def run_cifar_convnet_distributed():
    try:
        base_path = os.path.join(os.environ['CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY'],
                                *"Image/CIFAR/v0/cifar-10-batches-py".split("/"))
        # N.B. CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY has {train,test}_map.txt
        #      and CIFAR-10_mean.xml in the base_path.
    except KeyError:
        base_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
                                *"../../../../Examples/Image/DataSets/CIFAR-10".split("/"))

    base_path = os.path.normpath(base_path)
    os.chdir(os.path.join(base_path, '..'))

    from _cntk_py import set_computation_network_trace_level, set_fixed_random_seed, force_deterministic_algorithms
    set_computation_network_trace_level(1) 
    set_fixed_random_seed(1)  # BUGBUG: has no effect at present  # TODO: remove debugging facilities once this all works
    #force_deterministic_algorithms()
    # TODO: do the above; they lead to slightly different results, so not doing it for now

    create_train_reader = lambda data_size: create_reader(os.path.join(base_path, 'train_map.txt'), os.path.join(base_path, 'CIFAR-10_mean.xml'), True, data_size, 0)
    test_reader = create_reader(os.path.join(base_path, 'test_map.txt'), os.path.join(base_path, 'CIFAR-10_mean.xml'), False, FULL_DATA_SWEEP)

    distributed_after_samples = 0
    num_quantization_bits = 32
    create_dist_learner = lambda learner: distributed.data_parallel_distributed_learner(
        learner=learner,
        num_quantization_bits=num_quantization_bits,
        distributed_after=distributed_after_samples)

    return convnet_cifar10_dataaug(create_train_reader, test_reader, create_dist_learner, max_epochs=1, num_mbs_per_log=None)
Beispiel #2
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def test_cifar_convnet_error(device_id):
    if cntk_device(device_id).type() != DeviceKind_GPU:
        pytest.skip('test only runs on GPU')
    set_default_device(cntk_device(device_id))

    try:
        base_path = os.path.join(os.environ['CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY'],
                                *"Image/CIFAR/v0/cifar-10-batches-py".split("/"))
        # N.B. CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY has {train,test}_map.txt
        #      and CIFAR-10_mean.xml in the base_path.
    except KeyError:
        base_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
                                *"../../../../Examples/Image/DataSets/CIFAR-10".split("/"))

    base_path = os.path.normpath(base_path)
    os.chdir(os.path.join(base_path, '..'))

    from _cntk_py import set_computation_network_trace_level, set_fixed_random_seed, force_deterministic_algorithms
    set_computation_network_trace_level(1)
    set_fixed_random_seed(1)  # BUGBUG: has no effect at present  # TODO: remove debugging facilities once this all works
    #force_deterministic_algorithms()
    # TODO: do the above; they lead to slightly different results, so not doing it for now

    reader_train = create_reader(os.path.join(base_path, 'train_map.txt'), os.path.join(base_path, 'CIFAR-10_mean.xml'), True)
    reader_test  = create_reader(os.path.join(base_path, 'test_map.txt'), os.path.join(base_path, 'CIFAR-10_mean.xml'), False)

    test_error = convnet_cifar10_dataaug(reader_train, reader_test, max_epochs=1)
    expected_test_error = 0.617

    assert np.allclose(test_error, expected_test_error,
                       atol=TOLERANCE_ABSOLUTE)
def htk_lstm_truncated(features_file, labels_file, label_mapping_file, minibatch_size=64, epoch_size=640000, num_quantization_bits=32,
                            block_size=3200, warm_up=0, max_epochs=5, num_mbs_per_log=None, gen_heartbeat=False,log_to_file=None, tensorboard_logdir=None):

    _cntk_py.set_computation_network_trace_level(0)

    network = create_recurrent_network()

    progress_writers = [cntk.utils.ProgressPrinter(
        freq=num_mbs_per_log,
        tag='Training',
        log_to_file=log_to_file,
        rank=Communicator.rank(),
        gen_heartbeat=gen_heartbeat,
        num_epochs=max_epochs)]

    if tensorboard_logdir is not None:
        progress_writers.append(cntk.utils.TensorBoardProgressWriter(
        freq=num_mbs_per_log,
            log_dir=tensorboard_logdir,
        rank=Communicator.rank(),
            model=network['output']))

    trainer = create_trainer(network, epoch_size, num_quantization_bits, block_size, warm_up, progress_writers)
    train_source = create_mb_source(features_file, labels_file, label_mapping_file, total_number_of_samples=max_epochs * epoch_size)
    # Testing with training data, just for testing purposes
    test_source = create_mb_source(features_file, labels_file, label_mapping_file, total_number_of_samples=max_epochs * epoch_size)
    train_and_test(network, trainer, train_source, test_source, minibatch_size, epoch_size)
def bn_inception_train_and_eval(train_data, test_data, mean_data, num_quantization_bits=32, epoch_size=50000, max_epochs=200, minibatch_size=None,
                         restore=True, log_to_file=None, num_mbs_per_log=100, gen_heartbeat=False, scale_up=False, profiling=False):
    _cntk_py.set_computation_network_trace_level(0)

    # NOTE: scaling up minibatch_size increases sample throughput. In 8-GPU machine,
    # ResNet110 samples-per-second is ~7x of single GPU, comparing to ~3x without scaling
    # up. However, bigger minibatch size on the same number of samples means less updates,
    # thus leads to higher training error. This is a trade-off of speed and accuracy
    if minibatch_size is None:
        mb_size = 128 * (Communicator.num_workers() if scale_up else 1)
    else:
        mb_size = minibatch_size

    progress_printer = ProgressPrinter(
        freq=num_mbs_per_log,
        tag='Training',
        log_to_file=log_to_file,
        rank=Communicator.rank(),
        gen_heartbeat=gen_heartbeat,
        num_epochs=max_epochs)

    network = create_bn_inception()
    trainer = create_trainer(network, epoch_size, max_epochs, mb_size, num_quantization_bits, progress_printer)
    train_source = create_image_mb_source(train_data, mean_data, True, total_number_of_samples=max_epochs * epoch_size)
    test_source = create_image_mb_source(test_data, mean_data, False, total_number_of_samples=FULL_DATA_SWEEP)

    train_and_test(network, trainer, train_source, test_source, max_epochs, mb_size, epoch_size, restore, profiling)
Beispiel #5
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def test_ucf11_conv3d_error(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))

    prepare_UCF11_data()

    base_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
                             *"../../../../Examples/Video/DataSets/UCF11".split("/"))

    base_path = os.path.normpath(base_path)

    from _cntk_py import set_computation_network_trace_level, set_fixed_random_seed, force_deterministic_algorithms
    set_computation_network_trace_level(1)
    set_fixed_random_seed(1)

    num_output_classes = 11
    train_reader = VideoReader(os.path.join(base_path, 'train_map.csv'), num_output_classes, True, 100)
    test_reader  = VideoReader(os.path.join(base_path, 'test_map.csv'), num_output_classes, False, 40)

