model.py

import tensorflow as tf


CAP = 3501 # Cap for the number of notes

class Encoder_Z(tf.keras.layers.Layer):

  def __init__(self, dim_z, name="encoder", **kwargs):
    super(Encoder_Z, self).__init__(name=name, **kwargs)
    self.dim_x = (3, CAP, 1)
    self.dim_z = dim_z

  def build(self):
    layers = [tf.keras.layers.InputLayer(input_shape=self.dim_x)]

    layers.append(tf.keras.layers.Conv2D(filters=64, kernel_size=3, strides=(2, 2)))
    layers.append(tf.keras.layers.ReLU())
    layers.append(tf.keras.layers.Flatten())

    layers.append(tf.keras.layers.Dense(2000))
    layers.append(tf.keras.layers.ReLU())

    layers.append(tf.keras.layers.Dense(500))
    layers.append(tf.keras.layers.ReLU())

    layers.append(tf.keras.layers.Dense(self.dim_z * 2, activation=None, name="dist_params"))

    return tf.keras.Sequential(layers)


class Decoder_X(tf.keras.layers.Layer):

  def __init__(self, dim_z, name="decoder", **kwargs):
    super(Decoder_X, self).__init__(name=name, **kwargs)
    self.dim_z = dim_z

  def build(self):
    # Build architecture
      
    layers = [tf.keras.layers.InputLayer(input_shape=(self.dim_z,))]

    layers.append(tf.keras.layers.Dense(500))
    layers.append(tf.keras.layers.ReLU())

    layers.append(tf.keras.layers.Dense(2000))
    layers.append(tf.keras.layers.ReLU())

    layers.append(tf.keras.layers.Dense((CAP - 1) / 2 * 32, activation=None))
    layers.append(tf.keras.layers.Reshape((1, int((CAP - 1) / 2), 32)))

    layers.append(tf.keras.layers.Conv2DTranspose(
        filters=64, kernel_size=3, strides=2, padding='valid'))
    layers.append(tf.keras.layers.ReLU())

    layers.append(tf.keras.layers.Conv2DTranspose(
        filters=1, kernel_size=3, strides=1, padding='same'))

    return tf.keras.Sequential(layers)

kl_weight = tf.keras.backend.variable(0.125)



class VAECost:
    # VAE cost with a schedule based on the Microsoft Research Blog's article
    # "Less pain, more gain: A simple method for VAE training with less of that KL-vanishing agony"
    #
    # The KL weight increases linearly, until it meets a certain threshold and keeps constant
    # for the same number of epochs. After that, it decreases abruptly to zero again, and the
    # cycle repeats.

  def __init__(self, model):
    self.model = model
    self.kl_weight_increasing = True
    self.epoch = 1


  # The loss should have the form loss(y_true, y_pred), but in this
  # case y_pred is computed in the cost function

  @tf.function()
  def __call__(self, x_true):
    x_true = tf.cast(x_true, tf.float32)
      
    # Encode "song map" to get its latent representation and the parameters
    # of the distribution
    z_sample, mu, sd = self.model.encode(x_true)

    # Decode the latent representation. Due to the VAE architecture, we should
    # ideally get a reconstructed song map similar to the input.
    x_recons = self.model.decoder(z_sample)

    # Compute mean squared error, where our ground truth is the song map
    # we pass as input, so we "compare" the reconstruction to it.
      
    recons_error = tf.cast(
        tf.reduce_mean((x_true - x_recons) ** 2, axis=[1, 2, 3]),
        tf.float32)

    # Compute reverse KL divergence
    kl_divergence = -0.5 * tf.math.reduce_sum(
          1 + tf.math.log(tf.math.square(sd)) - tf.math.square(mu) - tf.math.square(sd),
          axis=1) # shape=(batch_size,)

    # Return metrics
    elbo = tf.reduce_mean(-kl_weight * kl_divergence - recons_error)
    mean_kl_divergence = tf.reduce_mean(kl_divergence)
    mean_recons_error = tf.reduce_mean(recons_error)

    return -elbo, mean_kl_divergence, mean_recons_error


class VAE(tf.keras.Model):

  def __init__(self, dim_z, seed=2000, analytic_kl=True, name="autoencoder", **kwargs):
    super(VAE, self).__init__(name=name, **kwargs)
    self.dim_x = (3, CAP, 1)
    self.dim_z = dim_z
    self.seed = seed
    self.analytic_kl = analytic_kl
    self.encoder = Encoder_Z(dim_z=self.dim_z).build()
    self.decoder = Decoder_X(dim_z=self.dim_z).build()
    self.cost_func = VAECost(self)

  @tf.function()
  def train_step(self, data):
    # Gradient descent
      
    with tf.GradientTape() as tape:
      neg_elbo, mean_kl_divergence, mean_recons_error = self.cost_func(data)

    gradients = tape.gradient(neg_elbo, self.trainable_variables)
    self.optimizer.apply_gradients(zip(gradients, self.trainable_variables))

    return {"abs ELBO": neg_elbo, "mean KL": mean_kl_divergence,
            "mean recons": mean_recons_error,
            "kl weight": kl_weight}

  def encode(self, x_input):
    # Get a "song map" and make a forward pass through the encoder, in order
    # to return the latent representation and the distribution's parameters
      
    mu, rho = tf.split(self.encoder(x_input), num_or_size_splits=2, axis=1)
    sd = tf.math.log(1 + tf.math.exp(rho))
    z_sample = mu + sd * tf.random.normal(shape=(self.dim_z,))
    return z_sample, mu, sd

  def generate(self, z_sample=None):
    # Decode a latent representation of a song, which is provided or sampled
      
    if z_sample == None:
      z_sample = tf.expand_dims(tf.random.normal(shape=(self.dim_z,)), axis=0)
    return self.decoder(z_sample)