62392a4b5e92ad5a6aefbc2d1379c7423437ff07,keras/layers/embeddings.py,Embedding,__init__,#Embedding#Any#Any#Any#Any#Any#Any#Any#Any#,20

Before Change

        if zero_is_mask:
            // This doesn"t seem particularly elegant
            self.W = sharedX(T.set_subtensor(self.W[0, :], mask_val).eval())

        self.params = [self.W]
        self.constraints = [W_constraint]
        self.regularizers = [W_regularizer]

        if weights is not None:

After Change

        of finding the context word in the context of the pivot word
        (or reciprocally depending on your training procedure).

        The layer ingests integer tensors of shape:
        (nb_samples, 2)
        and outputs a float tensor of shape
        (nb_samples, 1)

Instances