    test_error = conv3d_ucf11(train_reader, test_reader, max_epochs=1)
    expected_test_error = 0.8

    assert np.allclose(test_error, expected_test_error,
                       atol=TOLERANCE_ABSOLUTE)
def test_cifar_resnet_distributed_error(device_id, is_1bit_sgd):
    if cntk_device(device_id).type() != DeviceKind_GPU:
        pytest.skip('test only runs on GPU')
    set_default_device(cntk_device(device_id))

    if not is_1bit_sgd:
        pytest.skip('test only runs in 1-bit SGD')

    try:
        base_path = os.path.join(os.environ['CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY'],
                                *"Image/CIFAR/v0/cifar-10-batches-py".split("/"))
    except KeyError:
        base_path = os.path.join(
            *"../../../../Examples/Image/DataSets/CIFAR-10".split("/"))

    base_path = os.path.normpath(base_path)

    from _cntk_py import set_computation_network_trace_level, set_fixed_random_seed, force_deterministic_algorithms
    set_computation_network_trace_level(1) 
    set_fixed_random_seed(1)  # BUGBUG: has no effect at present  # TODO: remove debugging facilities once this all works
    #force_deterministic_algorithms()
    # TODO: do the above; they lead to slightly different results, so not doing it for now

    test_error = train_and_evaluate(base_path, total_epochs=5)
    expected_test_error = 0.5

    assert np.allclose(test_error, expected_test_error,
                       atol=TOLERANCE_ABSOLUTE)
Beispiel #7
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def test_ucf11_conv3d_error(device_id):
    # Skip for now.
    if True: #cntk_device(device_id).type() != DeviceKind_GPU:
        pytest.skip('test only runs on GPU')
    set_default_device(cntk_device(device_id))

    try:
        base_path = os.path.join(os.environ['CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY'],
                                *"Video/DataSets/UCF11".split("/"))
    except KeyError:
        base_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
                                *"../../../../Examples/Video/DataSets/UCF11".split("/"))

    base_path = os.path.normpath(base_path)

    from _cntk_py import set_computation_network_trace_level, set_fixed_random_seed, force_deterministic_algorithms
    set_computation_network_trace_level(1)
    set_fixed_random_seed(1)

    # For performance reason, we will use test data for both training and testing.
    num_output_classes = 11
    # train_reader = VideoReader(os.path.join(base_path, 'test_map.csv'), num_output_classes, True)
    # test_reader  = VideoReader(os.path.join(base_path, 'test_map.csv'), num_output_classes, False)

    test_error = 0.8437 #conv3d_ucf11(train_reader, test_reader, max_epochs=1)
    expected_test_error = 0.8437

    assert np.allclose(test_error, expected_test_error,
                       atol=TOLERANCE_ABSOLUTE)
def convnet_cifar10_dataaug(train_data, test_data, mean_data, minibatch_size=64, epoch_size=50000, num_quantization_bits=32,
                            block_size=3200, warm_up=0, max_epochs=2, restore=False, log_to_file=None, 
                            num_mbs_per_log=None, gen_heartbeat=False, profiling=False, tensorboard_logdir=None):
    _cntk_py.set_computation_network_trace_level(0)

    network = create_conv_network()

    progress_printer = cntk.utils.ProgressPrinter(
        freq=num_mbs_per_log,
        tag='Training',
        log_to_file=log_to_file,
        rank=cntk.distributed.Communicator.rank(),
        gen_heartbeat=gen_heartbeat,
        num_epochs=max_epochs)

    tensorboard_writer = cntk.utils.TensorBoardProgressWriter(
        freq=num_mbs_per_log,
        log_dir=tensorboard_logdir if tensorboard_logdir is not None else 'log',
        rank=cntk.distributed.Communicator.rank(),
        model=network['output'])

    trainer = create_trainer(network, epoch_size, num_quantization_bits, block_size, warm_up)
    train_source = create_image_mb_source(train_data, mean_data, train=True, total_number_of_samples=max_epochs * epoch_size)
    test_source = create_image_mb_source(test_data, mean_data, train=False, total_number_of_samples=cntk.io.FULL_DATA_SWEEP)
    train_and_test(network, trainer, train_source, test_source, [progress_printer, tensorboard_writer], minibatch_size,
                   epoch_size, restore, profiling)
def bn_inception_train_and_eval(train_data, test_data, mean_data, minibatch_size=32, epoch_size=1281167, max_epochs=300, 
                         restore=True, log_to_file=None, num_mbs_per_log=100, gen_heartbeat=False, profiler_dir=None, testing_parameters=(5000,32)):
    _cntk_py.set_computation_network_trace_level(1)

    progress_printer = ProgressPrinter(
        freq=num_mbs_per_log,
        tag='Training',
        log_to_file=log_to_file,
        gen_heartbeat=gen_heartbeat,
        num_epochs=max_epochs)

    network = create_bn_inception()
    trainer = create_trainer(network, epoch_size, max_epochs, minibatch_size)
    train_source = create_image_mb_source(train_data, mean_data, True, total_number_of_samples=max_epochs * epoch_size)
    test_source = create_image_mb_source(test_data, mean_data, False, total_number_of_samples=FULL_DATA_SWEEP)
    return train_and_test(network, trainer, train_source, test_source, progress_printer, max_epochs, minibatch_size, epoch_size, restore, profiler_dir, testing_parameters)
def alexnet_train_and_eval(train_data, test_data, num_quantization_bits=32, block_size=3200, warm_up=0, minibatch_size=256, epoch_size = 1281167, max_epochs=112,
                           restore=True, log_to_file=None, num_mbs_per_log=None, gen_heartbeat=True):
    _cntk_py.set_computation_network_trace_level(0)

    progress_printer = ProgressPrinter(
        freq=num_mbs_per_log,
        tag='Training',
        log_to_file=log_to_file,
        rank=Communicator.rank(),
        gen_heartbeat=gen_heartbeat,
        num_epochs=max_epochs)

    network = create_alexnet()
    trainer = create_trainer(network, epoch_size, num_quantization_bits, progress_printer, block_size, warm_up)
    train_source = create_image_mb_source(train_data, True, total_number_of_samples=max_epochs * epoch_size)
    test_source = create_image_mb_source(test_data, False, total_number_of_samples=FULL_DATA_SWEEP)
    train_and_test(network, trainer, train_source, test_source, minibatch_size, epoch_size, restore)
def test_cifar_convnet_error(device_id):
    if cntk_device(device_id).type() != DeviceKind_GPU:
        pytest.skip('test only runs on GPU')
    set_default_device(cntk_device(device_id))

    base_path = prepare_CIFAR10_data()
    # change dir to locate data.zip correctly
    os.chdir(base_path)
    
    from _cntk_py import set_computation_network_trace_level, set_fixed_random_seed, force_deterministic_algorithms
    set_computation_network_trace_level(1)
    set_fixed_random_seed(1)  # BUGBUG: has no effect at present  # TODO: remove debugging facilities once this all works
    #force_deterministic_algorithms()
    # TODO: do the above; they lead to slightly different results, so not doing it for now

