resnet.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"resnet.ipynb","provenance":[{"file_id":"1ys6h7-GidGM-N38mcrnDOVn_P2gQGetu","timestamp":1577884924785}],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"code","metadata":{"id":"6dpu5TVGe7lW","colab_type":"code","outputId":"5d0e87ed-8488-4ed9-8585-e20c51a9834f","executionInfo":{"status":"ok","timestamp":1577884995886,"user_tz":-330,"elapsed":28514,"user":{"displayName":"Debajit 15","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCBYYIDdOa6I16b95zw4DFvlaEeX9mwdHLJ3mSdCg=s64","userId":"03166908186037683196"}},"colab":{"base_uri":"https://localhost:8080/","height":124}},"source":["from google.colab import drive\n","drive.mount('/content/gdrive')"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n","\n","Enter your authorization code:\n","··········\n","Mounted at /content/gdrive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"2l8zgkIIRs5U","colab_type":"code","outputId":"31194106-9aac-41fd-b6fa-398be7169032","executionInfo":{"status":"ok","timestamp":1577885850278,"user_tz":-330,"elapsed":3033,"user":{"displayName":"Debajit 15","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCBYYIDdOa6I16b95zw4DFvlaEeX9mwdHLJ3mSdCg=s64","userId":"03166908186037683196"}},"colab":{"base_uri":"https://localhost:8080/","height":81}},"source":["import pandas as pd\n","import numpy as np \n","import matplotlib.pyplot as plt\n","import os\n","from urllib.request import urlopen,urlretrieve\n","from PIL import Image\n","from tqdm import tqdm_notebook\n","%matplotlib inline\n","from sklearn.utils import shuffle\n","import cv2\n","from resnets_utils import *\n","\n","from keras.models import load_model\n","from sklearn.datasets import load_files   \n","from keras.utils import np_utils\n","from glob import glob\n","from keras import applications\n","from keras.preprocessing.image import ImageDataGenerator \n","from keras import optimizers\n","from keras.models import Sequential,Model,load_model\n","from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D,GlobalAveragePooling2D\n","from keras.callbacks import TensorBoard,ReduceLROnPlateau,ModelCheckpoint\n"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"text/html":["<p style=\"color: red;\">\n","The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n","We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n","or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n","<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["Using TensorFlow backend.\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"sGvtglT7fHoC","colab_type":"code","outputId":"1285e68b-3f4d-40f2-f3b6-09975ba8dc66","executionInfo":{"status":"ok","timestamp":1577885863429,"user_tz":-330,"elapsed":5139,"user":{"displayName":"Debajit 15","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCBYYIDdOa6I16b95zw4DFvlaEeX9mwdHLJ3mSdCg=s64","userId":"03166908186037683196"}},"colab":{"base_uri":"https://localhost:8080/","height":717}},"source":["from numpy import array\n","import pandas as pd\n","import numpy as np\n","from keras.models import Sequential\n","from keras.layers import Dense\n","from keras.layers import Flatten\n","from keras.layers.convolutional import Conv1D\n","from keras.layers.convolutional import MaxPooling1D\n","from keras.utils import to_categorical\n"," \n","\n","df = pd.read_csv(\"/content/gdrive/My Drive/ampds/data/data.csv\")\n","from sklearn.model_selection import train_test_split\n","import tensorflow as tf\n","\n","X=df.iloc[:524540,1:2]\n","y=df.iloc[:524540,3:4]\n","print(y)\n","l=[]\n","for i in range(0,524540):\n","  chk=i%86400\n","  if(chk<21600):\n","    l.append(0)\n","  elif (21600<=chk<43200):\n","    l.append(1)\n","  elif (43200<=chk<64800):\n","    l.append(2)\n","  else:\n","    l.append(3)\n","encoded = to_categorical(l)\n","dataset = pd.DataFrame({'N': encoded[:, 0], 'M': encoded[:, 1], 'A': encoded[:, 2], 'E': encoded[:, 3]})\n","print(dataset.head())\n","\n","df = pd.concat([X, dataset], axis=1)\n","print(df.head())\n","import matplotlib.pyplot as plt\n","plt.plot(df)\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["        B2E\n","0       0.7\n","1       0.9\n","2       0.9\n","3       0.9\n","4       0.8\n","...     ...\n","524535  0.1\n","524536  0.1\n","524537  0.1\n","524538  0.1\n","524539  0.1\n","\n","[524540 rows x 1 columns]\n","     N    M    A    E\n","0  1.0  0.0  0.0  0.0\n","1  1.0  0.0  0.0  0.0\n","2  1.0  0.0  0.0  0.0\n","3  1.0  0.0  0.0  0.0\n","4  1.0  0.0  0.0  0.0\n","    WHE    N    M    A    E\n","0  60.7  1.0  0.0  0.0  0.0\n","1  60.7  1.0  0.0  0.0  0.0\n","2  60.6  1.0  0.0  0.0  0.0\n","3  44.7  1.0  0.0  0.0  0.0\n","4  44.7  1.0  0.0  0.0  0.0\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nY4z5EAd3xhjzIQ7ujDHmQ/8f6xOR38BVN9UAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"Xmyp9nUqB1Z0","colab_type":"code","colab":{}},"source":["import numpy\n","import matplotlib.pyplot as plt\n","import pandas\n","import math\n","from keras.models import Sequential\n","from keras.layers import Dense\n","from keras.layers import LSTM\n","from sklearn.preprocessing import MinMaxScaler\n","from sklearn.metrics import mean_squared_error"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Nj_yxjIEBaGu","colab_type":"code","colab":{}},"source":["dataset = df.values\n","dataset = df.astype('float32')"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"BC9WzVmABuPG","colab_type":"code","outputId":"a5349409-e7ee-45b2-8345-9525c320bd7a","executionInfo":{"status":"ok","timestamp":1577887902918,"user_tz":-330,"elapsed":2005,"user":{"displayName":"Debajit 15","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCBYYIDdOa6I16b95zw4DFvlaEeX9mwdHLJ3mSdCg=s64","userId":"03166908186037683196"}},"colab":{"base_uri":"https://localhost:8080/","height":139}},"source":["scaler = MinMaxScaler(feature_range=(0, 1))\n","dataset = scaler.fit_transform(dataset)\n","print (dataset)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["[[0.58253361 1.         0.         0.         0.        ]\n"," [0.58253361 1.         0.         0.         0.        ]\n"," [0.5815739  1.         0.         0.         0.        ]\n"," ...\n"," [0.30998081 1.         0.         0.         0.        ]\n"," [0.31381959 1.         0.         0.         0.        ]\n"," [0.31285988 1.         0.         0.         0.        ]]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"F4EIKcn-CGt5","colab_type":"code","colab":{}},"source":["datay = y.values\n","datay = y.astype('float32')"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"OLitKTyECK-S","colab_type":"code","outputId":"b27169ba-1e57-4d35-ab7d-76afdb14526f","executionInfo":{"status":"ok","timestamp":1577887911082,"user_tz":-330,"elapsed":2483,"user":{"displayName":"Debajit 15","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCBYYIDdOa6I16b95zw4DFvlaEeX9mwdHLJ3mSdCg=s64","userId":"03166908186037683196"}},"colab":{"base_uri":"https://localhost:8080/","height":156}},"source":["scaler = MinMaxScaler(feature_range=(0, 1))\n","datay = scaler.fit_transform(datay)\n","print (datay)\n","print(datay.shape)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["[[0.08235294]\n"," [0.10588235]\n"," [0.10588235]\n"," ...\n"," [0.01176471]\n"," [0.01176471]\n"," [0.01176471]]\n","(524540, 1)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Fl8wXBb2Dq7P","colab_type":"code","outputId":"7fd8722d-6a8d-4a4d-c08f-f864ce400da3","executionInfo":{"status":"ok","timestamp":1577888265225,"user_tz":-330,"elapsed":1162,"user":{"displayName":"Debajit 15","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCBYYIDdOa6I16b95zw4DFvlaEeX9mwdHLJ3mSdCg=s64","userId":"03166908186037683196"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["dataset = dataset.reshape((dataset.shape[0], dataset.shape[1], 1,1))\n","print(dataset.shape)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["(524540, 5, 1, 1)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"-aW8Q7FdFnr5","colab_type":"code","outputId":"94e55acf-737b-419f-9ccd-686dc717cb0d","executionInfo":{"status":"ok","timestamp":1577888267574,"user_tz":-330,"elapsed":1248,"user":{"displayName":"Debajit 