Python tensorflow.contrib.session_bundle.exporter.Exporter() Examples
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Example #1
Source File: export.py From lambda-packs with MIT License | 6 votes |
def _export_graph(graph, saver, checkpoint_path, export_dir, default_graph_signature, named_graph_signatures, exports_to_keep): """Exports graph via session_bundle, by creating a Session.""" with graph.as_default(): with tf_session.Session('') as session: variables.local_variables_initializer() lookup_ops.tables_initializer() saver.restore(session, checkpoint_path) export = exporter.Exporter(saver) export.init( init_op=control_flow_ops.group( variables.local_variables_initializer(), lookup_ops.tables_initializer()), default_graph_signature=default_graph_signature, named_graph_signatures=named_graph_signatures, assets_collection=ops.get_collection(ops.GraphKeys.ASSET_FILEPATHS)) return export.export(export_dir, contrib_variables.get_global_step(), session, exports_to_keep=exports_to_keep)
Example #2
Source File: export.py From auto-alt-text-lambda-api with MIT License | 6 votes |
def _export_graph(graph, saver, checkpoint_path, export_dir, default_graph_signature, named_graph_signatures, exports_to_keep): """Exports graph via session_bundle, by creating a Session.""" with graph.as_default(): with tf_session.Session('') as session: variables.local_variables_initializer() data_flow_ops.tables_initializer() saver.restore(session, checkpoint_path) export = exporter.Exporter(saver) export.init(init_op=control_flow_ops.group( variables.local_variables_initializer(), data_flow_ops.tables_initializer()), default_graph_signature=default_graph_signature, named_graph_signatures=named_graph_signatures, assets_collection=ops.get_collection( ops.GraphKeys.ASSET_FILEPATHS)) return export.export(export_dir, contrib_variables.get_global_step(), session, exports_to_keep=exports_to_keep)
Example #3
Source File: export.py From deep_image_model with Apache License 2.0 | 6 votes |
def _export_graph(graph, saver, checkpoint_path, export_dir, default_graph_signature, named_graph_signatures, exports_to_keep): """Exports graph via session_bundle, by creating a Session.""" with graph.as_default(): with tf_session.Session('') as session: variables.local_variables_initializer() data_flow_ops.initialize_all_tables() saver.restore(session, checkpoint_path) export = exporter.Exporter(saver) export.init(init_op=control_flow_ops.group( variables.local_variables_initializer(), data_flow_ops.initialize_all_tables()), default_graph_signature=default_graph_signature, named_graph_signatures=named_graph_signatures, assets_collection=ops.get_collection( ops.GraphKeys.ASSET_FILEPATHS)) return export.export(export_dir, contrib_variables.get_global_step(), session, exports_to_keep=exports_to_keep)
Example #4
Source File: export_half_plus_two.py From jetson with MIT License | 6 votes |
def Export(): export_path = "/tmp/half_plus_two" with tf.Session() as sess: # Make model parameters a&b variables instead of constants to # exercise the variable reloading mechanisms. a = tf.Variable(0.5) b = tf.Variable(2.0) # Calculate, y = a*x + b # here we use a placeholder 'x' which is fed at inference time. x = tf.placeholder(tf.float32) y = tf.add(tf.multiply(a, x), b) # Run an export. tf.global_variables_initializer().run() export = exporter.Exporter(tf.train.Saver()) export.init(named_graph_signatures={ "inputs": exporter.generic_signature({"x": x}), "outputs": exporter.generic_signature({"y": y}), "regress": exporter.regression_signature(x, y) }) export.export(export_path, tf.constant(123), sess)
Example #5
Source File: export.py From keras-lambda with MIT License | 6 votes |
def _export_graph(graph, saver, checkpoint_path, export_dir, default_graph_signature, named_graph_signatures, exports_to_keep): """Exports graph via session_bundle, by creating a Session.""" with graph.as_default(): with tf_session.Session('') as session: variables.local_variables_initializer() data_flow_ops.tables_initializer() saver.restore(session, checkpoint_path) export = exporter.Exporter(saver) export.init(init_op=control_flow_ops.group( variables.local_variables_initializer(), data_flow_ops.tables_initializer()), default_graph_signature=default_graph_signature, named_graph_signatures=named_graph_signatures, assets_collection=ops.get_collection( ops.GraphKeys.ASSET_FILEPATHS)) return export.export(export_dir, contrib_variables.get_global_step(), session, exports_to_keep=exports_to_keep)
Example #6
Source File: model.py From rec-rl with Apache License 2.0 | 5 votes |
