<jupyter_start><jupyter_text>Create Model and load weighting<jupyter_code>setup_growth()
num_classes = 10
base_model = fchollet_cnn(output=num_classes)

sample_size = 25
fit_params = {"epochs": 200, "batch_size": sample_size}
config = Config(
    fit=fit_params,
    query={"sample_size": sample_size},
    eval={"batch_size": 200, "sample_size": sample_size}
)

mc_model = McDropout(base_model, config=config)

optimizer = "adam"
loss = "sparse_categorical_crossentropy"
metrics = [keras.metrics.SparseCategoricalAccuracy()]
mc_model.compile(optimizer=optimizer, loss=loss, metrics=metrics)
mc_model.load_weights()
x_lab, y_lab = unlabeled_pool.get_labeled_data()
mc_model.fit(x_lab, y_lab)
mc_model.evaluate(x_test, y_test, batch_size=900)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1219195849546044, 'fraction_numerical': 0.016861219195849545, 'mean_word_length': 8.772151898734178, 'pattern_counts': {'":': 5, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '188638', 'license': 'MIT', 'n_tokens_mistral': 308, 'n_tokens_neox': 288, 'n_words': 46, 'path': 'wp/notebooks/debug/.ipynb_checkpoints/active_learning_model_works-checkpoint.ipynb', 'repo_name': 'ExLeonem/master-thesis-code', 'size': '771'}