def regression(incoming, placeholder=None, optimizer='adam',
loss='categorical_crossentropy', metric='default',
learning_rate=0.001, dtype=tf.float32, batch_size=64,
shuffle_batches=True, to_one_hot=False, n_classes=None,
trainable_vars=None, restore=True, op_name=None, name=None):
""" Regression.
The regression layer is used in TFLearn to apply a regression (linear or
logistic) to the provided input. It requires to specify a TensorFlow
gradient descent optimizer 'optimizer' that will minimize the provided
loss function 'loss' (which calculate the errors). A metric can also be
provided, to evaluate the model performance.
A 'TrainOp' is generated, holding all information about the optimization
process. It is added to TensorFlow collection 'tf.GraphKeys.TRAIN_OPS'
and later used by TFLearn 'models' classes to perform the training.
An optional placeholder 'placeholder' can be specified to use a custom
TensorFlow target placeholder instead of creating a new one. The target
placeholder is added to the 'tf.GraphKeys.TARGETS' TensorFlow
collection, so that it can be retrieved later.
Additionaly, a list of variables 'trainable_vars' can be specified,
so that only them will be updated when applying the backpropagation
algorithm.
Input:
2-D Tensor Layer.
Output:
2-D Tensor Layer (Same as input).
Arguments:
incoming: `Tensor`. Incoming 2-D Tensor.
placeholder: `Tensor`. This regression target (label) placeholder.
If 'None' provided, a placeholder will be added automatically.
You can retrieve that placeholder through graph key: 'TARGETS',
or the 'placeholder' attribute of this function's returned tensor.
optimizer: `str` (name), `Optimizer` or `function`. Optimizer to use.
Default: 'adam' (Adaptive Moment Estimation).
loss: `str` (name) or `function`. Loss function used by this layer
optimizer. Default: 'categorical_crossentropy'.
metric: `str`, `Metric` or `function`. The metric to be used.
Default: 'default' metric is 'accuracy'. To disable metric
calculation, set it to 'None'.
learning_rate: `float`. This layer optimizer's learning rate.
dtype: `tf.types`. This layer placeholder type. Default: tf.float32.
batch_size: `int`. Batch size of data to use for training. tflearn
supports different batch size for every optimizers. Default: 64.
shuffle_batches: `bool`. Shuffle or not this optimizer batches at
every epoch. Default: True.
to_one_hot: `bool`. If True, labels will be encoded to one hot vectors.
'n_classes' must then be specified.
n_classes: `int`. The total number of classes. Only required when using
'to_one_hot' option.
trainable_vars: list of `Variable`. If specified, this regression will
only update given variable weights. Else, all trainale variable
are going to be updated.
restore: `bool`. If False, variables related to optimizers such
as moving averages will not be restored when loading a
pre-trained model.
op_name: A name for this layer optimizer (optional).
Default: optimizer op name.
name: A name for this layer's placeholder scope.
Attributes:
placeholder: `Tensor`. Placeholder for feeding labels.
"""
input_shape = utils.get_incoming_shape(incoming)
if placeholder is None:
pscope = "TargetsData" if not name else name
with tf.name_scope(pscope):
placeholder = tf.placeholder(shape=input_shape, dtype=dtype, name="Y")
tf.add_to_collection(tf.GraphKeys.TARGETS, placeholder)
if to_one_hot:
if n_classes is None:
raise Exception("'n_classes' is required when using 'to_one_hot'.")
placeholder = core.one_hot_encoding(placeholder, n_classes)
step_tensor = None
# Building Optimizer
if isinstance(optimizer, str):
_opt = optimizers.get(optimizer)(learning_rate)
op_name = op_name if op_name else type(_opt).__name__
_opt.build()
optimizer = _opt.get_tensor()
elif isinstance(optimizer, optimizers.Optimizer):
op_name = op_name if op_name else type(optimizer).__name__
if optimizer.has_decay:
step_tensor = tf.Variable(0., name="Training_step",
trainable=False)
optimizer.build(step_tensor)
optimizer = optimizer.get_tensor()
elif hasattr(optimizer, '__call__'):
try:
optimizer, step_tensor = optimizer(learning_rate)
except Exception as e:
print(e.message)
print("Reminder: Custom Optimizer function must return (optimizer, "
"step_tensor) and take one argument: 'learning_rate'. "
"Note that returned step_tensor can be 'None' if no decay.")
