du16_2 -Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "imported\n",
      "WARNING:tensorflow:From C:\\Users\\ASUS\\Anaconda3\\envs\\myenv\\lib\\site-packages\\tensorflow\\python\\ops\\resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "WARNING:tensorflow:From C:\\Users\\ASUS\\Anaconda3\\envs\\myenv\\lib\\site-packages\\tensorflow\\python\\keras\\layers\\core.py:143: 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",
      "WARNING:tensorflow:From C:\\Users\\ASUS\\Anaconda3\\envs\\myenv\\lib\\site-packages\\tensorflow\\python\\keras\\utils\\losses_utils.py:170: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "WARNING:tensorflow:From C:\\Users\\ASUS\\Anaconda3\\envs\\myenv\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Epoch 1/100\n"
     ]
    }
   ],
   "source": [
    "#############i tested tfrecords here#####################\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#     history = model.fit( iterator, steps_per_epoch=steps,epochs=epochs_num, callbacks = [cp_callback,early_stop], verbose=1,validation_data=iterator_val,validation_steps=val_steps)\n",
    "history = model.fit(x=x,y=y, steps_per_epoch=steps,epochs=epochs_num, verbose=1)\n",
    "#     model.save(os.path.normpath(os.path.join(Data_path, 'models', \"model_3h_dataset.h5\")))\n",
    "#     tf.keras.models.save_model(model, os.path.normpath(os.path.join(Data_path, 'models', \"model_3h_dataset.h5\")))\n",
    "#     model.save_weights(os.path.normpath(os.path.join(Data_path, 'models', \"model_3h_dataset.h5\")))\n",
    "model_json = model.to_json()\n",
    "with open(os.path.normpath(os.path.join(Data_path, 'models', \"model_3h_dataset_16.json\")), \"w\") as json_file:\n",
    "    json_file.write(model_json)\n",
    "# # serialize weights to HDF5\n",
    "model.save_weights(os.path.normpath(os.path.join(Data_path, 'models', \"model_3h_dataset_16.h5\")))\n",
    "print(\"Saved model to disk\")\n",
    "%matplotlib inline\n",
    "# summarize history for loss\n",
    "plt.plot(history.history['loss'])\n",
    "# plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train'], loc='upper left')\n",
    "plt.show()\n",
    "# model_json = model.to_json()\n",
    "# with open(\"model_10h_dataset.json\", \"w\") as json_file:\n",
    "#     json_file.write(model_json)\n",
    "# model.save_weights(\"model_10h_dataset.h5\")\n",
    "# print(\"Saved model to disk\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "imported\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.utils import shuffle\n",
    "import tensorflow as tf\n",
    "# tf.enable_eager_execution()\n",
    "# from tensorflow import keras\n",
    "# from keras.models import Sequential\n",
    "# from keras.layers import Dense\n",
    "# from keras import layers\n",
    "# from keras.wrappers.scikit_learn import KerasRegressor\n",
    "# # import keras\n",
    "# from keras.layers import BatchNormalization\n",
    "# from keras import optimizers\n",
    "# from keras import regularizers\n",
    "\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.wrappers.scikit_learn import KerasRegressor\n",
    "# import keras\n",
    "from tensorflow.keras.layers import BatchNormalization\n",
    "from tensorflow.keras import optimizers\n",
    "from tensorflow.keras import regularizers\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import mean_squared_error\n",
    "#import sounddevice as sd\n",
    "# import tensorflow as tf\n",
    "# tf.enable_eager_execution()\n",
    "from pystoi.stoi import stoi\n",
    "import h5py\n",
    "######################\n",
    "#import libraries.\n",
    "import matplotlib.pyplot as plt\n",
    "from tabulate import tabulate\n",
    "import time\n",
    "import os\n",
    "import librosa\n",
    "from librosa.core import stft, istft\n",
    "####import sounddevice as sd\n",
    "import time\n",
    "print('imported')\n",
    "# #######################\n",
    "Data_path = 'D:/studies/university/thesis/speech_separation_codes/du16/donesomestuff'\n",
    "# Data_path = os.getcwd()\n",
    "input_tr_name = 'ftr_refrmd_3h'\n",
    "# input_val_name = 'ftr_refrmd_test'\n",
    "target_tr_name = 'single_dataset_log_3h'\n",
    "# target_val_name = 'clean_data_wav_norm'\n",
    "# tfrecord_folder_parent = 'tfrecord_files'\n",
    "# tfrecord_folder = 'tfrecord_files_10h'\n",
    "# tfrecord_val_folder = 'test_wav_norm'\n",
