problem.** \n","\n","**Then I predicted the accuacy.** \n","\n","**I used hyperparemeter tuning for the LSTM model and ran a GRU cell with some hyperparameter too.**\n"]},{"cell_type":"markdown","metadata":{"id":"9hr2QGohexbw","colab_type":"text"},"source":["**Importing packages from keras**"]},{"cell_type":"code","metadata":{"id":"-vDgUuKciK_G","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"de9ff0de-8acc-459d-c05a-c939e66b76aa","executionInfo":{"status":"ok","timestamp":1565965704954,"user_tz":240,"elapsed":1795,"user":{"displayName":"nupur deshpande","photoUrl":"https://lh4.googleusercontent.com/-AQjf2oEW7kE/AAAAAAAAAAI/AAAAAAAAA1Y/ZFcRZICzWa0/s64/photo.jpg","userId":"04753088851981756999"}}},"source":["import tensorflow as tf\n","device_name = tf.test.gpu_device_name()\n","if device_name != '/device:GPU:0':\n","  raise SystemError('GPU device not found')\n","print('Found GPU at: {}'.format(device_name))"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Found GPU at: /device:GPU:0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"nA_Zb6-SirM_","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"0f0ec8db-0a6f-4a4d-ec6d-3642b82cd986","executionInfo":{"status":"ok","timestamp":1565965708595,"user_tz":240,"elapsed":2915,"user":{"displayName":"nupur deshpande","photoUrl":"https://lh4.googleusercontent.com/-AQjf2oEW7kE/AAAAAAAAAAI/AAAAAAAAA1Y/ZFcRZICzWa0/s64/photo.jpg","userId":"04753088851981756999"}}},"source":["from google.colab import drive\n","\n","# This will prompt for authorization.\n","# drive.mount('/content/drive')\n","drive.mount(\"/content/drive\", force_remount=True)"],"execution_count":2,"outputs":[{"output_type":"stream","text":["Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"zo7yv_YQirTs","colab_type":"code","colab":{}},"source":["googlepath = \"/content/drive/My Drive/\""],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"P7rL_mRQjGdo","colab_type":"code","colab":{}},"source":["#!python /content/drive/'My Drive'/utils.py install"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"ZWSopsKs8Z8M","colab_type":"code","colab":{"resources":{"http://localhost:8080/nbextensions/google.colab/files.js":{"data":"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","ok":true,"headers":[["content-type","application/javascript"]],"status":200,"status_text":""}},"base_uri":"https://localhost:8080/","height":74},"outputId":"33243dc5-e1af-4722-8ff6-60714a0d3c47","executionInfo":{"status":"ok","timestamp":1565967668011,"user_tz":240,"elapsed":13227,"user":{"displayName":"nupur deshpande","photoUrl":"https://lh4.googleusercontent.com/-AQjf2oEW7kE/AAAAAAAAAAI/AAAAAAAAA1Y/ZFcRZICzWa0/s64/photo.jpg","userId":"04753088851981756999"}}},"source":["from google.colab import files\n","src = list(files.upload().values())[0]"],"execution_count":26,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","     <input type=\"file\" id=\"files-68bed8a5-51b9-4091-b9d9-bd2ed516763b\" name=\"files[]\" multiple disabled />\n","     <output id=\"result-68bed8a5-51b9-4091-b9d9-bd2ed516763b\">\n","      Upload widget is only available when the cell has been executed in the\n","      current browser session. Please rerun this cell to enable.\n","      </output>\n","      <script src=\"/nbextensions/google.colab/files.js\"></script> "]},"metadata":{"tags":[]}},{"output_type":"stream","text":["Saving utils.py to utils (1).py\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"TKU4vA-M8nQC","colab_type":"code","colab":{}},"source":["open('utils.py','wb').write(src)\n","from utils import *"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"p1DRNKYSx9C9","colab_type":"text"},"source":["**utils.py is a python package having tickers for time series data. You can** \n","\n","**upload it using the above command. Recommend to download it using**\n","https://pypi.org/project/python-utils/ **and storing in drive.** \n"]},{"cell_type":"code","metadata":{"id":"k9FesNZk4SEO","colab_type":"code","colab":{}},"source":["!pip install -U -q PyDrive\n","from pydrive.auth import GoogleAuth\n","from pydrive.drive import GoogleDrive\n","from google.colab import auth\n","from oauth2client.client import GoogleCredentials"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"QvZKtTC94SHr","colab_type":"code","colab":{}},"source":["# Authenticate and create the PyDrive client.\n","auth.authenticate_user()\n","gauth = GoogleAuth()\n","gauth.credentials = GoogleCredentials.get_application_default()\n","drive = GoogleDrive(gauth)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"CNGkPPvw4Yvt","colab_type":"code","colab":{}},"source":["link = 'https://drive.google.com/open?id=1CQDa0VJP0cEKxO9hRpyxngnJIxYaVwTV' # The shareable link"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"QmzxCTA74Y2U","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"5981948d-02c3-4935-f8a7-977ee853beeb","executionInfo":{"status":"ok","timestamp":1565966681183,"user_tz":240,"elapsed":548,"user":{"displayName":"nupur deshpande","photoUrl":"https://lh4.googleusercontent.com/-AQjf2oEW7kE/AAAAAAAAAAI/AAAAAAAAA1Y/ZFcRZICzWa0/s64/photo.jpg","userId":"04753088851981756999"}}},"source":["fluff, id = link.split('=')\n","print (id) # Verify that you have everything after '='"],"execution_count":16,"outputs":[{"output_type":"stream","text":["1CQDa0VJP0cEKxO9hRpyxngnJIxYaVwTV\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"U-rBC4sw41cM","colab_type":"code","colab":{}},"source":["downloaded = drive.CreateFile({'id':id}) \n","downloaded.GetContentFile('AMZN.csv')  \n","data_original = pd.read_csv('AMZN.csv')[::-1]"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"9yxcx78fexbx","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"9a19b74d-9707-40e0-9086-ba3f268d02d6","executionInfo":{"status":"ok","timestamp":1565967817563,"user_tz":240,"elapsed":504,"user":{"displayName":"nupur deshpande","photoUrl":"https://lh4.googleusercontent.com/-AQjf2oEW7kE/AAAAAAAAAAI/AAAAAAAAA1Y/ZFcRZICzWa0/s64/photo.jpg","userId":"04753088851981756999"}}},"source":["\n","\n","import pandas as pd\n","import matplotlib.pylab as plt\n","\n","from keras.models import Sequential\n","from keras.layers.core import Dense, Dropout, Activation, Flatten\n","from keras.layers.recurrent import LSTM, GRU\n","from keras.layers import Convolution1D, MaxPooling1D, AtrousConvolution1D, RepeatVector\n","from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, CSVLogger\n","from keras.layers.wrappers import Bidirectional\n","from keras import regularizers\n","from keras.layers.normalization import BatchNormalization\n","from keras.layers.advanced_activations import *\n","from keras.optimizers import RMSprop, Adam, SGD, Nadam\n","from keras.initializers import *\n","\n","import seaborn as sns\n","sns.despine()\n"],"execution_count":30,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"0L07ek2nzSbm","colab_type":"text"},"source":["**Imported all keras files**"]},{"cell_type":"markdown","metadata":{"id":"fLwgwSOMzYWj","colab_type":"text"},"source":["# NEXT STEPS"]},{"cell_type":"markdown","metadata":{"id":"KJRpe70gexb0","colab_type":"text"},"source":["**DATA MANIPULATION, NORMALIZATION AND EMA(Exponential moving average) CALCULATION**"]},{"cell_type":"markdown","metadata":{"id":"FfVkAA0oz05A","colab_type":"text"},"source":["**WHAT I AM TRYING TO DO:** \n","\n","**INSTEAD OF MAE I WILL TRY TO MEASURE ACCURACY OF MY LSTM NETWORK BY CONVERTING THE REGRESSION PROBLEM INTO CLASSIFICATION.** \n","\n","**I WILL TRY TO PREDICT CLOSING PRICES DATAPOINT TO DATAPOINT AND SEE HOW WELL MY NORMALIZED DATASET CAN PREDICT THE CLOSING VALUES**"]},{"cell_type":"code","metadata":{"id":"T5ZAk09cexb1","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":689},"outputId":"8c8edc65-360f-439f-8eeb-c256e93d629c","executionInfo":{"status":"ok","timestamp":1565967856010,"user_tz":240,"elapsed":13960,"user":{"displayName":"nupur deshpande","photoUrl":"https://lh4.googleusercontent.com/-AQjf2oEW7kE/AAAAAAAAAAI/AAAAAAAAA1Y/ZFcRZICzWa0/s64/photo.jpg","userId":"04753088851981756999"}}},"source":["#data_original = pd.read_csv('datastrat2.csv')[::-1]\n","\n","openp = data_original.ix[:, 'Open'].tolist()  #convert all OHLCV data into list formats so appending will be easy\n","highp = data_original.ix[:, 'High'].tolist()\n","lowp = data_original.ix[:, 'Low'].tolist()\n","closep = data_original.ix[:, 'Close'].tolist()\n","volumep = data_original.ix[:, 'Volume'].tolist()\n","\n","\n","WINDOW = 30   #Keep a window of 30 days for EMA\n","EMB_SIZE = 5  \n","STEP = 1\n","FORECAST = 1 #One day ahead forecast as before \n","X, Y = [], []\n","for i in range(0, len(data_original), STEP):  #create a counter for data with step size 1\n","    try:\n","        o = openp[i:i+WINDOW]  #declare a window and sample through each stock price\n","        h = highp[i:i+WINDOW]\n","        l = lowp[i:i+WINDOW]\n","        c = closep[i:i+WINDOW]\n","        v = volumep[i:i+WINDOW]\n","\n","        o = (np.array(o) - np.mean(o)) / np.std(o)  # normalize the stock values by fiding differnce in array lenth and mean and dividing by standard dev\n","        h = (np.array(h) - np.mean(h)) / np.std(h)\n","        l = (np.array(l) - np.mean(l)) / np.std(l)\n","        c = (np.array(c) - np.mean(c)) / np.std(c)\n","        v = (np.array(v) - np.mean(v)) / np.std(v)\n","\n","        x_i = closep[i:i+WINDOW]           #create train for close price and add forecast for output price\n","        y_i = closep[i+WINDOW+FORECAST]  \n","\n","        last_close = x_i[-1]\n","        next_close = y_i\n","\n","        if last_close < next_close: \n","            y_i = [1, 0]\n","        else:\n","            y_i = [0, 1] \n","\n","        x_i = np.column_stack((o, h, l, c, v))   #do not consider any tickers that we conidered in former model.\n","                                                  #by creating close price treshold we create our own theshold indicator\n","\n","    except Exception as e:\n","        break\n","\n","    X.append(x_i)\n","    Y.append(y_i)\n","\n","X, Y = np.array(X), np.array(Y)\n","X_train, X_test, Y_train, Y_test = create_Xt_Yt(X, Y)\n","\n","X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], EMB_SIZE))  #reshape train so that can be fed into LSTM network\n","X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], EMB_SIZE))\n"],"execution_count":31,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:2: DeprecationWarning: \n",".ix is deprecated. Please use\n",".loc for label based indexing or\n",".iloc for positional indexing\n","\n","See the documentation here:\n","http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n","  \n","/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:3: DeprecationWarning: \n",".ix is deprecated. Please use\n",".loc for label based indexing or\n",".iloc for positional indexing\n","\n","See the documentation here:\n","http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n","  This is separate from the ipykernel package so we can avoid doing imports until\n","/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:4: DeprecationWarning: \n",".ix is deprecated. Please use\n",".loc for label based indexing or\n",".iloc for positional indexing\n","\n","See the documentation here:\n","http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n","  after removing the cwd from sys.path.\n","/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:5: DeprecationWarning: \n",".ix is deprecated. Please use\n",".loc for label based indexing or\n",".iloc for positional indexing\n","\n","See the documentation here:\n","http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n","  \"\"\"\n","/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:6: DeprecationWarning: \n",".ix is deprecated. Please use\n",".loc for label based indexing or\n",".iloc for positional indexing\n","\n","See the documentation here:\n","http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n","  \n"],"name":"stderr"}]},{"cell_type":"markdown","metadata":{"id":"Nyd8Sd1texb3","colab_type":"text"},"source":["**My Algorithm:**\n","\n","**1. create train for close price and add forecast for output price**\n","\n","**2. create train for close price and add forecast for output price**\n","\n","**3. do not consider any tickers that we conidered in former model.