exit()
elif not isinstance(optimizer, tf.train.Optimizer):
raise ValueError("Invalid Optimizer type.")
inputs = tf.get_collection(tf.GraphKeys.INPUTS)
#inputs = tf.concat(0, utils.get_tensor_parents_placeholders(incoming))
# Building metric
# No auto accuracy for linear regression
if len(input_shape) == 1 and metric == 'default':
metric = None
if metric is not None:
# Default metric is accuracy
if metric == 'default': metric = 'accuracy'
if isinstance(metric, str):
metric = metrics.get(metric)()
metric.build(incoming, placeholder, inputs)
metric = metric.get_tensor()
elif isinstance(metric, metrics.Metric):
metric.build(incoming, placeholder, inputs)
metric = metric.get_tensor()
elif hasattr(metric, '__call__'):
try:
metric = metric(incoming, placeholder, inputs)
except Exception as e:
print(e.message)
print('Reminder: Custom metric function arguments must be '
'define as follow: custom_metric(y_pred, y_true, x).')
exit()
elif not isinstance(metric, tf.Tensor):
ValueError("Invalid Metric type.")
# Building other ops (loss, training ops...)
if isinstance(loss, str):
loss = objectives.get(loss)(incoming, placeholder)
# Check if function
elif hasattr(loss, '__call__'):
try:
loss = loss(incoming, placeholder)
except Exception as e:
print(e.message)
print('Reminder: Custom loss function arguments must be define as '
'follow: custom_loss(y_pred, y_true).')
exit()
elif not isinstance(loss, tf.Tensor):
raise ValueError("Invalid Loss type.")
tr_vars = trainable_vars
if not tr_vars:
tr_vars = tf.trainable_variables()
if not restore:
tf.add_to_collection(tf.GraphKeys.EXCL_RESTORE_VARS, 'moving_avg')
tf.add_to_collection(tf.GraphKeys.EXCL_RESTORE_VARS,
optimizer._name + '/')
tr_op = TrainOp(loss=loss,
optimizer=optimizer,
metric=metric,
trainable_vars=tr_vars,
batch_size=batch_size,
shuffle=shuffle_batches,
step_tensor=step_tensor,
name=op_name)
tf.add_to_collection(tf.GraphKeys.TRAIN_OPS, tr_op)
if not hasattr(incoming, '__len__'):
incoming.placeholder = placeholder
return incoming
def regression(incoming,
placeholder=None,
optimizer='adam',
loss='categorical_crossentropy',
metric='default',
learning_rate=0.001,
dtype=tf.float32,
batch_size=64,
shuffle_batches=True,
to_one_hot=False,
n_classes=None,
trainable_vars=None,
restore=True,
op_name=None,
name=None):
""" Regression.
Input:
2-D Tensor Layer.
Output:
2-D Tensor Layer (Same as input).
Arguments:
incoming: `Tensor`. Incoming 2-D Tensor.
placeholder: `Tensor`. This regression target (label) placeholder.
If 'None' provided, a placeholder will be added automatically.
You can retrieve that placeholder through graph key: 'TARGETS',
or the 'placeholder' attribute of this function's returned tensor.
optimizer: `str` (name), `Optimizer` or `function`. Optimizer to use.
Default: 'sgd' (Stochastic Descent Gradient).
loss: `str` (name) or `function`. Loss function used by this layer
optimizer. Default: 'categorical_crossentropy'.
metric: `str`, `Metric` or `function`. The metric to be used.
Default: 'default' metric is 'accuracy'. To disable metric
calculation, set it to 'None'.
learning_rate: `float`. This layer optimizer's learning rate.
dtype: `tf.types`. This layer placeholder type. Default: tf.float32.
batch_size: `int`. Batch size of data to use for training. tflearn
supports different batch size for every optimizers. Default: 64.
shuffle_batches: `bool`. Shuffle or not this optimizer batches at
every epoch. Default: True.
to_one_hot: `bool`. If True, labels will be encoded to one hot vectors.
'n_classes' must then be specified.
n_classes: `int`. The total number of classes. Only required when using
'to_one_hot' option.
trainable_vars: list of `Variable`. If specified, this regression will
only update given variable weights. Else, all trainale variable
are going to be updated.
restore: `bool`. If False, variables related to optimizers such
as moving averages will not be restored when loading a
pre-trained model.
op_name: A name for this layer optimizer (optional).
