abaa902d9c27646a0528ab7c8b6ee182dce1de2c,deepchem/models/tensorgraph/layers.py,GraphEmbedPoolLayer,embedding_factors,#GraphEmbedPoolLayer#Any#Any#Any#,4408

Before Change

  def embedding_factors(self, V, no_filters, name="default"):
    no_features = V.get_shape()[-1].value
    W = tf.get_variable(
        "%s_weights" % name, [no_features, no_filters],
        initializer=tf.truncated_normal_initializer(
            stddev=1.0 / math.sqrt(no_features)),
        dtype=tf.float32)
    b = tf.get_variable(
        "%s_bias" % self.name, [no_filters],
        initializer=tf.constant_initializer(0.1),
        dtype=tf.float32)

After Change

  def embedding_factors(self, V, no_filters, name="default"):
    no_features = V.get_shape()[-1].value
    if tfe.in_eager_mode():
      if not self._built:
        self.variables = self._create_variables(no_features, no_filters, name)
        self._built = True
      W, b = self.variables
    else:
      W, b = self._create_variables(no_features, no_filters, name)
    V_reshape = tf.reshape(V, (-1, no_features))
    s = tf.slice(tf.shape(V), [0], [len(V.get_shape()) - 1])
    s = tf.concat([s, tf.stack([no_filters])], 0)
    result = tf.reshape(tf.matmul(V_reshape, W) + b, s)

Instances