def get_matrix_format_data(fe_fd, csv_file, n_concat, hop, scaler):
"""Get training data and ground truth in matrix format.
Args:
fe_fd: string. Feature folder.
csv_file: string. Path of csv file.
n_concat: integar. Number of frames to concatenate.
hop: integar. Number of hop frames.
scaler: None | object.
"""
with open(csv_file, 'rb') as f:
reader = csv.reader(f)
lis = list(reader)
x3d_all = []
y_all = []
for li in lis:
[na, lb] = li[0].split('\t')
na = na.split('/')[1][0:-4]
path = fe_fd + '/' + na + '.f'
x = cPickle.load(open(path, 'rb'))
if scaler:
x = scaler.transform(x)
x3d = mat_2d_to_3d(x, n_concat, hop) # (n_blocks, n_concat, n_freq)
x3d_all.append(x3d)
y_all += [cfg.lb_to_id[lb]] * len(x3d)
x3d_all = np.concatenate(x3d_all) # (n_samples, n_concat, n_freq)
y_all = np.array(y_all)
y_all = sparse_to_categorical(y_all,
len(cfg.labels)) # (n_samples, n_labels)
return x3d_all, y_all
def get_matrix_format_data(fe_fd, csv_file, n_concat, hop, scaler):
"""Get training data and ground truth in matrix format.
Args:
fe_fd: string. Feature folder.
csv_file: string. Path of csv file.
n_concat: integar. Number of frames to concatenate.
hop: integar. Number of hop frames.
scaler: None | object.
"""
with open(csv_file, 'rb') as f:
reader = csv.reader(f)
lis = list(reader)
x3d_all = []
y_all = []
for li in lis:
[na, lb] = li[0].split('\t')
na = na.split('/')[1][0:-4]
path = fe_fd + '/' + na + '.f'
x = cPickle.load(open(path, 'rb'))
if scaler:
x = scaler.transform(x)
x3d = mat_2d_to_3d(x, n_concat, hop) # (n_blocks, n_concat, n_freq)
x3d_all.append(x3d)
y_all += [cfg.lb_to_id[lb]] * len(x3d)
x3d_all = np.concatenate(x3d_all) # (n_samples, n_concat, n_freq)
y_all = np.array(y_all)
y_all = sparse_to_categorical(y_all, len(cfg.labels)) # (n_samples, n_labels)
return x3d_all, y_all
def train_cv_model():
# init path
if type=='home':
fe_fd = cfg.dev_fe_mel_home_fd
labels = cfg.labels_home
lb_to_id = cfg.lb_to_id_home
tr_txt = cfg.dev_evaluation_fd + '/home_fold' + str(fold) + '_train.txt'
te_txt = cfg.dev_evaluation_fd + '/home_fold' + str(fold) + '_evaluate.txt'
if type=='resi':
fe_fd = cfg.dev_fe_mel_resi_fd
labels = cfg.labels_resi
lb_to_id = cfg.lb_to_id_resi
tr_txt = cfg.dev_evaluation_fd + '/residential_area_fold' + str(fold) + '_train.txt'
te_txt = cfg.dev_evaluation_fd + '/residential_area_fold' + str(fold) + '_evaluate.txt'
n_out = len( labels )
# load data to list
tr_X, tr_y = pp_dev_data.LoadAllData( fe_fd, tr_txt, lb_to_id, agg_num, hop )
tr_y = sparse_to_categorical( tr_y, n_out )
print tr_X.shape
print tr_y.shape
n_freq = tr_X.shape[2]
# build model
seq = Sequential()
seq.add( InputLayer( (agg_num, n_freq) ) )
seq.add( Flatten() )
seq.add( Dense( n_hid, act='relu' ) )
seq.add( Dropout( 0.1 ) )
seq.add( Dense( n_hid, act='relu' ) )
seq.add( Dropout( 0.1 ) )
seq.add( Dense( n_hid, act='relu' ) )
seq.add( Dropout( 0.1 ) )
seq.add( Dense( n_out, 'sigmoid' ) )
md = seq.combine()
# print summary info of model
md.summary()
# optimization method
optimizer = Adam(1e-3)
# callbacks (optional)
# save model every n epoch
pp_dev_data.CreateFolder( cfg.dev_md_fd )
save_model = SaveModel( dump_fd=cfg.dev_md_fd, call_freq=5 )
# validate model every n epoch
validation = Validation( tr_x=tr_X, tr_y=tr_y, va_x=None, va_y=None, te_x=None, te_y=None, metrics=['binary_crossentropy'], call_freq=1, dump_path=None )
# callbacks function
callbacks = [validation, save_model]
# train model
md.fit( x=tr_X, y=tr_y, batch_size=20, n_epochs=100, loss_func='binary_crossentropy', optimizer=optimizer, callbacks=callbacks )
def reshapeX(X):
N = len(X)
return X.reshape((N, 1, 28, 28))
### load & prepare data
tr_X, tr_y, va_X, va_y, te_X, te_y = load_data()
tr_X, va_X, te_X = reshapeX(tr_X), reshapeX(va_X), reshapeX(te_X)
# init params
n_in = 784
n_hid = 500
n_out = 10
# sparse label to 1 of K categorical label
tr_y = sparse_to_categorical(tr_y, n_out)
va_y = sparse_to_categorical(va_y, n_out)
te_y = sparse_to_categorical(te_y, n_out)
### Build model
act = 'relu'
seq = Sequential()
seq.add(InputLayer(in_shape=(1, 28, 28)))
seq.add(Convolution2D(n_outfmaps=32, n_row=3, n_col=3, act='relu'))
seq.add(MaxPool2D(pool_size=(2, 2)))
seq.add(Convolution2D(n_outfmaps=32, n_row=3, n_col=3, act='relu'))
seq.add(MaxPool2D(pool_size=(2, 2)))
seq.add(Dropout(0.2))
seq.add(Flatten())
seq.add(Dense(n_hid, act='relu'))
seq.add(Dropout(0.5))
from hat.layers.pool import Pool2D, GlobalMaxPool from hat.callbacks import Validation, SaveModel from hat.preprocessing import pad_trunc_seqs, sparse_to_categorical, reshape_3d_to_4d from hat.optimizers import Rmsprop import prepare_dev_data as pp_dev_data import config as cfg # hyper-params tr_fe_fd = cfg.dev_tr_fe_mel_fd max_len = 100 n_out = len( cfg.labels ) # prepare data tr_X, tr_y = pp_dev_data.GetAllData( tr_fe_fd, max_len ) tr_y = sparse_to_categorical( tr_y, n_out ) print tr_X.shape, tr_y.shape (_, n_time, n_freq) = tr_X.shape # build model seq = Sequential() seq.add( InputLayer( (n_time, n_freq) ) ) seq.add( Flatten() ) seq.add( Dropout(0.1) ) seq.add( Dense(500, 'relu') ) seq.add( Dropout(0.1) ) seq.add( Dense(500, 'relu') ) seq.add( Dropout(0.1) ) seq.add( Dense(500, 'relu') ) seq.add( Dropout(0.1) )
pred_all = np.concatenate(pred_all, axis=0)
y_all = np.concatenate(y_all, axis=0)
err = metrics.categorical_error(pred_all, y_all)
return err
if __name__ == '__main__':
# Load & prepare data
tr_x, tr_y, va_x, va_y, te_x, te_y = load_mnist()
# Init params
n_in = 784
n_hid = 500
n_out = 10
# Sparse label to 1-of-K categorical label
tr_y = sparse_to_categorical(tr_y, n_out)
va_y = sparse_to_categorical(va_y, n_out)
te_y = sparse_to_categorical(te_y, n_out)
# Build model
lay_in = InputLayer(in_shape=(n_in,))
