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%matplotlib inline

%matplotlib inline

Tensorflow image classification model example I¶

An example of a Tensorflow network that classifies Meta Album images.

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import openml
import openml_tensorflow
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import tensorflow as tf
from tensorflow.keras import layers, models

import openml import openml_tensorflow from tensorflow.keras.preprocessing.image import ImageDataGenerator import tensorflow as tf from tensorflow.keras import layers, models

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openml.config.apikey = 'KEY' # Paste your API key here

openml.config.apikey = 'KEY' # Paste your API key here

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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'])

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'])

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# 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)

# 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.

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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))

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

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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)

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)