Networks.ipynb

{
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    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'tensorflow'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[2], line 6\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mos\u001b[39;00m\n\u001b[1;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mtf\u001b[39;00m\n\u001b[0;32m      7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodel_selection\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m train_test_split\n\u001b[0;32m      8\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'tensorflow'"
     ]
    }
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   "source": [
    "import random\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import os\n",
    "import tensorflow as tf\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import interpolate\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from google.colab import drive\n",
    "import pickle\n",
    "from scipy.io import wavfile\n",
    "from scipy.signal import decimate\n",
    "from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Flatten, Dense, concatenate, Dropout\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "84c576f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Network Function\n",
    "def Multi_Modal():\n",
    "\n",
    "  # Define input layers for each head\n",
    "  input_w = Input(shape = (25,24,1))\n",
    "  input_p = Input(shape = (100,21,1))\n",
    "  input_s = Input(shape = (25,32,1))\n",
    "\n",
    "  # Encoders\n",
    "  conv1_w = Conv2D(64, (3, 3), activation='relu')(input_w)\n",
    "  pool1_w = MaxPooling2D((2, 2))(conv1_w)\n",
    "  conv2_w = Conv2D(32, (3, 3), activation='relu')(pool1_w)\n",
    "  pool2_w = MaxPooling2D((2, 2))(conv2_w)\n",
    "  flatten_w = Flatten()(pool2_w)\n",
    "\n",
    "  conv1_p = Conv2D(64, (3, 3), activation='relu')(input_p)\n",
    "  pool1_p = MaxPooling2D((2, 2))(conv1_p)\n",
    "  conv2_p = Conv2D(32, (3, 3), activation='relu')(pool1_p)\n",
    "  pool2_p = MaxPooling2D((2, 2))(conv2_p)\n",
    "  flatten_p = Flatten()(pool2_p)\n",
    "\n",
    "  conv1_s = Conv2D(64, (3, 3), activation='relu')(input_s)\n",
    "  pool1_s = MaxPooling2D((2, 2))(conv1_s)\n",
    "  conv2_s = Conv2D(32, (3, 3), activation='relu')(pool1_s)\n",
    "  pool2_s = MaxPooling2D((2, 2))(conv2_s)\n",
    "  flatten_s = Flatten()(pool2_s)\n",
    "\n",
    "\n",
    "  # Combine the outputs of hidden layers\n",
    "  combined = concatenate([flatten_w, flatten_p, flatten_s])\n",
    "\n",
    "  # Classifier\n",
    "  # 64-32\n",
    "  hidden1 = Dense(64, activation='relu')(combined)\n",
    "  hidden2 = Dense(32, activation='relu')(hidden1)\n",
    "\n",
    "  # Add more layers for further processing\n",
    "  output = Dense(9, activation='softmax')(hidden2)\n",
    "\n",
    "  model = Model(inputs=[input_w, input_p,input_s], outputs=output)\n",
    "  model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
    "  return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1350b39",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Network Function\n",
    "def Uni_Modal(modality):\n",
    "\n",
    "  if modality == 0:\n",
    "    input = Input(shape = (100,21,1))\n",
    "  elif modality == 1:\n",
    "    input = Input(shape = (25,24,1))\n",
    "  elif modality == 2:\n",
    "    input = Input(shape = (25,32,1))\n",
    "\n",
    "  # Encoders\n",
    "  conv1 = Conv2D(64, (3, 3), activation='relu')(input)\n",
    "  pool1 = MaxPooling2D((2, 2))(conv1)\n",
    "  conv2 = Conv2D(32, (3, 3), activation='relu')(pool1)\n",
    "  pool2 = MaxPooling2D((2, 2))(conv2)\n",
    "  flatten = Flatten()(pool2)\n",
    "  output = flatten\n",
    "  # Classifier\n",
    "  #hidden1 = Dense(64, activation='relu')(flatten)\n",
    "  #dropout1 = Dropout(0.2)(hidden1)\n",
    "  #hidden2 = Dense(32, activation='relu')(hidden1)\n",
    "  #dropout2 = Dropout(0.2)(hidden2)\n",
    "  #output = Dense(9, activation='softmax')(hidden2)\n",
    "\n",
    "  model = Model(inputs= input, outputs=output)\n",
    "  model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
    "  return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab54313a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def PS2W(input_shape1,input_shape2,input_shape3):\n",
    "  input_p = Input(input_shape1)\n",
    "  input_a = Input(input_shape2)\n",
    "  # encoded representation of input\n",
