def compute_q_targets(post_states, rewards, terminals): return element_select( terminals, rewards, gamma * reduce_max(self._target_net(post_states), axis=0) + rewards, )
def simple_mnist(): input_dim = 784 num_output_classes = 10 num_hidden_layers = 1 hidden_layers_dim = 200 # Input variables denoting the features and label data features = 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), features) netout = fully_connected_classifier_net( scaled_input, num_output_classes, hidden_layers_dim, num_hidden_layers, relu) ce = cross_entropy_with_softmax(netout, label) pe = classification_error(netout, 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(*"../Image/DataSets/MNIST/Train-28x28_cntk_text.txt".split("/")) 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 = { features: reader_train.streams.features, label: reader_train.streams.labels } # Instantiate progress writers. logdir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "mnist_log") tensorboard_writer = TensorBoardProgressWriter(freq=1, log_dir=logdir, model=netout) progress_printer = ProgressPrinter(freq=10, tag='Training') # Instantiate the trainer object to drive the model training lr_per_minibatch = learning_rate_schedule(0.2, UnitType.minibatch) learner = sgd(netout.parameters, lr=lr_per_minibatch) trainer = Trainer(netout, (ce, pe), learner, [tensorboard_writer, progress_printer]) # Get minibatches of images to train with and perform model training minibatch_size = 64 num_samples_per_sweep = 6000 num_sweeps_to_train_with = 2 num_minibatches_to_train = (num_samples_per_sweep * num_sweeps_to_train_with) / minibatch_size for minibatch_idx in range(0, int(num_minibatches_to_train)): trainer.train_minibatch(reader_train.next_minibatch(minibatch_size, input_map=input_map)) # Log max/min/mean of each parameter tensor, so that we can confirm that the parameters change indeed. # Don't want to do that very often though, otherwise will spend too much time computing min/max/mean. if minibatch_idx % 10 == 9: for p in netout.parameters: tensorboard_writer.write_value(p.uid + "/max", reduce_max(p).eval(), minibatch_idx) tensorboard_writer.write_value(p.uid + "/min", reduce_min(p).eval(), minibatch_idx) tensorboard_writer.write_value(p.uid + "/mean", reduce_mean(p).eval(), minibatch_idx) trainer.summarize_training_progress() # 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(*"../Image/DataSets/MNIST/Test-28x28_cntk_text.txt".split("/")) 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 = { features: 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) test_result += trainer.test_minibatch(mb) # Average of evaluation errors of all test minibatches trainer.summarize_test_progress() return test_result / num_minibatches_to_test
def simple_mnist(): input_dim = 784 num_output_classes = 10 num_hidden_layers = 1 hidden_layers_dim = 200 # Input variables denoting the features and label data features = 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), features) netout = fully_connected_classifier_net(scaled_input, num_output_classes, hidden_layers_dim, num_hidden_layers, relu) ce = cross_entropy_with_softmax(netout, label) pe = classification_error(netout, 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( *"../Image/DataSets/MNIST/Train-28x28_cntk_text.txt".split("/")) 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 = { features: reader_train.streams.features, label: reader_train.streams.labels } # Instantiate progress writers. logdir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "mnist_log") tensorboard_writer = TensorBoardProgressWriter(freq=1, log_dir=logdir, model=netout) progress_printer = ProgressPrinter(freq=10, tag='Training') # Instantiate the trainer object to drive the model training lr_per_minibatch = learning_rate_schedule(0.2, UnitType.minibatch) learner = sgd(netout.parameters, lr=lr_per_minibatch) trainer = Trainer(netout, (ce, pe), learner, [tensorboard_writer, progress_printer]) # Get minibatches of images to train with and perform model training minibatch_size = 64 num_samples_per_sweep = 6000 num_sweeps_to_train_with = 2 num_minibatches_to_train = (num_samples_per_sweep * num_sweeps_to_train_with) / minibatch_size for minibatch_idx in range(0, int(num_minibatches_to_train)): trainer.train_minibatch( reader_train.next_minibatch(minibatch_size, input_map=input_map)) # Log max/min/mean of each parameter tensor, so that we can confirm that the parameters change indeed. # Don't want to do that very often though, otherwise will spend too much time computing min/max/mean. if minibatch_idx % 10 == 9: for p in netout.parameters: tensorboard_writer.write_value(p.uid + "/max", reduce_max(p).eval(), minibatch_idx) tensorboard_writer.write_value(p.uid + "/min", reduce_min(p).eval(), minibatch_idx) tensorboard_writer.write_value(p.uid + "/mean", reduce_mean(p).eval(), minibatch_idx) trainer.summarize_training_progress() # 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( *"../Image/DataSets/MNIST/Test-28x28_cntk_text.txt".split("/")) 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 = { features: 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) test_result += trainer.test_minibatch(mb) # Average of evaluation errors of all test minibatches trainer.summarize_test_progress() return test_result / num_minibatches_to_test