Loading contrib/hagcn/hagcn_layers.py +5 −7 Original line number Diff line number Diff line Loading @@ -54,11 +54,9 @@ class AdaptiveFilter(Layer): def _build(self): if self.combine_method == "linear": self.Q = self.init( shape=(self.batch_size, self.num_nodes, self.num_nodes + self.num_node_features)) shape=(self.num_nodes + self.num_node_features, self.num_nodes)) else: self.Q = self.init( shape=(self.batch_size, self.num_node_features, self.num_nodes)) self.Q = self.init(shape=(self.num_node_features, self.num_nodes)) self.trainable_weights = [self.Q] Loading @@ -74,10 +72,10 @@ class AdaptiveFilter(Layer): if self.combine_method == "linear": concatenated = tf.concat([A_tilda_k, X], axis=2) transposed = tf.transpose(concatenated, perm=[0, 2, 1]) adp_fn_val = act_fn(tf.matmul(self.trainable_weights[0], transposed)) adp_fn_val = act_fn( tf.tensordot(concatenated, self.trainable_weights[0], axes=1)) else: adp_fn_val = act_fn(tf.matmul(A_tilda_k, tf.matmul(X, self.Q))) adp_fn_val = act_fn(tf.matmul(A_tilda_k, tf.tensordot(X, self.Q, axes=1))) out_tensor = adp_fn_val if set_tensors: self.variables = self.trainable_weights Loading contrib/hagcn/hagcn_model.py +32 −32 Original line number Diff line number Diff line Loading @@ -68,11 +68,11 @@ class HAGCN(TensorGraph): Feature( name="graph_adjacency_{}".format(k), dtype=tf.float32, shape=[self.batch_size, self.max_nodes, self.max_nodes])) shape=[None, self.max_nodes, self.max_nodes])) self.X = Feature( name='atom_features', dtype=tf.float32, shape=[self.batch_size, self.max_nodes, self.num_node_features]) shape=[None, self.max_nodes, self.num_node_features]) graph_layers = list() adaptive_filters = list() Loading Loading @@ -195,33 +195,33 @@ class HAGCN(TensorGraph): yield feed_dict def predict(self, dataset, transformers=[], outputs=None): """ Uses self to make predictions on provided Dataset object. Parameters ---------- dataset: dc.data.Dataset Dataset to make prediction on transformers: list List of dc.trans.Transformers. outputs: object If outputs is None, then will assume outputs=self.default_outputs. If outputs is a Layer/Tensor, then will evaluate and return as a single ndarray. If outputs is a list of Layers/Tensors, will return a list of ndarrays. Returns ------- results: numpy ndarray or list of numpy ndarrays """ generator = self.default_generator(dataset, predict=True, pad_batches=True) preds = self.predict_on_generator(generator, transformers, outputs) if len(dataset.y) % self.batch_size == 0: return preds else: after_pad = (len(dataset.y) // self.batch_size + 1) * self.batch_size closest = (len(dataset.y) // self.batch_size) * self.batch_size remainder = len(dataset.y) % self.batch_size num_added = after_pad - remainder - closest preds = preds[:-num_added] return preds # def predict(self, dataset, transformers=[], outputs=None): # """ # Uses self to make predictions on provided Dataset object. # # Parameters # ---------- # dataset: dc.data.Dataset # Dataset to make prediction on # transformers: list # List of dc.trans.Transformers. # outputs: object # If outputs is None, then will assume outputs=self.default_outputs. If outputs is # a Layer/Tensor, then will evaluate and return as a single ndarray. If # outputs is a list of Layers/Tensors, will return a list of ndarrays. # # Returns # ------- # results: numpy ndarray or list of numpy ndarrays # """ # generator = self.default_generator(dataset, predict=True, pad_batches=True) # preds = self.predict_on_generator(generator, transformers, outputs) # if len(dataset.y) % self.batch_size == 0: # return preds # else: # after_pad = (len(dataset.y) // self.batch_size + 1) * self.batch_size # closest = (len(dataset.y) // self.batch_size) * self.batch_size # remainder = len(dataset.y) % self.batch_size # num_added = after_pad - remainder - closest # preds = preds[:-num_added] # return preds Loading
