From fbc74ce50f1c2a628488cce6035b14eb3e30522a Mon Sep 17 00:00:00 2001 From: Bruno Fernandes Date: Wed, 21 Feb 2024 21:10:26 +0000 Subject: [PATCH 1/2] [lab-deep-learning-intro] Bruno --- your-code/Untitled.ipynb | 51 + .../[lab-deep-learning-intro] Bruno.ipynb | 2256 +++++++++++++++++ your-code/challenge-1.ipynb | 152 -- 3 files changed, 2307 insertions(+), 152 deletions(-) create mode 100644 your-code/Untitled.ipynb create mode 100644 your-code/[lab-deep-learning-intro] Bruno.ipynb delete mode 100644 your-code/challenge-1.ipynb diff --git a/your-code/Untitled.ipynb b/your-code/Untitled.ipynb new file mode 100644 index 0000000..3ddce7a --- /dev/null +++ b/your-code/Untitled.ipynb @@ -0,0 +1,51 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "ddb071ce", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "^C\n" + ] + } + ], + "source": [ + "!pip install tensorflow pandas" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3d72af19", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/your-code/[lab-deep-learning-intro] Bruno.ipynb b/your-code/[lab-deep-learning-intro] Bruno.ipynb new file mode 100644 index 0000000..ad79857 --- /dev/null +++ b/your-code/[lab-deep-learning-intro] Bruno.ipynb @@ -0,0 +1,2256 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting tensorflow\n", + " Obtaining dependency 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google-pasta, gast, cachetools, astunparse, absl-py, requests-oauthlib, google-auth, google-auth-oauthlib, tensorboard, tensorflow-intel, tensorflow\n", + "Successfully installed absl-py-2.1.0 astunparse-1.6.3 cachetools-5.3.2 flatbuffers-23.5.26 gast-0.5.4 google-auth-2.28.0 google-auth-oauthlib-1.2.0 google-pasta-0.2.0 grpcio-1.60.1 keras-2.15.0 libclang-16.0.6 ml-dtypes-0.2.0 oauthlib-3.2.2 opt-einsum-3.3.0 protobuf-4.25.3 requests-oauthlib-1.3.1 rsa-4.9 tensorboard-2.15.2 tensorboard-data-server-0.7.2 tensorflow-2.15.0 tensorflow-estimator-2.15.0 tensorflow-intel-2.15.0 tensorflow-io-gcs-filesystem-0.31.0 termcolor-2.4.0\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "pip install tensorflow pandas" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Challenge 1 - Tic Tac Toe\n", + "\n", + "In this lab you will perform deep learning analysis on a dataset of playing [Tic Tac Toe](https://en.wikipedia.org/wiki/Tic-tac-toe).\n", + "\n", + "There are 9 grids in Tic Tac Toe that are coded as the following picture shows:\n", + "\n", + "![Tic Tac Toe Grids](tttboard.jpg)\n", + "\n", + "In the first 9 columns of the dataset you can find which marks (`x` or `o`) exist in the grids. If there is no mark in a certain grid, it is labeled as `b`. The last column is `class` which tells you whether Player X (who always moves first in Tic Tac Toe) wins in this configuration. Note that when `class` has the value `False`, it means either Player O wins the game or it ends up as a draw." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Follow the steps suggested below to conduct a neural network analysis using Tensorflow and Keras. You will build a deep learning model to predict whether Player X wins the game or not.\n", + "\n", + "## Step 1: Data Engineering\n", + "\n", + "This dataset is almost in the ready-to-use state so you do not need to worry about missing values and so on. Still, some simple data engineering is needed.\n", + "\n", + "1. Read `tic-tac-toe.csv` into a dataframe.\n", + "1. Inspect the dataset. Determine if the dataset is reliable by eyeballing the data.\n", + "1. Convert the categorical values to numeric in all columns.\n", + "1. Separate the inputs and output.\n", + "1. Normalize the input data." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import LabelEncoder" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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LabelEncoder()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "LabelEncoder()" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.preprocessing import LabelEncoder\n", + "le = LabelEncoder()\n", + "columns = ['TL', 'TM', 'TR', 'ML', 'MM', 'MR', 'BL', 'BM', 'BR']\n", + "for c in columns:\n", + " df[c] = le.fit_transform(df[c])\n", + "le" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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LabelEncoder()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "LabelEncoder()" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "label_encoder = LabelEncoder()\n", + "for column in df.columns:\n", + " df[column] = label_encoder.fit_transform(df[column])\n", + "label_encoder" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x (958, 9)\n", + "y (958,)\n" + ] + } + ], + "source": [ + "X = df.drop(columns=['class'])\n", + "y = df['class']\n", + "\n", + "print('x ',X.shape)\n", + "print('y ',y.