du16_2_with_pretraining-vislinear.ipynb

{
 "cells": [
  {
   "cell_type": "code",
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   },
   "outputs": [],
   "source": [
    "#3#######leakyrelu makes us able to use bigger lr\n",
    "import glob\n",
    "import tensorflow as tf\n",
    "from tensorflow.python.ops import control_flow_ops\n",
    "# from tqdm import tqdm\n",
    "from progressbar import ProgressBar\n",
    "import h5py\n",
    "import matplotlib.pyplot as plt\n",
    "from natsort import natsorted\n",
    "I=0\n",
    "def _parse_function(example_proto):\n",
    "    print('1')\n",
    "    w=1\n",
    "    features = {\"X\": tf.FixedLenFeature((w*257), tf.float32),\n",
    "              \"Y\": tf.FixedLenFeature((257), tf.float32)}\n",
    "    parsed_features = tf.parse_single_example(example_proto, features)\n",
    "    print(\"i was here\")\n",
    "    print('2')\n",
    "    return parsed_features[\"X\"], parsed_features[\"Y\"]\n",
    "\n",
    "def rbm_layer(n_visible, n_hidden, num_epochs, numcases, lr, lrh, ws, bs, layer_n, len_data, directories):\n",
    "    Data_path = directories[0]\n",
    "    tfrecord_folder_parent = directories[1]\n",
    "    tfrecord_folder = directories[2]\n",
    "    \n",
    "    tfrecord_path_x = os.path.normpath(os.path.join(Data_path,tfrecord_folder_parent,tfrecord_folder))\n",
    "    sorted_names_x = natsorted(os.listdir(tfrecord_path_x))\n",
    "    trainfilenames_x = []\n",
    "    for i in sorted_names_x:\n",
    "        trainfilenames_x.append(os.path.normpath(os.path.join(tfrecord_path_x,i)))\n",
    "#     filenames_x = tf.placeholder(tf.string, shape=[None])\n",
    "#     dataset_x = tf.data.TFRecordDataset(filenames_x)\n",
    "    dataset_x = tf.data.TFRecordDataset(trainfilenames_x)\n",
    "    dataset_x = dataset_x.map(_parse_function)  # Parse the record into tensors.\n",
    "#     dataset_x = dataset_x.repeat()  # Repeat the input indefinitely.\n",
    "    dataset_x = dataset_x.batch(numcases)\n",
    "#     iterator_x = dataset_x.make_initializable_iterator()\n",
    "    \n",
    "    weightcost  = 0.009\n",
    "    initialmomentum  = 0.5\n",
    "    finalmomentum    = 0.9\n",
    "\n",
    "#     weightcost  = 0\n",
    "#     initialmomentum  = 0\n",
    "#     finalmomentum    = 0\n",
    "#     momentum  = 0.5\n",
    "    W_adder  = tf.zeros((n_visible,n_hidden),dtype=tf.dtypes.float32)\n",
    "    bh_adder = tf.zeros((1,n_hidden),dtype=tf.dtypes.float32)\n",
    "    bv_adder = tf.zeros((1,n_visible),dtype=tf.dtypes.float32)\n",
    "#     x  = tf.placeholder(tf.float32, [None, n_visible], name=\"x\") #The placeholder variable that holds our data\n",
    "#     h = tf.placeholder(tf.float32, [None, n_hidden], name=\"h\")\n",
    "#     h = tf.zeros((numcases, n_hidden),dtype=tf.dtypes.float32)\n",
    "#     h_empty  = np.zeros_like(h)\n",
    "#     h_empty = np.zeros((, n_hidden),dtype=np.float32)\n",
    "#     m  = tf.Variable(0.5, dtype=np.float32, name='m')\n",
    "    W  = tf.Variable(tf.random_normal([n_visible, n_hidden], 0.01), name=\"W\") #The weight matrix that stores the edge weights\n",
    "    bh = tf.Variable(tf.zeros([1, n_hidden],  tf.float32, name=\"bh\")) #The bias vector for the hidden layer\n",
    "    bv = tf.Variable(tf.zeros([1, n_visible],  tf.float32, name=\"bv\")) #The bias vector for the visible layer\n",
    "\n",
    "    def sample(probs):\n",
    "        #Takes in a vector of probabilities, and returns a random vector of 0s and 1s sampled from the input vector\n",
    "        return tf.floor(probs + tf.random_uniform(tf.shape(probs), 0, 1))\n",
    "    def sample_gauss(probs):\n",
    "        #Takes in a vector of probabilities, and returns a random vector of 0s and 1s sampled from the input vector\n",
    "        return tf.floor(probs + tf.random.normal(tf.shape(probs),mean=0.0,stddev=1))\n",
    "#     hk = sample(tf.sigmoid(tf.matmul(x, W) + bh)) #Propagate the visible values to sample the hidden values\n",
    "#     #128*512\n",