15","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCBYYIDdOa6I16b95zw4DFvlaEeX9mwdHLJ3mSdCg=s64","userId":"03166908186037683196"}},"colab":{"base_uri":"https://localhost:8080/","height":52}},"source":["from sklearn.model_selection import train_test_split\n","X_train, X_test, y_train, y_test = train_test_split(dataset, datay, test_size=0.10)\n","print(X_train.shape)\n","print(y_train.shape)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["(472086, 5, 1, 1)\n","(472086, 1)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"rIU2Tx6wE1p1","colab_type":"code","colab":{}},"source":["import tensorflow.compat.v1 as tf\n","tf.disable_v2_behavior()\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"g8zJf1ZjdI1x","colab_type":"code","outputId":"5f3b138e-3911-4b57-ef29-6a0559799f88","executionInfo":{"status":"error","timestamp":1577888297539,"user_tz":-330,"elapsed":1216,"user":{"displayName":"Debajit 15","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCBYYIDdOa6I16b95zw4DFvlaEeX9mwdHLJ3mSdCg=s64","userId":"03166908186037683196"}},"colab":{"base_uri":"https://localhost:8080/","height":402}},"source":["base_model = applications.resnet50.ResNet50(weights= None, include_top=False, input_shape= (5,1,1))\n","x = base_model.output\n","x = GlobalAveragePooling2D()(x)\n","x = Dropout(0.7)(x)\n","predictions = Dense(num_classes, activation= 'softmax')(x)\n","model = Model(inputs = base_model.input, outputs = predictions)\n","\n","\n","from keras.optimizers import SGD, Adam\n","# sgd = SGD(lr=lrate, momentum=0.9, decay=decay, nesterov=False)\n","adam = Adam(lr=0.0001)\n","model.compile(optimizer= adam, loss='categorical_crossentropy', metrics=['accuracy'])\n","\n"],"execution_count":0,"outputs":[{"output_type":"error","ename":"ValueError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","\u001b[0;32m<ipython-input-18-b2efdb1c5480>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mbase_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mapplications\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresnet50\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mResNet50\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweights\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minclude_top\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_shape\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbase_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGlobalAveragePooling2D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDropout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.7\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDense\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_classes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mactivation\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;34m'softmax'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/applications/__init__.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     18\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'models'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'utils'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mbase_fun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/applications/resnet50.py\u001b[0m in \u001b[0;36mResNet50\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mkeras_modules_injection\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mResNet50\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mresnet50\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mResNet50\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras_applications/resnet50.py\u001b[0m in \u001b[0;36mResNet50\u001b[0;34m(include_top, weights, input_tensor, input_shape, pooling, classes, **kwargs)\u001b[0m\n\u001b[1;32m    209\u001b[0m                                       \u001b[0mdata_format\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbackend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimage_data_format\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m                                       \u001b[0mrequire_flatten\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minclude_top\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m                                       weights=weights)\n\u001b[0m\u001b[1;32m    212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0minput_tensor\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras_applications/imagenet_utils.py\u001b[0m in \u001b[0;36m_obtain_input_shape\u001b[0;34m(input_shape, default_size, min_size, data_format, require_flatten, weights)\u001b[0m\n\u001b[1;32m    320\u001b[0m                                      \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmin_size\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'x'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmin_size\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    321\u001b[0m                                      \u001b[0;34m'; got `input_shape='\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 322\u001b[0;31m                                      str(input_shape) + '`')\n\u001b[0m\u001b[1;32m    323\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    324\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrequire_flatten\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: Input size must be at least 32x32; got `input_shape=(5, 1, 1)`"]}]},{"cell_type":"code","metadata":{"id":"hVY1CWj4jP6H","colab_type":"code","colab":{}},"source":["\n","model = Sequential()\n","model.add(Conv1D(filters=64, kernel_size=2, activation='relu', input_shape=(5, 1)))\n","model.add(MaxPooling1D(pool_size=2))\n","model.add(Flatten())\n","model.add(Dense(50, activation='relu'))\n","model.add(Dense(1))\n","model.compile(optimizer='adam', loss='mse')"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"49quOsr_D-3_","colab_type":"code","outputId":"b43aadc2-890d-44c1-cbb7-bebbac60d251","executionInfo":{"status":"ok","timestamp":1577733930040,"user_tz":-330,"elapsed":561409,"user":{"displayName":"Debajit 15","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCBYYIDdOa6I16b95zw4DFvlaEeX9mwdHLJ3mSdCg=s64","userId":"03166908186037683196"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["history=model.fit(X_train, y_train, epochs=50, batch_size=72, validation_data=(X_test, y_test), verbose=2, shuffle=False)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Train on 472086 samples, validate on 52454 samples\n","Epoch 1/50\n"," - 11s - loss: 8.3360e-04 - val_loss: 8.4274e-04\n","Epoch 2/50\n"," - 11s - loss: 7.8557e-04 - val_loss: 7.8650e-04\n","Epoch 3/50\n"," - 11s - loss: 7.7367e-04 - val_loss: 7.8145e-04\n","Epoch 4/50\n"," - 11s - loss: 7.7035e-04 - val_loss: 7.7795e-04\n","Epoch 5/50\n"," - 11s - loss: 7.6866e-04 - val_loss: 7.8022e-04\n","Epoch 6/50\n"," - 11s - loss: 7.6718e-04 - val_loss: 7.8016e-04\n","Epoch 7/50\n"," - 11s - loss: 7.6526e-04 - val_loss: 7.7846e-04\n","Epoch 8/50\n"," - 11s - loss: 7.6304e-04 - val_loss: 7.7769e-04\n","Epoch 9/50\n"," - 11s - loss: 7.6233e-04 - val_loss: 7.7940e-04\n","Epoch 10/50\n"," - 11s - loss: 7.6167e-04 - val_loss: 7.8162e-04\n","Epoch 11/50\n"," - 11s - loss: 7.6155e-04 - val_loss: 7.7936e-04\n","Epoch 12/50\n"," - 11s - loss: 7.6065e-04 - val_loss: 7.8198e-04\n","Epoch 13/50\n"," - 11s - loss: 7.6000e-04 - val_loss: 7.8168e-04\n","Epoch 14/50\n"," - 11s - loss: 7.5946e-04 - val_loss: 7.8187e-04\n","Epoch 15/50\n"," - 11s - loss: 7.5864e-04 - val_loss: 7.7914e-04\n","Epoch 16/50\n"," - 11s - loss: 7.5796e-04 - val_loss: 7.7868e-04\n","Epoch 17/50\n"," - 11s - loss: 7.5750e-04 - val_loss: 7.8065e-04\n","Epoch 18/50\n"," - 11s - loss: 7.5712e-04 - val_loss: 7.7906e-04\n","Epoch 19/50\n"," - 11s - loss: 7.5690e-04 - val_loss: 7.7961e-04\n","Epoch 20/50\n"," - 11s - loss: 7.5652e-04 - val_loss: 7.7750e-04\n","Epoch 21/50\n"," - 11s - loss: 7.5618e-04 - val_loss: 7.7956e-04\n","Epoch 22/50\n"," - 11s - loss: 7.5590e-04 - val_loss: 7.7866e-04\n","Epoch 23/50\n"," - 11s - loss: 7.5569e-04 - val_loss: 7.7796e-04\n","Epoch 24/50\n"," - 11s - loss: 7.5552e-04 - val_loss: 7.7748e-04\n","Epoch 25/50\n"," - 12s - loss: 7.5531e-04 - val_loss: 7.7740e-04\n","Epoch 