def add_layer(inputs, in_size, out_size, n_layer, activation_function=None):
# add one more layer and return the output of this layer
layer_name = 'layer%s' % n_layer
with tf.name_scope('layer'):
with tf.name_scope('weights'):
Weights = tf.Variable(tf.random_normal([in_size, out_size]),
name='W')
# histogram_summary(layer_name + '/weights', Weights)
tf.summary.histogram(layer_name + '/weights',
Weights) # tensorflow >= 0.12
with tf.name_scope('biases'):
biases = tf.Variable(tf.zeros([1, out_size]) + 0.1, name='b')
# histogram_summary(layer_name + '/biase', biases)
tf.summary.histogram(layer_name + '/biases',
biases) # Tensorflow >= 0.12
with tf.name_scope('Wx_plus_b'):
Wx_plus_b = tf.add(tf.matmul(inputs, Weights), biases)
if activation_function is None:
outputs = Wx_plus_b
else:
outputs = activation_function(Wx_plus_b)
histogram_summary(layer_name + '/outputs', outputs)
# tf.summary.histogram(layer_name + '/outputs', outputs) # Tensorflow >= 0.12
return outputs
def add_gradients_summaries(grads_and_vars):
"""Add summaries to gradients.
Args:
grads_and_vars: A list of gradient to variable pairs (tuples).
Returns:
The list of created summaries.
"""
summaries = []
for grad, var in grads_and_vars:
if grad is not None:
if isinstance(grad, ops.IndexedSlices):
grad_values = grad.values
else:
grad_values = grad
summaries.append(
logging_ops.histogram_summary(var.op.name + ':gradient',
grad_values))
summaries.append(
logging_ops.histogram_summary(
var.op.name + ':gradient_norm',
clip_ops.global_norm([grad_values])))
else:
logging.info('Var %s has no gradient', var.op.name)
return summaries
def logistic_regression(X, y, class_weight=None, init_mean=None,
init_stddev=1.0):
"""Creates logistic regression TensorFlow subgraph.
Args:
X: tensor or placeholder for input features,
shape should be [batch_size, n_features].
y: tensor or placeholder for target,
shape should be [batch_size, n_classes].
class_weight: tensor, [n_classes], where for each class
it has weight of the class. If not provided
will check if graph contains tensor `class_weight:0`.
If that is not provided either all ones are used.
init_mean: the mean value to use for initialization.
init_stddev: the standard devation to use for initialization.
Returns:
Predictions and loss tensors.
Side effects:
The variables linear_regression.weights and linear_regression.bias are
initialized as follows. If init_mean is not None, then initialization
will be done using a random normal initializer with the given init_mean
and init_stddv. (These may be set to 0.0 each if a zero initialization
is desirable for convex use cases.) If init_mean is None, then the
uniform_unit_scaling_initialzer will be used.
"""
with vs.variable_scope('logistic_regression'):
logging_ops.histogram_summary('logistic_regression.X', X)
logging_ops.histogram_summary('logistic_regression.y', y)
# Set up the requested initialization.
if (init_mean is None):
weights = vs.get_variable('weights',
[X.get_shape()[1], y.get_shape()[-1]])
bias = vs.get_variable('bias',
[y.get_shape()[-1]])
else:
weights = vs.get_variable('weights',
[X.get_shape()[1], y.get_shape()[-1]],
initializer=init_ops.random_normal_initializer(
init_mean, init_stddev))
bias = vs.get_variable('bias',
[y.get_shape()[-1]],
initializer=init_ops.random_normal_initializer(
init_mean, init_stddev))
logging_ops.histogram_summary('logistic_regression.weights', weights)
logging_ops.histogram_summary('logistic_regression.bias', bias)
# If no class weight provided, try to retrieve one from pre-defined
# tensor name in the graph.
if not class_weight:
try:
class_weight = ops.get_default_graph().get_tensor_by_name('class_weight:0')
except KeyError:
pass
return losses_ops.softmax_classifier(X, y, weights, bias,
class_weight=class_weight)
def linear_regression(x, y, init_mean=None, init_stddev=1.0):
"""Creates linear regression TensorFlow subgraph.
Args:
x: tensor or placeholder for input features.
y: tensor or placeholder for target.
init_mean: the mean value to use for initialization.
init_stddev: the standard devation to use for initialization.
Returns:
Predictions and loss tensors.
Side effects:
The variables linear_regression.weights and linear_regression.bias are
initialized as follows. If init_mean is not None, then initialization
will be done using a random normal initializer with the given init_mean
and init_stddv. (These may be set to 0.0 each if a zero initialization
is desirable for convex use cases.) If init_mean is None, then the
uniform_unit_scaling_initialzer will be used.
"""
with vs.variable_scope('linear_regression'):
scope_name = vs.get_variable_scope().name
logging_ops.histogram_summary('%s.x' % scope_name, x)
logging_ops.histogram_summary('%s.y' % scope_name, y)
dtype = x.dtype.base_dtype
y_shape = y.get_shape()
if len(y_shape) == 1:
output_shape = 1
else:
output_shape = y_shape[1]
# Set up the requested initialization.
if init_mean is None:
weights = vs.get_variable('weights',
[x.get_shape()[1], output_shape],
dtype=dtype)
bias = vs.get_variable('bias', [output_shape], dtype=dtype)
else:
weights = vs.get_variable(
'weights', [x.get_shape()[1], output_shape],
initializer=init_ops.random_normal_initializer(init_mean,
init_stddev,
dtype=dtype),
dtype=dtype)
bias = vs.get_variable(
'bias', [output_shape],
initializer=init_ops.random_normal_initializer(init_mean,
init_stddev,
dtype=dtype),
dtype=dtype)
logging_ops.histogram_summary('%s.weights' % scope_name, weights)
logging_ops.histogram_summary('%s.bias' % scope_name, bias)
return losses_ops.mean_squared_error_regressor(x, y, weights, bias)
def linear_regression(x, y, init_mean=None, init_stddev=1.0):
"""Creates linear regression TensorFlow subgraph.
Args:
x: tensor or placeholder for input features.
y: tensor or placeholder for target.
init_mean: the mean value to use for initialization.
init_stddev: the standard devation to use for initialization.
Returns:
Predictions and loss tensors.
Side effects:
The variables linear_regression.weights and linear_regression.bias are
initialized as follows. If init_mean is not None, then initialization
will be done using a random normal initializer with the given init_mean
and init_stddv. (These may be set to 0.0 each if a zero initialization
is desirable for convex use cases.) If init_mean is None, then the
uniform_unit_scaling_initialzer will be used.
"""
with vs.variable_scope('linear_regression'):
scope_name = vs.get_variable_scope().name
logging_ops.histogram_summary('%s.x' % scope_name, x)
logging_ops.histogram_summary('%s.y' % scope_name, y)
dtype = x.dtype.base_dtype
y_shape = y.get_shape()
if len(y_shape) == 1:
output_shape = 1
else:
output_shape = y_shape[1]
# Set up the requested initialization.
if init_mean is None:
weights = vs.get_variable(
'weights', [x.get_shape()[1], output_shape], dtype=dtype)
bias = vs.get_variable('bias', [output_shape], dtype=dtype)
else:
weights = vs.get_variable('weights', [x.get_shape()[1], output_shape],
initializer=init_ops.random_normal_initializer(
init_mean, init_stddev, dtype=dtype),
dtype=dtype)
bias = vs.get_variable('bias', [output_shape],
initializer=init_ops.random_normal_initializer(
init_mean, init_stddev, dtype=dtype),
dtype=dtype)
logging_ops.histogram_summary('%s.weights' % scope_name, weights)
logging_ops.histogram_summary('%s.bias' % scope_name, bias)
return losses_ops.mean_squared_error_regressor(x, y, weights, bias)
def add_gradients_summaries(grads_and_vars):
"""Add summaries to gradients.
