def ffnet(): input_dim = 2 num_output_classes = 2 num_hidden_layers = 2 hidden_layers_dim = 50 # Input variables denoting the features and label data input = input_variable((input_dim), np.float32) label = input_variable((num_output_classes), np.float32) # Instantiate the feedforward classification model netout = fully_connected_classifier_net(input, num_output_classes, hidden_layers_dim, num_hidden_layers, sigmoid) ce = cross_entropy_with_softmax(netout, label) pe = classification_error(netout, label) lr_per_minibatch = learning_rate_schedule(0.5, UnitType.minibatch) # Instantiate the trainer object to drive the model training learner = sgd(netout.parameters, lr=lr_per_minibatch) progress_printer = ProgressPrinter(128) trainer = Trainer(netout, (ce, pe), learner, progress_printer) # Get minibatches of training data and perform model training minibatch_size = 25 for i in range(1024): features, labels = generate_random_data(minibatch_size, input_dim, num_output_classes) # Specify the mapping of input variables in the model to actual # minibatch data to be trained with trainer.train_minibatch({input: features, label: labels}) trainer.summarize_training_progress() test_features, test_labels = generate_random_data(minibatch_size, input_dim, num_output_classes) avg_error = trainer.test_minibatch({ input: test_features, label: test_labels }) return avg_error
def ffnet(): input_dim = 2 num_output_classes = 2 num_hidden_layers = 2 hidden_layers_dim = 50 # Input variables denoting the features and label data input = input_variable((input_dim), np.float32) label = input_variable((num_output_classes), np.float32) # Instantiate the feedforward classification model netout = fully_connected_classifier_net( input, num_output_classes, hidden_layers_dim, num_hidden_layers, sigmoid) ce = cross_entropy_with_softmax(netout, label) pe = classification_error(netout, label) lr_per_minibatch=learning_rate_schedule(0.5, UnitType.minibatch) # Instantiate the trainer object to drive the model training learner = sgd(netout.parameters, lr=lr_per_minibatch) progress_printer = ProgressPrinter(128) trainer = Trainer(netout, (ce, pe), learner, progress_printer) # Get minibatches of training data and perform model training minibatch_size = 25 for i in range(1024): features, labels = generate_random_data( minibatch_size, input_dim, num_output_classes) # Specify the mapping of input variables in the model to actual # minibatch data to be trained with trainer.train_minibatch({input: features, label: labels}) trainer.summarize_training_progress() test_features, test_labels = generate_random_data( minibatch_size, input_dim, num_output_classes) avg_error = trainer.test_minibatch( {input: test_features, label: test_labels}) return avg_error
def simple_mnist(tensorboard_logdir=None): input_dim = 784 num_output_classes = 10 num_hidden_layers = 1 hidden_layers_dim = 200 # Input variables denoting the features and label data feature = input(input_dim, np.float32) label = input(num_output_classes, np.float32) # Instantiate the feedforward classification model scaled_input = element_times(constant(0.00390625), feature) z = fully_connected_classifier_net(scaled_input, num_output_classes, hidden_layers_dim, num_hidden_layers, relu) ce = cross_entropy_with_softmax(z, label) pe = classification_error(z, label) data_dir = os.path.join(abs_path, "..", "..", "..", "DataSets", "MNIST") path = os.path.normpath(os.path.join(data_dir, "Train-28x28_cntk_text.txt")) check_path(path) reader_train = create_reader(path, True, input_dim, num_output_classes) input_map = { feature: reader_train.streams.features, label: reader_train.streams.labels } # Training config minibatch_size = 64 num_samples_per_sweep = 60000 num_sweeps_to_train_with = 10 # Instantiate progress writers. #training_progress_output_freq = 100 progress_writers = [ ProgressPrinter( #freq=training_progress_output_freq, tag='Training', num_epochs=num_sweeps_to_train_with) ] if tensorboard_logdir is not None: progress_writers.append( TensorBoardProgressWriter(freq=10, log_dir=tensorboard_logdir, model=z)) # Instantiate the trainer object to drive the model training trainer = Trainer(z, (ce, pe), adadelta(z.parameters), progress_writers) training_session(trainer=trainer, mb_source=reader_train, mb_size=minibatch_size, var_to_stream=input_map, max_samples=num_samples_per_sweep * num_sweeps_to_train_with, progress_frequency=num_samples_per_sweep).train() # Load test data path = os.path.normpath(os.path.join(data_dir, "Test-28x28_cntk_text.txt")) check_path(path) reader_test = create_reader(path, False, input_dim, num_output_classes) input_map = { feature: reader_test.streams.features, label: reader_test.streams.labels } # Test data for trained model test_minibatch_size = 1024 num_samples = 10000 num_minibatches_to_test = num_samples / test_minibatch_size test_result = 0.0 for i in range(0, int(num_minibatches_to_test)): mb = reader_test.next_minibatch(test_minibatch_size, input_map=input_map) eval_error = trainer.test_minibatch(mb) test_result = test_result + eval_error # Average of evaluation errors of all test minibatches return test_result / num_minibatches_to_test
def simple_mnist(debug_output=False): input_dim = 784 num_output_classes = 10 num_hidden_layers = 1 hidden_layers_dim = 200 # Input variables denoting the features and label data input = input_variable(input_dim, np.float32) label = input_variable(num_output_classes, np.float32) # Instantiate the feedforward classification model scaled_input = element_times(constant(0.00390625), input) z = fully_connected_classifier_net( scaled_input, num_output_classes, hidden_layers_dim, num_hidden_layers, relu) ce = cross_entropy_with_softmax(z, label) pe = classification_error(z, label) data_dir = os.path.join(abs_path, "..", "..", "..", "DataSets", "MNIST") path = os.path.normpath(os.path.join(data_dir, "Train-28x28_cntk_text.txt")) check_path(path) reader_train = create_reader(path, True, input_dim, num_output_classes) input_map = { input : reader_train.streams.features, label : reader_train.streams.labels } lr_per_minibatch=learning_rate_schedule(0.2, UnitType.minibatch) # Instantiate the trainer object to drive the model training trainer = Trainer(z, ce, pe, sgd(z.parameters, lr=lr_per_minibatch)) # Get minibatches of images to train with and perform model training minibatch_size = 64 num_samples_per_sweep = 60000 num_sweeps_to_train_with = 10 num_minibatches_to_train = (num_samples_per_sweep * num_sweeps_to_train_with) / minibatch_size training_progress_output_freq = 500 if debug_output: training_progress_output_freq = training_progress_output_freq/4 for i in range(0, int(num_minibatches_to_train)): mb = reader_train.next_minibatch(minibatch_size, input_map=input_map) trainer.train_minibatch(mb) print_training_progress(trainer, i, training_progress_output_freq) # Load test data path = os.path.normpath(os.path.join(data_dir, "Test-28x28_cntk_text.txt")) check_path(path) reader_test = create_reader(path, False, input_dim, num_output_classes) input_map = { input : reader_test.streams.features, label : reader_test.streams.labels } # Test data for trained model test_minibatch_size = 1024 num_samples = 10000 num_minibatches_to_test = num_samples / test_minibatch_size test_result = 0.0 for i in range(0, int(num_minibatches_to_test)): mb = reader_test.next_minibatch(test_minibatch_size, input_map=input_map) eval_error = trainer.test_minibatch(mb) test_result = test_result + eval_error # Average of evaluation errors of all test minibatches return test_result / num_minibatches_to_test
def simple_mnist(tensorboard_logdir=None): input_dim = 784 num_output_classes = 10 num_hidden_layers = 1 hidden_layers_dim = 200 # Input variables denoting the features and label data input = input_variable(input_dim, np.float32) label = input_variable(num_output_classes, np.float32) # Instantiate the feedforward classification model scaled_input = element_times(constant(0.00390625), input) z = fully_connected_classifier_net( scaled_input, num_output_classes, hidden_layers_dim, num_hidden_layers, relu) ce = cross_entropy_with_softmax(z, label) pe = classification_error(z, label) data_dir = os.path.join(abs_path, "..", "..", "..", "DataSets", "MNIST") path = os.path.normpath(os.path.join(data_dir, "Train-28x28_cntk_text.txt")) check_path(path) reader_train = create_reader(path, True, input_dim, num_output_classes) input_map = { input : reader_train.streams.features, label : reader_train.streams.labels } # Training config minibatch_size = 64 num_samples_per_sweep = 60000 num_sweeps_to_train_with = 10 # Instantiate progress writers. #training_progress_output_freq = 100 progress_writers = [ProgressPrinter( #freq=training_progress_output_freq, tag='Training', num_epochs=num_sweeps_to_train_with)] if tensorboard_logdir is not None: progress_writers.append(TensorBoardProgressWriter(freq=10, log_dir=tensorboard_logdir, model=z)) # Instantiate the trainer object to drive the model training lr_per_minibatch = learning_rate_schedule(0.2, UnitType.minibatch) trainer = Trainer(z, (ce, pe), sgd(z.parameters, lr=lr_per_minibatch), progress_writers) training_session( trainer=trainer, mb_source = reader_train, mb_size = minibatch_size, var_to_stream = input_map, max_samples = num_samples_per_sweep * num_sweeps_to_train_with, progress_frequency=num_samples_per_sweep ).train() # Load test data path = os.path.normpath(os.path.join(data_dir, "Test-28x28_cntk_text.txt")) check_path(path) reader_test = create_reader(path, False, input_dim, num_output_classes) input_map = { input : reader_test.streams.features, label : reader_test.streams.labels } # Test data for trained model test_minibatch_size = 1024 num_samples = 10000 num_minibatches_to_test = num_samples / test_minibatch_size test_result = 0.0 for i in range(0, int(num_minibatches_to_test)): mb = reader_test.next_minibatch(test_minibatch_size, input_map=input_map) eval_error = trainer.test_minibatch(mb) test_result = test_result + eval_error # Average of evaluation errors of all test minibatches return test_result / num_minibatches_to_test
def simple_mnist(debug_output=False): input_dim = 784 num_output_classes = 10 num_hidden_layers = 1 hidden_layers_dim = 200 # Input variables denoting the features and label data input = input_variable(input_dim, np.float32) label = input_variable(num_output_classes, np.float32) # Instantiate the feedforward classification model scaled_input = element_times(constant(0.00390625), input) z = fully_connected_classifier_net( scaled_input, num_output_classes, hidden_layers_dim, num_hidden_layers, relu) ce = cross_entropy_with_softmax(z, label) pe = classification_error(z, label) try: rel_path = os.path.join(os.environ['CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY'], *"Image/MNIST/v0/Train-28x28_cntk_text.txt".split("/")) except KeyError: rel_path = os.path.join(abs_path, "..", "..", "..", "..", "..", "Examples", "Image", "DataSets", "MNIST", "Train-28x28_cntk_text.txt") path = os.path.normpath(os.path.join(abs_path, rel_path)) check_path(path) reader_train = create_reader(path, True, input_dim, num_output_classes) input_map = { input : reader_train.streams.features, label : reader_train.streams.labels } lr_per_minibatch=learning_rate_schedule(0.2, UnitType.minibatch) # Instantiate the trainer object to drive the model training trainer = Trainer(z, ce, pe, sgd(z.parameters, lr=lr_per_minibatch)) # Get minibatches of images to train with and perform model training minibatch_size = 64 num_samples_per_sweep = 60000 num_sweeps_to_train_with = 10 num_minibatches_to_train = (num_samples_per_sweep * num_sweeps_to_train_with) / minibatch_size training_progress_output_freq = 500 if debug_output: training_progress_output_freq = training_progress_output_freq/4 for i in range(0, int(num_minibatches_to_train)): mb = reader_train.next_minibatch(minibatch_size, input_map=input_map) trainer.train_minibatch(mb) print_training_progress(trainer, i, training_progress_output_freq) # Load test data try: rel_path = os.path.join(os.environ['CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY'], *"Image/MNIST/v0/Test-28x28_cntk_text.txt".split("/")) except KeyError: rel_path = os.path.join(abs_path, "..", "..", "..", "..", "..", "Examples", "Image", "DataSets", "MNIST", "Test-28x28_cntk_text.txt") path = os.path.normpath(os.path.join(abs_path, rel_path)) check_path(path) reader_test = create_reader(path, False, input_dim, num_output_classes) input_map = { input : reader_test.streams.features, label : reader_test.streams.labels } # Test data for trained model test_minibatch_size = 1024 num_samples = 10000 num_minibatches_to_test = num_samples / test_minibatch_size test_result = 0.0 for i in range(0, int(num_minibatches_to_test)): mb = reader_test.next_minibatch(test_minibatch_size, input_map=input_map) eval_error = trainer.test_minibatch(mb) test_result = test_result + eval_error # Average of evaluation errors of all test minibatches return test_result / num_minibatches_to_test