def test_io_api(tmp_path):
num_instances = 100
(image_x, train_y), (test_x, test_y) = mnist.load_data()
(text_x, train_y), (test_x,
test_y) = utils.imdb_raw(num_instances=num_instances)
image_x = image_x[:num_instances]
text_x = text_x[:num_instances]
structured_data_x = utils.generate_structured_data(
num_instances=num_instances)
classification_y = utils.generate_one_hot_labels(
num_instances=num_instances, num_classes=3)
regression_y = utils.generate_data(num_instances=num_instances,
shape=(1, ))
# Build model and train.
automodel = ak.AutoModel(
inputs=[ak.ImageInput(),
ak.TextInput(),
ak.StructuredDataInput()],
outputs=[
ak.RegressionHead(metrics=['mae']),
ak.ClassificationHead(loss='categorical_crossentropy',
metrics=['accuracy'])
],
directory=tmp_path,
max_trials=2,
seed=utils.SEED)
automodel.fit([image_x, text_x, structured_data_x],
[regression_y, classification_y],
epochs=1,
validation_split=0.2)
def test_text_classifier(tmp_path):
(train_x, train_y), (test_x, test_y) = utils.imdb_raw()
clf = ak.TextClassifier(directory=tmp_path, max_trials=2, seed=utils.SEED,
metrics=['accuracy'], objective='accuracy')
clf.fit(train_x, train_y, epochs=2, validation_data=(test_x, test_y))
clf.export_model()
assert clf.predict(test_x).shape == (len(test_x), 1)
assert clf.tuner._get_best_trial_epochs() == 2
def test_text_regressor(tmp_path):
(train_x, train_y), (test_x, test_y) = utils.imdb_raw()
train_y = utils.generate_data(num_instances=train_y.shape[0], shape=(1, ))
test_y = utils.generate_data(num_instances=test_y.shape[0], shape=(1, ))
clf = ak.TextRegressor(directory=tmp_path, max_trials=2, seed=utils.SEED)
clf.fit(train_x, train_y, epochs=1, validation_data=(test_x, test_y))
clf.export_model()
assert clf.predict(test_x).shape == (len(test_x), 1)
def test_functional_api(tmp_path):
# Prepare the data.
num_instances = 80
(image_x, train_y), (test_x, test_y) = mnist.load_data()
(text_x, train_y), (test_x, test_y) = utils.imdb_raw()
(structured_data_x, train_y), (test_x, test_y) = utils.dataframe_numpy()
image_x = image_x[:num_instances]
text_x = text_x[:num_instances]
structured_data_x = structured_data_x[:num_instances]
classification_y = utils.generate_one_hot_labels(
num_instances=num_instances, num_classes=3)
regression_y = utils.generate_data(num_instances=num_instances,
shape=(1, ))
# Build model and train.
image_input = ak.ImageInput()
output = ak.Normalization()(image_input)
output = ak.ImageAugmentation()(output)
outputs1 = ak.ResNetBlock(version='next')(output)
outputs2 = ak.XceptionBlock()(output)
image_output = ak.Merge()((outputs1, outputs2))
structured_data_input = ak.StructuredDataInput()
structured_data_output = ak.CategoricalToNumerical()(structured_data_input)
structured_data_output = ak.DenseBlock()(structured_data_output)
text_input = ak.TextInput()
outputs1 = ak.TextToIntSequence()(text_input)
outputs1 = ak.Embedding()(outputs1)
outputs1 = ak.ConvBlock(separable=True)(outputs1)
outputs1 = ak.SpatialReduction()(outputs1)
outputs2 = ak.TextToNgramVector()(text_input)
outputs2 = ak.DenseBlock()(outputs2)
text_output = ak.Merge()((outputs1, outputs2))
merged_outputs = ak.Merge()(
(structured_data_output, image_output, text_output))
regression_outputs = ak.RegressionHead()(merged_outputs)
classification_outputs = ak.ClassificationHead()(merged_outputs)
automodel = ak.AutoModel(
inputs=[image_input, text_input, structured_data_input],
directory=tmp_path,
outputs=[regression_outputs, classification_outputs],
max_trials=2,
tuner=ak.Hyperband,
seed=utils.SEED)
automodel.fit((image_x, text_x, structured_data_x),
(regression_y, classification_y),
validation_split=0.2,
epochs=1)
def test_text_and_structured_data(tmp_path):