**\n","\n","**4. by creating close price treshold we create our own theshold indicator**"]},{"cell_type":"markdown","metadata":{"id":"QMN0hu4pexb4","colab_type":"text"},"source":["**USE LSTM NETWORK ARCHITECTURE**"]},{"cell_type":"code","metadata":{"id":"PnAxdqL8exb4","colab_type":"code","colab":{}},"source":["\n","def mean_pred(X_test, Y_test):\n","    return K.mean(Y_test)\n","\n","def build_model(inputs, output_size, neurons, activ_func=\"relu\",\n","                dropout=0.10, loss=\"mae\", optimizer=\"adam\"):\n","    \n","    model = Sequential()\n","\n","    model.add(LSTM(neurons, input_shape=(WINDOW,EMB_SIZE)))\n","    model.add(Dropout(dropout))\n","    model.add(Dense(units=output_size))\n","    model.add(Activation(activ_func))\n","\n","    model.compile(loss=loss, optimizer=optimizer, metrics=['accuracy', mean_pred])\n","    return model"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"K3Vz5MR8exb6","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":1000},"outputId":"384263f9-8257-474d-8a8f-bf9ea2d89ca4","executionInfo":{"status":"ok","timestamp":1565968111647,"user_tz":240,"elapsed":235531,"user":{"displayName":"nupur deshpande","photoUrl":"https://lh4.googleusercontent.com/-AQjf2oEW7kE/AAAAAAAAAAI/AAAAAAAAA1Y/ZFcRZICzWa0/s64/photo.jpg","userId":"04753088851981756999"}}},"source":["nn_model = build_model(X_train, output_size=2, neurons = 32)\n","# model output is next price normalised to 10th previous closing price\n","# train model on data\n","# note: eth_history contains information on the training error per epoch\n","nn_history = nn_model.fit(X_train, Y_train,\n","                            epochs=20, batch_size=256, verbose=1,  validation_data=(X_test, Y_test),shuffle=True)"],"execution_count":33,"outputs":[{"output_type":"stream","text":["W0816 15:04:35.385684 139769722800000 deprecation_wrapper.py:119] From /usr/local/lib/python2.7/dist-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","W0816 15:04:35.392836 139769722800000 deprecation_wrapper.py:119] From /usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n","\n","W0816 15:04:35.404253 139769722800000 deprecation_wrapper.py:119] From /usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n","\n","W0816 15:04:35.625117 139769722800000 deprecation_wrapper.py:119] From /usr/local/lib/python2.7/dist-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","W0816 15:04:35.635040 139769722800000 deprecation.py:506] From /usr/local/lib/python2.7/dist-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","W0816 15:04:35.663301 139769722800000 deprecation_wrapper.py:119] From /usr/local/lib/python2.7/dist-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n","\n","W0816 15:04:35.943429 139769722800000 deprecation.py:323] From /usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/math_grad.py:1250: where (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","W0816 15:04:36.463542 139769722800000 deprecation_wrapper.py:119] From /usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py:986: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n","\n"],"name":"stderr"},{"output_type":"stream","text":["Train on 40973 samples, validate on 4553 samples\n","Epoch 1/20\n","40973/40973 [==============================] - 15s 357us/step - loss: 0.4681 - acc: 0.5405 - mean_pred: 0.2660 - val_loss: 0.4484 - val_acc: 0.5519 - val_mean_pred: 0.4311\n","Epoch 2/20\n","40973/40973 [==============================] - 12s 284us/step - loss: 0.3996 - acc: 0.6535 - mean_pred: 0.4482 - val_loss: 0.4029 - val_acc: 0.6440 - val_mean_pred: 0.4054\n","Epoch 3/20\n","40973/40973 [==============================] - 12s 283us/step - loss: 0.3740 - acc: 0.6887 - mean_pred: 0.4195 - val_loss: 0.3941 - val_acc: 0.6563 - val_mean_pred: 0.4590\n","Epoch 4/20\n","40973/40973 [==============================] - 12s 283us/step - loss: 0.3656 - acc: 0.6949 - mean_pred: 0.4187 - val_loss: 0.3886 - val_acc: 0.6613 - val_mean_pred: 0.3830\n","Epoch 5/20\n","40973/40973 [==============================] - 12s 283us/step - loss: 0.3616 - acc: 0.6977 - mean_pred: 0.4176 - val_loss: 0.3866 - val_acc: 0.6640 - val_mean_pred: 0.3846\n","Epoch 6/20\n","40973/40973 [==============================] - 12s 283us/step - loss: 0.3593 - acc: 0.6988 - mean_pred: 0.4167 - val_loss: 0.3858 - val_acc: 0.6648 - val_mean_pred: 0.4083\n","Epoch 7/20\n","40973/40973 [==============================] - 12s 282us/step - loss: 0.3571 - acc: 0.7008 - mean_pred: 0.4211 - val_loss: 0.3828 - val_acc: 0.6646 - val_mean_pred: 0.4091\n","Epoch 8/20\n","40973/40973 [==============================] - 12s 282us/step - loss: 0.3551 - acc: 0.7010 - mean_pred: 0.4202 - val_loss: 0.3827 - val_acc: 0.6637 - val_mean_pred: 0.3940\n","Epoch 9/20\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.3536 - acc: 0.7018 - mean_pred: 0.4215 - val_loss: 0.3825 - val_acc: 0.6655 - val_mean_pred: 0.4081\n","Epoch 10/20\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.3518 - acc: 0.7017 - mean_pred: 0.4220 - val_loss: 0.3848 - val_acc: 0.6602 - val_mean_pred: 0.4227\n","Epoch 11/20\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.3503 - acc: 0.7025 - mean_pred: 0.4256 - val_loss: 0.3810 - val_acc: 0.6666 - val_mean_pred: 0.4353\n","Epoch 12/20\n","40973/40973 [==============================] - 12s 281us/step - loss: 0.3497 - acc: 0.7049 - mean_pred: 0.4260 - val_loss: 0.3794 - val_acc: 0.6646 - val_mean_pred: 0.4427\n","Epoch 13/20\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.3479 - acc: 0.7034 - mean_pred: 0.4301 - val_loss: 0.3781 - val_acc: 0.6646 - val_mean_pred: 0.4340\n","Epoch 14/20\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.3468 - acc: 0.7045 - mean_pred: 0.4304 - val_loss: 0.3816 - val_acc: 0.6618 - val_mean_pred: 0.4452\n","Epoch 15/20\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.3453 - acc: 0.7056 - mean_pred: 0.4313 - val_loss: 0.3760 - val_acc: 0.6690 - val_mean_pred: 0.4333\n","Epoch 16/20\n","40973/40973 [==============================] - 12s 282us/step - loss: 0.3440 - acc: 0.7063 - mean_pred: 0.4382 - val_loss: 0.3739 - val_acc: 0.6694 - val_mean_pred: 0.4423\n","Epoch 17/20\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.3423 - acc: 0.7055 - mean_pred: 0.4390 - val_loss: 0.3732 - val_acc: 0.6659 - val_mean_pred: 0.4467\n","Epoch 18/20\n","40973/40973 [==============================] - 12s 281us/step - loss: 0.3412 - acc: 0.7046 - mean_pred: 0.4410 - val_loss: 0.3712 - val_acc: 0.6697 - val_mean_pred: 0.4465\n","Epoch 19/20\n","40973/40973 [==============================] - 12s 282us/step - loss: 0.3396 - acc: 0.7069 - mean_pred: 0.4433 - val_loss: 0.3723 - val_acc: 0.6670 - val_mean_pred: 0.4578\n","Epoch 20/20\n","40973/40973 [==============================] - 12s 281us/step - loss: 0.3388 - acc: 0.7060 - mean_pred: 0.4423 - val_loss: 0.3680 - val_acc: 0.6719 - val_mean_pred: 0.4445\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"EIcP_G10-VZg","colab_type":"text"},"source":["**TRAINING ACCURACY AT 70.6%**"]},{"cell_type":"code","metadata":{"id":"oZukq1gWexb8","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":269},"outputId":"75531933-b7d8-4e94-f4a8-4828d2a48181","executionInfo":{"status":"ok","timestamp":1565968150846,"user_tz":240,"elapsed":508,"user":{"displayName":"nupur deshpande","photoUrl":"https://lh4.googleusercontent.com/-AQjf2oEW7kE/AAAAAAAAAAI/AAAAAAAAA1Y/ZFcRZICzWa0/s64/photo.jpg","userId":"04753088851981756999"}}},"source":["nn_model.summary()\n"],"execution_count":34,"outputs":[{"output_type":"stream","text":["_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","lstm_1 (LSTM)                (None, 32)                4864      \n","_________________________________________________________________\n","dropout_1 (Dropout)          (None, 32)                0         \n","_________________________________________________________________\n","dense_1 (Dense)              (None, 2)                 66        \n","_________________________________________________________________\n","activation_1 (Activation)    (None, 2)                 0         \n","=================================================================\n","Total params: 4,930\n","Trainable params: 4,930\n","Non-trainable params: 0\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"WoJOq_rgexb-","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":50},"outputId":"2a4e54e8-204e-407b-d0a6-7950b426b27b","executionInfo":{"status":"ok","timestamp":1565968165151,"user_tz":240,"elapsed":7999,"user":{"displayName":"nupur deshpande","photoUrl":"https://lh4.googleusercontent.com/-AQjf2oEW7kE/AAAAAAAAAAI/AAAAAAAAA1Y/ZFcRZICzWa0/s64/photo.jpg","userId":"04753088851981756999"}}},"source":["acc = nn_model.evaluate(X_test, Y_test, batch_size=16)\n","print('Test accuracy:', acc)"],"execution_count":35,"outputs":[{"output_type":"stream","text":["4553/4553 [==============================] - 7s 1ms/step\n","('Test accuracy:', [0.36795512862240065, 0.6718647046427452, 0.44446592047637096])\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"FTliD7NC-j2Y","colab_type":"text"},"source":["**TESTING ACCURACY AT 67.18%**"]},{"cell_type":"code","metadata":{"id":"bi51jqQgexcA","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":295},"outputId":"1141bd92-717c-4256-d6bd-958e81322765","executionInfo":{"status":"ok","timestamp":1565968217171,"user_tz":240,"elapsed":929,"user":{"displayName":"nupur deshpande","photoUrl":"https://lh4.googleusercontent.com/-AQjf2oEW7kE/AAAAAAAAAAI/AAAAAAAAA1Y/ZFcRZICzWa0/s64/photo.jpg","userId":"04753088851981756999"}}},"source":["#Plotting training data loss and validation data loss\n","plt.plot(nn_history.history['acc'], label = 'trained data')\n","plt.plot(nn_history.history['val_acc'], label = 'validated data')\n","plt.legend()\n","plt.xlabel('Epochs')\n","plt.ylabel('Accuracy')\n","plt.title('Measure of accuracy')\n","plt.show()"],"execution_count":36,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAZIAAAEWCAYAAABMoxE0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nnA35ZWzIL2djfhn7SmoBNxIpMyma1LhIRidEMS0joW3IZ0psBKltP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945XxSRbqZtDa6C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\nRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"wGOS0SMX-s2D","colab_type":"text"},"source":["**A GOOD MODEL. NORMALIZED VALUES DIFFERENCE AT 0.025 SO NOT OVERFIT OR UNDERFIT.**"]},{"cell_type":"code","metadata":{"id":"FFhxD0rZexcC","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":295},"outputId":"8e9c1c1a-6a10-4a79-c111-43007de91d81","executionInfo":{"status":"ok","timestamp":1565968289085,"user_tz":240,"elapsed":767,"user":{"displayName":"nupur deshpande","photoUrl":"https://lh4.googleusercontent.com/-AQjf2oEW7kE/AAAAAAAAAAI/AAAAAAAAA1Y/ZFcRZICzWa0/s64/photo.jpg","userId":"04753088851981756999"}}},"source":["#plateau:\n","plt.plot(nn_history.history['loss'], label = 'training curve')\n","plt.plot(nn_history.history['val_loss'], label = 'testing curve')\n","plt.legend()\n","plt.xlabel('Epochs')\n","plt.ylabel('Training loss')\n","plt.title('Measure of plateau')\n","plt.show()"],"execution_count":37,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYsAAAEWCAYAAACXGLsWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nqXFstwkFjel1Wk9RDvDggw8ydepUJkyYwD333ANARUUFF154IRMnTm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jk+Oz7vnfe5yJy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\nAoAkFgAksQAgiQUA6QLHxOoTcEE+8QAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"5umLf4MEexcE","colab_type":"text"},"source":["**SUMMARY OF THIS STRATEGY: \n","        great results compared to just pure stock prediction and even \n","        previous simple moving average algorithm\n","I will now try to find the best LSTM model by tweaking epoch, cost function, actvation function and other parameters. \n","Go down for other network architectures-mainly GRU and conv1D**"]},{"cell_type":"markdown","metadata":{"id":"PN79AKBH_A3L","colab_type":"text"},"source":["**HYPERPARAMETER TUNING**"]},{"cell_type":"markdown","metadata":{"id":"oia4KmT2excF","colab_type":"text"},"source":["**For LSTM: change activation function, cost functionand other params. We know which ones are good for seq model by previous analysis. Trying a few combinations based on past analysis:**\n","\n","**1.elu and selu with rmsprop and sgd along with mse and logcosh**\n","\n","**2.will try epochs 100 and 50** \n","\n","**3. will try initialzers random uniform and zeros**"]},{"cell_type":"code","metadata":{"id":"PMqGTlVJexcG","colab_type":"code","colab":{}},"source":["#elu with mse and rmsprop. epoch of 100. \n","#initializer random uniform.  \n","def mean_pred(X_test, Y_test):\n","    return K.mean(Y_test)\n","\n","def build_model(inputs, output_size, neurons, activ_func=\"elu\",\n","                dropout=0.10, loss=\"mse\", optimizer=\"rmsprop\"):\n","    \n","    model = Sequential()\n","\n","    model.add(LSTM(neurons, input_shape=(WINDOW,EMB_SIZE)))\n","    model.add(Dropout(dropout))\n","    model.add(Dense(units=output_size, kernel_initializer='random_uniform'))\n","    model.add(Activation(activ_func))\n","\n","    model.compile(loss=loss, optimizer=optimizer, metrics=['accuracy', mean_pred])\n","    return model"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"UeMsiLZaexcI","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":1000},"outputId":"adab8fc7-da00-41b1-c51a-4830e88c5e53","executionInfo":{"status":"ok","timestamp":1565969486381,"user_tz":240,"elapsed":1148722,"user":{"displayName":"nupur deshpande","photoUrl":"https://lh4.googleusercontent.com/-AQjf2oEW7kE/AAAAAAAAAAI/AAAAAAAAA1Y/ZFcRZICzWa0/s64/photo.jpg","userId":"04753088851981756999"}}},"source":["nn_model = build_model(X_train, output_size=2, neurons = 32)\n","# model output is next price normalised to 10th previous closing price\n","# train model on data\n","# note: eth_history contains information on the training error per epoch\n","nn_history = nn_model.fit(X_train, Y_train,\n","                            epochs=100, batch_size=256, verbose=1,  validation_data=(X_test, Y_test),shuffle=True)"],"execution_count":39,"outputs":[{"output_type":"stream","text":["Train on 40973 samples, validate on 4553 samples\n","Epoch 1/100\n","40973/40973 [==============================] - 12s 300us/step - loss: 0.2644 - acc: 0.5440 - mean_pred: 0.4557 - val_loss: 0.2433 - val_acc: 0.6029 - val_mean_pred: 0.5067\n","Epoch 2/100\n","40973/40973 [==============================] - 12s 283us/step - loss: 0.2275 - acc: 0.6393 - mean_pred: 0.4993 - val_loss: 0.2280 - val_acc: 0.6093 - val_mean_pred: 0.4928\n","Epoch 3/100\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.2069 - acc: 0.6881 - mean_pred: 0.4966 - val_loss: 0.2198 - val_acc: 0.6499 - val_mean_pred: 0.4768\n","Epoch 4/100\n","40973/40973 [==============================] - 12s 283us/step - loss: 0.2033 - acc: 0.6948 - mean_pred: 0.4965 - val_loss: 0.2204 - val_acc: 0.6473 - val_mean_pred: 0.4920\n","Epoch 5/100\n","40973/40973 [==============================] - 12s 281us/step - loss: 0.2014 - acc: 0.6991 - mean_pred: 0.4968 - val_loss: 0.2163 - val_acc: 0.6589 - val_mean_pred: 0.4988\n","Epoch 6/100\n","40973/40973 [==============================] - 12s 283us/step - loss: 0.2007 - acc: 0.7005 - mean_pred: 0.4969 - val_loss: 0.2157 - val_acc: 0.6631 - val_mean_pred: 0.5008\n","Epoch 7/100\n","40973/40973 [==============================] - 12s 284us/step - loss: 0.1999 - acc: 0.7016 - mean_pred: 0.4966 - val_loss: 0.2265 - val_acc: 0.6413 - val_mean_pred: 0.5194\n","Epoch 8/100\n","40973/40973 [==============================] - 12s 281us/step - loss: 0.1993 - acc: 0.7009 - mean_pred: 0.4971 - val_loss: 0.2154 - val_acc: 0.6642 - val_mean_pred: 0.5119\n","Epoch 9/100\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.1989 - acc: 0.7017 - mean_pred: 0.4969 - val_loss: 0.2211 - val_acc: 0.6532 - val_mean_pred: 0.4846\n","Epoch 10/100\n","40973/40973 [==============================] - 11s 281us/step - loss: 0.1982 - acc: 0.7043 - mean_pred: 0.4970 - val_loss: 0.2242 - val_acc: 0.6446 - val_mean_pred: 0.4879\n","Epoch 11/100\n","40973/40973 [==============================] - 12s 281us/step - loss: 0.1979 - acc: 0.7022 - mean_pred: 0.4968 - val_loss: 0.2183 - val_acc: 0.6536 - val_mean_pred: 0.4996\n","Epoch 12/100\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.1970 - acc: 0.7038 - mean_pred: 0.4973 - val_loss: 0.2216 - val_acc: 0.6569 - val_mean_pred: 0.4897\n","Epoch 13/100\n","40973/40973 [==============================] - 12s 281us/step - loss: 0.1966 - acc: 0.7049 - mean_pred: 0.4967 - val_loss: 0.2187 - val_acc: 0.6598 - val_mean_pred: 0.5067\n","Epoch 14/100\n","40973/40973 [==============================] - 12s 283us/step - loss: 0.1962 - acc: 0.7058 - mean_pred: 0.4976 - val_loss: 0.2210 - val_acc: 0.6604 - val_mean_pred: 0.5239\n","Epoch 15/100\n","40973/40973 [==============================] - 12s 281us/step - loss: 0.1954 - acc: 0.7059 - mean_pred: 0.4976 - val_loss: 0.2236 - val_acc: 0.6607 - val_mean_pred: 0.5008\n","Epoch 16/100\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.1954 - acc: 0.7048 - mean_pred: 0.4974 - val_loss: 0.2243 - val_acc: 0.6391 - val_mean_pred: 0.4887\n","Epoch 17/100\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.1948 - acc: 0.7057 - mean_pred: 0.4973 - val_loss: 0.2225 - val_acc: 0.6574 - val_mean_pred: 0.5062\n","Epoch 18/100\n","40973/40973 [==============================] - 11s 281us/step - loss: 0.1945 - acc: 0.7059 - mean_pred: 0.4976 - val_loss: 0.2147 - val_acc: 0.6659 - val_mean_pred: 0.4914\n","Epoch 19/100\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.1942 - acc: 0.7062 - mean_pred: 0.4976 - val_loss: 0.2138 - val_acc: 0.6655 - val_mean_pred: 0.5045\n","Epoch 20/100\n","40973/40973 [==============================] - 11s 278us/step - loss: 0.1939 - acc: 0.7078 - mean_pred: 0.4977 - val_loss: 0.2150 - val_acc: 0.6607 - val_mean_pred: 0.4940\n","Epoch 21/100\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.1938 - acc: 0.7069 - mean_pred: 0.4979 - val_loss: 0.2132 - val_acc: 0.6664 - val_mean_pred: 0.4913\n","Epoch 22/100\n","40973/40973 [==============================] - 11s 278us/step - loss: 0.1935 - acc: 0.7071 - mean_pred: 0.4979 - val_loss: 0.2136 - val_acc: 0.6600 - val_mean_pred: 0.4944\n","Epoch 23/100\n","40973/40973 [==============================] - 11s 278us/step - loss: 0.1933 - acc: 0.7074 - mean_pred: 0.4978 - val_loss: 0.2146 - val_acc: 0.6657 - val_mean_pred: 0.4997\n","Epoch 24/100\n","40973/40973 [==============================] - 11s 277us/step - loss: 0.1930 - acc: 0.7081 - mean_pred: 0.4978 - val_loss: 0.2244 - val_acc: 0.6611 - val_mean_pred: 0.4930\n","Epoch 25/100\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.1925 - acc: 0.7085 - mean_pred: 0.4982 - val_loss: 0.2214 - val_acc: 0.6556 - val_mean_pred: 0.4993\n","Epoch 26/100\n","40973/40973 [==============================] - 11s 278us/step - loss: 0.1922 - acc: 0.7089 - mean_pred: 0.4981 - val_loss: 0.2172 - val_acc: 0.6624 - val_mean_pred: 0.4963\n","Epoch 27/100\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.1927 - acc: 0.7097 - mean_pred: 0.4979 - val_loss: 0.2185 - val_acc: 0.6640 - val_mean_pred: 0.5011\n","Epoch 28/100\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.1919 - acc: 0.7092 - mean_pred: 0.4981 - val_loss: 0.2234 - val_acc: 0.6558 - val_mean_pred: 0.5037\n","Epoch 29/100\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.1921 - acc: 0.7082 - mean_pred: 0.4983 - val_loss: 0.2119 - val_acc: 0.6712 - val_mean_pred: 0.4983\n","Epoch 30/100\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.1916 - acc: 0.7096 - mean_pred: 0.4982 - val_loss: 0.2166 - val_acc: 0.6615 - val_mean_pred: 0.4984\n","Epoch 31/100\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.1911 - acc: 0.7119 - mean_pred: 0.4982 - val_loss: 0.2177 - val_acc: 0.6686 - val_mean_pred: 0.4966\n","Epoch 32/100\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.1915 - acc: 0.7099 - mean_pred: 0.4984 - val_loss: 0.2147 - val_acc: 0.6651 - val_mean_pred: 0.4959\n","Epoch 33/100\n","40973/40973 [==============================] - 11s 281us/step - loss: 0.1913 - acc: 0.7090 - mean_pred: 0.4986 - val_loss: 0.2129 - val_acc: 0.6692 - val_mean_pred: 0.4977\n","Epoch 34/100\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.1911 - acc: 0.7112 - mean_pred: 0.4983 - val_loss: 0.2194 - val_acc: 0.6637 - val_mean_pred: 0.5034\n","Epoch 35/100\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.1907 - acc: 0.7115 - mean_pred: 0.4985 - val_loss: 0.2172 - val_acc: 0.6763 - val_mean_pred: 0.5022\n","Epoch 36/100\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.1908 - acc: 0.7105 - mean_pred: 0.4986 - val_loss: 0.2180 - val_acc: 0.6668 - val_mean_pred: 0.5010\n","Epoch 37/100\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.1905 - acc: 0.7113 - mean_pred: 0.4984 - val_loss: 0.2129 - val_acc: 0.6725 - val_mean_pred: 0.4945\n","Epoch 38/100\n","40973/40973 [==============================] - 11s 278us/step - loss: 0.1902 - acc: 0.7117 - mean_pred: 0.4987 - val_loss: 0.2137 - val_acc: 0.6719 - val_mean_pred: 0.4977\n","Epoch 39/100\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.1904 - acc: 0.7120 - mean_pred: 0.4985 - val_loss: 0.2169 - val_acc: 0.6684 - val_mean_pred: 0.5006\n","Epoch 40/100\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.1901 - acc: 0.7080 - mean_pred: 0.4988 - val_loss: 0.2146 - val_acc: 0.6721 - val_mean_pred: 0.5006\n","Epoch 41/100\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.1901 - acc: 0.7113 - mean_pred: 0.4988 - val_loss: 0.2152 - val_acc: 0.6716 - val_mean_pred: 0.4985\n","Epoch 42/100\n","40973/40973 [==============================] - 12s 281us/step - loss: 0.1899 - acc: 0.7117 - mean_pred: 0.4986 - val_loss: 0.2137 - val_acc: 0.6648 - val_mean_pred: 0.4988\n","Epoch 43/100\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.1893 - acc: 0.7142 - mean_pred: 0.4990 - val_loss: 0.2104 - val_acc: 0.6760 - val_mean_pred: 0.4993\n","Epoch 44/100\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.1894 - acc: 0.7140 - mean_pred: 0.4988 - val_loss: 0.2153 - val_acc: 0.6640 - val_mean_pred: 0.4981\n","Epoch 45/100\n","40973/40973 [==============================] - 11s 278us/step - loss: 0.1894 - acc: 0.7121 - mean_pred: 0.4990 - val_loss: 0.2111 - val_acc: 0.6730 - val_mean_pred: 0.4976\n","Epoch 46/100\n","40973/40973 [==============================] - 12s 281us/step - loss: 0.1893 - acc: 0.7134 - mean_pred: 0.4989 - val_loss: 0.2147 - val_acc: 0.6754 - val_mean_pred: 0.4986\n","Epoch 47/100\n","40973/40973 [==============================] - 11s 281us/step - loss: 0.1892 - acc: 0.7140 - mean_pred: 0.4990 - val_loss: 0.2115 - val_acc: 0.6688 - val_mean_pred: 0.4976\n","Epoch 48/100\n","40973/40973 [==============================] - 12s 282us/step - loss: 0.1889 - acc: 0.7119 - mean_pred: 0.4989 - val_loss: 0.2140 - val_acc: 0.6679 - val_mean_pred: 0.4999\n","Epoch 49/100\n","40973/40973 [==============================] - 12s 281us/step - loss: 0.1886 - acc: 0.7148 - mean_pred: 0.4991 - val_loss: 0.2171 - val_acc: 0.6699 - val_mean_pred: 0.5012\n","Epoch 50/100\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.1886 - acc: 0.7134 - mean_pred: 0.4993 - val_loss: 0.2173 - val_acc: 0.6736 - val_mean_pred: 0.4992\n","Epoch 51/100\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.1885 - acc: 0.7143 - mean_pred: 0.4990 - val_loss: 0.2108 - val_acc: 0.6760 - val_mean_pred: 0.4982\n","Epoch 52/100\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.1884 - acc: 0.7150 - mean_pred: 0.4992 - val_loss: 0.2137 - val_acc: 0.6725 - val_mean_pred: 0.5000\n","Epoch 53/100\n","40973/40973 [==============================] - 11s 278us/step - loss: 0.1884 - acc: 0.7155 - mean_pred: 0.4993 - val_loss: 0.2103 - val_acc: 0.6763 - val_mean_pred: 0.4981\n","Epoch 54/100\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.1878 - acc: 0.7158 - mean_pred: 0.4992 - val_loss: 0.2148 - val_acc: 0.6745 - val_mean_pred: 0.5000\n","Epoch 55/100\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.1884 - acc: 0.7147 - mean_pred: 0.4992 - val_loss: 0.2114 - val_acc: 0.6820 - val_mean_pred: 0.4998\n","Epoch 56/100\n","40973/40973 [==============================] - 12s 