Default: optimizer op name.
name: A name for this layer's placeholder scope.
Attributes:
placeholder: `Tensor`. Placeholder for feeding labels.
"""
input_shape = utils.get_incoming_shape(incoming)
if placeholder is None:
pscope = "TargetsData" if not name else name
with tf.name_scope(pscope):
placeholder = tf.placeholder(shape=input_shape,
dtype=dtype,
name="Y")
tf.add_to_collection(tf.GraphKeys.TARGETS, placeholder)
if to_one_hot:
if n_classes is None:
raise Exception("'n_classes' is required when using 'to_one_hot'.")
placeholder = core.one_hot_encoding(placeholder, n_classes)
step_tensor = None
# Building Optimizer
if isinstance(optimizer, str):
_opt = optimizers.get(optimizer)(learning_rate)
op_name = op_name if op_name else type(_opt).__name__
_opt.build()
optimizer = _opt.get_tensor()
elif isinstance(optimizer, optimizers.Optimizer):
op_name = op_name if op_name else type(optimizer).__name__
if optimizer.has_decay:
step_tensor = tf.Variable(0.,
name="Training_step",
trainable=False)
optimizer.build(step_tensor)
optimizer = optimizer.get_tensor()
elif hasattr(optimizer, '__call__'):
try:
optimizer, step_tensor = optimizer(learning_rate)
except Exception as e:
print(e.message)
print(
"Reminder: Custom Optimizer function must return (optimizer, "
"step_tensor) and take one argument: 'learning_rate'. "
"Note that returned step_tensor can be 'None' if no decay.")
exit()
elif not isinstance(optimizer, tf.train.Optimizer):
raise ValueError("Invalid Optimizer type.")
inputs = tf.get_collection(tf.GraphKeys.INPUTS)
#inputs = tf.concat(0, utils.get_tensor_parents_placeholders(incoming))
# Building metric
# No auto accuracy for linear regression
if len(input_shape) == 1 and metric == 'default':
metric = None
if metric is not None:
# Default metric is accuracy
if metric == 'default': metric = 'accuracy'
if isinstance(metric, str):
metric = metrics.get(metric)()
metric.build(incoming, placeholder, inputs)
metric = metric.get_tensor()
elif isinstance(metric, metrics.Metric):
metric.build(incoming, placeholder, inputs)
metric = metric.get_tensor()
elif hasattr(metric, '__call__'):
try:
metric = metric(incoming, placeholder, inputs)
except Exception as e:
print(e.message)
print('Reminder: Custom metric function arguments must be '
'define as follow: custom_metric(y_pred, y_true, x).')
exit()
elif not isinstance(metric, tf.Tensor):
ValueError("Invalid Metric type.")
# Building other ops (loss, training ops...)
if isinstance(loss, str):
loss = objectives.get(loss)(incoming, placeholder)
# Check if function
elif hasattr(loss, '__call__'):
try:
loss = loss(incoming, placeholder)
except Exception as e:
print(e.message)
print('Reminder: Custom loss function arguments must be define as '
'follow: custom_loss(y_pred, y_true).')
exit()
elif not isinstance(loss, tf.Tensor):
raise ValueError("Invalid Loss type.")
tr_vars = trainable_vars
if not tr_vars:
tr_vars = tf.trainable_variables()
if not restore:
tf.add_to_collection(tf.GraphKeys.EXCL_RESTORE_VARS, 'moving_avg')
tf.add_to_collection(tf.GraphKeys.EXCL_RESTORE_VARS,
optimizer._name + '/')
tr_op = TrainOp(loss=loss,
optimizer=optimizer,
metric=metric,
trainable_vars=tr_vars,
batch_size=batch_size,
shuffle=shuffle_batches,
step_tensor=step_tensor,
name=op_name)
tf.add_to_collection(tf.GraphKeys.TRAIN_OPS, tr_op)
if not hasattr(incoming, '__len__'):
incoming.placeholder = placeholder
return incoming
def regression(incoming,
placeholder='default',
optimizer='adam',
loss='categorical_crossentropy',
metric='default',
learning_rate=0.001,
dtype=tf.float32,
batch_size=64,
shuffle_batches=True,
to_one_hot=False,
n_classes=None,
trainable_vars=None,
restore=True,
op_name=None,
validation_monitors=None,
validation_batch_size=None,
name=None):
""" Regression.
