diff options
author | 2017-01-04 21:25:34 -0800 | |
---|---|---|
committer | 2017-01-04 21:46:08 -0800 | |
commit | 333dc32ff79af21484695157f3d141dc776f7c02 (patch) | |
tree | b379bcaa56bfa54d12ea839fb7e62ab163490743 /tensorflow/python | |
parent | d9541696b068cfcc1fab66b03d0b8d605b64f14d (diff) |
Change arg order for {softmax,sparse_softmax,sigmoid}_cross_entropy_with_logits to be (labels, predictions), and force use of named args to avoid accidents.
Change: 143629623
Diffstat (limited to 'tensorflow/python')
-rw-r--r-- | tensorflow/python/kernel_tests/sparse_xent_op_test.py | 19 | ||||
-rw-r--r-- | tensorflow/python/kernel_tests/xent_op_test.py | 5 | ||||
-rw-r--r-- | tensorflow/python/ops/gradient_checker_test.py | 2 | ||||
-rw-r--r-- | tensorflow/python/ops/losses/losses_impl.py | 9 | ||||
-rw-r--r-- | tensorflow/python/ops/nn_impl.py | 35 | ||||
-rw-r--r-- | tensorflow/python/ops/nn_ops.py | 51 | ||||
-rw-r--r-- | tensorflow/python/ops/nn_xent_test.py | 34 | ||||
-rw-r--r-- | tensorflow/python/training/saver_test.py | 11 |
8 files changed, 107 insertions, 59 deletions
diff --git a/tensorflow/python/kernel_tests/sparse_xent_op_test.py b/tensorflow/python/kernel_tests/sparse_xent_op_test.py index ef94af54fe..d2a815a0d7 100644 --- a/tensorflow/python/kernel_tests/sparse_xent_op_test.py +++ b/tensorflow/python/kernel_tests/sparse_xent_op_test.py @@ -141,25 +141,26 @@ class SparseXentTest(test.TestCase): with self.test_session(use_gpu=True): with self.assertRaisesRegexp(ValueError, ".*Rank mismatch:*"): nn_ops.sparse_softmax_cross_entropy_with_logits( - [[0., 1.], [2., 3.], [2., 3.]], [[0, 2]]) + labels=[[0, 2]], logits=[[0., 1.], [2., 3.], [2., 3.]]) def testScalar(self): with self.test_session(use_gpu=True): with self.assertRaisesRegexp(ValueError, ".*Logits cannot be scalars*"): nn_ops.sparse_softmax_cross_entropy_with_logits( - constant_op.constant(1.0), constant_op.constant(0)) + labels=constant_op.constant(0), logits=constant_op.constant(1.0)) def testLabelsPlaceholderScalar(self): with self.test_session(use_gpu=True): labels = array_ops.placeholder(np.int32) - y = nn_ops.sparse_softmax_cross_entropy_with_logits([[7.]], labels) + y = nn_ops.sparse_softmax_cross_entropy_with_logits( + labels=labels, logits=[[7.]]) with self.assertRaisesOpError("labels must be 1-D"): y.eval(feed_dict={labels: 0}) def testVector(self): with self.test_session(use_gpu=True): loss = nn_ops.sparse_softmax_cross_entropy_with_logits( - constant_op.constant([1.0]), constant_op.constant(0)) + labels=constant_op.constant(0), logits=constant_op.constant([1.0])) self.assertAllClose(0.0, loss.eval()) def testFloat(self): @@ -191,7 +192,8 @@ class SparseXentTest(test.TestCase): shape=[3, 4], dtype=dtypes.float64, name="f") - x = nn_ops.sparse_softmax_cross_entropy_with_logits(f, l, name="xent") + x = nn_ops.sparse_softmax_cross_entropy_with_logits( + labels=l, logits=f, name="xent") err = gradient_checker.compute_gradient_error(f, [3, 4], x, [3]) print("cross entropy gradient err = ", err) self.assertLess(err, 5e-8) @@ -201,7 +203,8 @@ class SparseXentTest(test.TestCase): # manually reshape loss np_loss = np.reshape(np_loss, np.array(labels).shape) with self.test_session(use_gpu=True) as sess: - loss = nn_ops.sparse_softmax_cross_entropy_with_logits(features, labels) + loss = nn_ops.sparse_softmax_cross_entropy_with_logits( + labels=labels, logits=features) backprop = loss.op.inputs[0].op.outputs[1] tf_loss, tf_backprop = sess.run([loss, backprop]) self.assertAllCloseAccordingToType(np_loss, tf_loss) @@ -225,7 +228,7 @@ class SparseXentTest(test.TestCase): labels = array_ops.placeholder(dtypes.int32, shape=[None, 1]) logits = array_ops.placeholder(dtypes.float32, shape=[None, 3]) ce = nn_ops.sparse_softmax_cross_entropy_with_logits( - logits, array_ops.squeeze(labels)) + labels=array_ops.squeeze(labels), logits=logits) labels_v2 = np.zeros((1, 1), dtype=np.int32) logits_v2 = np.random.randn(1, 3) sess.run([ce], feed_dict={labels: labels_v2, logits: logits_v2}) @@ -243,7 +246,7 @@ def _sparse_vs_dense_xent_benchmark_dense(labels, logits): array_ops.stack([length]), 1.0, 0.0) target = array_ops.reshape(target, array_ops.stack([-1, num_entries])) crossent = nn_ops.softmax_cross_entropy_with_logits( - logits, target, name="SequenceLoss/CrossEntropy") + labels=target, logits=logits, name="SequenceLoss/CrossEntropy") crossent_sum = math_ops.reduce_sum(crossent) grads = gradients_impl.gradients([crossent_sum], [logits])[0] diff --git a/tensorflow/python/kernel_tests/xent_op_test.py b/tensorflow/python/kernel_tests/xent_op_test.py index ac56f567ce..e1e0566124 100644 --- a/tensorflow/python/kernel_tests/xent_op_test.py +++ b/tensorflow/python/kernel_tests/xent_op_test.py @@ -57,7 +57,7 @@ class XentTest(test.TestCase): np_loss, _ = self._npXent(np_features, np_labels, dim=dim) with self.test_session(use_gpu=use_gpu) as sess: loss = nn_ops.softmax_cross_entropy_with_logits( - np_features, np_labels, dim=dim) + labels=np_labels, logits=np_features, dim=dim) tf_loss = sess.run(loss) print("np_loss:", np_loss) print("tf_loss:", tf_loss) @@ -166,7 +166,8 @@ class XentTest(test.TestCase): shape=[3, 4], dtype=dtypes.float64, name="f") - x = nn_ops.softmax_cross_entropy_with_logits(f, l, name="xent") + x = nn_ops.softmax_cross_entropy_with_logits(labels=l, logits=f, + name="xent") err = gradient_checker.compute_gradient_error(f, [3, 4], x, [3]) print("cross entropy gradient err = ", err) self.assertLess(err, 5e-8) diff --git a/tensorflow/python/ops/gradient_checker_test.py b/tensorflow/python/ops/gradient_checker_test.py index 8182352658..3ea8f3798c 100644 --- a/tensorflow/python/ops/gradient_checker_test.py +++ b/tensorflow/python/ops/gradient_checker_test.py @@ -267,7 +267,7 @@ class MiniMNISTTest(test.TestCase): dtype=dtypes.float64, name="labels") cost = nn_ops.softmax_cross_entropy_with_logits( - logits, labels, name="cost") + labels=labels, logits=logits, name="cost") # Test the gradients. err = gradient_checker.compute_gradient_error( diff --git a/tensorflow/python/ops/losses/losses_impl.py b/tensorflow/python/ops/losses/losses_impl.py index 749e46cb77..c23d046d70 100644 --- a/tensorflow/python/ops/losses/losses_impl.py +++ b/tensorflow/python/ops/losses/losses_impl.py @@ -559,7 +559,8 @@ def sigmoid_cross_entropy( multi_class_labels = (multi_class_labels * (1 - label_smoothing) + 0.5 * label_smoothing) - losses = nn.sigmoid_cross_entropy_with_logits(logits, multi_class_labels, + losses = nn.sigmoid_cross_entropy_with_logits(labels=multi_class_labels, + logits=logits, name="xentropy") return compute_weighted_loss(losses, weights, scope, loss_collection) @@ -613,7 +614,8 @@ def softmax_cross_entropy( smooth_negatives = label_smoothing / num_classes onehot_labels = onehot_labels * smooth_positives + smooth_negatives - losses = nn.softmax_cross_entropy_with_logits(logits, onehot_labels, + losses = nn.softmax_cross_entropy_with_logits(labels=onehot_labels, + logits=logits, name="xentropy") return compute_weighted_loss(losses, weights, scope, loss_collection) @@ -653,7 +655,8 @@ def sparse_softmax_cross_entropy(labels, logits, weights=1.0, scope=None, [logits, labels, weights]) as scope: labels = array_ops.reshape(labels, shape=[array_ops.shape(labels)[0]]) - losses = nn.sparse_softmax_cross_entropy_with_logits(logits, labels, + losses = nn.sparse_softmax_cross_entropy_with_logits(labels=labels, + logits=logits, name="xentropy") # Reshape losses to [batch_size, 1] to be consistent with weights. losses = array_ops.reshape(losses, shape=[array_ops.shape(losses)[0], 1]) diff --git a/tensorflow/python/ops/nn_impl.py b/tensorflow/python/ops/nn_impl.py index 5aba723017..60499c36ec 100644 --- a/tensorflow/python/ops/nn_impl.py +++ b/tensorflow/python/ops/nn_impl.py @@ -96,7 +96,9 @@ def log_poisson_loss(targets, log_input, compute_full_loss=False, name=None): return result -def sigmoid_cross_entropy_with_logits(logits, targets, name=None): +def sigmoid_cross_entropy_with_logits(_sentinel=None, # pylint: disable=invalid-name + labels=None, logits=None, + name=None): """Computes