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# Copyright 2020 The AutoKeras Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from pathlib import Path
from typing import List
from typing import Optional
from typing import Tuple
from typing import Type
from typing import Union
import tensorflow as tf
from tensorflow import keras
from autokeras import auto_model
from autokeras import blocks
from autokeras import nodes as input_module
from autokeras.engine import tuner
from autokeras.tuners import greedy
from autokeras.tuners import task_specific
from autokeras.utils import types
class SupervisedImagePipeline(auto_model.AutoModel):
def __init__(self, outputs, **kwargs):
super().__init__(inputs=input_module.ImageInput(), outputs=outputs, **kwargs)
class ImageClassifier(SupervisedImagePipeline):
"""AutoKeras image classification class.
# Arguments
num_classes: Int. Defaults to None. If None, it will be inferred from the
data.
multi_label: Boolean. Defaults to False.
loss: A Keras loss function. Defaults to use 'binary_crossentropy' or
'categorical_crossentropy' based on the number of classes.
metrics: A list of Keras metrics. Defaults to use 'accuracy'.
project_name: String. The name of the AutoModel.
Defaults to 'image_classifier'.
max_trials: Int. The maximum number of different Keras Models to try.
The search may finish before reaching the max_trials. Defaults to 100.
directory: String. The path to a directory for storing the search outputs.
Defaults to None, which would create a folder with the name of the
AutoModel in the current directory.
objective: String. Name of model metric to minimize
or maximize, e.g. 'val_accuracy'. Defaults to 'val_loss'.
tuner: String or subclass of AutoTuner. If string, it should be one of
'greedy', 'bayesian', 'hyperband' or 'random'. It can also be a subclass
of AutoTuner. If left unspecified, it uses a task specific tuner, which
first evaluates the most commonly used models for the task before
exploring other models.
overwrite: Boolean. Defaults to `False`. If `False`, reloads an existing
project of the same name if one is found. Otherwise, overwrites the
project.
seed: Int. Random seed.
max_model_size: Int. Maximum number of scalars in the parameters of a
model. Models larger than this are rejected.
**kwargs: Any arguments supported by AutoModel.
"""
def __init__(
self,
num_classes: Optional[int] = None,
multi_label: bool = False,
loss: types.LossType = None,
metrics: Optional[types.MetricsType] = None,
project_name: str = "image_classifier",
max_trials: int = 100,
directory: Union[str, Path, None] = None,
objective: str = "val_loss",
tuner: Union[str, Type[tuner.AutoTuner]] = None,
overwrite: bool = False,
seed: Optional[int] = None,
max_model_size: Optional[int] = None,
**kwargs
):
if tuner is None:
tuner = task_specific.ImageClassifierTuner
super().__init__(
outputs=blocks.ClassificationHead(
num_classes=num_classes,
multi_label=multi_label,
loss=loss,
metrics=metrics,
),
max_trials=max_trials,
directory=directory,
project_name=project_name,
objective=objective,
tuner=tuner,
overwrite=overwrite,
seed=seed,
max_model_size=max_model_size,
**kwargs
)
def fit(
self,
x: Optional[types.DatasetType] = None,
y: Optional[types.DatasetType] = None,
epochs: Optional[int] = None,
callbacks: Optional[List[keras.callbacks.Callback]] = None,
validation_split: Optional[float] = 0.2,
validation_data: Union[
tf.data.Dataset, Tuple[types.DatasetType, types.DatasetType], None
] = None,
**kwargs
):
"""Search for the best model and hyperparameters for the AutoModel.
It will search for the best model based on the performances on
validation data.
# Arguments
x: numpy.ndarray or tensorflow.Dataset. Training data x. The shape of
the data should be (samples, width, height)
or (samples, width, height, channels).
y: numpy.ndarray or tensorflow.Dataset. Training data y. It can be raw
labels, one-hot encoded if more than two classes, or binary encoded
for binary classification.
epochs: Int. The number of epochs to train each model during the search.
