tflite_model_maker.text_classifier.BertClassifierSpec

A specification of BERT model for text classification.

tflite_model_maker.text_classifier.BertClassifierSpec(
    uri='https://hub.tensorflow.google.cn/tensorflow/bert_en_uncased_L-12_H-768_A-12/1',
    model_dir=None,
    seq_len=128,
    dropout_rate=0.1,
    initializer_range=0.02,
    learning_rate=3e-05,
    distribution_strategy='mirrored',
    num_gpus=-1,
    tpu='',
    trainable=True,
    do_lower_case=True,
    is_tf2=True,
    name='Bert',
    tflite_input_name=None,
    default_batch_size=32,
    index_to_label=None
)

uri TF-Hub path/url to Bert module.
model_dir The location of the model checkpoint files.
seq_len Length of the sequence to feed into the model.
dropout_rate The rate for dropout.
initializer_range The stdev of the truncated_normal_initializer for initializing all weight matrices.
learning_rate The initial learning rate for Adam.
distribution_strategy A string specifying which distribution strategy to use. Accepted values are 'off', 'one_device', 'mirrored', 'parameter_server', 'multi_worker_mirrored', and 'tpu' -- case insensitive. 'off' means not to use Distribution Strategy; 'tpu' means to use TPUStrategy using tpu_address.
num_gpus How many GPUs to use at each worker with the DistributionStrategies API. The default is -1, which means utilize all available GPUs.
tpu TPU address to connect to.
trainable boolean, whether pretrain layer is trainable.
do_lower_case boolean, whether to lower case the input text. Should be True for uncased models and False for cased models.
is_tf2 boolean, whether the hub module is in TensorFlow 2.x format.
name The name of the object.
tflite_input_name Dict, input names for the TFLite model.
default_batch_size Default batch size for training.
index_to_label List of labels in the training data. e.g. ['neg', 'pos'].

Methods

build

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build()

Builds the class. Used for lazy initialization.

convert_examples_to_features

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convert_examples_to_features(
    examples, tfrecord_file, label_names
)

Converts examples to features and write them into TFRecord file.

create_model

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create_model(
    num_classes, optimizer='adam', with_loss_and_metrics=True
)

Creates the keras model.

get_config

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get_config()

Gets the configuration.

get_default_quantization_config

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get_default_quantization_config()

Gets the default quantization configuration.

get_name_to_features

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get_name_to_features()

Gets the dictionary describing the features.

reorder_input_details

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reorder_input_details(
    tflite_input_details
)

Reorders the tflite input details to map the order of keras model.

run_classifier

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run_classifier(
    train_ds, validation_ds, epochs, steps_per_epoch, num_classes, **kwargs
)

Creates classifier and runs the classifier training.

Args
train_ds tf.data.Dataset, training data to be fed in tf.keras.Model.fit().
validation_ds tf.data.Dataset, validation data to be fed in tf.keras.Model.fit().
epochs Integer, training epochs.
steps_per_epoch Integer or None. Total number of steps (batches of samples) before declaring one epoch finished and starting the next epoch. If steps_per_epoch is None, the epoch will run until the input dataset is exhausted.
num_classes Interger, number of classes.
**kwargs Other parameters used in the tf.keras.Model.fit().

Returns
tf.keras.Model, the keras model that's already trained.

save_vocab

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save_vocab(
    vocab_filename
)

Prints the file path to the vocabulary.

select_data_from_record

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select_data_from_record(
    record
)

Dispatches records to features and labels.

compat_tf_versions [2]
convert_from_saved_model_tf2 True
need_gen_vocab False