def initialize_metrics(self, metric_names):
"""metric names can be list or dict"""
if 'acc' in metric_names:
self.metric_update['acc'] = tf.keras.metrics.BinaryAccuracy()
self.metric['acc'] = []
if 'pearsonr' in metric_names:
self.metric_update['pearsonr'] = metrics.PearsonR(self.num_targets)
self.metric['pearsonr'] = []
if 'auroc' in metric_names:
self.metric_update['auroc'] = tf.keras.metrics.AUC(curve='ROC')
self.metric['auroc'] = []
if 'aupr' in metric_names:
self.metric_update['aupr'] = tf.keras.metrics.AUC(curve='PR')
self.metric['aupr'] = []
if 'cosine' in metric_names:
self.metric_update['cosine'] = tf.keras.metrics.CosineSimilarity()
self.metric['cosine'] = []
if 'kld' in metric_names:
self.metric_update['kld'] = tf.keras.metrics.KLDivergence()
self.metric['kld'] = []
if 'mse' in metric_names:
self.metric_update['mse'] = tf.keras.metrics.MeanSquaredError()
self.metric['mse'] = []
if 'mae' in metric_names:
self.metric_update['mae'] = tf.keras.metrics.MeanAbsoluteError()
self.metric['mae'] = []
if 'poisson' in metric_names:
self.metric_update['poisson'] = tf.keras.metrics.Poisson()
self.metric['poisson'] = []
def evaluate(self, seq_data, head_i=None, loss='poisson'):
""" Evaluate model on SeqDataset. """
# choose model
if self.ensemble is not None:
model = self.ensemble
elif head_i is not None:
model = self.models[head_i]
else:
model = self.model
# compile with dense metrics
num_targets = model.output_shape[-1]
if loss == 'bce':
model.compile(optimizer=tf.keras.optimizers.SGD(),
loss=loss,
metrics=[
metrics.SeqAUC(curve='ROC', summarize=False),
metrics.SeqAUC(curve='PR', summarize=False)
])
else:
model.compile(optimizer=tf.keras.optimizers.SGD(),
loss=loss,
metrics=[
metrics.PearsonR(num_targets, summarize=False),
metrics.R2(num_targets, summarize=False)
])
# evaluate
return model.evaluate(seq_data.dataset)
def compile(self, seqnn_model):
# for model in seqnn_model.models:
if self.loss == 'bce':
model_metrics = [metrics.SeqAUC(curve='ROC'), metrics.SeqAUC(curve='PR')]
else:
# num_targets = model.output_shape[-1]
num_targets = seqnn_model.layers[-1].output_shape[-1]
model_metrics = [metrics.PearsonR(num_targets), metrics.R2(num_targets)]
seqnn_model.compile(loss=self.loss_fn,
optimizer=self.optimizer,
metrics=model_metrics)
self.compiled = True
def fit_tape(self, seqnn_model):
if not self.compiled:
self.compile(seqnn_model)
model = seqnn_model.model
# metrics
num_targets = model.output_shape[-1]
train_loss = tf.keras.metrics.Mean(name='train_loss')
train_r = metrics.PearsonR(num_targets, name='train_r')
train_r2 = metrics.R2(num_targets, name='train_r2')
valid_loss = tf.keras.metrics.Mean(name='valid_loss')
valid_r = metrics.PearsonR(num_targets, name='valid_r')
valid_r2 = metrics.R2(num_targets, name='valid_r2')
if self.strategy is None:
@tf.function
def train_step(x, y):
with tf.GradientTape() as tape:
pred = model(x, training=True)
loss = self.loss_fn(y, pred) + sum(model.losses)
train_loss(loss)
train_r(y, pred)
train_r2(y, pred)
gradients = tape.gradient(loss, model.trainable_variables)
self.optimizer.apply_gradients(
zip(gradients, model.trainable_variables))
@tf.function
def eval_step(x, y):
pred = model(x, training=False)
loss = self.loss_fn(y, pred) + sum(model.losses)
valid_loss(loss)
valid_r(y, pred)
valid_r2(y, pred)
else:
def train_step(x, y):
with tf.GradientTape() as tape:
pred = model(x, training=True)
loss_batch_len = self.loss_fn(y, pred)
loss_batch = tf.reduce_mean(loss_batch_len, axis=-1)
