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metrics.py
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metrics.py
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from __future__ import division, print_function, absolute_import
from .utils import get_from_module
import tensorflow as tf
def get(identifier):
return get_from_module(identifier, globals(), 'optimizer')
"""
Metric classes are meant to be used with TFLearn models (such as DNN). For
direct operations to be used with Tensorflow, see below (accuracy_op, ...).
"""
# --------------
# Metric classes
# --------------
class Metric(object):
""" Base Metric Class.
Metric class is meant to be used by TFLearn models class. It can be
first initialized with desired parameters, and a model class will
build it later using the given network output and targets.
Attributes:
tensor: `Tensor`. The metric tensor.
"""
def __init__(self, name=None):
self.name = name
self.tensor = None
self.built = False
def build(self, predictions, targets, inputs):
""" build.
Build metric method, with common arguments to all Metrics.
Arguments:
prediction: `Tensor`. The network to perform prediction.
targets: `Tensor`. The targets (labels).
inputs: `Tensor`. The input data.
"""
raise NotImplementedError
def get_tensor(self):
""" get_tensor.
Get the metric tensor.
Returns:
The metric `Tensor`.
"""
if not self.built:
raise Exception("Metric class Tensor hasn't be built. 'build' "
"method must be invoked before using 'get_tensor'.")
return self.tensor
class Accuracy(Metric):
""" Accuracy.
Computes the model accuracy. The target predictions are assumed
to be logits.
If the predictions tensor is 1D (ie shape [?], or [?, 1]), then the
labels are assumed to be binary (cast as float32), and accuracy is
computed based on the average number of equal binary outcomes,
thresholding predictions on logits > 0.
Otherwise, accuracy is computed based on categorical outcomes,
and assumes the inputs (both the model predictions and the labels)
are one-hot encoded. tf.argmax is used to obtain categorical
predictions, for equality comparison.
Examples:
```python
# To be used with TFLearn estimators
acc = Accuracy()
regression = regression(net, metric=acc)
```
Arguments:
name: The name to display.
"""
def __init__(self, name=None):
super(Accuracy, self).__init__(name)
def build(self, predictions, targets, inputs=None):
""" Build accuracy, comparing predictions and targets. """
self.built = True
pshape = predictions.get_shape()
if len(pshape)==1 or (len(pshape)==2 and int(pshape[1])==1):
self.name = self.name or "binary_acc" # clearly indicate binary accuracy being used
self.tensor = binary_accuracy_op(predictions, targets)
else:
self.name = self.name or "acc" # traditional categorical accuracy
self.tensor = accuracy_op(predictions, targets)
# Add a special name to that tensor, to be used by monitors
self.tensor.m_name = self.name
accuracy = Accuracy
class Top_k(Metric):
""" Top-k.
Computes Top-k mean accuracy (whether the targets are in the top 'K'
predictions).
Examples:
```python
# To be used with TFLearn estimators
top5 = Top_k(k=5)
regression = regression(net, metric=top5)
```
Arguments:
k: `int`. Number of top elements to look at for computing precision.
name: The name to display.
"""
def __init__(self, k=1, name=None):
super(Top_k, self).__init__(name)
self.name = "top" + str(k) if not name else name
self.k = k
def build(self, predictions, targets, inputs=None):
""" Build top-k accuracy, comparing top-k predictions and targets. """
self.built = True
self.tensor = top_k_op(predictions, targets, self.k)
# Add a special name to that tensor, to be used by monitors
self.tensor.m_name = self.name
top_k = Top_k
class R2(Metric):
""" Standard Error.
Computes coefficient of determination. Useful to evaluate a linear
regression.
Examples:
```python
# To be used with TFLearn estimators
r2 = R2()
regression = regression(net, metric=r2)
```
Arguments:
name: The name to display.
"""
def __init__(self, name=None):
super(R2, self).__init__(name)
self.name = "R2" if not name else name
def build(self, predictions, targets, inputs=None):
""" Build standard error tensor. """
self.built = True
self.tensor = r2_op(predictions, targets)
# Add a special name to that tensor, to be used by monitors
self.tensor.m_name = self.name
class WeightedR2(Metric):
""" Weighted Standard Error.
Computes coefficient of determination. Useful to evaluate a linear
regression.
Examples:
```python
# To be used with TFLearn estimators
r2 = R2()
regression = regression(net, metric=r2)
```
Arguments:
name: The name to display.
"""
def __init__(self, name=None):
super(WeightedR2, self).__init__(name)
self.name = "R2" if not name else name
def build(self, predictions, targets, inputs):
""" Build standard error tensor. """
self.built = True
self.tensor = weighted_r2_op(predictions, targets, inputs)
# Add a special name to that tensor, to be used by monitors
self.tensor.m_name = self.name
class Prediction_Counts(Metric):
""" Prints the count of each category of prediction that is present in the predictions.
Can be useful to see, for example, to see if the model only gives one type of predictions,
or if the predictions given are in the expected proportions """
def __init__(self, inner_metric, name=None):
super(Prediction_Counts, self).__init__(name)
self.inner_metric = inner_metric
def build(self, predictions, targets, inputs=None):
""" Prints the number of each kind of prediction """
self.built = True
pshape = predictions.get_shape()
self.inner_metric.build(predictions, targets, inputs)
with tf.name_scope(self.name):
if len(pshape) == 1 or (len(pshape) == 2 and int(pshape[1]) == 1):
self.name = self.name or "binary_prediction_counts"
y, idx, count = tf.unique_with_counts(tf.argmax(predictions))
self.tensor = tf.Print(self.inner_metric, [y, count], name=self.inner_metric.name)
else:
self.name = self.name or "categorical_prediction_counts"
y, idx, count = tf.unique_with_counts(tf.argmax(predictions, dimension=1))
self.tensor = tf.Print(self.inner_metric.tensor, [y, count], name=self.inner_metric.name)
prediction_counts = Prediction_Counts
# ----------
# Metric ops
# ----------
def accuracy_op(predictions, targets):
""" accuracy_op.