    "\n",
    "#     #Next, we update the values of W, bh, and bv, based on the difference between the samples that we drew and the original values\n",
    "#     posprods  = tf.matmul(tf.transpose(x),hk) #771*512\n",
    "#     poshidacts = tf.reduce_sum(hk) #(512)\n",
    "#     posvisacts = tf.reduce_sum(x,axis=0)#771\n",
    "    \n",
    "#     xk   = tf.sigmoid(tf.matmul(hk, tf.transpose(W)) + bv)#128*711\n",
    "#     neghidprobs   = tf.sigmoid(tf.matmul(xk, W) + bh) #128*512\n",
    "#     negprods  = tf.matmul(tf.transpose(xk),neghidprobs); #771*512##################################\n",
    "#     neghidacts = tf.reduce_sum(neghidprobs, axis=0)\n",
    "#     negvisacts = tf.reduce_sum(xk, axis=0)\n",
    "#     print(negvisacts)#771\n",
    "# #     print(type(posprod-negprod))\n",
    "# #     print(type(lr))\n",
    "#     m=0.5\n",
    "#     print('here')\n",
    "#     W_adder = (m * W_adder)+ (lr*(posprods-negprods)/numcases)-(weightcost*W)\n",
    "#     bv_adder = (m * bv_adder)+ ((lr/numcases)*(posvisacts-negvisacts))\n",
    "#     bh_adder = (m * bh_adder)+ ((lr/numcases)*(poshidacts-neghidacts))\n",
    "#     #When we do sess.run(updt), TensorFlow will run all 3 update steps\n",
    "#     updt = [W.assign_add(W_adder), bv.assign_add(bv_adder), bh.assign_add(bh_adder)]\n",
    "\n",
    "    ### Run the graph!\n",
    "    # Now it's time to start a session and run the graph! \n",
    "\n",
    "#     with tf.Session() as sess:\n",
    "        #First, we train the model\n",
    "        #initialize the variables of the model\n",
    "#         sess.run(tf.global_variables_initializer())\n",
    "#         print(m)\n",
    "#         sess.run(iterator_x.initializer, feed_dict={filenames_x: trainfilenames_x})\n",
    "#         print(iterator_x.get_next()[0])\n",
    "        #Run through all of the training data num_epochs times\n",
    "#         for epoch in tqdm(range(num_epochs)):\n",
    "            #Train the RBM on batch_size examples at a time\n",
    "#             for X_batch in da(batch_size, layer_n, ws, bs, len_data, name, data_name):\n",
    "#             Data = sess.run(iterator_x.get_next()[0])\n",
    "#             print('blah')\n",
    "#             print(Data.shape)\n",
    "#             for Data in dataset_x:\n",
    "#                 for j in range(layer_n):\n",
    "#                     Data = np.matmul(Data,ws[j])+bs[j]\n",
    "    #             if epoch>5:\n",
    "    #                 momentum=finalmomentum;\n",
    "    #             else:\n",
    "    #                 momentum=initialmomentum;\n",
    "#                 feed_dict = {x: Data}\n",
    "#                 var1 = sess.run(updt, feed_dict)\n",
    "    count=0\n",
    "    pbar = ProgressBar()\n",
    "    for epoch in pbar(range(num_epochs)):\n",
    "        count+=1\n",
    "        incount=0\n",
    "        for x in dataset_x:\n",
    "            incount+=1\n",
    "            xx=x[0]\n",
    "            if incount%1000==0:\n",
    "#                 plt.imshow(updt[0].numpy(),cmap='gray')\n",
    "                plt.imshow(neghidprobs.numpy(),cmap='gray')\n",
    "                plt.show()\n",
    "#                 print(updt[0])\n",
    "                print(incount)\n",
    "            if layer_n>1:\n",
    "                for j in range(1,layer_n):\n",
    "                    xx = tf.sigmoid(tf.matmul(xx,ws[j])+bs[j])\n",
    "#             poshidprobs = tf.sigmoid(tf.matmul(x[0], W) + bh+ tf.random.normal((len(x[0]),len(W[0])),mean=0.0,stddev=0.1)) #Propagate the visible values to sample the hidden values\n",
    "            poshidprobs = tf.sigmoid(tf.matmul(xx, W) + bh)\n",
    "#             poshidprobs = tf.matmul(x[0], W) + bh\n",
    "            poshidstates = sample(poshidprobs)\n",
    "            #128*512\n",
    "            #Next, we update the values of W, bh, and bv, based on the difference between the samples that we drew and the original values\n",
    "            posprods  = tf.matmul(tf.transpose(x[0]),poshidprobs) #771*512\n",
