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256 changes: 243 additions & 13 deletions your-code/challenge-1.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -34,11 +34,139 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 107,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 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"
]
}
],
"source": [
"# your code here\n",
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"df = pd.read_csv('tic-tac-toe.csv')\n",
"\n",
"print(df.head())\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 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",
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 958 entries, 0 to 957\n",
"Data columns (total 10 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 TL 958 non-null object\n",
" 1 TM 958 non-null object\n",
" 2 TR 958 non-null object\n",
" 3 ML 958 non-null object\n",
" 4 MM 958 non-null object\n",
" 5 MR 958 non-null object\n",
" 6 BL 958 non-null object\n",
" 7 BM 958 non-null object\n",
" 8 BR 958 non-null object\n",
" 9 class 958 non-null bool \n",
"dtypes: bool(1), object(9)\n",
"memory usage: 68.4+ KB\n",
"None\n",
"TL 0\n",
"TM 0\n",
"TR 0\n",
"ML 0\n",
"MM 0\n",
"MR 0\n",
"BL 0\n",
"BM 0\n",
"BR 0\n",
"class 0\n",
"dtype: int64\n"
]
}
],
"source": [
"print(df.head())\n",
"print(df.info())\n",
"print(df.isnull().sum())"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {},
"outputs": [],
"source": [
"# your code here"
"# Apply explicit mapping for grid features\n",
"# Map 'x' -> 1, 'o' -> -1, 'b' -> 0\n",
"value_map = {'x': 1, 'o': -1, 'b': 0}\n",
"for col in df.columns[:-1]: # Skip the last column which is the target ('class')\n",
" df[col] = df[col].map(value_map)"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {},
"outputs": [],
"source": [
"X = df.drop(columns=['class'])\n",
"y = df['class']"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" TL TM TR ML MM MR BL BM BR\n",
"302 -1 1 1 -1 1 -1 0 1 0\n",
"467 -1 0 -1 -1 1 0 1 1 1\n",
"294 1 0 0 0 1 0 -1 -1 1\n",
"548 0 -1 1 -1 0 1 0 0 1\n",
"465 -1 0 -1 1 -1 0 1 1 1\n",
"302 True\n",
"467 True\n",
"294 True\n",
"548 True\n",
"465 True\n",
"Name: class, dtype: bool\n"
]
}
],
"source": [
"x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
"\n",
"# Print the first few rows to verify\n",
"print(x_train.head())\n",
"print(y_train.head())"
]
},
{
Expand All @@ -60,11 +188,53 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 112,
"metadata": {},
"outputs": [],
"source": [
"# your code here\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"from keras import layers\n",
"from keras import models"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Step 1: Split the Traning and test Data"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {},
"outputs": [],
"source": [
"# your code here"
"model = models.Sequential()\n",
"\n",
"model.add(layers.Dense(64, activation='relu', input_shape=(x_train.shape[1],)))\n",
"model.add(layers.Dense(32, activation='relu'))\n",
"model.add(layers.Dense(1, activation='sigmoid'))\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Step 2: Compile the model"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {},
"outputs": [],
"source": [
"model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])"
]
},
{
Expand All @@ -78,11 +248,59 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 118,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/20\n",
"31/31 [==============================] - 5s 155ms/step - loss: 0.0098 - accuracy: 1.0000 - val_loss: 0.0443 - val_accuracy: 0.9870\n",
"Epoch 2/20\n",
"31/31 [==============================] - 5s 152ms/step - loss: 0.0091 - accuracy: 1.0000 - val_loss: 0.0409 - val_accuracy: 0.9870\n",