Args:
grads_and_vars: A list of gradient to variable pairs (tuples).
Returns:
The list of created summaries.
"""
summaries = []
for grad, var in grads_and_vars:
if grad is not None:
if isinstance(grad, ops.IndexedSlices):
grad_values = grad.values
else:
grad_values = grad
summaries.append(logging_ops.histogram_summary(
var.op.name + ':gradient', grad_values))
summaries.append(logging_ops.histogram_summary(
var.op.name + ':gradient_norm', clip_ops.global_norm([grad_values])))
else:
logging.info('Var %s has no gradient', var.op.name)
return summaries
def _add_hidden_layer_summary(value, tag):
logging_ops.scalar_summary("%s/fraction_of_zero_values" % tag,
nn.zero_fraction(value))
logging_ops.histogram_summary("%s/activation" % tag, value)
def _setup_training(self):
"""Sets up graph, model and trainer."""
# Create config if not given.
if self._config is None:
self._config = RunConfig(verbose=self.verbose)
# Create new graph.
self._graph = ops.Graph()
self._graph.add_to_collection("IS_TRAINING", True)
with self._graph.as_default():
random_seed.set_random_seed(self._config.tf_random_seed)
self._global_step = variables.Variable(
0, name="global_step", trainable=False)
# Setting up inputs and outputs.
self._inp, self._out = self._data_feeder.input_builder()
# If class weights are provided, add them to the graph.
# Different loss functions can use this tensor by name.
if self.class_weight:
self._class_weight_node = constant_op.constant(
self.class_weight, name='class_weight')
# Add histograms for X and y if they are floats.
if self._data_feeder.input_dtype in (np.float32, np.float64):
logging_ops.histogram_summary("X", self._inp)
if self._data_feeder.output_dtype in (np.float32, np.float64):
logging_ops.histogram_summary("y", self._out)
# Create model's graph.
self._model_predictions, self._model_loss = self.model_fn(
self._inp, self._out)
# Create trainer and augment graph with gradients and optimizer.
# Additionally creates initialization ops.
learning_rate = self.learning_rate
optimizer = self.optimizer
if callable(learning_rate):
learning_rate = learning_rate(self._global_step)
if callable(optimizer):
optimizer = optimizer(learning_rate)
self._train = optimizers.optimize_loss(self._model_loss, self._global_step,
learning_rate=learning_rate,
optimizer=optimizer, clip_gradients=self.clip_gradients)
# Update ops during training, e.g. batch_norm_ops
self._train = control_flow_ops.group(self._train, *ops.get_collection('update_ops'))
# Merge all summaries into single tensor.
self._summaries = logging_ops.merge_all_summaries()
# Get all initializers for all trainable variables.
self._initializers = variables.initialize_all_variables()
# Create model's saver capturing all the nodes created up until now.
self._saver = train.Saver(
max_to_keep=self._config.keep_checkpoint_max,
keep_checkpoint_every_n_hours=self._config.keep_checkpoint_every_n_hours)
# Enable monitor to create validation data dict with appropriate tf placeholders
self._monitor.create_val_feed_dict(self._inp, self._out)
# Create session to run model with.
self._session = session.Session(self._config.tf_master, config=self._config.tf_config)
# Run parameter initializers.
self._session.run(self._initializers)
def add_histogram_summary(self, v, name):
logging_ops.histogram_summary("{}:{}".format(self.name, name), v)
def optimize_loss(loss,
global_step,
learning_rate,
optimizer,
gradient_noise_scale=None,
gradient_multipliers=None,
clip_gradients=None,
moving_average_decay=None,
learning_rate_decay_fn=None,
update_ops=None,
variables=None,
name=None,
summaries=None):
"""Given loss and parameters for optimizer, returns a training op.
Args:
loss: Tensor, 0 dimensional.
global_step: Tensor, step counter for each update.
learning_rate: float or Tensor, magnitude of update per each training step.
optimizer: string, class or optimizer instance, used as trainer.
string should be name of optimizer, like 'SGD',
'Adam', 'Adagrad'. Full list in OPTIMIZER_CLS_NAMES constant.
class should be sub-class of tf.Optimizer that implements
`compute_gradients` and `apply_gradients` functions.
optimizer instance should be instantion of tf.Optimizer sub-class
and have `compute_gradients` and `apply_gradients` functions.
gradient_noise_scale: float or None, adds 0-mean normal noise scaled by this
value.
gradient_multipliers: dict of variables or variable names to floats.
If present, gradients for specified
variables will be multiplied by given constant.
clip_gradients: float or `None`, clips gradients by this value.
moving_average_decay: Deprecated. float or None, takes into account previous
loss to make learning smoother due to outliers.
learning_rate_decay_fn: function, takes `learning_rate` and `global_step`
`Tensor`s, returns `Tensor`.
Can be used to implement any learning rate decay
functions.
For example: tf.train.exponential_decay.
update_ops: list of update `Operation`s to execute at each step. If `None`,
uses elements of UPDATE_OPS collection. The order of execution
between `update_ops` and `loss` is non-deterministic.
variables: list of variables to optimize or
`None` to use all trainable variables.
name: The name for this operation is used to scope operations and summaries.
summaries: List of internal quantities to visualize on tensorboard. If not
set only the loss and the learning rate will be reported. The
complete list is in OPTIMIZER_SUMMARIES.
Returns:
Training op.
Raises:
ValueError: if optimizer is wrong type.
"""
with vs.variable_scope(name, "OptimizeLoss", [loss, global_step]):
# Update ops take UPDATE_OPS collection if not provided.
if update_ops is None:
update_ops = set(ops.get_collection(ops.GraphKeys.UPDATE_OPS))
# Make sure update ops are ran before computing loss.
if update_ops:
loss = control_flow_ops.with_dependencies(update_ops, loss)
# Moving average of the loss with decay.
# TODO(b/30439864): moving_average_decay should be removed.
if moving_average_decay is not None:
logging.warn("'moving_average_decay' is deprecated. Please use "
"tensorboard's builtin averaging instead.")
# Generate moving averages of the loss.
loss_averages = train.ExponentialMovingAverage(moving_average_decay,
name="avg")
loss_averages_op = loss_averages.apply([loss])
logging_ops.scalar_summary("loss/mean", loss_averages.average(loss))
loss = control_flow_ops.with_dependencies([loss_averages_op], loss)
# Learning rate variable, with possible decay.
if (isinstance(learning_rate, ops.Tensor)
and learning_rate.get_shape().ndims == 0):
lr = learning_rate
elif isinstance(learning_rate, float):
lr = vs.get_variable(
"learning_rate", [], trainable=False,
initializer=init_ops.constant_initializer(learning_rate))
else:
raise ValueError("Learning rate should be 0d Tensor or float. "
"Got %s of type %s" % (
str(learning_rate), str(type(learning_rate))))
if summaries is None:
summaries = ["loss", "learning_rate"]
if learning_rate_decay_fn is not None:
lr = learning_rate_decay_fn(lr, global_step)
if "learning_rate" in summaries:
logging_ops.scalar_summary("learning_rate", lr)
# Create optimizer, given specified parameters.
if isinstance(optimizer, six.string_types):
if optimizer not in OPTIMIZER_CLS_NAMES:
raise ValueError(
"Optimizer name should be one of [%s], you provided %s."
