def __init__(self, seed, lr, optimizer=None):
self.lr = lr
self.seed = seed
self._optimizer = optimizer
self.graph = tf.Graph()
with self.graph.as_default():
tf.set_random_seed(123 + self.seed)
self.features, self.labels, self.train_op, self.eval_metric_ops, self.loss = self.create_model(
)
self.saver = tf.train.Saver()
self.sess = tf.Session(graph=self.graph,
config=tf.ConfigProto(
inter_op_parallelism_threads=1,
intra_op_parallelism_threads=1))
self.size = graph_size(self.graph)
with self.graph.as_default():
self.sess.run(tf.global_variables_initializer())
metadata = tf.RunMetadata()
opts = tf.profiler.ProfileOptionBuilder.float_operation()
self.flops = tf.profiler.profile(self.graph,
run_meta=metadata,
cmd='scope',
options=opts).total_float_ops
np.random.seed(self.seed)
def __init__(self, seed, lr, optimizer=None, gpu_fraction=0.2):
self.lr = lr
self.seed = seed
self._optimizer = optimizer
self.graph = tf.Graph()
with self.graph.as_default():
tf.set_random_seed(123 + self.seed)
self.features, self.labels, self.train_op, self.eval_metric_ops, self.loss = self.create_model()
self.saver = tf.train.Saver()
config=tf.ConfigProto(log_device_placement=False)
# config.gpu_options.per_process_gpu_memory_fraction = gpu_fraction
self.sess = tf.Session(graph=self.graph, config=config)
self.size = graph_size(self.graph)
with self.graph.as_default():
self.sess.run(tf.global_variables_initializer())
metadata = tf.RunMetadata()
opts = tf.profiler.ProfileOptionBuilder.float_operation()
self.flops = tf.profiler.profile(self.graph, run_meta=metadata, cmd='scope', options=opts).total_float_ops
np.random.seed(self.seed)
def __init__(self, lr):
self.lr = lr
self._optimizer = None
self.graph = tf.Graph()
with self.graph.as_default():
self.features, self.labels, self.is_train, self.train_op, self.eval_metric_ops, self.loss, self.var_grad = self.create_model()
self.saver = tf.train.Saver()
config = tf.ConfigProto()
config.gpu_options.allow_growth=True
self.sess = tf.Session(graph=self.graph, config=config)
self.size = graph_size(self.graph)
#self.profiler = tf.profiler.Profiler(self.sess.graph)
#self.profiler = model_analyzer.Profiler(self.sess.graph)
with self.graph.as_default():
self.sess.run(tf.global_variables_initializer())
metadata = tf.RunMetadata()
opts = tf.profiler.ProfileOptionBuilder.float_operation()
self.flops = tf.profiler.profile(self.graph, run_meta=metadata, cmd='scope', options=opts).total_float_ops
def __init__(self, seed, lr, optimizer=None):
self.lr = lr
self._optimizer = optimizer
self.graph = tf.Graph()
with self.graph.as_default():
tf.set_random_seed(123 + seed)
self.features, self.labels, self.train_op, self.eval_metric_ops, self.loss = self.create_model(
)
self.gradient_op = tf.gradients(self.loss,
tf.trainable_variables())
self.saver = tf.train.Saver()
self.sess = tf.Session(graph=self.graph)
self.size = graph_size(self.graph)
with self.graph.as_default():
self.sess.run(tf.global_variables_initializer())
metadata = tf.RunMetadata()
opts = tf.profiler.ProfileOptionBuilder.float_operation()
self.flops = tf.profiler.profile(self.graph,
run_meta=metadata,
cmd='scope',
options=opts).total_float_ops
def __init__(self, lr, seed, max_batch_size):
self.lr = lr
self._optimizer = None
self.rng = random.Random(seed)
self.graph = tf.Graph()
if seed is not None:
self.graph.seed = seed
with self.graph.as_default():
self.learning_rate_tensor = tf.placeholder(tf.float32, shape=[])
self.features, self.labels, self.loss_op, self.train_op, self.eval_metric_ops = self.create_model()
self.saver = tf.train.Saver()
self.sess = tf.Session(graph=self.graph)
self.size = graph_size(self.graph)
# largest batch size for which GPU will not run out of memory
self.max_batch_size = max_batch_size if max_batch_size is not None else 2 ** 14
with self.graph.as_default():
self.sess.run(tf.global_variables_initializer())
# metadata = tf.RunMetadata()
# opts = tf.profiler.ProfileOptionBuilder(
# tf.profiler.ProfileOptionBuilder.float_operation()
# ).with_empty_output().build()
# self.flops = tf.profiler.profile(self.graph, run_meta=metadata, cmd='scope', options=opts).total_float_ops
self.flops = 0
def __init__(self, seed, lr, optimizer=None):
self.lr = lr
self._optimizer = optimizer
self.graph = tf.Graph()
with self.graph.as_default():
tf.set_random_seed(seed)
self.features, self.labels, self.train_op, self.eval_metric_ops, self.loss, self.pred_ops = self.create_model()
self.saver = tf.train.Saver()
self.sess = tf.Session(graph=self.graph)
self.size = graph_size(self.graph)
with self.graph.as_default():
self.sess.run(tf.global_variables_initializer())