281us/step - loss: 0.1879 - acc: 0.7149 - mean_pred: 0.4993 - val_loss: 0.2128 - val_acc: 0.6736 - val_mean_pred: 0.4988\n","Epoch 57/100\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.1877 - acc: 0.7158 - mean_pred: 0.4992 - val_loss: 0.2125 - val_acc: 0.6752 - val_mean_pred: 0.4999\n","Epoch 58/100\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.1872 - acc: 0.7171 - mean_pred: 0.4994 - val_loss: 0.2122 - val_acc: 0.6749 - val_mean_pred: 0.4992\n","Epoch 59/100\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.1877 - acc: 0.7158 - mean_pred: 0.4993 - val_loss: 0.2114 - val_acc: 0.6765 - val_mean_pred: 0.5002\n","Epoch 60/100\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.1876 - acc: 0.7163 - mean_pred: 0.4994 - val_loss: 0.2116 - val_acc: 0.6771 - val_mean_pred: 0.5004\n","Epoch 61/100\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.1874 - acc: 0.7172 - mean_pred: 0.4994 - val_loss: 0.2134 - val_acc: 0.6804 - val_mean_pred: 0.4992\n","Epoch 62/100\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.1871 - acc: 0.7177 - mean_pred: 0.4994 - val_loss: 0.2125 - val_acc: 0.6760 - val_mean_pred: 0.4993\n","Epoch 63/100\n","40973/40973 [==============================] - 11s 278us/step - loss: 0.1869 - acc: 0.7163 - mean_pred: 0.4995 - val_loss: 0.2091 - val_acc: 0.6749 - val_mean_pred: 0.4991\n","Epoch 64/100\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.1871 - acc: 0.7162 - mean_pred: 0.4995 - val_loss: 0.2097 - val_acc: 0.6756 - val_mean_pred: 0.4995\n","Epoch 65/100\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.1866 - acc: 0.7172 - mean_pred: 0.4995 - val_loss: 0.2141 - val_acc: 0.6791 - val_mean_pred: 0.4999\n","Epoch 66/100\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.1866 - acc: 0.7164 - mean_pred: 0.4995 - val_loss: 0.2153 - val_acc: 0.6732 - val_mean_pred: 0.4995\n","Epoch 67/100\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.1865 - acc: 0.7177 - mean_pred: 0.4995 - val_loss: 0.2138 - val_acc: 0.6833 - val_mean_pred: 0.4995\n","Epoch 68/100\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.1861 - acc: 0.7170 - mean_pred: 0.4995 - val_loss: 0.2111 - val_acc: 0.6719 - val_mean_pred: 0.4990\n","Epoch 69/100\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.1859 - acc: 0.7185 - mean_pred: 0.4996 - val_loss: 0.2108 - val_acc: 0.6787 - val_mean_pred: 0.4997\n","Epoch 70/100\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.1860 - acc: 0.7182 - mean_pred: 0.4996 - val_loss: 0.2110 - val_acc: 0.6769 - val_mean_pred: 0.4993\n","Epoch 71/100\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.1858 - acc: 0.7185 - mean_pred: 0.4996 - val_loss: 0.2124 - val_acc: 0.6822 - val_mean_pred: 0.4996\n","Epoch 72/100\n","40973/40973 [==============================] - 11s 278us/step - loss: 0.1857 - acc: 0.7196 - mean_pred: 0.4996 - val_loss: 0.2148 - val_acc: 0.6778 - val_mean_pred: 0.4995\n","Epoch 73/100\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.1853 - acc: 0.7191 - mean_pred: 0.4996 - val_loss: 0.2161 - val_acc: 0.6758 - val_mean_pred: 0.4999\n","Epoch 74/100\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.1856 - acc: 0.7197 - mean_pred: 0.4996 - val_loss: 0.2124 - val_acc: 0.6813 - val_mean_pred: 0.4998\n","Epoch 75/100\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.1852 - acc: 0.7211 - mean_pred: 0.4997 - val_loss: 0.2146 - val_acc: 0.6675 - val_mean_pred: 0.4995\n","Epoch 76/100\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.1848 - acc: 0.7203 - mean_pred: 0.4997 - val_loss: 0.2131 - val_acc: 0.6782 - val_mean_pred: 0.4997\n","Epoch 77/100\n","40973/40973 [==============================] - 11s 278us/step - loss: 0.1846 - acc: 0.7217 - mean_pred: 0.4997 - val_loss: 0.2119 - val_acc: 0.6846 - val_mean_pred: 0.4997\n","Epoch 78/100\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.1847 - acc: 0.7213 - mean_pred: 0.4997 - val_loss: 0.2121 - val_acc: 0.6782 - val_mean_pred: 0.4998\n","Epoch 79/100\n","40973/40973 [==============================] - 11s 278us/step - loss: 0.1845 - acc: 0.7201 - mean_pred: 0.4997 - val_loss: 0.2123 - val_acc: 0.6747 - val_mean_pred: 0.4998\n","Epoch 80/100\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.1842 - acc: 0.7216 - mean_pred: 0.4997 - val_loss: 0.2117 - val_acc: 0.6877 - val_mean_pred: 0.4996\n","Epoch 81/100\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.1839 - acc: 0.7231 - mean_pred: 0.4997 - val_loss: 0.2123 - val_acc: 0.6866 - val_mean_pred: 0.4997\n","Epoch 82/100\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.1835 - acc: 0.7221 - mean_pred: 0.4997 - val_loss: 0.2121 - val_acc: 0.6811 - val_mean_pred: 0.4998\n","Epoch 83/100\n","40973/40973 [==============================] - 12s 281us/step - loss: 0.1840 - acc: 0.7221 - mean_pred: 0.4997 - val_loss: 0.2118 - val_acc: 0.6815 - val_mean_pred: 0.4997\n","Epoch 84/100\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.1834 - acc: 0.7236 - mean_pred: 0.4998 - val_loss: 0.2116 - val_acc: 0.6853 - val_mean_pred: 0.4995\n","Epoch 85/100\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.1833 - acc: 0.7233 - mean_pred: 0.4997 - val_loss: 0.2141 - val_acc: 0.6725 - val_mean_pred: 0.4998\n","Epoch 86/100\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.1831 - acc: 0.7247 - mean_pred: 0.4997 - val_loss: 0.2143 - val_acc: 0.6760 - val_mean_pred: 0.4996\n","Epoch 87/100\n","40973/40973 [==============================] - 11s 277us/step - loss: 0.1832 - acc: 0.7247 - mean_pred: 0.4997 - val_loss: 0.2106 - val_acc: 0.6879 - val_mean_pred: 0.4997\n","Epoch 88/100\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.1826 - acc: 0.7271 - mean_pred: 0.4997 - val_loss: 0.2099 - val_acc: 0.6892 - val_mean_pred: 0.4997\n","Epoch 89/100\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.1825 - acc: 0.7256 - mean_pred: 0.4998 - val_loss: 0.2129 - val_acc: 0.6853 - val_mean_pred: 0.4998\n","Epoch 90/100\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.1823 - acc: 0.7250 - mean_pred: 0.4998 - val_loss: 0.2099 - val_acc: 0.6844 - val_mean_pred: 0.4998\n","Epoch 91/100\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.1823 - acc: 0.7253 - mean_pred: 0.4998 - val_loss: 0.2124 - val_acc: 0.6798 - val_mean_pred: 0.4998\n","Epoch 92/100\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.1821 - acc: 0.7270 - mean_pred: 0.4998 - val_loss: 0.2115 - val_acc: 0.6857 - val_mean_pred: 0.4997\n","Epoch 93/100\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.1816 - acc: 0.7281 - mean_pred: 0.4997 - val_loss: 0.2103 - val_acc: 0.6853 - val_mean_pred: 0.4998\n","Epoch 94/100\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.1817 - acc: 0.7262 - mean_pred: 0.4998 - val_loss: 0.2136 - val_acc: 0.6846 - val_mean_pred: 0.5000\n","Epoch 95/100\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.1813 - acc: 0.7285 - mean_pred: 0.4998 - val_loss: 0.2125 - val_acc: 0.6905 - val_mean_pred: 0.4999\n","Epoch 96/100\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.1812 - acc: 0.7272 - mean_pred: 0.4998 - val_loss: 0.2123 - val_acc: 0.6877 - val_mean_pred: 0.4998\n","Epoch 97/100\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.1812 - acc: 0.7288 - mean_pred: 0.4998 - val_loss: 0.2102 - val_acc: 0.6842 - val_mean_pred: 0.4997\n","Epoch 98/100\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.1810 - acc: 0.7290 - mean_pred: 0.4998 - val_loss: 0.2123 - val_acc: 0.6828 - val_mean_pred: 0.4997\n","Epoch 99/100\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.1806 - acc: 0.7284 - mean_pred: 0.4998 - val_loss: 0.2109 - val_acc: 0.6831 - val_mean_pred: 0.4997\n","Epoch 100/100\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.1805 - acc: 0.7291 - mean_pred: 0.4998 - val_loss: 0.2104 - val_acc: 0.6839 - val_mean_pred: 0.4998\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"3r_pvc8rexcJ","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":269},"outputId":"06b1c937-fd47-41c5-863b-be561b0b8e13","executionInfo":{"status":"ok","timestamp":1565970093224,"user_tz":240,"elapsed":643,"user":{"displayName":"nupur deshpande","photoUrl":"https://lh4.googleusercontent.com/-AQjf2oEW7kE/AAAAAAAAAAI/AAAAAAAAA1Y/ZFcRZICzWa0/s64/photo.jpg","userId":"04753088851981756999"}}},"source":["nn_model.summary()"],"execution_count":40,"outputs":[{"output_type":"stream","text":["_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","lstm_2 (LSTM)                (None, 32)                4864      \n","_________________________________________________________________\n","dropout_2 (Dropout)          (None, 32)                0         \n","_________________________________________________________________\n","dense_2 (Dense)              (None, 2)                 66        \n","_________________________________________________________________\n","activation_2 (Activation)    (None, 2)                 0         \n","=================================================================\n","Total params: 4,930\n","Trainable params: 4,930\n","Non-trainable params: 0\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"CXmQRN0aexcL","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":50},"outputId":"b3080e36-e6c5-4e85-ba86-52fe2532993f","executionInfo":{"status":"ok","timestamp":1565970101769,"user_tz":240,"elapsed":7588,"user":{"displayName":"nupur deshpande","photoUrl":"https://lh4.googleusercontent.com/-AQjf2oEW7kE/AAAAAAAAAAI/AAAAAAAAA1Y/ZFcRZICzWa0/s64/photo.jpg","userId":"04753088851981756999"}}},"source":["acc = nn_model.evaluate(X_test, Y_test, batch_size=16)\n","print('Test accuracy:', acc)"],"execution_count":41,"outputs":[{"output_type":"stream","text":["4553/4553 [==============================] - 7s 2ms/step\n","('Test accuracy:', [0.21036483873358514, 0.6839446518844283, 0.499842308934589])\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"CGMHFgPQF5pb","colab_type":"text"},"source":["**Test Accuracy is at 68.39%**"]},{"cell_type":"markdown","metadata":{"id":"yrYWxXOWHAWG","colab_type":"text"},"source":["**Plotting training data loss and validation data loss**"]},{"cell_type":"code","metadata":{"id":"nZY6fRQeexcN","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":295},"outputId":"d70ee196-5df9-4324-d15c-6c370922a4b8","executionInfo":{"status":"ok","timestamp":1565970557777,"user_tz":240,"elapsed":142472,"user":{"displayName":"nupur deshpande","photoUrl":"https://lh4.googleusercontent.com/-AQjf2oEW7kE/AAAAAAAAAAI/AAAAAAAAA1Y/ZFcRZICzWa0/s64/photo.jpg","userId":"04753088851981756999"}}},"source":["plt.plot(nn_history.history['acc'], label = 'trained data')\n","plt.plot(nn_history.history['val_acc'], label = 'validated data')\n","plt.legend()\n","plt.xlabel('Epochs')\n","plt.ylabel('Accuracy')\n","plt.title('Measure of accuracy')\n","plt.show()"],"execution_count":47,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYsAAAEWCAYAAACXGLsWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nq3ZnM6ZHIv+4diiRYYH9dRzY0RljTCOwft9RXlq6kwVr9lFcWs7Yn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f6KxxgvHMw9xoxF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\nWM0AAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"6ButieWJHE4s","colab_type":"text"},"source":["**Model is perfectly fit if not slightly overfit but we trained for 100 epochs so that is expected**"]},{"cell_type":"code","metadata":{"id":"YtdyhmNPexcQ","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":295},"outputId":"d563c3a3-009b-471c-f47b-1b71170eaeb4","executionInfo":{"status":"ok","timestamp":1565970146367,"user_tz":240,"elapsed":678,"user":{"displayName":"nupur deshpande","photoUrl":"https://lh4.googleusercontent.com/-AQjf2oEW7kE/AAAAAAAAAAI/AAAAAAAAA1Y/ZFcRZICzWa0/s64/photo.jpg","userId":"04753088851981756999"}}},"source":["plt.plot(nn_history.history['loss'], label = 'training curve')\n","plt.plot(nn_history.history['val_loss'], label = 'testing curve')\n","plt.legend()\n","plt.xlabel('Epochs')\n","plt.ylabel('Training loss')\n","plt.title('Measure of plateau')\n","plt.show()"],"execution_count":43,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYsAAAEWCAYAAACXGLsWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noKSkhNmzZzNq1CiGDx/OvHnzePjhh9m9ezcnnXQSJ510EhDYtOXLli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jQ/E4m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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"2UmlBy9nHU3_","colab_type":"text"},"source":["**Measuring