If unspecified, by default we train for a maximum of 1000 epochs,
but we stop training if the validation loss stops improving for 10
epochs (unless you specified an EarlyStopping callback as part of
the callbacks argument, in which case the EarlyStopping callback you
specified will determine early stopping).
callbacks: List of Keras callbacks to apply during training and
validation.
validation_split: Float between 0 and 1. Defaults to 0.2.
Fraction of the training data to be used as validation data.
The model will set apart this fraction of the training data,
will not train on it, and will evaluate
the loss and any model metrics
on this data at the end of each epoch.
The validation data is selected from the last samples
in the `x` and `y` data provided, before shuffling. This argument is
not supported when `x` is a dataset.
The best model found would be fit on the entire dataset including the
validation data.
validation_data: Data on which to evaluate the loss and any model metrics
at the end of each epoch. The model will not be trained on this data.
`validation_data` will override `validation_split`. The type of the
validation data should be the same as the training data.
The best model found would be fit on the training dataset without the
validation data.
**kwargs: Any arguments supported by
[keras.Model.fit](https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit).
# Returns
history: A Keras History object corresponding to the best model.
Its History.history attribute is a record of training
loss values and metrics values at successive epochs, as well as
validation loss values and validation metrics values (if applicable).
"""
history = super().fit(
x=x,
y=y,
epochs=epochs,
callbacks=callbacks,
validation_split=validation_split,
validation_data=validation_data,
**kwargs
)
return history
class ImageRegressor(SupervisedImagePipeline):
"""AutoKeras image regression class.
# Arguments
output_dim: Int. The number of output dimensions. Defaults to None.
If None, it will be inferred from the data.
loss: A Keras loss function. Defaults to use 'mean_squared_error'.
metrics: A list of Keras metrics. Defaults to use 'mean_squared_error'.
project_name: String. The name of the AutoModel.
Defaults to 'image_regressor'.
max_trials: Int. The maximum number of different Keras Models to try.
The search may finish before reaching the max_trials. Defaults to 100.
directory: String. The path to a directory for storing the search outputs.
Defaults to None, which would create a folder with the name of the
AutoModel in the current directory.
objective: String. Name of model metric to minimize
or maximize, e.g. 'val_accuracy'. Defaults to 'val_loss'.
tuner: String or subclass of AutoTuner. If string, it should be one of
'greedy', 'bayesian', 'hyperband' or 'random'. It can also be a subclass
of AutoTuner. If left unspecified, it uses a task specific tuner, which
first evaluates the most commonly used models for the task before
exploring other models.
overwrite: Boolean. Defaults to `False`. If `False`, reloads an existing
project of the same name if one is found. Otherwise, overwrites the
project.
seed: Int. Random seed.
max_model_size: Int. Maximum number of scalars in the parameters of a
model. Models larger than this are rejected.
**kwargs: Any arguments supported by AutoModel.
"""
def __init__(
self,
output_dim: Optional[int] = None,
loss: types.LossType = "mean_squared_error",
metrics: Optional[types.MetricsType] = None,
project_name: str = "image_regressor",
max_trials: int = 100,
directory: Union[str, Path, None] = None,
objective: str = "val_loss",
tuner: Union[str, Type[tuner.AutoTuner]] = None,
overwrite: bool = False,
seed: Optional[int] = None,
max_model_size: Optional[int] = None,
**kwargs
):
if tuner is None:
tuner = greedy.Greedy
super().__init__(
outputs=blocks.RegressionHead(
output_dim=output_dim, loss=loss, metrics=metrics
),
max_trials=max_trials,
directory=directory,
project_name=project_name,
objective=objective,
tuner=tuner,
overwrite=overwrite,
seed=seed,
max_model_size=max_model_size,
**kwargs
)
def fit(
self,
x: Optional[types.DatasetType] = None,
y: Optional[types.DatasetType] = None,
epochs: Optional[int] = None,
callbacks: Optional[List[keras.callbacks.Callback]] = None,
validation_split: Optional[float] = 0.2,
validation_data: Union[
types.DatasetType, Tuple[types.DatasetType], None
] = None,
**kwargs
):
"""Search for the best model and hyperparameters for the AutoModel.
