complete Lab24

Lab2/examples/Lab24-xor.ipynb

@@ -0,0 +1,127 @@
+{
+ "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

@@ -0,0 +1,30 @@
+5.9,3,4.2,1.5,1
+6.9,3.1,5.4,2.1,2
+5.1,3.3,1.7,0.5,0
+6,3.4,4.5,1.6,1
+5.5,2.5,4,1.3,1
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+5.6,3,4.1,1.3,1
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+5.6,3,4.5,1.5,1
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+5.2,4.1,1.5,0.1,0
+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

@@ -0,0 +1,120 @@
+6.4,2.8,5.6,2.2,2
+5,2.3,3.3,1,1
+4.9,2.5,4.5,1.7,2
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+7.7,3.8,6.7,2.2,2
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+6.8,3.2,5.9,2.3,2
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+6.7,3.3,5.7,2.1,2
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+5.8,2.6,4,1.2,1
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+5.4,3,4.5,1.5,1
+6.7,3,5,1.7,1
+6.3,2.3,4.4,1.3,1
+5.1,2.5,3,1.1,1
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+6.8,3,5.5,2.1,2
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+6.9,3.2,5.7,2.3,2
+6.5,3.2,5.1,2,2
+5.8,2.8,5.1,2.4,2
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+5.8,2.7,5.1,1.9,2
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