Loading deepchem/models/tensorgraph/layers.py +16 −10 Original line number Diff line number Diff line Loading @@ -152,9 +152,8 @@ class Layer(object): def set_summary(self, summary_op, summary_description=None, collections=None): """Annotates a tensor with a tf.summary operation Collects data from self.out_tensor by default but can be changed by setting self.tb_input to another tensor in create_tensor This causes self.out_tensor to be logged to Tensorboard. Parameters ---------- Loading @@ -175,21 +174,28 @@ class Layer(object): self.collections = collections self.tensorboard = True def add_summary_to_tg(self): def add_summary_to_tg(self, tb_input=None): """ Can only be called after self.create_layer to gaurentee that name is not none Create the summary operation for this layer, if set_summary() has been called on it. Can only be called after self.create_layer to guarantee that name is not None. Parameters ---------- tb_input: tensor the tensor to log to Tensorboard. If None, self.out_tensor is used. """ if self.tensorboard == False: return if self.tb_input == None: self.tb_input = self.out_tensor if tb_input == None: tb_input = self.out_tensor if self.summary_op == "tensor_summary": tf.summary.tensor_summary(self.name, self.tb_input, self.summary_description, self.collections) tf.summary.tensor_summary(self.name, tb_input, self.summary_description, self.collections) elif self.summary_op == 'scalar': tf.summary.scalar(self.name, self.tb_input, self.collections) tf.summary.scalar(self.name, tb_input, self.collections) elif self.summary_op == 'histogram': tf.summary.histogram(self.name, self.tb_input, self.collections) tf.summary.histogram(self.name, tb_input, self.collections) def copy(self, replacements={}, variables_graph=None, shared=False): """Duplicate this Layer and all its inputs. Loading deepchem/models/tensorgraph/tensor_graph.py +4 −1 Original line number Diff line number Diff line Loading @@ -894,6 +894,7 @@ class TensorGraph(Model): inputs = [create_tensors(in_layer, tensors, training) for in_layer in layer.in_layers] tensor = layer.create_tensor(in_layers=inputs, set_tensors=False, training=training) tensors[layer] = tensor layer.add_summary_to_tg(tensor) return tensor # Define the model function. Loading @@ -907,6 +908,8 @@ class TensorGraph(Model): if weight_column is not None: tensors[self.task_weights[0]] = tf.feature_column.input_layer(features, [weight_column]) tensors[self.labels[0]] = labels for layer, tensor in tensors.items(): layer.add_summary_to_tg(tensor) # Create the correct outputs, based on the mode. Loading Loading @@ -936,7 +939,7 @@ class TensorGraph(Model): # Create the Estimator. return tf.estimator.Estimator(model_fn=model_fn) return tf.estimator.Estimator(model_fn=model_fn, model_dir=model_dir) def _enqueue_batch(tg, generator, graph, sess, n_enqueued, final_sample): """ Loading Loading
deepchem/models/tensorgraph/layers.py +16 −10 Original line number Diff line number Diff line Loading @@ -152,9 +152,8 @@ class Layer(object): def set_summary(self, summary_op, summary_description=None, collections=None): """Annotates a tensor with a tf.summary operation Collects data from self.out_tensor by default but can be changed by setting self.tb_input to another tensor in create_tensor This causes self.out_tensor to be logged to Tensorboard. Parameters ---------- Loading @@ -175,21 +174,28 @@ class Layer(object): self.collections = collections self.tensorboard = True def add_summary_to_tg(self): def add_summary_to_tg(self, tb_input=None): """ Can only be called after self.create_layer to gaurentee that name is not none Create the summary operation for this layer, if set_summary() has been called on it. Can only be called after self.create_layer to guarantee that name is not None. Parameters ---------- tb_input: tensor the tensor to log to Tensorboard. If None, self.out_tensor is used. """ if self.tensorboard == False: return if self.tb_input == None: self.tb_input = self.out_tensor if tb_input == None: tb_input = self.out_tensor if self.summary_op == "tensor_summary": tf.summary.tensor_summary(self.name, self.tb_input, self.summary_description, self.collections) tf.summary.tensor_summary(self.name, tb_input, self.summary_description, self.collections) elif self.summary_op == 'scalar': tf.summary.scalar(self.name, self.tb_input, self.collections) tf.summary.scalar(self.name, tb_input, self.collections) elif self.summary_op == 'histogram': tf.summary.histogram(self.name, self.tb_input, self.collections) tf.summary.histogram(self.name, tb_input, self.collections) def copy(self, replacements={}, variables_graph=None, shared=False): """Duplicate this Layer and all its inputs. Loading
deepchem/models/tensorgraph/tensor_graph.py +4 −1 Original line number Diff line number Diff line Loading @@ -894,6 +894,7 @@ class TensorGraph(Model): inputs = [create_tensors(in_layer, tensors, training) for in_layer in layer.in_layers] tensor = layer.create_tensor(in_layers=inputs, set_tensors=False, training=training) tensors[layer] = tensor layer.add_summary_to_tg(tensor) return tensor # Define the model function. Loading @@ -907,6 +908,8 @@ class TensorGraph(Model): if weight_column is not None: tensors[self.task_weights[0]] = tf.feature_column.input_layer(features, [weight_column]) tensors[self.labels[0]] = labels for layer, tensor in tensors.items(): layer.add_summary_to_tg(tensor) # Create the correct outputs, based on the mode. Loading Loading @@ -936,7 +939,7 @@ class TensorGraph(Model): # Create the Estimator. return tf.estimator.Estimator(model_fn=model_fn) return tf.estimator.Estimator(model_fn=model_fn, model_dir=model_dir) def _enqueue_batch(tg, generator, graph, sess, n_enqueued, final_sample): """ Loading
