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218 changes: 202 additions & 16 deletions your-code/challenge-1.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -34,11 +34,57 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 7,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" TL TM TR ML MM MR BL BM BR class\n",
"0 2 2 2 2 1 1 2 1 1 1\n",
"1 2 2 2 2 1 1 1 2 1 1\n",
"2 2 2 2 2 1 1 1 1 2 1\n",
"3 2 2 2 2 1 1 1 0 0 1\n",
"4 2 2 2 2 1 1 0 1 0 1\n"
]
}
],
"source": [
"# your code here"
"# your code here\n",
"\n",
"# import libraries\n",
"import pandas as pd\n",
"from sklearn import preprocessing\n",
"le = preprocessing.LabelEncoder()\n",
"\n",
"# read data\n",
"tictac = pd.read_csv(\"/Users/rickardramhoj/ironhack_assignments/lab-deep-learning/your-code/tic-tac-toe.csv\")\n",
"\n",
"# convert to numerical\n",
"tictac[\"TL\"] = le.fit_transform(tictac[\"TL\"])\n",
"tictac[\"TM\"] = le.fit_transform(tictac[\"TM\"])\n",
"tictac[\"TR\"] = le.fit_transform(tictac[\"TR\"])\n",
"tictac[\"ML\"] = le.fit_transform(tictac[\"ML\"])\n",
"tictac[\"MM\"] = le.fit_transform(tictac[\"MM\"])\n",
"tictac[\"MR\"] = le.fit_transform(tictac[\"MR\"])\n",
"tictac[\"BL\"] = le.fit_transform(tictac[\"BL\"])\n",
"tictac[\"BM\"] = le.fit_transform(tictac[\"BM\"])\n",
"tictac[\"BR\"] = le.fit_transform(tictac[\"BR\"])\n",
"tictac[\"class\"] = le.fit_transform(tictac[\"class\"])\n",
"\n",
"# define input\n",
"features = tictac.drop([\"class\"], axis=1)\n",
"\n",
"# define output\n",
"target = tictac[\"class\"]\n",
"\n",
"# normalize input\n",
"scaler = preprocessing.StandardScaler()\n",
"features = scaler.fit_transform(features)\n",
"\n",
"# print head\n",
"print(tictac.head())\n"
]
},
{
Expand All @@ -60,11 +106,72 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 11,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/3\n",
"23/23 [==============================] - 3s 25ms/step - loss: 0.6181 - accuracy: 0.6630\n",
"Epoch 2/3\n",
"23/23 [==============================] - 0s 21ms/step - loss: 0.5508 - accuracy: 0.7312\n",
"Epoch 3/3\n",
"23/23 [==============================] - 0s 12ms/step - loss: 0.5080 - accuracy: 0.7855 0s - loss: 0.5403 - accuracy: \n",
"8/8 [==============================] - 0s 3ms/step - loss: 0.5051 - accuracy: 0.7542\n",
"0.5050691366195679\n",
"0.7541666626930237\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2021-09-28 21:11:03.580642: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:Assets written to: tic-tac-toe.model/assets\n"
]
}
],
"source": [
"# your code here"
"# your code here\n",
"\n",
"# import libraries\n",
"from sklearn.model_selection import train_test_split\n",
"import tensorflow as tf\n",
"\n",
"# split into train and test\n",
"X_train, X_test, y_train, y_test = train_test_split(features, target, random_state=1)\n",
"\n",
"# define model\n",
"model = tf.keras.models.Sequential()\n",
"\n",
"# add layers\n",
"model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))\n",
"model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))\n",
"model.add(tf.keras.layers.Dense(2, activation=tf.nn.softmax))\n",
"\n",
"# compile model\n",
"model.compile(optimizer='adam',\n",
" loss='sparse_categorical_crossentropy',\n",
" metrics=['accuracy'])\n",
"\n",
"# fit model\n",
"model.fit(X_train, y_train, epochs=3)\n",
"\n",
"# evaluate model\n",
"val_loss, val_acc = model.evaluate(X_test, y_test)\n",
"print(val_loss)\n",
"print(val_acc)\n",
"\n",
"# save model\n",
"model.save('tic-tac-toe.model')"
]
},
{
Expand All @@ -78,11 +185,39 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 12,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[0.35851267 0.64148736]\n",
" [0.25878274 0.7412172 ]\n",
" [0.2545635 0.74543643]\n",
" [0.22912289 0.7708771 ]\n",
" [0.29479566 0.70520437]]\n",
"241 1\n",
"849 0\n",
"436 1\n",
"386 1\n",
"345 1\n",
"Name: class, dtype: int64\n"
]
}
],
"source": [
"# your code here"
"# your code here\n",
"\n",
"# load model\n",
"model = tf.keras.models.load_model('tic-tac-toe.model')\n",
"\n",
"# make predictions\n",
"predictions = model.predict(X_test[:5])\n",
"\n",
"# compare predictions with true values\n",
"print(predictions)\n",
"print(y_test[:5])\n"
]
},
{
Expand All @@ -104,11 +239,59 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 24,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/5\n",
"23/23 [==============================] - 1s 5ms/step - loss: 0.6424 - accuracy: 0.6435\n",
"Epoch 2/5\n",
"23/23 [==============================] - 0s 3ms/step - loss: 0.5486 - accuracy: 0.7159\n",
"Epoch 3/5\n",
"23/23 [==============================] - 0s 4ms/step - loss: 0.4277 - accuracy: 0.8148\n",
"Epoch 4/5\n",
"23/23 [==============================] - 0s 6ms/step - loss: 0.3145 - accuracy: 0.8677\n",
"Epoch 5/5\n",
"23/23 [==============================] - 0s 5ms/step - loss: 0.1806 - accuracy: 0.9415\n",
"8/8 [==============================] - 0s 3ms/step - loss: 0.2420 - accuracy: 0.8958\n",
"0.2419986128807068\n",
"0.8958333134651184\n"
]
}
],
"source": [
"# your code here"
"# your code here\n",
"\n",
"# define model\n",
"model = tf.keras.models.Sequential()\n",
"\n",
"# add layers\n",
"model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))\n",
"model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))\n",
"model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))\n",
"model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))\n",
"model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))\n",
"model.add(tf.keras.layers.Dense(2, activation=tf.nn.softmax))\n",
"\n",
"opt = tf.keras.optimizers.Adam(\n",
" learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07, amsgrad=False,\n",
" name='Adam'\n",
")\n",
"# compile model\n",
"model.compile(optimizer=opt,\n",
" loss='sparse_categorical_crossentropy',\n",
" metrics=['accuracy'])\n",
"\n",
"# fit model\n",
"model.fit(X_train, y_train, epochs=5)\n",
"\n",
"# evaluate model\n",
"val_loss, val_acc = model.evaluate(X_test, y_test)\n",
"print(val_loss)\n",
"print(val_acc)"
]
},
{
Expand All @@ -120,17 +303,20 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"# your answer here"
"# your answer here\n",
"\n",
"# Adding more epochs increased the score, but leads to overfitting if too many are used. \n",
"# Adding extra layers also increased the score, as well as changing the learning rate. "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
Expand All @@ -144,7 +330,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
"version": "3.7.6"
}
},
"nbformat": 4,
Expand Down
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7 changes: 7 additions & 0 deletions your-code/tic-tac-toe.model/keras_metadata.pb
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@@ -0,0 +1,7 @@

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