loss = tf.reduce_sum(loss_batch) / self.batch_size
loss += sum(model.losses) / self.num_gpu
train_r(y, pred)
train_r2(y, pred)
gradients = tape.gradient(loss, model.trainable_variables)
self.optimizer.apply_gradients(
zip(gradients, model.trainable_variables))
return loss
@tf.function
def train_step_distr(xd, yd):
replica_losses = self.strategy.run(train_step, args=(xd, yd))
loss = self.strategy.reduce(tf.distribute.ReduceOp.SUM,
replica_losses,
axis=None)
train_loss(loss)
def eval_step(x, y):
pred = model(x, training=False)
loss = self.loss_fn(y, pred) + sum(model.losses)
valid_loss(loss)
valid_r(y, pred)
valid_r2(y, pred)
@tf.function
def eval_step_distr(xd, yd):
return self.strategy.run(eval_step, args=(xd, yd))
# improvement variables
valid_best = -np.inf
unimproved = 0
# training loop
for ei in range(self.train_epochs_max):
if ei >= self.train_epochs_min and unimproved > self.patience:
break
else:
# train
t0 = time.time()
train_iter = iter(self.train_data[0].dataset)
for si in range(self.train_epoch_batches[0]):
x, y = next(train_iter)
if self.strategy is not None:
train_step_distr(x, y)
else:
train_step(x, y)
# evaluate
# eval_iter = iter(self.eval_data[0].dataset)
# for si in range(self.eval_epoch_batches[0]):
# x, y = next(eval_iter)
for x, y in self.eval_data[0].dataset:
if self.strategy is not None:
eval_step_distr(x, y)
else:
eval_step(x, y)
# print training accuracy
train_loss_epoch = train_loss.result().numpy()
train_r_epoch = train_r.result().numpy()
train_r2_epoch = train_r2.result().numpy()
print('Epoch %d - %ds - train_loss: %.4f - train_r: %.4f - train_r2: %.4f' % \
(ei, (time.time()-t0), train_loss_epoch, train_r_epoch, train_r2_epoch), end='')
# print validation accuracy
# valid_loss, valid_pr, valid_r2 = model.evaluate(self.eval_data[0].dataset, verbose=0)
valid_loss_epoch = valid_loss.result().numpy()
valid_r_epoch = valid_r.result().numpy()
valid_r2_epoch = valid_r2.result().numpy()
print(' - valid_loss: %.4f - valid_r: %.4f - valid_r2: %.4f' % \
(valid_loss_epoch, valid_r_epoch, valid_r2_epoch), end='')
# checkpoint
seqnn_model.save('%s/model_check.h5' % self.out_dir)
# check best
if valid_r_epoch > valid_best:
print(' - best!', end='')
unimproved = 0
valid_best = valid_r_epoch
seqnn_model.save('%s/model_best.h5' % self.out_dir)
else:
unimproved += 1
print('', flush=True)
# reset metrics
train_loss.reset_states()
train_r.reset_states()
train_r2.reset_states()
valid_loss.reset_states()
valid_r.reset_states()
valid_r2.reset_states()
def fit2(self, seqnn_model):
if not self.compiled:
self.compile(seqnn_model)
assert (len(seqnn_model.models) >= self.num_datasets)
################################################################
# prep
# metrics
train_loss, train_r, train_r2 = [], [], []
valid_loss, valid_r, valid_r2 = [], [], []
for di in range(self.num_datasets):
num_targets = seqnn_model.models[di].output_shape[-1]
train_loss.append(tf.keras.metrics.Mean(name='train%d_loss' % di))
train_r.append(metrics.PearsonR(num_targets,
name='train%d_r' % di))
train_r2.append(metrics.R2(num_targets, name='train%d_r2' % di))
valid_loss.append(tf.keras.metrics.Mean(name='valid%d_loss' % di))
valid_r.append(metrics.PearsonR(num_targets,
name='valid%d_r' % di))
valid_r2.append(metrics.R2(num_targets, name='valid%d_r2' % di))
# generate decorated train steps
"""
train_steps = []
for di in range(self.num_datasets):
model = seqnn_model.models[di]
@tf.function
def train_step(x, y):
with tf.GradientTape() as tape:
pred = model(x, training=tf.constant(True))
