mirror of
https://github.com/clearml/clearml
synced 2025-01-31 17:17:00 +00:00
67 lines
2.0 KiB
Python
67 lines
2.0 KiB
Python
try:
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import autokeras as ak
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except ImportError:
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import sys
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raise ImportError(
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"autokeras package is missing, you can install it using pip: pip install autokeras"
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if sys.version_info.minor > 6
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else "AutoKeras does not support python version < 3.7"
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)
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import numpy as np
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import tensorflow as tf
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from tensorflow import keras
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from clearml import Task
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# Connecting ClearML with the current process,
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# from here on everything is logged automatically
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task = Task.init(project_name="autokeras", task_name="AutoKeras IMDB example with scalars")
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def imdb_raw():
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max_features = 20000
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index_offset = 3 # word index offset
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(x_train, y_train), (x_test, y_test) = tf.keras.datasets.imdb.load_data(
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num_words=max_features, index_from=index_offset
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)
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x_train = x_train
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y_train = y_train.reshape(-1, 1)
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x_test = x_test
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y_test = y_test.reshape(-1, 1)
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word_to_id = tf.keras.datasets.imdb.get_word_index()
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word_to_id = {k: (v + index_offset) for k, v in word_to_id.items()}
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word_to_id["<PAD>"] = 0
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word_to_id["<START>"] = 1
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word_to_id["<UNK>"] = 2
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id_to_word = {value: key for key, value in word_to_id.items()}
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x_train = list(map(lambda sentence: " ".join(id_to_word[i] for i in sentence), x_train))
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x_test = list(map(lambda sentence: " ".join(id_to_word[i] for i in sentence), x_test))
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x_train = np.array(x_train, dtype=str)
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x_test = np.array(x_test, dtype=str)
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return (x_train, y_train), (x_test, y_test)
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# Prepare the data.
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(x_train, y_train), (x_test, y_test) = imdb_raw()
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print(x_train.shape) # (25000,)
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print(y_train.shape) # (25000, 1)
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print(x_train[0][:50]) # <START> this film was just brilliant casting <UNK>
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# Initialize the TextClassifier
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clf = ak.TextClassifier(max_trials=2)
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# Tensorboard Callbacks
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tb_callbacks = [keras.callbacks.TensorBoard(log_dir="log")]
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# Search for the best model.
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clf.fit(x_train, y_train, epochs=2, callbacks=tb_callbacks)
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# Evaluate on the testing data.
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clf_accuracy = clf.evaluate(x_test, y_test)
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print("Accuracy:", clf_accuracy)
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