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mediapipe_model_maker.image_classifier.HParams
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The hyperparameters for training image classifiers.
Inherits From: BaseHParams
mediapipe_model_maker . image_classifier . HParams (
learning_rate : float = 0.001 ,
batch_size : int = 2 ,
epochs : int = 10 ,
steps_per_epoch : Optional [ int ] = None ,
class_weights : Optional [ Mapping [ int , float ]] = None ,
shuffle : bool = False ,
repeat : bool = False ,
export_dir : str = tempfile . mkdtemp (),
distribution_strategy : str = & #x27;off',
num_gpus : int = 0 ,
tpu : str = & #x27;',
do_fine_tuning : bool = False ,
l1_regularizer : float = 0.0 ,
l2_regularizer : float = 0.0001 ,
label_smoothing : float = 0.1 ,
do_data_augmentation : bool = True ,
decay_samples : int = ( 10000 * 256 ),
warmup_epochs : int = 2 ,
checkpoint_frequency : int = 1 ,
one_hot : bool = True ,
multi_labels : bool = False
)
Attributes
learning_rate
Learning rate to use for gradient descent training.
batch_size
Batch size for training.
epochs
Number of training iterations over the dataset.
do_fine_tuning
If true, the base module is trained together with the
classification layer on top.
l1_regularizer
A regularizer that applies a L1 regularization penalty.
l2_regularizer
A regularizer that applies a L2 regularization penalty.
label_smoothing
Amount of label smoothing to apply. See tf.keras.losses for
more details.
do_data_augmentation
A boolean controlling whether the training dataset is
augmented by randomly distorting input images, including random cropping,
flipping, etc. See utils.image_preprocessing documentation for details.
decay_samples
Number of training samples used to calculate the decay steps
and create the training optimizer.
warmup_steps
Number of warmup steps for a linear increasing warmup schedule
on learning rate. Used to set up warmup schedule by model_util.WarmUp.
checkpoint_frequency
Frequency to save checkpoint.
one_hot
Whether the label data is score input or one-hot.
multi_labels
Whether the model predict multi labels.
steps_per_epoch
Dataclass field
class_weights
Dataclass field
shuffle
Dataclass field
repeat
Dataclass field
export_dir
Dataclass field
distribution_strategy
Dataclass field
num_gpus
Dataclass field
tpu
Dataclass field
warmup_epochs
Dataclass field
Methods
get_strategy
View source
get_strategy ()
__eq__
__eq__ (
other
)
Class Variables
batch_size
2
checkpoint_frequency
1
class_weights
None
decay_samples
2560000
distribution_strategy
'off'
do_data_augmentation
True
do_fine_tuning
False
epochs
10
export_dir
'/tmpfs/tmp/tmpnt_h4p9w'
l1_regularizer
0.0
l2_regularizer
0.0001
label_smoothing
0.1
learning_rate
0.001
multi_labels
False
num_gpus
0
one_hot
True
repeat
False
shuffle
False
steps_per_epoch
None
tpu
''
warmup_epochs
2
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Last updated 2024-05-07 UTC.
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{"lastModified": "Last updated 2024-05-07 UTC."}
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