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 1.00322257 1.08495342 1.00322257 ... 1.00322257 -0.16731812\n", + " -0.28682739]\n", + " [ 1.00322257 1.08495342 1.00322257 ... -0.28682739 1.08495342\n", + " -0.28682739]\n", + " [ 1.00322257 1.08495342 1.00322257 ... -0.28682739 -0.16731812\n", + " 1.00322257]\n", + " ...\n", + " [-0.28682739 1.08495342 -0.28682739 ... 1.00322257 -0.16731812\n", + " 1.00322257]\n", + " [-0.28682739 1.08495342 -0.28682739 ... 1.00322257 -0.16731812\n", + " 1.00322257]\n", + " [-0.28682739 -0.16731812 1.00322257 ... -0.28682739 1.08495342\n", + " 1.00322257]]\n", + "0 True\n", + "1 True\n", + "2 True\n", + "3 True\n", + "4 True\n", + " ... \n", + "953 False\n", + "954 False\n", + "955 False\n", + "956 False\n", + "957 False\n", + "Name: class, Length: 958, dtype: bool\n" + ] + } + ], + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "stdr = StandardScaler().fit(X)\n", + "X = stdr.transform(X)\n", + "print(X)\n", + "print(y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Build Neural Network\n", + "\n", + "To build the neural network, you can refer to your own codes you wrote while following the [Deep Learning with Python, TensorFlow, and Keras tutorial](https://www.youtube.com/watch?v=wQ8BIBpya2k) in the lesson. It's pretty similar to what you will be doing in this lab.\n", + "\n", + "1. Split the training and test data.\n", + "1. Create a `Sequential` model.\n", + "1. Add several layers to your model. Make sure you use ReLU as the activation function for the middle layers. Use Softmax for the output layer because each output has a single lable and all the label probabilities add up to 1.\n", + "1. Compile the model using `adam` as the optimizer and `sparse_categorical_crossentropy` as the loss function. For metrics, use `accuracy` for now.\n", + "1. Fit the training data.\n", + "1. Evaluate your neural network model with the test data.\n", + "1. Save your model as `tic-tac-toe.model`." + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense\n", + "from tensorflow.keras.utils import to_categorical\n", + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(718, 9)\n", + "(718,)\n", + "(240, 9)\n", + "(240,)\n" + ] + } + ], + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(X,y)\n", + "\n", + "print(X_train.shape)\n", + "print(y_train.shape)\n", + "print(X_test.shape)\n", + "print(y_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential([ \n", + " Dense(64, activation='relu', input_shape=(9,)),\n", + " Dense(64, activation='relu'), \n", + " Dense(10, activation='softmax'),\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [], + "source": [ + "model.compile(\n", + " optimizer='adam', \n", + " loss='sparse_categorical_crossentropy',\n", + " metrics=['accuracy'],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/30\n", + "WARNING:tensorflow:From C:\\Users\\btdjf\\anaconda3\\Lib\\site-packages\\keras\\src\\utils\\tf_utils.py:492: The name tf.ragged.RaggedTensorValue is deprecated. Please use tf.compat.v1.ragged.RaggedTensorValue instead.\n", + "\n", + "WARNING:tensorflow:From C:\\Users\\btdjf\\anaconda3\\Lib\\site-packages\\keras\\src\\engine\\base_layer_utils.py:384: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead.\n", + "\n", + "90/90 [==============================] - 2s 3ms/step - loss: 1.0810 - accuracy: 0.6267\n", + "Epoch 2/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.5848 - accuracy: 0.7103\n", + "Epoch 3/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.5223 - accuracy: 0.7674\n", + "Epoch 4/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.4758 - accuracy: 0.8064\n", + "Epoch 5/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.4346 - accuracy: 0.8287\n", + "Epoch 6/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.3901 - accuracy: 0.8524\n", + "Epoch 7/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.3622 - accuracy: 0.8677\n", + "Epoch 8/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.3360 - accuracy: 0.8816\n", + "Epoch 9/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.3118 - accuracy: 0.8914\n", + "Epoch 10/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.2917 - accuracy: 0.8983\n", + "Epoch 11/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.2646 - accuracy: 0.9053\n", + "Epoch 12/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.2504 - accuracy: 0.9150\n", + "Epoch 13/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.2363 - accuracy: 0.9220\n", + "Epoch 