models.py 7.92 KB
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# @author lucasmiranda42

from tensorflow.keras import Input, Model, Sequential
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from tensorflow.keras.constraints import UnitNorm
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from tensorflow.keras.layers import Bidirectional, Dense, Dropout
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from tensorflow.keras.layers import Lambda, LSTM
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from tensorflow.keras.layers import RepeatVector, TimeDistributed
from tensorflow.keras.losses import Huber
from tensorflow.keras.optimizers import Adam
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from source.model_utils import *
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import tensorflow as tf


class SEQ_2_SEQ_AE:
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    def __init__(
        self,
        input_shape,
        CONV_filters,
        LSTM_units_1,
        LSTM_units_2,
        DENSE_2,
        DROPOUT_RATE,
        ENCODING,
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        learn_rate,
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    ):
        self.input_shape = input_shape
        self.CONV_filters = CONV_filters
        self.LSTM_units_1 = LSTM_units_1
        self.LSTM_units_2 = LSTM_units_2
        self.DENSE_1 = LSTM_units_2
        self.DENSE_2 = DENSE_2
        self.DROPOUT_RATE = DROPOUT_RATE
        self.ENCODING = ENCODING
        self.learn_rate = learn_rate

    def build(self):
        # Encoder Layers
        Model_E0 = tf.keras.layers.Conv1D(
            filters=self.CONV_filters,
            kernel_size=5,
            strides=1,
            padding="causal",
            activation="relu",
            input_shape=self.input_shape[1:],
        )
        Model_E1 = Bidirectional(
            LSTM(
                self.LSTM_units_1,
                activation="tanh",
                return_sequences=True,
                kernel_constraint=UnitNorm(axis=0),
            )
        )
        Model_E2 = Bidirectional(
            LSTM(
                self.LSTM_units_2,
                activation="tanh",
                return_sequences=False,
                kernel_constraint=UnitNorm(axis=0),
            )
        )
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        Model_E3 = Dense(
            self.DENSE_1, activation="relu", kernel_constraint=UnitNorm(axis=0)
        )
        Model_E4 = Dense(
            self.DENSE_2, activation="relu", kernel_constraint=UnitNorm(axis=0)
        )
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        Model_E5 = Dense(
            self.ENCODING,
            activation="relu",
            kernel_constraint=UnitNorm(axis=1),
            activity_regularizer=UncorrelatedFeaturesConstraint(3, weightage=1.0),
        )

        # Decoder layers
        Model_D4 = Bidirectional(
            LSTM(
                self.LSTM_units_1,
                activation="tanh",
                return_sequences=True,
                kernel_constraint=UnitNorm(axis=1),
            )
        )
        Model_D5 = Bidirectional(
            LSTM(
                self.LSTM_units_1,
                activation="sigmoid",
                return_sequences=True,
                kernel_constraint=UnitNorm(axis=1),
            )
        )

        # Define and instanciate encoder
        encoder = Sequential(name="DLC_encoder")
        encoder.add(Model_E0)
        encoder.add(Model_E1)
        encoder.add(Model_E2)
        encoder.add(Model_E3)
        encoder.add(Dropout(self.DROPOUT_RATE))
        encoder.add(Model_E4)
        encoder.add(Model_E5)

        # Define and instanciate decoder
        decoder = Sequential(name="DLC_Decoder")
        decoder.add(
            DenseTranspose(
                Model_E5, activation="relu", input_shape=(self.ENCODING,), output_dim=64
            )
        )
        decoder.add(DenseTranspose(Model_E4, activation="relu", output_dim=128))
        decoder.add(DenseTranspose(Model_E3, activation="relu", output_dim=256))
        decoder.add(RepeatVector(self.input_shape[1]))
        decoder.add(Model_D4)
        decoder.add(Model_D5)
        decoder.add(TimeDistributed(Dense(self.input_shape[2])))

        model = Sequential([encoder, decoder], name="DLC_Autoencoder")

        model.compile(
            loss=Huber(reduction="sum", delta=100.0),
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            optimizer=Adam(lr=self.learn_rate, clipvalue=0.5,),
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            metrics=["mae"],
        )