loss = self.loss_fn(y, pred) + sum(model.losses)
train_loss[di](loss)
train_r[di](y, pred)
train_r2[di](y, pred)
gradients = tape.gradient(loss, model.trainable_variables)
self.optimizer.apply_gradients(zip(gradients, model.trainable_variables))
train_steps.append(train_step)
"""
@tf.function
def train_step0(x, y):
with tf.GradientTape() as tape:
pred = seqnn_model.models[0](x, training=True)
loss = self.loss_fn(y, pred) + sum(
seqnn_model.models[0].losses)
train_loss[0](loss)
train_r[0](y, pred)
train_r2[0](y, pred)
gradients = tape.gradient(
loss, seqnn_model.models[0].trainable_variables)
self.optimizer.apply_gradients(
zip(gradients, seqnn_model.models[0].trainable_variables))
@tf.function
def eval_step0(x, y):
pred = seqnn_model.models[0](x, training=False)
loss = self.loss_fn(y, pred) + sum(seqnn_model.models[0].losses)
valid_loss[0](loss)
valid_r[0](y, pred)
valid_r2[0](y, pred)
if self.num_datasets > 1:
@tf.function
def train_step1(x, y):
with tf.GradientTape() as tape:
pred = seqnn_model.models[1](x, training=True)
loss = self.loss_fn(y, pred) + sum(
seqnn_model.models[1].losses)
train_loss[1](loss)
train_r[1](y, pred)
train_r2[1](y, pred)
gradients = tape.gradient(
loss, seqnn_model.models[1].trainable_variables)
self.optimizer.apply_gradients(
zip(gradients, seqnn_model.models[1].trainable_variables))
@tf.function
def eval_step1(x, y):
pred = seqnn_model.models[1](x, training=False)
loss = self.loss_fn(y, pred) + sum(
seqnn_model.models[1].losses)
valid_loss[1](loss)
valid_r[1](y, pred)
valid_r2[1](y, pred)
# improvement variables
valid_best = [-np.inf] * self.num_datasets
unimproved = [0] * self.num_datasets
################################################################
# training loop
for ei in range(self.train_epochs_max):
if ei >= self.train_epochs_min and np.min(
unimproved) > self.patience:
break
else:
# shuffle datasets
np.random.shuffle(self.dataset_indexes)
# get iterators
train_data_iters = [iter(td.dataset) for td in self.train_data]
# train
t0 = time.time()
for di in self.dataset_indexes:
x, y = next(train_data_iters[di])
if di == 0:
train_step0(x, y)
else:
train_step1(x, y)
print('Epoch %d - %ds' % (ei, (time.time() - t0)))
for di in range(self.num_datasets):
print(' Data %d' % di, end='')
model = seqnn_model.models[di]
# print training accuracy
print(' - train_loss: %.4f' %
train_loss[di].result().numpy(),
end='')
print(' - train_r: %.4f' % train_r[di].result().numpy(),
end='')
print(' - train_r: %.4f' % train_r2[di].result().numpy(),
end='')
# evaluate
for x, y in self.eval_data[di].dataset:
if di == 0:
eval_step0(x, y)
else:
eval_step1(x, y)
# print validation accuracy
print(' - valid_loss: %.4f' %
valid_loss[di].result().numpy(),
end='')
print(' - valid_r: %.4f' % valid_r[di].result().numpy(),
end='')
print(' - valid_r2: %.4f' % valid_r2[di].result().numpy(),
end='')
early_stop_stat = valid_r[di].result().numpy()
# checkpoint
model.save('%s/model%d_check.h5' % (self.out_dir, di))
# check best
if early_stop_stat > valid_best[di]:
print(' - best!', end='')
unimproved[di] = 0
valid_best[di] = early_stop_stat
model.save('%s/model%d_best.h5' % (self.out_dir, di))
else:
unimproved[di] += 1
print('', flush=True)
# reset metrics
train_loss[di].reset_states()
train_r[di].reset_states()
train_r2[di].reset_states()
valid_loss[di].reset_states()
valid_r[di].reset_states()
valid_r2[di].reset_states()
def main():
usage = 'usage: %prog [options] <data_dir> <model_name> <output_dir> <params_file>...'