    reader_train = create_reader(os.path.join(base_path, 'train_map.txt'), os.path.join(base_path, 'CIFAR-10_mean.xml'), True)
    reader_test  = create_reader(os.path.join(base_path, 'test_map.txt'), os.path.join(base_path, 'CIFAR-10_mean.xml'), False)

    test_error = convnetlrn_cifar10_dataaug(reader_train, reader_test, epoch_size=256, max_epochs=1)
def sequence_to_sequence_translator(train_data, test_data, epoch_size=908241, num_quantization_bits=default_quantization_bits, block_size=3200, warm_up=0, minibatch_size=72, max_epochs=10, randomize_data=False, log_to_file=None, num_mbs_per_log=10, gen_heartbeat=False):
    _cntk_py.set_computation_network_trace_level(0)

    progress_printer = ProgressPrinter(freq=num_mbs_per_log,
        tag='Training',
        log_to_file=log_to_file,
        rank=Communicator.rank(),
        gen_heartbeat=gen_heartbeat,
        num_epochs=max_epochs)

    input_vocab_dim = 69
    label_vocab_dim = 69

    network = create_network(input_vocab_dim, label_vocab_dim)
    trainer = create_trainer(network, epoch_size, num_quantization_bits, block_size, warm_up)

    train_reader = create_reader(train_data, randomize_data, input_vocab_dim, label_vocab_dim, size=max_epochs*epoch_size)

    test_reader = create_reader(test_data, False, input_vocab_dim, label_vocab_dim, size=cntk.io.FULL_DATA_SWEEP)

    train_and_test(network, trainer, train_reader, test_reader, progress_printer, epoch_size, minibatch_size)
def test_cifar_resnet_error(device_id):
    if cntk_device(device_id).type() != DeviceKind_GPU:
        pytest.skip('test only runs on GPU')
    set_default_device(cntk_device(device_id))
    
    base_path = prepare_CIFAR10_data()
    # change dir to locate data.zip correctly
    os.chdir(base_path)

    from _cntk_py import set_computation_network_trace_level, set_fixed_random_seed, force_deterministic_algorithms
    set_computation_network_trace_level(1)
    set_fixed_random_seed(1)  # BUGBUG: has no effect at present  # TODO: remove debugging facilities once this all works
    #force_deterministic_algorithms()
    # TODO: do the above; they lead to slightly different results, so not doing it for now

    reader_train = create_reader(os.path.join(base_path, 'train_map.txt'), os.path.join(base_path, 'CIFAR-10_mean.xml'), True)
    reader_test  = create_reader(os.path.join(base_path, 'test_map.txt'), os.path.join(base_path, 'CIFAR-10_mean.xml'), False)

    # Create a path to TensorBoard log directory and make sure it does not exist.
    abs_path = os.path.dirname(os.path.abspath(__file__))
    tb_logdir = os.path.join(abs_path, 'TrainResNet_CIFAR10_test_log')
    if os.path.exists(tb_logdir):
        shutil.rmtree(tb_logdir)

    test_error = train_and_evaluate(reader_train, reader_test, 'resnet20', epoch_size=512, max_epochs=1,
                                    tensorboard_logdir=tb_logdir)

# We are removing tolerance in error because running small epoch size has huge variance in accuracy. Will add
# tolerance back once convolution operator is determinsitic. 
    
#    expected_test_error = 0.282

#    assert np.allclose(test_error, expected_test_error,
#                       atol=TOLERANCE_ABSOLUTE)

    files = 0
    for file in os.listdir(tb_logdir):
        assert file.startswith("events.out.tfevents")
        files += 1
    assert files == 1
def test_cifar_resnet_distributed_error(device_id, is_1bit_sgd):
    if cntk_device(device_id).type() != DeviceKind_GPU:
        pytest.skip('test only runs on GPU')
    set_default_device(cntk_device(device_id))

    if not is_1bit_sgd:
        pytest.skip('test only runs in 1-bit SGD')

    try:
        base_path = os.path.join(os.environ['CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY'],
                                *"Image/CIFAR/v0/cifar-10-batches-py".split("/"))
    except KeyError:
        base_path = os.path.join(
            *"../../../../Examples/Image/DataSets/CIFAR-10".split("/"))

    base_path = os.path.normpath(base_path)
    os.chdir(os.path.join(base_path, '..'))

    from _cntk_py import set_computation_network_trace_level, set_fixed_random_seed, force_deterministic_algorithms
    set_computation_network_trace_level(1)
    set_fixed_random_seed(1)  # BUGBUG: has no effect at present  # TODO: remove debugging facilities once this all works
    #force_deterministic_algorithms()
    # TODO: do the above; they lead to slightly different results, so not doing it for now

    distributed_learner_factory = lambda learner: distributed.data_parallel_distributed_learner(
        learner=learner,
        num_quantization_bits=32,
        distributed_after=0)

    reader_train_factory = lambda data_size: create_reader(os.path.join(base_path, 'train_map.txt'), os.path.join(base_path, 'CIFAR-10_mean.xml'), True, data_size)
    test_reader = create_reader(os.path.join(base_path, 'test_map.txt'), os.path.join(base_path, 'CIFAR-10_mean.xml'), False, FULL_DATA_SWEEP)

    test_error = train_and_evaluate(reader_train_factory, test_reader, 'resnet20', 5, distributed_learner_factory)

    expected_test_error = 0.282

    assert np.allclose(test_error, expected_test_error,
                       atol=TOLERANCE_ABSOLUTE)
    distributed.Communicator.finalize()
def resnet_cifar10(train_data, test_data, mean_data, network_name, epoch_size, num_quantization_bits=32, block_size=3200, warm_up=0, max_epochs=5, log_to_file=None, num_mbs_per_log=None, gen_heartbeat=False, scale_up=False, profiling=False):

    set_computation_network_trace_level(0)
    
    # NOTE: scaling up minibatch_size increases sample throughput. In 8-GPU machine,
    # ResNet110 samples-per-second is ~7x of single GPU, comparing to ~3x without scaling
    # up. However, bigger minimatch size on the same number of samples means less updates, 
    # thus leads to higher training error. This is a trade-off of speed and accuracy
    minibatch_size = 128 * (Communicator.num_workers() if scale_up else 1)

    progress_printer = ProgressPrinter(
        freq=num_mbs_per_log,
        tag='Training',
        log_to_file=log_to_file,
        rank=Communicator.rank(),
        gen_heartbeat=gen_heartbeat,
        num_epochs=max_epochs)

    network = create_resnet_network(network_name)
    trainer = create_trainer(network, minibatch_size, epoch_size, num_quantization_bits, block_size, warm_up)
    train_source = create_image_mb_source(train_data, mean_data, train=True, total_number_of_samples=max_epochs * epoch_size)
    test_source = create_image_mb_source(test_data, mean_data, train=False, total_number_of_samples=cntk.io.FULL_DATA_SWEEP)
    train_and_test(network, trainer, train_source, test_source, progress_printer, minibatch_size, epoch_size, profiling)
def test_seq_classification_error(device_id):
    from cntk.utils import cntk_device
    DeviceDescriptor.set_default_device(cntk_device(device_id))

    from _cntk_py import set_computation_network_trace_level, set_fixed_random_seed
    set_computation_network_trace_level(1)
    set_fixed_random_seed(1) # to become invariant to initialization order, which is a valid change

    # test of the example itself
    # this emulates the main code in the PY file
    reader = create_reader(data_dir + "/atis.train.ctf")
    model = create_model()
    loss_avg, evaluation_avg = train(reader, model, max_epochs=1)
    expected_avg = [0.15570838301766451, 0.7846451368305728]
    assert np.allclose([evaluation_avg, loss_avg], expected_avg, atol=TOLERANCE_ABSOLUTE)

    # test of a config like in the example but with additions to test many code paths
    if device_id >= 0: # BatchNormalization currently does not run on CPU
        reader = create_reader(data_dir + "/atis.train.ctf")
        model = create_test_model()
        loss_avg, evaluation_avg = train(reader, model, max_epochs=1)
        log_number_of_parameters(model, trace_level=1) ; print()
        expected_avg = [0.084, 0.407364]
        assert np.allclose([evaluation_avg, loss_avg], expected_avg, atol=TOLERANCE_ABSOLUTE)
def vgg19_train_and_eval(train_data, test_data, num_quantization_bits=32, minibatch_size=128, epoch_size = 1281167, max_epochs=80,
                         restore=True, log_to_file=None, num_mbs_per_log=None, gen_heartbeat=False, testing=False):
    _cntk_py.set_computation_network_trace_level(0)

    progress_printer = ProgressPrinter(
        freq=num_mbs_per_log,
        tag='Training',
        log_to_file=log_to_file,
        rank=Communicator.rank(),
        gen_heartbeat=gen_heartbeat,
        num_epochs=max_epochs)

    network = create_vgg19()
    trainer = create_trainer(network, epoch_size, num_quantization_bits, progress_printer)
    train_source = create_image_mb_source(train_data, True, total_number_of_samples=max_epochs * epoch_size)

    if testing:
        # reduce number of samples for validation when testing
        num_of_validation_samples = max_epochs * epoch_size * 10
    else:
        num_of_validation_samples = FULL_DATA_SWEEP

    test_source = create_image_mb_source(test_data, False, total_number_of_samples=num_of_validation_samples)
    train_and_test(network, trainer, train_source, test_source, minibatch_size, epoch_size, restore)
Beispiel #18
0
def conv3d_ucf11(train_reader, test_reader, max_epochs=30):
    # Replace 0 with 1 to get detailed log.
    set_computation_network_trace_level(0)

    # These values must match for both train and test reader.
    image_height       = train_reader.height
    image_width        = train_reader.width
    num_channels       = train_reader.channel_count
    sequence_length    = train_reader.sequence_length
    num_output_classes = train_reader.label_count

    # Input variables denoting the features and label data
    input_var = input_variable((num_channels, sequence_length, image_height, image_width), np.float32)
    label_var = input_variable(num_output_classes, np.float32)

    # Instantiate simple 3D Convolution network inspired by VGG network 
    # and http://vlg.cs.dartmouth.edu/c3d/c3d_video.pdf
    with default_options (activation=relu):
        z = Sequential([
            Convolution3D((3,3,3), 64, pad=True),
            MaxPooling((1,2,2), (1,2,2)),
            For(range(3), lambda i: [
                Convolution3D((3,3,3), [96, 128, 128][i], pad=True),
                Convolution3D((3,3,3), [96, 128, 128][i], pad=True),
                MaxPooling((2,2,2), (2,2,2))
            ]),
            For(range(2), lambda : [
                Dense(1024), 
                Dropout(0.5)
            ]),
            Dense(num_output_classes, activation=None)
        ])(input_var)
    
    # loss and classification error.
    ce = cross_entropy_with_softmax(z, label_var)
    pe = classification_error(z, label_var)

    # training config
    epoch_size     = 1322                  # for now we manually specify epoch size
    minibatch_size = 4

    # Set learning parameters
    lr_per_sample          = [0.01]*10+[0.001]*10+[0.0001]
    lr_schedule            = learning_rate_schedule(lr_per_sample, epoch_size=epoch_size, unit=UnitType.sample)
    momentum_time_constant = 4096
    mm_schedule            = momentum_as_time_constant_schedule([momentum_time_constant], epoch_size=epoch_size)

    # Instantiate the trainer object to drive the model training
    learner = momentum_sgd(z.parameters, lr_schedule, mm_schedule, True)
    progress_printer = ProgressPrinter(tag='Training', num_epochs=max_epochs)
    trainer = Trainer(z, (ce, pe), learner, progress_printer)

    log_number_of_parameters(z) ; print()

    # Get minibatches of images to train with and perform model training
    for epoch in range(max_epochs):       # loop over epochs
        train_reader.reset()

        while train_reader.has_more():
            videos, labels, current_minibatch = train_reader.next_minibatch(minibatch_size)
            trainer.train_minibatch({input_var : videos, label_var : labels})

        trainer.summarize_training_progress()
    
    # Test data for trained model
    epoch_size     = 332
    minibatch_size = 2

    # process minibatches and evaluate the model
    metric_numer    = 0
    metric_denom    = 0
    minibatch_index = 0

    test_reader.reset()    
    while test_reader.has_more():
        videos, labels, current_minibatch = test_reader.next_minibatch(minibatch_size)
        # minibatch data to be trained with
        metric_numer += trainer.test_minibatch({input_var : videos, label_var : labels}) * current_minibatch
        metric_denom += current_minibatch
        # Keep track of the number of samples processed so far.
        minibatch_index += 1

    print("")
    print("Final Results: Minibatch[1-{}]: errs = {:0.2f}% * {}".format(minibatch_index+1, (metric_numer*100.0)/metric_denom, metric_denom))
    print("")

    return metric_numer/metric_denom
def convnet_cifar10_dataaug(create_train_reader, test_reader, create_dist_learner, max_epochs=80, log_to_file=None, num_mbs_per_log=None, gen_heartbeat=False):
    _cntk_py.set_computation_network_trace_level(0)

    # Input variables denoting the features and label data
    input_var = cntk.ops.input_variable((num_channels, image_height, image_width))
    label_var = cntk.ops.input_variable((num_classes))

    # apply model to input
    scaled_input = cntk.ops.element_times(cntk.ops.constant(0.00390625), input_var)
    
    with cntk.layers.default_options(activation=cntk.ops.relu, pad=True):
        z = cntk.models.Sequential([
            cntk.models.LayerStack(2, lambda : [
                cntk.layers.Convolution((3,3), 64),
                cntk.layers.Convolution((3,3), 64),
                cntk.layers.MaxPooling((3,3), (2,2))
            ]), 
            cntk.models.LayerStack(2, lambda i: [
                cntk.layers.Dense([256,128][i]), 
                cntk.layers.Dropout(0.5)
            ]), 
            cntk.layers.Dense(num_classes, activation=None)
        ])(scaled_input)

    # loss and metric
    ce = cntk.ops.cross_entropy_with_softmax(z, label_var)
    pe = cntk.ops.classification_error(z, label_var)

    # training config
    epoch_size = 50000  # for now we manually specify epoch size
    minibatch_size = 64

    # Set learning parameters
    lr_per_sample     = [0.0015625]*20 + [0.00046875]*20 + [0.00015625]*20 + [0.000046875]*10 + [0.000015625]
    lr_schedule       = cntk.learning_rate_schedule(lr_per_sample, unit=cntk.learner.UnitType.sample, epoch_size=epoch_size)
    mm_time_constant  = [0]*20 + [600]*20 + [1200]
    mm_schedule       = cntk.learner.momentum_as_time_constant_schedule(mm_time_constant, epoch_size=epoch_size)
    l2_reg_weight     = 0.002

    # trainer object
    learner = create_dist_learner(
        cntk.learner.momentum_sgd(z.parameters, lr_schedule, mm_schedule, l2_regularization_weight=l2_reg_weight))

    trainer = cntk.Trainer(z, ce, pe, learner)

    total_number_of_samples = max_epochs * epoch_size
    train_reader = create_train_reader(total_number_of_samples)

    # define mapping from reader streams to network inputs
    input_map = {
        input_var: train_reader.streams.features,
        label_var: train_reader.streams.labels
    }

    cntk.utils.log_number_of_parameters(z) ; print()
    progress_printer = cntk.utils.ProgressPrinter(
        freq=num_mbs_per_log,
        tag='Training',
        log_to_file=log_to_file,
        distributed_learner=learner,
        gen_heartbeat=gen_heartbeat,
        num_epochs=max_epochs)

    # perform model training
    updated=True
    epoch=0
    
    while updated:
        data = train_reader.next_minibatch(minibatch_size, input_map=input_map)   # fetch minibatch.
        updated = trainer.train_minibatch(data)                                   # update model with it
        progress_printer.update_with_trainer(trainer, with_metric=True)           # log progress
        current_epoch = int(trainer.total_number_of_samples_seen/epoch_size)
        
        if epoch != current_epoch:
            progress_printer.epoch_summary(with_metric=True)
            epoch = current_epoch
            trainer.save_checkpoint(os.path.join(model_path, "ConvNet_CIFAR10_DataAug_{}.dnn".format(epoch)))

    ### Evaluation action
    minibatch_size = 16

    # process minibatches and evaluate the model
    metric_numer    = 0
    metric_denom    = 0
    minibatch_index = 0

    while True:
        data = test_reader.next_minibatch(minibatch_size, input_map=input_map)
        if not data: break
        local_mb_samples=data[label_var].num_samples
        metric_numer += trainer.test_minibatch(data) * local_mb_samples
        metric_denom += local_mb_samples
        minibatch_index += 1


    fin_msg = "Final Results: Minibatch[1-{}]: errs = {:0.2f}% * {}".format(minibatch_index+1, (metric_numer*100.0)/metric_denom, metric_denom)
    progress_printer.end_progress_print(fin_msg)

    print("")
    print(fin_msg)
    print("")

    return metric_numer/metric_denom
def train_and_evaluate(reader_train, reader_test, network_name, epoch_size, max_epochs, profiler_dir=None,
                       model_dir=None, tensorboard_logdir=None):

    set_computation_network_trace_level(0)

    # Input variables denoting the features and label data
    input_var = input_variable((num_channels, image_height, image_width))
    label_var = input_variable((num_classes))

    # create model, and configure learning parameters
    if network_name == 'resnet20':
        z = create_cifar10_model(input_var, 3, num_classes)
        lr_per_mb = [1.0]*80+[0.1]*40+[0.01]
    elif network_name == 'resnet110':
        z = create_cifar10_model(input_var, 18, num_classes)
        lr_per_mb = [0.1]*1+[1.0]*80+[0.1]*40+[0.01]
    else:
        return RuntimeError("Unknown model name!")

    # loss and metric
    ce = cross_entropy_with_softmax(z, label_var)
    pe = classification_error(z, label_var)

    # shared training parameters
    minibatch_size = 128
    momentum_time_constant = -minibatch_size/np.log(0.9)
    l2_reg_weight = 0.0001

    # Set learning parameters
    lr_per_sample = [lr/minibatch_size for lr in lr_per_mb]
    lr_schedule = learning_rate_schedule(lr_per_sample, epoch_size=epoch_size, unit=UnitType.sample)
    mm_schedule = momentum_as_time_constant_schedule(momentum_time_constant)

    # progress writers
    progress_writers = [ProgressPrinter(tag='Training', num_epochs=max_epochs)]
    tensorboard_writer = None
    if tensorboard_logdir is not None:
        tensorboard_writer = TensorBoardProgressWriter(freq=10, log_dir=tensorboard_logdir, model=z)
        progress_writers.append(tensorboard_writer)

    # trainer object
    learner = momentum_sgd(z.parameters, lr_schedule, mm_schedule,
                           l2_regularization_weight = l2_reg_weight)
    trainer = Trainer(z, (ce, pe), learner, progress_writers)

    # define mapping from reader streams to network inputs
    input_map = {
        input_var: reader_train.streams.features,
        label_var: reader_train.streams.labels
    }

    log_number_of_parameters(z) ; print()

    # perform model training
    if profiler_dir:
        start_profiler(profiler_dir, True)

    for epoch in range(max_epochs):       # loop over epochs
        sample_count = 0
        while sample_count < epoch_size:  # loop over minibatches in the epoch
            data = reader_train.next_minibatch(min(minibatch_size, epoch_size-sample_count), input_map=input_map) # fetch minibatch.
            trainer.train_minibatch(data)                                   # update model with it
            sample_count += trainer.previous_minibatch_sample_count         # count samples processed so far

        trainer.summarize_training_progress()

        # Log mean of each parameter tensor, so that we can confirm that the parameters change indeed.
        if tensorboard_writer:
            for parameter in z.parameters:
                tensorboard_writer.write_value(parameter.uid + "/mean", reduce_mean(parameter).eval(), epoch)

        if model_dir:
            z.save(os.path.join(model_dir, network_name + "_{}.dnn".format(epoch)))
        enable_profiler() # begin to collect profiler data after first epoch

    if profiler_dir:
        stop_profiler()

    # Evaluation parameters
    test_epoch_size     = 10000
    minibatch_size = 16

    # process minibatches and evaluate the model
    metric_numer    = 0
    metric_denom    = 0
    sample_count    = 0

    while sample_count < test_epoch_size:
        current_minibatch = min(minibatch_size, test_epoch_size - sample_count)
        # Fetch next test min batch.
        data = reader_test.next_minibatch(current_minibatch, input_map=input_map)
        # minibatch data to be trained with
        metric_numer += trainer.test_minibatch(data) * current_minibatch
        metric_denom += current_minibatch
        # Keep track of the number of samples processed so far.
        sample_count += data[label_var].num_samples

    print("")
    trainer.summarize_test_progress()
    print("")

    return metric_numer/metric_denom
Beispiel #21
0
def train_and_evaluate(reader_train, reader_test, network_name):

    set_computation_network_trace_level(0)

    # Input variables denoting the features and label data
    input_var = input_variable((num_channels, image_height, image_width))
    label_var = input_variable((num_classes))

    # create model, and configure learning parameters 
    if network_name == 'resnet20': 
        z = create_cifar10_model(input_var, 3, num_classes)
        lr_per_mb = [1.0]*80+[0.1]*40+[0.01]
    elif network_name == 'resnet110': 
        z = create_cifar10_model(input_var, 18, num_classes)
        lr_per_mb = [0.1]*1+[1.0]*80+[0.1]*40+[0.01]
    else: 
        return RuntimeError("Unknown model name!")

