test_autoencoder.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "REFS: https://www.dataquest.io/blog/jupyter-notebook-tips-tricks-shortcuts/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "from __future__ import division\n",
    "from __future__ import absolute_import\n",
    "\n",
    "import argparse\n",
    "import json\n",
    "import keras\n",
    "import numpy as np\n",
    "import os\n",
    "import random\n",
    "import time\n",
    "\n",
    "import robust_autoencoder\n",
    "\n",
    "def run_autoencoder(params, STORE=False):\n",
    "    encoder = robust_autoencoder.RobustAutoencoder()\n",
    "    encoder.train_scaled = encoder.load_dataset(params['data_json'])#train[0]: ecg_data train[1]: labels \n",
    "    encoder.dev_scaled = encoder.load_dataset(params['dev_json'])\n",
    "    \n",
    "    # scale the data\n",
    "    encoder.train_scaled, _, _ = encoder.scale_input(encoder.train_scaled[0])\n",
    "    encoder.dev_scaled, _, _ = encoder.scale_input(encoder.dev_scaled[0])\n",
    "    \n",
    "    # add noisy\n",
    "    encoder.train_noisy = encoder.add_noise_snr(encoder.train_scaled, des_snr=params['des_snr'], noise_type=params['noisy_type'])\n",
    "    encoder.dev_noisy = encoder.add_noise_snr(encoder.dev_scaled, des_snr=params['des_snr'], noise_type=params['noisy_type'])\n",
    "    \n",
    "    # build autoencoder\n",
    "    encoder.build_encoder(params)\n",
    "    \n",
    "    # train the autoencoder\n",
    "    encoder.train_encoder(params)\n",
    "    \n",
    "    # reconstruct the noisy signals\n",
    "    encoder.reconstruct(params)\n",
    "    \n",
    "    # store the reconstructed/noisy signals\n",
    "    if STORE:\n",
    "        encoder.store_results(params)\n",
    "    \n",
    "    reset_keras(encoder.model)\n",
    "    del encoder.history\n",
    "    gc.collect()\n",
    "    \n",
    "    \n",
    "from keras.backend.tensorflow_backend import set_session\n",
    "from keras.backend.tensorflow_backend import clear_session\n",
    "from keras.backend.tensorflow_backend import get_session\n",
    "import tensorflow\n",
    "import gc\n",
    "# Reset Keras Session\n",
    "def reset_keras(model=None):\n",
    "    sess = get_session()\n",
    "    clear_session()\n",
    "    sess.close()\n",
    "    sess = get_session()\n",
    "\n",
    "    try:\n",
    "        del model # this is from global space - change this as you need\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "    print(gc.collect()) # if it's done something you should see a number being outputted\n",
    "\n",
    "    # use the same config as you used to create the session\n",
    "    config = tensorflow.ConfigProto()\n",
    "    config.gpu_options.per_process_gpu_memory_fraction = 1\n",
    "    config.gpu_options.visible_device_list = \"0\"\n",
    "    set_session(tensorflow.Session(config=config))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  5%|▌         | 400/7676 [00:00<00:01, 3988.00it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7676/7676 [00:01<00:00, 3962.04it/s]\n",
      "100%|██████████| 852/852 [00:00<00:00, 3842.03it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 290368 samples, validate on 32315 samples\n",
      "Epoch 1/80\n",
      "290368/290368 [==============================] - 7s 24us/step - loss: -1.7214 - val_loss: -1.8260\n",
      "Epoch 2/80\n",
      "290368/290368 [==============================] - 6s 22us/step - loss: -1.8350 - val_loss: -1.8429\n",
      "Epoch 3/80\n",
      "290368/290368 [==============================] - 6s 22us/step - loss: -1.8474 - val_loss: -1.8521\n",
      "Epoch 4/80\n",
      "290368/290368 [==============================] - 7s 23us/step - loss: -1.8548 - val_loss: -1.8561\n",
      "Epoch 5/80\n",
      "290368/290368 [==============================] - 6s 22us/step - loss: -1.8602 - val_loss: -1.8625\n",
      "Epoch 6/80\n",
      "290368/290368 [==============================] - 6s 22us/step - loss: -1.8641 - val_loss: -1.8659\n",
      "Epoch 7/80\n",
      "290368/290368 [==============================] - 6s 22us/step - loss: -1.8667 - val_loss: -1.8669\n",
      "Epoch 8/80\n",
      "290368/290368 [==============================] - 6s 22us/step - loss: -1.8686 - val_loss: -1.8637\n",
      "Epoch 9/80\n",
      "290368/290368 [==============================] - 7s 22us/step - loss: -1.8699 - val_loss: -1.8706\n",
      "Epoch 10/80\n",
      "290368/290368 [==============================] - 7s 24us/step - loss: -1.8710 - val_loss: -1.8710\n",
      "Epoch 11/80\n",
