Exemple #1
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def create_trainer(network, minibatch_size, epoch_size, num_quantization_bits):
    if network['name'] == 'resnet20':
        lr_per_mb = [1.0] * 80 + [0.1] * 40 + [0.01]
    elif network['name'] == 'resnet110':
        lr_per_mb = [0.1] * 1 + [1.0] * 80 + [0.1] * 40 + [0.01]
    else:
        return RuntimeError("Unknown model name!")

    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)

    # learner object
    local_learner = momentum_sgd(network['output'].parameters,
                                 lr_schedule,
                                 mm_schedule,
                                 unit_gain=True,
                                 l2_regularization_weight=l2_reg_weight)

    learner = data_parallel_distributed_learner(
        learner=local_learner,
        num_quantization_bits=num_quantization_bits,
        distributed_after=0)
    return Trainer(network['output'], network['ce'], network['pe'], learner)
def create_trainer(network, minibatch_size, epoch_size, num_quantization_bits, block_size, warm_up):
    if network['name'] == 'resnet20': 
        lr_per_mb = [1.0]*80+[0.1]*40+[0.01]
    elif network['name'] == 'resnet110': 
        lr_per_mb = [0.1]*1+[1.0]*80+[0.1]*40+[0.01]
    else: 
        return RuntimeError("Unknown model name!")

    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)
    
    # learner object
    if block_size != None and num_quantization_bits != 32:
        raise RuntimeError("Block momentum cannot be used with quantization, please remove quantized_bits option.")

    local_learner = momentum_sgd(network['output'].parameters, lr_schedule, mm_schedule,
                                 l2_regularization_weight = l2_reg_weight)

    if block_size != None:
        learner = block_momentum_distributed_learner(local_learner, block_size=block_size)
    else:
        learner = data_parallel_distributed_learner(local_learner, num_quantization_bits=num_quantization_bits, distributed_after=warm_up)
    
    return Trainer(network['output'], (network['ce'], network['pe']), learner)
Exemple #3
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def create_trainer(network, minibatch_size, epoch_size, num_quantization_bits, block_size, warm_up, progress_printer):
    if network['name'] == 'resnet20': 
        lr_per_mb = [1.0]*80+[0.1]*40+[0.01]
    elif network['name'] == 'resnet110': 
        lr_per_mb = [0.1]*1+[1.0]*80+[0.1]*40+[0.01]
    else: 
        return RuntimeError("Unknown model name!")

    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)
    
    # learner object
    if block_size != None and num_quantization_bits != 32:
        raise RuntimeError("Block momentum cannot be used with quantization, please remove quantized_bits option.")

    local_learner = momentum_sgd(network['output'].parameters, lr_schedule, mm_schedule,
                                 l2_regularization_weight = l2_reg_weight)

    if block_size != None:
        learner = block_momentum_distributed_learner(local_learner, block_size=block_size)
    else:
        learner = data_parallel_distributed_learner(local_learner, num_quantization_bits=num_quantization_bits, distributed_after=warm_up)

    return Trainer(network['output'], (network['ce'], network['pe']), learner, progress_printer)
Exemple #4
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def train_lm(training_file, epochs, max_num_minibatches):

    # load the data and vocab
    data, char_to_ix, ix_to_char, data_size, vocab_dim = load_data_and_vocab(training_file)

    # Model the source and target inputs to the model
    input_sequence, label_sequence = create_inputs(vocab_dim)

    # create the model
    model = create_model(vocab_dim)
    
    # and apply it to the input sequence    
    z = model(input_sequence)

    # setup the criterions (loss and metric)
    ce = cross_entropy_with_softmax(z, label_sequence)
    errs = classification_error(z, label_sequence)

    # Instantiate the trainer object to drive the model training
    lr_per_sample = learning_rate_schedule(0.001, UnitType.sample)
    momentum_time_constant = momentum_as_time_constant_schedule(1100)
    clipping_threshold_per_sample = 5.0
    gradient_clipping_with_truncation = True
    learner = momentum_sgd(z.parameters, lr_per_sample, momentum_time_constant,
                           gradient_clipping_threshold_per_sample=clipping_threshold_per_sample,
                           gradient_clipping_with_truncation=gradient_clipping_with_truncation)
    progress_printer = ProgressPrinter(freq=100, tag='Training')
    trainer = Trainer(z, (ce, errs), learner, progress_printer)

    sample_freq = 1000
    minibatches_per_epoch = min(data_size // minibatch_size, max_num_minibatches // epochs)

    # print out some useful training information
    log_number_of_parameters(z)
    print ("Running %d epochs with %d minibatches per epoch" % (epochs, minibatches_per_epoch))
    print()

    for e in range(0, epochs):
        # Specify the mapping of input variables in the model to actual minibatch data to be trained with
        # If it's the start of the data, we specify that we are looking at a new sequence (True)
        mask = [True]
        for b in range(0, minibatches_per_epoch):
            # get the data            
            features, labels = get_data(b, minibatch_size, data, char_to_ix, vocab_dim)
            arguments = ({input_sequence : features, label_sequence : labels}, mask)
            mask = [False] 
            trainer.train_minibatch(arguments)

            global_minibatch = e*minibatches_per_epoch + b
            if global_minibatch % sample_freq == 0:
                print(sample(z, ix_to_char, vocab_dim, char_to_ix))

        model_filename = "models/shakespeare_epoch%d.dnn" % (e+1)
        z.save_model(model_filename)
        print("Saved model to '%s'" % model_filename)
Exemple #5
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def train_lm():
    data = DataReader(token_to_id_path, segment_sepparator)

    # Create model nodes for the source and target inputs
    input_sequence, label_sequence = create_inputs(data.vocab_dim)

    # Create the model. It has three output nodes
    # z: the input to softmax that  provides the latent representation of the next token
    # cross_entropy: this is used training criterion
    # error: this a binary indicator if the model predicts the correct token
    z, cross_entropy, error = create_model(input_sequence, label_sequence, data.vocab_dim, hidden_dim)

    # For measurement we use the (build in) full softmax.
    full_ce = C.cross_entropy_with_softmax(z, label_sequence)

    # print out some useful training information
    log_number_of_parameters(z) ; print()
    
    # Run the training loop
    num_trained_samples = 0
    num_trained_samples_since_last_report = 0

    # Instantiate the trainer object to drive the model training
    lr_schedule = learning_rate_schedule(learning_rate, UnitType.sample)
    momentum_schedule = momentum_as_time_constant_schedule(momentum_as_time_constant)
    gradient_clipping_with_truncation = True
    learner = momentum_sgd(z.parameters, lr_schedule, momentum_schedule,
                            gradient_clipping_threshold_per_sample=clipping_threshold_per_sample,
                            gradient_clipping_with_truncation=gradient_clipping_with_truncation)
    trainer = Trainer(z, (cross_entropy, error), learner)
  
    for epoch_count in range(num_epochs):
        for features, labels, token_count in data.minibatch_generator(train_file_path, sequence_length, sequences_per_batch):
            arguments = ({input_sequence : features, label_sequence : labels})

            t_start = timeit.default_timer()
            trainer.train_minibatch(arguments)
            t_end =  timeit.default_timer()

            samples_per_second = token_count / (t_end - t_start)

            # Print progress report every num_samples_between_progress_report samples

            if num_trained_samples_since_last_report >= num_samples_between_progress_report or num_trained_samples == 0:
                av_ce = average_cross_entropy(full_ce, input_sequence, label_sequence, data)
                print_progress(samples_per_second, av_ce, num_trained_samples, t_start)
                num_trained_samples_since_last_report = 0

            num_trained_samples += token_count
            num_trained_samples_since_last_report += token_count

        # after each epoch save the model
        model_filename = "models/lm_epoch%d.dnn" % epoch_count
        z.save_model(model_filename)
        print("Saved model to '%s'" % model_filename)
Exemple #6
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def train_fast_rcnn(debug_output=False):
    if debug_output:
        print("Storing graphs and intermediate models to %s." % os.path.join(abs_path, "Output"))

    # Create the minibatch source
    minibatch_source = create_mb_source(image_height, image_width, num_channels,
                                        num_classes, num_rois, base_path, "train")

    # Input variables denoting features, rois and label data
    image_input = input_variable((num_channels, image_height, image_width))
    roi_input   = input_variable((num_rois, 4))
    label_input = input_variable((num_rois, num_classes))

    # define mapping from reader streams to network inputs
    input_map = {
        image_input: minibatch_source[features_stream_name],
        roi_input: minibatch_source[roi_stream_name],
        label_input: minibatch_source[label_stream_name]
    }

    # Instantiate the Fast R-CNN prediction model and loss function
    frcn_output = frcn_predictor(image_input, roi_input, num_classes)
    ce = cross_entropy_with_softmax(frcn_output, label_input, axis=1)
    pe = classification_error(frcn_output, label_input, axis=1)
    if debug_output:
        plot(frcn_output, os.path.join(abs_path, "Output", "graph_frcn.png"))

    # Set learning parameters
    l2_reg_weight = 0.0005
    lr_per_sample = [0.00001] * 10 + [0.000001] * 5 + [0.0000001]
    lr_schedule = learning_rate_schedule(lr_per_sample, unit=UnitType.sample)
    mm_schedule = momentum_as_time_constant_schedule(momentum_time_constant)

    # Instantiate the trainer object
    learner = momentum_sgd(frcn_output.parameters, lr_schedule, mm_schedule, l2_regularization_weight=l2_reg_weight)
    trainer = Trainer(frcn_output, (ce, pe), learner)

    # Get minibatches of images and perform model training
    print("Training Fast R-CNN model for %s epochs." % max_epochs)
    log_number_of_parameters(frcn_output)
    progress_printer = ProgressPrinter(tag='Training', num_epochs=max_epochs)
    for epoch in range(max_epochs):       # loop over epochs
        sample_count = 0
        while sample_count < epoch_size:  # loop over minibatches in the epoch
            data = minibatch_source.next_minibatch(min(mb_size, epoch_size-sample_count), input_map=input_map)
            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 debug_output:
            frcn_output.save(os.path.join(abs_path, "Output", "frcn_py_%s.model" % (epoch+1)))

    return frcn_output
def Evaluator(model, criterion):
    from cntk import Trainer
    from cntk.learner import momentum_sgd, learning_rate_schedule, UnitType, momentum_as_time_constant_schedule
    loss, metric = Trainer._get_loss_metric(criterion)
    parameters = set(loss.parameters)
    if model:
        parameters |= set(model.parameters)
    if metric:
        parameters |= set(metric.parameters)
    dummy_learner = momentum_sgd(tuple(parameters), 
                                 lr = learning_rate_schedule(1, UnitType.minibatch),
                                 momentum = momentum_as_time_constant_schedule(0))
    return Trainer(model, (loss, metric), dummy_learner)
def Evaluator(model, criterion):
    from cntk import Trainer
    from cntk.learner import momentum_sgd, learning_rate_schedule, UnitType, momentum_as_time_constant_schedule
    loss, metric = Trainer._get_loss_metric(criterion)
    parameters = set(loss.parameters)
    if model:
        parameters |= set(model.parameters)
    if metric:
        parameters |= set(metric.parameters)
    dummy_learner = momentum_sgd(
        tuple(parameters),
        lr=learning_rate_schedule(1, UnitType.minibatch),
        momentum=momentum_as_time_constant_schedule(0))
    return Trainer(model, (loss, metric), dummy_learner)
Exemple #9
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def train_model(base_model_file, feature_node_name, last_hidden_node_name,
                image_width, image_height, num_channels, num_classes, train_map_file,
                num_epochs, max_images=-1, freeze=False):
    epoch_size = sum(1 for line in open(train_map_file))
    if max_images > 0:
        epoch_size = min(epoch_size, max_images)