% (", ".join(OPTIMIZER_CLS_NAMES), optimizer))
opt = OPTIMIZER_CLS_NAMES[optimizer](learning_rate=lr)
elif isinstance(optimizer, type) and issubclass(optimizer,
optimizer_.Optimizer):
opt = optimizer(learning_rate=lr)
elif isinstance(optimizer, optimizer_.Optimizer):
opt = optimizer
else:
raise ValueError("Unrecognized optimizer: should be string, "
"subclass of Optimizer or instance of "
"subclass of Optimizer. Got %s." % str(optimizer))
# All trainable variables, if specific variables are not specified.
if variables is None:
variables = vars_.trainable_variables()
# Compute gradients.
gradients = opt.compute_gradients(loss, variables)
# Optionally add gradient noise.
if gradient_noise_scale is not None:
gradients = _add_scaled_noise_to_gradients(
gradients, gradient_noise_scale)
# Multiply some gradients.
if gradient_multipliers is not None:
gradients = _multiply_gradients(gradients, gradient_multipliers)
# Optionally clip gradients by global norm.
if clip_gradients is not None:
gradients = _clip_gradients_by_norm(gradients, clip_gradients)
# Add scalar summary for loss.
if "loss" in summaries:
logging_ops.scalar_summary("loss", loss)
# Add histograms for variables, gradients and gradient norms.
for gradient, variable in gradients:
if isinstance(gradient, ops.IndexedSlices):
grad_values = gradient.values
else:
grad_values = gradient
if grad_values is not None:
if "gradients" in summaries:
logging_ops.histogram_summary(variable.name + "/gradients",
grad_values)
if "gradient_norm" in summaries:
logging_ops.histogram_summary(variable.name + "/gradient_norm",
clip_ops.global_norm([grad_values]))
# Create gradient updates.
grad_updates = opt.apply_gradients(gradients,
global_step=global_step,
name="train")
# Ensure the train_tensor computes grad_updates.
train_tensor = control_flow_ops.with_dependencies([grad_updates], loss)
return train_tensor
def optimize_loss(loss,
global_step,
learning_rate,
optimizer,
gradient_noise_scale=None,
gradient_multipliers=None,
clip_gradients=None,
learning_rate_decay_fn=None,
update_ops=None,
variables=None,
name=None,
summaries=None,
colocate_gradients_with_ops=False):
"""Given loss and parameters for optimizer, returns a training op.
Various ways of passing optimizers, include:
- string, name of the optimizer like 'SGD', 'Adam', see OPTIMIZER_CLS_NAMES
for full list. E.g. `optimize_loss(..., optimizer='Adam')`.
- function, takes learning rate `Tensor` as argument and must return
`Optimizer` instance. E.g. `optimize_loss(...,
optimizer=lambda lr: tf.train.MomentumOptimizer(lr, momentum=0.5))`.
Alternatively, if `learning_rate` is `None`, the function takes no
arguments. E.g. `optimize_loss(..., learning_rate=None,
optimizer=lambda: tf.train.MomentumOptimizer(0.5, momentum=0.5))`.
- class, subclass of `Optimizer` that takes only one required argument -
learning rate, such as AdamOptimizer, AdagradOptimizer.
E.g. `optimize_loss(..., optimizer=tf.train.AdagradOptimizer)`.
- object, instance of subclass of `Optimizer`.
E.g., `optimizer_loss(..., optimizer=tf.train.AdagradOptimizer(0.5))`.
Args:
loss: Tensor, 0 dimensional.
global_step: Tensor, step counter for each update.
learning_rate: float or Tensor, magnitude of update per each training step.
optimizer: string, class or optimizer instance, used as trainer.
string should be name of optimizer, like 'SGD',
'Adam', 'Adagrad'. Full list in OPTIMIZER_CLS_NAMES constant.
class should be sub-class of `tf.Optimizer` that implements
`compute_gradients` and `apply_gradients` functions.
optimizer instance should be instantiation of `tf.Optimizer`
sub-class and have `compute_gradients` and `apply_gradients`
functions.
gradient_noise_scale: float or None, adds 0-mean normal noise scaled by this
value.
gradient_multipliers: dict of variables or variable names to floats.
If present, gradients for specified
variables will be multiplied by given constant.
clip_gradients: float or `None`, clips gradients by this value.
learning_rate_decay_fn: function, takes `learning_rate` and `global_step`
`Tensor`s, returns `Tensor`.
Can be used to implement any learning rate decay
functions.
For example: `tf.train.exponential_decay`.
update_ops: list of update `Operation`s to execute at each step. If `None`,
uses elements of UPDATE_OPS collection. The order of execution
between `update_ops` and `loss` is non-deterministic.
variables: list of variables to optimize or
`None` to use all trainable variables.
name: The name for this operation is used to scope operations and summaries.
summaries: List of internal quantities to visualize on tensorboard. If not
set only the loss and the learning rate will be reported. The
complete list is in OPTIMIZER_SUMMARIES.
colocate_gradients_with_ops: If True, try colocating gradients with the
corresponding op.
Returns:
Training op.
Raises:
ValueError: if optimizer is wrong type.
"""
with vs.variable_scope(name, "OptimizeLoss", [loss, global_step]):
# Update ops take UPDATE_OPS collection if not provided.
if update_ops is None:
update_ops = set(ops.get_collection(ops.GraphKeys.UPDATE_OPS))
# Make sure update ops are ran before computing loss.
if update_ops:
loss = control_flow_ops.with_dependencies(list(update_ops), loss)
# Learning rate variable, with possible decay.
lr = None
if learning_rate is not None:
if (isinstance(learning_rate, ops.Tensor)
and learning_rate.get_shape().ndims == 0):
lr = learning_rate
elif isinstance(learning_rate, float):
lr = vs.get_variable(
"learning_rate", [],
trainable=False,
initializer=init_ops.constant_initializer(learning_rate))
else:
raise ValueError(
"Learning rate should be 0d Tensor or float. "
"Got %s of type %s" %
(str(learning_rate), str(type(learning_rate))))
if summaries is None:
summaries = ["loss", "learning_rate"]
if learning_rate is not None and learning_rate_decay_fn is not None:
lr = learning_rate_decay_fn(lr, global_step)
if "learning_rate" in summaries:
logging_ops.scalar_summary("learning_rate", lr)
# Create optimizer, given specified parameters.
if isinstance(optimizer, six.string_types):
if lr is None:
raise ValueError(
"Learning rate is None, but should be specified if "
"optimizer is string (%s)." % optimizer)
if optimizer not in OPTIMIZER_CLS_NAMES:
raise ValueError(
"Optimizer name should be one of [%s], you provided %s." %
(", ".join(OPTIMIZER_CLS_NAMES), optimizer))
opt = OPTIMIZER_CLS_NAMES[optimizer](learning_rate=lr)
elif (isinstance(optimizer, type)
and issubclass(optimizer, optimizer_.Optimizer)):
if lr is None:
raise ValueError(
"Learning rate is None, but should be specified if "
"optimizer is class (%s)." % optimizer)
opt = optimizer(learning_rate=lr)
elif isinstance(optimizer, optimizer_.Optimizer):
opt = optimizer
elif callable(optimizer):
if learning_rate is not None:
opt = optimizer(lr)
else:
opt = optimizer()
if not isinstance(opt, optimizer_.Optimizer):
raise ValueError(
"Unrecognized optimizer: function should return "
"subclass of Optimizer. Got %s." % str(opt))
else:
raise ValueError(
"Unrecognized optimizer: should be string, "
"subclass of Optimizer, instance of "
"subclass of Optimizer or function with one argument. "
"Got %s." % str(optimizer))
# All trainable variables, if specific variables are not specified.
if variables is None:
variables = vars_.trainable_variables()
# Compute gradients.
gradients = opt.compute_gradients(
loss,
variables,
colocate_gradients_with_ops=colocate_gradients_with_ops)
