def train(self, input_fn, steps=None):
"""Trains a model given training data `input_fn`.
:param input_fn: A function that constructs the input data for evaluation. The
function should construct and return one of the following:
* A `TFDataset` object, each elements of which is a tuple `(features, labels)`.
* A `tf.data.Dataset` object: Outputs of `Dataset` object must be a tuple
`(features, labels)` with same constraints as below.
* A tuple `(features, labels)`: Where `features` is a `tf.Tensor` or a dictionary
of string feature name to `Tensor` and `labels` is a `Tensor` or a
dictionary of string label name to `Tensor`. Both `features` and
`labels` are consumed by `model_fn`. They should satisfy the expectation
of `model_fn` from inputs.
:param steps: Number of steps for which to train the model.
Returns:
`self`, for chaining.
"""
with tf.Graph().as_default() as g:
global_step_tensor = self.estimator._create_and_assert_global_step(
g)
add_step_input = tf.placeholder(dtype=tf.int64, shape=())
assign_step = tf.assign_add(global_step_tensor, add_step_input)
result = self.estimator._call_input_fn(input_fn,
tf.estimator.ModeKeys.TRAIN)
if isinstance(result, TFDataset):
if not result.has_batch:
raise ValueError("The batch_size of TFDataset must be " +
"specified when used for training.")
spec = self._call_model_fn(result.feature_tensors,
result.label_tensors,
tf.estimator.ModeKeys.TRAIN,
self.config)
optim_method = TFOptimizer.to_bigdl_optim_method(
koptim_method=self.optimizer)
latest_checkpoint = self.estimator.latest_checkpoint()
with tf.Session() as sess:
saver = tf.train.Saver()
if latest_checkpoint:
saver.restore(sess, latest_checkpoint)
else:
sess.run(tf.global_variables_initializer())
opt = TFOptimizer.from_loss(
spec.loss,
optim_method,
session=sess,
clip_norm=self.gradient_clipping_norm,
clip_value=self.gradient_clipping_constant)
opt.optimize(MaxIteration(steps))
sess.run(assign_step, feed_dict={add_step_input: steps})
final_step = sess.run(global_step_tensor)
saver.save(sess,
self.estimator.model_dir + "/model",
global_step=final_step)
return self
return self.estimator.train(input_fn, steps=steps)
def train(self, input_fn, steps=None):
with tf.Graph().as_default() as g:
global_step_tensor = self.estimator._create_and_assert_global_step(
g)
add_step_input = tf.placeholder(dtype=tf.int64, shape=())
assign_step = tf.assign_add(global_step_tensor, add_step_input)
result = self.estimator._call_input_fn(input_fn,
tf.estimator.ModeKeys.TRAIN)
if isinstance(result, TFDataset):
if not result.has_batch:
raise ValueError("The batch_size of TFDataset must be " +
"specified when used for training.")
spec = self._call_model_fn(result.feature_tensors,
result.label_tensors,
tf.estimator.ModeKeys.TRAIN,
self.config)
optim_method = TFOptimizer.to_bigdl_optim_method(
koptim_method=self.optimizer)
latest_checkpoint = self.estimator.latest_checkpoint()
with tf.Session() as sess:
saver = tf.train.Saver()
if latest_checkpoint:
saver.restore(sess, latest_checkpoint)
else:
sess.run(tf.global_variables_initializer())
opt = TFOptimizer.from_loss(spec.loss,
optim_method,
session=sess)
opt.optimize(MaxIteration(steps))
sess.run(assign_step, feed_dict={add_step_input: steps})
final_step = sess.run(global_step_tensor)
saver.save(sess,
self.estimator.model_dir + "/model",
global_step=final_step)
return self
return self.estimator.train(input_fn, steps=steps)