    # Create the minibatch source and input variables
    minibatch_source = create_mb_source(train_map_file, image_width, image_height, num_channels, num_classes)
    image_input = input_variable((num_channels, image_height, image_width))
    label_input = input_variable(num_classes)

    # Define mapping from reader streams to network inputs
    input_map = {
        image_input: minibatch_source[features_stream_name],
        label_input: minibatch_source[label_stream_name]
    }

    # Instantiate the transfer learning model and loss function
    tl_model = create_model(base_model_file, feature_node_name, last_hidden_node_name, num_classes, image_input, freeze)
    ce = cross_entropy_with_softmax(tl_model, label_input)
    pe = classification_error(tl_model, label_input)

    # Instantiate the trainer object
    lr_schedule = learning_rate_schedule(lr_per_mb, unit=UnitType.minibatch)
    mm_schedule = momentum_schedule(momentum_per_mb)
    learner = momentum_sgd(tl_model.parameters, lr_schedule, mm_schedule, l2_regularization_weight=l2_reg_weight)
    trainer = Trainer(tl_model, (ce, pe), learner)

    # Get minibatches of images and perform model training
    print("Training transfer learning model for {0} epochs (epoch_size = {1}).".format(num_epochs, epoch_size))
    log_number_of_parameters(tl_model)
    progress_printer = ProgressPrinter(tag='Training', num_epochs=num_epochs)
    for epoch in range(num_epochs):       # loop over epochs
        sample_count = 0
        while sample_count < epoch_size:  # loop over minibatches in the epoch
            data = minibatch_source.next_minibatch(min(mb_size, epoch_size-sample_count), input_map=input_map)
            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
            if sample_count % (100 * mb_size) == 0:
                print ("Processed {0} samples".format(sample_count))

        progress_printer.epoch_summary(with_metric=True)

    return tl_model
def create_trainer(network, epoch_size, num_quantization_bits, block_size, warm_up):
    # Instantiate the trainer object to drive the model training
    lr_per_minibatch = learning_rate_schedule(0.5, UnitType.minibatch)
    momentum_time_constant = momentum_as_time_constant_schedule(1100)
    clipping_threshold_per_sample = 2.3
    gradient_clipping_with_truncation = True

    # Create learner
    if block_size is not None and num_quantization_bits != default_quantization_bits:
        raise RuntimeError("Block momentum cannot be used with quantization, please remove quantized_bits option.")

    local_learner = momentum_sgd(network['output'].parameters,
                           lr_per_minibatch, momentum_time_constant,
                           gradient_clipping_threshold_per_sample=clipping_threshold_per_sample,
                           gradient_clipping_with_truncation=gradient_clipping_with_truncation)

    if block_size != None:
        learner = block_momentum_distributed_learner(local_learner, block_size=block_size)
    else:
        learner = data_parallel_distributed_learner(local_learner, num_quantization_bits=num_quantization_bits, distributed_after=warm_up)

    return Trainer(network['output'], (network['ce'], network['pe']), learner)
Exemple #11
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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
Exemple #12
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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([
            Convolution((3, 3, 3), 64, pad=True),
            MaxPooling((1, 2, 2), (1, 2, 2)),
            LayerStack(
                3, lambda i: [
                    Convolution((3, 3, 3), [96, 128, 128][i], pad=True),
                    Convolution((3, 3, 3), [96, 128, 128][i], pad=True),
                    MaxPooling((2, 2, 2), (2, 2, 2))
                ]),
            LayerStack(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)
    trainer = Trainer(z, ce, pe, learner)

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

    # 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})

            progress_printer.update_with_trainer(
                trainer, with_metric=True)  # log progress
        progress_printer.epoch_summary(with_metric=True)

    # 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 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
Exemple #14
0
def cifar_resnet(base_path, debug_output=False):
    image_height = 32
    image_width = 32
    num_channels = 3
    num_classes = 10
    feats_stream_name = 'features'
    labels_stream_name = 'labels'

    minibatch_source = create_mb_source(feats_stream_name, labels_stream_name, 
                        image_height, image_width, num_channels, num_classes, base_path)
    features_si = minibatch_source[feats_stream_name]
    labels_si = minibatch_source[labels_stream_name]

    # Input variables denoting the features and label data
    image_input = input_variable(
        (num_channels, image_height, image_width), features_si.m_element_type)
    label_var = input_variable((num_classes), features_si.m_element_type)

    # Instantiate the resnet classification model
    classifier_output = resnet_classifer(image_input, num_classes)

    ce = cross_entropy_with_softmax(classifier_output, label_var)
    pe = classification_error(classifier_output, label_var)

    mb_size = 128
    num_mb_per_epoch = 100
    num_epochs = 10
    num_mbs = num_mb_per_epoch * num_epochs

    lr_per_sample = [1/mb_size]*80+[0.1/mb_size]*40+[0.01/mb_size]
    lr_schedule = learning_rate_schedule(lr_per_sample, units=mb_size * num_mb_per_epoch)
    momentum_per_sample=0.9**(1.0/128)
    
    # Instantiate the trainer object to drive the model training
    trainer = Trainer(classifier_output, ce, pe,
                      [momentum_sgd(classifier_output.parameters, lr_schedule, momentum_per_sample, l2_regularization_weight=0.0001)])

    # Get minibatches of images to train with and perform model training
    training_progress_output_freq = 100

    if debug_output:
        training_progress_output_freq = training_progress_output_freq/3

    for i in range(0, num_mbs):
        mb = minibatch_source.next_minibatch(mb_size)

        # Specify the mapping of input variables in the model to actual
        # minibatch data to be trained with
        arguments = {
                image_input: mb[features_si], 
                label_var: mb[labels_si]
                }
        trainer.train_minibatch(arguments)

        print_training_progress(trainer, i, training_progress_output_freq)

    test_minibatch_source = create_test_mb_source(feats_stream_name, labels_stream_name,
                    image_height, image_width, num_channels, num_classes, base_path)
    features_si = test_minibatch_source[feats_stream_name]
    labels_si = test_minibatch_source[labels_stream_name]

    mb_size = 128
    num_mbs = 100

    total_error = 0.0
    for i in range(0, num_mbs):
        mb = test_minibatch_source.next_minibatch(mb_size)

        # Specify the mapping of input variables in the model to actual
        # minibatch data to be trained with
        arguments = {
                image_input: mb[features_si], 
                label_var: mb[labels_si]
                }
        error = trainer.test_minibatch(arguments)
        total_error += error

    return total_error / num_mbs
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
Exemple #16
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def convnet_cifar10_dataaug(reader_train, reader_test, 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)

    # 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)
        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 += 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 cifar_resnet_distributed(data_path, run_test, num_epochs, communicator=None, save_model_filename=None, load_model_filename=None, debug_output=False):
    image_height = 32
    image_width = 32
    num_channels = 3
    num_classes = 10

    feats_stream_name = 'features'
    labels_stream_name = 'labels'

    minibatch_source = create_reader(os.path.join(data_path, 'train_map.txt'), os.path.join(data_path, 'CIFAR-10_mean.xml'), True,
                                     distributed_communicator = communicator)

    features_si = minibatch_source[feats_stream_name]
    labels_si = minibatch_source[labels_stream_name]

    # Instantiate the resnet classification model, or load from file
    
    if load_model_filename:
        print("Loading model:", load_model_filename)
        classifier_output = persist.load_model(load_model_filename)
        image_input = classifier_output.arguments[0]
    else:
        image_input = input_variable(
            (num_channels, image_height, image_width), features_si.m_element_type)
        classifier_output = create_resnet_model(image_input, num_classes)

    # Input variables denoting the features and label data
    label_var = input_variable((num_classes), features_si.m_element_type)

    ce = cross_entropy_with_softmax(classifier_output, label_var)
    pe = classification_error(classifier_output, label_var)

    # Instantiate the trainer object to drive the model training

    mb_size = 128
    num_mb_per_epoch = 100
    
    num_mbs = num_mb_per_epoch * num_epochs

    lr_per_sample = [1/mb_size]*80+[0.1/mb_size]*40+[0.01/mb_size]
    lr_schedule = learning_rate_schedule(lr_per_sample, units = mb_size * num_mb_per_epoch)
    momentum_time_constant = -mb_size/np.log(0.9)

    # create data parallel distributed trainer if needed
    dist_trainer = distributed.data_parallel_distributed_trainer(communicator, False) if communicator else None

    # Instantiate the trainer object to drive the model training
    trainer = Trainer(classifier_output, ce, pe,
                      [momentum_sgd(classifier_output.parameters, lr_schedule, momentum_time_constant, l2_regularization_weight=0.0001)],
                      distributed_trainer = dist_trainer)
    
    # Get minibatches of images to train with and perform model training
    training_progress_output_freq = 100 if communicator else 20

    if debug_output:
        training_progress_output_freq = training_progress_output_freq/4
        
    for i in range(0, num_mbs):
    
        # NOTE: depends on network, the mb_size can be changed dynamically here
        mb = minibatch_source.next_minibatch(mb_size)

        # Specify the mapping of input variables in the model to actual
        # minibatch data to be trained with
        arguments = {
                image_input: mb[features_si], 
                label_var: mb[labels_si]
                }
        trainer.train_minibatch(arguments)

        print_training_progress(trainer, i, training_progress_output_freq)
        
    if save_model_filename:
        print("Saving model:", save_model_filename)
        persist.save_model(classifier_output, save_model_filename)

    if run_test:
        test_minibatch_source = create_reader(os.path.join(data_path, 'test_map.txt'), os.path.join(data_path, 'CIFAR-10_mean.xml'), False)
        features_si = test_minibatch_source[feats_stream_name]
        labels_si = test_minibatch_source[labels_stream_name]

        mb_size = 128
        num_mbs = 100

        total_error = 0.0
        for i in range(0, num_mbs):
            mb = test_minibatch_source.next_minibatch(mb_size)

            # Specify the mapping of input variables in the model to actual
            # minibatch data to be trained with
            arguments = {
                    image_input: mb[features_si], 
                    label_var: mb[labels_si]
                    }
            error = trainer.test_minibatch(arguments)
            total_error += error

        return total_error / num_mbs
    else:
        return 0
def train_and_evaluate(reader_train, reader_test, network_name, max_epochs):

    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
    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 = 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 += 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)
        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
Exemple #19
0
def train(train_reader, valid_reader, vocab, i2w, model, max_epochs):

    # do some hooks that we won't need in the future
    label_sequence = model.find_by_name('label_sequence')
    decoder_history_hook = model.find_by_name('decoder_history_hook')

    # Criterion nodes
    ce = cross_entropy_with_softmax(model, label_sequence)
    errs = classification_error(model, label_sequence)

    def clone_and_hook():
        # network output for decoder history
        net_output = hardmax(model)

        # make a clone of the graph where the ground truth is replaced by the network output
        return model.clone(CloneMethod.share,
                           {decoder_history_hook.output: net_output.output})

    # get a new model that uses the past network output as input to the decoder
    new_model = clone_and_hook()

    # Instantiate the trainer object to drive the model training
    lr_per_sample = learning_rate_schedule(0.007, UnitType.sample)
    minibatch_size = 72
    momentum_time_constant = momentum_as_time_constant_schedule(1100)
    clipping_threshold_per_sample = 2.3
    gradient_clipping_with_truncation = True
    learner = momentum_sgd(
        model.parameters,
        lr_per_sample,
        momentum_time_constant,
        gradient_clipping_threshold_per_sample=clipping_threshold_per_sample,
        gradient_clipping_with_truncation=gradient_clipping_with_truncation)
    trainer = Trainer(model, ce, errs, learner)

    # Get minibatches of sequences to train with and perform model training
    i = 0
    mbs = 0

    # Set epoch size to a larger number of lower training error
    epoch_size = 5000 if isFast else 908241

    training_progress_output_freq = 100

    # bind inputs to data from readers
    train_bind = {
        find_arg_by_name('raw_input', model): train_reader.streams.features,
        find_arg_by_name('raw_labels', model): train_reader.streams.labels
    }
    valid_bind = {
        find_arg_by_name('raw_input', new_model):
        valid_reader.streams.features,
        find_arg_by_name('raw_labels', new_model): valid_reader.streams.labels
    }

    for epoch in range(max_epochs):
        loss_numer = 0
        metric_numer = 0
        denom = 0

        while i < (epoch + 1) * epoch_size:
            # get next minibatch of training data
            mb_train = train_reader.next_minibatch(minibatch_size,
                                                   input_map=train_bind)
            trainer.train_minibatch(mb_train)

            # collect epoch-wide stats
            samples = trainer.previous_minibatch_sample_count
            loss_numer += trainer.previous_minibatch_loss_average * samples
            metric_numer += trainer.previous_minibatch_evaluation_average * samples
            denom += samples

            # every N MBs evaluate on a test sequence to visually show how we're doing; also print training stats
            if mbs % training_progress_output_freq == 0:

                print(
                    "Minibatch: {0}, Train Loss: {1:2.3f}, Train Evaluation Criterion: {2:2.3f}"
                    .format(mbs, get_train_loss(trainer),
                            get_train_eval_criterion(trainer)))

                mb_valid = valid_reader.next_minibatch(minibatch_size,
                                                       input_map=valid_bind)
                e = new_model.eval(mb_valid)
                print_sequences(e, i2w)

            i += mb_train[find_arg_by_name('raw_labels', model)].num_samples
            mbs += 1

        print("--- EPOCH %d DONE: loss = %f, errs = %f ---" %
              (epoch, loss_numer / denom, 100.0 * (metric_numer / denom)))
        return 100.0 * (metric_numer / denom)
Exemple #20
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label_sequence = model.find_by_name('label_sequence')

# Criterion nodes
ce = cross_entropy_with_softmax(model, label_sequence)
errs = classification_error(model, label_sequence)

# let's show the required arguments for this model
print([x.name for x in model.arguments])
lr_per_sample = learning_rate_schedule(0.007, UnitType.sample)
minibatch_size = 72
momentum_time_constant = momentum_as_time_constant_schedule(1100)
clipping_threshold_per_sample = 2.3
gradient_clipping_with_truncation = True
learner = momentum_sgd(
    model.parameters,
    lr_per_sample,
    momentum_time_constant,
    gradient_clipping_threshold_per_sample=clipping_threshold_per_sample,
    gradient_clipping_with_truncation=gradient_clipping_with_truncation)
trainer = Trainer(model, ce, errs, learner)


# helper function to find variables by name
def find_arg_by_name(name, expression):
    vars = [i for i in expression.arguments if i.name == name]
    assert len(vars) == 1
    return vars[0]


train_bind = {
    find_arg_by_name('raw_input', model): train_reader.streams.features,
    find_arg_by_name('raw_labels', model): train_reader.streams.labels
Exemple #21
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def train_lm():
    data = DataReader(token_to_id_path, segment_sepparator)

    # Create model nodes for the source and target inputs
    input_sequence, label_sequence = create_inputs(data.vocab_dim)

    # Create the model. It has three output nodes
    # z: the input to softmax that  provides the latent representation of the next token
    # cross_entropy: this is used training criterion
    # error: this a binary indicator if the model predicts the correct token
    z, cross_entropy, error = create_model(input_sequence, label_sequence,
                                           data.vocab_dim, hidden_dim)

    # For measurement we use the (build in) full softmax.
    full_ce = C.cross_entropy_with_softmax(z, label_sequence)

    # print out some useful training information
    log_number_of_parameters(z)
    print()

    # Run the training loop
    num_trained_samples = 0
    num_trained_samples_since_last_report = 0

    # Instantiate the trainer object to drive the model training
    lr_schedule = learning_rate_schedule(learning_rate, UnitType.sample)
    momentum_schedule = momentum_as_time_constant_schedule(
        momentum_as_time_constant)
    gradient_clipping_with_truncation = True
    learner = momentum_sgd(
        z.parameters,
        lr_schedule,
        momentum_schedule,
        gradient_clipping_threshold_per_sample=clipping_threshold_per_sample,
        gradient_clipping_with_truncation=gradient_clipping_with_truncation)
    trainer = Trainer(z, (cross_entropy, error), learner)

    for epoch_count in range(num_epochs):
        for features, labels, token_count in data.minibatch_generator(
                train_file_path, sequence_length, sequences_per_batch):
            arguments = ({input_sequence: features, label_sequence: labels})

            t_start = timeit.default_timer()
            trainer.train_minibatch(arguments)
            t_end = timeit.default_timer()

            samples_per_second = token_count / (t_end - t_start)

            # Print progress report every num_samples_between_progress_report samples

            if num_trained_samples_since_last_report >= num_samples_between_progress_report or num_trained_samples == 0:
                av_ce = average_cross_entropy(full_ce, input_sequence,
                                              label_sequence, data)
                print_progress(samples_per_second, av_ce, num_trained_samples,
                               t_start)
                num_trained_samples_since_last_report = 0

            num_trained_samples += token_count
            num_trained_samples_since_last_report += token_count

        # after each epoch save the model
        model_filename = "models/lm_epoch%d.dnn" % epoch_count
        z.save_model(model_filename)
        print("Saved model to '%s'" % model_filename)
Exemple #22
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def sequence_to_sequence_translator(debug_output=False):

    input_vocab_dim = 69
    label_vocab_dim = 69

    hidden_dim = 512
    num_layers = 2

    # Source and target inputs to the model
    batch_axis = Axis.default_batch_axis()
    input_seq_axis = Axis('inputAxis')
    label_seq_axis = Axis('labelAxis')

    input_dynamic_axes = [batch_axis, input_seq_axis]
    raw_input = input_variable(shape=(input_vocab_dim),
                               dynamic_axes=input_dynamic_axes)

    label_dynamic_axes = [batch_axis, label_seq_axis]
    raw_labels = input_variable(shape=(label_vocab_dim),
                                dynamic_axes=label_dynamic_axes)

    # Instantiate the sequence to sequence translation model
    input_sequence = raw_input

    # Drop the sentence start token from the label, for decoder training
    label_sequence = slice(raw_labels, label_seq_axis, 1, 0)
    label_sentence_start = sequence.first(raw_labels)

    is_first_label = sequence.is_first(label_sequence)
    label_sentence_start_scattered = sequence.scatter(label_sentence_start,
                                                      is_first_label)

    # Encoder
    encoder_outputH = stabilize(input_sequence)
    for i in range(0, num_layers):
        (encoder_outputH,
         encoder_outputC) = LSTMP_component_with_self_stabilization(
             encoder_outputH.output(), hidden_dim, hidden_dim, future_value,
             future_value)

    thought_vectorH = sequence.first(encoder_outputH)
    thought_vectorC = sequence.first(encoder_outputC)

    thought_vector_broadcastH = sequence.broadcast_as(thought_vectorH,
                                                      label_sequence)
    thought_vector_broadcastC = sequence.broadcast_as(thought_vectorC,
                                                      label_sequence)

    # Decoder
    decoder_history_from_ground_truth = label_sequence
    decoder_input = element_select(
        is_first_label, label_sentence_start_scattered,
        past_value(decoder_history_from_ground_truth))

    decoder_outputH = stabilize(decoder_input)
    for i in range(0, num_layers):
        if (i > 0):
            recurrence_hookH = past_value
            recurrence_hookC = past_value
        else:
            isFirst = sequence.is_first(label_sequence)
            recurrence_hookH = lambda operand: element_select(
                isFirst, thought_vector_broadcastH, past_value(operand))
            recurrence_hookC = lambda operand: element_select(
                isFirst, thought_vector_broadcastC, past_value(operand))

        (decoder_outputH,
         encoder_outputC) = LSTMP_component_with_self_stabilization(
             decoder_outputH.output(), hidden_dim, hidden_dim,
             recurrence_hookH, recurrence_hookC)

    decoder_output = decoder_outputH
    decoder_dim = hidden_dim

    # Softmax output layer
    z = linear_layer(stabilize(decoder_output), label_vocab_dim)
    ce = cross_entropy_with_softmax(z, label_sequence)
    errs = classification_error(z, label_sequence)

    # Instantiate the trainer object to drive the model training
    lr = 0.007
    momentum_time_constant = 1100
    momentum_per_sample = momentums_per_sample(
        math.exp(-1.0 / momentum_time_constant))
    clipping_threshold_per_sample = 2.3
    gradient_clipping_with_truncation = True

    trainer = Trainer(z, ce, errs, [
        momentum_sgd(z.parameters(), lr, momentum_per_sample,
                     clipping_threshold_per_sample,
                     gradient_clipping_with_truncation)
    ])

    rel_path = r"../../../../Examples/SequenceToSequence/CMUDict/Data/cmudict-0.7b.train-dev-20-21.ctf"
    path = os.path.join(os.path.dirname(os.path.abspath(__file__)), rel_path)
    feature_stream_name = 'features'
    labels_stream_name = 'labels'

    mb_source = text_format_minibatch_source(path, [
        StreamConfiguration(feature_stream_name, input_vocab_dim, True, 'S0'),
        StreamConfiguration(labels_stream_name, label_vocab_dim, True, 'S1')
    ], 10000)
    features_si = mb_source[feature_stream_name]
    labels_si = mb_source[labels_stream_name]

    # Get minibatches of sequences to train with and perform model training
    minibatch_size = 72
    training_progress_output_freq = 30
    if debug_output:
        training_progress_output_freq = training_progress_output_freq / 3

    while True:
        mb = mb_source.get_next_minibatch(minibatch_size)
        if len(mb) == 0:
            break

        # Specify the mapping of input variables in the model to actual
        # minibatch data to be trained with
        arguments = {raw_input: mb[features_si], raw_labels: mb[labels_si]}
        trainer.train_minibatch(arguments)

        print_training_progress(trainer, i, training_progress_output_freq)
        i += 1

    rel_path = r"../../../../Examples/SequenceToSequence/CMUDict/Data/cmudict-0.7b.test.ctf"
    path = os.path.join(os.path.dirname(os.path.abspath(__file__)), rel_path)

    test_mb_source = text_format_minibatch_source(path, [
        StreamConfiguration(feature_stream_name, input_vocab_dim, True, 'S0'),
        StreamConfiguration(labels_stream_name, label_vocab_dim, True, 'S1')
    ], 10000, False)
    features_si = test_mb_source[feature_stream_name]
    labels_si = test_mb_source[labels_stream_name]

    # choose this to be big enough for the longest sentence
    train_minibatch_size = 1024

    # Get minibatches of sequences to test and perform testing
    i = 0
    total_error = 0.0
    while True:
        mb = test_mb_source.get_next_minibatch(train_minibatch_size)
        if len(mb) == 0:
            break

        # Specify the mapping of input variables in the model to actual
        # minibatch data to be tested with
        arguments = {raw_input: mb[features_si], raw_labels: mb[labels_si]}
        mb_error = trainer.test_minibatch(arguments)

        total_error += mb_error

        if debug_output:
            print("Minibatch {}, Error {} ".format(i, mb_error))

        i += 1

    # Average of evaluation errors of all test minibatches
    return total_error / i
Exemple #23
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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(debug_output=False):
    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 = input_variable((num_channels, image_height, image_width),
                               np.float32)
    label_var = input_variable(num_output_classes, np.float32)

    # Instantiate the feedforward classification model
    input_removemean = minus(input_var, constant(128))
    scaled_input = element_times(constant(0.00390625), input_removemean)
    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_output_classes, activation=None)
        ])(scaled_input)

    ce = cross_entropy_with_softmax(z, label_var)
    pe = 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

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

    # Instantiate the trainer object to drive the model training
    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')

    # Get minibatches of images to train with and perform model training
    max_epochs = 30
    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)
        persist.save_model(
            z, 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
Exemple #25
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def train_and_evaluate(reader_train, reader_test, max_epochs):

    # 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
    z = create_vgg9_model(input_var, 10)

    #
    # Training action
    #

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

    # training config
    epoch_size     = 50000
    minibatch_size = 64

    # For basic model
    lr_per_sample       = [0.00015625]*10+[0.000046875]*10+[0.0000156]
    momentum_per_sample = 0.9 ** (1.0 / minibatch_size)  # BUGBUG: why does this work? Should be as time const, no?
    l2_reg_weight       = 0.03

    # trainer object
    lr_schedule = learning_rate_schedule(lr_per_sample, units=epoch_size)
    learner     = momentum_sgd(z.parameters, lr_schedule, momentum_per_sample, 
                               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)
    
    #
    # 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

    #progress_printer = ProgressPrinter(freq=100, first=10, tag='Eval')
    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("")
Exemple #26
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def train_lm(training_file, max_num_minibatches):

    # load the data and vocab
    data, char_to_ix, ix_to_char, data_size, vocab_dim = load_data_and_vocab(
        training_file)

    # Model the source and target inputs to the model
    input_sequence, label_sequence = create_inputs(vocab_dim)

    # create the model
    model = create_model(vocab_dim)

    # and apply it to the input sequence
    z = model(input_sequence)

    # setup the criterions (loss and metric)
    ce = cross_entropy_with_softmax(z, label_sequence)
    errs = classification_error(z, label_sequence)

    # Instantiate the trainer object to drive the model training
    lr_per_sample = learning_rate_schedule(0.001, UnitType.sample)
    momentum_time_constant = momentum_as_time_constant_schedule(1100)
    clipping_threshold_per_sample = 5.0
    gradient_clipping_with_truncation = True
    learner = momentum_sgd(
        z.parameters,
        lr_per_sample,
        momentum_time_constant,
        gradient_clipping_threshold_per_sample=clipping_threshold_per_sample,
        gradient_clipping_with_truncation=gradient_clipping_with_truncation)
    trainer = Trainer(z, (ce, errs), learner)

    sample_freq = 1000
    epochs = 50
    minibatches_per_epoch = int((data_size / minibatch_size))
    minibatches = min(epochs * minibatches_per_epoch, max_num_minibatches)

    # print out some useful training information
    log_number_of_parameters(z)
    print()
    progress_printer = ProgressPrinter(freq=100, tag='Training')

    e = 0
    p = 0
    for i in range(0, minibatches):

        if p + minibatch_size + 1 >= data_size:
            p = 0
            e += 1
            model_filename = "models/shakespeare_epoch%d.dnn" % e
            z.save(model_filename)
            print("Saved model to '%s'" % model_filename)

        # get the data
        features, labels = get_data(p, minibatch_size, data, char_to_ix,
                                    vocab_dim)

        # Specify the mapping of input variables in the model to actual minibatch data to be trained with
        # If it's the start of the data, we specify that we are looking at a new sequence (True)
        mask = [False]
        if p == 0:
            mask = [True]
        arguments = ({input_sequence: features, label_sequence: labels}, mask)
        trainer.train_minibatch(arguments)

        progress_printer.update_with_trainer(trainer,
                                             with_metric=True)  # log progress

        if i % sample_freq == 0:
            print(sample(z, ix_to_char, vocab_dim, char_to_ix))

        p += minibatch_size

    # Do a final save of the model
    model_filename = "models/shakespeare_epoch%d.dnn" % e
    z.save(model_filename)
Exemple #27
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def sequence_to_sequence_translator(debug_output=False, run_test=False):

    input_vocab_dim = 69
    label_vocab_dim = 69

    # network complexity; initially low for faster testing
    hidden_dim = 256
    num_layers = 1

    # Source and target inputs to the model
    batch_axis = Axis.default_batch_axis()
    input_seq_axis = Axis('inputAxis')
    label_seq_axis = Axis('labelAxis')

    input_dynamic_axes = [batch_axis, input_seq_axis]
    raw_input = input_variable(shape=(input_vocab_dim),
                               dynamic_axes=input_dynamic_axes,
                               name='raw_input')

    label_dynamic_axes = [batch_axis, label_seq_axis]
    raw_labels = input_variable(shape=(label_vocab_dim),
                                dynamic_axes=label_dynamic_axes,
                                name='raw_labels')

    # Instantiate the sequence to sequence translation model
    input_sequence = raw_input

    # Drop the sentence start token from the label, for decoder training
    label_sequence = slice(raw_labels, label_seq_axis, 1,
                           0)  # <s> A B C </s> --> A B C </s>
    label_sentence_start = sequence.first(raw_labels)  # <s>

    is_first_label = sequence.is_first(label_sequence)  # <s> 0 0 0 ...
    label_sentence_start_scattered = sequence.scatter(label_sentence_start,
                                                      is_first_label)

    # Encoder
    encoder_outputH = stabilize(input_sequence)
    for i in range(0, num_layers):
        (encoder_outputH,
         encoder_outputC) = LSTMP_component_with_self_stabilization(
             encoder_outputH.output, hidden_dim, hidden_dim, future_value,
             future_value)

    thought_vectorH = sequence.first(encoder_outputH)
    thought_vectorC = sequence.first(encoder_outputC)

    thought_vector_broadcastH = sequence.broadcast_as(thought_vectorH,
                                                      label_sequence)
    thought_vector_broadcastC = sequence.broadcast_as(thought_vectorC,
                                                      label_sequence)

    # Decoder
    decoder_history_hook = alias(
        label_sequence, name='decoder_history_hook')  # copy label_sequence

    decoder_input = element_select(is_first_label,
                                   label_sentence_start_scattered,
                                   past_value(decoder_history_hook))

    decoder_outputH = stabilize(decoder_input)
    for i in range(0, num_layers):
        if (i > 0):
            recurrence_hookH = past_value
            recurrence_hookC = past_value
        else:
            isFirst = sequence.is_first(label_sequence)
            recurrence_hookH = lambda operand: element_select(
                isFirst, thought_vector_broadcastH, past_value(operand))
            recurrence_hookC = lambda operand: element_select(
                isFirst, thought_vector_broadcastC, past_value(operand))

        (decoder_outputH,
         encoder_outputC) = LSTMP_component_with_self_stabilization(
             decoder_outputH.output, hidden_dim, hidden_dim, recurrence_hookH,
             recurrence_hookC)

    decoder_output = decoder_outputH

    # Softmax output layer
    z = linear_layer(stabilize(decoder_output), label_vocab_dim)

    # Criterion nodes
    ce = cross_entropy_with_softmax(z, label_sequence)
    errs = classification_error(z, label_sequence)

    # network output for decoder history
    net_output = hardmax(z)

    # make a clone of the graph where the ground truth is replaced by the network output
    ng = z.clone(CloneMethod.share,
                 {decoder_history_hook.output: net_output.output})

    # Instantiate the trainer object to drive the model training
    lr = 0.007
    minibatch_size = 72
    momentum_time_constant = 1100
    m_schedule = momentum_schedule(momentum_time_constant)
    clipping_threshold_per_sample = 2.3
    gradient_clipping_with_truncation = True
    learner = momentum_sgd(z.parameters, lr, m_schedule,
                           clipping_threshold_per_sample,
                           gradient_clipping_with_truncation)
    trainer = Trainer(z, ce, errs, learner)

    # setup data
    rel_path = r"../../../../Examples/SequenceToSequence/CMUDict/Data/cmudict-0.7b.train-dev-20-21.ctf"
    train_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
                              rel_path)
    valid_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
                              "tiny.ctf")

    feature_stream_name = 'features'
    labels_stream_name = 'labels'

    # readers
    randomize_data = True
    if run_test:
        randomize_data = False  # because we want to get an exact error
    train_reader = text_format_minibatch_source(train_path, [
        StreamConfiguration(feature_stream_name, input_vocab_dim, True, 'S0'),
        StreamConfiguration(labels_stream_name, label_vocab_dim, True, 'S1')
    ],
                                                randomize=randomize_data)
    features_si_tr = train_reader.stream_info(feature_stream_name)
    labels_si_tr = train_reader.stream_info(labels_stream_name)

    valid_reader = text_format_minibatch_source(valid_path, [
        StreamConfiguration(feature_stream_name, input_vocab_dim, True, 'S0'),
        StreamConfiguration(labels_stream_name, label_vocab_dim, True, 'S1')
    ],
                                                randomize=False)
    features_si_va = valid_reader.stream_info(feature_stream_name)
    labels_si_va = valid_reader.stream_info(labels_stream_name)

    # get the vocab for printing output sequences in plaintext
    rel_path = r"../../../../Examples/SequenceToSequence/CMUDict/Data/cmudict-0.7b.mapping"
    vocab_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
                              rel_path)
    vocab = [w.strip() for w in open(vocab_path).readlines()]
    i2w = {i: ch for i, ch in enumerate(vocab)}

    # Get minibatches of sequences to train with and perform model training
    i = 0
    mbs = 0
    epoch_size = 908241
    max_epochs = 10
    training_progress_output_freq = 500

    # make things more basic for running a quicker test
    if run_test:
        epoch_size = 5000
        max_epochs = 1
        training_progress_output_freq = 30

    for epoch in range(max_epochs):
        loss_numer = 0
        metric_numer = 0
        denom = 0

        while i < (epoch + 1) * epoch_size:

            # get next minibatch of training data
            mb_train = train_reader.next_minibatch(minibatch_size)

            train_args = {
                'raw_input': mb_train[features_si_tr],
                'raw_labels': mb_train[labels_si_tr]
            }
            trainer.train_minibatch(train_args)

            # collect epoch-wide stats
            samples = trainer.previous_minibatch_sample_count
            loss_numer += trainer.previous_minibatch_loss_average * samples
            metric_numer += trainer.previous_minibatch_evaluation_average * samples
            denom += samples

            # every N MBs evaluate on a test sequence to visually show how we're doing
            if mbs % training_progress_output_freq == 0:
                mb_valid = valid_reader.next_minibatch(minibatch_size)
                valid_args = {
                    'raw_input': mb_valid[features_si_va],
                    'raw_labels': mb_valid[labels_si_va]
                }

                e = ng.eval(valid_args)
                print_sequences(e, i2w)

            print_training_progress(trainer, mbs,
                                    training_progress_output_freq)
            i += mb_train[labels_si_tr].num_samples
            mbs += 1

        print("--- EPOCH %d DONE: loss = %f, errs = %f ---" %
              (epoch, loss_numer / denom, 100.0 * (metric_numer / denom)))

    # now setup a test run
    rel_path = r"../../../../Examples/SequenceToSequence/CMUDict/Data/cmudict-0.7b.test.ctf"
    test_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
                             rel_path)

    test_reader = text_format_minibatch_source(test_path, [
        StreamConfiguration(feature_stream_name, input_vocab_dim, True, 'S0'),
        StreamConfiguration(labels_stream_name, label_vocab_dim, True, 'S1')
    ],
                                               10000,
                                               randomize=False)
    features_si_te = test_reader.stream_info(feature_stream_name)
    labels_si_te = test_reader.stream_info(labels_stream_name)

    test_minibatch_size = 1024

    # Get minibatches of sequences to test and perform testing
    i = 0
    total_error = 0.0
    while True:
        mb = test_reader.next_minibatch(test_minibatch_size)
        if len(mb) == 0:
            break

        # Specify the mapping of input variables in the model to actual
        # minibatch data to be tested with
        arguments = {
            raw_input: mb[features_si_te],
            raw_labels: mb[labels_si_te]
        }
        mb_error = trainer.test_minibatch(arguments)

        total_error += mb_error

        if debug_output:
            print("Minibatch {}, Error {} ".format(i, mb_error))

        i += 1

    # Average of evaluation errors of all test minibatches
    return total_error / i
def sequence_to_sequence_translator(debug_output=False, run_test=False):

    input_vocab_dim = 69
    label_vocab_dim = 69

    # network complexity; initially low for faster testing
    hidden_dim = 256
    num_layers = 1

    # Source and target inputs to the model
    batch_axis = Axis.default_batch_axis()
    input_seq_axis = Axis('inputAxis')
    label_seq_axis = Axis('labelAxis')

    input_dynamic_axes = [batch_axis, input_seq_axis]
    raw_input = input_variable(shape=(input_vocab_dim),
                               dynamic_axes=input_dynamic_axes,
                               name='raw_input')

    label_dynamic_axes = [batch_axis, label_seq_axis]
    raw_labels = input_variable(shape=(label_vocab_dim),
                                dynamic_axes=label_dynamic_axes,
                                name='raw_labels')

    # Instantiate the sequence to sequence translation model
    input_sequence = raw_input

    # Drop the sentence start token from the label, for decoder training
    label_sequence = sequence.slice(raw_labels, 1,
                                    0)  # <s> A B C </s> --> A B C </s>
    label_sentence_start = sequence.first(raw_labels)  # <s>

    is_first_label = sequence.is_first(label_sequence)  # <s> 0 0 0 ...
    label_sentence_start_scattered = sequence.scatter(label_sentence_start,
                                                      is_first_label)

    # Encoder
    encoder_outputH = stabilize(input_sequence)
    for i in range(0, num_layers):
        (encoder_outputH,
         encoder_outputC) = LSTMP_component_with_self_stabilization(
             encoder_outputH.output, hidden_dim, hidden_dim, future_value,
             future_value)

    thought_vectorH = sequence.first(encoder_outputH)
    thought_vectorC = sequence.first(encoder_outputC)

    thought_vector_broadcastH = sequence.broadcast_as(thought_vectorH,
                                                      label_sequence)
    thought_vector_broadcastC = sequence.broadcast_as(thought_vectorC,
                                                      label_sequence)

    # Decoder
    decoder_history_hook = alias(
        label_sequence, name='decoder_history_hook')  # copy label_sequence

    decoder_input = element_select(is_first_label,
                                   label_sentence_start_scattered,
                                   past_value(decoder_history_hook))

    decoder_outputH = stabilize(decoder_input)
    for i in range(0, num_layers):
        if (i > 0):
            recurrence_hookH = past_value
            recurrence_hookC = past_value
        else:
            isFirst = sequence.is_first(label_sequence)
            recurrence_hookH = lambda operand: element_select(
                isFirst, thought_vector_broadcastH, past_value(operand))
            recurrence_hookC = lambda operand: element_select(
                isFirst, thought_vector_broadcastC, past_value(operand))

        (decoder_outputH,
         encoder_outputC) = LSTMP_component_with_self_stabilization(
             decoder_outputH.output, hidden_dim, hidden_dim, recurrence_hookH,
             recurrence_hookC)

    decoder_output = decoder_outputH

    # Softmax output layer
    z = linear_layer(stabilize(decoder_output), label_vocab_dim)

    # Criterion nodes
    ce = cross_entropy_with_softmax(z, label_sequence)
    errs = classification_error(z, label_sequence)

    # network output for decoder history
    net_output = hardmax(z)

    # make a clone of the graph where the ground truth is replaced by the network output
    ng = z.clone(CloneMethod.share,
                 {decoder_history_hook.output: net_output.output})

    # Instantiate the trainer object to drive the model training
    lr_per_minibatch = learning_rate_schedule(0.5, UnitType.minibatch)
    momentum_time_constant = momentum_as_time_constant_schedule(1100)
    clipping_threshold_per_sample = 2.3
    gradient_clipping_with_truncation = True
    learner = momentum_sgd(
        z.parameters,
        lr_per_minibatch,
        momentum_time_constant,
        gradient_clipping_threshold_per_sample=clipping_threshold_per_sample,
        gradient_clipping_with_truncation=gradient_clipping_with_truncation)
    trainer = Trainer(z, ce, errs, learner)

    # setup data
    train_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..",
                              "Data", "cmudict-0.7b.train-dev-20-21.ctf")
    valid_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..",
                              "Data", "tiny.ctf")

    # readers
    randomize_data = True
    if run_test:
        randomize_data = False  # because we want to get an exact error

    train_reader = create_reader(train_path, randomize_data, input_vocab_dim,
                                 label_vocab_dim)
    train_bind = {
        raw_input: train_reader.streams.features,
        raw_labels: train_reader.streams.labels
    }

    # get the vocab for printing output sequences in plaintext
    vocab_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..",
                              "Data", "cmudict-0.7b.mapping")
    vocab = [w.strip() for w in open(vocab_path).readlines()]
    i2w = {i: ch for i, ch in enumerate(vocab)}

    # Get minibatches of sequences to train with and perform model training
    i = 0
    mbs = 0
    minibatch_size = 72
    epoch_size = 908241
    max_epochs = 10
    training_progress_output_freq = 500

    # make things more basic for running a quicker test
    if run_test:
        epoch_size = 5000
        max_epochs = 1
        training_progress_output_freq = 30

    valid_reader = create_reader(valid_path, False, input_vocab_dim,
                                 label_vocab_dim)
    valid_bind = {
        find_arg_by_name('raw_input', ng): valid_reader.streams.features,
        find_arg_by_name('raw_labels', ng): valid_reader.streams.labels
    }

    for epoch in range(max_epochs):
        loss_numer = 0
        metric_numer = 0
        denom = 0

        while i < (epoch + 1) * epoch_size:
            # get next minibatch of training data
            mb_train = train_reader.next_minibatch(minibatch_size,
                                                   input_map=train_bind)
            trainer.train_minibatch(mb_train)

            # collect epoch-wide stats
            samples = trainer.previous_minibatch_sample_count
            loss_numer += trainer.previous_minibatch_loss_average * samples
            metric_numer += trainer.previous_minibatch_evaluation_average * samples
            denom += samples

            # every N MBs evaluate on a test sequence to visually show how we're doing
            if mbs % training_progress_output_freq == 0:
                mb_valid = valid_reader.next_minibatch(minibatch_size,
                                                       input_map=valid_bind)
                e = ng.eval(mb_valid)
                print_sequences(e, i2w)

            print_training_progress(trainer, mbs,
                                    training_progress_output_freq)
            i += mb_train[raw_labels].num_samples
            mbs += 1

        print("--- EPOCH %d DONE: loss = %f, errs = %f ---" %
              (epoch, loss_numer / denom, 100.0 * (metric_numer / denom)))

    error1 = translator_test_error(z, trainer, input_vocab_dim,
                                   label_vocab_dim)

    save_model(z, "seq2seq.dnn")
    z = load_model("seq2seq.dnn")

    label_seq_axis = Axis('labelAxis')
    label_sequence = sequence.slice(find_arg_by_name('raw_labels', z), 1, 0)
    ce = cross_entropy_with_softmax(z, label_sequence)
    errs = classification_error(z, label_sequence)
    trainer = Trainer(z, ce, errs, [
        momentum_sgd(z.parameters, lr_per_minibatch, momentum_time_constant,
                     clipping_threshold_per_sample,
                     gradient_clipping_with_truncation)
    ])

    error2 = translator_test_error(z, trainer, input_vocab_dim,
                                   label_vocab_dim)

    assert error1 == error2

    return error1
Exemple #29
0
def train_and_evaluate(reader_train, reader_test, max_epochs):

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

    # Normalize the input
    feature_scale = 1.0 / 256.0
    input_var_norm = element_times(feature_scale, input_var)

    # apply model to input
    z = create_vgg9_model(input_var_norm, 10)

    #
    # Training action
    #

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

    # training config
    epoch_size = 50000
    minibatch_size = 64

    # Set learning parameters
    lr_per_minibatch = learning_rate_schedule(
        [0.01] * 10 + [0.003] * 10 + [0.001], epoch_size, UnitType.minibatch)
    momentum_time_constant = momentum_as_time_constant_schedule(
        -minibatch_size / np.log(0.9))
    l2_reg_weight = 0.0001

    # trainer object
    learner = momentum_sgd(z.parameters,
                           lr=lr_per_minibatch,
                           momentum=momentum_time_constant,
                           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)

    #
    # 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

    #progress_printer = ProgressPrinter(freq=100, first=10, tag='Eval')
    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("")

    # return evaluation error.
    return metric_numer / metric_denom
def train_and_evaluate(reader,
                       reader_test,
                       model,
                       epoch_size=50000,
                       max_epochs=5):

    # declare the model's input dimension
    # Training does not require this, but it is needed for deployment.
    model.update_signature((num_channels, image_height, image_width))

    # criterion function. This is what is being trained trained.
    # Model gets "sandwiched" between normalization (not part of model proper) and criterion.
    criterion = create_criterion_function(model, normalize=lambda x: x / 256)
    #debughelpers.dump_function(criterion, 'criterion')

    #from cntk.graph import plot
    #plot(criterion, filename=os.path.join(model_path, "ConvNet_CIFAR10_DataAug.pdf"))

    # iteration parameters
    minibatch_size = 64
    #epoch_size = 1000 ; max_epochs = 1 # for faster testing

    # learning parameters
    learner = momentum_sgd(
        model.parameters,
        lr=learning_rate_schedule([0.0015625] * 20 + [0.00046875] * 20 +
                                  [0.00015625] * 20 + [0.000046875] * 10 +
                                  [0.000015625],
                                  unit=UnitType.sample,
                                  epoch_size=epoch_size),
        momentum=momentum_as_time_constant_schedule([0] * 20 + [600] * 20 +
                                                    [1200],
                                                    epoch_size=epoch_size),
        l2_regularization_weight=0.002)

    # trainer object
    trainer = Trainer(None, criterion, learner)

    # perform model training
    log_number_of_parameters(model)
    print()
    progress_printer = ProgressPrinter(tag='Training')

    for epoch in range(max_epochs):  # loop over epochs
        sample_count = 0
        while sample_count < epoch_size:  # loop over minibatches in the epoch
            mb = reader.next_minibatch(
                min(minibatch_size,
                    epoch_size - sample_count))  # fetch minibatch.
            #trainer.train_minibatch(mb[reader.streams.features], mb[reader.streams.labels])
            trainer.train_minibatch({
                criterion.arguments[0]:
                mb[reader.streams.features],
                criterion.arguments[1]:
                mb[reader.streams.labels]
            })
            sample_count += mb[
                reader.streams.
                labels].num_samples  # count samples processed so far
            progress_printer.update_with_trainer(
                trainer, with_metric=True)  # log progress
        loss, metric, actual_samples = progress_printer.epoch_summary(
            with_metric=True)
        model.save(
            os.path.join(model_path,
                         "ConvNet_CIFAR10_DataAug_{}.dnn".format(epoch)))
        progress_printer.epoch_summary(with_metric=True)

    # TODO: we should be done here
    #return metric_numer/metric_denom

    ### Evaluation action

    # evaluate with current Trainer instance; just to make sure we save and load the model correctly and BN works now --TODO: delete once confirmed
    epoch_size = 10000
    minibatch_size = 16
    metric_numer = 0
    metric_denom = 0
    sample_count = 0
    minibatch_index = 0

    while sample_count < epoch_size:
        mbsize = min(minibatch_size, epoch_size - sample_count)
        mb = reader_test.next_minibatch(mbsize)
        metric_numer += mbsize * trainer.test_minibatch(
            {
                criterion.arguments[0]: mb[reader_test.streams.features],
                criterion.arguments[1]: mb[reader_test.streams.labels]
            })
        metric_denom += mbsize
        sample_count += mb[reader_test.streams.labels].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 evaluation error.
    return loss, metric  # return values from last epoch
Exemple #31
0
def train_model(base_model_file,
                feature_node_name,
                last_hidden_node_name,
                image_width,
                image_height,
                num_channels,
                num_classes,
                train_map_file,
                num_epochs,
                max_images=-1,
                freeze=False):
    epoch_size = sum(1 for line in open(train_map_file))
    if max_images > 0:
        epoch_size = min(epoch_size, max_images)

    # Create the minibatch source and input variables
    minibatch_source = create_mb_source(train_map_file, image_width,
                                        image_height, num_channels,
                                        num_classes)
    image_input = input_variable((num_channels, image_height, image_width))
    label_input = input_variable(num_classes)

    # Define mapping from reader streams to network inputs
    input_map = {
        image_input: minibatch_source[features_stream_name],
        label_input: minibatch_source[label_stream_name]
    }

    # Instantiate the transfer learning model and loss function
    tl_model = create_model(base_model_file, feature_node_name,
                            last_hidden_node_name, num_classes, image_input,
                            freeze)
    ce = cross_entropy_with_softmax(tl_model, label_input)
    pe = classification_error(tl_model, label_input)

    # Instantiate the trainer object
    lr_schedule = learning_rate_schedule(lr_per_mb, unit=UnitType.minibatch)
    mm_schedule = momentum_schedule(momentum_per_mb)
    learner = momentum_sgd(tl_model.parameters,
                           lr_schedule,
                           mm_schedule,
                           l2_regularization_weight=l2_reg_weight)
    trainer = Trainer(tl_model, (ce, pe), learner)

    # Get minibatches of images and perform model training
    print(
        "Training transfer learning model for {0} epochs (epoch_size = {1}).".
        format(num_epochs, epoch_size))
    log_number_of_parameters(tl_model)
    progress_printer = ProgressPrinter(tag='Training', num_epochs=num_epochs)
    for epoch in range(num_epochs):  # loop over epochs
        sample_count = 0
        while sample_count < epoch_size:  # loop over minibatches in the epoch
            data = minibatch_source.next_minibatch(min(
                mb_size, epoch_size - sample_count),
                                                   input_map=input_map)
            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
            if sample_count % (100 * mb_size) == 0:
                print("Processed {0} samples".format(sample_count))

        progress_printer.epoch_summary(with_metric=True)

    return tl_model
Exemple #32
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def convnet_mnist(debug_output=False):
    image_height = 28
    image_width  = 28
    num_channels = 1
    input_dim = image_height * image_width * num_channels
    num_output_classes = 10

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

    # Instantiate the feedforward classification model
    scaled_input = element_times(constant(0.00390625), input_var)
    with default_options (activation=relu, pad=False): 
        conv1 = Convolution((5,5), 32, pad=True)(scaled_input)
        pool1 = MaxPooling((3,3), (2,2))(conv1)
        conv2 = Convolution((3,3), 48)(pool1)
        pool2 = MaxPooling((3,3), (2,2))(conv2)
        conv3 = Convolution((3,3), 64)(pool2)
        f4    = Dense(96)(conv3)
        drop4 = Dropout(0.5)(f4)
        z     = Dense(num_output_classes, activation=None)(drop4)

    ce = cross_entropy_with_softmax(z, label_var)
    pe = classification_error(z, label_var)

    reader_train = create_reader(os.path.join(data_path, 'Train-28x28_cntk_text.txt'), True, input_dim, num_output_classes)

    # training config
    epoch_size = 60000                    # for now we manually specify epoch size
    minibatch_size = 128

    # Set learning parameters
    lr_per_sample          = [0.001]*10+[0.0005]*10+[0.0001]
    lr_schedule            = learning_rate_schedule(lr_per_sample, epoch_size)
    momentum_time_constant = [0]*5+[1024] 

    # Instantiate the trainer object to drive the model training
    learner     = momentum_sgd(z.parameters, lr_schedule, momentum_time_constant)
    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')

    # Get minibatches of images to train with and perform model training
    max_epochs = 40
    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, "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)

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

    # Test data for trained model
    epoch_size = 10000
    minibatch_size = 1024

    # 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
Exemple #33
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def sequence_to_sequence_translator(debug_output=False, run_test=False):

    input_vocab_dim = 69
    label_vocab_dim = 69

    # network complexity; initially low for faster testing
    hidden_dim = 256
    num_layers = 1

    # Source and target inputs to the model
    batch_axis = Axis.default_batch_axis()
    input_seq_axis = Axis('inputAxis')
    label_seq_axis = Axis('labelAxis')

    input_dynamic_axes = [batch_axis, input_seq_axis]
    raw_input = input_variable(
        shape=(input_vocab_dim), dynamic_axes=input_dynamic_axes, name='raw_input')

    label_dynamic_axes = [batch_axis, label_seq_axis]
    raw_labels = input_variable(
        shape=(label_vocab_dim), dynamic_axes=label_dynamic_axes, name='raw_labels')

    # Instantiate the sequence to sequence translation model
    input_sequence = raw_input

    # Drop the sentence start token from the label, for decoder training
    label_sequence = sequence.slice(raw_labels, 1, 0) # <s> A B C </s> --> A B C </s>
    label_sentence_start = sequence.first(raw_labels)        # <s>

    is_first_label = sequence.is_first(label_sequence)       # <s> 0 0 0 ...
    label_sentence_start_scattered = sequence.scatter(
        label_sentence_start, is_first_label)

    # Encoder
    encoder_outputH = stabilize(input_sequence)
    for i in range(0, num_layers):
        (encoder_outputH, encoder_outputC) = LSTMP_component_with_self_stabilization(
            encoder_outputH.output, hidden_dim, hidden_dim, future_value, future_value)

    thought_vectorH = sequence.first(encoder_outputH)
    thought_vectorC = sequence.first(encoder_outputC)

    thought_vector_broadcastH = sequence.broadcast_as(
        thought_vectorH, label_sequence)
    thought_vector_broadcastC = sequence.broadcast_as(
        thought_vectorC, label_sequence)

    # Decoder
    decoder_history_hook = alias(label_sequence, name='decoder_history_hook') # copy label_sequence

    decoder_input = element_select(is_first_label, label_sentence_start_scattered, past_value(
        decoder_history_hook))

    decoder_outputH = stabilize(decoder_input)
    for i in range(0, num_layers):
        if (i > 0):
            recurrence_hookH = past_value
            recurrence_hookC = past_value
        else:
            isFirst = sequence.is_first(label_sequence)
            recurrence_hookH = lambda operand: element_select(
                isFirst, thought_vector_broadcastH, past_value(operand))
            recurrence_hookC = lambda operand: element_select(
                isFirst, thought_vector_broadcastC, past_value(operand))

        (decoder_outputH, encoder_outputC) = LSTMP_component_with_self_stabilization(
            decoder_outputH.output, hidden_dim, hidden_dim, recurrence_hookH, recurrence_hookC)

    decoder_output = decoder_outputH

    # Softmax output layer
    z = linear_layer(stabilize(decoder_output), label_vocab_dim)

    # Criterion nodes
    ce = cross_entropy_with_softmax(z, label_sequence)
    errs = classification_error(z, label_sequence)

    # network output for decoder history
    net_output = hardmax(z)

    # make a clone of the graph where the ground truth is replaced by the network output
    ng = z.clone(CloneMethod.share, {decoder_history_hook.output : net_output.output})

    # Instantiate the trainer object to drive the model training
    lr_per_minibatch = learning_rate_schedule(0.5, UnitType.minibatch)
    momentum_time_constant = momentum_as_time_constant_schedule(1100)
    clipping_threshold_per_sample = 2.3
    gradient_clipping_with_truncation = True
    learner = momentum_sgd(z.parameters, 
                           lr_per_minibatch, momentum_time_constant, 
                           gradient_clipping_threshold_per_sample=clipping_threshold_per_sample, 
                           gradient_clipping_with_truncation=gradient_clipping_with_truncation)
    trainer = Trainer(z, ce, errs, learner)

    # setup data
    train_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "Data", "cmudict-0.7b.train-dev-20-21.ctf")
    valid_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "Data", "tiny.ctf")

    # readers
    randomize_data = True
    if run_test:
        randomize_data = False # because we want to get an exact error

    train_reader = create_reader(train_path, randomize_data, input_vocab_dim, label_vocab_dim)
    train_bind = {
        raw_input  : train_reader.streams.features,
        raw_labels : train_reader.streams.labels
    }

    # get the vocab for printing output sequences in plaintext
    vocab_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "Data", "cmudict-0.7b.mapping")
    vocab = [w.strip() for w in open(vocab_path).readlines()]
    i2w = { i:ch for i,ch in enumerate(vocab) }

    # Get minibatches of sequences to train with and perform model training
    i = 0
    mbs = 0
    minibatch_size = 72
    epoch_size = 908241
    max_epochs = 10
    training_progress_output_freq = 500

    # make things more basic for running a quicker test
    if run_test:
        epoch_size = 5000
        max_epochs = 1
        training_progress_output_freq = 30

    valid_reader = create_reader(valid_path, False, input_vocab_dim, label_vocab_dim)
    valid_bind = {
            find_arg_by_name('raw_input',ng)  : valid_reader.streams.features,
            find_arg_by_name('raw_labels',ng) : valid_reader.streams.labels
        }

    for epoch in range(max_epochs):
        loss_numer = 0
        metric_numer = 0
        denom = 0

        while i < (epoch+1) * epoch_size:
            # get next minibatch of training data
            mb_train = train_reader.next_minibatch(minibatch_size, input_map=train_bind)
            trainer.train_minibatch(mb_train)

            # collect epoch-wide stats
            samples = trainer.previous_minibatch_sample_count
            loss_numer += trainer.previous_minibatch_loss_average * samples
            metric_numer += trainer.previous_minibatch_evaluation_average * samples
            denom += samples

            # every N MBs evaluate on a test sequence to visually show how we're doing
            if mbs % training_progress_output_freq == 0:
                mb_valid = valid_reader.next_minibatch(minibatch_size, input_map=valid_bind)
                e = ng.eval(mb_valid)
                print_sequences(e, i2w)

            print_training_progress(trainer, mbs, training_progress_output_freq)
            i += mb_train[raw_labels].num_samples
            mbs += 1

        print("--- EPOCH %d DONE: loss = %f, errs = %f ---" % (epoch, loss_numer/denom, 100.0*(metric_numer/denom)))


    error1 = translator_test_error(z, trainer, input_vocab_dim, label_vocab_dim)

    z.save_model("seq2seq.dnn")
    z.restore_model("seq2seq.dnn")

    label_seq_axis = Axis('labelAxis')
    label_sequence = sequence.slice(find_arg_by_name('raw_labels',z), 1, 0)
    ce = cross_entropy_with_softmax(z, label_sequence)
    errs = classification_error(z, label_sequence)
    trainer = Trainer(z, ce, errs, [momentum_sgd(
                    z.parameters, lr_per_minibatch, momentum_time_constant, clipping_threshold_per_sample, gradient_clipping_with_truncation)])

    error2 = translator_test_error(z, trainer, input_vocab_dim, label_vocab_dim)

    assert error1 == error2

    return error1
Exemple #34
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def convnet_cifar10(debug_output=False):
    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 = input_variable((num_channels, image_height, image_width), np.float32)
    label_var = input_variable(num_output_classes, np.float32)

    # Instantiate the feedforward classification model
    input_removemean = minus(input_var, constant(128))
    scaled_input = element_times(constant(0.00390625), input_removemean)
    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_output_classes, activation=None)
        ])(scaled_input)
    
    ce = cross_entropy_with_softmax(z, label_var)
    pe = 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

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

    # Instantiate the trainer object to drive the model training
    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')

    # Get minibatches of images to train with and perform model training
    max_epochs = 30
    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, "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
Exemple #35
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def train_and_evaluate(reader_train, reader_test, max_epochs):

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

    # Normalize the input
    feature_scale = 1.0 / 256.0
    input_var_norm = element_times(feature_scale, input_var)
   
    # apply model to input
    z = create_vgg9_model(input_var_norm, 10)

    #
    # Training action
    #

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

    # training config
    epoch_size     = 50000
    minibatch_size = 64

    # Set learning parameters
    lr_per_minibatch       = learning_rate_schedule([0.01]*10 + [0.003]*10 + [0.001], epoch_size, UnitType.minibatch)
    momentum_time_constant = momentum_as_time_constant_schedule(-minibatch_size/np.log(0.9))
    l2_reg_weight          = 0.0001

    # trainer object
    learner     = momentum_sgd(z.parameters, 
                               lr = lr_per_minibatch, momentum = momentum_time_constant,
                               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)
    
    #
    # 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

    #progress_printer = ProgressPrinter(freq=100, first=10, tag='Eval')
    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("")

    # return evaluation error.
    return metric_numer/metric_denom
def train_and_evaluate(reader_train,
                       reader_test,
                       network_name,
                       max_epochs,
                       distributed_trainer,
                       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 * (len(distributed_trainer.communicator().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 = 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)
        # save model only in worker0, otherwise there will be file write conflicts for multi GPU on the same machine
        if distributed_trainer.communicator().current_worker(
        ).global_rank == 0:
            z.save_model(
                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
Exemple #37
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def main(base_folder, training_mode='majority', model_name='VGG13', max_epochs = 100):

    # create needed folders.
    output_model_path   = os.path.join(base_folder, R'models')
    output_model_folder = os.path.join(output_model_path, model_name + '_' + training_mode)
    if not os.path.exists(output_model_folder):
        os.makedirs(output_model_folder)

    # creating logging file 
    logging.basicConfig(filename = os.path.join(output_model_folder, "train.log"), filemode = 'w', level = logging.INFO)
    logging.getLogger().addHandler(logging.StreamHandler())

    logging.info("Starting with training mode {} using {} model and max epochs {}.".format(training_mode, model_name, max_epochs))

    # create the model
    num_classes = len(emotion_table)
    model       = build_model(num_classes, model_name)

    # set the input variables.
    input_var = ct.input_variable((1, model.input_height, model.input_width), np.float32)
    label_var = ct.input_variable((num_classes), np.float32)
    
    # read FER+ dataset.
    logging.info("Loading data...")
    train_params        = FERPlusParameters(num_classes, model.input_height, model.input_width, training_mode, False)
    test_and_val_params = FERPlusParameters(num_classes, model.input_height, model.input_width, "majority", True)

    train_data_reader   = FERPlusReader.create(base_folder, train_folders, "label.csv", train_params)
    val_data_reader     = FERPlusReader.create(base_folder, valid_folders, "label.csv", test_and_val_params)
    test_data_reader    = FERPlusReader.create(base_folder, test_folders, "label.csv", test_and_val_params)
    
    # print summary of the data.
    display_summary(train_data_reader, val_data_reader, test_data_reader)
    
    # get the probalistic output of the model.
    z    = model.model(input_var)
    pred = ct.softmax(z)
    
    epoch_size     = train_data_reader.size()
    minibatch_size = 32

    # Training config
    lr_per_minibatch       = [model.learning_rate]*20 + [model.learning_rate / 2.0]*20 + [model.learning_rate / 10.0]
    mm_time_constant       = -minibatch_size/np.log(0.9)
    lr_schedule            = learning_rate_schedule(lr_per_minibatch, unit=UnitType.minibatch, epoch_size=epoch_size)
    mm_schedule            = momentum_as_time_constant_schedule(mm_time_constant)

    # loss and error cost
    train_loss = cost_func(training_mode, pred, label_var)
    pe         = classification_error(z, label_var)

    # construct the trainer
    learner = momentum_sgd(z.parameters, lr_schedule, mm_schedule)
    trainer = Trainer(z, (train_loss, pe), learner)

    # Get minibatches of images to train with and perform model training
    max_val_accuracy    = 0.0
    final_test_accuracy = 0.0
    best_test_accuracy  = 0.0

    logging.info("Start training...")
    epoch      = 0
    best_epoch = 0
    while epoch < max_epochs: 
        train_data_reader.reset()
        val_data_reader.reset()
        test_data_reader.reset()
        
        # Training 
        start_time = time.time()
        training_loss = 0
        training_accuracy = 0
        while train_data_reader.has_more():
            images, labels, current_batch_size = train_data_reader.next_minibatch(minibatch_size)

            # Specify the mapping of input variables in the model to actual minibatch data to be trained with
            trainer.train_minibatch({input_var : images, label_var : labels})

            # keep track of statistics.
            training_loss     += trainer.previous_minibatch_loss_average * current_batch_size
            training_accuracy += trainer.previous_minibatch_evaluation_average * current_batch_size
                
        training_accuracy /= train_data_reader.size()
        training_accuracy = 1.0 - training_accuracy
        
        # Validation
        val_accuracy = 0
        while val_data_reader.has_more():
            images, labels, current_batch_size = val_data_reader.next_minibatch(minibatch_size)
            val_accuracy += trainer.test_minibatch({input_var : images, label_var : labels}) * current_batch_size
            
        val_accuracy /= val_data_reader.size()
        val_accuracy = 1.0 - val_accuracy
        
        # if validation accuracy goes higher, we compute test accuracy
        test_run = False
        if val_accuracy > max_val_accuracy:
            best_epoch = epoch
            max_val_accuracy = val_accuracy

            trainer.save_checkpoint(os.path.join(output_model_folder, "model_{}".format(best_epoch)))

            test_run = True
            test_accuracy = 0
            while test_data_reader.has_more():
                images, labels, current_batch_size = test_data_reader.next_minibatch(minibatch_size)
                test_accuracy += trainer.test_minibatch({input_var : images, label_var : labels}) * current_batch_size
            
            test_accuracy /= test_data_reader.size()
            test_accuracy = 1.0 - test_accuracy
            final_test_accuracy = test_accuracy
            if final_test_accuracy > best_test_accuracy: 
                best_test_accuracy = final_test_accuracy
 
        logging.info("Epoch {}: took {:.3f}s".format(epoch, time.time() - start_time))
        logging.info("  training loss:\t{:e}".format(training_loss))
        logging.info("  training accuracy:\t\t{:.2f} %".format(training_accuracy * 100))
        logging.info("  validation accuracy:\t\t{:.2f} %".format(val_accuracy * 100))
        if test_run:
            logging.info("  test accuracy:\t\t{:.2f} %".format(test_accuracy * 100))
            
        epoch += 1

    logging.info("")
    logging.info("Best validation accuracy:\t\t{:.2f} %, epoch {}".format(max_val_accuracy * 100, best_epoch))
    logging.info("Test accuracy corresponding to best validation:\t\t{:.2f} %".format(final_test_accuracy * 100))
    logging.info("Best test accuracy:\t\t{:.2f} %".format(best_test_accuracy * 100))
def cifar_resnet_distributed(data_path,
                             run_test,
                             num_epochs,
                             communicator=None,
                             save_model_filename=None,
                             load_model_filename=None,
                             debug_output=False):
    image_height = 32
    image_width = 32
    num_channels = 3
    num_classes = 10

    feats_stream_name = 'features'
    labels_stream_name = 'labels'

    minibatch_source = create_reader(os.path.join(data_path, 'train_map.txt'),
                                     os.path.join(data_path,
                                                  'CIFAR-10_mean.xml'),
                                     True,
                                     distributed_communicator=communicator)

    features_si = minibatch_source[feats_stream_name]
    labels_si = minibatch_source[labels_stream_name]

    # Instantiate the resnet classification model, or load from file

    if load_model_filename:
        print("Loading model:", load_model_filename)
        classifier_output = persist.load_model(load_model_filename)
        image_input = classifier_output.arguments[0]
    else:
        image_input = input_variable((num_channels, image_height, image_width),
                                     features_si.m_element_type)
        classifier_output = create_resnet_model(image_input, num_classes)

    # Input variables denoting the features and label data
    label_var = input_variable((num_classes), features_si.m_element_type)

    ce = cross_entropy_with_softmax(classifier_output, label_var)
    pe = classification_error(classifier_output, label_var)

    # Instantiate the trainer object to drive the model training

    mb_size = 128
    num_mb_per_epoch = 100

    num_mbs = num_mb_per_epoch * num_epochs

    lr_schedule = [1.0 / mb_size] * 80 + [0.1 / mb_size] * 40 + [
        0.01 / mb_size
    ]
    lr_per_minibatch = learning_rate_schedule(lr_schedule, UnitType.minibatch,
                                              mb_size * num_mb_per_epoch)
    momentum_time_constant = momentum_as_time_constant_schedule(-mb_size /
                                                                np.log(0.9))

    # create data parallel distributed trainer if needed
    dist_trainer = distributed.data_parallel_distributed_trainer(
        communicator, False) if communicator else None

    # Instantiate the trainer object to drive the model training
    trainer = Trainer(classifier_output,
                      ce,
                      pe, [
                          momentum_sgd(classifier_output.parameters,
                                       lr=lr_per_minibatch,
                                       momentum=momentum_time_constant,
                                       l2_regularization_weight=0.0001)
                      ],
                      distributed_trainer=dist_trainer)

    # Get minibatches of images to train with and perform model training
    training_progress_output_freq = 100 if communicator else 20

    if debug_output:
        training_progress_output_freq = training_progress_output_freq / 4

    for i in range(0, num_mbs):

        # NOTE: depends on network, the mb_size can be changed dynamically here
        mb = minibatch_source.next_minibatch(mb_size)

        # Specify the mapping of input variables in the model to actual
        # minibatch data to be trained with
        arguments = {image_input: mb[features_si], label_var: mb[labels_si]}
        trainer.train_minibatch(arguments)

        print_training_progress(trainer, i, training_progress_output_freq)

    if save_model_filename:
        print("Saving model:", save_model_filename)
        persist.save_model(classifier_output, save_model_filename)

    if run_test:
        test_minibatch_source = create_reader(
            os.path.join(data_path, 'test_map.txt'),
            os.path.join(data_path, 'CIFAR-10_mean.xml'), False)
        features_si = test_minibatch_source[feats_stream_name]
        labels_si = test_minibatch_source[labels_stream_name]

        mb_size = 128
        num_mbs = 100

        total_error = 0.0
        for i in range(0, num_mbs):
            mb = test_minibatch_source.next_minibatch(mb_size)

            # Specify the mapping of input variables in the model to actual
            # minibatch data to be trained with
            arguments = {
                image_input: mb[features_si],
                label_var: mb[labels_si]
            }
            error = trainer.test_minibatch(arguments)
            total_error += error

        return total_error / num_mbs
    else:
        return 0
Exemple #39
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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)
    momentum_time_constant = [0] * 20 + [600] * 20 + [1200]
    mm_schedule = momentum_as_time_constant_schedule(momentum_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
Exemple #40
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def sequence_to_sequence_translator(debug_output=False):

    input_vocab_dim = 69
    label_vocab_dim = 69

    hidden_dim = 512
    num_layers = 2

    # Source and target inputs to the model
    batch_axis = Axis.default_batch_axis()
    input_seq_axis = Axis("inputAxis")
    label_seq_axis = Axis("labelAxis")

    input_dynamic_axes = [batch_axis, input_seq_axis]
    raw_input = input_variable(shape=(input_vocab_dim), dynamic_axes=input_dynamic_axes)

    label_dynamic_axes = [batch_axis, label_seq_axis]
    raw_labels = input_variable(shape=(label_vocab_dim), dynamic_axes=label_dynamic_axes)

    # Instantiate the sequence to sequence translation model
    input_sequence = raw_input

    # Drop the sentence start token from the label, for decoder training
    label_sequence = slice(raw_labels, label_seq_axis, 1, 0)
    label_sentence_start = sequence.first(raw_labels)

    is_first_label = sequence.is_first(label_sequence)
    label_sentence_start_scattered = sequence.scatter(label_sentence_start, is_first_label)

    # Encoder
    encoder_outputH = stabilize(input_sequence)
    for i in range(0, num_layers):
        (encoder_outputH, encoder_outputC) = LSTMP_component_with_self_stabilization(
            encoder_outputH.output(), hidden_dim, hidden_dim, future_value, future_value
        )

    thought_vectorH = sequence.first(encoder_outputH)
    thought_vectorC = sequence.first(encoder_outputC)

    thought_vector_broadcastH = sequence.broadcast_as(thought_vectorH, label_sequence)
    thought_vector_broadcastC = sequence.broadcast_as(thought_vectorC, label_sequence)

    # Decoder
    decoder_history_from_ground_truth = label_sequence
    decoder_input = element_select(
        is_first_label, label_sentence_start_scattered, past_value(decoder_history_from_ground_truth)
    )

    decoder_outputH = stabilize(decoder_input)
    for i in range(0, num_layers):
        if i > 0:
            recurrence_hookH = past_value
            recurrence_hookC = past_value
        else:
            isFirst = sequence.is_first(label_sequence)
            recurrence_hookH = lambda operand: element_select(isFirst, thought_vector_broadcastH, past_value(operand))
            recurrence_hookC = lambda operand: element_select(isFirst, thought_vector_broadcastC, past_value(operand))

        (decoder_outputH, encoder_outputC) = LSTMP_component_with_self_stabilization(
            decoder_outputH.output(), hidden_dim, hidden_dim, recurrence_hookH, recurrence_hookC
        )

    decoder_output = decoder_outputH
    decoder_dim = hidden_dim

    # Softmax output layer
    z = linear_layer(stabilize(decoder_output), label_vocab_dim)
    ce = cross_entropy_with_softmax(z, label_sequence)
    errs = classification_error(z, label_sequence)

    # Instantiate the trainer object to drive the model training
    lr = 0.007
    momentum_time_constant = 1100
    momentum_per_sample = momentums_per_sample(math.exp(-1.0 / momentum_time_constant))
    clipping_threshold_per_sample = 2.3
    gradient_clipping_with_truncation = True

    trainer = Trainer(
        z,
        ce,
        errs,
        [
            momentum_sgd(
                z.parameters(),
                lr,
                momentum_per_sample,
                clipping_threshold_per_sample,
                gradient_clipping_with_truncation,
            )
        ],
    )

    rel_path = r"../../../../Examples/SequenceToSequence/CMUDict/Data/cmudict-0.7b.train-dev-20-21.ctf"
    path = os.path.join(os.path.dirname(os.path.abspath(__file__)), rel_path)
    feature_stream_name = "features"
    labels_stream_name = "labels"

    mb_source = text_format_minibatch_source(
        path,
        [
            StreamConfiguration(feature_stream_name, input_vocab_dim, True, "S0"),
            StreamConfiguration(labels_stream_name, label_vocab_dim, True, "S1"),
        ],
        10000,
    )
    features_si = mb_source[feature_stream_name]
    labels_si = mb_source[labels_stream_name]

    # Get minibatches of sequences to train with and perform model training
    minibatch_size = 72
    training_progress_output_freq = 30
    if debug_output:
        training_progress_output_freq = training_progress_output_freq / 3

    while True:
        mb = mb_source.get_next_minibatch(minibatch_size)
        if len(mb) == 0:
            break

        # Specify the mapping of input variables in the model to actual
        # minibatch data to be trained with
        arguments = {raw_input: mb[features_si], raw_labels: mb[labels_si]}
        trainer.train_minibatch(arguments)

        print_training_progress(trainer, i, training_progress_output_freq)
        i += 1

    rel_path = r"../../../../Examples/SequenceToSequence/CMUDict/Data/cmudict-0.7b.test.ctf"
    path = os.path.join(os.path.dirname(os.path.abspath(__file__)), rel_path)

    test_mb_source = text_format_minibatch_source(
        path,
        [
            StreamConfiguration(feature_stream_name, input_vocab_dim, True, "S0"),
            StreamConfiguration(labels_stream_name, label_vocab_dim, True, "S1"),
        ],
        10000,
        False,
    )
    features_si = test_mb_source[feature_stream_name]
    labels_si = test_mb_source[labels_stream_name]

    # choose this to be big enough for the longest sentence
    train_minibatch_size = 1024

    # Get minibatches of sequences to test and perform testing
    i = 0
    total_error = 0.0
    while True:
        mb = test_mb_source.get_next_minibatch(train_minibatch_size)
        if len(mb) == 0:
            break

        # Specify the mapping of input variables in the model to actual
        # minibatch data to be tested with
        arguments = {raw_input: mb[features_si], raw_labels: mb[labels_si]}
        mb_error = trainer.test_minibatch(arguments)

        total_error += mb_error

        if debug_output:
            print("Minibatch {}, Error {} ".format(i, mb_error))

        i += 1

    # Average of evaluation errors of all test minibatches
    return total_error / i
Exemple #41
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def convnet_mnist(debug_output=False):
    image_height = 28
    image_width = 28
    num_channels = 1
    input_dim = image_height * image_width * num_channels
    num_output_classes = 10

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

    # Instantiate the feedforward classification model
    scaled_input = element_times(constant(0.00390625), input_var)
    with default_options(activation=relu, pad=False):
        conv1 = Convolution((5, 5), 32, pad=True)(scaled_input)
        pool1 = MaxPooling((3, 3), (2, 2))(conv1)
        conv2 = Convolution((3, 3), 48)(pool1)
        pool2 = MaxPooling((3, 3), (2, 2))(conv2)
        conv3 = Convolution((3, 3), 64)(pool2)
        f4 = Dense(96)(conv3)
        drop4 = Dropout(0.5)(f4)
        z = Dense(num_output_classes, activation=None)(drop4)

    ce = cross_entropy_with_softmax(z, label_var)
    pe = classification_error(z, label_var)

    reader_train = create_reader(
        os.path.join(data_path, 'Train-28x28_cntk_text.txt'), True, input_dim,
        num_output_classes)

    # training config
    epoch_size = 60000  # for now we manually specify epoch size
    minibatch_size = 128

    # Set learning parameters
    lr_per_sample = [0.001] * 10 + [0.0005] * 10 + [0.0001]
    lr_schedule = learning_rate_schedule(lr_per_sample, UnitType.sample,
                                         epoch_size)
    mm_time_constant = [0] * 5 + [1024]
    mm_schedule = momentum_as_time_constant_schedule(mm_time_constant,
                                                     epoch_size)

    # Instantiate the trainer object to drive the model training
    learner = momentum_sgd(z.parameters, lr_schedule, mm_schedule)
    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')

    # Get minibatches of images to train with and perform model training
    max_epochs = 40
    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)
        z.save_model(
            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)

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

    # Test data for trained model
    epoch_size = 10000
    minibatch_size = 1024

    # 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
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,
                     unit_gain=True,
                     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