The regression layer is used in TFLearn to apply a regression (linear or
logistic) to the provided input. It requires to specify a TensorFlow
gradient descent optimizer 'optimizer' that will minimize the provided
loss function 'loss' (which calculate the errors). A metric can also be
provided, to evaluate the model performance.
A 'TrainOp' is generated, holding all information about the optimization
process. It is added to TensorFlow collection 'tf.GraphKeys.TRAIN_OPS'
and later used by TFLearn 'models' classes to perform the training.
An optional placeholder 'placeholder' can be specified to use a custom
TensorFlow target placeholder instead of creating a new one. The target
placeholder is added to the 'tf.GraphKeys.TARGETS' TensorFlow
collection, so that it can be retrieved later. In case no target is used,
set the placeholder to None.
Additionaly, a list of variables 'trainable_vars' can be specified,
so that only them will be updated when applying the backpropagation
algorithm.
Input:
2-D Tensor Layer.
Output:
2-D Tensor Layer (Same as input).
Arguments:
incoming: `Tensor`. Incoming 2-D Tensor.
placeholder: `Tensor`. This regression target (label) placeholder.
If 'default', a placeholder will be added automatically.
You can retrieve that placeholder through graph key: 'TARGETS',
or the 'placeholder' attribute of this function's returned tensor.
If you do not want to use any target, set placeholder to 'None'.
optimizer: `str` (name), `Optimizer` or `function`. Optimizer to use.
Default: 'adam' (Adaptive Moment Estimation).
loss: `str` (name) or `function`. Loss function used by this layer
optimizer. Default: 'categorical_crossentropy'.
metric: `str`, `Metric` or `function`. The metric to be used.
Default: 'default' metric is 'accuracy'. To disable metric
calculation, set it to 'None'.
learning_rate: `float`. This layer optimizer's learning rate.
dtype: `tf.types`. This layer placeholder type. Default: tf.float32.
batch_size: `int`. Batch size of data to use for training. tflearn
supports different batch size for every optimizers. Default: 64.
shuffle_batches: `bool`. Shuffle or not this optimizer batches at
every epoch. Default: True.
to_one_hot: `bool`. If True, labels will be encoded to one hot vectors.
'n_classes' must then be specified.
n_classes: `int`. The total number of classes. Only required when using
'to_one_hot' option.
trainable_vars: list of `Variable`. If specified, this regression will
only update given variable weights. Else, all trainale variable
are going to be updated.
restore: `bool`. If False, variables related to optimizers such
as moving averages will not be restored when loading a
pre-trained model.
op_name: A name for this layer optimizer (optional).
Default: optimizer op name.
validation_monitors: `list` of `Tensor` objects. List of variables
to compute during validation, which are also used to produce
summaries for output to TensorBoard. For example, this can be
used to periodically record a confusion matrix or AUC metric,
during training. Each variable should have rank 1, i.e.
shape [None].
validation_batch_size: `int` or None. Specifies the batch
size to be used for the validation data feed.
name: A name for this layer's placeholder scope.
Attributes:
placeholder: `Tensor`. Placeholder for feeding labels.
"""
input_shape = utils.get_incoming_shape(incoming)
if placeholder == 'default':
pscope = "TargetsData" if not name else name
with tf.name_scope(pscope):
p_shape = [None] if to_one_hot else input_shape
placeholder = tf.placeholder(shape=p_shape, dtype=dtype, name="Y")
elif placeholder is None:
placeholder = None
if placeholder is not None:
if placeholder not in tf.get_collection(tf.GraphKeys.TARGETS):
tf.add_to_collection(tf.GraphKeys.TARGETS, placeholder)
if to_one_hot:
if n_classes is None:
raise Exception("'n_classes' is required when using 'to_one_hot'.")