the training loss to recheck model**"]},{"cell_type":"markdown","metadata":{"id":"rneMkPa9GwJR","colab_type":"text"},"source":["**More Hyperparameter tuning: selu with logcosh amd sgd. epoch of 35. initializer zeros**"]},{"cell_type":"code","metadata":{"id":"4lRvaj_WexcT","colab_type":"code","colab":{}},"source":["def mean_pred(X_test, Y_test):\n","    return K.mean(Y_test)\n","\n","def build_model(inputs, output_size, neurons, activ_func=\"selu\",\n","                dropout=0.10, loss=\"logcosh\", optimizer=\"sgd\"):\n","    \n","    model = Sequential()\n","\n","    model.add(LSTM(neurons, input_shape=(WINDOW,EMB_SIZE)))\n","    model.add(Dropout(dropout))\n","    model.add(Dense(units=output_size, kernel_initializer='random_uniform'))\n","    model.add(Activation(activ_func))\n","\n","    model.compile(loss=loss, optimizer=optimizer, metrics=['accuracy', mean_pred])\n","    return model"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"9ZPjnGEjexcV","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":1000},"outputId":"c5d7bf82-cdd8-4f46-ba71-3b1fb5e4216b","executionInfo":{"status":"ok","timestamp":1565970557459,"user_tz":240,"elapsed":406936,"user":{"displayName":"nupur deshpande","photoUrl":"https://lh4.googleusercontent.com/-AQjf2oEW7kE/AAAAAAAAAAI/AAAAAAAAA1Y/ZFcRZICzWa0/s64/photo.jpg","userId":"04753088851981756999"}}},"source":["nn_model = build_model(X_train, output_size=2, neurons = 32)\n","# model output is next price normalised to 10th previous closing price\n","# train model on data\n","# note: eth_history contains information on the training error per epoch\n","nn_history = nn_model.fit(X_train, Y_train,\n","                            epochs=35, batch_size=256, verbose=1,  validation_data=(X_test, Y_test),shuffle=True)"],"execution_count":46,"outputs":[{"output_type":"stream","text":["Train on 40973 samples, validate on 4553 samples\n","Epoch 1/35\n","40973/40973 [==============================] - 13s 309us/step - loss: 0.1720 - acc: 0.5163 - mean_pred: 0.1431 - val_loss: 0.1441 - val_acc: 0.5032 - val_mean_pred: 0.2543\n","Epoch 2/35\n","40973/40973 [==============================] - 12s 282us/step - loss: 0.1321 - acc: 0.5312 - mean_pred: 0.3270 - val_loss: 0.1262 - val_acc: 0.5036 - val_mean_pred: 0.3795\n","Epoch 3/35\n","40973/40973 [==============================] - 12s 284us/step - loss: 0.1225 - acc: 0.5314 - mean_pred: 0.4162 - val_loss: 0.1218 - val_acc: 0.5036 - val_mean_pred: 0.4406\n","Epoch 4/35\n","40973/40973 [==============================] - 12s 282us/step - loss: 0.1202 - acc: 0.5314 - mean_pred: 0.4593 - val_loss: 0.1207 - val_acc: 0.5036 - val_mean_pred: 0.4705\n","Epoch 5/35\n","40973/40973 [==============================] - 12s 281us/step - loss: 0.1195 - acc: 0.5314 - mean_pred: 0.4801 - val_loss: 0.1204 - val_acc: 0.5036 - val_mean_pred: 0.4851\n","Epoch 6/35\n","40973/40973 [==============================] - 12s 282us/step - loss: 0.1193 - acc: 0.5314 - mean_pred: 0.4901 - val_loss: 0.1202 - val_acc: 0.5036 - val_mean_pred: 0.4923\n","Epoch 7/35\n","40973/40973 [==============================] - 12s 281us/step - loss: 0.1192 - acc: 0.5314 - mean_pred: 0.4950 - val_loss: 0.1202 - val_acc: 0.5036 - val_mean_pred: 0.4959\n","Epoch 8/35\n","40973/40973 [==============================] - 12s 281us/step - loss: 0.1191 - acc: 0.5315 - mean_pred: 0.4974 - val_loss: 0.1201 - val_acc: 0.5036 - val_mean_pred: 0.4978\n","Epoch 9/35\n","40973/40973 [==============================] - 12s 281us/step - loss: 0.1191 - acc: 0.5315 - mean_pred: 0.4986 - val_loss: 0.1201 - val_acc: 0.5038 - val_mean_pred: 0.4988\n","Epoch 10/35\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.1190 - acc: 0.5318 - mean_pred: 0.4992 - val_loss: 0.1200 - val_acc: 0.5041 - val_mean_pred: 0.4993\n","Epoch 11/35\n","40973/40973 [==============================] - 12s 282us/step - loss: 0.1190 - acc: 0.5320 - mean_pred: 0.4995 - val_loss: 0.1199 - val_acc: 0.5038 - val_mean_pred: 0.4996\n","Epoch 12/35\n","40973/40973 [==============================] - 12s 282us/step - loss: 0.1189 - acc: 0.5325 - mean_pred: 0.4997 - val_loss: 0.1199 - val_acc: 0.5038 - val_mean_pred: 0.4997\n","Epoch 13/35\n","40973/40973 [==============================] - 12s 282us/step - loss: 0.1188 - acc: 0.5336 - mean_pred: 0.4998 - val_loss: 0.1198 - val_acc: 0.5041 - val_mean_pred: 0.4997\n","Epoch 14/35\n","40973/40973 [==============================] - 12s 281us/step - loss: 0.1188 - acc: 0.5340 - mean_pred: 0.4998 - val_loss: 0.1198 - val_acc: 0.5045 - val_mean_pred: 0.4998\n","Epoch 15/35\n","40973/40973 [==============================] - 12s 281us/step - loss: 0.1187 - acc: 0.5349 - mean_pred: 0.4998 - val_loss: 0.1198 - val_acc: 0.5052 - val_mean_pred: 0.4998\n","Epoch 16/35\n","40973/40973 [==============================] - 12s 283us/step - loss: 0.1187 - acc: 0.5362 - mean_pred: 0.4999 - val_loss: 0.1197 - val_acc: 0.5056 - val_mean_pred: 0.4999\n","Epoch 17/35\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.1186 - acc: 0.5372 - mean_pred: 0.4999 - val_loss: 0.1196 - val_acc: 0.5063 - val_mean_pred: 0.4999\n","Epoch 18/35\n","40973/40973 [==============================] - 12s 282us/step - loss: 0.1185 - acc: 0.5387 - mean_pred: 0.4999 - val_loss: 0.1196 - val_acc: 0.5063 - val_mean_pred: 0.4999\n","Epoch 19/35\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.1185 - acc: 0.5392 - mean_pred: 0.4999 - val_loss: 0.1196 - val_acc: 0.5069 - val_mean_pred: 0.5000\n","Epoch 20/35\n","40973/40973 [==============================] - 12s 281us/step - loss: 0.1184 - acc: 0.5406 - mean_pred: 0.4999 - val_loss: 0.1196 - val_acc: 0.5071 - val_mean_pred: 0.4999\n","Epoch 21/35\n","40973/40973 [==============================] - 12s 282us/step - loss: 0.1183 - acc: 0.5407 - mean_pred: 0.4999 - val_loss: 0.1195 - val_acc: 0.5085 - val_mean_pred: 0.4999\n","Epoch 22/35\n","40973/40973 [==============================] - 12s 281us/step - loss: 0.1183 - acc: 0.5426 - mean_pred: 0.4999 - val_loss: 0.1194 - val_acc: 0.5111 - val_mean_pred: 0.5000\n","Epoch 23/35\n","40973/40973 [==============================] - 12s 282us/step - loss: 0.1182 - acc: 0.5453 - mean_pred: 0.5000 - val_loss: 0.1194 - val_acc: 0.5111 - val_mean_pred: 0.4999\n","Epoch 24/35\n","40973/40973 [==============================] - 12s 281us/step - loss: 0.1181 - acc: 0.5448 - mean_pred: 0.4999 - val_loss: 0.1193 - val_acc: 0.5113 - val_mean_pred: 0.4999\n","Epoch 25/35\n","40973/40973 [==============================] - 12s 282us/step - loss: 0.1181 - acc: 0.5467 - mean_pred: 0.4999 - val_loss: 0.1192 - val_acc: 0.5120 - val_mean_pred: 0.4999\n","Epoch 26/35\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.1180 - acc: 0.5488 - mean_pred: 0.4999 - val_loss: 0.1192 - val_acc: 0.5133 - val_mean_pred: 0.4999\n","Epoch 27/35\n","40973/40973 [==============================] - 12s 283us/step - loss: 0.1179 - acc: 0.5507 - mean_pred: 0.4999 - val_loss: 0.1191 - val_acc: 0.5148 - val_mean_pred: 0.4999\n","Epoch 28/35\n","40973/40973 [==============================] - 12s 281us/step - loss: 0.1179 - acc: 0.5527 - mean_pred: 0.4999 - val_loss: 0.1191 - val_acc: 0.5168 - val_mean_pred: 0.4999\n","Epoch 29/35\n","40973/40973 [==============================] - 12s 282us/step - loss: 0.1178 - acc: 0.5546 - mean_pred: 0.4999 - val_loss: 0.1190 - val_acc: 0.5183 - val_mean_pred: 0.5000\n","Epoch 30/35\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.1177 - acc: 0.5565 - mean_pred: 0.4999 - val_loss: 0.1190 - val_acc: 0.5188 - val_mean_pred: 0.5000\n","Epoch 31/35\n","40973/40973 [==============================] - 12s 281us/step - loss: 0.1177 - acc: 0.5569 - mean_pred: 0.5000 - val_loss: 0.1189 - val_acc: 0.5197 - val_mean_pred: 0.4999\n","Epoch 32/35\n","40973/40973 [==============================] - 12s 281us/step - loss: 0.1176 - acc: 0.5581 - mean_pred: 0.4999 - val_loss: 0.1189 - val_acc: 0.5194 - val_mean_pred: 0.5000\n","Epoch 33/35\n","40973/40973 [==============================] - 12s 281us/step - loss: 0.1176 - acc: 0.5579 - mean_pred: 0.4999 - val_loss: 0.1188 - val_acc: 0.5210 - val_mean_pred: 0.5000\n","Epoch 34/35\n","40973/40973 [==============================] - 12s 282us/step - loss: 0.1174 - acc: 0.5604 - mean_pred: 0.5000 - val_loss: 0.1187 - val_acc: 0.5245 - val_mean_pred: 0.5000\n","Epoch 35/35\n","40973/40973 [==============================] - 12s 281us/step - loss: 0.1173 - acc: 0.5617 - mean_pred: 0.5000 - val_loss: 0.1187 - val_acc: 0.5254 - val_mean_pred: 0.4999\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"13AZedetexcX","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":269},"outputId":"1ec0ca88-9b59-4a63-a702-087f1961d8fe","executionInfo":{"status":"ok","timestamp":1565970600412,"user_tz":240,"elapsed":1411,"user":{"displayName":"nupur deshpande","photoUrl":"https://lh4.googleusercontent.com/-AQjf2oEW7kE/AAAAAAAAAAI/AAAAAAAAA1Y/ZFcRZICzWa0/s64/photo.jpg","userId":"04753088851981756999"}}},"source":["nn_model.summary()"],"execution_count":48,"outputs":[{"output_type":"stream","text":["_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","lstm_3 (LSTM)                (None, 32)                4864      \n","_________________________________________________________________\n","dropout_3 (Dropout)          (None, 32)                0         \n","_________________________________________________________________\n","dense_3 (Dense)              (None, 2)                 66        \n","_________________________________________________________________\n","activation_3 (Activation)    (None, 2)                 0         \n","=================================================================\n","Total params: 4,930\n","Trainable params: 4,930\n","Non-trainable params: 0\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"RP2K9L_6excb","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":50},"outputId":"47575a5d-0760-43dd-9587-3d0432ca3010","executionInfo":{"status":"ok","timestamp":1565970610522,"user_tz":240,"elapsed":7884,"user":{"displayName":"nupur deshpande","photoUrl":"https://lh4.googleusercontent.com/-AQjf2oEW7kE/AAAAAAAAAAI/AAAAAAAAA1Y/ZFcRZICzWa0/s64/photo.jpg","userId":"04753088851981756999"}}},"source":["acc = nn_model.evaluate(X_test, Y_test, batch_size=16)\n","print('Test accuracy:', acc)"],"execution_count":49,"outputs":[{"output_type":"stream","text":["4553/4553 [==============================] - 7s 2ms/step\n","('Test accuracy:', [0.11868744594708344, 0.5253678893103014, 0.49994405417449655])\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"TjqMZDeqHymh","colab_type":"text"},"source":["**Accuracy at 52.53%**"]},{"cell_type":"markdown","metadata":{"id":"vodfKu9YH8vT","colab_type":"text"},"source":["**Plotting training data loss and validation data loss**\n","\n","**As seeb data is overfit and doesn't do great with selu and logcosh because logcosh could remove non-linearity of data**"]},{"cell_type":"code","metadata":{"id":"PvhRexaSexcf","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":295},"outputId":"599b56f0-80c9-4ea5-8ec4-deedf423d500","executionInfo":{"status":"ok","timestamp":1565970633321,"user_tz":240,"elapsed":1023,"user":{"displayName":"nupur deshpande","photoUrl":"https://lh4.googleusercontent.com/-AQjf2oEW7kE/AAAAAAAAAAI/AAAAAAAAA1Y/ZFcRZICzWa0/s64/photo.jpg","userId":"04753088851981756999"}}},"source":["plt.plot(nn_history.history['acc'], label = 'trained data')\n","plt.plot(nn_history.history['val_acc'], label = 'validated data')\n","plt.legend()\n","plt.xlabel('Epochs')\n","plt.ylabel('Accuracy')\n","plt.title('Measure of accuracy')\n","plt.show()"],"execution_count":50,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYsAAAEWCAYAAACXGLsWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nq3ZnM6ZHIv+4diiRYYH9dRzY0RljTCOwft9RXlq6kwVr9lFcWs7Yn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f6KxxgvHMw9xoxF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\nWM0AAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"IPiIOWVwexck","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":295},"outputId":"251efd53-5434-4f5e-e779-8027e3253497","executionInfo":{"status":"ok","timestamp":1565970732741,"user_tz":240,"elapsed":1513,"user":{"displayName":"nupur