Lab2/examples/Lab24-xor.ipynb

@@ -1,127 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "ab7d7dbd-f2e0-4384-8c55-d4aeee74dd7c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The tensorboard extension is already loaded. To reload it, use:\n",
-      "  %reload_ext tensorboard\n",
-      "WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe3e8248550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
-      "1/1 [==============================] - 0s 33ms/step\n",
-      "[[0.03452728]\n",
-      " [0.9867295 ]\n",
-      " [0.9883936 ]\n",
-      " [0.01205833]]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "Reusing TensorBoard on port 6008 (pid 911540), started 0:22:54 ago. (Use '!kill 911540' to kill it.)"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "      <iframe id=\"tensorboard-frame-e941beefef1b33a0\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
-       "      </iframe>\n",
-       "      <script>\n",
-       "        (function() {\n",
-       "          const frame = document.getElementById(\"tensorboard-frame-e941beefef1b33a0\");\n",
-       "          const url = new URL(\"/\", window.location);\n",
-       "          const port = 6008;\n",
-       "          if (port) {\n",
-       "            url.port = port;\n",
-       "          }\n",
-       "          frame.src = url;\n",
-       "        })();\n",
-       "      </script>\n",
-       "    "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%load_ext tensorboard\n",
-    "\n",
-    "import tensorflow as tf\n",
-    "import numpy as np \n",
-    "import datetime, os\n",
-    "\n",
-    "tf.config.experimental.set_visible_devices([], \"GPU\") \n",
-    "\n",
-    "X = np.array([[0,0],[0,1],[1,0],[1,1]])\n",
-    "y = np.array([[0],[1],[1],[0]])\n",
-    "    \n",
-    "model = tf.keras.models.Sequential([\n",
-    "    tf.keras.layers.Dense(8, activation='relu', name='layers_dense_1'),\n",
-    "    tf.keras.layers.Dense(8, activation='relu', name='layers_dense_2'),\n",
-    "    tf.keras.layers.Dense(1, activation='sigmoid', name='layers_dense_3')\n",
-    "])\n",
-    "    \n",
-    "loss_fn = tf.keras.losses.binary_crossentropy\n",
-    "\n",
-    "simple = True\n",
-    "\n",
-    "if simple == True:\n",
-    "    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
-    "    \n",
-    "    logdir = os.path.join(\"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
-    "    tensorboard_callback = tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)\n",
-    "    model.fit(X, y, batch_size=4, epochs=1000, verbose=0, callbacks = [tensorboard_callback])\n",
-    "else:\n",
-    "    for i in range(100):\n",
-    "        with tf.GradientTape() as tape:\n",
-    "            # Forward pass.\n",
-    "            predictions = model(X)\n",
-    "            # Compute the loss value for this batch.\n",
-    "            loss_value = loss_fn(y, predictions)\n",
-    "\n",
-    "        # Get gradients of loss wrt the weights.\n",
-    "        gradients = tape.gradient(loss_value, model.trainable_weights)\n",
-    "        # Update the weights of the model.\n",
-    "        optimizer.apply_gradients(zip(gradients, model.trainable_weights))\n",
-    "\n",
-    "print(model.predict(X))\n",
-    "\n",
-    "%tensorboard --logdir logs"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "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": "3.8.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

Lab2/examples/datasets/iris_test.csv

@@ -1,30 +0,0 @@
-5.9,3,4.2,1.5,1
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-6.7,3.1,4.7,1.5,1
-6.7,3.3,5.7,2.5,2
-6.4,2.9,4.3,1.3,1

Lab2/examples/datasets/iris_train.csv

@@ -1,120 +0,0 @@
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-4.9,2.5,4.5,1.7,2
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