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)) seq.add(Dense(n_hid, act='relu')) seq.add(Dense(n_out, act='softmax')) md = seq.combine() # print summary info of model md.summary()
def train():
# prepare data
tr_X, tr_y, _, te_X, te_y, te_na_list = pp_dev_data.GetAllData( fe_fd, agg_num, hop, fold )
[batch_num, n_time, n_freq] = tr_X.shape
print tr_X.shape, tr_y.shape
print te_X.shape, te_y.shape
# build model
seq = Sequential()
seq.add( InputLayer( (n_time, n_freq) ) )
seq.add( Flatten() ) # flatten to 2d: (n_time, n_freq) to 1d:(n_time*n_freq)
seq.add( Dropout( 0.1 ) )
seq.add( Dense( n_hid, act=act ) )
seq.add( Dropout( 0.1 ) )
seq.add( Dense( n_hid, act=act ) )
seq.add( Dropout( 0.1 ) )
seq.add( Dense( n_hid, act=act ) )
seq.add( Dropout( 0.1 ) )
seq.add( Dense( n_out, act='sigmoid' ) )
md = seq.combine()
md.summary()
# optimizer
optimizer = Adam(1e-4)
# callbacks
# tr_err, te_err are frame based. To get event based err, run recognize.py
validation = Validation( tr_x=tr_X, tr_y=tr_y, va_x=None, va_y=None, te_x=te_X, te_y=te_y, batch_size=2000,
metrics=['binary_crossentropy'], call_freq=1, dump_path=None )
# save model
pp_dev_data.CreateFolder( cfg.dev_md_fd )
save_model = SaveModel( dump_fd=cfg.dev_md_fd, call_freq=10 )
# callbacks
callbacks = [ validation, save_model ]
# fit model
md.fit( x=tr_X, y=tr_y, batch_size=2000, n_epochs=100, loss_func='binary_crossentropy', optimizer=optimizer, callbacks=callbacks, verbose=1 )
hop = 5 # step_len
act = 'relu'
n_hid = 500
fold = 1
n_out = len(cfg.labels)
# prepare data
tr_X, tr_y, _ = pp_dev_data.GetAllData(cfg.dev_fe_mel_fd,
agg_num,
hop,
fold=None)
[batch_num, n_time, n_freq] = tr_X.shape
print tr_X.shape, tr_y.shape
# build model
seq = Sequential()
seq.add(InputLayer((n_time, n_freq)))
seq.add(Flatten()) # flatten to 2d: (n_time, n_freq) to 1d:(n_time*n_freq)
seq.add(Dropout(0.1))
seq.add(Dense(n_hid, act=act))
seq.add(Dropout(0.1))
seq.add(Dense(n_hid, act=act))
seq.add(Dropout(0.1))
seq.add(Dense(n_hid, act=act))
seq.add(Dropout(0.1))
seq.add(Dense(n_out, act='sigmoid'))
md = seq.compile()
md.summary()
# validation
# tr_err, te_err are frame based. To get event based err, run recognize.py
# 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) ) seq.add( Dense(n_out, 'sigmoid') ) md = seq.combine() md.summary() # optimizer
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)
# reshape X to shape: (n_pictures, n_fmaps=3, n_row=32, n_col=32)
tr_X = reshape_img_for_cnn(tr_X)
te_X = reshape_img_for_cnn(te_X)
# init params
n_out = 10
# sparse label to 1-of-K categorical label
tr_y = sparse_to_categorical(tr_y, n_out)
te_y = sparse_to_categorical(te_y, n_out)
print tr_X.shape
print tr_y.shape
### Build model
seq = Sequential()
seq.add(InputLayer(in_shape=(3, 32, 32)))
seq.add(
Convolution2D(n_outfmaps=64,
n_row=3,
n_col=3,
act='linear',
border_mode=(1, 1)))
seq.add(BN(axes=(0, 2, 3)))
seq.add(Activation('relu'))
seq.add(
Convolution2D(n_outfmaps=64,
n_row=3,
n_col=3,
act='linear',
def train():
# prepare data
tr_X, tr_y, _, te_X, te_y, te_na_list = pp_dev_data.GetAllData(
fe_fd, agg_num, hop, fold)
[batch_num, n_time, n_freq] = tr_X.shape
print tr_X.shape, tr_y.shape
print te_X.shape, te_y.shape
# build model
seq = Sequential()
seq.add(InputLayer((n_time, n_freq)))
seq.add(Flatten()) # flatten to 2d: (n_time, n_freq) to 1d:(n_time*n_freq)
seq.add(Dropout(0.1))
seq.add(Dense(n_hid, act=act))
seq.add(Dropout(0.1))
seq.add(Dense(n_hid, act=act))
seq.add(Dropout(0.1))
seq.add(Dense(n_hid, act=act))
seq.add(Dropout(0.1))
seq.add(Dense(n_out, act='sigmoid'))
md = seq.compile()
md.summary()
# optimizer
optimizer = Adam(1e-4)
# callbacks
# tr_err, te_err are frame based. To get event based err, run recognize.py
validation = Validation(tr_x=tr_X,
tr_y=tr_y,
va_x=None,
va_y=None,
te_x=te_X,
te_y=te_y,
batch_size=2000,
metrics=['binary_crossentropy'],
call_freq=1,
dump_path=None)
# save model
pp_dev_data.CreateFolder(cfg.dev_md_fd)
save_model = SaveModel(dump_fd=cfg.dev_md_fd, call_freq=10)
# callbacks
callbacks = [validation, save_model]
# fit model
md.fit(x=tr_X,
y=tr_y,
batch_size=2000,
n_epochs=100,
loss_func='binary_crossentropy',
optimizer=optimizer,
callbacks=callbacks,
verbose=1)
### load & prepare data tr_X, tr_y, va_X, va_y, te_X, te_y = load_data() # 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 seq = Sequential() seq.add(InputLayer(n_in)) seq.add(Dense(n_hid, act='relu')) seq.add(Dropout(p_drop=0.2)) seq.add(Dense(n_hid, act='relu')) seq.add(Dropout(p_drop=0.2)) seq.add(Dense(n_out, act='softmax')) md = seq.combine() # print summary info of model md.summary() # md.plot_connection() ### optimization method optimizer = Adam(lr=0.001) # Try SGD, Adagrad, Rmsprop, etc. instead
strides = (1, 1)
seq.add(
Convolution2D(n_outfmaps=n_outfmaps,
n_row=3,
n_col=3,
act='linear',
border_mode=(1, 1),
strides=strides))
seq.add(BN(axes=(0, 2, 3)))
seq.add(Activation('relu'))
return seq
### Build model
seq = Sequential()
seq.add(InputLayer(in_shape=(3, 32, 32)))
seq = add_n_blocks(seq, n_outfmaps=64, n_repeat=6, is_first_layer=True)
seq = add_n_blocks(seq, n_outfmaps=128, n_repeat=8, is_first_layer=False)
seq = add_n_blocks(seq, n_outfmaps=256, n_repeat=12, is_first_layer=True)
seq = add_n_blocks(seq, n_outfmaps=512, n_repeat=6, is_first_layer=True)
seq.add(Lambda(mean_pool))
seq.add(Flatten())
seq.add(Dense(n_out, act='softmax'))
md = seq.combine()
# print summary info of model
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 )
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)) seq.add(Dense(n_out, 'sigmoid')) md = seq.combine() md.summary() # optimizer
def train(tr_fe_fd, tr_csv_file, te_fe_fd, te_csv_file, n_concat, hop, scaler,
out_md_fd):
# Prepare data
tr_x, tr_y = pp_data.get_matrix_format_data(fe_fd=tr_fe_fd,
csv_file=tr_csv_file,
n_concat=n_concat,
hop=hop,
scaler=scaler)
te_x, te_y = pp_data.get_matrix_format_data(fe_fd=te_fe_fd,
csv_file=te_csv_file,
n_concat=n_concat,
hop=hop,
scaler=scaler)
n_freq = tr_x.shape[2]
print 'tr_x.shape:', tr_x.shape # (n_samples, n_concat, n_freq)
print 'tr_y.shape:', tr_y.shape # (n_samples, n_labels)
# Build model
n_out = len(cfg.labels)
seq = Sequential()
seq.add(InputLayer((n_concat, n_freq)))
seq.add(Flatten())
seq.add(Dropout(0.2))
seq.add(Dense(200, act='relu'))
seq.add(Dropout(0.2))
seq.add(Dense(200, act='relu'))
seq.add(Dropout(0.2))
seq.add(Dense(n_out, act='softmax'))
md = seq.compile()
md.summary()
# Validation.
# tr_err, te_err are frame based. To get event based err, run recognize.py
validation = Validation(tr_x=tr_x,
tr_y=tr_y,
va_x=None,
va_y=None,
te_x=te_x,
te_y=te_y,
batch_size=500,
call_freq=1,
dump_path=None)
# Save model
pp_data.create_folder(out_md_fd)
save_model = SaveModel(out_md_fd, call_freq=2)
# Callbacks
callbacks = [validation, save_model]
# Optimizer
optimizer = Adam(1e-3)
# fit model
md.fit(x=tr_x,
y=tr_y,
batch_size=100,
n_epochs=101,
loss_func='categorical_crossentropy',
optimizer=optimizer,
callbacks=callbacks)
# pad & truncate sequences
tr_X = pad_trunc_seqs(tr_X, max_len, pad_type='pre')
te_X = pad_trunc_seqs(te_X, max_len, pad_type='pre')
# sparse target to categorical target
tr_y = sparse_to_categorical(tr_y, n_out)
te_y = sparse_to_categorical(te_y, n_out)
### build model
def mean_pool(input):
return K.mean(input, axis=1)
seq = Sequential()
seq.add(InputLayer(max_len))
seq.add(Embedding(n_words, n_proj))
seq.add(LSTM(n_hid, 'tanh', return_sequence=True)) # Try RNN, GRU instead
seq.add(Lambda(mean_pool))
seq.add(Dense(n_out, 'softmax'))
md = seq.combine()
# print summary info of model
md.summary()
### optimization method
optimizer = Rmsprop(0.001)
### callbacks (optional)
# save model every n epoch (optional)
def train(tr_fe_fd, tr_csv_file, te_fe_fd, te_csv_file,
n_concat, hop, scaler, out_md_fd):
# Prepare data
tr_x, tr_y = pp_data.get_matrix_format_data(
fe_fd=tr_fe_fd,
csv_file=tr_csv_file,
n_concat=n_concat, hop=hop, scaler=scaler)
te_x, te_y = pp_data.get_matrix_format_data(
fe_fd=te_fe_fd,
csv_file=te_csv_file,
n_concat=n_concat, hop=hop, scaler=scaler)
n_freq = tr_x.shape[2]
print 'tr_x.shape:', tr_x.shape # (n_samples, n_concat, n_freq)
print 'tr_y.shape:', tr_y.shape # (n_samples, n_labels)
# Build model
n_out = len(cfg.labels)
seq = Sequential()
seq.add(InputLayer((n_concat, n_freq)))
seq.add(Flatten())
seq.add(Dropout(0.2))
seq.add(Dense(200, act='relu'))
seq.add(Dropout(0.2))
seq.add(Dense(200, act='relu'))
seq.add(Dropout(0.2))
seq.add(Dense(n_out, act='softmax'))
md = seq.compile()
md.summary()
# Validation.
# tr_err, te_err are frame based. To get event based err, run recognize.py
validation = Validation(tr_x=tr_x, tr_y=tr_y,
va_x=None, va_y=None,
te_x=te_x, te_y=te_y,
batch_size=500, call_freq=1, dump_path=None)
# Save model
pp_data.create_folder(out_md_fd)
save_model = SaveModel(out_md_fd, call_freq=2)
# Callbacks
callbacks = [validation, save_model]
# Optimizer
optimizer = Adam(1e-3)
# fit model
md.fit(x=tr_x, y=tr_y,
batch_size=100,
n_epochs=101,
loss_func='categorical_crossentropy',
optimizer=optimizer,
callbacks=callbacks)
# hyper-params agg_num = 11 # concatenate frames hop = 5 # step_len act = 'relu' n_hid = 500 fold = 1 n_out = len( cfg.labels ) # prepare data tr_X, tr_y, _ = pp_dev_data.GetAllData( cfg.dev_fe_mel_fd, agg_num, hop, fold=None ) [batch_num, n_time, n_freq] = tr_X.shape print tr_X.shape, tr_y.shape # build model seq = Sequential() seq.add( InputLayer( (n_time, n_freq) ) ) seq.add( Flatten() ) # flatten to 2d: (n_time, n_freq) to 1d:(n_time*n_freq) seq.add( Dropout( 0.1 ) ) seq.add( Dense( n_hid, act=act ) ) seq.add( Dropout( 0.1 ) ) seq.add( Dense( n_hid, act=act ) ) seq.add( Dropout( 0.1 ) ) seq.add( Dense( n_hid, act=act ) ) seq.add( Dropout( 0.1 ) ) seq.add( Dense( n_out, act='sigmoid' ) ) md = seq.combine() md.summary() # validation # tr_err, te_err are frame based. To get event based err, run recognize.py