It will search for the best model based on the performances on
validation data.
# Arguments
x: numpy.ndarray or tensorflow.Dataset. Training data x. The shape of
the data should be (samples, width, height) or
(samples, width, height, channels).
y: numpy.ndarray or tensorflow.Dataset. Training data y. The targets
passing to the head would have to be tf.data.Dataset, np.ndarray,
pd.DataFrame or pd.Series. It can be single-column or multi-column.
The values should all be numerical.
epochs: Int. The number of epochs to train each model during the search.
If unspecified, by default we train for a maximum of 1000 epochs,
but we stop training if the validation loss stops improving for 10
epochs (unless you specified an EarlyStopping callback as part of
the callbacks argument, in which case the EarlyStopping callback you
specified will determine early stopping).
callbacks: List of Keras callbacks to apply during training and
validation.
validation_split: Float between 0 and 1. Defaults to 0.2.
Fraction of the training data to be used as validation data.
The model will set apart this fraction of the training data,
will not train on it, and will evaluate
the loss and any model metrics
on this data at the end of each epoch.
The validation data is selected from the last samples
in the `x` and `y` data provided, before shuffling. This argument is
not supported when `x` is a dataset.
The best model found would be fit on the entire dataset including the
validation data.
validation_data: Data on which to evaluate the loss and any model metrics
at the end of each epoch. The model will not be trained on this data.
`validation_data` will override `validation_split`. The type of the
validation data should be the same as the training data.
The best model found would be fit on the training dataset without the
validation data.
**kwargs: Any arguments supported by
[keras.Model.fit](https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit).
# Returns
history: A Keras History object corresponding to the best model.
Its History.history attribute is a record of training
loss values and metrics values at successive epochs, as well as
validation loss values and validation metrics values (if applicable).
"""
history = super().fit(
x=x,
y=y,
epochs=epochs,
callbacks=callbacks,
validation_split=validation_split,
validation_data=validation_data,
**kwargs
)
return history
class ImageSegmenter(SupervisedImagePipeline):
"""AutoKeras image segmentation class.
# Arguments
num_classes: Int. Defaults to None. If None, it will be inferred from the
data.
loss: A Keras loss function. Defaults to use 'binary_crossentropy' or
'categorical_crossentropy' based on the number of classes.
metrics: A list of metrics used to measure the accuracy of the model,
default to 'accuracy'.
project_name: String. The name of the AutoModel.
Defaults to 'image_segmenter'.
max_trials: Int. The maximum number of different Keras Models to try.
The search may finish before reaching the max_trials. Defaults to 100.
directory: String. The path to a directory for storing the search outputs.
Defaults to None, which would create a folder with the name of the
AutoModel in the current directory.
objective: String. Name of model metric to minimize
or maximize, e.g. 'val_accuracy'. Defaults to 'val_loss'.
tuner: String or subclass of AutoTuner. If string, it should be one of
'greedy', 'bayesian', 'hyperband' or 'random'. It can also be a subclass
of AutoTuner. If left unspecified, it uses a task specific tuner, which
first evaluates the most commonly used models for the task before
exploring other models.
overwrite: Boolean. Defaults to `False`. If `False`, reloads an existing
project of the same name if one is found. Otherwise, overwrites the
project.
seed: Int. Random seed.
