def build_graph(): tf.keras.backend.clear_session() image_input = ak.ImageInput(shape=(32, 32, 3)) image_input.batch_size = 32 image_input.num_samples = 1000 merged_outputs = ak.SpatialReduction()(image_input) head = ak.ClassificationHead(num_classes=10, shape=(10, )) classification_outputs = head(merged_outputs) return ak.graph.Graph(inputs=image_input, outputs=classification_outputs)
def test_functional_api(tmp_dir): # Prepare the data. num_instances = 20 (image_x, train_y), (test_x, test_y) = mnist.load_data() (text_x, train_y), (test_x, test_y) = common.imdb_raw() (structured_data_x, train_y), (test_x, test_y) = common.dataframe_numpy() image_x = image_x[:num_instances] text_x = text_x[:num_instances] structured_data_x = structured_data_x[:num_instances] classification_y = common.generate_one_hot_labels( num_instances=num_instances, num_classes=3) regression_y = common.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')(image_input) outputs2 = ak.XceptionBlock()(image_input) image_output = ak.Merge()((outputs1, outputs2)) structured_data_input = ak.StructuredDataInput( column_names=common.COLUMN_NAMES_FROM_CSV, column_types=common.COLUMN_TYPES_FROM_CSV) structured_data_output = ak.FeatureEngineering()(structured_data_input) structured_data_output = ak.DenseBlock()(structured_data_output) text_input = ak.TextInput() outputs1 = ak.TextToIntSequence()(text_input) outputs1 = ak.EmbeddingBlock()(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.GraphAutoModel( inputs=[image_input, text_input, structured_data_input], directory=tmp_dir, outputs=[regression_outputs, classification_outputs], max_trials=2, seed=common.SEED) automodel.fit((image_x, text_x, structured_data_x), (regression_y, classification_y), validation_split=0.2, epochs=2)
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 functional_api(): (x_train, y_train), (x_test, y_test) = mnist.load_data() input_node = ak.ImageInput() output_node = input_node output_node = ak.Normalization()(output_node) output_node = ak.ConvBlock()(output_node) output_node = ak.SpatialReduction()(output_node) output_node = ak.DenseBlock()(output_node) output_node = ak.ClassificationHead()(output_node) clf = ak.GraphAutoModel(input_node, output_node, seed=5, max_trials=3) clf.fit(x_train, y_train, validation_split=0.2) return clf.evaluate(x_test, y_test)
def functional_api(): (x_train, y_train), (x_test, y_test) = cifar10.load_data() input_node = ak.ImageInput() output_node = input_node output_node = ak.Normalization()(output_node) output_node = ak.ImageAugmentation()(output_node) output_node = ak.ResNetBlock(version='next')(output_node) output_node = ak.SpatialReduction()(output_node) output_node = ak.DenseBlock()(output_node) output_node = ak.ClassificationHead()(output_node) clf = ak.AutoModel(input_node, output_node, seed=5, max_trials=3) clf.fit(x_train, y_train, validation_split=0.2) return clf.evaluate(x_test, y_test)
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 functional_api(): max_features = 20000 max_words = 400 (x_train, y_train), (x_test, y_test) = tf.keras.datasets.imdb.load_data( num_words=max_features, index_from=3) x_train = tf.keras.preprocessing.sequence.pad_sequences(x_train, maxlen=max_words) x_test = tf.keras.preprocessing.sequence.pad_sequences(x_test, maxlen=max_words) print(x_train.dtype) print(x_train[:10]) input_node = ak.Input() output_node = input_node output_node = ak.EmbeddingBlock(max_features=max_features)(output_node) output_node = ak.ConvBlock()(output_node) output_node = ak.SpatialReduction()(output_node) output_node = ak.DenseBlock()(output_node) output_node = ak.ClassificationHead()(output_node) clf = ak.AutoModel(input_node, output_node, seed=5, max_trials=3) clf.fit(x_train, y_train, validation_split=0.2) return clf.evaluate(x_test, y_test)