renamed Function.save_model() to save(), and restore_model() likewise;

made load_model a static method Function.load()

Examples/Image/Classification/ConvNet/Python/ConvNetLRN_CIFAR10_DataAug.py

@@ -129,7 +129,7 @@ def convnetlrn_cifar10_dataaug(reader_train, reader_test, epoch_size=50000, max_
             progress_printer.update_with_trainer(trainer, with_metric=True) # log progress
 
         progress_printer.epoch_summary(with_metric=True)
-        z.save_model(os.path.join(model_path, "ConvNet_CIFAR10_DataAug_{}.dnn".format(epoch)))
+        z.save(os.path.join(model_path, "ConvNet_CIFAR10_DataAug_{}.dnn".format(epoch)))
     
     ### Evaluation action
     epoch_size     = 10000

Examples/Image/Classification/ConvNet/Python/ConvNet_CIFAR10.py

@@ -97,7 +97,7 @@ def convnet_cifar10(debug_output=False):
             progress_printer.update_with_trainer(trainer, with_metric=True) # log progress
 
         progress_printer.epoch_summary(with_metric=True)
-        z.save_model(os.path.join(model_path, "ConvNet_CIFAR10_{}.dnn".format(epoch)))
+        z.save(os.path.join(model_path, "ConvNet_CIFAR10_{}.dnn".format(epoch)))
     
     # Load test data
     reader_test = create_reader(os.path.join(data_path, 'Test_cntk_text.txt'), False, input_dim, num_output_classes)

Examples/Image/Classification/ConvNet/Python/ConvNet_CIFAR10_DataAug.py

@@ -107,7 +107,7 @@ def convnet_cifar10_dataaug(reader_train, reader_test, epoch_size = 50000, max_e
             progress_printer.update_with_trainer(trainer, with_metric=True) # log progress
 
         progress_printer.epoch_summary(with_metric=True)
-        z.save_model(os.path.join(model_path, "ConvNet_CIFAR10_DataAug_{}.dnn".format(epoch)))
+        z.save(os.path.join(model_path, "ConvNet_CIFAR10_DataAug_{}.dnn".format(epoch)))
     
     ### Evaluation action
     epoch_size     = 10000

Examples/Image/Classification/ConvNet/Python/ConvNet_MNIST.py

@@ -87,7 +87,7 @@ def convnet_mnist(debug_output=False):
             progress_printer.update_with_trainer(trainer, with_metric=True) # log progress
 
         progress_printer.epoch_summary(with_metric=True)
-        z.save_model(os.path.join(model_path, "ConvNet_MNIST_{}.dnn".format(epoch)))
+        z.save(os.path.join(model_path, "ConvNet_MNIST_{}.dnn".format(epoch)))
     
     # Load test data
     reader_test = create_reader(os.path.join(data_path, 'Test-28x28_cntk_text.txt'), False, input_dim, num_output_classes)

Examples/Image/Classification/ResNet/Python/TrainResNet_CIFAR10.py

@@ -112,7 +112,7 @@ def train_and_evaluate(reader_train, reader_test, network_name, epoch_size, max_
             sample_count += trainer.previous_minibatch_sample_count         # count samples processed so far
             progress_printer.update_with_trainer(trainer, with_metric=True) # log progress
         progress_printer.epoch_summary(with_metric=True)
-        z.save_model(os.path.join(model_path, network_name + "_{}.dnn".format(epoch)))
+        z.save(os.path.join(model_path, network_name + "_{}.dnn".format(epoch)))
         enable_profiler() # begin to collect profiler data after first epoch
         
     if profiler_dir:

Examples/Image/Detection/FastRCNN/A2_RunCntk_py3.py

@@ -176,7 +176,7 @@ def train_fast_rcnn(debug_output=False):
 
         progress_printer.epoch_summary(with_metric=True)
         if debug_output:
-            frcn_output.save_model(os.path.join(abs_path, "Output", "frcn_py_%s.model" % (epoch+1)))
+            frcn_output.save(os.path.join(abs_path, "Output", "frcn_py_%s.model" % (epoch+1)))
 
     return frcn_output
 
@@ -217,7 +217,7 @@ if __name__ == '__main__':
         trained_model = load_model(model_path)
     else:
         trained_model = train_fast_rcnn()
-        trained_model.save_model(model_path)
+        trained_model.save(model_path)
         print("Stored trained model at %s" % model_path)
 
     # Evaluate the test set

Examples/Image/GettingStarted/07_Deconvolution_PY.py

@@ -82,7 +82,7 @@ def deconv_mnist(max_epochs=3):
             progress_printer.update_with_trainer(trainer, with_metric=True) # log progress
 
         progress_printer.epoch_summary(with_metric=True)
-        z.save_model(os.path.join(model_path, "07_Deconvolution_PY_{}.model".format(epoch)))
+        z.save(os.path.join(model_path, "07_Deconvolution_PY_{}.model".format(epoch)))
 
     # rename final model
     last_model_name = os.path.join(model_path, "07_Deconvolution_PY_{}.model".format(max_epochs - 1))

Examples/Image/TransferLearning/TransferLearning.py

@@ -203,7 +203,7 @@ if __name__ == '__main__':
         trained_model = train_model(_base_model_file, _feature_node_name, _last_hidden_node_name,
                                     _image_width, _image_height, _num_channels, _num_classes, _train_map_file,
                                     max_epochs, freeze=freeze_weights)
-        trained_model.save_model(tl_model_file)
+        trained_model.save(tl_model_file)
         print("Stored trained model at %s" % tl_model_file)
 
     # Evaluate the test set

Examples/Image/TransferLearning/TransferLearning_Extended.py

@@ -82,7 +82,7 @@ if __name__ == '__main__':
     trained_model = train_model(base_model_file, feature_node_name, last_hidden_node_name,
                                 image_width, image_height, num_channels,
                                 len(class_mapping), train_map_file, num_epochs=30, freeze=True)
-    trained_model.save_model(new_model_file)
+    trained_model.save(new_model_file)
     print("Stored trained model at %s" % tl_model_file)
 
     # evaluate test images

Examples/SequenceToSequence/CMUDict/Python/Sequence2Sequence.py

@@ -233,8 +233,8 @@ def sequence_to_sequence_translator(debug_output=False, run_test=False):
 
     error1 = translator_test_error(z, trainer, input_vocab_dim, label_vocab_dim)
 
-    z.save_model("seq2seq.dnn")
-    z.restore_model("seq2seq.dnn")
+    z.save("seq2seq.dnn")
+    z.restore("seq2seq.dnn")
 
     label_seq_axis = Axis('labelAxis')
     label_sequence = sequence.slice(find_arg_by_name('raw_labels',z), 1, 0)

Examples/Text/CharacterLM/char_rnn.py

@@ -184,7 +184,7 @@ def train_lm(training_file, max_num_minibatches):
             p = 0
             e += 1
             model_filename = "models/shakespeare_epoch%d.dnn" % e
-            z.save_model(model_filename)
+            z.save(model_filename)
             print("Saved model to '%s'" % model_filename)
 
         # get the data            
@@ -207,7 +207,7 @@ def train_lm(training_file, max_num_minibatches):
 
     # Do a final save of the model        
     model_filename = "models/shakespeare_epoch%d.dnn" % e
-    z.save_model(model_filename)
+    z.save(model_filename)
 
 
 def load_and_sample(model_filename, vocab_filename, prime_text='', use_hardmax=False, length=1000, temperature=1.0):

Tutorials/CNTK_203_Reinforcement_Learning_Basics.ipynb

@@ -751,7 +751,7 @@
     "            plot_weights([(agent.brain.params['W1'], 'Episode %i $W_1$'%episode_number)], figsize=(14,5))\n",
     "            break\n",
     "        reward_sum = 0\n",
-    "agent.brain.model.save_model('dqn.mod')"
+    "agent.brain.model.save('dqn.mod')"
    ]
   },
   {
@@ -1184,7 +1184,7 @@
     "\n",
     "        observation = env.reset()  # reset env\n",
     "        episode_number += 1\n",
-    "probability.save_model('pg.mod')"
+    "probability.save('pg.mod')"
    ]
   },
   {

bindings/python/cntk/ops/__init__.py

@@ -9,7 +9,7 @@ import numpy as np
 import numbers
 from numbers import Number
 from . import sequence
-from .functions import CloneMethod, Function, load_model
+from .functions import CloneMethod, Function
 from .variables import Variable, Parameter, Constant
 from ..utils import sanitize_input, sanitize_shape, get_data_type, sanitize_axis, sanitize_dynamic_axes, typemap
 from ..axis import Axis

bindings/python/cntk/ops/functions.py

@@ -621,7 +621,7 @@ class Function(cntk_py.Function):
         return graph.find_by_name(self, name)
 
     @typemap
-    def save_model(self, filename):
+    def save(self, filename):
         '''
         Save this function graph into a model file using protobuf-based
         serialization.
@@ -631,8 +631,11 @@ class Function(cntk_py.Function):
         '''
         return super(Function, self).save_model(filename)
 
+    def save_model(self, filename): # legacy name
+        return self.save(filename)
+
     @typemap
-    def restore_model(self, filename):
+    def restore(self, filename):
         '''
         Restore the models parameters (in-place) from a saved model file
 
@@ -644,6 +647,39 @@ class Function(cntk_py.Function):
         '''
         return super(Function, self).restore_model(filename)
 
+    def restore_model(self, filename): # legacy name
+        return self.restore(filename)
+
+    @staticmethod
+    @typemap
+    def load(filename, device=None):
+        '''
+        Load the model in ``filename``, that has been saved using
+        :func:`~cntk.ops.functions.Function.save`.
+
+        Args:
+            filename (str): filename to load the model from
+            device (:class:`~cntk.device.DeviceDescriptor`, default is the default device):
+             instance of DeviceDescriptor
+
+        Returns:
+            root node
+        '''
+        if not device:
+            device = DeviceDescriptor.use_default_device()
+        return cntk_py.Function.load_model(filename, device)
+
+@typemap
+def load_model(filename, device=None):
+    '''
+    Alias for :func:`~cntk.ops.functions.Function.load`.
+    '''
+    return Function.load(filename, device)
+
+@typemap
+def save_model(model, filename): # legacy name
+    return model.save(filename)
+
 
 class UserFunction(Function):
     '''
@@ -770,22 +806,3 @@ class UserFunction(Function):
         Returns the operator name.
         '''
         return 'UserFunction'
-
-
-@typemap
-def load_model(filename, device=None):
-    '''
-    Load the model in ``filename``, that has been saved using
-    :func:`~cntk.ops.functions.Function.save_model`.
-
-    Args:
-        filename (str): filename to load the model from
-        device (:class:`~cntk.device.DeviceDescriptor`, default is the default device):
-         instance of DeviceDescriptor
-
-    Returns:
-        root node
-    '''
-    if not device:
-        device = DeviceDescriptor.use_default_device()
-    return cntk_py.Function.load_model(filename, device)

bindings/python/cntk/tests/persist_test.py

@@ -10,6 +10,7 @@ from cntk.ops import *
 from cntk.debug import save_as_legacy_model
 from cntk.ops.functions import load_model
 
+# TODO: a test for restore_model?
 
 def test_load_save_constant(tmpdir):
     c = constant(value=[1,3])
@@ -20,19 +21,19 @@ def test_load_save_constant(tmpdir):
     assert np.allclose(result, expected)
 
     filename = str(tmpdir / 'c_plus_c.mod')
-    root_node.save_model(filename)
+    root_node.save(filename)
 
-    loaded_node = load_model(filename)
+    loaded_node = Function.load(filename)
     loaded_result = loaded_node.eval()
     assert np.allclose(loaded_result, expected)
 
     filename = filename + '.legacy'
     save_as_legacy_model(root_node, filename)
-    loaded_node = load_model(filename)
+    loaded_node = Function.load(filename)
     loaded_result = loaded_node.eval()
     assert np.allclose(loaded_result, expected)
 
-def test_load_save_input(tmpdir):
+def test_load_save_input_legacy_names(tmpdir):
     i1 = input_variable((1,2), name='i1')
     root_node = abs(i1)
     input1 = [[[-1,2]]]
@@ -68,9 +69,9 @@ def test_load_save_inputs(tmpdir):
     assert np.allclose(result, expected)
 
     filename = str(tmpdir / 'i_plus_i_0.mod')
-    root_node.save_model(filename)
+    root_node.save(filename)
 
-    loaded_node = load_model(filename)
+    loaded_node = Function.load(filename)
 
     # Test specifying the input nodes by name
     loaded_result = loaded_node.eval({'i1': input1, 'i2': input2})
@@ -78,7 +79,7 @@ def test_load_save_inputs(tmpdir):
 
     filename = filename + '.legacy'
     save_as_legacy_model(root_node, filename)
-    loaded_node = load_model(filename)
+    loaded_node = Function.load(filename)
     loaded_result = loaded_node.eval({'i1': input1, 'i2': input2})
     assert np.allclose(loaded_result, expected)
 
@@ -92,9 +93,9 @@ def test_load_save_unique_input(tmpdir):
     assert np.allclose(result, expected)
 
     filename = str(tmpdir / 'i_plus_0.mod')
-    root_node.save_model(filename)
+    root_node.save(filename)
 
-    loaded_node = load_model(filename)
+    loaded_node = Function.load(filename)
 
     # Test specifying the only value for an unique input
     loaded_result = loaded_node.eval(input1)
@@ -102,6 +103,6 @@ def test_load_save_unique_input(tmpdir):
 
     filename = filename + '.legacy'
     save_as_legacy_model(root_node, filename)
-    loaded_node = load_model(filename)
+    loaded_node = Function.load(filename)
     loaded_result = loaded_node.eval(input1)
     assert np.allclose(loaded_result, expected)