type=float,
default=0.96,
help='Required classificationa accuracy')
parser.add_argument('-lev',
'--levels',
type=int,
default=(75, 50, 35, 20, 15, 10, 7, 5, 3, 2, 1, 0),
nargs='+',
help='Pruning percentile levels')
return parser.parse_args()
if __name__ == '__main__':
args = parse_arguments()
print_message(message='EXAMPLE: MNIST dataset')
print_param(description='Number of experiment observations',
param_str=str(args.n_obs))
print_param(description='Initial number of hidden neurons',
param_str=str(args.hidden_structure))
print_param(description='Required accuracy', param_str=str(args.req_acc))
params_str = '_hs' + str(args.hidden_structure) + '_ra' + str(
args.req_acc).replace('.', '') + '_no' + str(args.n_obs)
if args.generate:
stats_data = list()
for i_obs in range(1, args.n_obs + 1):
print_message(message='MNIST experiment, observation ' +
str(i_obs) + '/' + str(args.n_obs))
net = FeedForwardNet(hidden=args.hidden_structure,
tf_name='Sigmoid')
dataset = open_shelve('../examples/mnist/dataset_mnist_1K.ds', 'c')
net.fit(x=dataset['x'],
def get_speech_data():
print_message(message='Reading alignments...')
read_alignments()
print_param(description='Number of loaded records (alignments)', param_str=str(len(mlf.keys())))
print_param(description='Number of alignment frames', param_str=str(len(mlf[mlf.keys()[0]])))
print_param(description='Number of found phonemes', param_str=str(len(samples)))
print_param(description='Found phonemes', param_str=str(sorted(samples.keys())))
print_message(message='Reading features...')
read_features()
print_param(description='Number of loaded records (features)', param_str=str(len(features.keys())))
print_param(description='Number of feature frames', param_str=str(len(features[features.keys()[0]])))
print_message(message='Adding samples...')
add_samples()
print_message(message='Splitting data...')
split_data()
print_param(description='Number of training samples', param_str=str(len(data['x'])))
print_param(description='Number of validation samples', param_str=str(len(data['x_val'])))
print_param(description='Number of testing samples', param_str=str(len(data['x_test'])))
print_param(description='Problem dimension', param_str=str(data['x'][0].shape[0]))
print_param(description='Number of classes', param_str=str(len(samples)))
print_message(message='Number of samples per class:')
for phonem in sorted(samples.keys()):
print_param(description=phonem, param_str=str(len(samples[phonem])))
''' Check args '''
if abs(sum(args_tmp.data_split) - 1) > 1e-5:
stderr.write('Error: data_split args must give 1.0 together (e.g. 0.8 0.1 0.1).\n')
exit()
else:
return args_tmp
if __name__ == '__main__':
args = parse_arguments()
split_bounds = (args.n_samples*args.data_split[0], args.n_samples*(args.data_split[0]+args.data_split[1]))
destination = 'dataset_karnin'+args.name_appendix+'.ds'
a = args.a
b = 2.0/(a+1)-1
print_message(message='Generating and splitting KARNIN data...')
print_param(description='parameter a', param_str=str(a))
print_param(description='parameter b', param_str=str(b))
data = {'x': list(), 'y': list(), 'x_val': list(), 'y_val': list(), 'x_test': list(), 'y_test': list()}
for ni in range(args.n_samples):
x1 = uniform(-1, 1)
x2 = uniform(-1, 1)
if x1 < a and x2 <= b:
y = 0.0
else:
y = 1.0
'''
x0 = uniform(-1, a)
y0 = uniform(-1, b)
x1 = uniform(a, 1)
y1 = uniform(-1, 1+(1-b))
print_message(message='Number of samples per class:')
for phonem in sorted(samples.keys()):
print_param(description=phonem, param_str=str(len(samples[phonem])))
if __name__ == '__main__':
args = parse_arguments()
destination = 'dataset_speech_bs'+str(args.border_size)
destination += '_cs'+str(args.context_size)+'_nf'+str(args.n_filters)
destination += '_ds'+str(int(args.data_split[0]*10))+str(int(args.data_split[1]*10))+str(int(args.data_split[2]*10))
destination += '_ns'+str(args.n_samples)+'_nr'+str(args.n_records)
if args.phonemes:
destination += '_'+str(args.phonemes).replace(',','+').replace(' ', '').replace('\'', '')[1:-1]
destination += args.name_appendix+'.ds'
print_message(message='Processing SPEECH data...')
print_param(description='Path to features', param_str=args.feature_filename)
print_param(description='Path to alignments', param_str=args.alignment_filename)
print_param(description='Border size (strictness)', param_str=str(args.border_size))
print_param(description='Context size', param_str=str(args.context_size))
print_param(description='Number of MEL filters', param_str=str(args.n_filters))
print_param(description='Number of records', param_str=str(args.n_records))
print_param(description='Number of samples', param_str=str(args.n_samples))
print_param(description='Maximum number of other phonemes', param_str=str(args.max_rest))
print_param(description='Phonemes as classes', param_str=str(args.phonemes) if args.phonemes else 'all')
print_param(description='Data split (train/val/test)', param_str=str(args.data_split))
print_param(description='Dataset destination file name', param_str=destination)
mlf = dict()
features = dict()
samples = dict()
data = {'x': list(), 'y': list(), 'x_val': list(), 'y_val': list(), 'x_test': list(), 'y_test': list(),
parser.add_argument('-g', '--generate', type=bool, default=False,
help='Generate new stats or load the dumped?')
parser.add_argument('-no', '--n_obs', type=int, default=10,
help='Number of experiment observations')
parser.add_argument('-hs', '--hidden_structure', type=int, default=[50], nargs='+',
help='Neural network structure')
parser.add_argument('-ra', '--req_acc', type=float, default=1.0,
help='Required classificationa accuracy')
parser.add_argument('-lev', '--levels', type=int, default=(75, 50, 35, 20, 10, 5, 1, 0), nargs='+',
help='Pruning percentile levels')
return parser.parse_args()
if __name__ == '__main__':
args = parse_arguments()
print_message(message='EXAMPLE: XOR dataset')
print_param(description='Number of experiment observations', param_str=str(args.n_obs))
print_param(description='Initial number of hidden neurons', param_str=str(args.hidden_structure))
print_param(description='Required accuracy', param_str=str(args.req_acc))
params_str = '_hs'+str(args.hidden_structure)+'_ra'+str(args.req_acc).replace('.', '')+'_no'+str(args.n_obs)
if args.generate:
stats_data = list()
for i_obs in range(1, args.n_obs+1):
print_message(message='XOR experiment, observation '+str(i_obs)+'/'+str(args.n_obs))
net = FeedForwardNet(hidden=args.hidden_structure, tf_name='Sigmoid')
dataset = open_shelve('../examples/xor/dataset_xor.ds', 'c')
net.fit(x=dataset['x'], y=dataset['y'], x_val=dataset['x_val'], y_val=dataset['y_val'], learning_rate=0.4,
n_epoch=50, req_acc=1.0)
res = net.evaluate(x=dataset['x_test'], y=dataset['y_test'])
print_message(message='Evaluation on test data after training:')
print_param(description='Accuracy', param_str=str(res[1]))