    "            poshidacts = tf.reduce_sum(poshidprobs, axis=0) #(512)\n",
    "            posvisacts = tf.reduce_sum(x[0],axis=0)#771\n",
    "\n",
    "#             negdata   = tf.sigmoid(tf.matmul(poshidstates, tf.transpose(W)) + bv+ tf.random.normal((len(x[0]),len(x[0][0])),mean=0.0,stddev=0.05))#128*711\n",
    "#             negdata   = tf.sigmoid(tf.matmul(poshidstates, tf.transpose(W))+bv)#128*711\n",
    "            negdata   = tf.matmul(poshidstates, tf.transpose(W))+bv\n",
    "            neghidprobs   = tf.sigmoid(tf.matmul(negdata, W) + bh) #128*512\n",
    "            negprods  = tf.matmul(tf.transpose(negdata),neghidprobs); #771*512##################################\n",
    "            neghidacts = tf.reduce_sum(neghidprobs, axis=0)\n",
    "            negvisacts = tf.reduce_sum(negdata, axis=0)\n",
    "#             print(negvisacts)#771\n",
    "        #     print(type(posprod-negprod))\n",
    "        #     print(type(lr))\n",
    "            if epoch>5:\n",
    "                m=finalmomentum\n",
    "            else:\n",
    "                m=initialmomentum\n",
    "            W_adder = (m * W_adder)+ (lr*(posprods-negprods/numcases))-(weightcost*W)\n",
    "            bv_adder = (m * bv_adder)+ ((lr/numcases)*(posvisacts-negvisacts))\n",
    "            bh_adder = (m * bh_adder)+ ((lr/numcases)*(poshidacts-neghidacts))\n",
    "            #When we do sess.run(updt), TensorFlow will run all 3 update steps\n",
    "            updt = [W.assign_add(W_adder).numpy(), bv.assign_add(bv_adder).numpy(), bh.assign_add(bh_adder).numpy()] \n",
    "            \n",
    "        print(count)\n",
    "    return updt\n",
    "\n",
    "\n"
   ]
  },
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      " 10% |#######                                                                 |\r"
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      " 20% |##############                                                          |\r"
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      " 30% |#####################                                                   |\r"
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      " 10% |#######                                                                 |\r"
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      " 20% |##############                                                          |\r"
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      " 30% |#####################                                                   |\r"
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   "source": [
    "# import libraries.\n",
    "import numpy as np\n",
    "from sklearn.utils import shuffle\n",
    "import tensorflow as tf\n",
    "import datetime\n",
    "tf.enable_eager_execution()\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",
    "from pystoi.stoi import stoi\n",
    "import h5py\n",
    "import sys\n",
    "from natsort import natsorted\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",
    "tfrecord_folder_parent = 'tfrecord_files'\n",
    "tfrecord_folder = 'mixed_10h_norm2'\n",
    "tfrecord_val_folder = 'validation_mixed_norm'\n",
    "ckpt_folder = '5'\n",
    "dirs = [Data_path, tfrecord_folder_parent, tfrecord_folder]\n",
    " \n",
    "# len_data = (684108, 257)\n",
    "# len_data = (2197278, 257)\n",
    "# val_len = (97278,257)\n",
    "len_data = (200000, 257)\n",
    "val_len = (100000,257)\n",
    "w=1\n",
    "h = [512,512]\n",
    "global batch_size\n",
    "batch_size = 128\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=256, win_length=512, window='hann')\n",
    "    return recon\n",
    "\n",
    "########################\n",
    "visible = w*len_data[1]\n",
    "hidden = h[0]\n",
    "visible1 = h[0]\n",
    "hidden1 = h[1]\n",
    "# visible2 = h[1]\n",
    "# hidden2 = len_data[1]\n",
    "\n",
    "layer1 = rbm_layer(visible, hidden, 10, batch_size, 0.00001, 0.00001, [np.eye(visible,hidden)], [np.zeros((1,visible))], 1, len_data[0],dirs)\n",