    "  conv1_p = Conv2D(128, (3, 3), activation='relu',padding = 'same')(input_p)\n",
    "  pool1_p = MaxPooling2D((2, 2))(conv1_p)\n",
    "  conv2_p = Conv2D(64, (3, 3), activation='relu',padding = 'same')(pool1_p)\n",
    "  pool2_p = MaxPooling2D((2, 2))(conv2_p)\n",
    "  conv3_p = Conv2D(32, (3, 3), activation='relu',padding = 'same')(pool2_p)\n",
    "  pool3_p = MaxPooling2D((2, 2))(conv3_p)\n",
    "\n",
    "  conv1_a = Conv2D(128, (3, 3), activation='relu',padding = 'same')(input_a)\n",
    "  pool1_a = MaxPooling2D((2, 2))(conv1_a)\n",
    "  conv2_a = Conv2D(64, (3, 3), activation='relu',padding = 'same')(pool1_a)\n",
    "  pool2_a = MaxPooling2D((2, 2))(conv2_a)\n",
    "  conv3_a = Conv2D(32, (3, 3), activation='relu',padding = 'same')(pool2_a)\n",
    "  pool3_a = MaxPooling2D((2, 2))(conv3_a)\n",
    "\n",
    "  flatten_p = Flatten()(pool3_p)\n",
    "  flatten_a = Flatten()(pool3_a)\n",
    "  combined = concatenate([flatten_p,flatten_a])\n",
    "  encoded = Dense(1000, activation='relu')(combined)\n",
    "  # decoded representation of code\n",
    "  decoded1 = Dense(np.prod(input_shape3), activation='relu')(encoded)\n",
    "  #decoded2 = Dense(600, activation='relu')(decoded1)\n",
    "  # Model which take input image and shows decoded images\n",
    "  autoencoder = Model([input_p,input_a], decoded1)\n",
    "\n",
    "  autoencoder.compile(optimizer='adam', loss='mse')\n",
    "  return autoencoder\n",
    "\n",
    "def P2W():\n",
    "  input_p = Input(shape=(100,21,1))\n",
    "  # encoded representation of input\n",
    "  conv1_p = Conv2D(128, (3, 3), activation='relu',padding = 'same')(input_p)\n",
    "  pool1_p = MaxPooling2D((2, 2))(conv1_p)\n",
    "  conv2_p = Conv2D(64, (3, 3), activation='relu',padding = 'same')(pool1_p)\n",
    "  pool2_p = MaxPooling2D((2, 2))(conv2_p)\n",
    "  conv3_p = Conv2D(32, (3, 3), activation='relu',padding = 'same')(pool2_p)\n",
    "  pool3_p = MaxPooling2D((2, 2))(conv3_p)\n",
    "\n",
    "  flatten_p = Flatten()(pool3_p)\n",
    "\n",
    "  encoded = Dense(1000, activation='relu')(flatten_p)\n",
    "  # decoded representation of code\n",
    "  decoded1 = Dense(600, activation='relu')(encoded)\n",
    "  # Model which take input image and shows decoded images\n",
    "  autoencoder = Model(input_p, decoded1)\n",
    "\n",
    "  autoencoder.compile(optimizer='adam', loss='mse')\n",
    "  return autoencoder\n",
    "\n",
    "def S2W():\n",
    "  input_p = Input(shape=(25,32,1))\n",
    "  # encoded representation of input\n",
    "  conv1_p = Conv2D(128, (3, 3), activation='relu',padding = 'same')(input_p)\n",
    "  pool1_p = MaxPooling2D((2, 2))(conv1_p)\n",
    "  conv2_p = Conv2D(64, (3, 3), activation='relu',padding = 'same')(pool1_p)\n",
    "  pool2_p = MaxPooling2D((2, 2))(conv2_p)\n",
    "  conv3_p = Conv2D(32, (3, 3), activation='relu',padding = 'same')(pool2_p)\n",
    "  pool3_p = MaxPooling2D((2, 2))(conv3_p)\n",
    "\n",
    "  flatten_p = Flatten()(pool3_p)\n",
    "\n",
    "  encoded = Dense(1000, activation='relu')(flatten_p)\n",
    "  # decoded representation of code\n",
    "  decoded1 = Dense(600, activation='relu')(encoded)\n",
    "  # Model which take input image and shows decoded images\n",
    "  autoencoder = Model(input_p, decoded1)\n",
    "\n",
    "  autoencoder.compile(optimizer='adam', loss='mse')\n",
    "  return autoencoder\n",
    "\n",
    "def W2P(input_shape=(20,30,1) ):\n",
    "  input_p = Input(input_shape)\n",
    "  # encoded representation of input\n",
    "  conv1_p = Conv2D(128, (3, 3), activation='relu',padding = 'same')(input_p)\n",
    "  pool1_p = MaxPooling2D((2, 2))(conv1_p)\n",
    "  conv2_p = Conv2D(64, (3, 3), activation='relu',padding = 'same')(pool1_p)\n",
    "  pool2_p = MaxPooling2D((2, 2))(conv2_p)\n",
    "  conv3_p = Conv2D(32, (3, 3), activation='relu',padding = 'same')(pool2_p)\n",
    "  pool3_p = MaxPooling2D((2, 2))(conv3_p)\n",
    "\n",
    "  flatten_p = Flatten()(pool3_p)\n",
    "\n",
    "  encoded = Dense(700, activation='relu')(flatten_p)\n",
    "  encoded1 = Dense(1500, activation='relu')(encoded)\n",
    "  # decoded representation of code\n",
    "  decoded1 = Dense(2100, activation='relu')(encoded1)\n",
    "  # Model which take input image and shows decoded images\n",
    "  autoencoder = Model(input_p, decoded1)\n",
    "\n",
    "  autoencoder.compile(optimizer='adam', loss='mse')\n",
    "  return autoencoder"
   ]
  }
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