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import tensorflow as tf |
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from keras.layers import Input, Dense, Dropout |
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from keras.models import Model |
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from keras.losses import binary_crossentropy |
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from load_data import read_data |
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import joblib |
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import numpy as np |
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KL = tf.keras.layers |
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def perceptual_label_predictor(): |
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"""Assemble and return the perceptual_label_predictor.""" |
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mini_input = Input((20,)) |
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p = Dense(20, activation='relu')(mini_input) |
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p = Dropout(0.2)(p) |
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p = Dense(16, activation='relu')(p) |
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p = Dropout(0.2)(p) |
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p = Dense(5, activation='sigmoid')(p) |
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style_predictor = Model(mini_input, p) |
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style_predictor.summary() |
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return style_predictor |
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def train_perceptual_label_predictor(perceptual_label_predictor, encoder): |
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"""Train the perceptual_label_predictor. (Including data loading.)""" |
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Input_synthetic = read_data("./data/labeled_dataset/synthetic_data") |
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Input_AU = read_data("./data/external_data/ARTURIA_data")[:100] |
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AU_labels = joblib.load("./data/labeled_dataset/ARTURIA_labels") |
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synth_labels = joblib.load("./data/labeled_dataset/synthetic_data_labels") |
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AU_encode = encoder.predict(Input_AU)[0] |
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Synth_encode = encoder.predict(Input_synthetic)[0] |
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perceptual_label_predictor.compile(optimizer='adam', loss=binary_crossentropy) |
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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) |
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perceptual_label_predictor.save(f"./models/new_trained_models/perceptual_label_predictor.h5") |
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