From dcc5e29db7b05cd114ab8edc6d383a61b0c76a14 Mon Sep 17 00:00:00 2001 From: Grimngor Date: Mon, 23 Sep 2024 17:39:47 +0200 Subject: [PATCH] Lab - Neural Networks --- your-code/challenge-1.ipynb | 380 ++++++++++++++++++++++++++++++++++-- your-code/challenge-2.png | Bin 52468 -> 282951 bytes your-code/tic-tac-toe.keras | Bin 0 -> 58670 bytes 3 files changed, 367 insertions(+), 13 deletions(-) create mode 100644 your-code/tic-tac-toe.keras diff --git a/your-code/challenge-1.ipynb b/your-code/challenge-1.ipynb index 2487c5f..47e1866 100644 --- a/your-code/challenge-1.ipynb +++ b/your-code/challenge-1.ipynb @@ -34,11 +34,260 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 326, "metadata": {}, "outputs": [], "source": [ - "# your code here" + "# Importing Libraries\n", + "\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import f1_score\n", + "\n", + "import tensorflow as tf\n", + "from keras.models import Sequential, load_model\n", + "from keras.layers import Dense, Input" + ] + }, + { + "cell_type": "code", + "execution_count": 327, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " TL TM TR ML MM MR BL BM BR class\n", + "0 x x x x o o x o o True\n", + "1 x x x x o o o x o True\n", + "2 x x x x o o o o x True\n", + "3 x x x x o o o b b True\n", + "4 x x x x o o b o b True\n", + ".. .. .. .. .. .. .. .. .. .. ...\n", + "953 o x x x o o o x x False\n", + "954 o x o x x o x o x False\n", + "955 o x o x o x x o x False\n", + "956 o x o o x x x o x False\n", + "957 o o x x x o o x x False\n", + "\n", + "[958 rows x 10 columns]" + ] + }, + "execution_count": 327, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ttt_df = pd.read_csv('tic-tac-toe.csv')\n", + "ttt_df" + ] + }, + { + "cell_type": "code", + "execution_count": 328, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Javi\\AppData\\Local\\Temp\\ipykernel_87964\\907362345.py:3: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", + " ttt_df = ttt_df.replace({'x':1, 'o':0, 'b':0.5, True:1, False:0})\n" + ] + } + ], + "source": [ + "# Assigning b to 0.5 instead of '2' or '-1' makes it so we don't need to normalize (0-1)\n", + "# as all the values are already in the range.\n", + "ttt_df = ttt_df.replace({'x':1, 'o':0, 'b':0.5, True:1, False:0})\n", + "\n", + "X = ttt_df.drop(\"class\", axis=1)\n", + "y = ttt_df[\"class\"]" ] }, { @@ -60,11 +309,63 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 331, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 331, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Splitting the dataset into the Training set and Test set (70/30)\n", + "X_test, X_train, y_test, y_train = train_test_split(X, y, test_size=0.3, random_state=42)\n", + "\n", + "# Building the model\n", + "def seq_model():\n", + " model = Sequential()\n", + " model.add(Input(shape=(X.shape[1],)))\n", + " model.add(Dense(50, activation='relu'))\n", + " model.add(Dense(50, activation='relu'))\n", + " model.add(Dense(2, activation='softmax'))\n", + "\n", + " model.compile(optimizer=\"adam\", metrics=['accuracy'], loss='sparse_categorical_crossentropy')\n", + " return model\n", + "\n", + "ttt_model = seq_model()\n", + "\n", + "# Training the model\n", + "ttt_model.fit(X_train, y_train, epochs=100, verbose=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 332, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 475us/step - accuracy: 0.9396 - loss: 0.1679\n", + "Accuracy: 0.9507\n", + "Loss:\t 0.1637\n" + ] + } + ], "source": [ - "# your code here" + "# Model evaluation on test data\n", + "eval = ttt_model.evaluate(X_test, y_test)\n", + "print(f\"Accuracy: {eval[1].__round__(4)}\\nLoss:\\t {eval[0].__round__(4)}\")\n", + "\n", + "# Saving the model\n", + "ttt_model.save(\"tic-tac-toe.keras\")" ] }, { @@ -78,11 +379,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 319, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n", + "F1 Score: 0.9841\n" + ] + } + ], "source": [ - "# your code here" + "model = load_model(\"tic-tac-toe.keras\")\n", + "\n", + "y_pred = model.predict(X_test)\n", + "y_pred_labels = np.argmax(y_pred, axis=1)\n", + "\n", + "f1_scr = f1_score(y_test, y_pred_labels)\n", + "print(f'F1 Score: {f1_scr:.4f}')" ] }, { @@ -104,11 +420,46 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 351, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 902us/step - accuracy: 0.9758 - loss: 0.0836\n", + "Accuracy: 0.9761\n", + "Loss:\t 0.0962\n" + ] + } + ], "source": [ - "# your code here" + "# Creating an instance of Adam optimizer to test changes in the learning rate\n", + "custom_adam = tf.keras.optimizers.Adam(learning_rate=0.0005) # Default = 0.001\n", + "\n", + "# Splitting the dataset into the Training set and Test set (70/30)\n", + "X_test, X_train, y_test, y_train = train_test_split(X, y, test_size=0.3, random_state=42)\n", + "\n", + "# Building the model\n", + "def seq_model():\n", + " model = Sequential()\n", + " model.add(Input(shape=(X.shape[1],)))\n", + " model.add(Dense(1000, activation='relu'))\n", + " model.add(Dense(500, activation='relu'))\n", + " model.add(Dense(50, activation='relu'))\n", + " model.add(Dense(2, activation='softmax'))\n", + "\n", + " model.compile(optimizer=custom_adam, metrics=['accuracy'], loss='sparse_categorical_crossentropy')\n", + " return model\n", + "\n", + "ttt_model = seq_model()\n", + "\n", + "# Training the model\n", + "ttt_model.fit(X_train, y_train, epochs=100, verbose=0)\n", + "\n", + "# Model evaluation on test data\n", + "eval = ttt_model.evaluate(X_test, y_test)\n", + "print(f\"Accuracy: {eval[1].__round__(4)}\\nLoss:\\t {eval[0].__round__(4)}\")" ] }, { @@ -124,7 +475,10 @@ "metadata": {}, "outputs": [], "source": [ - "# your answer here" + "\"\"\" \n", + "Reducing the learning rate, adding at least one layer and increasing the epochs a bit\n", + "all seemed to improve the model.\n", + "\"\"\"" ] } ], @@ -144,7 +498,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.12.4" } }, "nbformat": 4, diff --git a/your-code/challenge-2.png b/your-code/challenge-2.png index 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