placeholder = core.one_hot_encoding(placeholder, n_classes)
step_tensor = None
# Building Optimizer
if isinstance(optimizer, str):
_opt = optimizers.get(optimizer)(learning_rate)
op_name = op_name if op_name else type(_opt).__name__
_opt.build()
optimizer = _opt.get_tensor()
elif isinstance(optimizer, optimizers.Optimizer):
op_name = op_name if op_name else type(optimizer).__name__
if optimizer.has_decay:
step_tensor = tf.Variable(0.,
name="Training_step",
trainable=False)
optimizer.build(step_tensor)
optimizer = optimizer.get_tensor()
elif hasattr(optimizer, '__call__'):
try:
optimizer, step_tensor = optimizer(learning_rate)
except Exception as e:
print(str(e))
print(
"Reminder: Custom Optimizer function must return (optimizer, "
"step_tensor) and take one argument: 'learning_rate'. "
"Note that returned step_tensor can be 'None' if no decay.")
exit()
elif not isinstance(optimizer, tf.train.Optimizer):
raise ValueError("Invalid Optimizer type.")
inputs = tf.get_collection(tf.GraphKeys.INPUTS)
#inputs = tf.concat(0, utils.get_tensor_parents_placeholders(incoming))
# Building metric
# No auto accuracy for linear regression
if len(input_shape) == 1 and metric == 'default':
metric = None
# If no placeholder, only a Tensor can be pass as metric
if not isinstance(metric, tf.Tensor) and placeholder is None:
metric = None
if metric is not None:
# Default metric is accuracy
if metric == 'default':
metric = 'accuracy'
if isinstance(metric, str):
metric = metrics.get(metric)()
metric.build(incoming, placeholder, inputs)
metric = metric.get_tensor()
elif isinstance(metric, metrics.Metric):
metric.build(incoming, placeholder, inputs)
metric = metric.get_tensor()
elif hasattr(metric, '__call__'):
try:
metric = metric(incoming, placeholder, inputs)
except Exception as e:
print(str(e))
print('Reminder: Custom metric function arguments must be '
'defined as: custom_metric(y_pred, y_true, x).')
exit()
elif not isinstance(metric, tf.Tensor):
raise ValueError("Invalid Metric type.")
# Building other ops (loss, training ops...)
if isinstance(loss, str):
loss = objectives.get(loss)(incoming, placeholder)
# Check if function
elif hasattr(loss, '__call__'):
try:
loss = loss(incoming, placeholder)
except Exception as e:
print(str(e))
print(
'Reminder: Custom loss function arguments must be defined as: '
'custom_loss(y_pred, y_true).')
exit()
elif not isinstance(loss, tf.Tensor):
raise ValueError("Invalid Loss type.")
tr_vars = trainable_vars
if not tr_vars:
tr_vars = tf.trainable_variables()
if not restore:
tf.add_to_collection(tf.GraphKeys.EXCL_RESTORE_VARS, 'moving_avg')
tf.add_to_collection(tf.GraphKeys.EXCL_RESTORE_VARS,
optimizer._name + '/')
tr_op = TrainOp(loss=loss,
optimizer=optimizer,
metric=metric,
trainable_vars=tr_vars,
batch_size=batch_size,
shuffle=shuffle_batches,
step_tensor=step_tensor,
validation_monitors=validation_monitors,
validation_batch_size=validation_batch_size,
name=op_name)
tf.add_to_collection(tf.GraphKeys.TRAIN_OPS, tr_op)
if not hasattr(incoming, '__len__'):
incoming.placeholder = placeholder
return incoming
def regression(incoming,
placeholder='default',
optimizer='adam',
loss='categorical_crossentropy',
metric='default',
learning_rate=0.001,
dtype=tf.float32,
batch_size=64,
shuffle_batchs=True,
to_one_hot=False,
n_classes=None,
trainalbe_vars=None,
restore=True,
op_name=None,
validation_monitor=None,
validation_batch_size=None,
name=None):
""" Regression.
The regression layer is used in TFLearn to apply a regression (linear or
logistic) to the provided input. It requires to specify a TensorFlow
gradient descent optimizer 'optimizer' that will minimize the provided
loss function 'loss' (which calculate the errors). A metric can also be
provided, to evaluate the model performance.
A 'TrainOp' is generated, holding all information about the optimization
process. It is added to TensorFlow collection 'tf.GraphKeys.TRAIN_OPS'
and later used by TFLearn 'models' classes to perform the training.