sigmoid cross entropy given `logits`. Measures the probability error in discrete classification tasks in which each @@ -104,7 +106,7 @@ def sigmoid_cross_entropy_with_logits(logits, targets, name=None): perform multilabel classification where a picture can contain both an elephant and a dog at the same time. - For brevity, let `x = logits`, `z = targets`. The logistic loss is + For brevity, let `x = logits`, `z = labels`. The logistic loss is z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x)) = z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x))) @@ -124,11 +126,12 @@ def sigmoid_cross_entropy_with_logits(logits, targets, name=None): max(x, 0) - x * z + log(1 + exp(-abs(x))) - `logits` and `targets` must have the same type and shape. + `logits` and `labels` must have the same type and shape. Args: + _sentinel: Used to prevent positional parameters. Internal, do not use. + labels: A `Tensor` of the same type and shape as `logits`. logits: A `Tensor` of type `float32` or `float64`. - targets: A `Tensor` of the same type and shape as `logits`. name: A name for the operation (optional). Returns: @@ -136,16 +139,21 @@ def sigmoid_cross_entropy_with_logits(logits, targets, name=None): logistic losses. Raises: - ValueError: If `logits` and `targets` do not have the same shape. + ValueError: If `logits` and `labels` do not have the same shape. """ - with ops.name_scope(name, "logistic_loss", [logits, targets]) as name: + # pylint: disable=protected-access + nn_ops._ensure_xent_args("sigmoid_cross_entropy_with_logits", + _sentinel, labels, logits) + # pylint: enable=protected-access + + with ops.name_scope(name, "logistic_loss", [logits, labels]) as name: logits = ops.convert_to_tensor(logits, name="logits") - targets = ops.convert_to_tensor(targets, name="targets") + labels = ops.convert_to_tensor(labels, name="labels") try: - targets.get_shape().merge_with(logits.get_shape()) + labels.get_shape().merge_with(logits.get_shape()) except ValueError: - raise ValueError("logits and targets must have the same shape (%s vs %s)" - % (logits.get_shape(), targets.get_shape())) + raise ValueError("logits and labels must have the same shape (%s vs %s)" + % (logits.get_shape(), labels.get_shape())) # The logistic loss formula from above is # x - x * z + log(1 + exp(-x)) @@ -159,7 +167,7 @@ def sigmoid_cross_entropy_with_logits(logits, targets, name=None): cond = (logits >= zeros) relu_logits = array_ops.where(cond, logits, zeros) neg_abs_logits = array_ops.where(cond, -logits, logits) - return math_ops.add(relu_logits - logits * targets, + return math_ops.add(relu_logits - logits * labels, math_ops.log1p(math_ops.exp(neg_abs_logits)), name=name) @@ -1095,7 +1103,7 @@ def nce_loss(weights, partition_strategy=partition_strategy, name=name) sampled_losses = sigmoid_cross_entropy_with_logits( - logits, labels, name="sampled_losses") + labels=labels, logits=logits, name="sampled_losses") # sampled_losses is batch_size x {true_loss, sampled_losses...} # We sum out true and sampled losses. return _sum_rows(sampled_losses) @@ -1170,6 +1178,7 @@ def sampled_softmax_loss(weights, remove_accidental_hits=remove_accidental_hits, partition_strategy=partition_strategy, name=name) - sampled_losses = nn_ops.softmax_cross_entropy_with_logits(logits, labels) + sampled_losses = nn_ops.softmax_cross_entropy_with_logits(labels=labels, + logits=logits) # sampled_losses is a [batch_size] tensor. return sampled_losses diff --git a/tensorflow/python/ops/nn_ops.py b/tensorflow/python/ops/nn_ops.py index ee199efccf..f5075f0675 100644 --- a/tensorflow/python/ops/nn_ops.py +++ b/tensorflow/python/ops/nn_ops.py @@ -14,7 +14,6 @@ # ============================================================================== """Wrappers for primitive Neural Net (NN) Operations.""" -# pylint: disable=invalid-name from __future__ import absolute_import from __future__ import division from __future__ import print_function @@ -1047,7 +1046,7 @@ def conv2d_transpose(value, raise ValueError("data_format