def eval(md, x, y, out_dir, out_probs_dir, iter_):
# Predict
t1 = time.time()
(n_clips, n_time, n_freq) = x.shape
(x, y) = pp_data.transform_data(x, y)
prob = md.predict(x)
prob = prob.astype(np.float32)
if out_dir:
pp_data.create_folder(out_dir)
#out_prob_path = os.path.join(out_probs_dir, "prob_%d_iters.p" %iter_)
# Dump predicted probabilites for future average
if out_probs_dir:
pp_data.create_folder(out_probs_dir)
out_prob_path = os.path.join(out_probs_dir, "prob_%d_iters.p" % iter_)
cPickle.dump(prob,
open(out_prob_path, 'wb'),
protocol=cPickle.HIGHEST_PROTOCOL)
# Compute and dump stats
n_out = y.shape[1]
stats = []
t1 = time.time()
for k in range(n_out):
(precisions, recalls,
thresholds) = metrics.precision_recall_curve(y[:, k], prob[:, k])
avg_precision = metrics.average_precision_score(y[:, k],
prob[:, k],
average=None)
(fpr, tpr, thresholds) = metrics.roc_curve(y[:, k], prob[:, k])
auc = metrics.roc_auc_score(y[:, k], prob[:, k], average=None)
#eer = pp_data.eer(prob[:, k], y[:, k])
skip = 1000
dict = {
'precisions': precisions[0::skip],
'recalls': recalls[0::skip],
'AP': avg_precision,
'fpr': fpr[0::skip],
'fnr': 1. - tpr[0::skip],
'auc': auc
}
stats.append(dict)
logging.info("Callback time: %s" % (time.time() - t1, ))
dump_path = os.path.join(out_dir, "md%d_iters.p" % iter_)
cPickle.dump(stats,
open(dump_path, 'wb'),
protocol=cPickle.HIGHEST_PROTOCOL)
logging.info("mAP: %f" % np.mean([e['AP'] for e in stats]))
def eval(model, x, y, out_dir, out_probs_dir, md_iter):
pp_data.create_folder(out_dir)
# Predict
t1 = time.time()
(n_clips, n_time_, n_freq) = x.shape
(x, y) = pp_data.transform_data(x, y)
prob = model.predict(x)
prob = prob.astype(np.float32)
print("The %d time into evalution." % md_iter)
if out_probs_dir:
pp_data.create_folder(out_probs_dir)
out_prob_path = os.path.join(out_probs_dir,
"prob_%d_iters.p" % md_iter)
#cPickle.dump(prob, open(out_prob_path, 'wb'))
# Dump predicted probabilities for future average
n_out = y.shape[1]
stats = []
t1 = time.time()
for k in range(n_out):
(precisions, recalls,
thresholds) = metrics.precision_recall_curve(y[:, k], prob[:, k])
avg_precision = metrics.average_precision_score(y[:, k],
prob[:, k],
average=None)
(fpr, tpr, thresholds) = metrics.roc_curve(y[:, k], prob[:, k])
auc = metrics.roc_auc_score(y[:, k], prob[:, k], average=None)
eer = pp_data.eer(prob[:, k], y[:, k])
skip = 1000
dict = {
'precisions': precisions[0::skip],
'recalls': recalls[0::skip],
'AP': avg_precision,
'fpr': fpr[0::skip],
'fnr': 1. - tpr[0::skip],
'auc': auc
}
stats.append(dict)
logging.info("Callback time: %s" % (time.time() - t1, ))
dump_path = os.path.join(out_dir, "model_%d_iters.p" % (md_iter, ))
cPickle.dump(stats, open(dump_path, 'wb'))
mAP = np.mean([e['AP'] for e in stats])
logging.info("mAP of %d iteration: %f" % (md_iter, mAP))
return mAP
def train(args):
cpickle_dir = args.cpickle_dir
workspace = args.workspace
# Path of hdf5 data
bal_train_hdf5_path = os.path.join(cpickle_dir, "bal_train.h5")
unbal_train_hdf5_path = os.path.join(cpickle_dir, "unbal_train.h5")
eval_hdf5_path = os.path.join(cpickle_dir, "eval.h5")
# Load data
t1 = time.time()
(tr_x1, tr_y1, tr_id_list1) = pp_data.load_data(bal_train_hdf5_path)
(tr_x2, tr_y2, tr_id_list2) = pp_data.load_data(unbal_train_hdf5_path)
tr_x = np.concatenate((tr_x1, tr_x2))
tr_y = np.concatenate((tr_y1, tr_y2))
tr_id_list = tr_id_list1 + tr_id_list2
(te_x, te_y, te_id_list) = pp_data.load_data(eval_hdf5_path)
logging.info("Loading data time: %s s" % (time.time() - t1))
logging.info(tr_x1.shape, tr_x2.shape)
logging.info("tr_x.shape: %s" % (tr_x.shape, ))
(_, n_time, n_freq) = tr_x.shape
# Build model
n_hid = 500
n_out = tr_y.shape[1]
lay_in = InputLayer(in_shape=(n_time, n_freq))
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)
a = Dense(n_out=n_hid, act='relu')(a)
a = Dropout(p_drop=0.2)(a)
cla = Dense(n_out=n_out, act='sigmoid', name='cla')(a)
att = Dense(n_out=n_out, act='softmax', name='att')(a)
# Attention
lay_out = Lambda(_attention)([cla, att])
# Compile model
md = Model(in_layers=[lay_in], out_layers=[lay_out])
md.compile()
md.summary(is_logging=True)
# Save model every several iterations
call_freq = 1000
dump_fd = os.path.join(workspace, "models", pp_data.get_filename(__file__))
pp_data.create_folder(dump_fd)
save_model = SaveModel(dump_fd=dump_fd,
call_freq=call_freq,
type='iter',
is_logging=True)
# Callbacks function
callbacks = [save_model]
batch_size = 500
tr_gen = RatioDataGenerator(batch_size=batch_size, type='train')
# Optimization method
optimizer = Adam(lr=args.lr)
# Train
stat_dir = os.path.join(workspace, "stats", pp_data.get_filename(__file__))
pp_data.create_folder(stat_dir)
prob_dir = os.path.join(workspace, "probs", pp_data.get_filename(__file__))
pp_data.create_folder(prob_dir)
tr_time = time.time()
for (tr_batch_x, tr_batch_y) in tr_gen.generate(xs=[tr_x], ys=[tr_y]):
# Compute stats every several interations
if md.iter_ % call_freq == 0:
# Stats of evaluation dataset
t1 = time.time()
te_err = eval(md=md,
x=te_x,
y=te_y,
out_dir=os.path.join(stat_dir, "test"),
out_probs_dir=os.path.join(prob_dir, "test"))
logging.info("Evaluate test time: %s" % (time.time() - t1, ))
# Stats of training dataset
t1 = time.time()
tr_bal_err = eval(md=md,
x=tr_x1,
y=tr_y1,
out_dir=os.path.join(stat_dir, "train_bal"),
out_probs_dir=None)
logging.info("Evaluate tr_bal time: %s" % (time.time() - t1, ))
# Update params
(tr_batch_x,
tr_batch_y) = pp_data.transform_data(tr_batch_x, tr_batch_y)
md.train_on_batch(batch_x=tr_batch_x,
batch_y=tr_batch_y,
loss_func='binary_crossentropy',
optimizer=optimizer,
callbacks=callbacks)
# Stop training when maximum iteration achieves
if md.iter_ == call_freq * 31:
break
def train(args):
EVAL_MAP = -1000.
PATIENCE = 0
data_dir = args.data_dir
workspace = args.workspace
tag = args.tag
levels = args.levels
# Path for the hdf5 dara
bal_train_path = os.path.join(data_dir, "bal_train.h5")
unbal_train_path = os.path.join(data_dir, "unbal_train.h5")
eval_path = os.path.join(data_dir, "eval.h5")
# Load data
t1 = time.time()
(tr_x1, tr_y1, tr_id_list1) = pp_data.load_data(bal_train_path)
(tr_x2, tr_y2, tr_id_list2) = pp_data.load_data(unbal_train_path)
(eval_x, eval_y, eval_id_list) = pp_data.load_data(eval_path)
#tr_x = tr_x1
#tr_y = tr_y1
#tr_id_list = tr_id_list1
tr_x = np.concatenate((tr_x1, tr_x2))
tr_y = np.concatenate((tr_y1, tr_y2))
tr_id_list = tr_id_list1 + tr_id_list2
logging.info("Loading dat time: %s s" % (time.time() - t1))
logging.info(tr_x1.shape, tr_x2.shape)
logging.info("tr_x.shape: %s" % (tr_x.shape, ))
(_, n_time, n_freq) = tr_x.shape
# Build Model
model = get_ml_attention(levels)
logging.info(model.to_json())
# Optimization method
optimizer = Adam(lr=args.lr)
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['binary_accuracy'])
#logging.info(model.summary())
#
batch_size = 500
tr_gen = RatioDataGenerator(batch_size=batch_size, type='train')
# Save Model every call_freq iterations
model_iter = 0
call_freq = 1000
dump_fd = os.path.join(workspace, "models", pp_data.get_filename(__file__))
pp_data.create_folder(dump_fd)
# Train
stat_dir = os.path.join(workspace, "stats", pp_data.get_filename(__file__))
pp_data.create_folder(stat_dir)
prob_dir = os.path.join(workspace, "probs", pp_data.get_filename(__file__))
pp_data.create_folder(prob_dir)
tr_time = time.time()
for (tr_batch_x, tr_batch_y) in tr_gen.generate(xs=[tr_x], ys=[tr_y]):
# Computes stats every several iterations
print(model_iter)
if model_iter % call_freq == 0: # every 1000 iterations
# Stats of evaluation dataset
t1 = time.time()
eval_MAP = eval(model=model,
x=eval_x,
y=eval_y,
out_dir=os.path.join(stat_dir, "eval"),
out_probs_dir=os.path.join(prob_dir, "eval"),
md_iter=model_iter)
logging.info("Evaluate evaluation-set time: %s" %
(time.time() - t1, ))
if eval_MAP >= EVAL_MAP:
#md_name = "/scratch/work/xuz2/model_" + tag + "_.h5"
md_name = tag + "_.h5"
model.save(md_name)
EVAL_MAP = eval_MAP
PATIENCE = 0
else:
PATIENCE += 1
logging.info("Patience now: %d" % (PATIENCE, ))
if PATIENCE >= 10:
break
# print("Training stop at %s iterations" % (model_iter,))
# break
# Stats of training dataset
#t1 =time.time()
#tr_bal_err = eval(model=model, x=tr_x1, y=tr_y1,
# out_dir=os.path.join(stat_dir, "train_bal"),
## out_probs_dir=None,
# md_iter=model_iter)
#logging.info("Evaluate tr_bal time: %s" % (time.time() - t1,))
# Save Model
#if eval_MAP > 0.342:
# md_name = "/scratch/work/xuz2/model_" + str(model_iter) + "_.h5"
# model.save(md_name)
# Update params
(tr_batch_x,
tr_batch_y) = pp_data.transform_data(tr_batch_x, tr_batch_y)
model.train_on_batch(tr_batch_x, tr_batch_y)
model_iter += 1
# Stop training when maximum iteration achieves
if model_iter == call_freq * 151:
break
def train(args):
cpickle_dir = args.cpickle_dir
workspace = args.workspace
# Path of hdf5 data
bal_train_hdf5_path = os.path.join(cpickle_dir, "bal_train.h5")
unbal_train_hdf5_path = os.path.join(cpickle_dir, "unbal_train.h5")
eval_hdf5_path = os.path.join(cpickle_dir, "eval.h5")
# Load data
t1 = time.time()
(tr_x1, tr_y1, tr_id_list1) = pp_data.load_data(bal_train_hdf5_path)
(tr_x2, tr_y2, tr_id_list2) = pp_data.load_data(unbal_train_hdf5_path)
print(tr_x1.shape)
print(tr_x2.shape)
tr_x = np.concatenate((tr_x1, tr_x2))