# Optionally add gradient noise.
if gradient_noise_scale is not None:
gradients = _add_scaled_noise_to_gradients(gradients,
gradient_noise_scale)
# Multiply some gradients.
if gradient_multipliers is not None:
gradients = _multiply_gradients(gradients, gradient_multipliers)
# Optionally clip gradients by global norm.
if clip_gradients is not None:
gradients = _clip_gradients_by_norm(gradients, clip_gradients)
# Add scalar summary for loss.
if "loss" in summaries:
logging_ops.scalar_summary("loss", loss)
# Add histograms for variables, gradients and gradient norms.
for gradient, variable in gradients:
if isinstance(gradient, ops.IndexedSlices):
grad_values = gradient.values
else:
grad_values = gradient
if grad_values is not None:
if "gradients" in summaries:
logging_ops.histogram_summary(variable.name + "/gradients",
grad_values)
if "gradient_norm" in summaries:
logging_ops.histogram_summary(
variable.name + "/gradient_norm",
clip_ops.global_norm([grad_values]))
# Create gradient updates.
grad_updates = opt.apply_gradients(gradients,
global_step=global_step,
name="train")
# Ensure the train_tensor computes grad_updates.
train_tensor = control_flow_ops.with_dependencies([grad_updates], loss)
return train_tensor
def optimize_loss(
loss,
global_step,
learning_rate,
optimizer,
gradient_noise_scale=None,
gradient_multipliers=None,
clip_gradients=None,
learning_rate_decay_fn=None,
update_ops=None,
variables=None,
name=None,
summaries=None,
):
"""Given loss and parameters for optimizer, returns a training op.
Various ways of passing optimizers, include:
- string, name of the optimizer like 'SGD', 'Adam', see OPTIMIZER_CLS_NAMES
for full list. E.g. `optimize_loss(..., optimizer='Adam')`.
- function, takes learning rate `Tensor` as argument and must return
`Optimizer` instance. E.g. `optimize_loss(...,
optimizer=lambda lr: tf.train.MomentumOptimizer(lr, momentum=0.5))`.
Alternatively, if `learning_rate` is `None`, the function takes no
arguments. E.g. `optimize_loss(..., learning_rate=None,
optimizer=lambda: tf.train.MomentumOptimizer(0.5, momentum=0.5))`.
- class, subclass of `Optimizer` that takes only one required argument -
learning rate, such as AdamOptimizer, AdagradOptimizer.
E.g. `optimize_loss(..., optimizer=tf.train.AdagradOptimizer)`.
- object, instance of subclass of `Optimizer`.
E.g., `optimizer_loss(..., optimizer=tf.train.AdagradOptimizer(0.5))`.
Args:
loss: Tensor, 0 dimensional.
global_step: Tensor, step counter for each update.
learning_rate: float or Tensor, magnitude of update per each training step.
optimizer: string, class or optimizer instance, used as trainer.
string should be name of optimizer, like 'SGD',
'Adam', 'Adagrad'. Full list in OPTIMIZER_CLS_NAMES constant.
class should be sub-class of tf.Optimizer that implements
`compute_gradients` and `apply_gradients` functions.
optimizer instance should be instantion of `tf.Optimizer`
sub-class and have `compute_gradients` and `apply_gradients`
functions.
gradient_noise_scale: float or None, adds 0-mean normal noise scaled by this
value.
gradient_multipliers: dict of variables or variable names to floats.
If present, gradients for specified
variables will be multiplied by given constant.
clip_gradients: float or `None`, clips gradients by this value.
learning_rate_decay_fn: function, takes `learning_rate` and `global_step`
`Tensor`s, returns `Tensor`.
Can be used to implement any learning rate decay
functions.
For example: tf.train.exponential_decay.
update_ops: list of update `Operation`s to execute at each step. If `None`,
uses elements of UPDATE_OPS collection. The order of execution
between `update_ops` and `loss` is non-deterministic.
variables: list of variables to optimize or
`None` to use all trainable variables.
name: The name for this operation is used to scope operations and summaries.
summaries: List of internal quantities to visualize on tensorboard. If not
set only the loss and the learning rate will be reported. The
complete list is in OPTIMIZER_SUMMARIES.
Returns:
Training op.
Raises:
ValueError: if optimizer is wrong type.
"""
with vs.variable_scope(name, "OptimizeLoss", [loss, global_step]):
# Update ops take UPDATE_OPS collection if not provided.
if update_ops is None:
update_ops = set(ops.get_collection(ops.GraphKeys.UPDATE_OPS))
# Make sure update ops are ran before computing loss.
if update_ops:
loss = control_flow_ops.with_dependencies(list(update_ops), loss)
# Learning rate variable, with possible decay.
lr = None
if learning_rate is not None:
if isinstance(learning_rate, ops.Tensor) and learning_rate.get_shape().ndims == 0:
lr = learning_rate
elif isinstance(learning_rate, float):
lr = vs.get_variable(
"learning_rate", [], trainable=False, initializer=init_ops.constant_initializer(learning_rate)
)
else:
raise ValueError(
"Learning rate should be 0d Tensor or float. "
"Got %s of type %s" % (str(learning_rate), str(type(learning_rate)))
)
if summaries is None:
summaries = ["loss", "learning_rate"]
if learning_rate is not None and learning_rate_decay_fn is not None:
lr = learning_rate_decay_fn(lr, global_step)
if "learning_rate" in summaries:
logging_ops.scalar_summary("learning_rate", lr)
# Create optimizer, given specified parameters.
if isinstance(optimizer, six.string_types):
if lr is None:
raise ValueError(
"Learning rate is None, but should be specified if " "optimizer is string (%s)." % optimizer
)
if optimizer not in OPTIMIZER_CLS_NAMES:
raise ValueError(
"Optimizer name should be one of [%s], you provided %s."
% (", ".join(OPTIMIZER_CLS_NAMES), optimizer)
)
opt = OPTIMIZER_CLS_NAMES[optimizer](learning_rate=lr)
elif isinstance(optimizer, type) and issubclass(optimizer, optimizer_.Optimizer):
if lr is None:
raise ValueError(
"Learning rate is None, but should be specified if " "optimizer is class (%s)." % optimizer
)
opt = optimizer(learning_rate=lr)
elif isinstance(optimizer, optimizer_.Optimizer):
opt = optimizer
elif callable(optimizer):
if learning_rate is not None:
opt = optimizer(lr)
else:
opt = optimizer()
if not isinstance(opt, optimizer_.Optimizer):
raise ValueError(
"Unrecognized optimizer: function should return " "subclass of Optimizer. Got %s." % str(opt)
)
else:
raise ValueError(
"Unrecognized optimizer: should be string, "
"subclass of Optimizer, instance of "
"subclass of Optimizer or function with one argument. "
"Got %s." % str(optimizer)
)
# All trainable variables, if specific variables are not specified.
if variables is None:
variables = vars_.trainable_variables()
# Compute gradients.
gradients = opt.compute_gradients(loss, variables)
# Optionally add gradient noise.
if gradient_noise_scale is not None:
gradients = _add_scaled_noise_to_gradients(gradients, gradient_noise_scale)
# Multiply some gradients.
if gradient_multipliers is not None:
gradients = _multiply_gradients(gradients, gradient_multipliers)
# Optionally clip gradients by global norm.
if clip_gradients is not None:
gradients = _clip_gradients_by_norm(gradients, clip_gradients)
# Add scalar summary for loss.
if "loss" in summaries:
logging_ops.scalar_summary("loss", loss)
# Add histograms for variables, gradients and gradient norms.
for gradient, variable in gradients:
if isinstance(gradient, ops.IndexedSlices):
grad_values = gradient.values
else:
grad_values = gradient
if grad_values is not None:
if "gradients" in summaries:
logging_ops.histogram_summary(variable.name + "/gradients", grad_values)
if "gradient_norm" in summaries:
logging_ops.histogram_summary(variable.name + "/gradient_norm", clip_ops.global_norm([grad_values]))
# Create gradient updates.
grad_updates = opt.apply_gradients(gradients, global_step=global_step, name="train")
# Ensure the train_tensor computes grad_updates.
train_tensor = control_flow_ops.with_dependencies([grad_updates], loss)
return train_tensor
def optimize_loss(loss,
global_step,
learning_rate,
optimizer,
clip_gradients=None,
moving_average_decay=0.9,
learning_rate_decay_fn=None,
variables=None):
"""Given loss and parameters for optimizer, returns a training op.
