############# GET MODEL PARAMETERS #################
seth = GetValues()
prep, folds, save_model, model_type,\
model,feature, dropout, act1, act2,\
act3, input_neurons, epochs, batchsize,\
num_classes, agg_num, hop, loss, optimizer,\
dataset=seth.get_parameters(dataset='dcase_2016')
import config as cfg
############# EXTRACT FEATURES #####################
extract = False
if extract:
aud_audio.extract(feature,
cfg.wav_dev_fd,
cfg.dev_fd + '/' + feature,
'parameters.yaml',
dataset=dataset)
aud_audio.extract(feature,
cfg.wav_eva_fd,
cfg.eva_fd + '/' + feature,
'parameters.yaml',
dataset=dataset)
############# LOAD DATA ###########################
tr_X, tr_y = seth.get_train_data()
dimx = tr_X.shape[-2]
dimy = tr_X.shape[-1]
tr_X = aud_utils.mat_3d_to_nd(model, tr_X)
miz = aud_model.Functional_Model(input_neurons=input_neurons,
dropout=dropout,
input_neurons=400 # Number of Neurons
epochs=10 # Number of Epochs
batchsize=128 # Batch Size
num_classes=15 # Number of classes
filter_length=3 # Size of Filter
nb_filter=100 # Number of Filters
#Parameters that are passed to the features.
agg_num=10 # Agg Number(Integer) Number of frames
hop=10 # Hop Length(Integer)
dataset = 'dcase_2016'
extract = 0
## EXTRACT FEATURES
if extract:
aud_audio.extract(feature, wav_dev_fd, dev_fd+'/'+feature,'example.yaml',dataset=dataset)
aud_audio.extract(feature, wav_eva_fd, eva_fd+'/'+feature,'example.yaml',dataset=dataset)
def GetAllData(fe_fd, csv_file, agg_num, hop):
"""
Input: Features folder(String), CSV file(String), agg_num(Integer), hop(Integer).
Output: Loaded features(Numpy Array) and labels(Numpy Array).
Loads all the features saved as pickle files.
"""
# read csv
with open( csv_file, 'rb') as f:
reader = csv.reader(f)
lis = list(reader)
# init list
X3d_all = []
print "Epochs", epochs
print "Batchsize", batchsize
print "Number of filters", nb_filter
## UNPACK THE DATASET ACCORDING TO KERAS_AUD
# [NEEDED AT INITIAL STAGE]
path = 'E:/akshita_workspace/chime_home'
# [NEEDED AT INITIAL STAGE]
#aud_utils.unpack_chime_2k16(path,wav_dev_fd,wav_eva_fd,meta_train_csv,meta_test_csv,label_csv)
## EXTRACT FEATURES
aud_audio.extract(feature,
wav_dev_fd,
dev_fd + '/' + feature,
'defaults.yaml',
dataset='chime_2016')
aud_audio.extract(feature,
wav_eva_fd,
eva_fd + '/' + feature,
'defaults.yaml',
dataset='chime_2016')
def GetAllData(fe_fd, csv_file, agg_num, hop):
"""
Input: Features folder(String), CSV file(String), agg_num(Integer), hop(Integer).
Output: Loaded features(Numpy Array) and labels(Numpy Array).
Loads all the features saved as pickle files.
"""