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308 changes: 308 additions & 0 deletions reuters_mlflow.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 5,
"id": "c24f79d3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading data...\n",
"Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/reuters.npz\n",
"\u001b[1m2110848/2110848\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 0us/step\n",
"8982 train sequences\n",
"2246 test sequences\n",
"46 classes\n",
"Vectorizing sequence data...\n",
"x_train shape: (8982, 1000)\n",
"x_test shape: (2246, 1000)\n",
"Convert class vector to binary class matrix (for use with categorical_crossentropy)\n",
"y_train shape: (8982, 46)\n",
"y_test shape: (2246, 46)\n"
]
}
],
"source": [
"from __future__ import print_function\n",
"\n",
"import numpy as np\n",
"import keras\n",
"from keras.datasets import reuters\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense, Dropout, Activation\n",
"from tensorflow.keras.preprocessing.text import Tokenizer\n",
"\n",
"import mlflow\n",
"\n",
"max_words = 1000\n",
"batch_size = 32\n",
"epochs = 5\n",
"\n",
"print('Loading data...')\n",
"(x_train, y_train), (x_test, y_test) = reuters.load_data(num_words=max_words,\n",
" test_split=0.2)\n",
"print(len(x_train), 'train sequences')\n",
"print(len(x_test), 'test sequences')\n",
"\n",
"num_classes = np.max(y_train) + 1\n",
"print(num_classes, 'classes')\n",
"\n",
"print('Vectorizing sequence data...')\n",
"tokenizer = Tokenizer(num_words=max_words)\n",
"x_train = tokenizer.sequences_to_matrix(x_train, mode='binary')\n",
"x_test = tokenizer.sequences_to_matrix(x_test, mode='binary')\n",
"print('x_train shape:', x_train.shape)\n",
"print('x_test shape:', x_test.shape)\n",
"\n",
"print('Convert class vector to binary class matrix '\n",
" '(for use with categorical_crossentropy)')\n",
"y_train = keras.utils.to_categorical(y_train, num_classes)\n",
"y_test = keras.utils.to_categorical(y_test, num_classes)\n",
"print('y_train shape:', y_train.shape)\n",
"print('y_test shape:', y_test.shape)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "39bf87b7",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2025/11/04 14:05:21 INFO mlflow.tracking.fluent: Experiment with name 'Iron' does not exist. Creating a new experiment.\n"
]
},
{
"data": {
"text/plain": [
"<Experiment: artifact_location='mlflow-artifacts:/195699277622829892', creation_time=1762261521516, experiment_id='195699277622829892', last_update_time=1762261521516, lifecycle_stage='active', name='Iron', tags={}>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mlflow.set_tracking_uri(\"http://127.0.0.1:5000\")\n",
"mlflow.set_experiment(\"Iron\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "4d8e57f0",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2025/11/04 14:05:22 INFO mlflow.utils.autologging_utils: Created MLflow autologging run with ID '108204e58f894339b74c6380b8125882', which will track hyperparameters, performance metrics, model artifacts, and lineage information for the current tensorflow workflow\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Building model...\n"
]
},
{
"data": {
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/5\n",
"\u001b[1m248/253\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5868 - loss: 1.8561"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m253/253\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.6798 - loss: 1.4193 - val_accuracy: 0.7597 - val_loss: 1.0717\n",
"Epoch 2/5\n",
"\u001b[1m248/253\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8058 - loss: 0.8170"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m253/253\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8116 - loss: 0.7831 - val_accuracy: 0.7976 - val_loss: 0.9072\n",
"Epoch 3/5\n",
"\u001b[1m252/253\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8686 - loss: 0.5380"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m253/253\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8659 - loss: 0.5439 - val_accuracy: 0.8076 - val_loss: 0.8660\n",
"Epoch 4/5\n",
"\u001b[1m242/253\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9074 - loss: 0.4091"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m253/253\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9000 - loss: 0.4229 - val_accuracy: 0.8120 - val_loss: 0.8558\n",
"Epoch 5/5\n",
"\u001b[1m253/253\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9205 - loss: 0.3258 - val_accuracy: 0.8031 - val_loss: 0.9003\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2025/11/04 14:05:29 WARNING mlflow.models.model: `artifact_path` is deprecated. Please use `name` instead.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"🏃 View run aged-sow-379 at: http://127.0.0.1:5000/#/experiments/195699277622829892/runs/108204e58f894339b74c6380b8125882\n",
"🧪 View experiment at: http://127.0.0.1:5000/#/experiments/195699277622829892\n",
"\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7947 - loss: 0.8667\n",
"Test score: 0.8667305111885071\n",
"Test accuracy: 0.7947462201118469\n"
]
}
],
"source": [
"mlflow.tensorflow.autolog()\n",
"print('Building model...')\n",
"model = Sequential()\n",
"model.add(Dense(512, input_shape=(max_words,)))\n",
"model.add(Activation('relu'))\n",
"model.add(Dropout(0.5))\n",
"model.add(Dense(num_classes))\n",
"model.add(Activation('softmax'))\n",
"\n",
"model.compile(loss='categorical_crossentropy',\n",
" optimizer='adam',\n",
" metrics=['accuracy'])\n",
"\n",
"history = model.fit(x_train, y_train,\n",
" batch_size=batch_size,\n",
" epochs=epochs,\n",
" verbose=1,\n",
" validation_split=0.1)\n",
"score = model.evaluate(x_test, y_test,\n",
" batch_size=batch_size, verbose=1)\n",
"print('Test score:', score[0])\n",
"print('Test accuracy:', score[1])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "84e441c8",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.01"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mlflow.log_param(\"batch_size\", batch_size)\n",
"mlflow.log_param(\"learning_rate\", 0.01)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "cb7f9454",
"metadata": {},
"outputs": [],
"source": [
"mlflow.log_metric(\"value\", score[1])"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "75f8483b",
"metadata": {},
"outputs": [],
"source": [
"mlflow.set_tag(\"project\", \"reuters_classification\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "venv (3.12.7)",
"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": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}