Args:
loss: Tensor, 0 dimensional.
global_step: Tensor, step counter for each update.
learning_rate: float or Tensor, magnitude of update per each training step.
optimizer: string, class or optimizer instance, used as trainer.
string should be name of optimizer, like 'SGD',
'Adam', 'Adagrad'. Full list in OPTIMIZER_CLS_NAMES constant.
class should be sub-class of tf.Optimizer that implements
`compute_gradients` and `apply_gradients` functions.
optimizer instance should be instantion of tf.Optimizer sub-class
and have `compute_gradients` and `apply_gradients` functions.
clip_gradients: float or None, clips gradients by this value.
moving_average_decay: float or None, takes into account previous loss
to make learning smoother due to outliers.
learning_rate_decay_fn: function, takes learning_rate and global_step
Tensors, returns Tensor. Can be used to implement
any learning rate decay funcitons.
For example: tf.train.exponential_decay.
variables: list of variables to optimizer or none.
Returns:
Training op.
Raises:
ValueError: if optimizer is wrong type.
"""
# Moving average of the loss with decay.
if moving_average_decay is not None:
# Generate moving averages of the loss.
loss_averages = train.ExponentialMovingAverage(moving_average_decay,
name="avg")
loss_averages_op = loss_averages.apply([loss])
logging_ops.scalar_summary("loss/mean", loss_averages.average(loss))
loss = control_flow_ops.with_dependencies([loss_averages_op], loss)
# Learning rate variable, with possible decay.
if isinstance(learning_rate, ops.Tensor) and len(learning_rate.get_shape()) == 0:
lr = learning_rate
elif isinstance(learning_rate, float):
lr = vs.get_variable("learning_rate",
[],
trainable=False,
initializer=init_ops.constant_initializer(learning_rate))
else:
raise ValueError("Learning rate should be 0d Tensor or float. Got %s" %
str(learning_rate))
if learning_rate_decay_fn is not None:
lr = learning_rate_decay_fn(lr, global_step)
# Create optimizer, given specified parameters.
if isinstance(optimizer, six.string_types):
if optimizer not in OPTIMIZER_CLS_NAMES:
raise ValueError("Optimizer name should be one of [%s], you provided %s."
% (", ".join(OPTIMIZER_CLS_NAMES), optimizer))
opt = OPTIMIZER_CLS_NAMES[optimizer](learning_rate=lr)
elif isinstance(optimizer, type) and issubclass(optimizer,
optimizer_.Optimizer):
opt = optimizer(learning_rate=lr)
elif isinstance(optimizer, optimizer_.Optimizer):
opt = optimizer
else:
raise ValueError("Unrecognized optimizer: should be string, "
"subclass of Optimizer or instance of "
"subclass of Optimizer. Got %s." % str(optimizer))
# All trainable variables, if specific variables are not specified.
if variables is None:
variables = vars_.trainable_variables()
# Compute gradients and clip them if provided.
gradients = opt.compute_gradients(loss, variables)
if clip_gradients is not None:
gradients, variables = zip(*gradients)
clipped_gradients, _ = clip_ops.clip_by_global_norm(gradients,
clip_gradients)
gradients = list(zip(clipped_gradients, variables))
# Add scalar summary for loss.
logging_ops.scalar_summary("loss", loss)
# Add histograms for variables, gradients and gradient norms.
for gradient, variable in gradients:
if isinstance(gradient, ops.IndexedSlices):
grad_values = gradient.values
else:
grad_values = gradient
if grad_values is not None:
logging_ops.histogram_summary(variable.name, variable)
logging_ops.histogram_summary(variable.name + "/gradients", grad_values)
logging_ops.histogram_summary(variable.name + "/gradient_norm",
clip_ops.global_norm([grad_values]))
# Create gradient updates.
grad_updates = opt.apply_gradients(gradients,
global_step=global_step,
name="train")
# Make sure total_loss is valid.
final_loss = array_ops.check_numerics(loss, "Loss is inf or nan")
# Ensure the train_tensor computes grad_updates.
train_tensor = control_flow_ops.with_dependencies([grad_updates], final_loss)
return train_tensor
def _add_hidden_layer_summary(value, tag):
logging_ops.scalar_summary("%s/fraction_of_zero_values" % tag,
nn.zero_fraction(value))
logging_ops.histogram_summary("%s/activation" % tag, value)
def logistic_regression(X,
y,
class_weight=None,
init_mean=None,
init_stddev=1.0):
"""Creates logistic regression TensorFlow subgraph.
Args:
X: tensor or placeholder for input features,
shape should be [batch_size, n_features].
y: tensor or placeholder for target,
shape should be [batch_size, n_classes].
class_weight: tensor, [n_classes], where for each class
it has weight of the class. If not provided
will check if graph contains tensor `class_weight:0`.
If that is not provided either all ones are used.
init_mean: the mean value to use for initialization.
init_stddev: the standard devation to use for initialization.
Returns:
Predictions and loss tensors.
Side effects:
The variables linear_regression.weights and linear_regression.bias are
initialized as follows. If init_mean is not None, then initialization
will be done using a random normal initializer with the given init_mean
and init_stddv. (These may be set to 0.0 each if a zero initialization
is desirable for convex use cases.) If init_mean is None, then the
uniform_unit_scaling_initialzer will be used.
"""
with vs.variable_scope('logistic_regression'):
logging_ops.histogram_summary('logistic_regression.X', X)
logging_ops.histogram_summary('logistic_regression.y', y)
# Set up the requested initialization.
if (init_mean is None):
weights = vs.get_variable(
'weights',
[X.get_shape()[1], y.get_shape()[-1]])
bias = vs.get_variable('bias', [y.get_shape()[-1]])
else:
weights = vs.get_variable(
'weights',
[X.get_shape()[1], y.get_shape()[-1]],
initializer=init_ops.random_normal_initializer(
init_mean, init_stddev))
bias = vs.get_variable(
'bias', [y.get_shape()[-1]],
initializer=init_ops.random_normal_initializer(
init_mean, init_stddev))
logging_ops.histogram_summary('logistic_regression.weights', weights)
logging_ops.histogram_summary('logistic_regression.bias', bias)
# If no class weight provided, try to retrieve one from pre-defined
# tensor name in the graph.
if not class_weight:
try:
class_weight = ops.get_default_graph().get_tensor_by_name(
'class_weight:0')
except KeyError:
pass
return softmax_classifier(X,
y,
weights,
bias,
class_weight=class_weight)
def testHistogramSummary(self):
with self.cached_session():
c = constant_op.constant(3)
s = logging_ops.histogram_summary('tag', c)
self.assertEqual(s.op.type, u'HistogramSummary')
def _setup_training(self):
"""Sets up graph, model and trainer."""