has to be either NCHW or NHWC.") value = ops.convert_to_tensor(value, name="value") filter = ops.convert_to_tensor(filter, name="filter") - axis = 3 if data_format=="NHWC" else 1 + axis = 3 if data_format == "NHWC" else 1 if not value.get_shape()[axis].is_compatible_with(filter.get_shape()[3]): raise ValueError("input channels does not match filter's input channels, " "{} != {}".format(value.get_shape()[3], filter.get_shape( @@ -1528,7 +1527,18 @@ def log_softmax(logits, dim=-1, name=None): return _softmax(logits, gen_nn_ops._log_softmax, dim, name) -def softmax_cross_entropy_with_logits(logits, labels, dim=-1, name=None): +def _ensure_xent_args(name, sentinel, labels, logits): + # Make sure that all arguments were passed as named arguments. + if sentinel is not None: + raise ValueError("Only call `%s` with " + "named arguments (labels=..., logits=..., ...)" % name) + if labels is None or logits is None: + raise ValueError("Both labels and logits must be provided.") + + +def softmax_cross_entropy_with_logits(_sentinel=None, # pylint: disable=invalid-name + labels=None, logits=None, + dim=-1, name=None): """Computes softmax cross entropy between `logits` and `labels`. Measures the probability error in discrete classification tasks in which the @@ -1551,9 +1561,13 @@ def softmax_cross_entropy_with_logits(logits, labels, dim=-1, name=None): `logits` and `labels` must have the same shape `[batch_size, num_classes]` and the same dtype (either `float16`, `float32`, or `float64`). + **Note that to avoid confusion, it is required to pass only named arguments to + this function.** + Args: - logits: Unscaled log probabilities. + _sentinel: Used to prevent positional parameters. Internal, do not use. labels: Each row `labels[i]` must be a valid probability distribution. + logits: Unscaled log probabilities. dim: The class dimension. Defaulted to -1 which is the last dimension. name: A name for the operation (optional). @@ -1561,6 +1575,9 @@ def softmax_cross_entropy_with_logits(logits, labels, dim=-1, name=None): A 1-D `Tensor` of length `batch_size` of the same type as `logits` with the softmax cross entropy loss. """ + _ensure_xent_args("softmax_cross_entropy_with_logits", _sentinel, + labels, logits) + # TODO(pcmurray) Raise an error when the labels do not sum to 1. Note: This # could break users who call this with bad labels, but disregard the bad # results. @@ -1569,7 +1586,7 @@ def softmax_cross_entropy_with_logits(logits, labels, dim=-1, name=None): labels = ops.convert_to_tensor(labels) precise_logits = math_ops.cast(logits, dtypes.float32) if ( logits.dtype == dtypes.float16) else logits - # Labels and logits must be of the same type + # labels and logits must be of the same type labels = math_ops.cast(labels, precise_logits.dtype) input_rank = array_ops.rank(precise_logits) # For shape inference. @@ -1618,7 +1635,9 @@ def softmax_cross_entropy_with_logits(logits, labels, dim=-1, name=None): return cost -def sparse_softmax_cross_entropy_with_logits(logits, labels, name=None): +def sparse_softmax_cross_entropy_with_logits(_sentinel=None, # pylint: disable=invalid-name + labels=None, logits=None, + name=None): """Computes sparse softmax cross entropy between `logits` and `labels`. Measures the probability error in discrete classification tasks in which the @@ -1640,14 +1659,18 @@ def sparse_softmax_cross_entropy_with_logits(logits, labels, name=None): A common use case is to have logits of shape `[batch_size, num_classes]` and labels of shape `[batch_size]`. But higher dimensions are supported. + **Note that to avoid confusion, it is required to pass only named arguments to + this function.** + Args: - logits: Unscaled log probabilities of rank `r` and shape - `[d_0, d_1, ..., d_{r-2}, num_classes]` and dtype `float32` or `float64`. + _sentinel: Used to prevent positional parameters. Internal, do not use. labels: `Tensor` of shape `[d_0, d_1, ..., d_{r-2}]` and dtype `int32` or `int64`. Each entry in `labels` must be an index in `[0, num_classes)`. