def test_preprocessing():
input_shape = (33, )
output_shape = (1, )
x_train_1 = common.generate_data(num_instances=100,
shape=input_shape,
dtype='dataset')
x_train_2 = common.generate_data(num_instances=100,
shape=input_shape,
dtype='dataset')
y_train = common.generate_data(num_instances=100,
shape=output_shape,
dtype='dataset')
dataset = tf.data.Dataset.zip(((x_train_1, x_train_2), y_train))
input_node1 = ak.Input(shape=input_shape)
temp_node1 = ak.Normalization()(input_node1)
output_node1 = ak.DenseBlock()(temp_node1)
output_node3 = ak.Normalization()(temp_node1)
output_node3 = ak.DenseBlock()(output_node3)
input_node2 = ak.Input(shape=input_shape)
output_node2 = ak.Normalization()(input_node2)
output_node2 = ak.DenseBlock()(output_node2)
output_node = ak.Merge()([output_node1, output_node2, output_node3])
output_node = ak.RegressionHead()(output_node)
graph = graph_module.HyperBuiltGraphHyperModel([input_node1, input_node2],
output_node)
graph.preprocess(hp=kerastuner.HyperParameters(),
dataset=dataset,
validation_data=dataset,
fit=True)
def test_preprocessing(_, tmp_dir):
x_train = np.random.rand(100, 33)
y_train = np.random.rand(100, 1)
input_node1 = ak.Input()
temp_node1 = ak.Normalization()(input_node1)
output_node1 = ak.DenseBlock()(temp_node1)
output_node3 = ak.Normalization()(temp_node1)
output_node3 = ak.DenseBlock()(output_node3)
input_node2 = ak.Input()
output_node2 = ak.Normalization()(input_node2)
output_node2 = ak.DenseBlock()(output_node2)
output_node = ak.Merge()([output_node1, output_node2, output_node3])
output_node = ak.RegressionHead()(output_node)
graph = ak.GraphAutoModel([input_node1, input_node2],
output_node,
directory=tmp_dir,
max_trials=1)
graph.fit([x_train, x_train],
y_train,
epochs=1,
batch_size=100,
validation_data=([x_train, x_train], y_train),
validation_split=0.5,
verbose=False)
def test_image_blocks(tmp_path):
num_instances = 10
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train[:num_instances]
y_regression = utils.generate_data(num_instances=num_instances,
shape=(1, ))
input_node = ak.ImageInput()
output = ak.Normalization()(input_node)
output = ak.ImageAugmentation()(output)
outputs1 = ak.ResNetBlock(version="v2")(output)
outputs2 = ak.XceptionBlock()(output)
output_node = ak.Merge()((outputs1, outputs2))
output_node = ak.ClassificationHead()(output_node)
automodel = ak.AutoModel(
inputs=input_node,
outputs=output_node,
directory=tmp_path,
max_trials=1,
seed=utils.SEED,
)
automodel.fit(x_train,
y_regression,
validation_data=(x_train, y_regression),
epochs=1)
def test_image_blocks(tmp_path):
num_instances = 10
x_train = test_utils.generate_data(num_instances=num_instances,
shape=(28, 28))
y_train = np.random.randint(0, 10, num_instances)
input_node = ak.ImageInput()
output = ak.Normalization()(input_node)
output = ak.ImageAugmentation()(output)
outputs1 = ak.ResNetBlock(version="v2")(output)
outputs2 = ak.XceptionBlock()(output)
output_node = ak.Merge()((outputs1, outputs2))
output_node = ak.ClassificationHead()(output_node)
automodel = ak.AutoModel(
inputs=input_node,
outputs=output_node,
directory=tmp_path,
max_trials=1,
seed=test_utils.SEED,
)
automodel.fit(x_train,
y_train,
validation_data=(x_train, y_train),
epochs=1)
def train_ak():
image_count = len(list(config.database_path.glob('**/*.jpg')))
print("# of images found:", image_count)
list_ds = tf.data.Dataset.list_files(str(config.database_path / '*/*.jpg'),
shuffle=False)
list_ds = list_ds.shuffle(image_count, reshuffle_each_iteration=False)
