SmilingWolf

/

wd-v1-4-moat-tagger-v2

like 80

I tried write one but I wander is Sequential modal, Dense layer, sigmoid activation, adam optimizer, categorical_crossentropy loss function ok?
if there's any advice, I would be appreciated.

import tensorflow as tf
import tf2onnx


def generate_mixed_modal(
        model_path,
        feature_extraction_layer,
        batch_size,  # count of images in each batch
        # a list of (448, 448, 3) image in shape of (batch_size, 448, 448, 3)
        # can also use Horizontal Flipping,  Rotation,  Cropping,  Color Jittering,  Gaussian Noise,  etc. to increase the dataset?
        new_data,
        # a tag list in (batch_size, num_classes) (like [[0,0,1], [0,1,0],...] when it is a 3 tag problem)
        # maybe float value is also ok? using original prediction confidence value?
        new_labels,
        num_classes,  # total tag number
        num_epochs,  # train epochs, each epoch will train all the data
        new_modal_name,
):
    full_model = tf.keras.models.load_model(model_path)
    # get the feature extractor of the layer before last layer
    feature_extractor = tf.keras.models.Model(
        full_model.inputs, full_model.get_layer(feature_extraction_layer[-2]).output
    )

    features = feature_extractor.predict(new_data)
    print(features.shape)

    # dense layer & use sigmoid to predict multiple tags?
    classifier = tf.keras.models.Sequential([
        tf.keras.layers.Dense(num_classes, activation='sigmoid')
    ])

    # train the classifier
    # use adam to automatically adjust learning rate
    classifier.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
    classifier.fit(features, new_labels, epochs=num_epochs, batch_size=batch_size)

    # combine the feature extractor and classifier
    new_model = tf.keras.models.Sequential([
        feature_extractor,
        classifier
    ])

    new_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
    tf.keras.models.save_model(new_model, new_modal_name + '.pd')
    # also save to onnx
    tf2onnx.convert.from_keras(new_model, output_path=new_modal_name + '.onnx')