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Fix SparseTopKCategoricalAccuracy for predictions with rank > 2.

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PiperOrigin-RevId: 281769284
Change-Id: Icf5ee1addcee83d2b77ee6de644f5fb7e83b2971
This commit is contained in:
Pavithra Vijay 2019-11-21 09:43:43 -08:00 committed by TensorFlower Gardener
parent 82e32167b6
commit ddca4b92b4
2 changed files with 41 additions and 6 deletions
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21
tensorflow/python/keras/metrics.py
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@ -2997,12 +2997,25 @@ def top_k_categorical_accuracy(y_true, y_pred, k=5):
@keras_export('keras.metrics.sparse_top_k_categorical_accuracy')
def sparse_top_k_categorical_accuracy(y_true, y_pred, k=5):
"""Computes how often integer targets are in the top `K` predictions.
Args:
y_true: tensor of true targets.
y_pred: tensor of predicted targets.
k: (Optional) Number of top elements to look at for computing accuracy.
Defaults to 5.
Returns:
Sparse top K categorical accuracy value.
"""
y_pred_rank = ops.convert_to_tensor(y_pred).shape.ndims
y_true_rank = ops.convert_to_tensor(y_true).shape.ndims
# If the shape of y_true is (num_samples, 1), squeeze to (num_samples,)
if (y_true_rank is not None) and (y_pred_rank is not None) and (len(
K.int_shape(y_true)) == len(K.int_shape(y_pred))):
y_true = array_ops.squeeze(y_true, [-1])
# Flatten y_pred to (batch_size, num_samples) and y_true to (num_samples,)
if (y_true_rank is not None) and (y_pred_rank is not None):
if y_pred_rank > 2:
y_pred = array_ops.reshape(y_pred, [-1, y_pred.shape[-1]])
if y_true_rank > 1:
y_true = array_ops.reshape(y_true, [-1])
return math_ops.cast(
nn.in_top_k(y_pred, math_ops.cast(y_true, 'int32'), k), K.floatx())

26
tensorflow/python/keras/metrics_functional_test.py
View File

@ -46,15 +46,21 @@ class KerasFunctionalMetricsTest(test.TestCase):
# Test correctness if the shape of y_true is (num_samples,)
y_true = K.variable([1., 0., 0., 0.])
y_pred = K.variable([[0.8, 0.2], [0.6, 0.4], [0.7, 0.3], [0.9, 0.1]])
print(K.eval(metric(y_true, y_pred)))
self.assertAllEqual(K.eval(metric(y_true, y_pred)), [0., 1., 1., 1.])
# Test correctness if the shape of y_true is (num_samples, 1)
y_true = K.variable([[1.], [0.], [0.], [0.]])
y_pred = K.variable([[0.8, 0.2], [0.6, 0.4], [0.7, 0.3], [0.9, 0.1]])
print(K.eval(metric(y_true, y_pred)))
self.assertAllEqual(K.eval(metric(y_true, y_pred)), [0., 1., 1., 1.])
# Test correctness if the shape of y_true is (batch_size, seq_length) and
# y_pred is (batch_size, seq_length, num_classes)
y_pred = K.variable(
np.array([[[0.2, 0.3, 0.1], [0.1, 0.2, 0.7]],
[[0.3, 0.2, 0.1], [0.7, 0.2, 0.1]]]))
y_true = K.variable(np.array([[1, 0], [1, 0]]))
self.assertAllEqual(K.eval(metric(y_true, y_pred)), [[1., 0.], [0., 1.]])
def test_sparse_categorical_accuracy_float(self):
with self.cached_session():
metric = metrics.sparse_categorical_accuracy
@ -106,6 +112,22 @@ class KerasFunctionalMetricsTest(test.TestCase):
metrics.sparse_top_k_categorical_accuracy(y_true, y_pred, k=1))
self.assertEqual(np.mean(result), 0.)
# Test correctness if the shape of y_true is (batch_size, seq_length) and
# y_pred is (batch_size, seq_length, num_classes)
y_pred = K.variable(
np.array([[[0.3, 0.2, 0.1], [0.1, 0.2, 0.7], [0.1, 0.2, 0.7]],
[[0.3, 0.2, 0.1], [0.1, 0.2, 0.7], [0.3, 0.2, 0.1]]]))
y_true = K.variable(np.array([[1, 0, 0], [1, 0, 1]]))
result = K.eval(
metrics.sparse_top_k_categorical_accuracy(y_true, y_pred, k=3))
self.assertEqual(np.mean(result), 1)
result = K.eval(
metrics.sparse_top_k_categorical_accuracy(y_true, y_pred, k=2))
self.assertEqual(np.mean(result), 0.5)
result = K.eval(
metrics.sparse_top_k_categorical_accuracy(y_true, y_pred, k=1))
self.assertEqual(np.mean(result), 0.)
def test_top_k_categorical_accuracy(self):
with self.cached_session():
y_pred = K.variable(np.array([[0.3, 0.2, 0.1], [0.1, 0.2, 0.7]]))