Regression metrics
MeanSquaredError class
tf_keras.metrics.MeanSquaredError(name="mean_squared_error", dtype=None)
Computes the mean squared error between y_true and y_pred.
Arguments
- name: (Optional) string name of the metric instance.
- dtype: (Optional) data type of the metric result.
Standalone usage:
>>> m = tf.keras.metrics.MeanSquaredError()
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
>>> m.result().numpy()
0.25
>>> m.reset_state()
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],
... sample_weight=[1, 0])
>>> m.result().numpy()
0.5
Usage with compile() API:
model.compile(
optimizer='sgd',
loss='mse',
metrics=[tf.keras.metrics.MeanSquaredError()])
RootMeanSquaredError class
tf_keras.metrics.RootMeanSquaredError(name="root_mean_squared_error", dtype=None)
Computes root mean squared error metric between y_true and y_pred.
Standalone usage:
>>> m = tf.keras.metrics.RootMeanSquaredError()
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
>>> m.result().numpy()
0.5
>>> m.reset_state()
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],
... sample_weight=[1, 0])
>>> m.result().numpy()
0.70710677
Usage with compile() API:
model.compile(
optimizer='sgd',
loss='mse',
metrics=[tf.keras.metrics.RootMeanSquaredError()])
MeanAbsoluteError class
tf_keras.metrics.MeanAbsoluteError(name="mean_absolute_error", dtype=None)
Computes the mean absolute error between the labels and predictions.
Arguments
- name: (Optional) string name of the metric instance.
- dtype: (Optional) data type of the metric result.
Standalone usage:
>>> m = tf.keras.metrics.MeanAbsoluteError()
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
>>> m.result().numpy()
0.25
>>> m.reset_state()
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],
... sample_weight=[1, 0])
>>> m.result().numpy()
0.5
Usage with compile() API:
model.compile(
optimizer='sgd',
loss='mse',
metrics=[tf.keras.metrics.MeanAbsoluteError()])
MeanAbsolutePercentageError class
tf_keras.metrics.MeanAbsolutePercentageError(
name="mean_absolute_percentage_error", dtype=None
)
Computes the mean absolute percentage error between y_true and
y_pred.
Arguments
- name: (Optional) string name of the metric instance.
- dtype: (Optional) data type of the metric result.
Standalone usage:
>>> m = tf.keras.metrics.MeanAbsolutePercentageError()
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
>>> m.result().numpy()
250000000.0
>>> m.reset_state()
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],
... sample_weight=[1, 0])
>>> m.result().numpy()
500000000.0
Usage with compile() API:
model.compile(
optimizer='sgd',
loss='mse',
metrics=[tf.keras.metrics.MeanAbsolutePercentageError()])
MeanSquaredLogarithmicError class
tf_keras.metrics.MeanSquaredLogarithmicError(
name="mean_squared_logarithmic_error", dtype=None
)
Computes the mean squared logarithmic error between y_true and
y_pred.
Arguments
- name: (Optional) string name of the metric instance.
- dtype: (Optional) data type of the metric result.
Standalone usage:
>>> m = tf.keras.metrics.MeanSquaredLogarithmicError()
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
>>> m.result().numpy()
0.12011322
>>> m.reset_state()
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],
... sample_weight=[1, 0])
>>> m.result().numpy()
0.24022643
Usage with compile() API:
model.compile(
optimizer='sgd',
loss='mse',
metrics=[tf.keras.metrics.MeanSquaredLogarithmicError()])
CosineSimilarity class
tf_keras.metrics.CosineSimilarity(name="cosine_similarity", dtype=None, axis=-1)
Computes the cosine similarity between the labels and predictions.
cosine similarity = (a . b) / ||a|| ||b||
See: Cosine Similarity.
This metric keeps the average cosine similarity between predictions and
labels over a stream of data.
Arguments
- name: (Optional) string name of the metric instance.
- dtype: (Optional) data type of the metric result.
- axis: (Optional) The dimension along which the cosine
similarity is computed. Defaults to
-1.
Standalone usage:
>>> # l2_norm(y_true) = [[0., 1.], [1./1.414, 1./1.414]]
>>> # l2_norm(y_pred) = [[1., 0.], [1./1.414, 1./1.414]]
>>> # l2_norm(y_true) . l2_norm(y_pred) = [[0., 0.], [0.5, 0.5]]
>>> # result = mean(sum(l2_norm(y_true) . l2_norm(y_pred), axis=1))
>>> # = ((0. + 0.) + (0.5 + 0.5)) / 2
>>> m = tf.keras.metrics.CosineSimilarity(axis=1)
>>> m.update_state([[0., 1.], [1., 1.]], [[1., 0.], [1., 1.]])
>>> m.result().numpy()
0.49999997
>>> m.reset_state()
>>> m.update_state([[0., 1.], [1., 1.]], [[1., 0.], [1., 1.]],
... sample_weight=[0.3, 0.7])
>>> m.result().numpy()
0.6999999
Usage with compile() API:
model.compile(
optimizer='sgd',
loss='mse',
metrics=[tf.keras.metrics.CosineSimilarity(axis=1)])
LogCoshError class
tf_keras.metrics.LogCoshError(name="logcosh", dtype=None)
Computes the logarithm of the hyperbolic cosine of the prediction error.
logcosh = log((exp(x) + exp(-x))/2), where x is the error (y_pred -
y_true)
Arguments
- name: (Optional) string name of the metric instance.
- dtype: (Optional) data type of the metric result.
Standalone usage:
>>> m = tf.keras.metrics.LogCoshError()
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
>>> m.result().numpy()
0.10844523
>>> m.reset_state()
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],
... sample_weight=[1, 0])
>>> m.result().numpy()
0.21689045
Usage with compile() API:
model.compile(optimizer='sgd',
loss='mse',
metrics=[tf.keras.metrics.LogCoshError()])