# Prepare the data.
num_instances = 80
(x_text, y_train), (x_test, y_test) = utils.imdb_raw()
x_structured_data = pd.read_csv(utils.TRAIN_CSV_PATH)
x_text = x_text[:num_instances]
x_structured_data = x_structured_data[:num_instances]
y_classification = utils.generate_one_hot_labels(
num_instances=num_instances, num_classes=3)
y_regression = utils.generate_data(num_instances=num_instances,
shape=(1, ))
# Build model and train.
structured_data_input = ak.StructuredDataInput()
structured_data_output = ak.CategoricalToNumerical()(structured_data_input)
structured_data_output = ak.DenseBlock()(structured_data_output)
text_input = ak.TextInput()
outputs1 = ak.TextToIntSequence()(text_input)
outputs1 = ak.Embedding()(outputs1)
outputs1 = ak.ConvBlock(separable=True)(outputs1)
outputs1 = ak.SpatialReduction()(outputs1)
outputs2 = ak.TextToNgramVector()(text_input)
outputs2 = ak.DenseBlock()(outputs2)
text_output = ak.Merge()((outputs1, outputs2))
merged_outputs = ak.Merge()((structured_data_output, text_output))
regression_outputs = ak.RegressionHead()(merged_outputs)
classification_outputs = ak.ClassificationHead()(merged_outputs)
automodel = ak.AutoModel(
inputs=[text_input, structured_data_input],
directory=tmp_path,
outputs=[regression_outputs, classification_outputs],
max_trials=2,
tuner=ak.Hyperband,
seed=utils.SEED,
)
automodel.fit(
(x_text, x_structured_data),
(y_regression, y_classification),
validation_split=0.2,
epochs=1,
)
def test_preprocessing_adapt_with_text_vec():
class MockLayer(preprocessing.TextVectorization):
def adapt(self, *args, **kwargs):
super().adapt(*args, **kwargs)
self.is_called = True
(x_train, y_train), (x_test, y_test) = utils.imdb_raw()
dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(32)
layer1 = MockLayer(max_tokens=5000, output_mode="int", output_sequence_length=40)
model = tf.keras.models.Sequential()
model.add(tf.keras.Input(shape=(1,), dtype=tf.string))
model.add(layer1)
model.add(tf.keras.layers.Embedding(50001, 10))
model.add(tf.keras.layers.Dense(1))
tuner_module.AutoTuner.adapt(model, dataset)
assert layer1.is_called
def test_io_api(tmp_path):
num_instances = 20
(image_x, train_y), (test_x, test_y) = mnist.load_data()
(text_x, train_y), (test_x,
test_y) = utils.imdb_raw(num_instances=num_instances)
image_x = image_x[:num_instances]
text_x = text_x[:num_instances]
structured_data_x = (pd.read_csv(utils.TRAIN_CSV_PATH).to_numpy().astype(
np.unicode)[:num_instances])
classification_y = utils.generate_one_hot_labels(
num_instances=num_instances, num_classes=3)
regression_y = utils.generate_data(num_instances=num_instances,
shape=(1, ))
# Build model and train.
automodel = ak.AutoModel(
inputs=[ak.ImageInput(),
ak.TextInput(),
ak.StructuredDataInput()],
outputs=[
ak.RegressionHead(metrics=["mae"]),
ak.ClassificationHead(loss="categorical_crossentropy",
metrics=["accuracy"]),
],
directory=tmp_path,
max_trials=2,
tuner=ak.RandomSearch,
seed=utils.SEED,
)
automodel.fit(
[image_x, text_x, structured_data_x],
[regression_y, classification_y],
epochs=1,
validation_split=0.2,
batch_size=4,
)
def test_imdb_accuracy_over_84(tmp_path):
(x_train, y_train), (x_test, y_test) = utils.imdb_raw(num_instances=None)
clf = ak.TextClassifier(max_trials=2, directory=tmp_path)
clf.fit(x_train, y_train, epochs=2)
accuracy = clf.evaluate(x_test, y_test)[1]
assert accuracy >= 0.84
def test_text_classifier(tmp_dir):
(train_x, train_y), (test_x, test_y) = utils.imdb_raw()
clf = ak.TextClassifier(directory=tmp_dir, max_trials=2, seed=utils.SEED)
clf.fit(train_x, train_y, epochs=1, validation_data=(test_x, test_y))
clf.export_model()
assert clf.predict(test_x).shape == (len(test_x), 1)