**kwargs: Any arguments supported by AutoModel.
"""
def __init__(
self,
num_classes: Optional[int] = None,
loss: types.LossType = None,
metrics: Optional[types.MetricsType] = None,
project_name: str = "image_segmenter",
max_trials: int = 100,
directory: Union[str, Path, None] = None,
objective: str = "val_loss",
tuner: Union[str, Type[tuner.AutoTuner]] = None,
overwrite: bool = False,
seed: Optional[int] = None,
**kwargs
):
if tuner is None:
tuner = greedy.Greedy
super().__init__(
outputs=blocks.SegmentationHead(
num_classes=num_classes, loss=loss, metrics=metrics
),
max_trials=max_trials,
directory=directory,
project_name=project_name,
objective=objective,
tuner=tuner,
overwrite=overwrite,
seed=seed,
**kwargs
)
def fit(
self,
x: Optional[types.DatasetType] = None,
y: Optional[types.DatasetType] = None,
epochs: Optional[int] = None,
callbacks: Optional[List[keras.callbacks.Callback]] = None,
validation_split: Optional[float] = 0.2,
validation_data: Union[
types.DatasetType, Tuple[types.DatasetType], None
] = None,
**kwargs
):
"""Search for the best model and hyperparameters for the AutoModel.
It will search for the best model based on the performances on
validation data.
# Arguments
x: numpy.ndarray or tensorflow.Dataset. Training image dataset x.
The shape of the data should be (samples, width, height) or
(samples, width, height, channels).
y: numpy.ndarray or tensorflow.Dataset. Training image data set y.
It should be a tensor and the height and width should be the same
as x. Each element in the tensor is the label of the corresponding
pixel.
epochs: Int. The number of epochs to train each model during the search.
If unspecified, by default we train for a maximum of 1000 epochs,
but we stop training if the validation loss stops improving for 10
epochs (unless you specified an EarlyStopping callback as part of
the callbacks argument, in which case the EarlyStopping callback you
specified will determine early stopping).
callbacks: List of Keras callbacks to apply during training and
validation.
validation_split: Float between 0 and 1. Defaults to 0.2.
Fraction of the training data to be used as validation data.
The model will set apart this fraction of the training data,
will not train on it, and will evaluate
the loss and any model metrics
on this data at the end of each epoch.
The validation data is selected from the last samples
in the `x` and `y` data provided, before shuffling. This argument is
not supported when `x` is a dataset.
The best model found would be fit on the entire dataset including the
validation data.
validation_data: Data on which to evaluate the loss and any model metrics
at the end of each epoch. The model will not be trained on this data.
`validation_data` will override `validation_split`. The type of the
validation data should be the same as the training data.
The best model found would be fit on the training dataset without the
validation data.
**kwargs: Any arguments supported by
[keras.Model.fit](https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit).
# Returns
history: A Keras History object corresponding to the best model.
Its History.history attribute is a record of training
loss values and metrics values at successive epochs, as well as
validation loss values and validation metrics values (if applicable).
"""
history = super().fit(
x=x,
y=y,
epochs=epochs,
callbacks=callbacks,
validation_split=validation_split,
validation_data=validation_data,
**kwargs
)
return history
class ImageObjectDetector(SupervisedImagePipeline):
"""AutoKeras image object detector class.
# Arguments
num_classes: Int. Defaults to None. If None, it will be inferred from the
data.
multi_label: Boolean. Defaults to False.
loss: A Keras loss function. Defaults to use 'binary_crossentropy' or
'categorical_crossentropy' based on the number of classes.
metrics: A list of Keras metrics. Defaults to use 'accuracy'.
project_name: String. The name of the AutoModel.
Defaults to 'image_classifier'.
max_trials: Int. The maximum number of different Keras Models to try.
The search may finish before reaching the max_trials. Defaults to 100.
directory: String. The path to a directory for storing the search outputs.
Defaults to None, which would create a folder with the name of the
AutoModel in the current directory.
objective: String. Name of model metric to minimize
or maximize, e.g. 'val_accuracy'. Defaults to 'val_loss'.
tuner: String or subclass of AutoTuner. If string, it should be one of
'greedy', 'bayesian', 'hyperband' or 'random'. It can also be a subclass
of AutoTuner. If left unspecified, it uses a task specific tuner, which
first evaluates the most commonly used models for the task before
exploring other models.
overwrite: Boolean. Defaults to `False`. If `False`, reloads an existing
project of the same name if one is found. Otherwise, overwrites the
project.
seed: Int. Random seed.
max_model_size: Int. Maximum number of scalars in the parameters of a
model. Models larger than this are rejected.