contrib/hagcn/hagcn_layers.py +5 −7 Original line number Diff line number Diff line Loading @@ -54,11 +54,9 @@ class AdaptiveFilter(Layer): def _build(self): if self.combine_method == "linear": self.Q = self.init( shape=(self.batch_size, self.num_nodes, self.num_nodes + self.num_node_features)) shape=(self.num_nodes + self.num_node_features, self.num_nodes)) else: self.Q = self.init( shape=(self.batch_size, self.num_node_features, self.num_nodes)) self.Q = self.init(shape=(self.num_node_features, self.num_nodes)) self.trainable_weights = [self.Q] Loading @@ -74,10 +72,10 @@ class AdaptiveFilter(Layer): if self.combine_method == "linear": concatenated = tf.concat([A_tilda_k, X], axis=2) transposed = tf.transpose(concatenated, perm=[0, 2, 1]) adp_fn_val = act_fn(tf.matmul(self.trainable_weights[0], transposed)) adp_fn_val = act_fn( tf.tensordot(concatenated, self.trainable_weights[0], axes=1)) else: adp_fn_val = act_fn(tf.matmul(A_tilda_k, tf.matmul(X, self.Q))) adp_fn_val = act_fn(tf.matmul(A_tilda_k, tf.tensordot(X, self.Q, axes=1))) out_tensor = adp_fn_val if set_tensors: self.variables = self.trainable_weights Loading
contrib/hagcn/hagcn_model.py +32 −32 Original line number Diff line number Diff line Loading @@ -68,11 +68,11 @@ class HAGCN(TensorGraph): Feature( name="graph_adjacency_{}".format(k), dtype=tf.float32, shape=[self.batch_size, self.max_nodes, self.max_nodes])) shape=[None, self.max_nodes, self.max_nodes])) self.X = Feature( name='atom_features', dtype=tf.float32, shape=[self.batch_size, self.max_nodes, self.num_node_features]) shape=[None, self.max_nodes, self.num_node_features]) graph_layers = list() adaptive_filters = list() Loading Loading @@ -195,33 +195,33 @@ class HAGCN(TensorGraph): yield feed_dict def predict(self, dataset, transformers=[], outputs=None): """ Uses self to make predictions on provided Dataset object. Parameters ---------- dataset: dc.data.Dataset Dataset to make prediction on transformers: list List of dc.trans.Transformers. outputs: object If outputs is None, then will assume outputs=self.default_outputs. If outputs is a Layer/Tensor, then will evaluate and return as a single ndarray. If outputs is a list of Layers/Tensors, will return a list of ndarrays. Returns ------- results: numpy ndarray or list of numpy ndarrays """ generator = self.default_generator(dataset, predict=True, pad_batches=True) preds = self.predict_on_generator(generator, transformers, outputs) if len(dataset.y) % self.batch_size == 0: return preds else: after_pad = (len(dataset.y) // self.batch_size + 1) * self.batch_size closest = (len(dataset.y) // self.batch_size) * self.batch_size remainder = len(dataset.y) % self.batch_size num_added = after_pad - remainder - closest preds = preds[:-num_added] return preds # def predict(self, dataset, transformers=[], outputs=None): # """ # Uses self to make predictions on provided Dataset object. # # Parameters # ---------- # dataset: dc.data.Dataset # Dataset to make prediction on # transformers: list # List of dc.trans.Transformers. # outputs: object # If outputs is None, then will assume outputs=self.default_outputs. If outputs is # a Layer/Tensor, then will evaluate and return as a single ndarray. If # outputs is a list of Layers/Tensors, will return a list of ndarrays. # # Returns # ------- # results: numpy ndarray or list of numpy ndarrays # """ # generator = self.default_generator(dataset, predict=True, pad_batches=True) # preds = self.predict_on_generator(generator, transformers, outputs) # if len(dataset.y) % self.batch_size == 0: # return preds # else: # after_pad = (len(dataset.y) // self.batch_size + 1) * self.batch_size # closest = (len(dataset.y) // self.batch_size) * self.batch_size # remainder = len(dataset.y) % self.batch_size # num_added = after_pad - remainder - closest # preds = preds[:-num_added] # return preds