    "layer2 = rbm_layer(visible1, hidden1, 10, batch_size, 0.00001, 0.00001, [np.eye(visible,hidden),layer1[0]], [np.zeros((1,visible)),layer1[2]], 2, len_data[0], dirs)\n",
    "# layer3 = rbm_layer(visible2, hidden2, 50, batch_size, 0.01, [np.eye(visible),layer1[0],layer2[0]], [np.zeros((1,visible)),layer1[2],layer2[2]], 3, len_data[0], dirs)\n",
    "\n",
    "###############################\n",
    "#######################\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "I=0\n",
    "\n",
    "# epochs_num=50\n",
    "global datalen\n",
    "datalen=len_data[0]\n",
    "\n",
    "\n",
    "seed = 7\n",
    "rate1 = 0.1\n",
    "rate2 = 0.2\n",
    "buffersize = 1000\n",
    "from tensorflow.keras.layers import Activation\n",
    "# from keras.layers import Activation\n",
    "np.random.seed(seed)\n",
    "act1 = layers.LeakyReLU(alpha=0.1)\n",
    "model = Sequential()\n",
    "# model.add(layers.Dropout(rate1, 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(Dense(h[0], input_dim = w*len_data[1], kernel_initializer= 'glorot_normal', bias_initializer = 'glorot_normal'))\n",
    "# model.add(Dense(h[0], input_dim = w*len_data[1], kernel_initializer= tf.constant_initializer(layer1[0]), bias_initializer = tf.constant_initializer(layer1[2])))\n",
    "# , kernel_initializer= tf.constant_initializer(layer1[0]), bias_initializer = tf.constant_initializer(layer1[2])\n",
    "# tf.constant_initializer(layer1[0])\n",
    "# tf.constant_initializer(layer1[2])\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(rate2, noise_shape=None, seed=None))\n",
    "# model.add(Dense(h[1]))\n",
    "# model.add(act2)\n",
    "# act3=layers.LeakyReLU(alpha=0.1)\n",
    "# # model.add(layers.Dropout(rate, noise_shape=None, seed=None))\n",
    "# model.add(Dense(h[2]))\n",
    "# model.add(act3)\n",
    "# act=layers.LeakyReLU(alpha=0.01)\n",
    "model.add(Dense(len_data[1]))\n",
    "#############################################\n",
    "import os\n",
    "\n",
    "def _parse_function(example_proto):\n",
    "    features = {\"X\": tf.FixedLenFeature((w*257), tf.float32),\n",
    "              \"Y\": tf.FixedLenFeature((257), tf.float32)}\n",
    "    parsed_features = tf.parse_single_example(example_proto, features)\n",
    "    return parsed_features[\"X\"], parsed_features[\"Y\"]\n",
    "\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=[None])\n",
    "# dataset = tf.data.TFRecordDataset(filenames)\n",
    "dataset = tf.data.TFRecordDataset(trainfilenames)\n",
    "dataset = dataset.map(_parse_function)  # Parse the record into tensors.\n",
    "dataset = dataset.repeat()  # Repeat the input indefinitely.\n",
    "dataset = dataset.batch(batch_size)\n",
    "dataset = dataset.shuffle(buffersize)\n",
    "# iterator = dataset.make_initializable_iterator()\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 = tf.data.TFRecordDataset(trainfilenames_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 = 50\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",
    "print(datetime.datetime.now())\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=30)\n",
    "opt = tf.keras.optimizers.Adamax()\n",
    "# opt = tf.train.AdamOptimizer()\n",
    "# opt = tf.keras.optimizers.SGD()\n",
    "model.compile(loss='mean_squared_error', optimizer=opt)\n",
    "history = model.fit( dataset, steps_per_epoch=steps,epochs=epochs_num, callbacks = [cp_callback,early_stop], verbose=1,validation_data=dataset_val,validation_steps=val_steps)\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_\"+ckpt_folder+\".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_\"+ckpt_folder+\".h5\")))\n",
    "print(\"Saved model to disk\")\n",
    "    \n",
    "print(datetime.datetime.now())\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",
