6a8815de0cec97480e2f0ce4769ceaf1121274bd,keras/engine/topology.py,Merge,compute_mask,#Merge#Any#Any#,1349

Before Change

            masks = [K.expand_dims(m, 0) for m in mask if m is not None]
            return K.all(K.concatenate(masks, axis=0), axis=0, keepdims=False)
        elif self.mode == "concat":
            masks = [K.ones_like(inputs[i][:-1]) if m is None else m for i, m in zip(inputs, mask)]
            expanded_dims = [K.expand_dims(m) for m in masks]
            concatenated = K.concatenate(expanded_dims, axis=self.concat_axis)
            return K.all(concatenated, axis=-1, keepdims=False)
        elif self.mode in ["cos", "dot"]:
            return None

After Change

            // Make a list of masks while making sure the dimensionality of each mask 
            // is the same as the corresponding input.
            masks = []
            for input_i, mask_i in zip(inputs, mask):
                if mask_i is None:
                    // Input is unmasked. Append all 1s to masks, but cast it to uint8 first
                    masks.append(K.cast(K.ones_like(input_i), "uint8"))
                elif K.ndim(mask_i) < K.ndim(input_i):
                    // Mask is smaller than the input, expand it
                    masks.append(K.expand_dims(mask_i))
                else:
                    masks.append(mask_i)
            concatenated = K.concatenate(masks, axis=self.concat_axis)
            return K.all(concatenated, axis=-1, keepdims=False)
        elif self.mode in ["cos", "dot"]:
            return None

Instances