tr_y = np.concatenate((tr_y1, tr_y2))
tr_id_list = tr_id_list1 + tr_id_list2
(te_x, te_y, te_id_list) = pp_data.load_data(eval_hdf5_path)
logging.info("Loading data time: %s s" % (time.time() - t1))
logging.info(tr_x1.shape, tr_x2.shape)
logging.info("tr_x.shape: %s" % (tr_x.shape, ))
(_, n_time, n_freq) = tr_x.shape
# Build model
n_hid = 600
n_out = tr_y.shape[1]
lay_in = Input(shape=(n_time, n_freq))
a_0 = BatchNormalization()(lay_in)
a_1 = Dense(n_hid, kernel_regularizer=regularizers.l2(0.001))(a_0)
a_1 = BatchNormalization()(a_1)
a_1 = Activation('relu')(a_1)
a_1 = Dropout(rate=0.4)(a_1)
a_2 = Dense(n_hid, kernel_regularizer=regularizers.l2(0.001))(a_1)
a_2 = BatchNormalization()(a_2)
a_2 = Activation('relu')(a_2)
a_2 = Dropout(rate=0.4)(a_2)
a_3 = Dense(n_hid, kernel_regularizer=regularizers.l2(0.001))(a_2)
a_3 = BatchNormalization()(a_3)
a_3 = Activation('relu')(a_3)
a_3 = Dropout(rate=0.4)(a_3)
cla_1 = Dense(n_out, name='cla_1')(a_3)
cla_1 = BatchNormalization()(cla_1)
cla_1 = Activation('sigmoid')(cla_1)
att_1 = Dense(n_out, name='att_1')(a_3)
att_1 = BatchNormalization()(att_1)
att_1 = Activation('softmax')(att_1)
# Attention
lay_out_a = Lambda(_attention,
output_shape=_att_output_shape)([cla_1, att_1])
cla_2 = Dense(n_out, name='cla_2')(a_2)
cla_2 = BatchNormalization()(cla_2)
cla_2 = Activation('sigmoid')(cla_2)
att_2 = Dense(n_out, name='att2')(a_2)
att_2 = BatchNormalization()(att_2)
att_2 = Activation('softmax')(att_2)
lay_out_b = Lambda(_attention,
output_shape=_att_output_shape)([cla_2, att_2])
lay_out_c = Concatenate(axis=1)([lay_out_a, lay_out_b])
#lay_out = Dense(n_out, activation='sigmoid', name='output')(lay_out_c)
lay_out = Dense(n_out, name='output')(lay_out_c)
lay_out = BatchNormalization()(lay_out)
lay_out = Activation('sigmoid')(lay_out)
# Compile model
md = Model(inputs=lay_in, outputs=lay_out)
md.summary()
# Save model every several iterations
call_freq = 1000
dump_fd = os.path.join(workspace, "models", pp_data.get_filename(__file__))
pp_data.create_folder(dump_fd)
# save_model = SaveModel(dump_fd=dump_fd, call_freq=call_freq, type='iter', is_logging=True)
# Callbacks function
#callbacks = []#save_model]
batch_size = 500
tr_gen = RatioDataGenerator(batch_size=batch_size, type='train')
# Optimization method
optimizer = Adam(lr=args.lr)
md.compile(loss='binary_crossentropy', optimizer=optimizer)
#callbacks=callbacks)
# Train
stat_dir = os.path.join(workspace, "stats", pp_data.get_filename(__file__))
pp_data.create_folder(stat_dir)
prob_dir = os.path.join(workspace, "probs", pp_data.get_filename(__file__))
pp_data.create_folder(prob_dir)
tr_time = time.time()
iter_ = 1
for (tr_batch_x, tr_batch_y) in tr_gen.generate(xs=[tr_x], ys=[tr_y]):
# Compute stats every several interations
if iter_ % call_freq == 0:
# Stats of evaluation dataset
t1 = time.time()
te_err = eval(md=md,
x=te_x,
y=te_y,
out_dir=os.path.join(stat_dir, "test"),
out_probs_dir=os.path.join(prob_dir, "test"),
iter_=iter_)
logging.info("Evaluate test time: %s" % (time.time() - t1, ))
# Stats of training dataset
t1 = time.time()
tr_bal_err = eval(md=md,
x=tr_x1,
y=tr_y1,
out_dir=os.path.join(stat_dir, "train_bal"),
out_probs_dir=None,
iter_=iter_)
logging.info("Evaluate tr_bal time: %s" % (time.time() - t1, ))
iter_ += 1
# Update params
(tr_batch_x,
tr_batch_y) = pp_data.transform_data(tr_batch_x, tr_batch_y)
md.train_on_batch(x=tr_batch_x, y=tr_batch_y)
# Stop training when maximum iteration achieves
if iter_ == call_freq * 151:
break