"Epoch 3/20\n",
"31/31 [==============================] - 2s 74ms/step - loss: 0.0081 - accuracy: 1.0000 - val_loss: 0.0415 - val_accuracy: 0.9870\n",
"Epoch 4/20\n",
"31/31 [==============================] - 2s 81ms/step - loss: 0.0073 - accuracy: 1.0000 - val_loss: 0.0390 - val_accuracy: 0.9870\n",
"Epoch 5/20\n",
"31/31 [==============================] - 2s 49ms/step - loss: 0.0066 - accuracy: 1.0000 - val_loss: 0.0393 - val_accuracy: 0.9870\n",
"Epoch 6/20\n",
"31/31 [==============================] - 1s 30ms/step - loss: 0.0061 - accuracy: 1.0000 - val_loss: 0.0383 - val_accuracy: 0.9870\n",
"Epoch 7/20\n",
"31/31 [==============================] - 2s 51ms/step - loss: 0.0058 - accuracy: 1.0000 - val_loss: 0.0373 - val_accuracy: 0.9870\n",
"Epoch 8/20\n",
"31/31 [==============================] - 1s 35ms/step - loss: 0.0054 - accuracy: 1.0000 - val_loss: 0.0383 - val_accuracy: 0.9870\n",
"Epoch 9/20\n",
"31/31 [==============================] - 2s 70ms/step - loss: 0.0048 - accuracy: 1.0000 - val_loss: 0.0367 - val_accuracy: 0.9870\n",
"Epoch 10/20\n",
"31/31 [==============================] - 2s 58ms/step - loss: 0.0045 - accuracy: 1.0000 - val_loss: 0.0350 - val_accuracy: 0.9870\n",
"Epoch 11/20\n",
"31/31 [==============================] - 2s 60ms/step - loss: 0.0041 - accuracy: 1.0000 - val_loss: 0.0350 - val_accuracy: 0.9870\n",
"Epoch 12/20\n",
"31/31 [==============================] - 1s 44ms/step - loss: 0.0039 - accuracy: 1.0000 - val_loss: 0.0353 - val_accuracy: 0.9870\n",
"Epoch 13/20\n",
"31/31 [==============================] - 3s 91ms/step - loss: 0.0036 - accuracy: 1.0000 - val_loss: 0.0334 - val_accuracy: 0.9870\n",
"Epoch 14/20\n",
"31/31 [==============================] - 2s 64ms/step - loss: 0.0034 - accuracy: 1.0000 - val_loss: 0.0331 - val_accuracy: 0.9870\n",
"Epoch 15/20\n",
"31/31 [==============================] - 2s 72ms/step - loss: 0.0032 - accuracy: 1.0000 - val_loss: 0.0333 - val_accuracy: 0.9870\n",
"Epoch 16/20\n",
"31/31 [==============================] - 3s 86ms/step - loss: 0.0030 - accuracy: 1.0000 - val_loss: 0.0321 - val_accuracy: 0.9870\n",
"Epoch 17/20\n",
"31/31 [==============================] - 3s 109ms/step - loss: 0.0028 - accuracy: 1.0000 - val_loss: 0.0332 - val_accuracy: 0.9870\n",
"Epoch 18/20\n",
"31/31 [==============================] - 2s 80ms/step - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.0330 - val_accuracy: 0.9870\n",
"Epoch 19/20\n",
"31/31 [==============================] - 2s 62ms/step - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.0314 - val_accuracy: 0.9870\n",
"Epoch 20/20\n",
"31/31 [==============================] - 1s 29ms/step - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.0308 - val_accuracy: 0.9870\n"
]
}
],
"source": [
"# your code here"
"# your code here\n",
"history = model.fit(x_train, y_train, epochs=26, batch_size=16, validation_split=0.2)\n"
]
},
{
Expand All @@ -104,11 +322,22 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 116,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"6/6 [==============================] - 0s 54ms/step - loss: 0.0615 - accuracy: 0.9792\n",
"Test Accuracy: 0.9792\n"
]
}
],
"source": [
"# your code here"
"# your code here\n",
"test_loss, test_accuracy = model.evaluate(x_test, y_test)\n",
"print(f'Test Accuracy: {test_accuracy:.4f}')"
]
},
{
Expand All @@ -120,11 +349,12 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 117,
"metadata": {},
"outputs": [],
"source": [
"# your answer here"
"# your answer here\n",
"#THe use of 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."
]
}
],
Expand All @@ -144,7 +374,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
"version": "3.10.13"
}
},
"nbformat": 4,
Expand Down