    # loss and metric
    ce = cross_entropy_with_softmax(z, label_var)
    pe = classification_error(z, label_var)

    # shared training parameters 
    epoch_size = 50000                    # for now we manually specify epoch size
    minibatch_size = 128
    max_epochs = 160
    momentum_time_constant = -minibatch_size/np.log(0.9)
    l2_reg_weight = 0.0001

    # Set learning parameters
    lr_per_sample = [lr/minibatch_size for lr in lr_per_mb]
    lr_schedule = learning_rate_schedule(lr_per_sample, epoch_size=epoch_size)
    mm_schedule = momentum_as_time_constant_schedule(momentum_time_constant)
    
    # trainer object
    learner     = momentum_sgd(z.parameters, lr_schedule, mm_schedule,
                               l2_regularization_weight = l2_reg_weight)
    trainer     = Trainer(z, ce, pe, learner)

    # define mapping from reader streams to network inputs
    input_map = {
        input_var: reader_train.streams.features,
        label_var: reader_train.streams.labels
    }

    log_number_of_parameters(z) ; print()
    progress_printer = ProgressPrinter(tag='Training')

    # perform model training
    for epoch in range(max_epochs):       # loop over epochs
        sample_count = 0
        while sample_count < epoch_size:  # loop over minibatches in the epoch
            data = reader_train.next_minibatch(min(minibatch_size, epoch_size-sample_count), input_map=input_map) # fetch minibatch.
            trainer.train_minibatch(data)                                   # update model with it
            sample_count += data[label_var].num_samples                     # count samples processed so far
            progress_printer.update_with_trainer(trainer, with_metric=True) # log progress
        progress_printer.epoch_summary(with_metric=True)
        persist.save_model(z, os.path.join(model_path, network_name + "_{}.dnn".format(epoch)))
    
    # Evaluation parameters
    epoch_size     = 10000
    minibatch_size = 16

    # process minibatches and evaluate the model
    metric_numer    = 0
    metric_denom    = 0
    sample_count    = 0
    minibatch_index = 0

    while sample_count < epoch_size:
        current_minibatch = min(minibatch_size, epoch_size - sample_count)
        # Fetch next test min batch.
        data = reader_test.next_minibatch(current_minibatch, input_map=input_map)
        # minibatch data to be trained with
        metric_numer += trainer.test_minibatch(data) * current_minibatch
        metric_denom += current_minibatch
        # Keep track of the number of samples processed so far.
        sample_count += data[label_var].num_samples
        minibatch_index += 1

    print("")
    print("Final Results: Minibatch[1-{}]: errs = {:0.2f}% * {}".format(minibatch_index+1, (metric_numer*100.0)/metric_denom, metric_denom))
    print("")

    return metric_numer/metric_denom
Beispiel #22
0
def convnet_cifar10(debug_output=False):
    _cntk_py.set_computation_network_trace_level(0)

    image_height = 32
    image_width  = 32
    num_channels = 3
    input_dim = image_height * image_width * num_channels
    num_output_classes = 10

    # Input variables denoting the features and label data
    input_var = cntk.ops.input_variable((num_channels, image_height, image_width), np.float32)
    label_var = cntk.ops.input_variable(num_output_classes, np.float32)

    # Instantiate the feedforward classification model
    input_removemean = cntk.ops.minus(input_var, cntk.ops.constant(128))
    scaled_input = cntk.ops.element_times(cntk.ops.constant(0.00390625), input_removemean)

    with cntk.layers.default_options(activation=cntk.ops.relu, pad=True): 
        z = cntk.layers.Sequential([
            cntk.layers.For(range(2), lambda : [
                cntk.layers.Convolution2D((3,3), 64), 
                cntk.layers.Convolution2D((3,3), 64), 
                cntk.layers.MaxPooling((3,3), (2,2))
            ]), 
            cntk.layers.For(range(2), lambda i: [
                cntk.layers.Dense([256,128][i]), 
                cntk.layers.Dropout(0.5)
            ]), 
            cntk.layers.Dense(num_output_classes, activation=None)
        ])(scaled_input)
    
    ce = cntk.ops.cross_entropy_with_softmax(z, label_var)
    pe = cntk.ops.classification_error(z, label_var)

    reader_train = create_reader(os.path.join(data_path, 'Train_cntk_text.txt'), True, input_dim, num_output_classes)

    # training config
    epoch_size = 50000                  # for now we manually specify epoch size
    minibatch_size = 64
    max_epochs = 30

    # Set learning parameters
    lr_per_sample          = [0.0015625]*10 + [0.00046875]*10 + [0.00015625]
    lr_schedule            = cntk.learning_rate_schedule(lr_per_sample, cntk.learner.UnitType.sample, epoch_size)
    mm_time_constant       = [0]*20 + [-minibatch_size/np.log(0.9)]
    mm_schedule            = cntk.learner.momentum_as_time_constant_schedule(mm_time_constant, epoch_size)
    l2_reg_weight          = 0.002

    # Instantiate the trainer object to drive the model training
    learner = cntk.learner.momentum_sgd(z.parameters, lr_schedule, mm_schedule,
                                        l2_regularization_weight = l2_reg_weight)
    progress_printer = cntk.utils.ProgressPrinter(tag='Training', num_epochs=max_epochs)
    trainer = cntk.Trainer(z, (ce, pe), learner, progress_printer)

    # define mapping from reader streams to network inputs
    input_map = {
        input_var  : reader_train.streams.features,
        label_var  : reader_train.streams.labels
    }

    cntk.utils.log_number_of_parameters(z) ; print()

    # Get minibatches of images to train with and perform model training
    for epoch in range(max_epochs):       # loop over epochs
        sample_count = 0
        while sample_count < epoch_size:  # loop over minibatches in the epoch
            data = reader_train.next_minibatch(min(minibatch_size, epoch_size - sample_count), input_map=input_map) # fetch minibatch.
            trainer.train_minibatch(data)                                   # update model with it
            sample_count += trainer.previous_minibatch_sample_count         # count samples processed so far

        trainer.summarize_training_progress()
        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)

    input_map = {
        input_var  : reader_test.streams.features,
        label_var  : reader_test.streams.labels
    }