An optional placeholder 'placeholder' can be specified to use a custom
TensorFlow target placeholder instead of creating a new one. The target
placeholder is added to the 'tf.GraphKeys.TARGETS' TensorFlow
collection, so that it can be retrieved later. In case no target is used,
set the placeholder to None.
Additionaly, a list of variables 'trainable_vars' can be specified,
so that only them will be updated when applying the backpropagation
algorithms.
"""
input_shape = utils.get_incoming_shape(incoming)
if palceholder == 'default':
pscope = "TargetsData" if not name else name
with tf.name_scope(pscope):
p_shape = [None] if to_one_hot else input_shape
placeholder = tf.placeholder(shape=p_shape, dtype=dtype, name="Y")
elif palceholder is None:
palceholder = None
if placeholder is not None:
if palceholder not in tf.get_collection(tf.GraphKeys.TARGETS):
tf.add_to_collection(tf.GraphKeys.TARGETS, palceholder)
if to_one_hot:
if n_classes is None:
raise Exception("'n_classes' is required when using 'to_one_hot'.")
placeholder = core.one_hot_encoding(placeholder, n_classes)
step_tensor = None
#Building Optimizer
if isinstance(optimizer, str):
_opt = optimizers.get(optimizer)(learning_rate)
op_name = op_name if op_name else type(_opt).__name__
_opt.build()
optimizer = _opt.get_tensor()
elif isinstance(optimizer, optimizers.Optimizer):
op_name = op_name if op_name else type(optimizer).__name__
if optomizer.has_decay:
step_tensor = tf.Variable(0.,
name="Training_step",
trainable=False)
optimizer.build(step_tensor)
optimizer = optimizer.get_tensor()
elif hasattr(optimizer, '__call__'):
try:
optimizer, step_tensor = optimizer(learning_rate)
except Exception as e:
print(str(e))
print(
"Reminder: Custom Optimizer function must return (optimizer, "
"step_tensor) and take one argument: 'learning_rate'. "
"Note that returned step_tensor can be 'None' if no decay.")
exit()
elif not isinstance(optimizer, tf.train.Optimizer):
raise ValueError("Invalid Optimizer type.")
inputs = tf.get_collection(tf.GraphKeys.INPUTS)
#Building metric
#No auto accuracy for liner regression
if len(input_shape) == 1 and metric == 'default':
mteric = None
# If no placeholder, only a Tensor can be pass as metric
if not isinstance(metric, tf.Tensor) and placeholder is None:
metric = None
if metric is not None:
# Default metric is accuracy
if metric == 'default':
metric = 'accuracy'
if isinstance(metric, str):
metric = metric.get(metric)()
metric.build(incoming, placeholder, inputs)
metric = metric.get_tensor()
elif isinstance(metric, metrics.Metric):
metric.build(incoming, placeholder, inputs)
metric = metric.get_tensor()
elif hasattr(metric, '__call__'):
try:
metric = metric(incoming, placeholder, inputs)
except Exception as e:
print(str(e))
print('Reminder: Custom metric function arguments must be '
'defined as: custom_metric(y_pred, y_true, x).')
exit()
elif not isinstance(metric, tf.Tensor):
raise ValueError("Invalid Metric type.")
#Building other ops(loss, training ops...)
if isinstance(loss, str):
loss = objectives.get(loss)(incoming, placeholder)
#Check if function
elif hasattr(loss, '__call__'):
try:
loss = loss(incoming, placeholder)
except Exception as e:
print(str(e))
print(
'Reminder: Custom loss function arguments must be defined as: '
'custom_loss(y_pred, y_true).')
exit()
elif not isinstance(loss, tf.Tensor):
raise ValueError("Invalid Loss type.")
tr_vars = trainable_vars
if not tr_vars:
tr_vars = tf.trainable_variables()
if not restore:
tf.add_to_collection(tf.GraphKeys.EXCL_RESTORE_VARS, 'moving_avg')
tf.add_to_collection(tf.GraphKeys.EXCL_RESTORE_VARS,
optimizer._name + '/')
tr_op = TrainOp(loss=loss,
optimizer=optimizer,
metric=metric,
trainable_vars=tr_vars,
batch_size=batch_size,
shuffle=shuffle_batches,
step_tensor=step_tensor,
validation_monitors=validation_monitors,
validation_batch_size=validation_batch_size,
name=op_name)
tf.add_to_collection(tf.GraphKeys.TRAIN_OPS, tr_op)
if not hasattr(incoming, '__len__'):
incoming.palceholder = palceholder
return incoming
def regression(incoming, placeholder=None, optimizer='adam',
loss='categorical_crossentropy', metric='default',
learning_rate=0.001, dtype=tf.float32, batch_size=64,
shuffle_batches=True, trainable_vars=None, op_name=None,
name=None):
""" Regression.
