(VQ-VAE)Neural Discrete Representation Learning - van den Oord - NIPS 2017 - TensorFlow & PyTorch Code

class VectorQuantizer(base.AbstractModule):
  """Sonnet module representing the VQ-VAE layer.
  Implements the algorithm presented in
  'Neural Discrete Representation Learning' by van den Oord et al.
  https://arxiv.org/abs/1711.00937
  Input any tensor to be quantized. Last dimension will be used as space in
  which to quantize. All other dimensions will be flattened and will be seen
  as different examples to quantize.
  The output tensor will have the same shape as the input.
  For example a tensor with shape [16, 32, 32, 64] will be reshaped into
  [16384, 64] and all 16384 vectors (each of 64 dimensions)  will be quantized
  independently.
  Args:
    embedding_dim: integer representing the dimensionality of the tensors in the
      quantized space. Inputs to the modules must be in this format as well.
    num_embeddings: integer, the number of vectors in the quantized space.
    commitment_cost: scalar which controls the weighting of the loss terms
      (see equation 4 in the paper - this variable is Beta).
  """

  def __init__(self, embedding_dim, num_embeddings, commitment_cost,
               name='vq_layer'):
    super(VectorQuantizer, self).__init__(name=name)
    self._embedding_dim = embedding_dim
    self._num_embeddings = num_embeddings
    self._commitment_cost = commitment_cost

    with self._enter_variable_scope():
      initializer = tf.uniform_unit_scaling_initializer()
      self._w = tf.get_variable('embedding', [embedding_dim, num_embeddings],
                                initializer=initializer, trainable=True)

  def _build(self, inputs, is_training):
    """Connects the module to some inputs.
    Args:
      inputs: Tensor, final dimension must be equal to embedding_dim. All other
        leading dimensions will be flattened and treated as a large batch.
      is_training: boolean, whether this connection is to training data.
    Returns:
      dict containing the following keys and values:
        quantize: Tensor containing the quantized version of the input.
        loss: Tensor containing the loss to optimize.
        perplexity: Tensor containing the perplexity of the encodings.
        encodings: Tensor containing the discrete encodings, ie which element
          of the quantized space each input element was mapped to.
        encoding_indices: Tensor containing the discrete encoding indices, ie
          which element of the quantized space each input element was mapped to.
    """
    # Assert last dimension is same as self._embedding_dim
    input_shape = tf.shape(inputs)
    with tf.control_dependencies([
        tf.Assert(tf.equal(input_shape[-1], self._embedding_dim),
                  [input_shape])]):
      flat_inputs = tf.reshape(inputs, [-1, self._embedding_dim])

    distances = (tf.reduce_sum(flat_inputs**2, 1, keepdims=True)
                 - 2 * tf.matmul(flat_inputs, self._w)
                 + tf.reduce_sum(self._w ** 2, 0, keepdims=True))

    encoding_indices = tf.argmax(- distances, 1)
    encodings = tf.one_hot(encoding_indices, self._num_embeddings)
    encoding_indices = tf.reshape(encoding_indices, tf.shape(inputs)[:-1])
    quantized = self.quantize(encoding_indices)

    e_latent_loss = tf.reduce_mean((tf.stop_gradient(quantized) - inputs) ** 2)
    q_latent_loss = tf.reduce_mean((quantized - tf.stop_gradient(inputs)) ** 2)
    loss = q_latent_loss + self._commitment_cost * e_latent_loss

    quantized = inputs + tf.stop_gradient(quantized - inputs)
    avg_probs = tf.reduce_mean(encodings, 0)
    perplexity = tf.exp(- tf.reduce_sum(avg_probs * tf.log(avg_probs + 1e-10)))

    return {'quantize': quantized,
            'loss': loss,
            'perplexity': perplexity,
            'encodings': encodings,
            'encoding_indices': encoding_indices,}

  @property
  def embeddings(self):
    return self._w

  def quantize(self, encoding_indices):
    with tf.control_dependencies([encoding_indices]):
      w = tf.transpose(self.embeddings.read_value(), [1, 0])
    return tf.nn.embedding_lookup(w, encoding_indices, validate_indices=False)