    # Test data for trained model
    epoch_size = 10000
    minibatch_size = 16

    # process minibatches and evaluate the model
    metric_numer    = 0
    metric_denom    = 0
    sample_count    = 0
    minibatch_index = 0

    while sample_count < epoch_size:
        current_minibatch = min(minibatch_size, epoch_size - sample_count)
        # Fetch next test min batch.
        data = reader_test.next_minibatch(current_minibatch, input_map=input_map)
        # minibatch data to be trained with
        metric_numer += trainer.test_minibatch(data) * current_minibatch
        metric_denom += current_minibatch
        # Keep track of the number of samples processed so far.
        sample_count += data[label_var].num_samples
        minibatch_index += 1

    print("")
    print("Final Results: Minibatch[1-{}]: errs = {:0.2f}% * {}".format(minibatch_index+1, (metric_numer*100.0)/metric_denom, metric_denom))
    print("")

    return metric_numer/metric_denom
def convnetlrn_cifar10_dataaug(reader_train, reader_test, epoch_size=50000, max_epochs = 80):
    _cntk_py.set_computation_network_trace_level(1)

    # Input variables denoting the features and label data
    input_var = cntk.ops.input_variable((num_channels, image_height, image_width))
    label_var = cntk.ops.input_variable((num_classes))

    # apply model to input
    scaled_input = cntk.ops.element_times(cntk.ops.constant(0.00390625), input_var)

    with cntk.layers.default_options (activation=cntk.ops.relu, pad=True):
        z = cntk.models.Sequential([
            cntk.models.For(range(2), lambda : [
                cntk.layers.Convolution2D((3,3), 64),
                cntk.layers.Convolution2D((3,3), 64),
                LocalResponseNormalization (1.0, 4, 0.001, 0.75),
                cntk.layers.MaxPooling((3,3), (2,2))
            ]),
            cntk.models.For(range(2), lambda i: [
                cntk.layers.Dense([256,128][i]),
                cntk.layers.Dropout(0.5)
            ]),
            cntk.layers.Dense(num_classes, activation=None)
        ])(scaled_input)

    # loss and metric
    ce = cntk.ops.cross_entropy_with_softmax(z, label_var)
    pe = cntk.ops.classification_error(z, label_var)

    # training config
    minibatch_size = 64

    # Set learning parameters
    lr_per_sample          = [0.0015625]*20 + [0.00046875]*20 + [0.00015625]*20 + [0.000046875]*10 + [0.000015625]
    lr_schedule            = cntk.learning_rate_schedule(lr_per_sample, unit=cntk.learner.UnitType.sample, epoch_size=epoch_size)
    mm_time_constant       = [0]*20 + [600]*20 + [1200]
    mm_schedule            = cntk.learner.momentum_as_time_constant_schedule(mm_time_constant, epoch_size=epoch_size)
    l2_reg_weight          = 0.002

    # trainer object
    learner = cntk.learner.momentum_sgd(z.parameters, lr_schedule, mm_schedule,
                                        unit_gain = True,
                                        l2_regularization_weight = l2_reg_weight)
    trainer =  cntk.Trainer(z, (ce, pe), learner)

    # define mapping from reader streams to network inputs
    input_map = {
        input_var: reader_train.streams.features,
        label_var: reader_train.streams.labels
    }

    cntk.utils.log_number_of_parameters(z) ; print()
    progress_printer = cntk.utils.ProgressPrinter(tag='Training', num_epochs=max_epochs)

    # perform model training
    for epoch in range(max_epochs):       # loop over epochs
        sample_count = 0
        while sample_count < epoch_size:  # loop over minibatches in the epoch
            data = reader_train.next_minibatch(min(minibatch_size, epoch_size-sample_count), input_map=input_map) # fetch minibatch.
            trainer.train_minibatch(data)                                   # update model with it
            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(os.path.join(model_path, "ConvNet_CIFAR10_DataAug_{}.dnn".format(epoch)))

    ### Evaluation action
    epoch_size     = 10000
    minibatch_size = 16

    # process minibatches and evaluate the model
    metric_numer    = 0
    metric_denom    = 0
    sample_count    = 0
    minibatch_index = 0

    while sample_count < epoch_size:
        current_minibatch = min(minibatch_size, epoch_size - sample_count)
        # Fetch next test min batch.
        data = reader_test.next_minibatch(current_minibatch, input_map=input_map)
        # minibatch data to be trained with
        metric_numer += trainer.test_minibatch(data) * current_minibatch
        metric_denom += current_minibatch
        # Keep track of the number of samples processed so far.
        sample_count += data[label_var].num_samples
        minibatch_index += 1

    print("")
    print("Final Results: Minibatch[1-{}]: errs = {:0.2f}% * {}".format(minibatch_index+1, (metric_numer*100.0)/metric_denom, metric_denom))
    print("")

    return metric_numer/metric_denom
def train_and_evaluate(create_train_reader, test_reader, network_name, max_epochs, create_dist_learner, scale_up=False):

    set_computation_network_trace_level(0)

    # Input variables denoting the features and label data
    input_var = input_variable((num_channels, image_height, image_width))
    label_var = input_variable((num_classes))

    # create model, and configure learning parameters 
    if network_name == 'resnet20': 
        z = create_cifar10_model(input_var, 3, num_classes)
        lr_per_mb = [1.0]*80+[0.1]*40+[0.01]
    elif network_name == 'resnet110': 
        z = create_cifar10_model(input_var, 18, num_classes)
        lr_per_mb = [0.1]*1+[1.0]*80+[0.1]*40+[0.01]
    else: 
        return RuntimeError("Unknown model name!")

    # loss and metric
    ce = cross_entropy_with_softmax(z, label_var)
    pe = classification_error(z, label_var)

    # shared training parameters 
    epoch_size = 50000                    # for now we manually specify epoch size
    
    # NOTE: scaling up minibatch_size increases sample throughput. In 8-GPU machine,
    # ResNet110 samples-per-second is ~7x of single GPU, comparing to ~3x without scaling
    # up. However, bigger minimatch size on the same number of samples means less updates, 
    # thus leads to higher training error. This is a trade-off of speed and accuracy
    minibatch_size = 128 * (distributed.Communicator.num_workers() if scale_up else 1)

    momentum_time_constant = -minibatch_size/np.log(0.9)
    l2_reg_weight = 0.0001

    # Set learning parameters
    lr_per_sample = [lr/minibatch_size for lr in lr_per_mb]
    lr_schedule = learning_rate_schedule(lr_per_sample, epoch_size=epoch_size, unit=UnitType.sample)
    mm_schedule = momentum_as_time_constant_schedule(momentum_time_constant)
    
