def _auc_hist_accumulate(hist_true, hist_false, nbins, collections):
"""Accumulate histograms in new variables."""
with variable_scope.variable_op_scope(
[hist_true, hist_false], None, 'hist_accumulate'):
# Holds running total histogram of scores for records labeled True.
hist_true_acc = variable_scope.get_variable(
'hist_true_acc',
initializer=array_ops.zeros_initializer(
[nbins],
dtype=hist_true.dtype),
collections=collections,
trainable=False)
# Holds running total histogram of scores for records labeled False.
hist_false_acc = variable_scope.get_variable(
'hist_false_acc',
initializer=array_ops.zeros_initializer(
[nbins],
dtype=hist_false.dtype),
collections=collections,
trainable=False)
update_op = control_flow_ops.group(
hist_true_acc.assign_add(hist_true),
hist_false_acc.assign_add(hist_false),
name='update_op')
return hist_true_acc, hist_false_acc, update_op
def _auc_hist_accumulate(hist_true, hist_false, nbins, collections):
"""Accumulate histograms in new variables."""
with variable_scope.variable_scope(None, 'hist_accumulate',
[hist_true, hist_false]):
# Holds running total histogram of scores for records labeled True.
hist_true_acc = variable_scope.get_variable(
'hist_true_acc',
initializer=array_ops.zeros_initializer([nbins],
dtype=hist_true.dtype),
collections=collections,
trainable=False)
# Holds running total histogram of scores for records labeled False.
hist_false_acc = variable_scope.get_variable(
'hist_false_acc',
initializer=array_ops.zeros_initializer([nbins],
dtype=hist_false.dtype),
collections=collections,
trainable=False)
update_op = control_flow_ops.group(
hist_true_acc.assign_add(hist_true),
hist_false_acc.assign_add(hist_false),
name='update_op')
return hist_true_acc, hist_false_acc, update_op
def weighted_moving_average(value,
decay,
weight,
truediv=True,
name="WeightedMovingAvg"):
"""Compute the weighted moving average of `value`.
Conceptually, the weighted moving average is:
moving_average(value * weight) / moving_average(weight),
where a moving average updates by the rule
new_value = decay * old_value + (1 - decay) * update
Args:
value: A tensor.
decay: A float Tensor or float value. The moving average decay.
weight: A tensor that keeps the current value of a weight.
Shape should be able to multiply `value`.
truediv: Boolean, if True, dividing by moving_average(weight) is floating
point division. If False, use division implied by dtypes.
name: Optional name of the returned operation.
Returns:
An Operation that updates the weighted moving average.
"""
# Unlike assign_moving_average, the weighted moving average doesn't modify
# user-visible variables. It is the ratio of two internal variables, which are
# moving averages of the updates. Thus, the signature of this function is
# quite different than assign_moving_average.
with variable_scope.variable_op_scope([value, weight, decay], name,
name) as scope:
value_variable = variable_scope.get_variable(
"value",
initializer=array_ops.zeros_initializer(value.get_shape(),
dtype=value.dtype),
trainable=False)
weight_variable = variable_scope.get_variable(
"weight",
initializer=array_ops.zeros_initializer(weight.get_shape(),
dtype=weight.dtype),
trainable=False)
numerator = assign_moving_average(value_variable, value * weight,
decay)
denominator = assign_moving_average(weight_variable, weight, decay)
if truediv:
return math_ops.truediv(numerator, denominator, name=scope.name)
else:
return math_ops.div(numerator, denominator, name=scope.name)
def weighted_moving_average(
value, decay, weight, truediv=True, name="WeightedMovingAvg"):
"""Compute the weighted moving average of `value`.
Conceptually, the weighted moving average is:
moving_average(value * weight) / moving_average(weight),
where a moving average updates by the rule
new_value = decay * old_value + (1 - decay) * update
Args:
value: A tensor.
decay: A float Tensor or float value. The moving average decay.
weight: A tensor that keeps the current value of a weight.
Shape should be able to multiply `value`.
truediv: Boolean, if True, dividing by moving_average(weight) is floating
point division. If False, use division implied by dtypes.
name: Optional name of the returned operation.
Returns:
An Operation that updates the weighted moving average.
"""
# Unlike assign_moving_average, the weighted moving average doesn't modify
# user-visible variables. It is the ratio of two internal variables, which are
# moving averages of the updates. Thus, the signature of this function is
# quite different than assign_moving_average.
with variable_scope.variable_op_scope(
[value, weight, decay], name, name) as scope:
value_variable = variable_scope.get_variable(
"value",
initializer=array_ops.zeros_initializer(
value.get_shape(), dtype=value.dtype),
trainable=False
)
weight_variable = variable_scope.get_variable(
"weight",
initializer=array_ops.zeros_initializer(
weight.get_shape(), dtype=weight.dtype),
trainable=False
)
numerator = assign_moving_average(value_variable, value * weight, decay)
denominator = assign_moving_average(weight_variable, weight, decay)
if truediv:
return math_ops.truediv(numerator, denominator, name=scope.name)
else:
return math_ops.div(numerator, denominator, name=scope.name)
def histogram_fixed_width(values, value_range, nbins=100, use_locking=True, dtype=dtypes.int32, name=None):
"""Return histogram of values.