14/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.2148 - accuracy: 0.9359\n", + "Epoch 15/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.2001 - accuracy: 0.9304\n", + "Epoch 16/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.1807 - accuracy: 0.9485\n", + "Epoch 17/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.1664 - accuracy: 0.9568\n", + "Epoch 18/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.1495 - accuracy: 0.9596\n", + "Epoch 19/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.1400 - accuracy: 0.9652\n", + "Epoch 20/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.1235 - accuracy: 0.9777\n", + "Epoch 21/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.1147 - accuracy: 0.9749\n", + "Epoch 22/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0993 - accuracy: 0.9847\n", + "Epoch 23/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0901 - accuracy: 0.9833\n", + "Epoch 24/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0809 - accuracy: 0.9875\n", + "Epoch 25/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0743 - accuracy: 0.9916\n", + "Epoch 26/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0655 - accuracy: 0.9930\n", + "Epoch 27/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0567 - accuracy: 0.9958\n", + "Epoch 28/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0516 - accuracy: 0.9972\n", + "Epoch 29/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0458 - accuracy: 0.9986\n", + "Epoch 30/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0399 - accuracy: 1.0000\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fit(X_train,y_train, batch_size=8, epochs=30)" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8/8 [==============================] - 0s 3ms/step - loss: 0.2547 - accuracy: 0.9125\n" + ] + }, + { + "data": { + "text/plain": [ + "[0.25474846363067627, 0.9125000238418579]" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.evaluate(X_test,y_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Make Predictions\n", + "\n", + "Now load your saved model and use it to make predictions on a few random rows in the test dataset. Check if the predictions are correct." + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 0s 154ms/step\n", + "[0 1 1 1 1]\n", + "859 False\n", + "360 True\n", + "6 True\n", + "376 True\n", + "27 True\n", + "Name: class, dtype: bool\n" + ] + } + ], + "source": [ + "predictions = model.predict(X_test[:5])\n", + "print(np.argmax(predictions, axis=1))\n", + "print(y_test[:5])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Improve Your Model\n", + "\n", + "Did your model achieve low loss (<0.1) and high accuracy (>0.95)? If not, try to improve your model.\n", + "\n", + "But how? There are so many things you can play with in Tensorflow and in the next challenge you'll learn about these things. But in this challenge, let's just do a few things to see if they will help.\n", + "\n", + "* Add more layers to your model. If the data are complex you need more layers. But don't use more layers than you need. If adding more layers does not improve the model performance you don't need additional layers.\n", + "* Adjust the learning rate when you compile the model. This means you will create a custom `tf.keras.optimizers.Adam` instance where you specify the learning rate you want. Then pass the instance to `model.compile` as the optimizer.\n", + " * `tf.keras.optimizers.Adam` [reference](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Adam).\n", + " * Don't worry if you don't understand what the learning rate does. You'll learn about it in the next challenge.\n", + "* Adjust the number of epochs when you fit the training data to the model. Your model performance continues to improve as you train more epochs. But eventually it will reach the ceiling and the performance will stay the same." + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [], + "source": [ + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential([\n", + " Dense(64, activation='relu', input_shape=(9,)),\n", + " Dense(64, activation='relu'), \n", + " Dense(64, activation='relu'), \n", + " Dense(10, activation='softmax'), \n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [], + "source": [ + "opt = tf.keras.optimizers.Adam(learning_rate=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [], + "source": [ + "model.compile(\n", + " optimizer=opt, \n", + " loss='sparse_categorical_crossentropy',\n", + " metrics=['accuracy'],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/30\n", + "90/90 [==============================] - 1s 4ms/step - loss: 0.7123 - accuracy: 0.6685\n", + "Epoch 2/30\n", + "90/90 [==============================] - 0s 3ms/step - loss: 0.5083 - accuracy: 0.7758\n", + "Epoch 3/30\n", + "90/90 [==============================] - 0s 3ms/step - loss: 0.4696 - accuracy: 0.7799\n", + "Epoch 4/30\n", + "90/90 [==============================] - 0s 3ms/step - loss: 0.3465 - accuracy: 0.8579\n", + "Epoch 5/30\n", + "90/90 [==============================] - 0s 3ms/step - loss: 0.2878 - accuracy: 0.8844\n", + "Epoch 6/30\n", + "90/90 [==============================] - 0s 3ms/step - loss: 0.2304 - accuracy: 0.9123\n", + "Epoch 7/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.1282 - accuracy: 0.9554\n", + "Epoch 8/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.1705 - accuracy: 0.9359\n", + "Epoch 9/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.1621 - accuracy: 0.9415\n", + "Epoch 10/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0880 - accuracy: 0.9680\n", + "Epoch 11/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0289 - accuracy: 0.9903\n", + "Epoch 12/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0395 - accuracy: 0.9847\n", + "Epoch 13/30\n", + "90/90 [==============================] - 0s 3ms/step - loss: 0.1082 - accuracy: 0.9610\n", + "Epoch 14/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0844 - accuracy: 0.9708\n", + "Epoch 15/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0084 - accuracy: 0.9972\n", + "Epoch 16/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0303 - accuracy: 0.9903\n", + "Epoch 17/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0160 - accuracy: 0.9930\n", + "Epoch 18/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 9.1237e-04 - accuracy: 1.0000\n", + "Epoch 19/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 2.7773e-04 - accuracy: 1.0000\n", + "Epoch 20/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 1.7968e-04 - accuracy: 1.0000\n", + "Epoch 21/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 1.2872e-04 - accuracy: 1.0000\n", + "Epoch 22/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 9.9185e-05 - accuracy: 1.0000\n", + "Epoch 23/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 7.9463e-05 - accuracy: 1.0000\n", + "Epoch 24/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 6.6224e-05 - accuracy: 1.0000\n", + "Epoch 25/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 5.4306e-05 - accuracy: 1.0000\n", + "Epoch 26/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 4.5206e-05 - accuracy: 1.0000\n", + "Epoch 27/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 3.6903e-05 - accuracy: 1.0000\n", + "Epoch 28/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 2.9268e-05 - accuracy: 1.0000\n", + "Epoch 29/30\n", + "90/90 [==============================] - 0s 2ms/step - loss: 2.4178e-05 - accuracy: 1.0000\n", + "Epoch 30/30\n", + "90/90 [==============================] - 0s 3ms/step - loss: 2.0749e-05 - accuracy: 1.0000\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fit(X_train,y_train, batch_size=8, epochs=30)" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8/8 [==============================] - 0s 3ms/step - loss: 0.0201 - accuracy: 0.9875\n" + ] + }, + { + "data": { + "text/plain": [ + "[0.02011324092745781, 0.987500011920929]" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.evaluate(X_test,y_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Which approach(es) did you find helpful to improve your model performance?**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your answer here" + ] + } + ], + "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": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-code/challenge-1.ipynb b/your-code/challenge-1.ipynb deleted file mode 100644 index 2487c5f..0000000 --- a/your-code/challenge-1.ipynb +++ /dev/null @@ -1,152 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Challenge 1 - Tic Tac Toe\n", - "\n", - "In this lab you will perform deep learning analysis on a dataset of playing [Tic Tac Toe](https://en.wikipedia.org/wiki/Tic-tac-toe).\n", - "\n", - "There are 9 grids in Tic Tac Toe that are coded as the following picture shows:\n", - "\n", - "![Tic Tac Toe Grids](tttboard.jpg)\n", - "\n", - "In the first 9 columns of the dataset you can find which marks (`x` or `o`) exist in the grids. If there is no mark in a certain grid, it is labeled as `b`. The last column is `class` which tells you whether Player X (who always moves first in Tic Tac Toe) wins in this configuration. Note that when `class` has the value `False`, it means either Player O wins the game or it ends up as a draw." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Follow the steps suggested below to conduct a neural network analysis using Tensorflow and Keras. You will build a deep learning model to predict whether Player X wins the game or not.\n", - "\n", - "## Step 1: Data Engineering\n", - "\n", - "This dataset is almost in the ready-to-use state so you do not need to worry about missing values and so on. Still, some simple data engineering is needed.\n", - "\n", - "1. Read `tic-tac-toe.csv` into a dataframe.\n", - "1. Inspect the dataset. Determine if the dataset is reliable by eyeballing the data.\n", - "1. Convert the categorical values to numeric in all columns.\n", - "1. Separate the inputs and output.\n", - "1. Normalize the input data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 2: Build Neural Network\n", - "\n", - "To build the neural network, you can refer to your own codes you wrote while following the [Deep Learning with Python, TensorFlow, and Keras tutorial](https://www.youtube.com/watch?v=wQ8BIBpya2k) in the lesson. It's pretty similar to what you will be doing in this lab.\n", - "\n", - "1. Split the training and test data.\n", - "1. Create a `Sequential` model.\n", - "1. Add several layers to your model. Make sure you use ReLU as the activation function for the middle layers. Use Softmax for the output layer because each output has a single lable and all the label probabilities add up to 1.\n", - "1. Compile the model using `adam` as the optimizer and `sparse_categorical_crossentropy` as the loss function. For metrics, use `accuracy` for now.\n", - "1. Fit the training data.\n", - "1. Evaluate your neural network model with the test data.\n", - "1. Save your model as `tic-tac-toe.model`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 3: Make Predictions\n", - "\n", - "Now load your saved model and use it to make predictions on a few random rows in the test dataset. Check if the predictions are correct." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 4: Improve Your Model\n", - "\n", - "Did your model achieve low loss (<0.1) and high accuracy (>0.95)? If not, try to improve your model.\n", - "\n", - "But how? There are so many things you can play with in Tensorflow and in the next challenge you'll learn about these things. But in this challenge, let's just do a few things to see if they will help.\n", - "\n", - "* Add more layers to your model. If the data are complex you need more layers. But don't use more layers than you need. If adding more layers does not improve the model performance you don't need additional layers.\n", - "* Adjust the learning rate when you compile the model. This means you will create a custom `tf.keras.optimizers.Adam` instance where you specify the learning rate you want. Then pass the instance to `model.compile` as the optimizer.\n", - " * `tf.keras.optimizers.Adam` [reference](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Adam).\n", - " * Don't worry if you don't understand what the learning rate does. You'll learn about it in the next challenge.\n", - "* Adjust the number of epochs when you fit the training data to the model. Your model performance continues to improve as you train more epochs. But eventually it will reach the ceiling and the performance will stay the same." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Which approach(es) did you find helpful to improve your model performance?**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your answer here" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "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": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 19df48a8b3050559174636219944d1c0a385bf78 Mon Sep 17 00:00:00 2001 From: Bruno2391 <145012336+Bruno2391@users.noreply.github.com> Date: Wed, 21 Feb 2024 21:13:14 +0000 Subject: [PATCH 2/2] Delete your-code/Untitled.ipynb --- your-code/Untitled.ipynb | 51 ---------------------------------------- 1 file changed, 51 deletions(-) delete mode 100644 your-code/Untitled.ipynb diff --git a/your-code/Untitled.ipynb b/your-code/Untitled.ipynb deleted file mode 100644 index 3ddce7a..0000000 --- a/your-code/Untitled.ipynb +++ /dev/null @@ -1,51 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "ddb071ce", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "^C\n" - ] - } - ], - "source": [ - "!pip install tensorflow pandas" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3d72af19", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "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": "3.11.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -}