Softmax, Tanh, LookupTable) from ngraph.frontends.neon import UniformInit, RMSProp from ngraph.frontends.neon import ax, loop_train, make_bound_computation, make_default_callbacks from ngraph.frontends.neon import NgraphArgparser from ngraph.frontends.neon import ArrayIterator import ngraph.transformers as ngt from imdb import IMDB # parse the command line arguments parser = NgraphArgparser(__doc__) parser.add_argument('--layer_type', default='rnn', choices=['rnn', 'birnn'], help='type of recurrent layer to use (rnn or birnn)') parser.set_defaults(gen_be=False) args = parser.parse_args() # these hyperparameters are from the paper args.batch_size = 128 time_steps = 128 hidden_size = 10 gradient_clip_value = 15 embed_size = 128 vocab_size = 20000 pad_idx = 0 # download IMDB imdb_dataset = IMDB(path=args.data_dir, sentence_length=time_steps, pad_idx=pad_idx)
# parse the command line arguments parser = NgraphArgparser(__doc__) parser.add_argument('--data_path', help='enter path for training data', type=str) parser.add_argument('--gpu_id', default="0", help='enter gpu id', type=str,action=check_size(0,10)) parser.add_argument('--max_para_req', default=100, help='enter the max length of paragraph', type=int, action=check_size(30,300)) parser.add_argument('--batch_size_squad',default=16, help='enter the batch size', type=int, action=check_size(1,256)) parser.set_defaults() args = parser.parse_args() os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id hidden_size = 150 gradient_clip_value = 15 embed_size = 300 params_dict = {} params_dict['batch_size'] = args.batch_size_squad params_dict['embed_size'] = 300 params_dict['pad_idx'] = 0 params_dict['hs'] = hidden_size params_dict['glove_dim'] = 300 params_dict['iter_interval'] = 8000
import ngraph as ng from ngraph.frontends.neon import (Layer, Sequential, Preprocess, LSTM, Affine, Softmax, Tanh, Logistic) from ngraph.frontends.neon import UniformInit, RMSProp from ngraph.frontends.neon import ax, loop_train from ngraph.frontends.neon import NgraphArgparser, make_bound_computation, make_default_callbacks from ngraph.frontends.neon import SequentialArrayIterator import ngraph.transformers as ngt from ngraph.frontends.neon import PTB # parse the command line arguments parser = NgraphArgparser(__doc__) parser.add_argument('--layer_type', default='lstm', choices=['lstm'], help='type of recurrent layer to use (lstm)') parser.set_defaults() args = parser.parse_args() # these hyperparameters are from the paper args.batch_size = 64 time_steps = 50 hidden_size = 128 gradient_clip_value = 5 # download penn treebank tree_bank_data = PTB(path=args.data_dir) ptb_data = tree_bank_data.load_data() train_set = SequentialArrayIterator(ptb_data['train'], batch_size=args.batch_size, time_steps=time_steps, total_iterations=args.num_iterations) valid_set = SequentialArrayIterator(ptb_data['valid'], batch_size=args.batch_size,
import ngraph.transformers as ngt from tqdm import tqdm from contextlib import closing from ngraph.frontends.neon import NgraphArgparser, ArrayIterator from ngraph.frontends.neon import GaussianInit, UniformInit from ngraph.frontends.neon import Affine, Convolution, Pooling, Sequential from ngraph.frontends.neon import Rectlin, Softmax, GradientDescentMomentum from ngraph.frontends.neon import ax np.seterr(all='raise') parser = NgraphArgparser( description='Train convnet-vgg_a model on random dataset') # Default batch_size for convnet-vgg_a is 64 parser.set_defaults(batch_size=64, num_iterations=100) args = parser.parse_args() # Setup data provider image_size = 224 X_train = np.random.uniform(-1, 1, (args.batch_size, 3, image_size, image_size)) y_train = np.ones(shape=(args.batch_size), dtype=np.int32) train_data = { 'image': { 'data': X_train, 'axes': ('N', 'C', 'H', 'W') }, 'label': { 'data': y_train, 'axes': ('N', )
return placeholders parser = NgraphArgparser(description=__doc__) parser.add_argument("--learning_rate", type=float, default=0.01, help="Learning rate") parser.add_argument("--epochs", type=int, default=41, help="Number of epochs") parser.add_argument( "--deep_parameters", default='100,50', type=str, help= "Comma separated list of hidden neurons on the deep section of the model") parser.set_defaults(batch_size=40) args = parser.parse_args() fc_layers_deep = [int(s) for s in args.deep_parameters.split(',')] cs_loader = data.CensusDataset(args.batch_size) inputs = make_placeholders(args.batch_size, cs_loader) model = WideDeepClassifier(cs_loader.parameters['dimensions_embeddings'], cs_loader.parameters['tokens_in_embeddings'], fc_layers_deep, deep_activation_fn=Rectlin()) wide_deep = model(args.batch_size, inputs)
dest='mini', action='store_true', help='If given, builds a mini version of Inceptionv3') parser.add_argument("--image_dir", default='/dataset/aeon/I1K/i1k-extracted/', help="Path to extracted imagenet data") parser.add_argument("--train_manifest_file", default='train-index-tabbed.csv', help="Name of tab separated Aeon training manifest file") parser.add_argument("--valid_manifest_file", default='val-index-tabbed.csv', help="Name of tab separated Aeon validation manifest file") parser.add_argument("--optimizer_name", default='rmsprop', help="Name of optimizer (rmsprop or sgd)") parser.set_defaults(batch_size=4, num_iterations=10000000, iter_interval=2000) args = parser.parse_args() # Set the random seed np.random.seed(1) # Number of outputs of last layer. ax.Y.length = 1000 ax.N.length = args.batch_size # Build AEON data loader objects train_set, valid_set = make_aeon_loaders( train_manifest=args.train_manifest_file, valid_manifest=args.valid_manifest_file, batch_size=args.batch_size, train_iterations=args.num_iterations, dataset='i1k',
'--restore', default=False, action='store_true', help='Restore weights if found.') parser.add_argument( '--interactive', default=False, action='store_true', help='enable interactive mode at the end of training.') parser.add_argument( '--test', default=False, action='store_true', help='evaluate on the test set at the end of training.') parser.set_defaults(batch_size=32, epochs=200) args = parser.parse_args() validate((args.emb_size, int, 1, 10000), (args.eps, float, 1e-15, 1e-2), (args.lr, float, 1e-8, 10), (args.grad_clip_norm, float, 1e-3, 1e5)) # Validate inputs validate_parent_exists(args.log_file) log_file = args.log_file validate_parent_exists(args.weights_save_path) weights_save_path = args.weights_save_path validate_parent_exists(args.data_dir) data_dir = args.data_dir assert weights_save_path.endswith('.npz')
parser = NgraphArgparser(__doc__) parser.add_argument( '--use_embedding', default=False, dest='use_embedding', action='store_true', help='If given, embedding layer is used as the first layer') parser.add_argument('--seq_len', type=int, help="Number of time points in each input sequence", default=32) parser.add_argument('--recurrent_units', type=int, help="Number of recurrent units in the network", default=256) parser.set_defaults(num_iterations=20000) args = parser.parse_args() use_embedding = args.use_embedding recurrent_units = args.recurrent_units batch_size = args.batch_size seq_len = args.seq_len num_iterations = args.num_iterations # Ratio of the text to use for training train_ratio = 0.95 # Define initialization method of neurons in the network init_uni = UniformInit(-0.1, 0.1) # Create the object that includes the sample text shakes = Shakespeare(train_split=train_ratio)
import numpy as np from contextlib import closing import ngraph as ng from ngraph.frontends.neon import Layer, Preprocess, Recurrent, Affine, Softmax, Tanh from ngraph.frontends.neon import UniformInit, RMSProp from ngraph.frontends.neon import ax, loop_train from ngraph.frontends.neon import NgraphArgparser, make_bound_computation, make_default_callbacks from ngraph.frontends.neon import SequentialArrayIterator import ngraph.transformers as ngt from ngraph.frontends.neon import PTB # parse the command line arguments parser = NgraphArgparser(__doc__) parser.set_defaults(batch_size=128, num_iterations=2000) args = parser.parse_args() # model parameters time_steps = 5 hidden_size = 256 gradient_clip_value = 5 # download penn treebank # set shift_target to be False, since it is going to predict the same sequence tree_bank_data = PTB(path=args.data_dir, shift_target=False) ptb_data = tree_bank_data.load_data() train_set = SequentialArrayIterator(ptb_data['train'], batch_size=args.batch_size, time_steps=time_steps, total_iterations=args.num_iterations,
parser.add_argument('--use_oov', default=False, action='store_true', help='use OOV test set') parser.add_argument( '--eps', type=float, default=1e-8, help='epsilon used to avoid divide by zero in softmax renormalization.', action=check_size(1e-100, 1e-2)) parser.add_argument('--model_file', default='memn2n_weights.npz', help='File to load model weights from.', type=str) parser.set_defaults(batch_size=32, epochs=200) args = parser.parse_args() validate((args.emb_size, int, 1, 10000), (args.eps, float, 1e-15, 1e-2)) # Sanitize inputs validate_existing_filepath(args.model_file) model_file = args.model_file assert model_file.endswith('.npz') validate_parent_exists(args.data_dir) data_dir = args.data_dir babi = BABI_Dialog(path=data_dir, task=args.task, oov=args.use_oov, use_match_type=args.use_match_type,