      "290368/290368 [==============================] - 6s 22us/step - loss: -1.8718 - val_loss: -1.8707\n",
      "Epoch 12/80\n",
      "290368/290368 [==============================] - 6s 22us/step - loss: -1.8725 - val_loss: -1.8688\n",
      "Epoch 13/80\n",
      "290368/290368 [==============================] - 6s 22us/step - loss: -1.8746 - val_loss: -1.8741\n",
      "Epoch 14/80\n",
      "290368/290368 [==============================] - 6s 22us/step - loss: -1.8747 - val_loss: -1.8742\n",
      "Epoch 15/80\n",
      "290368/290368 [==============================] - 6s 22us/step - loss: -1.8748 - val_loss: -1.8744\n",
      "Epoch 16/80\n",
      "290368/290368 [==============================] - 6s 22us/step - loss: -1.8749 - val_loss: -1.8744\n",
      "Epoch 17/80\n",
      "290368/290368 [==============================] - 6s 22us/step - loss: -1.8750 - val_loss: -1.8745\n",
      "Epoch 18/80\n",
      "290368/290368 [==============================] - 6s 22us/step - loss: -1.8754 - val_loss: -1.8748\n",
      "Epoch 19/80\n",
      "290368/290368 [==============================] - 6s 22us/step - loss: -1.8754 - val_loss: -1.8749\n",
      "Epoch 20/80\n",
      "290368/290368 [==============================] - 8s 28us/step - loss: -1.8754 - val_loss: -1.8749\n",
      "Epoch 21/80\n",
      "290368/290368 [==============================] - 7s 25us/step - loss: -1.8755 - val_loss: -1.8749\n",
      "Epoch 22/80\n",
      "290368/290368 [==============================] - 7s 23us/step - loss: -1.8755 - val_loss: -1.8749\n",
      "Epoch 23/80\n",
      "290368/290368 [==============================] - 10s 35us/step - loss: -1.8755 - val_loss: -1.8749\n",
      "Epoch 24/80\n",
      "290368/290368 [==============================] - 6s 19us/step - loss: -1.8755 - val_loss: -1.8749\n",
      "Epoch 25/80\n",
      "290368/290368 [==============================] - 5s 18us/step - loss: -1.8755 - val_loss: -1.8749\n",
      "Epoch 26/80\n",
      "290368/290368 [==============================] - 5s 19us/step - loss: -1.8755 - val_loss: -1.8749\n",
      "Epoch 27/80\n",
      "290368/290368 [==============================] - 6s 20us/step - loss: -1.8755 - val_loss: -1.8749\n",
      "Epoch 28/80\n",
      "169472/290368 [================>.............] - ETA: 2s - loss: -1.8755"
     ]
    }
   ],
   "source": [
    "## inspect save data\n",
    "AUTO_ENCODER = True\n",
    "# initial parmas\n",
    "tran_json_prefix = ['_train_recon','_train']\n",
    "dev_json_prefix = ['_dev_recon','_dev']\n",
    "params = {\n",
    "    'data_json': \"cinc17/train.json\",\n",
    "    'dev_json': \"cinc17/dev.json\",\n",
    "    'des_snr' : 20,\n",
    "    'noisy_type': 4, #0:gaussian 1: cauchy 4:mix of 1,2\n",
    "    'train_type': 1, #0: clean-clean 1: noisy-clean 2: noisy-noisy\n",
    "    'loss': 'mcc',\n",
    "    'MAX_EPOCHS': 80,\n",
    "    'batch_size': 256,\n",
    "    'input_size': 256,\n",
    "    'hidden_size': 64,\n",
    "    'lr': 0.01,\n",
    "    'lambda_w': 4e-5\n",
    "         }\n",
    "db_list =  [5, 40]\n",
    "loss_list = ['mcc']\n",
    "noisy_type = [4]\n",
    "\n",
    "for noise in noisy_type:\n",
    "        for loss in loss_list:\n",
    "            train_jsons = []\n",
    "            dev_jsons = []\n",
    "            experiments = []\n",
    "            for db in db_list:\n",
    "                params['noisy_type'] = noise\n",
    "                params['des_snr'] = db\n",
    "                params['loss'] = loss\n",
    "                # update file path based on params\n",
    "                params['store_data_folder'] = 'cinc17/mcc_transformed/'\n",
    "                params['experiment_name'] = 'noisy'+str(params['noisy_type'])+'_db'+str(params['des_snr'])+'_'+params['loss']\n",
    "                params['model_name'] = 'autoencoder_model/'+'train'+ str(params['train_type'])+'_'+params['experiment_name']+ '_autoencoder.h5'\n",
    "                params['store_json'] = 'cinc17/'+params['experiment_name']\n",
    "                for t,d in zip(tran_json_prefix, dev_json_prefix):\n",
    "                    train_jsons.append(params['store_json'] + t +'.json')\n",
    "                    dev_jsons.append(params['store_json'] + d +'.json')\n",
    "                    experiments.append(params['experiment_name']+t+d)\n",
    "\n",
    "                # run auto_encoder\n",
    "                if AUTO_ENCODER == True:\n",
    "                    reset_keras()\n",
    "                    run_autoencoder(params,STORE=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}