def tf_signature(self): """ Adjacency matrix has shape [batch, n_nodes, n_nodes] Node features have shape [batch, n_nodes, n_node_features] Edge features have shape [batch, n_nodes, n_nodes, n_edge_features] Labels have shape [batch, n_labels] """ signature = self.signature for k in signature: signature[k]["shape"] = prepend_none(signature[k]["shape"]) if "x" in signature and self.mask: # In case we have a mask, the mask is concatenated to the features signature["x"]["shape"] = signature["x"]["shape"][:-1] + ( signature["x"]["shape"][-1] + 1, ) if "a" in signature: # Adjacency matrix in batch mode is dense signature["a"]["spec"] = tf.TensorSpec if "e" in signature: # Edge attributes have an extra None dimension in batch mode signature["e"]["shape"] = prepend_none(signature["e"]["shape"]) if "y" in signature and self.node_level: # Node labels have an extra None dimension signature["y"]["shape"] = prepend_none(signature["y"]["shape"]) return to_tf_signature(signature)
def tf_signature(self): signature = self.dataset.signature for k in signature: signature[k]['shape'] = prepend_none(signature[k]['shape']) if 'a' in signature: # Adjacency matrix in batch mode is dense signature['a']['spec'] = tf.TensorSpec if 'e' in signature: # Edge attributes have an extra None dimension in batch mode signature['e']['shape'] = prepend_none(signature['e']['shape']) return to_tf_signature(signature)
def tf_signature(self): """ Adjacency matrix has shape [batch, n_nodes, n_nodes] Node features have shape [batch, n_nodes, n_node_features] Edge features have shape [batch, n_nodes, n_nodes, n_edge_features] Targets have shape [batch, ..., n_labels] """ signature = self.dataset.signature for k in signature: signature[k]["shape"] = prepend_none(signature[k]["shape"]) if "a" in signature: # Adjacency matrix in batch mode is dense signature["a"]["spec"] = tf.TensorSpec if "e" in signature: # Edge attributes have an extra None dimension in batch mode signature["e"]["shape"] = prepend_none(signature["e"]["shape"]) return to_tf_signature(signature)
def tf_signature(self): """ Adjacency matrix has shape [n_nodes, n_nodes] Node features have shape [batch, n_nodes, n_node_features] Edge features have shape [batch, n_edges, n_edge_features] Targets have shape [batch, ..., n_labels] """ signature = self.dataset.signature for k in ["x", "e", "y"]: if k in signature: signature[k]["shape"] = prepend_none(signature[k]["shape"]) return to_tf_signature(signature)
def tf_signature(self): signature = self.dataset.signature if 'y' in signature: if not self.node_level: signature['y']['shape'] = prepend_none(signature['y']['shape']) if 'a' in signature: signature['a']['spec'] = tf.SparseTensorSpec signature['i'] = dict() signature['i']['spec'] = tf.TensorSpec signature['i']['shape'] = (None,) signature['i']['dtype'] = tf.as_dtype(tf.int64) return to_tf_signature(signature)
def tf_signature(self): """ Adjacency matrix has shape [n_nodes, n_nodes] Node features have shape [batch, n_nodes, n_node_features] Edge features have shape [batch, n_edges, n_edge_features] Targets have shape [batch, ..., n_labels] """ signature = self.dataset.signature for k in ["x", "e", "y"]: if k in signature: signature[k]["shape"] = prepend_none(signature[k]["shape"]) signature["a"] = dict() signature["a"]["spec"] = get_spec(self.dataset.a) signature["a"]["shape"] = (None, None) signature["a"]["dtype"] = tf.as_dtype(self.dataset.a.dtype) return to_tf_signature(signature)
def tf_signature(self): """ Adjacency matrix has shape [n_nodes, n_nodes] Node features have shape [n_nodes, n_node_features] Edge features have shape [n_edges, n_edge_features] Targets have shape [..., n_labels] """ signature = self.dataset.signature if "y" in signature: signature["y"]["shape"] = prepend_none(signature["y"]["shape"]) if "a" in signature: signature["a"]["spec"] = tf.SparseTensorSpec signature["i"] = dict() signature["i"]["spec"] = tf.TensorSpec signature["i"]["shape"] = (None, ) signature["i"]["dtype"] = tf.as_dtype(tf.int64) return to_tf_signature(signature)