# Create config if not given.
if self._config is None:
self._config = RunConfig(verbose=self.verbose)
# Create new graph.
self._graph = ops.Graph()
self._graph.add_to_collection("IS_TRAINING", True)
with self._graph.as_default():
random_seed.set_random_seed(self._config.tf_random_seed)
self._global_step = variables.Variable(0,
name="global_step",
trainable=False)
# Setting up inputs and outputs.
self._inp, self._out = self._data_feeder.input_builder()
# If class weights are provided, add them to the graph.
# Different loss functions can use this tensor by name.
if self.class_weight:
self._class_weight_node = constant_op.constant(
self.class_weight, name='class_weight')
# Add histograms for X and y if they are floats.
if self._data_feeder.input_dtype in (np.float32, np.float64):
logging_ops.histogram_summary("X", self._inp)
if self._data_feeder.output_dtype in (np.float32, np.float64):
logging_ops.histogram_summary("y", self._out)
# Create model's graph.
self._model_predictions, self._model_loss = self.model_fn(
self._inp, self._out)
# Set up a single operator to merge all the summaries
self._summaries = logging_ops.merge_all_summaries()
# Create trainer and augment graph with gradients and optimizer.
# Additionally creates initialization ops.
learning_rate = self.learning_rate
optimizer = self.optimizer
if callable(learning_rate):
learning_rate = learning_rate(self._global_step)
if callable(optimizer):
optimizer = optimizer(learning_rate)
self._train = optimizers.optimize_loss(
self._model_loss,
self._global_step,
learning_rate=learning_rate,
optimizer=optimizer,
clip_gradients=self.clip_gradients)
# Update ops during training, e.g. batch_norm_ops
self._train = control_flow_ops.group(
self._train, *ops.get_collection('update_ops'))
# Get all initializers for all trainable variables.
self._initializers = variables.initialize_all_variables()
# Create model's saver capturing all the nodes created up until now.
self._saver = train.Saver(
max_to_keep=self._config.keep_checkpoint_max,
keep_checkpoint_every_n_hours=self._config.
keep_checkpoint_every_n_hours)
# Enable monitor to create validation data dict with appropriate tf placeholders
self._monitor.create_val_feed_dict(self._inp, self._out)
# Create session to run model with.
self._session = session.Session(self._config.tf_master,
config=self._config.tf_config)
# Run parameter initializers.
self._session.run(self._initializers)
def optimize_loss(loss,
global_step,
learning_rate,
optimizer,
clip_gradients=None,
moving_average_decay=0.9,
learning_rate_decay_fn=None,
variables=None):
"""Given loss and parameters for optimizer, returns a training op.
Args:
loss: Tensor, 0 dimensional.
global_step: Tensor, step counter for each update.
learning_rate: float or Tensor, magnitude of update per each training step.
optimizer: string or function, used as optimizer for training.
clip_gradients: float or None, clips gradients by this value.
moving_average_decay: float or None, takes into account previous loss
to make learning smoother due to outliers.
learning_rate_decay_fn: function, takes learning_rate and global_step
Tensors, returns Tensor. Can be used to implement
any learning rate decay funcitons.
For example: tf.train.exponential_decay.
variables: list of variables to optimizer or none.
Returns:
Training op.
Raises:
ValueError: if optimizer is wrong type.
"""
# Moving average of the loss with decay.
if moving_average_decay is not None:
# Generate moving averages of the loss.
loss_averages = train.ExponentialMovingAverage(moving_average_decay,
name="avg")
loss_averages_op = loss_averages.apply([loss])
logging_ops.scalar_summary("loss/mean", loss_averages.average(loss))
loss = control_flow_ops.with_dependencies([loss_averages_op], loss)
# Convert optimizer into the optimizer class.
if isinstance(optimizer, str):
opt_cls = OPTIMIZER_CLS_NAMES[optimizer]
elif callable(optimizer):
opt_cls = optimizer
else:
raise ValueError("Unrecognized optimizer: should be string or function.")
# Learning rate variable, with possible decay.
lr = vs.get_variable("learning_rate",
[],
trainable=False,
initializer=init_ops.constant_initializer(learning_rate))
if learning_rate_decay_fn is not None:
lr = learning_rate_decay_fn(lr, global_step)
# Create optimizer.
opt = opt_cls(learning_rate=lr)
# All trainable variables, if specific variables are not specified.
if variables is None:
variables = vars_.trainable_variables()
# Compute gradients and clip them if provided.
gradients = opt.compute_gradients(loss, variables)
if clip_gradients is not None:
clipped_gradients, _ = clip_ops.clip_by_global_norm(gradients,
clip_gradients)
gradients = zip(clipped_gradients, variables)
# Add scalar summary for loss.
logging_ops.scalar_summary("loss", loss)
# Add histograms for variables, gradients and gradient norms.
for gradient, variable in gradients:
if isinstance(gradient, ops.IndexedSlices):
grad_values = gradient.values
else:
grad_values = gradient
logging_ops.histogram_summary(variable.name, variable)
logging_ops.histogram_summary(variable.name + "/gradients", grad_values)
logging_ops.histogram_summary(variable.name + "/gradient_norm",
clip_ops.global_norm([grad_values]))
# Create gradient updates.
grad_updates = opt.apply_gradients(gradients,
global_step=global_step,
name="train")
# Make sure total_loss is valid.
final_loss = array_ops.check_numerics(loss, "Loss is inf or nan")
# Ensure the train_tensor computes grad_updates.
train_tensor = control_flow_ops.with_dependencies([grad_updates], final_loss)
return train_tensor
def __init__(self, params, infer=False):
self.is_training = tf.placeholder(tf.bool)
self.output_keep_prob = tf.placeholder(tf.float32)
num_layers = params['nlayer']
rnn_size = params['n_hidden']
grad_clip = 10
cell_fn = tf.nn.rnn_cell.BasicLSTMCell
cell = cell_fn(rnn_size) #RNN size
cell = tf.nn.rnn_cell.MultiRNNCell([cell] * num_layers)
# cell = tf.nn.rnn_cell.DropoutWrapper(cell, output_keep_prob = self.output_keep_prob)
self.cell = cell
NOUT = params['n_output'] # end_of_stroke + prob + 2*(mu + sig) + corr
self.input_data = tf.placeholder(
dtype=tf.float32,
shape=[None, params['seq_length'], params['n_input']])
self.input_zero = tf.placeholder(
dtype=tf.float32,
shape=[None, params['seq_length'], params['n_input']])
self.target_data = tf.placeholder(
tf.float32,
[params["batch_size"] * params["seq_length"], params["n_output"]])
self.initial_state = cell.zero_state(batch_size=params['batch_size'],
dtype=tf.float32)
ran_noise = tf.random_normal(shape=[
params["batch_size"], params['seq_length'], params['n_input']
],
mean=0,
stddev=0.00008)
ran_noise = tf.mul(ran_noise, self.input_zero)
tmp_input = tf.nn.relu(self.input_data + ran_noise)
self.input_data = tf.select(self.is_training, tmp_input,
self.input_data)
outputs = []
state = self.initial_state
with tf.variable_scope("rnnlm"):
for time_step in range(params['seq_length']):
if time_step > 0: tf.get_variable_scope().reuse_variables()
(cell_output, state) = cell(self.input_data[:, time_step, :],
state)
outputs.append(cell_output)
rnn_output = tf.reshape(tf.concat(1, outputs),
[-1, params['n_hidden']])
with tf.variable_scope('rnnlm'):
output_w1 = tf.get_variable("output_w1", [rnn_size, rnn_size])
output_b1 = tf.get_variable("output_b1", [rnn_size])
output_w2 = tf.get_variable("output_w2", [rnn_size, NOUT])
output_b2 = tf.get_variable("output_b3", [NOUT])
hidden_1 = tf.add(tf.matmul(rnn_output, output_w1), output_b1)
self.final_output = tf.add(tf.matmul(hidden_1, output_w2), output_b2)
tmp = self.final_output - self.target_data
loss = tf.nn.l2_loss(tmp)
self.cost = tf.reduce_mean(loss)
self.final_state = state
tf.scalar_summary('losses/total_loss', loss)
self.lr = tf.Variable(0.0, trainable=False)
tvars = tf.trainable_variables()
grads, _ = tf.clip_by_global_norm(tf.gradients(self.cost, tvars),
grad_clip)
for grad in grads:
# if isinstance(grad, ops.grads):
# grad_values = grad.values
# else:
# grad_values = grad
grad_values = grad
logging_ops.histogram_summary(grad.op.name + ':gradient',
grad_values)
logging_ops.histogram_summary(grad.op.name + ':gradient_norm',
clip_ops.global_norm([grad_values]))
optimizer = tf.train.AdamOptimizer(self.lr)
self.train_op = optimizer.apply_gradients(zip(grads, tvars))
def testHistogramSummary(self):
with self.cached_session():
c = constant_op.constant(3)
s = logging_ops.histogram_summary('tag', c)
self.assertEqual(s.op.type, u'HistogramSummary')
def create_train_op(
total_loss,
optimizer,
global_step=None,
update_ops=None,
variables_to_train=None,
clip_gradient_norm=0,
summarize_gradients=False,
gate_gradients=tf_optimizer.Optimizer.GATE_OP,
aggregation_method=None,
colocate_gradients_with_ops=False):
"""Creates an `Operation` that evaluates the gradients and returns the loss.