    "plt.savefig(os.path.normpath(os.path.join(Data_path,'images',ckpt_folder+'.png')))\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\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'numpy.ndarray' object has no attribute 'numpy'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-158-c7f92f2a79e5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msavetxt\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'results'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'rbm'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'test_vislinear'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'layers10.txt'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlayer1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msavetxt\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'results'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'rbm'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'test_vislinear'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'layers11.txt'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlayer1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msavetxt\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'results'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'rbm'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'test_vislinear'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'layers12.txt'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlayer1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'numpy'"
     ]
    }
   ],
   "source": [
    "np.savetxt(os.path.normpath(os.path.join(Data_path,'results','rbm','test_vislinear','layers10.txt')),layer1[0].numpy())\n",
    "np.savetxt(os.path.normpath(os.path.join(Data_path,'results','rbm','test_vislinear','layers11.txt')),layer1[1].numpy())\n",
    "np.savetxt(os.path.normpath(os.path.join(Data_path,'results','rbm','test_vislinear','layers12.txt')),layer1[2].numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "write_path = 'D:/studies/university/thesis/speech_separation_codes/du16/donesomestuff/10hdata'\n",
    "mixed_folder = os.path.normpath(os.path.join(write_path,'mixed_log_10h_norm2'))\n",
    "h5f = h5py.File(mixed_folder+'.hdf5','r')\n",
    "ftr = h5f['mixed_log_10h_norm2'][0:126]\n",
    "h5f.close()\n",
    "mixed_folder = os.path.normpath(os.path.join(write_path,'mixed_phase_10h_nozeroinsert'))\n",
    "h5f = h5py.File(mixed_folder+'.hdf5','r')\n",
    "ftr_phase = h5f['mixed_phase_10h_nozeroinsert'][0:126]\n",
    "h5f.close()\n",
    "mixed_folder = os.path.normpath(os.path.join(write_path,'mixed_log_10h_nozeroinsert'))\n",
    "h5f = h5py.File(mixed_folder+'.hdf5','r')\n",
    "ftr_raw = h5f['mixed_log_10h_nozeroinsert'][0:126]\n",
    "h5f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "hidden1 = 1/(1+np.exp(-np.matmul(ftr,layer1[0])+layer1[2]))\n",
    "# hidden2 = np.matmul(hidden1,layer2[0])+layer2[2]\n",
    "# hidden3 = np.matmul(hidden2,layer3[0])+layer3[2]\n",
    "hidden1_b = np.matmul(hidden1,layer1[0].T)+layer1[1]\n",
    "# hidden2_b = np.matmul(hidden3_b,layer2[0].T)+layer2[1]\n",
    "# hidden1_b = np.matmul(hidden2_b,layer1[0].T)+layer1[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "hidden1 = 1/(1+np.exp(-np.matmul(ftr,layer1[0])+layer1[2]))\n",
    "hidden2 = 1/(1+np.exp(-np.matmul(hidden1,layer2[0])+layer2[2]))\n",
    "# hidden3 = 1/(1+np.exp(-np.matmul(hidden2,layer3[0])+layer3[2]))\n",
    "hidden2_b = np.matmul(hidden2,layer2[0].T)+layer2[1]\n",
    "hidden1_b = np.matmul(hidden2_b,layer1[0].T)+layer1[1]\n",
    "# hidden1_b = np.matmul(hidden2_b,layer1[0].T)+layer1[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.28766647,  0.11526573, -0.7069241 , ..., -0.55612373,\n",
       "        -0.5525803 , -0.50696164],\n",
       "       [ 0.12223668,  0.26682785, -0.4372746 , ..., -0.99636066,\n",
       "        -1.0043348 , -1.5150769 ],\n",
       "       [ 0.8128672 ,  0.6103882 , -0.9415356 , ..., -1.1297925 ,\n",
       "        -0.9657317 , -0.59864193],\n",
       "       ...,\n",
       "       [-4.825073  , -5.533287  , -4.9773455 , ..., -2.2312639 ,\n",
       "        -2.2086217 , -1.930099  ],\n",
       "       [-4.825073  , -5.533287  , -4.9773455 , ..., -2.2312639 ,\n",
       "        -2.2086217 , -1.930099  ],\n",