**kwargs: Any arguments supported by AutoModel.
"""
def __init__(
self,
num_classes: Optional[int] = None,
multi_label: bool = False,
loss: types.LossType = None,
metrics: Optional[types.MetricsType] = None,
project_name: str = "image_classifier",
max_trials: int = 100,
directory: Union[str, Path, None] = None,
objective: str = "val_loss",
tuner: Union[str, Type[tuner.AutoTuner]] = None,
overwrite: bool = False,
seed: Optional[int] = None,
max_model_size: Optional[int] = None,
**kwargs
):
pass # pragma: no cover
def fit(
self,
x: Optional[types.DatasetType] = None,
y: Optional[types.DatasetType] = None,
epochs: Optional[int] = None,
callbacks: Optional[List[keras.callbacks.Callback]] = None,
validation_split: Optional[float] = 0.2,
validation_data: Union[
tf.data.Dataset, Tuple[types.DatasetType, types.DatasetType], None
] = None,
**kwargs
):
"""Search for the best model and hyperparameters for the AutoModel.
It will search for the best model based on the performances on
validation data.
# Arguments
x: numpy.ndarray or tensorflow.Dataset. Training data x. The shape of
the data should be (samples, width, height)
or (samples, width, height, channels). If it's a tensorflow.Dataset
only x is used, and each sample has an image, and corresponding
(bboxes, classIDs).
y: numpy.ndarray. Training data y. They are the
tuples of bounding boxes and their corresponding class IDs w.r.t.
the images in x. Each bounding box is defined by 4 values
[ymin, xmin, ymax, xmax]. Box coordinates are measured from top left
image corner, are 0-indexed and proportional to sides i.e. between
[0,1]. Shape of the bounding boxes should be (None, 4), and shape of
the classIDs should be (None,) in each tuple, where None represents
the number of bounding boxes in a single image.
epochs: Int. The number of epochs to train each model during the search.
If unspecified, by default we train for a maximum of 1000 epochs,
but we stop training if the validation loss stops improving for 10
epochs (unless you specified an EarlyStopping callback as part of
the callbacks argument, in which case the EarlyStopping callback you
specified will determine early stopping).
callbacks: List of Keras callbacks to apply during training and
validation.
validation_split: Float between 0 and 1. Defaults to 0.2.
Fraction of the training data to be used as validation data.
The model will set apart this fraction of the training data,
will not train on it, and will evaluate
the loss and any model metrics
on this data at the end of each epoch.
The validation data is selected from the last samples
in the `x` and `y` data provided, before shuffling. This argument is
not supported when `x` is a dataset.
The best model found would be fit on the entire dataset including the
validation data.
validation_data: Data on which to evaluate the loss and any model metrics
at the end of each epoch. The model will not be trained on this data.
`validation_data` will override `validation_split`. The type of the
validation data should be the same as the training data.
The best model found would be fit on the training dataset without the
validation data.
**kwargs: Any arguments supported by
[keras.Model.fit](https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit).
"""
pass # pragma: no cover
def predict(self, x, **kwargs):
"""Predict the output for a given testing data.
# Arguments
x: numpy.ndarray or tensorflow.Dataset. Testing data x. The shape of
the data should be (samples, width, height) or (samples, width,
height, channels).
**kwargs: Any arguments supported by keras.Model.predict.
# Returns
labels: [batch_size, 3] shaped tensor containing tuples of
(bboxes, classIDs, scores) for each image in the testing data x,
where each bounding box is defined by 4 values [ymin, xmin, ymax,
xmax]. Box coordinates are measured from top left image corner,
are 0-indexed and proportional to sides i.e. between [0,1]. Shape
of the bounding boxes should be (None, 4), and shape of the
classIDs should be (None,) in each tuple, where None represents
the number of bounding boxes detected in an image. The scores
denote the probability with which a class is detected in the
corresponding bounding box.
"""
pass # pragma: no cover