Include the python test and bump up the model version

Source/ComputationNetworkLib/ComputationNode.h

@@ -60,7 +60,8 @@
                                  // Reduction: Add reduction over multiple axes
 #define CNTK_MODEL_VERSION_28 28 // Padding op
 #define CNTK_MODEL_VERSION_29 29 // Expose StopGradient in BS
-#define CURRENT_CNTK_MODEL_VERSION CNTK_MODEL_VERSION_29
+#define CNTK_MODEL_VERSION_30 30 // LatticeWithSequenceSoftmax node
+#define CURRENT_CNTK_MODEL_VERSION CNTK_MODEL_VERSION_30
 
 // helper mode for debugging
 // If TRACK_GAP_NANS is defined then initialize layout gaps to NaN and do NaN checks. Also do detailed logging of node computations.

Tests/EndToEndTests/CNTKv2Python/Examples/lattice_deserializer_test.py

@@ -0,0 +1,94 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+# Licensed under the MIT license. See LICENSE.md file in the project root
+# for full license information.
+# ==============================================================================
+
+import os
+import cntk as C
+import numpy as np
+from cntk.ops.tests.ops_test_utils import cntk_device
+from cntk.cntk_py import DeviceKind_GPU
+from cntk.device import try_set_default_device
+import pytest
+
+TOLERANCE_ABSOLUTE = 1E-2
+
+def test_lattice_deserializer(device_id):
+    if cntk_device(device_id).type() != DeviceKind_GPU:
+        pytest.skip('test only runs on GPU')
+    try_set_default_device(cntk_device(device_id))
+
+    data_dir = r"D:\users\vadimma\cntk_tut_deprecated\CNTK\Tests\EndToEndTests\Speech\Data\AN4Corpus\v0"
+
+    feature_dimension = 33
+    feature = C.sequence.input_variable(feature_dimension)
+
+    label_dimension = 133
+    label = C.sequence.input_variable(label_dimension)
+
+    axis_lattice = C.Axis.new_unique_dynamic_axis('lattice_axis')
+    lattice = C.sequence.input_variable(1, sequence_axis=axis_lattice)
+
+    train_feature_filepath = os.path.join(data_dir,"glob_0000.scp")
+    train_label_filepath = os.path.join(data_dir,"glob_0000.mlf")
+    train_lattice_index_path = os.path.join(data_dir,"latticeIndex.txt")
+    mapping_filepath = os.path.join(data_dir,"state.list")
+    train_feature_stream = C.io.HTKFeatureDeserializer(
+    C.io.StreamDefs(speech_feature = C.io.StreamDef(shape = feature_dimension, scp = train_feature_filepath)))
+    train_label_stream = C.io.HTKMLFDeserializer(
+    mapping_filepath, C.io.StreamDefs(speech_label = C.io.StreamDef(shape = label_dimension, mlf = train_label_filepath)), True)
+    train_lattice_stream = C.io.LatticeDeserializer(train_lattice_index_path,C.io.StreamDefs(speech_lattice = C.io.StreamDef()))
+    train_data_reader = C.io.MinibatchSource([train_feature_stream, train_label_stream, train_lattice_stream], frame_mode = False)
+    train_input_map = {feature: train_data_reader.streams.speech_feature, label: train_data_reader.streams.speech_label, lattice: train_data_reader.streams.speech_lattice}
+
+    feature_mean = np.fromfile(os.path.join("GlobalStats", "mean.363"), dtype=float, count=feature_dimension)
+    feature_inverse_stddev = np.fromfile(os.path.join("GlobalStats", "var.363"), dtype=float, count=feature_dimension)
+
+    feature_normalized = (feature - feature_mean) * feature_inverse_stddev
+
+    with C.default_options(activation=C.sigmoid):
+        z = C.layers.Sequential([
+            C.layers.For(range(3), lambda: C.layers.Recurrence(C.layers.LSTM(1024))),
+            C.layers.Dense(label_dimension)
+        ])(feature_normalized)
+    mbsize = 1024
+    mbs_per_epoch = 10
+    max_epochs = 2
+
+    symListPath = os.path.join(data_dir,"CY2SCH010061231_1369712653.numden.lats.symlist")
+    phonePath = os.path.join(data_dir,"model.overalltying")
+    stateListPath = os.path.join(data_dir,"state.list")
+    transProbPath = os.path.join(data_dir,"model.transprob")
+
+    criteria = C.lattice_sequence_with_softmax(label, z, z, lattice, symListPath, phonePath, stateListPath, transProbPath)
+    err = C.classification_error(label,z)
+    # Learning rate parameter schedule per sample:
+    # Use 0.01 for the first 3 epochs, followed by 0.001 for the remaining
+    lr = C.learning_parameter_schedule_per_sample([(3, .01), (1,.001)])
+    mm = C.momentum_schedule([(1000, 0.9), (0, 0.99)], mbsize)
+    learner = C.momentum_sgd(z.parameters, lr, mm)
+    trainer = C.Trainer(z, (criteria, err), learner)
+
+    C.logging.log_number_of_parameters(z)
+    progress_printer = C.logging.progress_print.ProgressPrinter(tag='Training', num_epochs = max_epochs)
+
+
+    for epoch in range(max_epochs):
+        for mb in range(mbs_per_epoch):
+    #        import pdb;pdb.set_trace()
+            minibatch = train_data_reader.next_minibatch(mbsize, input_map = train_input_map)
+            trainer.train_minibatch(minibatch)
+            progress_printer.update_with_trainer(trainer, with_metric = True)
+
+        print('Trained on a total of ' + str(trainer.total_number_of_samples_seen) + ' frames')
+        progress_printer.epoch_summary(with_metric = True)
+
+    assert np.allclose(trainer.previous_minibatch_evaluation_average, 0.15064, atol=TOLERANCE_ABSOLUTE)
+    assert np.allclose(trainer.previous_minibatch_loss_average, 0.035923, atol=TOLERANCE_ABSOLUTE)
+    assert (trainer.previous_minibatch_sample_count == 218)
+    assert (trainer.total_number_of_samples_seen == 5750)
+    print("Completed successfully.")
+
+if __name__=='__main__':
+    test_lattice_deserializer(0)

Tests/EndToEndTests/CNTKv2Python/Examples/multiple_distributed_learners_test.py

@@ -58,8 +58,7 @@ if __name__=='__main__':
             if learner.total_number_of_samples_seen != expected_number_of_samples:
                 print("Completed with exception.")
                 raise ValueError("%d samples expected, got %d" % (expected_number_of_samples, learner.total_number_of_samples_seen))
-    avg = trainer.previous_minibatch_evaluation_average
-    print(avg)
+
     trainer.train_minibatch(arguments, outputs=[z_output])
     check_samples(dist_learners, 2)