def export_session_bundle(self): export_dir_base = self.saver_spec.get('export_directory') if not export_dir_base: print("export_directory is None") checkpoint = tf.train.latest_checkpoint(self.saver_directory) if not checkpoint: raise NotFittedError("Couldn't find trained model at %s." % self.saver_directory) export_dir = saved_model_export_utils.get_timestamped_export_dir(export_dir_base) if self.distributed_spec: sess = tf.Session(target=self.server.target, graph=self.graph, config=self.session_config) else: sess = tf.Session(graph=self.graph) self.scaffold.saver.restore(sess, checkpoint) signature = {name: ts for name, ts in self.states_input.items()} signature["deterministic"] = self.deterministic_input signature["update"] = self.update_input exporter = Exporter(self.scaffold.saver) exporter.init(self.graph.as_graph_def(), clear_devices=True, default_graph_signature=generic_signature(signature)) exporter.export(export_dir_base=export_dir, global_step_tensor=self.timestep, sess=sess) return export_dir
Example #7
Source File: punctuator.py From keras-punctuator with MIT License | 5 votes |
def saveWithSavedModel(): # K.set_learning_phase(0) # all new operations will be in test mode from now on # wordIndex = loadWordIndex() model = createModel() model.load_weights(KERAS_WEIGHTS_FILE) export_path = os.path.join(PUNCTUATOR_DIR, 'graph') # where to save the exported graph shutil.rmtree(export_path, True) export_version = 1 # version number (integer) import tensorflow as tf sess = tf.Session() saver = tf.train.Saver(sharded=True) from tensorflow.contrib.session_bundle import exporter model_exporter = exporter.Exporter(saver) signature = exporter.classification_signature(input_tensor=model.input,scores_tensor=model.output) # model_exporter.init(sess.graph.as_graph_def(),default_graph_signature=signature) tf.initialize_all_variables().run(session=sess) # model_exporter.export(export_path, tf.constant(export_version), sess) from tensorflow.python.saved_model import builder as saved_model_builder builder = saved_model_builder.SavedModelBuilder(export_path) from tensorflow.python.saved_model import signature_constants from tensorflow.python.saved_model import tag_constants legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op') from tensorflow.python.saved_model.signature_def_utils_impl import predict_signature_def signature_def = predict_signature_def( {signature_constants.PREDICT_INPUTS: model.input}, {signature_constants.PREDICT_OUTPUTS: model.output}) builder.add_meta_graph_and_variables( sess, [tag_constants.SERVING], signature_def_map={ signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: signature_def }, legacy_init_op=legacy_init_op) builder.save()
Example #8
Source File: task.py From cloud-ml-sdk with Apache License 2.0 | 5 votes |
def export_model(sess, inputs_signature, outputs_signature): # Export the model for generic inference service print("Exporting trained model to {}".format(FLAGS.model_path)) saver = tf.train.Saver(sharded=True) model_exporter = exporter.Exporter(saver) model_exporter.init( sess.graph.as_graph_def(), named_graph_signatures={ "inputs": exporter.generic_signature(inputs_signature), "outputs": exporter.generic_signature(outputs_signature) }) model_exporter.export(FLAGS.model_path, tf.constant(FLAGS.model_version), sess) print("Done exporting!")
Example #9
Source File: train.py From tensorflow_template_application with Apache License 2.0 | 4 votes |
def main(): # Define training data x = np.ones(FLAGS.batch_size) y = np.ones(FLAGS.batch_size) # Define the model X = tf.placeholder(tf.float32, shape=[None]) Y = tf.placeholder(tf.float32, shape=[None]) w = tf.Variable(1.0, name="weight") b = tf.Variable(1.0, name="bias") loss = tf.square(Y - tf.mul(X, w) - b) train_op = tf.train.GradientDescentOptimizer(0.01).minimize(loss) predict_op = tf.mul(X, w) + b saver = tf.train.Saver() checkpoint_dir = FLAGS.checkpoint_dir checkpoint_file = checkpoint_dir + "/checkpoint.ckpt" if not os.path.exists(checkpoint_dir): os.makedirs(checkpoint_dir) # Start the session with tf.Session() as sess: sess.run(tf.initialize_all_variables()) ckpt = tf.train.get_checkpoint_state(checkpoint_dir) if ckpt and ckpt.model_checkpoint_path: print("Continue training from the model {}".format(ckpt.model_checkpoint_path)) saver.restore(sess, ckpt.model_checkpoint_path) # Start training start_time = time.time() for epoch in range(FLAGS.epoch_number): sess.run(train_op, feed_dict={X: x, Y: y}) # Start validating if epoch % FLAGS.steps_to_validate == 0: end_time = time.time() print("[{}] Epoch: {}".format(end_time - start_time, epoch)) saver.save(sess, checkpoint_file) start_time = end_time # Print model variables w_value, b_value = sess.run([w, b]) print("The model of w: {}, b: {}".format(w_value, b_value)) # Export the model print("Exporting trained model to {}".format(FLAGS.model_path)) model_exporter = exporter.Exporter(saver) model_exporter.init( sess.graph.as_graph_def(), named_graph_signatures={ 'inputs': exporter.generic_signature({"features": X}), 'outputs': exporter.generic_signature({"prediction": predict_op}) }) model_exporter.export(FLAGS.model_path, tf.constant(FLAGS.export_version), sess) print 'Done exporting!'