       "       [-4.825073  , -5.533287  , -4.9773455 , ..., -2.2312639 ,\n",
       "        -2.2086217 , -1.930099  ]], dtype=float32)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ftr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.5145833 , -0.25775322, -0.18894145, ...,  1.129334  ,\n",
       "        -2.7267003 , -1.451347  ],\n",
       "       [-0.8435445 , -0.29447064, -0.58014065, ...,  0.73581946,\n",
       "        -2.551829  , -1.2822368 ],\n",
       "       [-0.851307  , -0.29206118, -0.5919156 , ...,  0.7205376 ,\n",
       "        -2.5477114 , -1.2768832 ],\n",
       "       ...,\n",
       "       [-1.0426275 , -0.32202753, -0.8085412 , ...,  0.49998662,\n",
       "        -2.4439259 , -1.1828278 ],\n",
       "       [-1.0426275 , -0.32202753, -0.8085412 , ...,  0.49998662,\n",
       "        -2.4439259 , -1.1828278 ],\n",
       "       [-1.0426275 , -0.32202753, -0.8085412 , ...,  0.49998662,\n",
       "        -2.4439259 , -1.1828278 ]], dtype=float32)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hidden1_b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean = np.loadtxt(os.path.normpath(os.path.join(write_path,'mean_mixed_log.txt')))\n",
    "std = np.loadtxt(os.path.normpath(os.path.join(write_path,'std_mixed_log.txt')))\n",
    "hidden1_b_unnorm = (hidden1_b*std)+mean\n",
    "recon = reconstruct(hidden1_b_unnorm/3, ftr_phase)\n",
    "import sounddevice as sd\n",
    "sd.play(recon,16000)\n",
    "\n",
    "Pxx, freqs, bins, im = plt.specgram(recon, NFFT=512, Fs=16000, noverlap=256)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "sd.play(recon,16000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "recon = reconstruct(hidden1_b, ftr_phase)\n",
    "import sounddevice as sd\n",
    "sd.play(recon,16000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ASUS\\Anaconda3\\envs\\myenv\\lib\\site-packages\\matplotlib\\axes\\_axes.py:7725: RuntimeWarning: divide by zero encountered in log10\n",
      "  Z = 10. * np.log10(spec)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "recon = reconstruct(ftr_raw, ftr_phase)\n",
    "import sounddevice as sd\n",
    "sd.play(recon,16000)\n",
    "from scipy import signal\n",
    "# frequencies, times, spectrogram = signal.spectrogram(recon, 16000)\n",
    "# plt.pcolormesh(times, frequencies, spectrogram)\n",
    "# plt.imshow(spectrogram)\n",
    "# plt.ylabel('Frequency [Hz]')\n",
    "# plt.xlabel('Time [sec]')\n",
    "# plt.pcolormesh\n",
    "# plt.show()\n",
    "Pxx, freqs, bins, im = plt.specgram(recon, NFFT=512, Fs=16000, noverlap=256)\n",
    "# The `specgram` method returns 4 objects. They are:\n",
    "# - Pxx: the periodogram\n",
    "# - freqs: the frequency vector\n",
    "# - bins: the centers of the time bins\n",
    "# - im: the matplotlib.image.AxesImage instance representing the data in the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(126, 257)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ftr_phase.shape"
   ]
  },
  {
   "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": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(771,)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import h5py \n",
    "import tensorflow as tf\n",
    "hh = h5py.File('ftr_refrmd_10h.hdf5', 'r')\n",
    "d=hh['ftr_refrmd_10h'][0]\n",
    "len_data=d.shape\n",
    "hh.close()\n",
    "len_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "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=256, win_length=512, window='hann')\n",
    "    return recon"
   ]
  },
  {
   "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": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "h5f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.07630829, 0.77991879, 0.43840923, 0.72346518, 0.97798951,\n",
       "       0.53849587, 0.50112046, 0.07205113, 0.26843898, 0.4998825 ])"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.random_sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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