deshpande","photoUrl":"https://lh4.googleusercontent.com/-AQjf2oEW7kE/AAAAAAAAAAI/AAAAAAAAA1Y/ZFcRZICzWa0/s64/photo.jpg","userId":"04753088851981756999"}}},"source":["plt.plot(nn_history.history['loss'], label = 'training curve')\n","plt.plot(nn_history.history['val_loss'], label = 'testing curve')\n","plt.legend()\n","plt.xlabel('Epochs')\n","plt.ylabel('Training loss')\n","plt.title('Measure of plateau')\n","plt.show()"],"execution_count":51,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYsAAAEWCAYAAACXGLsWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi40LCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcv7US4rQAAIABJREFUeJzt3XmcVNWZ//HPU0tv0A00EKOi0G4J\ngoAIuCXGJSDqL5pVMXHLL5EsP2cmM4kTnCRuk12zmcUR4xKzuIxZdBIX1NGYxI1F1CBGEFFAEvad\nXqrq+f1xb1VXV1d3VUNXV9H9fb9e9br3nrvU09ekHs45955j7o6IiEh3IuUOQEREKp+ShYiIFKRk\nISIiBSlZiIhIQUoWIiJSkJKFiIgUpGQhUkZm9hkz+4eZ7TCz4T0892oz+0WpYhPJpmQhZWdmK82s\n1cxG5JQ/b2ZuZmPKE1lpmVkc+C4ww90Hu/vGEn7X7Wb21VJdX/o/JQupFK8D56c3zOwooK584XRm\ngd78/8x+QA2wpBevKVISShZSKX4OXJS1fTFwR/YBZlZtZteb2Zth081/mVltuG+Ymf3ezNab2eZw\nfVTWuZeY2Qoz225mr5vZx8LyDk05ZjYmrM3Ewu0nzOxrZvYXYBdwiJkNMbNbzGytma0xs6+aWTTf\nHxXG/H0zeyv8fD8sOwL4W3jYFjP73zznpmOZHZ671sy+0NUNNLP/NrO/m9lWM3vSzMaF5bOBjwH/\nHjZ3/U9YfoCZ/Tq8Z6+b2T9nXWuamT1tZlvC7/2RmVXlu0dZ9+mTXcUm+z4lC6kUzwANZjY2/OGd\nBeS2x38TOAKYBBwGHAhcGe6LALcBo4GDgd3AjwDMbBBwA3CGu9cDJwCLexDbhcBsoB54A7gdSIQx\nHA3MALr6ofwScFwY80RgGvBld38VGBceM9TdT+3m+08BDg+/54tm9t4ujnswPO5twCLglwDuPjdc\n/3bY3PW+sIb0P8ALBPfxNOBzZnZ6eK0k8K/ACOD4cP9nu4lR+jklC6kk6drFdGApsCa9w8yM4Af7\nX919k7tvB75OkFRw943u/mt33xXu+xrwnqxrp4DxZlbr7mvdvSdNP7e7+xJ3TwCNwJnA59x9p7uv\nA76XjiOPjwHXuvs6d18PXEOQfHrimvC7XiJIiOfnO8jdb3X37e7eAlwNTDSzIV1ccyow0t2vdfdW\nd18B3Ez7/Vzo7s+4e8LdVwI30fF+ygATK3yISJ/5OfAk0EROExQwkqAPY2GQNwAwIApgZnUEP9oz\ngWHh/nozi7r7TjM7D/gCcEvYpPR5d3+lyLhWZa2PBuLA2qw4IjnHZDuAoDaS9kZY1hPZ134DOCr3\ngLA29jXgIwT3KhXuGgFszXPN0cABZrYlqywK/Cm83hEEne9TCO57DFjYw7ilH1HNQiqGu79B0NF9\nJvCbnN0bCJqWxrn70PAzxN0Hh/s/D7wDONbdG4CTwnILr/2wu08H9gdeIfhXNMBOOnakvz1faFnr\nq4AWYERWHA3uPi7PeQBvEfwwpx0clvXEQUWc/1HgHOC9wBBgTFiezmi5w0uvAl7P+huGunu9u58Z\n7r+R4D4dHt7P/8i61s5wWei+ST+iZCGV5hPAqe6+M7vQ3VMEP/DfM7O3AZjZgVlt7PUEyWSLmTUC\nV6XPNbP9zOycsO+iBdhB+7+8FwMnmdnBYZPNFd0F5+5rgXnAd8yswcwiZnaomXXVRHMn8GUzGxk+\nGnwlnftiCvmKmdWFHdYfB+7Oc0x9+LdtJPgR/3rO/n8Ah2RtPwdsN7MvmlmtmUXNbLyZTc263jZg\nh5m9E/hM+sSwOW0NcEF43v8FDu3h3yT7GCULqSju/pq7L+hi9xeB5cAzZrYNeJSgNgHwfaCWoAby\nDPBQ1nkR4N8I/kW+iaDt/TPh9z1C8OP7IkEzy++LCPMioAp4GdgM3EtQY8nnq8CC8PovEXQ89/R9\nhz8S/N2PAde7+7w8x9xB0ES1JozrmZz9twBHhk83/c7dk8D/Ieh4f53gvv2UoFYCQZPdR4HtBEk6\nN0FdClxOkJzGAU/18G+SfYxp8iORyhS+jPg6EA8710XKRjULEREpSMlCREQKUjOUiIgUpJqFiIgU\n1G9eyhsxYoSPGTOm3GGIiOxTFi5cuMHdRxY6rt8kizFjxrBgQVdPXIqISD5m9kbho9QMJSIiRVCy\nEBGRgpQsRESkoH7TZyEi5dPW1sbq1atpbm4udyjShZqaGkaNGkU8Ht+j85UsRGSvrV69mvr6esaM\nGUPW0O1SIdydjRs3snr1apqamvboGmqGEpG91tzczPDhw5UoKpSZMXz48L2q+SlZiEivUKKobHv7\n32fAJ4vtzW1875FXWbxqS+GDRUQGqAGfLFIp+MFjy1j0xuZyhyIie2jLli385Cc/2aNzzzzzTLZs\n6f4fi1deeSWPPvroHl2/vxjwyWJwTdDHv625rcyRiMie6i5ZJBLdTwXywAMPMHTo0G6Pufbaa3nv\ne9+7x/H1BncnlUoVPrBEBnyyiEaMwdUxtu3W3DIi+6o5c+bw2muvMWnSJC6//HKeeOIJ3v3ud3P2\n2Wdz5JFHAvD+97+fY445hnHjxjF37tzMuWPGjGHDhg2sXLmSsWPHcumllzJu3DhmzJjB7t27Abjk\nkku49957M8dfddVVTJ48maOOOopXXnkFgPXr1zN9+nTGjRvHJz/5SUaPHs2GDRs6xfrQQw8xefJk\nJk6cyGmnnQbA1VdfzfXXX585Zvz48axcuZKVK1fyjne8g4suuojx48fzn//5n1x++eWZ426//XYu\nu+wyAH7xi18wbdo0Jk2axKc+9SmSyWRv3mI9OgvQUBNTzUKkl1zzP0t4+a1tvXrNIw9o4Kr3jety\n/ze/+U3++te/snjxYgCeeOIJFi1axF//+tfMo6K33norjY2N7N69m6lTp/KhD32I4cOHd7jOsmXL\nuPPOO7n55ps599xz+fWvf80FF1zQ6ftGjBjBokWL+MlPfsL111/PT3/6U6655hpOPfVUrrjiCh56\n6CFuueWWTuetX7+eSy+9lCeffJKmpiY2bdpU8G9ftmwZP/vZzzjuuONYv349xx9/PNdddx0Ad999\nN1/60pdYunQpd999N3/5y1+Ix+N89rOf5Ze//CUXXXRRwesXq6Q1CzObaWZ/M7PlZjYnz/6TzGyR\nmSXM7MNZ5aeY2eKsT7OZvb9UcTbUxtm2W8lCpD+ZNm1ah3cKbrjhBiZOnMhxxx3HqlWrWLZsWadz\nmpqamDRpEgDHHHMMK1euzHvtD37wg52O+fOf/8ysWbMAmDlzJsOGDet03jPPPMNJJ52UiauxsbHg\n3zF69GiOO+44AEaOHMkhhxzCM888w8aNG3nllVc48cQTeeyxx1i4cCFTp05l0qRJPPbYY6xYsaLg\ntXuiZDULM4sCPwamA6uB+WZ2v7u/nHXYm8AlBJPDZ7j74wQTyWNmjQST1eebpL5XNNTEVbMQ6SXd\n1QD60qBBgzLrTzzxBI8++ihPP/00dXV1nHzyyXnfOaiurs6sR6PRTDNUV8dFo9GCfSLFiMViHfoj\nsmPL/jsAZs2axT333MM73/lOPvCBD2BmuDsXX3wx3/jGN/Y6lq6UsmYxDVju7ivcvRW4Czgn+wB3\nX+nuLwLd9dp8GHjQ3XeVKtCGWvVZiOzL6uvr2b59e5f7t27dyrBhw6irq+OVV17hmWee6fUYTjzx\nRO655x4A5s2bx+bNnZ+wPO6443jyySd5/fXXATLNUGPGjGHRokUALFq0KLM/nw984APcd9993Hnn\nnZmazGmnnca9997LunXrMtd9442iRh4vWimTxYHAqqzt1WFZT80C7sy3w8xmm9kCM1uwfv36Pbh0\nQDULkX3b8OHDOfHEExk/fnyHDuC0mTNnkkgkGDt2LHPmzMk06/Smq666innz5jF+/Hj++7//m7e/\n/e3U19d3OGbkyJHMnTuXD37wg0ycOJHzzjsPgA996ENs2rSJcePG8aMf/Ygjjjiiy+8ZNmwYY8eO\n5Y033mDatGkAHHnkkXz1q19lxowZTJgwgenTp7N27dpe/ftKNgd32Acx090/GW5fCBzr7pflOfZ2\n4Pfufm9O+f7Ai8AB7t7tr/mUKVN8Tyc/uvr+Jfz2+TW8cNWMPTpfZKBbunQpY8eOLXcYZdXS0kI0\nGiUWi/H000/zmc98JtPhXiny/Xcys4XuPqXQuaV8GmoNcFDW9qiwrCfOBX5bKFHsrYaaGNub20il\nnEhEQxaISM+9+eabnHvuuaRSKaqqqrj55pvLHVKvKmWymA8cbmZNBEliFvDRHl7jfOCK3g4sV0Nt\nnJTDztYE9TV7NnyviAxshx9+OM8//3y5wyiZkvVZuHsCuAx4GFgK3OPuS8zsWjM7G8DMpprZauAj\nwE1mtiR9vpmNIaiZ/LFUMabVZ97iVie3iEg+JX0pz90fAB7IKbsya30+QfNUvnNXsmcd4j3WENYm\ntu1u48ChtX3xlSIi+5QBP9wHBM1QgF7MExHpgpIFWTULNUOJiOSlZEHwUh6oZiGyr9qbIcoBvv/9\n77NrV/t7v8UMWz7QKFmQXbNQshDZF/V2sihm2PJS641hRHqTkgXtT0NtVzOUyD4pd4hygOuuu46p\nU6cyYcIErrrqKgB27tzJWWedxcSJExk/fjx33303N9xwA2+99RannHIKp5xyClDcsOXz589nwoQJ\nme8cP3583ti+9a1vcdRRRzFx4kTmzAnGUz355JNJv0S8YcMGxowZAw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sYgs7vO0qeKCBI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\ni8XbRCGRo0cAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"jWLCeZDNITcw","colab_type":"text"},"source":["**Not a lot of training and testing loss**"]},{"cell_type":"markdown","metadata":{"id":"e14za5AJLoMJ","colab_type":"text"},"source":["**More Hyperparamete tuning: Create a LSTM model with relu as activation function, mae as loss function, adam as optimizer and train for 50 epochs.**"]},{"cell_type":"code","metadata":{"id":"3h8Ba4AIexcn","colab_type":"code","colab":{}},"source":["\n","def mean_pred(X_test, Y_test):\n","    return K.mean(Y_test)\n","\n","def build_model(inputs, output_size, neurons, activ_func=\"relu\",\n","                dropout=0.10, loss=\"mae\", optimizer=\"adam\"):\n","    \n","    model = Sequential()\n","\n","    model.add(LSTM(neurons, input_shape=(WINDOW,EMB_SIZE)))\n","    model.add(Dropout(dropout))\n","    model.add(Dense(units=output_size))\n","    model.add(Activation(activ_func))\n","\n","    model.compile(loss=loss, optimizer=optimizer, metrics=['accuracy', mean_pred])\n","    return model"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"FY_aUumpexcp","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":1000},"outputId":"a73a62c2-ea46-4eee-b209-1ca62c9a984c","executionInfo":{"status":"ok","timestamp":1565971779243,"user_tz":240,"elapsed":579283,"user":{"displayName":"nupur deshpande","photoUrl":"https://lh4.googleusercontent.com/-AQjf2oEW7kE/AAAAAAAAAAI/AAAAAAAAA1Y/ZFcRZICzWa0/s64/photo.jpg","userId":"04753088851981756999"}}},"source":["nn_model = build_model(X_train, output_size=2, neurons = 32)\n","# model output is next price normalised to 10th previous closing price\n","# train model on data\n","# note: eth_history contains information on the training error per epoch\n","nn_history = nn_model.fit(X_train, Y_train,\n","                            epochs=50, batch_size=256, verbose=1,  validation_data=(X_test, Y_test),shuffle=True)"],"execution_count":54,"outputs":[{"output_type":"stream","text":["Train on 40973 samples, validate on 4553 samples\n","Epoch 1/50\n","40973/40973 [==============================] - 13s 314us/step - loss: 0.4701 - acc: 0.5279 - mean_pred: 0.2650 - val_loss: 0.4665 - val_acc: 0.5148 - val_mean_pred: 0.3484\n","Epoch 2/50\n","40973/40973 [==============================] - 12s 282us/step - loss: 0.4089 - acc: 0.6385 - mean_pred: 0.3944 - val_loss: 0.4039 - val_acc: 0.6501 - val_mean_pred: 0.4177\n","Epoch 3/50\n","40973/40973 [==============================] - 12s 283us/step - loss: 0.3759 - acc: 0.6888 - mean_pred: 0.4134 - val_loss: 0.3931 - val_acc: 0.6600 - val_mean_pred: 0.3717\n","Epoch 4/50\n","40973/40973 [==============================] - 12s 282us/step - loss: 0.3677 - acc: 0.6941 - mean_pred: 0.4166 - val_loss: 0.3891 - val_acc: 0.6574 - val_mean_pred: 0.4103\n","Epoch 5/50\n","40973/40973 [==============================] - 12s 283us/step - loss: 0.3632 - acc: 0.6964 - mean_pred: 0.4120 - val_loss: 0.3925 - val_acc: 0.6591 - val_mean_pred: 0.4556\n","Epoch 6/50\n","40973/40973 [==============================] - 12s 283us/step - loss: 0.3606 - acc: 0.7005 - mean_pred: 0.4138 - val_loss: 0.3867 - val_acc: 0.6611 - val_mean_pred: 0.4120\n","Epoch 7/50\n","40973/40973 [==============================] - 12s 282us/step - loss: 0.3580 - acc: 0.6999 - mean_pred: 0.4167 - val_loss: 0.3843 - val_acc: 0.6646 - val_mean_pred: 0.4081\n","Epoch 8/50\n","40973/40973 [==============================] - 12s 282us/step - loss: 0.3564 - acc: 0.7003 - mean_pred: 0.4176 - val_loss: 0.3850 - val_acc: 0.6600 - val_mean_pred: 0.4389\n","Epoch 9/50\n","40973/40973 [==============================] - 12s 282us/step - loss: 0.3547 - acc: 0.7024 - mean_pred: 0.4194 - val_loss: 0.3856 - val_acc: 0.6635 - val_mean_pred: 0.4449\n","Epoch 10/50\n","40973/40973 [==============================] - 12s 283us/step - loss: 0.3546 - acc: 0.7020 - mean_pred: 0.4168 - val_loss: 0.3812 - val_acc: 0.6655 - val_mean_pred: 0.4005\n","Epoch 11/50\n","40973/40973 [==============================] - 12s 282us/step - loss: 0.3534 - acc: 0.7045 - mean_pred: 0.4201 - val_loss: 0.3818 - val_acc: 0.6655 - val_mean_pred: 0.4182\n","Epoch 12/50\n","40973/40973 [==============================] - 12s 281us/step - loss: 0.3513 - acc: 0.7044 - mean_pred: 0.4210 - val_loss: 0.3798 - val_acc: 0.6655 - val_mean_pred: 0.4170\n","Epoch 13/50\n","40973/40973 [==============================] - 12s 281us/step - loss: 0.3506 - acc: 0.7050 - mean_pred: 0.4224 - val_loss: 0.3806 - val_acc: 0.6646 - val_mean_pred: 0.4132\n","Epoch 14/50\n","40973/40973 [==============================] - 12s 282us/step - loss: 0.3497 - acc: 0.7038 - mean_pred: 0.4231 - val_loss: 0.3773 - val_acc: 0.6673 - val_mean_pred: 0.4204\n","Epoch 15/50\n","40973/40973 [==============================] - 12s 283us/step - loss: 0.3482 - acc: 0.7051 - mean_pred: 0.4250 - val_loss: 0.3765 - val_acc: 0.6664 - val_mean_pred: 0.4279\n","Epoch 16/50\n","40973/40973 [==============================] - 12s 283us/step - loss: 0.3473 - acc: 0.7049 - mean_pred: 0.4255 - val_loss: 0.3745 - val_acc: 0.6716 - val_mean_pred: 0.4131\n","Epoch 17/50\n","40973/40973 [==============================] - 12s 281us/step - loss: 0.3467 - acc: 0.7068 - mean_pred: 0.4282 - val_loss: 0.3759 - val_acc: 0.6673 - val_mean_pred: 0.4300\n","Epoch 18/50\n","40973/40973 [==============================] - 12s 282us/step - loss: 0.3451 - acc: 0.7052 - mean_pred: 0.4277 - val_loss: 0.3755 - val_acc: 0.6716 - val_mean_pred: 0.4458\n","Epoch 19/50\n","40973/40973 [==============================] - 12s 282us/step - loss: 0.3442 - acc: 0.7060 - mean_pred: 0.4307 - val_loss: 0.3781 - val_acc: 0.6690 - val_mean_pred: 0.4689\n","Epoch 20/50\n","40973/40973 [==============================] - 11s 281us/step - loss: 0.3432 - acc: 0.7064 - mean_pred: 0.4294 - val_loss: 0.3732 - val_acc: 0.6657 - val_mean_pred: 0.4301\n","Epoch 21/50\n","40973/40973 [==============================] - 12s 281us/step - loss: 0.3426 - acc: 0.7059 - mean_pred: 0.4336 - val_loss: 0.3717 - val_acc: 0.6694 - val_mean_pred: 0.4359\n","Epoch 22/50\n","40973/40973 [==============================] - 12s 281us/step - loss: 0.3411 - acc: 0.7071 - mean_pred: 0.4322 - val_loss: 0.3713 - val_acc: 0.6675 - val_mean_pred: 0.4192\n","Epoch 23/50\n","40973/40973 [==============================] - 12s 283us/step - loss: 0.3399 - acc: 0.7079 - mean_pred: 0.4347 - val_loss: 0.3708 - val_acc: 0.6679 - val_mean_pred: 0.4536\n","Epoch 24/50\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.3389 - acc: 0.7078 - mean_pred: 0.4383 - val_loss: 0.3686 - val_acc: 0.6716 - val_mean_pred: 0.4418\n","Epoch 25/50\n","40973/40973 [==============================] - 12s 281us/step - loss: 0.3376 - acc: 0.7091 - mean_pred: 0.4379 - val_loss: 0.3694 - val_acc: 0.6637 - val_mean_pred: 0.4266\n","Epoch 26/50\n","40973/40973 [==============================] - 12s 281us/step - loss: 0.3363 - acc: 0.7100 - mean_pred: 0.4400 - val_loss: 0.3697 - val_acc: 0.6642 - val_mean_pred: 0.4428\n","Epoch 27/50\n","40973/40973 [==============================] - 12s 281us/step - loss: 0.3355 - acc: 0.7078 - mean_pred: 0.4403 - val_loss: 0.3660 - val_acc: 0.6692 - val_mean_pred: 0.4322\n","Epoch 28/50\n","40973/40973 [==============================] - 12s 282us/step - loss: 0.3342 - acc: 0.7094 - mean_pred: 0.4421 - val_loss: 0.3660 - val_acc: 0.6679 - val_mean_pred: 0.4448\n","Epoch 29/50\n","40973/40973 [==============================] - 12s 281us/step - loss: 0.3336 - acc: 0.7088 - mean_pred: 0.4434 - val_loss: 0.3645 - val_acc: 0.6714 - val_mean_pred: 0.4447\n","Epoch 30/50\n","40973/40973 [==============================] - 12s 282us/step - loss: 0.3324 - acc: 0.7097 - mean_pred: 0.4427 - val_loss: 0.3643 - val_acc: 0.6732 - val_mean_pred: 0.4418\n","Epoch 31/50\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.3312 - acc: 0.7079 - mean_pred: 0.4476 - val_loss: 0.3630 - val_acc: 0.6688 - val_mean_pred: 0.4510\n","Epoch 32/50\n","40973/40973 [==============================] - 12s 281us/step - loss: 0.3305 - acc: 0.7080 - mean_pred: 0.4494 - val_loss: 0.3610 - val_acc: 0.6697 - val_mean_pred: 0.4339\n","Epoch 33/50\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.3294 - acc: 0.7093 - mean_pred: 0.4485 - val_loss: 0.3587 - val_acc: 0.6716 - val_mean_pred: 0.4530\n","Epoch 34/50\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.3283 - acc: 0.7105 - mean_pred: 0.4526 - val_loss: 0.3610 - val_acc: 0.6719 - val_mean_pred: 0.4379\n","Epoch 35/50\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.3277 - acc: 0.7101 - mean_pred: 0.4518 - val_loss: 0.3575 - val_acc: 0.6776 - val_mean_pred: 0.4518\n","Epoch 36/50\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.3264 - acc: 0.7096 - mean_pred: 0.4521 - val_loss: 0.3574 - val_acc: 0.6712 - val_mean_pred: 0.4547\n","Epoch 37/50\n","40973/40973 [==============================] - 12s 281us/step - loss: 0.3256 - acc: 0.7102 - mean_pred: 0.4543 - val_loss: 0.3553 - val_acc: 0.6732 - val_mean_pred: 0.4507\n","Epoch 38/50\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.3255 - acc: 0.7100 - mean_pred: 0.4548 - val_loss: 0.3604 - val_acc: 0.6662 - val_mean_pred: 0.4610\n","Epoch 39/50\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.3241 - acc: 0.7112 - mean_pred: 0.4563 - val_loss: 0.3576 - val_acc: 0.6670 - val_mean_pred: 0.4627\n","Epoch 40/50\n","40973/40973 [==============================] - 11s 281us/step - loss: 0.3228 - acc: 0.7115 - mean_pred: 0.4575 - val_loss: 0.3534 - val_acc: 0.6692 - val_mean_pred: 0.4518\n","Epoch 41/50\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.3226 - acc: 0.7107 - mean_pred: 0.4609 - val_loss: 0.3545 - val_acc: 0.6760 - val_mean_pred: 0.4654\n","Epoch 42/50\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.3222 - acc: 0.7090 - mean_pred: 0.4623 - val_loss: 0.3550 - val_acc: 0.6666 - val_mean_pred: 0.4596\n","Epoch 43/50\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.3205 - acc: 0.7116 - mean_pred: 0.4628 - val_loss: 0.3522 - val_acc: 0.6756 - val_mean_pred: 0.4512\n","Epoch 44/50\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.3196 - acc: 0.7111 - mean_pred: 0.4624 - val_loss: 0.3529 - val_acc: 0.6778 - val_mean_pred: 0.4489\n","Epoch 45/50\n","40973/40973 [==============================] - 11s 279us/step - loss: 0.3199 - acc: 0.7106 - mean_pred: 0.4643 - val_loss: 0.3523 - val_acc: 0.6697 - val_mean_pred: 0.4702\n","Epoch 46/50\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.3182 - acc: 0.7122 - mean_pred: 0.4666 - val_loss: 0.3551 - val_acc: 0.6686 - val_mean_pred: 0.4616\n","Epoch 47/50\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.3170 - acc: 0.7117 - mean_pred: 0.4664 - val_loss: 0.3486 - val_acc: 0.6754 - val_mean_pred: 0.4753\n","Epoch 48/50\n","40973/40973 [==============================] - 12s 283us/step - loss: 0.3169 - acc: 0.7119 - mean_pred: 0.4705 - val_loss: 0.3521 - val_acc: 0.6653 - val_mean_pred: 0.4734\n","Epoch 49/50\n","40973/40973 [==============================] - 11s 280us/step - loss: 0.3155 - acc: 0.7121 - mean_pred: 0.4696 - val_loss: 0.3504 - val_acc: 0.6741 - val_mean_pred: 0.4720\n","Epoch 50/50\n","40973/40973 [==============================] - 12s 282us/step - loss: 0.3149 - acc: 0.7117 - mean_pred: 0.4716 - val_loss: 0.3511 - val_acc: 0.6694 - val_mean_pred: 0.4629\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"MGURXxNMexcs","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":269},"outputId":"77af3f3e-f5fc-4a5c-ac2e-d71b8b9274a6","executionInfo":{"status":"ok","timestamp":1565971933358,"user_tz":240,"elapsed":905,"user":{"displayName":"nupur deshpande","photoUrl":"https://lh4.googleusercontent.com/-AQjf2oEW7kE/AAAAAAAAAAI/AAAAAAAAA1Y/ZFcRZICzWa0/s64/photo.jpg","userId":"04753088851981756999"}}},"source":["nn_model.summary()"],"execution_count":55,"outputs":[{"output_type":"stream","text":["_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","lstm_5 (LSTM)                (None, 32)                4864      \n","_________________________________________________________________\n","dropout_5 (Dropout)          (None, 32)                0         \n","_________________________________________________________________\n","dense_5 (Dense)              (None, 2)                 66        \n","_________________________________________________________________\n","activation_5 (Activation)    (None, 2)                 0         \n","=================================================================\n","Total params: 4,930\n","Trainable params: 4,930\n","Non-trainable params: 0\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"n4wGBS9iexcu","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":50},"outputId":"3686b5f3-428b-460e-9bdd-b6385db2b7b2","executionInfo":{"status":"ok","timestamp":1565971943365,"user_tz":240,"elapsed":7783,"user":{"displayName":"nupur deshpande","photoUrl":"https://lh4.googleusercontent.com/-AQjf2oEW7kE/AAAAAAAAAAI/AAAAAAAAA1Y/ZFcRZICzWa0/s64/photo.jpg","userId":"04753088851981756999"}}},"source":["acc = nn_model.evaluate(X_test, Y_test, batch_size=16)\n","print('Test accuracy:', acc)"],"execution_count":56,"outputs":[{"output_type":"stream","text":["4553/4553 [==============================] - 7s 2ms/step\n","('Test accuracy:', [0.3511478917535363, 0.6694487151525164, 0.46290524976478153])\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"KtZChnbjM7Pe","colab_type":"text"},"source":["**Test Accuracy at 66.94%**"]},{"cell_type":"code","metadata":{"id":"ZjbCSSL-excv","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":295},"outputId":"1f403c24-9d20-49ca-adf8-354473e15ca0","executionInfo":{"status":"ok","timestamp":1565971979520,"user_tz":240,"elapsed":756,"user":{"displayName":"nupur deshpande","photoUrl":"https://lh4.googleusercontent.com/-AQjf2oEW7kE/AAAAAAAAAAI/AAAAAAAAA1Y/ZFcRZICzWa0/s64/photo.jpg","userId":"04753088851981756999"}}},"source":["#Plotting training data loss and validation data loss\n","plt.plot(nn_history.history['acc'], label = 'trained data')\n","plt.plot(nn_history.history['val_acc'], label = 'validated data')\n","plt.legend()\n","plt.xlabel('Epochs')\n","plt.ylabel('Accuracy')\n","plt.title('Measure of accuracy')\n","plt.show()"],"execution_count":57,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAZIAAAEWCAYAAABMoxE0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nyqq6EAvW7eDDNduZt7qUR95bRbIvgcK8NAry0jirdw6FeWkU5qXRP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cqVR8ftGAekQLWr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nEpHZqlo/bsFtqjojXnmvd+HQXPgPFkiMMaYZ8SyRHAusVtW1qhoApgNNP+oZRVVXquoq7/MmYBuQ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\nHvyDayEAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"LIc4QKwnexcx","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":295},"outputId":"13758710-f1cd-40b4-8896-79dab6d9b1cd","executionInfo":{"status":"ok","timestamp":1565971981127,"user_tz":240,"elapsed":593,"user":{"displayName":"nupur deshpande","photoUrl":"https://lh4.googleusercontent.com/-AQjf2oEW7kE/AAAAAAAAAAI/AAAAAAAAA1Y/ZFcRZICzWa0/s64/photo.jpg","userId":"04753088851981756999"}}},"source":["#plateau:\n","plt.plot(nn_history.history['loss'], label = 'training curve')\n","plt.plot(nn_history.history['val_loss'], label = 'testing curve')\n","plt.legend()\n","plt.xlabel('Epochs')\n","plt.ylabel('Training loss')\n","plt.title('Measure of plateau')\n","plt.show()"],"execution_count":58,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYsAAAEWCAYAAACXGLsWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSQnnnnsuo0aNYvjw4bz88ss88sgjZGVlceaZZ3LmmWcCrUtbvnjxYk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krIhvrXsejcu1F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6JscxYBuMfRPiSY1PoLgIEEEgkQQEYIEosLc474xEdYy8aeAr8F9vFmwMKb9Kq6oZsPeIrbsL2bT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\nIiIiaiVYRERErQSLiIiolWARERG1/gYWtr6jFY9AhgAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"EIhOphWUexcz","colab_type":"text"},"source":["**USE GRU NETWORK ARCHITECTURE**"]},{"cell_type":"markdown","metadata":{"id":"PLvZFqtSNmEl","colab_type":"text"},"source":["**Created a GRU network in Keras and then run it for some time. Here I haven't performed much parameter tuning. Trained for 20 epochs on relu, mae and adam.** \n"]},{"cell_type":"code","metadata":{"id":"WW-taS5fexcz","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":706},"outputId":"97e4c55a-c4f6-422c-8c23-ac83a8c869de","executionInfo":{"status":"ok","timestamp":1565972182279,"user_tz":240,"elapsed":199365,"user":{"displayName":"nupur deshpande","photoUrl":"https://lh4.googleusercontent.com/-AQjf2oEW7kE/AAAAAAAAAAI/AAAAAAAAA1Y/ZFcRZICzWa0/s64/photo.jpg","userId":"04753088851981756999"}}},"source":["def mean_pred(X_test, Y_test):\n","    return K.mean(Y_test)\n","\n","def build_model(inputs, output_size, neurons, activ_func=\"relu\",\n","                dropout=0.10, loss=\"mae\", optimizer=\"adam\"):\n","    \n","    model = Sequential()\n","\n","    model.add(GRU(neurons, input_shape=(WINDOW,EMB_SIZE)))\n","    model.add(Dropout(dropout))\n","    model.add(Dense(units=output_size))\n","    model.add(Activation(activ_func))\n","\n","    model.compile(loss=loss, optimizer=optimizer, metrics=['accuracy', mean_pred])\n","    return model\n","\n","nn_model = build_model(X_train, output_size=2, neurons = 32)\n","# model output is next price normalised to 10th previous closing price\n","# train model on data\n","# note: eth_history contains information on the training error per epoch\n","nn_history = nn_model.fit(X_train, Y_train,\n","                            epochs=20, batch_size=256, verbose=1,  validation_data=(X_test, Y_test),shuffle=True)"],"execution_count":59,"outputs":[{"output_type":"stream","text":["Train on 40973 samples, validate on 4553 samples\n","Epoch 1/20\n","40973/40973 [==============================] - 11s 273us/step - loss: 0.4955 - acc: 0.4835 - mean_pred: 0.1472 - val_loss: 0.4840 - val_acc: 0.4968 - val_mean_pred: 0.1995\n","Epoch 2/20\n","40973/40973 [==============================] - 10s 238us/step - loss: 0.4567 - acc: 0.5254 - mean_pred: 0.2413 - val_loss: 0.4425 - val_acc: 0.5678 - val_mean_pred: 0.2791\n","Epoch 3/20\n","40973/40973 [==============================] - 10s 240us/step - loss: 0.3961 - acc: 0.6640 - mean_pred: 0.4123 - val_loss: 0.4014 - val_acc: 0.6503 - val_mean_pred: 0.4085\n","Epoch 4/20\n","40973/40973 [==============================] - 10s 240us/step - loss: 0.3748 - acc: 0.6900 - mean_pred: 0.4206 - val_loss: 0.3931 - val_acc: 0.6565 - val_mean_pred: 0.3948\n","Epoch 5/20\n","40973/40973 [==============================] - 10s 239us/step - loss: 0.3682 - acc: 0.6939 - mean_pred: 0.4198 - val_loss: 0.3895 - val_acc: 0.6618 - val_mean_pred: 0.3986\n","Epoch 6/20\n","40973/40973 [==============================] - 10s 241us/step - loss: 0.3649 - acc: 0.6967 - mean_pred: 0.4194 - val_loss: 0.3871 - val_acc: 0.6611 - val_mean_pred: 0.3965\n","Epoch 7/20\n","40973/40973 [==============================] - 10s 241us/step - loss: 0.3624 - acc: 0.6987 - mean_pred: 0.4217 - val_loss: 0.3847 - val_acc: 0.6662 - val_mean_pred: 0.4177\n","Epoch 8/20\n","40973/40973 [==============================] - 10s 239us/step - loss: 0.3603 - acc: 0.6984 - mean_pred: 0.4205 - val_loss: 0.3848 - val_acc: 0.6644 - val_mean_pred: 0.4161\n","Epoch 9/20\n","40973/40973 [==============================] - 10s 238us/step - loss: 0.3577 - acc: 0.7025 - mean_pred: 0.4224 - val_loss: 0.3841 - val_acc: 0.6644 - val_mean_pred: 0.4176\n","Epoch 10/20\n","40973/40973 [==============================] - 10s 241us/step - loss: 0.3564 - acc: 0.7037 - mean_pred: 0.4231 - val_loss: 0.3823 - val_acc: 0.6692 - val_mean_pred: 0.4058\n","Epoch 11/20\n","40973/40973 [==============================] - 10s 240us/step - loss: 0.3549 - acc: 0.7027 - mean_pred: 0.4226 - val_loss: 0.3819 - val_acc: 0.6637 - val_mean_pred: 0.4337\n","Epoch 12/20\n","40973/40973 [==============================] - 10s 240us/step - loss: 0.3543 - acc: 0.7020 - mean_pred: 0.4240 - val_loss: 0.3830 - val_acc: 0.6642 - val_mean_pred: 0.4379\n","Epoch 13/20\n","40973/40973 [==============================] - 10s 239us/step - loss: 0.3530 - acc: 0.7022 - mean_pred: 0.4282 - val_loss: 0.3796 - val_acc: 0.6705 - val_mean_pred: 0.4311\n","Epoch 14/20\n","40973/40973 [==============================] - 10s 240us/step - loss: 0.3516 - acc: 0.7049 - mean_pred: 0.4264 - val_loss: 0.3797 - val_acc: 0.6708 - val_mean_pred: 0.4255\n","Epoch 15/20\n","40973/40973 [==============================] - 10s 240us/step - loss: 0.3502 - acc: 0.7065 - mean_pred: 0.4315 - val_loss: 0.3794 - val_acc: 0.6626 - val_mean_pred: 0.4357\n","Epoch 16/20\n","40973/40973 [==============================] - 10s 239us/step - loss: 0.3495 - acc: 0.7054 - mean_pred: 0.4311 - val_loss: 0.3804 - val_acc: 0.6657 - val_mean_pred: 0.4371\n","Epoch 17/20\n","40973/40973 [==============================] - 10s 238us/step - loss: 0.3490 - acc: 0.7056 - mean_pred: 0.4307 - val_loss: 0.3781 - val_acc: 0.6686 - val_mean_pred: 0.4404\n","Epoch 18/20\n","40973/40973 [==============================] - 10s 239us/step - loss: 0.3481 - acc: 0.7049 - mean_pred: 0.4319 - val_loss: 0.3771 - val_acc: 0.6690 - val_mean_pred: 0.4177\n","Epoch 19/20\n","40973/40973 [==============================] - 10s 240us/step - loss: 0.3468 - acc: 0.7066 - mean_pred: 0.4323 - val_loss: 0.3762 - val_acc: 0.6692 - val_mean_pred: 0.4254\n","Epoch 20/20\n","40973/40973 [==============================] - 10s 237us/step - loss: 0.3464 - acc: 0.7061 - mean_pred: 0.4361 - val_loss: 0.3760 - val_acc: 0.6681 - val_mean_pred: 0.4441\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"nkBH3ldoexc1","colab_type":"code","colab":{},"outputId":"6512d357-c9c4-4a7d-b0e6-bd583d2d9a84"},"source":["nn_model.summary()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","gru_1 (GRU)                  (None, 32)                3648      \n","_________________________________________________________________\n","dropout_3 (Dropout)          (None, 32)                0         \n","_________________________________________________________________\n","dense_3 (Dense)              (None, 2)                 66        \n","_________________________________________________________________\n","activation_3 (Activation)    (None, 2)                 0         \n","=================================================================\n","Total params: 3,714\n","Trainable params: 3,714\n","Non-trainable params: 0\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"HWJ6nWL2exc3","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":50},"outputId":"7592b54f-49c6-445c-9e47-6db1ca059d1f","executionInfo":{"status":"ok","timestamp":1565972227995,"user_tz":240,"elapsed":6802,"user":{"displayName":"nupur deshpande","photoUrl":"https://lh4.googleusercontent.com/-AQjf2oEW7kE/AAAAAAAAAAI/AAAAAAAAA1Y/ZFcRZICzWa0/s64/photo.jpg","userId":"04753088851981756999"}}},"source":["acc = nn_model.evaluate(X_test, Y_test, batch_size=16)\n","print('Test accuracy:', acc)"],"execution_count":60,"outputs":[{"output_type":"stream","text":["4553/4553 [==============================] - 6s 1ms/step\n","('Test accuracy:', [0.3759548671555053, 0.6681309027080611, 0.444116572992843])\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"bs7cYRpWOAD3","colab_type":"text"},"source":["Accuracy at 66.81%"]},{"cell_type":"code","metadata":{"id":"nsqjOzT4exc4","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":295},"outputId":"70f98b23-a2c9-46a7-834d-0ee0f30374dd","executionInfo":{"status":"ok","timestamp":1565972255839,"user_tz":240,"elapsed":689,"user":{"displayName":"nupur deshpande","photoUrl":"https://lh4.googleusercontent.com/-AQjf2oEW7kE/AAAAAAAAAAI/AAAAAAAAA1Y/ZFcRZICzWa0/s64/photo.jpg","userId":"04753088851981756999"}}},"source":["#Plotting training data loss and validation data loss\n","plt.plot(nn_history.history['acc'], label = 'trained data')\n","plt.plot(nn_history.history['val_acc'], label = 'validated data')\n","plt.legend()\n","plt.xlabel('Epochs')\n","plt.ylabel('Accuracy')\n","plt.title('Measure of accuracy')\n","plt.show()"],"execution_count":61,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYsAAAEWCAYAAACXGLsWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nHGkEnOPup4wcwsJZo5k9NovZYzMpzEmz7iNjTMgsWcS4ptZ2vvXMO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CQZ+BowDejscFfV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size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"0tQ7wviSOGj5","colab_type":"text"},"source":["**A great model for both accuracy and loss. GRU does a good job like LSTM network.**"]},{"cell_type":"code","metadata":{"id":"dZtwP6JBexc6","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":295},"outputId":"9df25e66-180f-402e-98f1-32849e434ae2","executionInfo":{"status":"ok","timestamp":1565972309515,"user_tz":240,"elapsed":692,"user":{"displayName":"nupur deshpande","photoUrl":"https://lh4.googleusercontent.com/-AQjf2oEW7kE/AAAAAAAAAAI/AAAAAAAAA1Y/ZFcRZICzWa0/s64/photo.jpg","userId":"04753088851981756999"}}},"source":["plt.plot(nn_history.history['loss'], label = 'training curve')\n","plt.plot(nn_history.history['val_loss'], label = 'testing curve')\n","plt.legend()\n","plt.xlabel('Epochs')\n","plt.ylabel('Training loss')\n","plt.title('Measure of plateau')\n","plt.show()"],"execution_count":63,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYsAAAEWCAYAAACXGLsWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi40LCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcv7US4rQAAIABJREFUeJzs3Xl8VPW9+P/XO5M9JBMgkEDYJSAh\nLCIg1A1BAmqLtbYVbau1rdiFX9vbatVbv27tbav1WmtdWtzaaq/LbW9v9YoCYhG1oiyyr2GThAAJ\nkH1P3r8/zskwCZlkQjJMlvfz8TiPOedzlnlnSObN5/M55/MRVcUYY4xpTUS4AzDGGNP1WbIwxhjT\nJksWxhhj2mTJwhhjTJssWRhjjGmTJQtjjDFtsmRhTJiIyHdE5KiIlIlI/3aee5+IvBiq2IxpzpKF\nCSsROSAiNSKS0qz8ExFRERkRnshCS0SigEeAbFXto6rHQ/hefxSRn4fq+qZ3sGRhuoL9wPWNGyIy\nAYgPXzinE0dn/r2kArHAtk68pjEhY8nCdAUvADf6bd8E/Nn/ABGJEZGHReRTt+nm9yIS5+7rKyL/\nJyIFInLSXR/id+7XRWSfiJSKyH4R+Ypb3qQpR0RGuLWZSHd7lYj8h4h8AFQAo0TEKyLPiki+iOSJ\nyM9FxNPSD+XG/KiIHHaXR92yMcAu97AiEXmnhXMbY1nknpsvIrcF+gBF5L9F5IiIFIvIahEZ75Yv\nAr4C/MRt7nrdLR8sIn9zP7P9IvJ9v2tNF5EPRaTIfd/HRSS6pc/I73P6VqDYTM9gycJ0BWuAJBEZ\n537xLgSat8f/ChgDTAZGA+nAPe6+COB5YDgwDKgEHgcQkQTgMeAKVU0EPgNsbEdsXwMWAYnAQeCP\nQJ0bw3lANhDoi/KnwAw35knAdOBuVd0NjHePSVbV2a28/2VAhvs+d4jI5QGOe9M9biCwAfgLgKou\ncdcfcpu7PufWkF4HNuF8jnOAH4rIPPda9cC/ASnATHf/d1uJ0fQClixMV9FYu5gL7ADyGneIiOB8\nYf+bqp5Q1VLgFzhJBVU9rqp/U9UKd99/AJf6XbsByBKROFXNV9X2NP38UVW3qWod0A+4Evihqpar\n6jHgN41xtOArwAOqekxVC4D7cZJPe9zvvtcWnIR4fUsHqepzqlqqqtXAfcAkEfEGuOY0YICqPqCq\nNaq6D3iaU5/nelVdo6p1qnoA+ANNP0/TC0W2fYgxZ8ULwGpgJM2aoIABOH0Y6528AYAAHgARicf5\n0p4P9HX3J4qIR1XLReQ64DbgWbdJ6cequjPIuA75rQ8HooB8vzgimh3jbzBObaTRQbesPfyvfRCY\n0PwAtzb2H8CXcD6rBndXClDcwjWHA4NFpMivzAO8515vDE7n+1Sczz0SWN/OuE0PYzUL0yWo6kGc\nju4rgf9ptrsQp2lpvKomu4tXVfu4+38MjAUuUNUk4BK3XNxrL1PVucAgYCfO/6IBymnakZ7WUmh+\n64eAaiDFL44kVR3fwnkAh3G+mBsNc8vaY2gQ598AXA1cDniBEW55Y0ZrPrT0IWC/38+QrKqJqnql\nu/8pnM8pw/08/93vWuXua1ufm+lhLFmYruSbwGxVLfcvVNUGnC/434jIQAARSfdrY0/ESSZFItIP\nuLfxXBFJFZGr3b6LaqCMU//z3ghcIiLD3Cabu1oLTlXzgeXAf4pIkohEiMg5IhKoieYl4G4RGeDe\nGnwPp/fFtOX/iUi822F9M/BKC8ckuj/bcZwv8V80238UGOW3/TFQKiJ3iEiciHhEJEtEpvldrwQo\nE5Fzge80nug2p+UBX3XP+wZwTjt/JtMNWbIwXYaq7lXVdQF23wHkAGtEpAR4G6c2AfAoEIdTA1kD\nvOV3XgTwI5z/kZ/AaXv/jvt+K3C+fDfjNLP8XxBh3ghEA9uBk8BfcWosLfk5sM69/hacjuf2Pu/w\nLs7PvRJ4WFWXt3DMn3GaqPLcuNY02/8skOne3fS/qloPfBan430/zuf2DE6tBJwmuxuAUpwk3TxB\n3QLcjpOcxgP/aufPZLohscmPjOl63IcR9wNRbue6MWFlNQtjjDFtsmRhjDGmTdYMZYwxpk1WszDG\nGNOmHvNQXkpKio4YMSLcYRhjTLeyfv36QlUd0NZxIU0WIjIf+C3O06HPqOqvmu3/OvBrTg3t8Liq\nPuPuuwm42y3/uar+qbX3GjFiBOvWBbrr0hhjTEtE5GDbR4UwWbhDEDyBM9ZPLrBWRF5T1e3NDn1F\nVRc3O7fxwaqpOE+frnfPPRmqeI0xxgQWyj6L6UCOqu5T1RrgZZwhCYIxD1jhDhp3EliBM+6PMcaY\nMAhlskin6SBouW5Zc9eKyGYR+auINI6DE9S57lj/60RkXUFBQWfFbYwxpplwd3C/DrykqtUicivw\nJ6C1sf2bcMfqXwIwdepUuwfYmDCpra0lNzeXqqqqcIdiAoiNjWXIkCFERUWd0fmhTBZ5NB0xcwh+\ncxSAMw+B3+YzwEN+585qdu6qTo/QGNMpcnNzSUxMZMSIEfgN3266CFXl+PHj5ObmMnLkyDO6Riib\nodYCGSIy0p2ScSHwmv8BIuI/ANsCnElvAJYB2eJMl9kXZ5awZSGM1RjTAVVVVfTv398SRRclIvTv\n379DNb+Q1SxUtU5EFuN8yXuA51R1m4g8AKxT1deA74vIApxpKk8AX3fPPSEiP8NJOODMNnYiVLEa\nYzrOEkXX1tF/n5D2WajqUmBps7J7/NbvIsAcAqr6HPBcKOMDyCuq5IUPD3LzhSNITYoN9dsZY0y3\n1OuH+6ioruP37+5lxfaj4Q7FGHOGioqKePLJJ8/o3CuvvJKioqJWj7nnnnt4++23z+j6PUWvTxaj\nB/ZhZEoCyy1ZGNNttZYs6upanw5k6dKlJCcnt3rMAw88wOWXX37G8XUGVaWhoaHtA0Ok1ycLESE7\nM5UP9xZSUlUb7nCMMWfgzjvvZO/evUyePJnbb7+dVatWcfHFF7NgwQIyMzMB+PznP8/555/P+PHj\nWbJkie/cESNGUFhYyIEDBxg3bhy33HIL48ePJzs7m8rKSgC+/vWv89e//tV3/L333suUKVOYMGEC\nO3fuBKCgoIC5c+cyfvx4vvWtbzF8+HAKCwtPi/Wtt95iypQpTJo0iTlz5gBw33338fDDD/uOycrK\n4sCBAxw4cICxY8dy4403kpWVxc9+9jNuv/1233F//OMfWbzYGQDjxRdfZPr06UyePJlbb72V+vr6\nzvyIw/6cRZeQPT6VP6zex6pdBSyYNDjc4RjTrd3/+ja2Hy7p1GtmDk7i3s+ND7j/V7/6FVu3bmXj\nxo0ArFq1ig0bNrB161bfraLPPfcc/fr1o7KykmnTpnHttdfSv3//JtfZs2cPL730Ek8//TRf/vKX\n+dvf/sZXv/rV094vJSWFDRs28OSTT/Lwww/zzDPPcP/99zN79mzuuusu3nrrLZ599tnTzisoKOCW\nW25h9erVjBw5khMn2r5vZ8+ePfzpT39ixowZFBQUMHPmTH79618D8Morr/DTn/6UHTt28Morr/DB\nBx8QFRXFd7/7Xf7yl79w4403tnn9YPX6mgXA5KF9SekTw/JtR8IdijGmk0yfPr3JMwWPPfYYkyZN\nYsaMGRw6dIg9e/acds7IkSOZPHkyAOeffz4HDhxo8dpf+MIXTjvm/fffZ+HChQDMnz+fvn37nnbe\nmjVruOSSS3xx9evXr82fY/jw4cyYMQOAAQMGMGrUKNasWcPx48fZuXMnF154IStXrmT9+vVMmzaN\nyZMns3LlSvbt29fmtdvDahaAJ0KYmzmQ1zflU11XT0ykJ9whGdNttVYDOJsSEhJ866tWreLtt9/m\nww8/JD4+nlmzZrX4zEFMTIxv3ePx+JqhAh3n8Xja7BMJRmRkZJP+CP/Y/H8OgIULF/Lqq69y7rnn\ncs011yAiqCo33XQTv/zlLzscSyBWs3BlZ6ZRVl3Hmn32OIcx3U1iYiKlpaUB9xcXF9O3b1/i4+PZ\nuXMna9as6fQYLrzwQl599VUAli9fzsmTpw+SPWPGDFavXs3+/fsBfM1QI0aMYMOGDQBs2LDBt78l\n11xzDf/4xz946aWXfDWZOXPm8Ne//pVjx475rnvwYFAjjwfNkoVr5jn9iY/2WFOUMd1Q//79ufDC\nC8nKymrSAdxo/vz51NXVMW7cOO68805fs05nuvfee1m+fDlZWVn893//N2lpaSQmJjY5ZsCAASxZ\nsoQvfOELTJo0ieuuuw6Aa6+9lhMnTjB+/Hgef/xxxowZE/B9+vbty7hx4zh48CDTp08HIDMzk5//\n/OdkZ2czceJE5s6dS35+fqf+fD1mDu6pU6dqRyc/+u5f1rPuwEnW3DWHiAh7GtWYYO3YsYNx48aF\nO4ywqq6uxuPxEBkZyYcffsh3vvMdX4d7V9HSv5OIrFfVqW2da30WfrIz01i65Qibcos4b9jpnVPG\nGBPIp59+ype//GUaGhqIjo7m6aefDndIncqShZ/Lxg4kMkJYvv2oJQtjTLtkZGTwySefhDuMkLE+\nCz/e+ChmjOpv/RbGGNOMJYtmssensregnL0FZeEOxRhjugxLFs1cPi4VwAYWNMYYP5Ysqoph0ytQ\n4dzvPDg5jolDvNYUZYwxfixZFO6Bvy+CnJW+ornjUvnkUBHHSmw+YWO6g44MUQ7w6KOPUlFR4dsO\nZtjy3saSxeDzIL4/7FnuK8oen4YqvL3jWBgDM8YEq7OTRTDDlodaZw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WXAkubld0T4NhZ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hKKKWooqagLWchrvHktzxwpLS4ol1eu8OusxpCTEdNsRji1ZGGN6lYgIcZ+Ob//XX0ODUlpVxwlf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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"h-O2_vEtOUkq","colab_type":"text"},"source":["#**CONCLUSIONS:**\n","\n","**I found that LSTM and GRU can predict Closing prices of Amazon Data on an average of 65% of the time(best model at 75% worst at 58%) if you normalize the data, stack input data and perform hyperparameter tuning.** \n"]}]}