a = Dense(n_out=n_hid, act='relu')(lay_in)
a = Dropout(p_drop=0.2)(a)
a = Dense(n_out=n_hid, act='relu')(a)
a = Dropout(p_drop=0.2)(a)
lay_out = Dense(n_out=n_out, act='softmax')(a)
md = Model(in_layers=[lay_in], out_layers=[lay_out])
md.compile()
md.summary()
def train_cv_model():
# init path
if type == 'home':
fe_fd = cfg.dev_fe_mel_home_fd
labels = cfg.labels_home
lb_to_id = cfg.lb_to_id_home
tr_txt = cfg.dev_evaluation_fd + '/home_fold' + str(
fold) + '_train.txt'
te_txt = cfg.dev_evaluation_fd + '/home_fold' + str(
fold) + '_evaluate.txt'
if type == 'resi':
fe_fd = cfg.dev_fe_mel_resi_fd
labels = cfg.labels_resi
lb_to_id = cfg.lb_to_id_resi
tr_txt = cfg.dev_evaluation_fd + '/residential_area_fold' + str(
fold) + '_train.txt'
te_txt = cfg.dev_evaluation_fd + '/residential_area_fold' + str(
fold) + '_evaluate.txt'
n_out = len(labels)
# load data to list
tr_X, tr_y = pp_dev_data.LoadAllData(fe_fd, tr_txt, lb_to_id, agg_num, hop)
tr_y = sparse_to_categorical(tr_y, n_out)
print tr_X.shape
print tr_y.shape
n_freq = tr_X.shape[2]
# build model
seq = Sequential()
seq.add(InputLayer((agg_num, n_freq)))
seq.add(Flatten())
seq.add(Dense(n_hid, act='relu'))
seq.add(Dropout(0.1))
seq.add(Dense(n_hid, act='relu'))
seq.add(Dropout(0.1))
seq.add(Dense(n_hid, act='relu'))
seq.add(Dropout(0.1))
seq.add(Dense(n_out, 'sigmoid'))
md = seq.combine()
# print summary info of model
md.summary()
# optimization method
optimizer = Adam(1e-3)
# callbacks (optional)
# save model every n epoch
pp_dev_data.CreateFolder(cfg.dev_md_fd)
save_model = SaveModel(dump_fd=cfg.dev_md_fd, call_freq=5)
# validate model every n epoch
validation = Validation(tr_x=tr_X,
tr_y=tr_y,
va_x=None,
va_y=None,
te_x=None,
te_y=None,
metrics=['binary_crossentropy'],
call_freq=1,
dump_path=None)
# callbacks function
callbacks = [validation, save_model]
# train model
md.fit(x=tr_X,
y=tr_y,
batch_size=20,
n_epochs=100,
loss_func='binary_crossentropy',
optimizer=optimizer,
callbacks=callbacks)
from hat.layers.cnn import Convolution2D, Convolution1D from hat.layers.pool import Pool2D, GlobalMaxPool from hat.callbacks import Validation, SaveModel from hat.preprocessing import pad_trunc_seqs, sparse_to_categorical, reshape_3d_to_4d from hat.optimizers import Rmsprop import prepare_dev_data as pp_dev_data import config as cfg # hyper-params tr_fe_fd = cfg.dev_tr_fe_mel_fd max_len = 100 n_out = len(cfg.labels) # prepare data tr_X, tr_y = pp_dev_data.GetAllData(tr_fe_fd, max_len) tr_y = sparse_to_categorical(tr_y, n_out) print tr_X.shape, tr_y.shape (_, n_time, n_freq) = tr_X.shape # build model seq = Sequential() seq.add(InputLayer((n_time, n_freq))) seq.add(Flatten()) seq.add(Dropout(0.1)) seq.add(Dense(500, 'relu')) seq.add(Dropout(0.1)) seq.add(Dense(500, 'relu')) seq.add(Dropout(0.1)) seq.add(Dense(500, 'relu')) seq.add(Dropout(0.1))