An op that calculates mean accuracy, assuming predictiosn are targets
are both one-hot encoded.
Examples:
```python
input_data = placeholder(shape=[None, 784])
y_pred = my_network(input_data) # Apply some ops
y_true = placeholder(shape=[None, 10]) # Labels
acc_op = accuracy_op(y_pred, y_true)
# Calculate accuracy by feeding data X and labels Y
accuracy = sess.run(acc_op, feed_dict={input_data: X, y_true: Y})
```
Arguments:
predictions: `Tensor`.
targets: `Tensor`.
Returns:
`Float`. The mean accuracy.
"""
if not isinstance(targets, tf.Tensor):
raise ValueError("mean_accuracy 'input' argument only accepts type "
"Tensor, '" + str(type(input)) + "' given.")
with tf.name_scope('Accuracy'):
correct_pred = tf.equal(tf.argmax(predictions, 1), tf.argmax(targets, 1))
acc = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
return acc
def binary_accuracy_op(predictions, targets):
""" binary_accuracy_op.
An op that calculates mean accuracy, assuming predictions are logits, and
targets are binary encoded (and represented as int32).
Examples:
```python
input_data = placeholder(shape=[None, 784])
y_pred = my_network(input_data) # Apply some ops
y_true = placeholder(shape=[None, 10]) # Labels
acc_op = binary_accuracy_op(y_pred, y_true)
# Calculate accuracy by feeding data X and labels Y
binary_accuracy = sess.run(acc_op, feed_dict={input_data: X, y_true: Y})
```
Arguments:
predictions: `Tensor` of `float` type.
targets: `Tensor` of `float` type.
Returns:
`Float`. The mean accuracy.
"""
if not isinstance(targets, tf.Tensor):
raise ValueError("mean_accuracy 'input' argument only accepts type "
"Tensor, '" + str(type(input)) + "' given.")
with tf.name_scope('BinaryAccuracy'):
predictions = tf.cast(tf.greater(predictions, 0), tf.float32)
correct_pred = tf.equal(predictions, tf.cast(targets, tf.float32))
acc = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
return acc
def top_k_op(predictions, targets, k=1):
""" top_k_op.
An op that calculates top-k mean accuracy.
Examples:
```python
input_data = placeholder(shape=[None, 784])
y_pred = my_network(input_data) # Apply some ops
y_true = placeholder(shape=[None, 10]) # Labels
top3_op = top_k_op(y_pred, y_true, 3)
# Calculate Top-3 accuracy by feeding data X and labels Y
top3_accuracy = sess.run(top3_op, feed_dict={input_data: X, y_true: Y})
```
Arguments:
predictions: `Tensor`.
targets: `Tensor`.
k: `int`. Number of top elements to look at for computing precision.
Returns:
`Float`. The top-k mean accuracy.
"""
with tf.name_scope('Top_' + str(k)):
targets = tf.cast(targets, tf.int32)
correct_pred = tf.nn.in_top_k(predictions, tf.argmax(targets, 1), k)
acc = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
return acc
def r2_op(predictions, targets):
""" r2_op.
An op that calculates the standard error.
Examples:
```python
input_data = placeholder(shape=[None, 784])
y_pred = my_network(input_data) # Apply some ops
y_true = placeholder(shape=[None, 10]) # Labels
stderr_op = r2_op(y_pred, y_true)
# Calculate standard error by feeding data X and labels Y
std_error = sess.run(stderr_op, feed_dict={input_data: X, y_true: Y})
```
Arguments:
predictions: `Tensor`.
targets: `Tensor`.
Returns:
`Float`. The standard error.
"""
with tf.name_scope('StandardError'):
a = tf.reduce_sum(tf.square(predictions))
b = tf.reduce_sum(tf.square(targets))
return tf.div(a, b)
def weighted_r2_op(predictions, targets, inputs):
""" r2_op.
An op that calculates the standard error.
Examples:
```python
input_data = placeholder(shape=[None, 784])
y_pred = my_network(input_data) # Apply some ops
y_true = placeholder(shape=[None, 10]) # Labels
stderr_op = r2_op(y_pred, y_true, input_data)
# Calculate standard error by feeding data X and labels Y
std_error = sess.run(stderr_op, feed_dict={input_data: X, y_true: Y})
```
Arguments:
predictions: `Tensor`.
targets: `Tensor`.
inputs: `Tensor`.
Returns:
`Float`. The standard error.
"""
with tf.name_scope('WeightedStandardError'):
if hasattr(inputs, '__len__'):
inputs = tf.add_n(inputs)
if inputs.get_shape().as_list() != targets.get_shape().as_list():
raise Exception("Weighted R2 metric requires Inputs and Targets to "
"have same shape.")
a = tf.reduce_sum(tf.square(predictions - inputs))
b = tf.reduce_sum(tf.square(targets - inputs))
return tf.div(a, b)