26/50\n"," - 11s - loss: 7.5506e-04 - val_loss: 7.7978e-04\n","Epoch 27/50\n"," - 11s - loss: 7.5489e-04 - val_loss: 7.7941e-04\n","Epoch 28/50\n"," - 11s - loss: 7.5474e-04 - val_loss: 7.7810e-04\n","Epoch 29/50\n"," - 11s - loss: 7.5451e-04 - val_loss: 7.7833e-04\n","Epoch 30/50\n"," - 11s - loss: 7.5435e-04 - val_loss: 7.7907e-04\n","Epoch 31/50\n"," - 11s - loss: 7.5426e-04 - val_loss: 7.7818e-04\n","Epoch 32/50\n"," - 11s - loss: 7.5410e-04 - val_loss: 7.7783e-04\n","Epoch 33/50\n"," - 11s - loss: 7.5396e-04 - val_loss: 7.7689e-04\n","Epoch 34/50\n"," - 11s - loss: 7.5380e-04 - val_loss: 7.7790e-04\n","Epoch 35/50\n"," - 11s - loss: 7.5361e-04 - val_loss: 7.7813e-04\n","Epoch 36/50\n"," - 11s - loss: 7.5360e-04 - val_loss: 7.7792e-04\n","Epoch 37/50\n"," - 11s - loss: 7.5337e-04 - val_loss: 7.7829e-04\n","Epoch 38/50\n"," - 11s - loss: 7.5337e-04 - val_loss: 7.7811e-04\n","Epoch 39/50\n"," - 11s - loss: 7.5322e-04 - val_loss: 7.7791e-04\n","Epoch 40/50\n"," - 11s - loss: 7.5287e-04 - val_loss: 7.7805e-04\n","Epoch 41/50\n"," - 11s - loss: 7.5206e-04 - val_loss: 7.8019e-04\n","Epoch 42/50\n"," - 11s - loss: 7.5137e-04 - val_loss: 7.7589e-04\n","Epoch 43/50\n"," - 11s - loss: 7.5092e-04 - val_loss: 7.7535e-04\n","Epoch 44/50\n"," - 11s - loss: 7.5073e-04 - val_loss: 7.7549e-04\n","Epoch 45/50\n"," - 11s - loss: 7.5045e-04 - val_loss: 7.7593e-04\n","Epoch 46/50\n"," - 12s - loss: 7.5039e-04 - val_loss: 7.7575e-04\n","Epoch 47/50\n"," - 11s - loss: 7.5031e-04 - val_loss: 7.7731e-04\n","Epoch 48/50\n"," - 11s - loss: 7.5029e-04 - val_loss: 7.7634e-04\n","Epoch 49/50\n"," - 11s - loss: 7.5019e-04 - val_loss: 7.7722e-04\n","Epoch 50/50\n"," - 11s - loss: 7.5018e-04 - val_loss: 7.7647e-04\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"JPTfT9MnEH7v","colab_type":"code","outputId":"91fbd217-c115-4f48-bd54-d1830fec9987","executionInfo":{"status":"ok","timestamp":1577735899177,"user_tz":-330,"elapsed":1355,"user":{"displayName":"Debajit 15","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCBYYIDdOa6I16b95zw4DFvlaEeX9mwdHLJ3mSdCg=s64","userId":"03166908186037683196"}},"colab":{"base_uri":"https://localhost:8080/","height":265}},"source":["from matplotlib import pyplot\n","pyplot.plot(history.history['loss'], label='train')\n","pyplot.plot(history.history['val_loss'], label='test')\n","pyplot.legend()\n","pyplot.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAY0AAAD4CAYAAAAQP7oXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhYaIiKTs/wPl9iiEw1IQFAAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"YSkL1X87PAHE","colab_type":"code","outputId":"b395ae4b-0fe0-49bd-8c40-fedc488bd333","executionInfo":{"status":"ok","timestamp":1577737423942,"user_tz":-330,"elapsed":1808,"user":{"displayName":"Debajit 15","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCBYYIDdOa6I16b95zw4DFvlaEeX9mwdHLJ3mSdCg=s64","userId":"03166908186037683196"}},"colab":{"base_uri":"https://localhost:8080/","height":156}},"source":["from math import sqrt\n","from numpy import concatenate\n","\n","yhat = model.predict(X_test)\n","print(yhat)\n","X_test = X_test.reshape((X_test.shape[0], X_test.shape[1]))\n","# invert scaling for forecast\n","# inv_yhat = concatenate((yhat, test[:, 1:]), axis=1)\n","# inv=inv_yhat\n","# inv_yhat = scaler.inverse_transform(inv_yhat)\n","# inv_yhat = inv_yhat[:,0]\n","# # invert scaling for actual\n","# testy = testy.reshape((len(testy), 1))\n","# inv_y = concatenate((testy, test[:, 1:]), axis=1)\n","rmse = sqrt(mean_squared_error(y_test, yhat))\n","print('Test RMSE: %.3f' % rmse)\n","# inv_y = scaler.inverse_transform(inv_y)\n","# inv_y = inv_y[:,0]\n","# # calculate RMSE\n","# rmse = sqrt(mean_squared_error(inv_y, inv_yhat))\n","# print('Test RMSE: %.3f' % rmse)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["[[0.03332122]\n"," [0.02838878]\n"," [0.06045851]\n"," ...\n"," [0.03947836]\n"," [0.02772163]\n"," [0.03233481]]\n","Test RMSE: 0.028\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"36v2NwORedy5","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]}]}