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bel_semantics.ipynb

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+{
+ "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": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "semantics",
+   "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": "3.12.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}