tiny image classification example added

docs/Examples/index.md

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+# Examples
+
+This folder contains examples of how to use the `openml-tensorflow` extension for different datasets and models. 

docs/Examples/tf_image_classification.py

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+"""
+Tensorflow image classification model example
+==================
+
+An example of a tensorflow network that classifies meta album images.
+"""
+
+import openml
+import openml_tensorflow
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+import tensorflow as tf
+from tensorflow.keras import layers, models
+
+############################################################################
+openml.config.apikey = 'KEY' # Paste your API key here
+
+############################################################################
+
+openml_tensorflow.config.epoch = 1 #  small epoch for test runs
+
+datagen = ImageDataGenerator()
+openml_tensorflow.config.datagen = datagen
+openml_tensorflow.config.dir = openml.config.get_cache_directory()+'/datasets/44312/PNU_Micro/images/'
+openml_tensorflow.config.x_col = "FILE_NAME"
+openml_tensorflow.config.y_col = 'encoded_labels'
+openml_tensorflow.config.datagen = datagen
+openml_tensorflow.config.batch_size = 32
+openml_tensorflow.config.class_mode = "categorical"
+
+# Perform cross-validation during traning 
+openml_tensorflow.config.perform_validation = True
+openml_tensorflow.config.validation_split = 0.1
+openml_tensorflow.config.datagen_valid = ImageDataGenerator()
+
+IMG_SIZE = (128, 128)
+IMG_SHAPE = IMG_SIZE + (3,)
+
+# Example tensorflow image classification model. 
+model = models.Sequential()
+model.add(layers.Conv2D(128, (3, 3), activation='relu', input_shape=IMG_SHAPE))
+model.add(layers.MaxPooling2D((2, 2)))
+model.add(layers.Conv2D(64, (3, 3), activation='relu'))
+model.add(layers.MaxPooling2D((2, 2)))
+model.add(layers.Conv2D(64, (3, 3), activation='relu'))
+model.add(layers.Flatten())
+model.add(layers.Dense(64, activation='relu'))
+model.add(layers.Dense(84, activation='relu'))
+model.add(layers.Dense(19, activation='softmax'))  # Adjust output size
+model.compile(optimizer='adam',
+              loss='categorical_crossentropy',
+              metrics=['AUC'])
+
+############################################################################
+
+# Download the OpenML task for the Meta_Album_PNU_Micro dataset.
+task = openml.tasks.get_task(362071)
+
+# Run the Keras model on the task (requires an API key).
+run = openml.runs.run_model_on_task(model, task, avoid_duplicate_runs=False)
+
+"""
+If you want to publish the run with the onnx file, 
+then you must call openml_tensorflow.add_onnx_to_run() immediately before run.publish(). 
+When you publish, onnx file of last trained model is uploaded. 
+Careful to not call this function when another run_model_on_task is called in between, 
+as during publish later, only the last trained model (from last run_model_on_task call) is uploaded.
+"""   
+run = openml_tensorflow.add_onnx_to_run(run)
+
+run.publish()
+
+print('URL for run: %s/run/%d?api_key=%s' % (openml.config.server, run.run_id, openml.config.apikey))
+
+############################################################################
+# Optional: Visualize model in netron
+
+from urllib.request import urlretrieve
+
+published_run = openml.runs.get_run(run.run_id)
+url = 'https://api.openml.org/data/download/{}/model.onnx'.format(published_run.output_files['onnx_model'])
+
+file_path, _ = urlretrieve(url, 'model.onnx')
+
+import netron
+# Visualize the ONNX model using Netron
+netron.start(file_path)
+

mkdocs.yml

@@ -57,6 +57,7 @@ nav:
     - "OpenML Integration": "API reference/OpenML integration.md"
   - Docker Reference: 
     - "Docker": "Docker reference/Docker.md"
-  # - Examples:
+  - Examples:
+    - "Image Classification Task": "Examples/tf_image_classification.py"
   # - "Limitations of the API":  "Limitations of the API.md"