Given the tensor `values`, this operation returns a rank 1 histogram counting
the number of entries in `values` that fell into every bin. The bins are
equal width and determined by the arguments `value_range` and `nbins`.
Args:
values: Numeric `Tensor`.
value_range: Shape [2] `Tensor`. new_values <= value_range[0] will be
mapped to hist[0], values >= value_range[1] will be mapped to hist[-1].
Must be same dtype as new_values.
nbins: Integer number of bins in this histogram.
use_locking: Boolean.
If `True`, use locking during the operation (optional).
dtype: dtype for returned histogram.
name: A name for this operation (defaults to 'histogram_fixed_width').
Returns:
A `Variable` holding histogram of values.
Examples:
```python
# Bins will be: (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf)
nbins = 5
value_range = [0.0, 5.0]
new_values = [-1.0, 0.0, 1.5, 2.0, 5.0, 15]
with tf.default_session() as sess:
hist = tf.histogram_fixed_width(new_values, value_range, nbins=5)
variables.initialize_all_variables().run()
sess.run(hist) => [2, 1, 1, 0, 2]
```
"""
with variable_scope.variable_op_scope([values, value_range], name, "histogram_fixed_width") as scope:
values = ops.convert_to_tensor(values, name="values")
values = array_ops.reshape(values, [-1])
value_range = ops.convert_to_tensor(value_range, name="value_range")
# Map tensor values that fall within value_range to [0, 1].
scaled_values = math_ops.truediv(values - value_range[0], value_range[1] - value_range[0], name="scaled_values")
# map tensor values within the open interval value_range to {0,.., nbins-1},
# values outside the open interval will be zero or less, or nbins or more.
indices = math_ops.floor(nbins * scaled_values, name="indices")
# Clip edge cases (e.g. value = value_range[1]) or "outliers."
indices = math_ops.cast(clip_ops.clip_by_value(indices, 0, nbins - 1), dtypes.int32)
# Dummy vector to scatter.
# TODO(langmore) Replace non-ideal creation of large dummy vector once an
# alternative to scatter is available.
updates = array_ops.ones_like(indices, dtype=dtype)
hist = variable_scope.get_variable(
"hist", initializer=array_ops.zeros_initializer([nbins], dtype=dtype), trainable=False
)
hist_assign_zero = hist.assign(array_ops.zeros_like(hist))
with ops.control_dependencies([hist_assign_zero]):
return state_ops.scatter_add(hist, indices, updates, use_locking=use_locking, name=scope.name)
def histogram_fixed_width(values,
value_range,
nbins=100,
use_locking=True,
dtype=dtypes.int32,
name=None):
"""Return histogram of values.
Given the tensor `values`, this operation returns a rank 1 histogram counting
the number of entries in `values` that fell into every bin. The bins are
equal width and determined by the arguments `value_range` and `nbins`.
Args:
values: Numeric `Tensor`.
value_range: Shape [2] `Tensor`. new_values <= value_range[0] will be
mapped to hist[0], values >= value_range[1] will be mapped to hist[-1].
Must be same dtype as new_values.
nbins: Integer number of bins in this histogram.
use_locking: Boolean.
If `True`, use locking during the operation (optional).
dtype: dtype for returned histogram.
name: A name for this operation (defaults to 'histogram_fixed_width').
Returns:
A `Variable` holding histogram of values.
Examples:
```python
# Bins will be: (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf)
nbins = 5
value_range = [0.0, 5.0]
new_values = [-1.0, 0.0, 1.5, 2.0, 5.0, 15]
with tf.default_session() as sess:
hist = tf.histogram_fixed_width(new_values, value_range, nbins=5)
variables.initialize_all_variables().run()
sess.run(hist) => [2, 1, 1, 0, 2]
```
"""
with variable_scope.variable_op_scope([values, value_range], name,
'histogram_fixed_width') as scope:
values = ops.convert_to_tensor(values, name='values')
values = array_ops.reshape(values, [-1])
value_range = ops.convert_to_tensor(value_range, name='value_range')
# Map tensor values that fall within value_range to [0, 1].
scaled_values = math_ops.truediv(values - value_range[0],
value_range[1] - value_range[0],
name='scaled_values')
# map tensor values within the open interval value_range to {0,.., nbins-1},
# values outside the open interval will be zero or less, or nbins or more.
indices = math_ops.floor(nbins * scaled_values, name='indices')
# Clip edge cases (e.g. value = value_range[1]) or "outliers."
indices = math_ops.cast(clip_ops.clip_by_value(indices, 0, nbins - 1),
dtypes.int32)
# Dummy vector to scatter.
# TODO(langmore) Replace non-ideal creation of large dummy vector once an
# alternative to scatter is available.
updates = array_ops.ones_like(indices, dtype=dtype)
hist = variable_scope.get_variable(
'hist',
initializer=array_ops.zeros_initializer([nbins], dtype=dtype),
trainable=False)
hist_assign_zero = hist.assign(array_ops.zeros_like(hist))
with ops.control_dependencies([hist_assign_zero]):
return state_ops.scatter_add(hist,
indices,
updates,
use_locking=use_locking,
name=scope.name)