    "ckpt_folder = '16'\n",
    "input_tr_path = os.path.normpath(os.path.join(Data_path,'3hdata',input_tr_name))\n",
    "# input_val_path = os.path.normpath(os.path.join(Data_path,input_val_name))\n",
    "target_tr_path = os.path.normpath(os.path.join(Data_path,'3hdata',target_tr_name))\n",
    "# target_val_path = os.path.normpath(os.path.join(Data_path,target_val_name))\n",
    "\n",
    "file = h5py.File(target_tr_path+'.hdf5','r')\n",
    "y = file[target_tr_name][0:10000]\n",
    "file.close()\n",
    "file = h5py.File(input_tr_path+'.hdf5','r')\n",
    "x = file[input_tr_name][0:10000]\n",
    "file.close()\n",
    "\n",
    "# hh = h5py.File(target_tr_path+'.hdf5', 'r')\n",
    "# d=hh[target_tr_name]\n",
    "# len_data=d.shape\n",
    "# hh.close()\n",
    "len_data = y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# len_data = (2197000000000, 257)\n",
    "\n",
    "# file = h5py.File(target_val_path+'.hdf5','r')\n",
    "# val_y = file[target_val_name]\n",
    "# val_len=val_y.shape\n",
    "# file.close()\n",
    "\n",
    "w=3\n",
    "# print(len_data)\n",
    "#######################\n",
    "#define reconstruct function to reconstruct sound from framed signal.\n",
    "def reconstruct(wave,angle):\n",
    "    recon = np.sqrt(np.power(10, wave))\n",
    "    recon1 = recon*np.cos(angle)+recon*np.sin(angle)*1j\n",
    "    recon = librosa.core.istft((recon1.T), hop_length=200, win_length=500, window='hann')\n",
    "    return recon\n",
    "#######################\n",
    "I=0\n",
    "global batch_size\n",
    "batch_size=128\n",
    "# epochs_num=50\n",
    "global datalen\n",
    "datalen=len_data[0]\n",
    "\n",
    "file.close()\n",
    "h = [512,512]\n",
    "seed = 7\n",
    "rate = 0.1\n",
    "from tensorflow.keras.layers import Activation\n",
    "# from keras.layers import Activation\n",
    "np.random.seed(seed)\n",
    "model = Sequential()\n",
    "act1 = layers.LeakyReLU(alpha=0.1)\n",
    "model.add(layers.Dropout(rate, noise_shape=None, seed=None))\n",
    "# ,kernel_regularizer=regularizers.l2(0.001)\n",
    "model.add(Dense(h[0], input_dim = w*len_data[1]))\n",
    "model.add(BatchNormalization())\n",
    "# model.add(act1)\n",
    "model.add(Activation('sigmoid'))\n",
    "act2=layers.LeakyReLU(alpha=0.1)\n",
    "model.add(layers.Dropout(rate, noise_shape=None, seed=None))\n",
    "model.add(Dense(h[1]))\n",
    "model.add(act2)\n",
    "act=layers.LeakyReLU(alpha=0.1)\n",
    "model.add(Dense(len_data[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\ASUS\\Anaconda3\\envs\\myenv\\lib\\site-packages\\tensorflow\\python\\ops\\resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "#############################################\n",
    "import os\n",
    "from natsort import natsorted\n",
    "\n",
    "def _parse_function(example_proto):\n",
    "    features = {\"X\": tf.FixedLenFeature((3*257), tf.float32),\n",
    "              \"Y\": tf.FixedLenFeature((257), tf.float32)}\n",
    "#     print(example_proto)\n",
    "    parsed_features = tf.parse_single_example(example_proto, features)\n",
    "    print(\"i was here\")\n",
    "    return parsed_features[\"X\"], parsed_features[\"Y\"]\n",
    "\n",
    "# orig_path = os.getcwd()\n",
    "# tfrecord_path = os.path.normpath(os.path.join(Data_path,tfrecord_folder_parent,tfrecord_folder))\n",
    "# sorted_names = natsorted(os.listdir(tfrecord_path))\n",
    "# trainfilenames = []\n",
    "# for i in sorted_names:\n",
    "#     trainfilenames.append(os.path.normpath(os.path.join(tfrecord_path,i)))\n",
    "# filenames = tf.placeholder(tf.string, shape=[len(sorted_names)])\n",
    "# dataset = tf.data.TFRecordDataset(filenames)\n",
    "# print(dataset)\n",
    "# dataset = dataset.map(_parse_function)  # Parse the record into tensors.\n",
    "# dataset = dataset.repeat()  # Repeat the input indefinitely.\n",
    "# dataset = dataset.batch(128)\n",
    "# # print(dataset)\n",
    "# iterator = dataset.make_initializable_iterator()\n",
    "\n",
    "\n",
    "# tfrecord_path_val = os.path.normpath(os.path.join(Data_path,tfrecord_folder_parent,tfrecord_val_folder))\n",
    "# sorted_names_val = natsorted(os.listdir(tfrecord_path_val))\n",
    "# trainfilenames_val = []\n",
    "# for i in sorted_names_val:\n",
    "#     trainfilenames_val.append(os.path.normpath(os.path.join(tfrecord_path_val,i)))\n",