Args:
total_loss: A `Tensor` representing the total loss.
optimizer: A tf.Optimizer to use for computing the gradients.
global_step: A `Tensor` representing the global step variable. If left as
`None`, then slim.variables.global_step() is used.
update_ops: an optional list of updates to execute. Note that the update_ops
that are used are the union of those update_ops passed to the function and
the value of slim.ops.GetUpdateOps(). Therefore, if `update_ops` is None,
then the value of slim.ops.GetUpdateOps() is still used.
variables_to_train: an optional list of variables to train. If None, it will
default to all tf.trainable_variables().
clip_gradient_norm: If greater than 0 then the gradients would be clipped
by it.
summarize_gradients: Whether or not add summaries for each gradient.
gate_gradients: How to gate the computation of gradients. See tf.Optimizer.
aggregation_method: Specifies the method used to combine gradient terms.
Valid values are defined in the class `AggregationMethod`.
colocate_gradients_with_ops: Whether or not to try colocating the gradients
with the ops that generated them.
Returns:
A `Tensor` that when evaluated, computes the gradients and returns the total
loss value.
"""
if global_step is None:
global_step = variables.get_or_create_global_step()
update_ops = set(update_ops or [])
# Make sure update_ops are computed before total_loss.
if update_ops:
with control_flow_ops.control_dependencies(update_ops):
barrier = control_flow_ops.no_op(name='update_barrier')
total_loss = control_flow_ops.with_dependencies([barrier], total_loss)
if variables_to_train is None:
# Default to tf.trainable_variables()
variables_to_train = tf_variables.trainable_variables()
else:
# Make sure that variables_to_train are in tf.trainable_variables()
for v in variables_to_train:
assert v in tf_variables.trainable_variables()
assert variables_to_train
# Create the gradients. Note that apply_gradients adds the gradient
# computation to the current graph.
grads = optimizer.compute_gradients(
total_loss, variables_to_train, gate_gradients=gate_gradients,
aggregation_method=aggregation_method,
colocate_gradients_with_ops=colocate_gradients_with_ops)
# Clip gradients.
if clip_gradient_norm > 0:
grads = clip_gradient_norms(grads, clip_gradient_norm)
# Summarize gradients.
if summarize_gradients:
for grad, var in grads:
if grad is not None:
if isinstance(grad, ops.IndexedSlices):
grad_values = grad.values
else:
grad_values = grad
logging_ops.histogram_summary(var.op.name + ':gradient', grad_values)
logging_ops.histogram_summary(var.op.name + ':gradient_norm',
clip_ops.global_norm([grad_values]))
else:
logging.info('Var %s has no gradient', var.op.name)
# Create gradient updates.
grad_updates = optimizer.apply_gradients(grads, global_step=global_step)
# Make sure total_loss is valid.
total_loss = array_ops.check_numerics(total_loss, 'LossTensor is inf or nan')
# Ensure the train_tensor computes grad_updates.
return control_flow_ops.with_dependencies([grad_updates], total_loss)
def _add_hidden_layer_summary(self, value, tag):
# TODO(zakaria): Move this code to tf.learn and add test.
logging_ops.scalar_summary("%s:fraction_of_zero_values" % tag, nn.zero_fraction(value))
logging_ops.histogram_summary("%s:activation" % tag, value)
def optimize_loss(loss,
global_step,
learning_rate,
optimizer,
gradient_noise_scale=None,
gradient_multipliers=None,
clip_gradients=None,
moving_average_decay=None,
learning_rate_decay_fn=None,
update_ops=None,
variables=None,
name=None,
summaries=None):
"""Given loss and parameters for optimizer, returns a training op.
Args:
loss: Tensor, 0 dimensional.
global_step: Tensor, step counter for each update.
learning_rate: float or Tensor, magnitude of update per each training step.
optimizer: string, class or optimizer instance, used as trainer.
string should be name of optimizer, like 'SGD',
'Adam', 'Adagrad'. Full list in OPTIMIZER_CLS_NAMES constant.
class should be sub-class of tf.Optimizer that implements
`compute_gradients` and `apply_gradients` functions.
optimizer instance should be instantion of tf.Optimizer sub-class
and have `compute_gradients` and `apply_gradients` functions.
gradient_noise_scale: float or None, adds 0-mean normal noise scaled by this
value.
gradient_multipliers: dict of variables or variable names to floats.
If present, gradients for specified
variables will be multiplied by given constant.
clip_gradients: float or `None`, clips gradients by this value.
moving_average_decay: Deprecated. float or None, takes into account previous
loss to make learning smoother due to outliers.
learning_rate_decay_fn: function, takes `learning_rate` and `global_step`
`Tensor`s, returns `Tensor`.
Can be used to implement any learning rate decay
functions.
For example: tf.train.exponential_decay.
update_ops: list of update `Operation`s to execute at each step. If `None`,
uses elements of UPDATE_OPS collection. The order of execution
between `update_ops` and `loss` is non-deterministic.
variables: list of variables to optimize or
`None` to use all trainable variables.
name: The name for this operation is used to scope operations and summaries.
summaries: List of internal quantities to visualize on tensorboard. If not
set only the loss and the learning rate will be reported. The
complete list is in OPTIMIZER_SUMMARIES.
Returns:
Training op.
Raises:
ValueError: if optimizer is wrong type.
"""
with vs.variable_scope(name, "OptimizeLoss", [loss, global_step]):
# Update ops take UPDATE_OPS collection if not provided.
if update_ops is None:
update_ops = set(ops.get_collection(ops.GraphKeys.UPDATE_OPS))
# Make sure update ops are ran before computing loss.
if update_ops:
loss = control_flow_ops.with_dependencies(list(update_ops), loss)
# Moving average of the loss with decay.
# TODO(b/30439864): moving_average_decay should be removed.
if moving_average_decay is not None:
logging.warn("'moving_average_decay' is deprecated. Please use "
"tensorboard's builtin averaging instead.")
# Generate moving averages of the loss.
loss_averages = train.ExponentialMovingAverage(
moving_average_decay, name="avg")
loss_averages_op = loss_averages.apply([loss])
logging_ops.scalar_summary("loss/mean",
loss_averages.average(loss))
loss = control_flow_ops.with_dependencies([loss_averages_op], loss)
# Learning rate variable, with possible decay.
if (isinstance(learning_rate, ops.Tensor)
and learning_rate.get_shape().ndims == 0):
lr = learning_rate
elif isinstance(learning_rate, float):
lr = vs.get_variable(
"learning_rate", [],
trainable=False,
initializer=init_ops.constant_initializer(learning_rate))
else:
raise ValueError("Learning rate should be 0d Tensor or float. "
"Got %s of type %s" %
(str(learning_rate), str(type(learning_rate))))
if summaries is None:
summaries = ["loss", "learning_rate"]
if learning_rate_decay_fn is not None:
lr = learning_rate_decay_fn(lr, global_step)
if "learning_rate" in summaries:
logging_ops.scalar_summary("learning_rate", lr)
# Create optimizer, given specified parameters.
if isinstance(optimizer, six.string_types):
if optimizer not in OPTIMIZER_CLS_NAMES:
raise ValueError(
"Optimizer name should be one of [%s], you provided %s." %
(", ".join(OPTIMIZER_CLS_NAMES), optimizer))
opt = OPTIMIZER_CLS_NAMES[optimizer](learning_rate=lr)
elif isinstance(optimizer, type) and issubclass(
optimizer, optimizer_.Optimizer):
opt = optimizer(learning_rate=lr)
elif isinstance(optimizer, optimizer_.Optimizer):
opt = optimizer
else:
raise ValueError("Unrecognized optimizer: should be string, "
"subclass of Optimizer or instance of "
"subclass of Optimizer. Got %s." % str(optimizer))
# All trainable variables, if specific variables are not specified.
if variables is None:
variables = vars_.trainable_variables()
# Compute gradients.
gradients = opt.compute_gradients(loss, variables)
# Optionally add gradient noise.
if gradient_noise_scale is not None:
gradients = _add_scaled_noise_to_gradients(gradients,
gradient_noise_scale)
# Multiply some gradients.
if gradient_multipliers is not None:
gradients = _multiply_gradients(gradients, gradient_multipliers)
# Optionally clip gradients by global norm.
if clip_gradients is not None:
gradients = _clip_gradients_by_norm(gradients, clip_gradients)
# Add scalar summary for loss.
if "loss" in summaries:
logging_ops.scalar_summary("loss", loss)
# Add histograms for variables, gradients and gradient norms.
for gradient, variable in gradients:
if isinstance(gradient, ops.IndexedSlices):
grad_values = gradient.values
else:
grad_values = gradient
if grad_values is not None:
if "gradients" in summaries:
logging_ops.histogram_summary(variable.name + "/gradients",
grad_values)
if "gradient_norm" in summaries:
logging_ops.histogram_summary(
variable.name + "/gradient_norm",
clip_ops.global_norm([grad_values]))
# Create gradient updates.
grad_updates = opt.apply_gradients(gradients,
global_step=global_step,
name="train")
# Ensure the train_tensor computes grad_updates.
train_tensor = control_flow_ops.with_dependencies([grad_updates], loss)
return train_tensor
def _add_hidden_layer_summary(self, value, tag):
# TODO(zakaria): Move this code to tf.learn and add test.
logging_ops.scalar_summary("%s:fraction_of_zero_values" % tag,
nn.zero_fraction(value))
logging_ops.histogram_summary("%s:activation" % tag, value)
def __init__(self, params, infer=False):
self.is_training = tf.placeholder(tf.bool)
self.output_keep_prob = tf.placeholder(tf.float32)
num_layers = params['nlayer']
rnn_size = params['n_hidden']
grad_clip = 10
cell_lst = []
for i in range(num_layers):
cell = tf.nn.rnn_cell.LSTMCell(
rnn_size,
initializer=tf.contrib.layers.xavier_initializer(
uniform=False),
forget_bias=1.0)
# if i==0:
# cell_drop = tf.nn.rnn_cell.DropoutWrapper(cell,input_keep_prob= self.output_keep_prob)
# cell=cell_drop
cell_drop = tf.nn.rnn_cell.DropoutWrapper(
cell, output_keep_prob=self.output_keep_prob)
cell = cell_drop
cell_lst.append(cell)
cell = tf.nn.rnn_cell.MultiRNNCell(cell_lst)
# cell_drop = tf.nn.rnn_cell.DropoutWrapper(cell,output_keep_prob= self.output_keep_prob)
# cell=cell_drop
self.cell = cell
NOUT = params['n_output'] # end_of_stroke + prob + 2*(mu + sig) + corr
self.input_data = tf.placeholder(
dtype=tf.float32,
shape=[None, params['seq_length'], params['n_input']])
self.input_zero = tf.placeholder(
dtype=tf.float32,
shape=[None, params['seq_length'], params['n_input']])
self.repeat_data = tf.placeholder(dtype=tf.int32,
shape=[None, params['seq_length']])
self.target_data = tf.placeholder(
tf.float32, [None, params["seq_length"], params["n_output"]])
self.initial_state = cell.zero_state(batch_size=params['batch_size'],
dtype=tf.float32)
#Noise applied only training phase and if only std bigger than 0
if (params["noise_std"] > 0.0):
ran_noise = tf.random_normal(shape=[
params["batch_size"], params['seq_length'], params['n_input']
],
mean=0,
stddev=params['noise_std'])
# ran_noise=tf.mul(ran_noise,self.input_zero)
tmp_input = tf.nn.relu(self.input_data + ran_noise)
self.input_data = tf.select(self.is_training, tmp_input,
self.input_data)
outputs = []
state = self.initial_state
with tf.variable_scope("rnnlm"):
for time_step in range(params['seq_length']):
if time_step > 0: tf.get_variable_scope().reuse_variables()
(cell_output, state) = cell(self.input_data[:, time_step, :],
state)
outputs.append(cell_output)
rnn_output = tf.reshape(tf.transpose(tf.pack(outputs), [1, 0, 2]),
[-1, params['n_hidden']])
with tf.variable_scope('rnnlm'):
output_w1 = tf.get_variable(
"output_w1", [rnn_size, NOUT],
initializer=tf.contrib.layers.xavier_initializer())
output_b1 = tf.get_variable("output_b1", [NOUT])
self.final_output = tf.add(tf.matmul(rnn_output, output_w1), output_b1)
flt = tf.squeeze(tf.reshape(self.repeat_data, [-1, 1]), [1])
where_flt = tf.not_equal(flt, 0)
indices = tf.where(where_flt)
tmp = self.final_output - tf.reshape(self.target_data,
[-1, params["n_output"]])
tmp = tf.gather(tmp, tf.squeeze(indices, [1]))
loss = tf.nn.l2_loss(tmp)
self.cost = tf.reduce_mean(loss)
self.final_state = state
tf.scalar_summary('losses/total_loss', loss)
self.lr = tf.Variable(0.0, trainable=False)
tvars = tf.trainable_variables()
total_parameters = 0
for variable in tvars:
# shape is an array of tf.Dimension
shape = variable.get_shape()
variable_parametes = 1
for dim in shape:
variable_parametes *= dim.value
total_parameters += variable_parametes
self.total_parameters = total_parameters
grads, _ = tf.clip_by_global_norm(tf.gradients(self.cost, tvars),
grad_clip)
for grad in grads:
# if isinstance(grad, ops.grads):
# grad_values = grad.values
# else:
# grad_values = grad
grad_values = grad
logging_ops.histogram_summary(grad.op.name + ':gradient',
grad_values)
logging_ops.histogram_summary(grad.op.name + ':gradient_norm',
clip_ops.global_norm([grad_values]))
optimizer = tf.train.AdamOptimizer(self.lr)
self.train_op = optimizer.apply_gradients(zip(grads, tvars))