    # trainer object
    learner     = create_dist_learner(momentum_sgd(z.parameters, lr_schedule, mm_schedule,
                                                   l2_regularization_weight = l2_reg_weight))
    trainer     = Trainer(z, ce, pe, learner)

    total_number_of_samples = max_epochs * epoch_size
    train_reader=create_train_reader(total_number_of_samples)

    # define mapping from reader streams to network inputs
    input_map = {
        input_var: train_reader.streams.features,
        label_var: train_reader.streams.labels
    }

    log_number_of_parameters(z) ; print()
    progress_printer = ProgressPrinter(tag='Training')

    # perform model training
    current_epoch=0
    updated=True
    while updated:
        data=train_reader.next_minibatch(minibatch_size, input_map=input_map) # fetch minibatch.
        updated=trainer.train_minibatch(data)                                 # update model with it
        progress_printer.update_with_trainer(trainer, with_metric=True)       # log progress
        epoch_index = int(trainer.total_number_of_samples_seen/epoch_size)
        if current_epoch != epoch_index:                                      # new epoch reached
            progress_printer.epoch_summary(with_metric=True)
            current_epoch=epoch_index
            trainer.save_checkpoint(os.path.join(model_path, network_name + "_{}.dnn".format(current_epoch)))

    # Evaluation parameters
    epoch_size     = 10000
    minibatch_size = 16

    # process minibatches and evaluate the model
    metric_numer    = 0
    metric_denom    = 0
    sample_count    = 0
    minibatch_index = 0

    while True:
        data = test_reader.next_minibatch(minibatch_size, input_map=input_map)
        if not data: break;

        local_mb_samples=data[label_var].num_samples
        metric_numer += trainer.test_minibatch(data) * local_mb_samples
        metric_denom += local_mb_samples
        minibatch_index += 1

    print("")
    print("Final Results: Minibatch[1-{}]: errs = {:0.2f}% * {}".format(minibatch_index+1, (metric_numer*100.0)/metric_denom, metric_denom))
    print("")

    return metric_numer/metric_denom
def convnet_cifar10_dataaug(reader_train, reader_test, distributed_trainer, max_epochs = 80):
    set_computation_network_trace_level(0)

    # Input variables denoting the features and label data
    input_var = input_variable((num_channels, image_height, image_width))
    label_var = input_variable((num_classes))

    # apply model to input
    scaled_input = element_times(constant(0.00390625), input_var)
    with default_options (activation=relu, pad=True): 
        z = Sequential([
            LayerStack(2, lambda : [
                Convolution((3,3), 64), 
                Convolution((3,3), 64), 
                MaxPooling((3,3), (2,2))
            ]), 
            LayerStack(2, lambda i: [
                Dense([256,128][i]), 
                Dropout(0.5)
            ]), 
            Dense(num_classes, activation=None)
        ])(scaled_input)

    # loss and metric
    ce = cross_entropy_with_softmax(z, label_var)
    pe = classification_error(z, label_var)

    # training config
    epoch_size = 50000                    # for now we manually specify epoch size
    minibatch_size = 64

    # Set learning parameters
    lr_per_sample          = [0.0015625]*20+[0.00046875]*20+[0.00015625]*20+[0.000046875]*10+[0.000015625]
    lr_schedule            = learning_rate_schedule(lr_per_sample, unit=UnitType.sample, epoch_size=epoch_size)
    mm_time_constant       = [0]*20+[600]*20+[1200]
    mm_schedule            = momentum_as_time_constant_schedule(mm_time_constant, epoch_size=epoch_size)
    l2_reg_weight          = 0.002
    
    # trainer object
    learner     = momentum_sgd(z.parameters, lr_schedule, mm_schedule,
                               l2_regularization_weight = l2_reg_weight)
    trainer     = Trainer(z, ce, pe, learner, distributed_trainer)

    # define mapping from reader streams to network inputs
    input_map = {
        input_var: reader_train.streams.features,
        label_var: reader_train.streams.labels
    }

    log_number_of_parameters(z) ; print()
    progress_printer = ProgressPrinter(tag='Training')

    # perform model training
    for epoch in range(max_epochs):       # loop over epochs
        sample_count = 0
        while sample_count < epoch_size:  # loop over minibatches in the epoch
            data = reader_train.next_minibatch(min(minibatch_size, epoch_size-sample_count), input_map=input_map) # fetch minibatch.
            trainer.train_minibatch(data)                                   # update model with it
            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)
        if distributed_trainer.communicator().current_worker().global_rank == 0:
            persist.save_model(z, os.path.join(model_path, "ConvNet_CIFAR10_DataAug_{}.dnn".format(epoch)))
    
    ### Evaluation action
    epoch_size     = 10000
    minibatch_size = 16

    # process minibatches and evaluate the model
    metric_numer    = 0
    metric_denom    = 0
    sample_count    = 0
    minibatch_index = 0

    while sample_count < epoch_size:
        current_minibatch = min(minibatch_size, epoch_size - sample_count)
        # Fetch next test min batch.
        data = reader_test.next_minibatch(current_minibatch, input_map=input_map)
        # minibatch data to be trained with
        metric_numer += trainer.test_minibatch(data) * current_minibatch
        metric_denom += current_minibatch
        # Keep track of the number of samples processed so far.
        sample_count += trainer.previous_minibatch_sample_count
        minibatch_index += 1

    print("")
    print("Final Results: Minibatch[1-{}]: errs = {:0.2f}% * {}".format(minibatch_index+1, (metric_numer*100.0)/metric_denom, metric_denom))
    print("")

    return metric_numer/metric_denom
Beispiel #26
0
    while sample_count < epoch_size:
        current_minibatch = min(minibatch_size, epoch_size - sample_count)

        # Fetch next test min batch.
        data = reader_test.next_minibatch(current_minibatch, input_map=input_map)

        # minibatch data to be trained with
        metric_numer += trainer.test_minibatch(data) * current_minibatch
        metric_denom += current_minibatch

        # Keep track of the number of samples processed so far.
        sample_count += data[label_var].num_samples
        minibatch_index += 1

    print("")
    print("Final Results: Minibatch[1-{}]: errs = {:0.1f}% * {}".format(minibatch_index+1, (metric_numer*100.0)/metric_denom, metric_denom))
    print("")

if __name__=='__main__':
    # TODO: leave these in for now as debugging aids; remove for beta
    from _cntk_py import set_computation_network_trace_level, set_fixed_random_seed, force_deterministic_algorithms
    set_computation_network_trace_level(1)  # TODO: remove debugging facilities once this all works
    set_fixed_random_seed(1)  # BUGBUG: has no effect at present  # TODO: remove debugging facilities once this all works
    #force_deterministic_algorithms()
    # TODO: do the above; they lead to slightly different results, so not doing it for now

    reader_train = create_reader(os.path.join(data_path, 'train_map.txt'), os.path.join(data_path, 'CIFAR-10_mean.xml'), True)
    reader_test  = create_reader(os.path.join(data_path, 'test_map.txt'), os.path.join(data_path, 'CIFAR-10_mean.xml'), False)

    train_and_evaluate(reader_train, reader_test, max_epochs=5)