parser = OptionParser(usage)
parser.add_option(
'-b',
dest='batch_size',
default=4,
help='Batch size for the model training [Default: %default]')
parser.add_option('-p',
dest='patience',
default=8,
help='Training patience [Default: %default]')
parser.add_option('-l',
dest='learning_rate',
default=0.1,
help='Learning rate [Default: %default]')
parser.add_option('-m',
dest='momentum',
default=0.99,
help='SGD momentum [Default: %default]')
parser.add_option('-e',
dest='n_epochs',
default=8,
help='Training patience [Default: %default]')
parser.add_option('--clip_norm',
dest='clip_norm',
default=1000000,
help='Training patience [Default: %default]')
(options, args) = parser.parse_args()
########TODO:ADD THE REST OF THE parameters
if len(args) < 4:
parser.error('Must provide data_dir, model and output directory.')
else:
data_dir = args[0]
model_name = args[1]
output_dir = args[2]
params_file = args[3]
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
####LOAD DATA
# read model parameters
with open(params_file) as params_open:
params = json.load(params_open)
params_model = params['model']
params_train = params['train']
# read datasets
train_data = []
eval_data = []
# load train data
train_data.append(
dataset.SeqDataset(data_dir,
split_label='train',
batch_size=params_train['batch_size'],
mode='train'))
# load eval data
eval_data.append(
dataset.SeqDataset(data_dir,
split_label='valid',
batch_size=params_train['batch_size'],
mode='eval'))
##########################################
# train, valid = load_data(data_dir, options.batch_size)
# print(type(valid[0]))
# print(len(valid))
# print(valid)
if model_name == 'basenji':
model = model_zoo.basenji_model((131072, 4), 3)
loss_fn = tf.keras.losses.Poisson(reduction=tf.keras.losses.Reduction.NONE)
early_stop = tf.keras.callbacks.EarlyStopping(
monitor='val_pearsonr', #'val_aupr',#
patience=options.patience,
verbose=1,
mode='max')
# early_stop = EarlyStoppingMin(monitor='val_pearsonr', mode='max', verbose=1,
# patience=options.patience, min_epoch=1)
save_best = tf.keras.callbacks.ModelCheckpoint(
'{}/model_best.h5'.format(output_dir),
save_best_only=True,
mode='max',
monitor='val_pearsonr',
verbose=1)
callbacks = [
early_stop,
tf.keras.callbacks.TensorBoard(output_dir),
tf.keras.callbacks.ModelCheckpoint('%s/model_check.h5' % output_dir),
save_best
]
# fit model
num_targets = model.output_shape[-1]
print('num_targets ', num_targets)
model_metrics = [metrics.PearsonR(num_targets), metrics.R2(num_targets)]
optimizer = tf.keras.optimizers.SGD(learning_rate=options.learning_rate,
momentum=options.momentum,
clipnorm=options.clip_norm)
model.compile(loss=loss_fn, optimizer=optimizer, metrics=model_metrics)
model.fit(train,
epochs=options.n_epochs,
callbacks=callbacks,
validation_data=valid)