# Set `num_parallel_calls` so multiple images are loaded/processed in parallel.
AUTOTUNE = tf.data.experimental.AUTOTUNE
train_ds = list_ds.map(utils.process_path, num_parallel_calls=AUTOTUNE)
features = np.array([list(x[0].numpy()) for x in list(train_ds)])
labels = np.array([x[1].numpy() for x in list(train_ds)])
input_node = ak.ImageInput()
output_node = ak.Normalization()(input_node)
output_node = ak.ImageAugmentation(horizontal_flip=False,
vertical_flip=False,
rotation_factor=False,
zoom_factor=False)(output_node)
output_node = ak.ClassificationHead()(output_node)
clf = ak.AutoModel(inputs=input_node,
outputs=output_node,
overwrite=True,
max_trials=config.max_trials,
directory=config.outpath_mpii)
# Feed the tensorflow Dataset to the classifier.
split = config.split
x_val = features[split:]
y_val = labels[split:]
x_train = features[:split]
y_train = labels[:split]
clf.fit(x_train,
y_train,
validation_data=(x_val, y_val),
epochs=config.epochs)
# Predict with the best model.
#predicted_y = clf.predict(x_val)
#print(predicted_y)
# Evaluate the best model with testing data.
print(clf.evaluate(x_val, y_val))
# Export as a Keras Model.
model = clf.export_model()
print(
type(model)) # <class 'tensorflow.python.keras.engine.training.Model'>
model.save(config.output_path + "model_ak_imgClsf.h5")
return 0
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)
id3 --> id5(ResNet V2)
id4 --> id6(Merge)
id5 --> id6
id7(StructuredDataInput) --> id8(CategoricalToNumerical)
id8 --> id9(DenseBlock)
id6 --> id10(Merge)
id9 --> id10
id10 --> id11(Classification Head)
id10 --> id12(Regression Head)
</div>
"""
import autokeras as ak
input_node1 = ak.ImageInput()
output_node = ak.Normalization()(input_node1)
output_node = ak.ImageAugmentation()(output_node)
output_node1 = ak.ConvBlock()(output_node)
output_node2 = ak.ResNetBlock(version='v2')(output_node)
output_node1 = ak.Merge()([output_node1, output_node2])
input_node2 = ak.StructuredDataInput()
output_node = ak.CategoricalToNumerical()(input_node2)
output_node2 = ak.DenseBlock()(output_node)
output_node = ak.Merge()([output_node1, output_node2])
output_node1 = ak.ClassificationHead()(output_node)
output_node2 = ak.RegressionHead()(output_node)
auto_model = ak.AutoModel(inputs=[input_node1, input_node2],
outputs=[output_node1, output_node2],
import tensorflow as tf
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
import autokeras as ak
import datasets
from sklearn.metrics import precision_score, recall_score, confusion_matrix, classification_report, accuracy_score, f1_score
x_train, x_test, y_train, y_test = datasets.load_data()
input_node = ak.StructuredDataInput()
# x1 = ak.StructuredDataBlock(categorical_encoding=True, normalize=False)(input_node)
x1 = ak.Normalization()(input_node)
x1 = ak.DenseBlock(num_layers=1, num_units=32)(x1)
x1 = ak.DenseBlock(num_layers=1, num_units=32, dropout=0.1, use_batchnorm=False)(x1)
output_node = ak.ClassificationHead(loss = "categorical_crossentropy", num_classes=2)(x1)
import kerastuner
auto_model = ak.AutoModel(inputs=input_node,
outputs=output_node,
# num_classes=2,
overwrite=True,
max_trials=3,
objective=kerastuner.Objective('val_auroc', direction='max'),
metrics = [tf.keras.metrics.AUC(name="auroc", curve="ROC")])
es = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=4)
auto_model.fit(x_train,
y_train,
epochs = 20,