Input:
2-D Tensor Layer.
Output:
2-D Tensor Layer (Same as input).
Arguments:
incoming: `Tensor`. Incoming 2-D Tensor.
placeholder: `Tensor`. This regression target (label) placeholder.
If 'None' provided, a placeholder will be added automatically.
You can retrieve that placeholder through graph key: 'TARGETS',
or the 'placeholder' attribute of this function's returned tensor.
optimizer: `str` (name) or `Optimizer`. Optimizer to use.
Default: 'sgd' (Stochastic Descent Gradient).
loss: `str` (name) or `Tensor`. Loss function used by this layer
optimizer. Default: 'categorical_crossentropy'.
metric: `str`, `Metric` or `Tensor`. The metric to be used.
Default: 'default' metric is 'accuracy'. To disable metric
calculation, set it to 'None'.
learning_rate: `float`. This layer optimizer's learning rate.
dtype: `tf.types`. This layer placeholder type. Default: tf.float32.
batch_size: `int`. Batch size of data to use for training. tflearn
supports different batch size for every optimizers. Default: 64.
shuffle_batches: `bool`. Shuffle or not this optimizer batches at
every epoch. Default: True.
trainable_vars: list of `Variable`. If specified, this regression will
only update given variable weights. Else, all trainale variable
are going to be updated.
op_name: A name for this layer optimizer (optional).
Default: optimizer op name.
name: A name for this layer's placeholder scope.
Attributes:
placeholder: `Tensor`. Placeholder for feeding labels.
"""
input_shape = utils.get_incoming_shape(incoming)
if not placeholder:
pscope = "TargetsData" if not name else name
with tf.name_scope(pscope):
pshape = [None, input_shape[-1]]
if len(input_shape) == 1:
pshape = [None]
placeholder = tf.placeholder(shape=pshape, dtype=dtype, name="Y")
tf.add_to_collection(tf.GraphKeys.TARGETS, placeholder)
step_tensor = None
# Building Optimizer
if isinstance(optimizer, str):
_opt = optimizers.get(optimizer)(learning_rate)
op_name = op_name if op_name else type(_opt).__name__
_opt.build()
optimizer = _opt.get_tensor()
elif isinstance(optimizer, optimizers.Optimizer):
op_name = op_name if op_name else type(optimizer).__name__
if optimizer.has_decay:
step_tensor = tf.Variable(0., name="Training_step",
trainable=False)
optimizer.build(step_tensor)
optimizer = optimizer.get_tensor()
elif not isinstance(optimizer, tf.train.Optimizer):
raise ValueError("Invalid Optimizer type.")
inputs = tf.get_collection(tf.GraphKeys.INPUTS)
#inputs = tf.concat(0, utils.get_tensor_parents_placeholders(incoming))
# Building metric
# No auto accuracy for linear regression
if len(input_shape) == 1 and metric == 'default':
metric = None
if metric is not None:
# Default metric is accuracy
if metric == 'default': metric = 'accuracy'
if isinstance(metric, str):
metric = metrics.get(metric)()
metric.build(incoming, placeholder, inputs)
metric = metric.get_tensor()
elif isinstance(metric, metrics.Metric):
metric.build(incoming, placeholder, inputs)
metric = metric.get_tensor()
elif not isinstance(metric, tf.Tensor):
ValueError("Invalid Metric type.")
# Building other ops (loss, training ops...)
if isinstance(loss, str):
loss = objectives.get(loss)(incoming, placeholder)
elif not isinstance(loss, tf.Tensor):
raise ValueError("Invalid Loss type.")
tr_vars = trainable_vars
if not tr_vars:
tr_vars = tf.trainable_variables()
tr_op = TrainOp(loss=loss,
optimizer=optimizer,
metric=metric,
trainable_vars=tr_vars,
batch_size=batch_size,
shuffle=shuffle_batches,
step_tensor=step_tensor,
name=op_name)
tf.add_to_collection(tf.GraphKeys.TRAIN_OPS, tr_op)
if not hasattr(incoming, '__len__'):
incoming.placeholder = placeholder
return incoming
def regression(incoming, placeholder=None, optimizer='adam',
loss='categorical_crossentropy', metric='default',
learning_rate=0.001, dtype=tf.float32, batch_size=64,
shuffle_batches=True, op_name=None, name=None):
""" Regression.
