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2 changes: 1 addition & 1 deletion your-code/bonus.ipynb
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -144,7 +144,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
"version": "3.9.6"
}
},
"nbformat": 4,
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320 changes: 313 additions & 7 deletions your-code/challenge-1.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -34,11 +34,253 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"# your code here"
"import numpy as np\n",
"import pandas as pd \n",
"from sklearn.model_selection import train_test_split\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Dense\n",
"from tensorflow.keras.utils import to_categorical"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# your code here\n",
"tic = pd.read_csv('tic-tac-toe.csv')\n",
"tic.replace('x', 1, inplace=True)\n",
"tic.replace('0', -1, inplace=True)\n",
"tic.replace('b', 0, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>TL</th>\n",
" <th>TM</th>\n",
" <th>TR</th>\n",
" <th>ML</th>\n",
" <th>MM</th>\n",
" <th>MR</th>\n",
" <th>BL</th>\n",
" <th>BM</th>\n",
" <th>BR</th>\n",
" <th>class</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>o</td>\n",
" <td>o</td>\n",
" <td>1</td>\n",
" <td>o</td>\n",
" <td>o</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>o</td>\n",
" <td>o</td>\n",
" <td>o</td>\n",
" <td>1</td>\n",
" <td>o</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>o</td>\n",
" <td>o</td>\n",
" <td>o</td>\n",
" <td>o</td>\n",
" <td>1</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>o</td>\n",
" <td>o</td>\n",
" <td>o</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>o</td>\n",
" <td>o</td>\n",
" <td>0</td>\n",
" <td>o</td>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>953</th>\n",
" <td>o</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>o</td>\n",
" <td>o</td>\n",
" <td>o</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>954</th>\n",
" <td>o</td>\n",
" <td>1</td>\n",
" <td>o</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>o</td>\n",
" <td>1</td>\n",
" <td>o</td>\n",
" <td>1</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>955</th>\n",
" <td>o</td>\n",
" <td>1</td>\n",
" <td>o</td>\n",
" <td>1</td>\n",
" <td>o</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>o</td>\n",
" <td>1</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>956</th>\n",
" <td>o</td>\n",
" <td>1</td>\n",
" <td>o</td>\n",
" <td>o</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>o</td>\n",
" <td>1</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>957</th>\n",
" <td>o</td>\n",
" <td>o</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>o</td>\n",
" <td>o</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>958 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" TL TM TR ML MM MR BL BM BR class\n",
"0 1 1 1 1 o o 1 o o True\n",
"1 1 1 1 1 o o o 1 o True\n",
"2 1 1 1 1 o o o o 1 True\n",
"3 1 1 1 1 o o o 0 0 True\n",
"4 1 1 1 1 o o 0 o 0 True\n",
".. .. .. .. .. .. .. .. .. .. ...\n",
"953 o 1 1 1 o o o 1 1 False\n",
"954 o 1 o 1 1 o 1 o 1 False\n",
"955 o 1 o 1 o 1 1 o 1 False\n",
"956 o 1 o o 1 1 1 o 1 False\n",
"957 o o 1 1 1 o o 1 1 False\n",
"\n",
"[958 rows x 10 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tic"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# your code here\n",
"y = np.array(tic['class'])\n",
"x = np.array(tic.drop(labels='class', axis=1))"
]
},
{
Expand All @@ -60,11 +302,29 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 26,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"array([[0., 1.],\n",
" [0., 1.],\n",
" [1., 0.],\n",
" ...,\n",
" [0., 1.],\n",
" [0., 1.],\n",
" [0., 1.]], dtype=float32)"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# your code here"
"x_train, x_test, y_train, y_test = train_test_split(x, y,test_size = 0.2, random_state=29)\n",
"to_categorical(y_train)"
]
},
{
Expand All @@ -82,7 +342,53 @@
"metadata": {},
"outputs": [],
"source": [
"# your code here"
"# your code here\n",
"#Create a Sequential model.\n",
"model = Sequential([\n",
" Dense(18, activation='relu'),\n",
" Dense(18, activation='relu'),\n",
" Dense(2, activation='softmax'),\n",
"])"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"# Compile the model using adam as the optimizer\n",
"\n",
"model.compile(\n",
" optimizer='adam',\n",
" loss='saparse_categorical_crossentropy',\n",
" metrics=['accuracy'],\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Fit the training data\n",
"history = model.fit(X_train, y_train, epochs=50, batch_size=40)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"\n",
"#Evaluate your neural network model with the test data.\n",
"model.evaluate(\n",
" x_test,\n",
" y_test\n",
")"
]
},
{
Expand Down Expand Up @@ -144,7 +450,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
"version": "3.9.6"
}
},
"nbformat": 4,
Expand Down