model/perceptual_label_predictor.py

import tensorflow as tf
from keras.layers import Input, Dense, Dropout
from keras.models import Model
from keras.losses import binary_crossentropy
from load_data import read_data
import joblib
import numpy as np

KL = tf.keras.layers


def perceptual_label_predictor():
    """Assemble and return the perceptual_label_predictor."""
    mini_input = Input((20,))
    p = Dense(20, activation='relu')(mini_input)
    p = Dropout(0.2)(p)
    p = Dense(16, activation='relu')(p)
    p = Dropout(0.2)(p)
    p = Dense(5, activation='sigmoid')(p)
    style_predictor = Model(mini_input, p)
    style_predictor.summary()
    return style_predictor
    
    
def train_perceptual_label_predictor(perceptual_label_predictor, encoder):
    """Train the perceptual_label_predictor. (Including data loading.)"""
    
    Input_synthetic = read_data("./data/labeled_dataset/synthetic_data")
    Input_AU = read_data("./data/external_data/ARTURIA_data")[:100]
    
    AU_labels = joblib.load("./data/labeled_dataset/ARTURIA_labels")
    synth_labels = joblib.load("./data/labeled_dataset/synthetic_data_labels")
    
    AU_encode = encoder.predict(Input_AU)[0]
    Synth_encode = encoder.predict(Input_synthetic)[0]
    
    perceptual_label_predictor.compile(optimizer='adam', loss=binary_crossentropy)
    perceptual_label_predictor.fit(np.vstack([AU_encode, Synth_encode]), np.vstack([AU_labels, synth_labels]), epochs=140, validation_split=0.05, batch_size=16)
    perceptual_label_predictor.save(f"./models/new_trained_models/perceptual_label_predictor.h5")