Base Metric Class
tflearn.metrics.Metric (name=None)
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.
Methods
build (predictions, targets, inputs)
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.
get_tensor (self)
Get the metric tensor.
Returns
The metric Tensor.
Accuracy
tflearn.metrics.Accuracy (name=None)
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
# To be used with TFLearn estimators
acc = Accuracy()
regression = regression(net, metric=acc)
Arguments
- name: The name to display.
Top-k
tflearn.metrics.Top_k (k=1, name=None)
Computes Top-k mean accuracy (whether the targets are in the top 'K' predictions).
Examples
# 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.
Standard Error
tflearn.metrics.R2 (name=None)
Computes coefficient of determination. Useful to evaluate a linear regression.
Examples
# To be used with TFLearn estimators
r2 = R2()
regression = regression(net, metric=r2)
Arguments
- name: The name to display.
Weighted Standard Error
tflearn.metrics.WeightedR2 (name=None)
Computes coefficient of determination. Useful to evaluate a linear regression.
Examples
# To be used with TFLearn estimators
weighted_r2 = WeightedR2()
regression = regression(net, metric=weighted_r2)
Arguments
- name: The name to display.
accuracy_op
tflearn.metrics.accuracy_op (predictions, targets)
An op that calculates mean accuracy, assuming predictiosn are targets are both one-hot encoded.
Examples
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.
binary_accuracy_op
tflearn.metrics.binary_accuracy_op (predictions, targets)
An op that calculates mean accuracy, assuming predictions are logits, and targets are binary encoded (and represented as int32).
Examples
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:
Tensoroffloattype. - targets:
Tensoroffloattype.
Returns
Float. The mean accuracy.
top_k_op
tflearn.metrics.top_k_op (predictions, targets, k=1)
An op that calculates top-k mean accuracy.
Examples
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.
r2_op
tflearn.metrics.r2_op (predictions, targets)
An op that calculates the standard error.
Examples
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.
weighted_r2_op
tflearn.metrics.weighted_r2_op (predictions, targets, inputs)
An op that calculates the standard error.
Examples
input_data = placeholder(shape=[None, 784])
y_pred = my_network(input_data) # Apply some ops
y_true = placeholder(shape=[None, 10]) # Labels
stderr_op = weighted_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.