semantics/bel_semantics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import net.modules\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "from net.transcoder import Transcoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "filepath = 'data/bel_data_test.csv'\n",
    "train_loader, test_loader, input_size = load_and_prepare_data(file_path=filepath)\n",
    "\n",
    "print(\"X_train shape:\", input_size.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# input_size = X_train.shape[0]\n",
    "# hidden_size = 128\n",
    "# output_size = 61\n",
    "\n",
    "architecture = [input_size, [128], 61]\n",
    "activations = ['leaky_relu','softmax']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize transcoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bl_transcoder = Transcoder(input_size, hidden_size, output_size, 'leaky_relu', 'softmax')\n",
    "bl_transcoder = Transcoder(architecture, hidden_activation='relu', output_activation='softmax')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Encoders and save weights\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Train the encoder if need\n",
    "\n",
    "bl_transcoder.train_model(train_loader, test_loader, learning_rate=0.001, epochs=1000)\n",
    "# bl_transcoder.train_with_validation(X_train, Y_train, alpha=0.1, iterations=1000)\n",
    "bl_transcoder.save_results('bt_1h128n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bl_transcoder.load_weights('weights/bt_1h128n_leaky_relu_weights.pth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot learning curves\n",
    "bl_transcoder.plot_learning_curves()\n",
    "\n",
    "# Visualize encoded space\n",
    "bl_transcoder.plot_encoded_space(X_test, Y_test)\n",
    "\n",
    "print(X_test.shape)\n",
    "print(X_train.shape)\n",
    "# Check reconstructions\n",
    "bl_transcoder.plot_reconstructions(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transcode images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_images = 2\n",
    "indices = np.random.choice(X_test.shape[1], num_images, replace=False)\n",
    "\n",
    "for idx in indices:\n",
    "    original_image = X_test[:, idx]\n",
    "    \n",
    "    # Encode the image\n",
    "    encoded = bl_transcoder.encode_image(original_image.reshape(-1, 1))\n",
    "    \n",
    "    # Decode the image\n",
    "    decoded = bl_transcoder.decode_image(encoded)\n",
    "\n",
    "    # Visualize original, encoded, and decoded images\n",
    "    visualize_transcoding(original_image, encoded, decoded, idx)\n",
    "\n",
    "    print(f\"Image {idx}:\")\n",
    "    print(\"Original shape:\", original_image.shape)\n",
    "    print(\"Encoded shape:\", encoded.shape)\n",
    "    print(\"Decoded shape:\", decoded.shape)\n",
    "    print(\"Encoded vector:\", encoded.flatten())  # Print flattened encoded vector\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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