    "# filenames_val = tf.placeholder(tf.string, shape=[None])\n",
    "# dataset_val = tf.data.TFRecordDataset(filenames_val)\n",
    "# dataset_val = dataset_val.map(_parse_function)  # Parse the record into tensors.\n",
    "# dataset_val = dataset_val.repeat()  # Repeat the input indefinitely.\n",
    "# dataset_val = dataset_val.batch(128)\n",
    "# iterator_val = dataset_val.make_initializable_iterator()\n",
    "\n",
    "epochs_num=100\n",
    "steps = len_data[0] // batch_size\n",
    "# val_steps = val_len[0] // batch_size\n",
    "# You can feed the initializer with the appropriate filenames for the current\n",
    "# phase of execution, e.g. training vs. validation.\n",
    "# next_elem = iterator_val.get_next()\n",
    "# Initialize `iterator` with training data.\n",
    "\n",
    "if not os.path.exists(os.path.join(Data_path,\"checkpoints\",ckpt_folder)):\n",
    "    os.makedirs(os.path.join(Data_path,\"checkpoints\",ckpt_folder))\n",
    "\n",
    "# with tf.Session() as sess:\n",
    "#     sess.run(tf.global_variables_initializer())\n",
    "#     sess.run(iterator.initializer, feed_dict={filenames: trainfilenames})\n",
    "#     sess.run(iterator_val.initializer, feed_dict={filenames_val: trainfilenames_val})\n",
    "#     print(\"initialized\")\n",
    "#     checkpoint_path = os.path.normpath(os.path.join(Data_path,\"checkpoints\",ckpt_folder,\"weights.{epoch:02d}.hdf5\"))\n",
    "#     checkpoint_dir = os.path.dirname(checkpoint_path)\n",
    "\n",
    "#     cp_callback = tf.keras.callbacks.ModelCheckpoint(\n",
    "#         checkpoint_path, verbose=1, save_best_only=True, save_weights_only=True)\n",
    "#         # Save weights, every 5-epochs.\n",
    "# #         period=1)\n",
    "#     early_stop = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=50)\n",
    "#     opt = tf.keras.optimizers.Adam()\n",
    "opt = tf.train.AdamOptimizer()\n",
    "model.compile(loss='mean_squared_error', optimizer=opt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\ASUS\\Anaconda3\\envs\\myenv\\lib\\site-packages\\tensorflow\\python\\keras\\layers\\core.py:143: 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",
      "WARNING:tensorflow:From C:\\Users\\ASUS\\Anaconda3\\envs\\myenv\\lib\\site-packages\\tensorflow\\python\\keras\\utils\\losses_utils.py:170: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "WARNING:tensorflow:From C:\\Users\\ASUS\\Anaconda3\\envs\\myenv\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "78/78 [==============================] - 75s 963ms/step - loss: 2.6563\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x=x,y=y, steps_per_epoch=steps,epochs=1, verbose=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=tf.constant([1,2,3])\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    label_numpy = a.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'Data_path' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-cd56679c7f1e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtensorflow\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mhh\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mh5py\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mFile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormpath\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mData_path\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'10hdata'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'ftr_refrmd_10h.hdf5'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'r'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[0md\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mhh\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'ftr_refrmd_10h_norm'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mlen_data\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'Data_path' is not defined"
     ]
    }
   ],
   "source": [
    "import h5py \n",
    "import tensorflow as tf\n",
    "import os\n",
    "hh = h5py.File(os.path.normpath(os.path.join(Data_path,'10hdata','ftr_refrmd_10h.hdf5')), 'r')\n",
    "d=hh['ftr_refrmd_10h_norm']\n",
    "len_data=d.shape\n",
    "hh.close()\n",
    "len_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<PrefetchDataset shapes: ((?, 257), (?,)), types: (tf.float32, tf.float32)>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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