Input:
2-D Tensor Layer.
Output:
2-D Tensor Layer (Same as input).
Arguments:
incoming: `Tensor`. Incoming 2-D Tensor.
placeholder: `Tensor`. This regression target (label) placeholder.
If 'None' provided, a placeholder will be added automatically.
You can retrieve that placeholder through graph key: 'TARGETS',
or the 'placeholder' attribute of this function's returned tensor.
optimizer: `str` (name) or `Optimizer`. Optimizer to use.
Default: 'sgd' (Stochastic Descent Gradient).
loss: `str` (name) or `Tensor`. Loss function used by this layer
optimizer. Default: 'categorical_crossentropy'.
metric: `str`, `Metric` or `Tensor`. The metric to be used.
Default: 'default' metric is 'accuracy'. To disable metric
calculation, set it to 'None'.
learning_rate: `float`. This layer optimizer's learning rate.
dtype: `tf.types`. This layer placeholder type. Default: tf.float32.
batch_size: `int`. Batch size of data to use for training. tflearn
supports different batch size for every optimizers. Default: 64.
shuffle_batches: `bool`. Shuffle or not this optimizer batches at
every epoch. Default: True.
op_name: A name for this layer optimizer (optional).
Default: optimizer op name.
name: A name for this layer's placeholder scope.
Attributes:
placeholder: `Tensor`. Placeholder for feeding labels.
"""
input_shape = utils.get_incoming_shape(incoming)
if not placeholder:
pscope = "TargetsData" if not name else name
with tf.name_scope(pscope):
pshape = [None, input_shape[-1]]
if len(input_shape) == 1:
pshape = [None]
placeholder = tf.placeholder(shape=pshape, dtype=dtype, name="Y")
tf.add_to_collection(tf.GraphKeys.TARGETS, placeholder)
step_tensor = None
# Building Optimizer
if isinstance(optimizer, str):
_opt = optimizers.get(optimizer)(learning_rate)
op_name = op_name if op_name else type(_opt).__name__
_opt.build()
optimizer = _opt.get_tensor()
elif isinstance(optimizer, optimizers.Optimizer):
op_name = op_name if op_name else type(optimizer).__name__
if optimizer.has_decay:
step_tensor = tf.Variable(0., name="Training_step",
trainable=False)
optimizer.build(step_tensor)
optimizer = optimizer.get_tensor()
elif not isinstance(optimizer, tf.train.Optimizer):
raise ValueError("Invalid Optimizer type.")
inputs = tf.get_collection(tf.GraphKeys.INPUTS)
#inputs = tf.concat(0, utils.get_tensor_parents_placeholders(incoming))
# Building metric
# No auto accuracy for linear regression
if len(input_shape) == 1 and metric == 'default':
metric = None
if metric is not None:
# Default metric is accuracy
if metric == 'default': metric = 'accuracy'
if isinstance(metric, str):
metric = metrics.get(metric)()
metric.build(incoming, placeholder, inputs)
metric = metric.get_tensor()
elif isinstance(metric, metrics.Metric):
metric.build(incoming, placeholder, inputs)
metric = metric.get_tensor()
elif not isinstance(metric, tf.Tensor):
ValueError("Invalid Metric type.")
# Building other ops (loss, training ops...)
if isinstance(loss, str):
loss = objectives.get(loss)(incoming, placeholder)
elif not isinstance(loss, tf.Tensor):
raise ValueError("Invalid Loss type.")
tr_op = TrainOp(loss=loss,
optimizer=optimizer,
metric=metric,
trainable_vars=tf.trainable_variables(),
batch_size=batch_size,
shuffle=shuffle_batches,
step_tensor=step_tensor,
name=op_name)
tf.add_to_collection(tf.GraphKeys.TRAIN_OPS, tr_op)
if not hasattr(incoming, '__len__'):
incoming.placeholder = placeholder
return incoming