        return model
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class SEQ_2_SEQ_VAE:
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    def __init__(
        self,
        input_shape,
        CONV_filters,
        LSTM_units_1,
        LSTM_units_2,
        DENSE_2,
        DROPOUT_RATE,
        ENCODING,
        learn_rate,
    ):
        self.input_shape = input_shape
        self.CONV_filters = CONV_filters
        self.LSTM_units_1 = LSTM_units_1
        self.LSTM_units_2 = LSTM_units_2
        self.DENSE_1 = LSTM_units_2
        self.DENSE_2 = DENSE_2
        self.DROPOUT_RATE = DROPOUT_RATE
        self.ENCODING = ENCODING
        self.learn_rate = learn_rate

    def build(self):
        # Encoder Layers
        Model_E0 = tf.keras.layers.Conv1D(
            filters=CONV_filters,
            kernel_size=5,
            strides=1,
            padding="causal",
            activation="relu",
        )
        Model_E1 = Bidirectional(
            LSTM(
                LSTM_units_1,
                activation="tanh",
                return_sequences=True,
                kernel_constraint=UnitNorm(axis=0),
            )
        )
        Model_E2 = Bidirectional(
            LSTM(
                LSTM_units_2,
                activation="tanh",
                return_sequences=False,
                kernel_constraint=UnitNorm(axis=0),
            )
        )
        Model_E3 = Dense(DENSE_1, activation="relu", kernel_constraint=UnitNorm(axis=0))
        Model_E4 = Dense(DENSE_2, activation="relu", kernel_constraint=UnitNorm(axis=0))
        Model_E5 = Dense(
            ENCODING,
            activation="relu",
            kernel_constraint=UnitNorm(axis=1),
            activity_regularizer=UncorrelatedFeaturesConstraint(3, weightage=1.0),
        )

        # Decoder layers
        Model_D4 = Bidirectional(
            LSTM(
                LSTM_units_1,
                activation="tanh",
                return_sequences=True,
                kernel_constraint=UnitNorm(axis=1),
            )
        )
        Model_D5 = Bidirectional(
            LSTM(
                LSTM_units_1,
                activation="sigmoid",
                return_sequences=True,
                kernel_constraint=UnitNorm(axis=1),
            )
        )

        # Define and instanciate encoder
        x = Input(shape=self.input_shape[1:])
        encoder = Model_E0(x)
        encoder = Model_E1(encoder)
        encoder = Model_E2(encoder)
        encoder = Model_E3(encoder)
        encoder = Dropout(DROPOUT_RATE)(encoder)
        encoder = Model_E4(encoder)
        encoder = Model_E5(encoder)

        z_mean = Dense(ENCODING)(encoder)
        z_log_sigma = Dense(ENCODING)(encoder)

        if "ELBO" in self.loss:
            z_mean, z_log_sigma = KLDivergenceLayer()([z_mean, z_log_sigma])

        z = Lambda(sampling)([z_mean, z_log_sigma])

        if "MMD" in self.loss:
            z = MMDiscrepancyLayer()(z)

        # Define and instanciate decoder
        decoder = DenseTranspose(Model_E5, activation="relu", output_dim=ENCODING)(z)
        decoder = DenseTranspose(Model_E4, activation="relu", output_dim=DENSE_2)(
            decoder
        )
        decoder = DenseTranspose(Model_E3, activation="relu", output_dim=DENSE_1)(
            decoder
        )
        decoder = RepeatVector(self.input_shape[1])(decoder)
        decoder = Model_D4(decoder)
        decoder = Model_D5(decoder)
        x_decoded_mean = TimeDistributed(Dense(self.input_shape[2]))(decoder)

        # end-to-end autoencoder
        vae = Model(x, x_decoded_mean)

        def huber_loss(x, x_decoded_mean):
            huber_loss = Huber(reduction="sum", delta=100.0)
            return self.input_shape[1:] * huber_loss(x, x_decoded_mean)

        vae.compile(
            loss=huber_loss,
            optimizer=Adam(
                lr=hp.Float(
                    "learning_rate",
                    min_value=1e-4,
                    max_value=1e-2,
                    sampling="LOG",
                    default=1e-3,
                ),
            ),
            metrics=["mae"],
            experimental_run_tf_function=False,
        )

        return encoder, generator, vae
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class SEQ_2_SEQ_MVAE:
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    pass


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class SEQ_2_SEQ_MMVAE:
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    pass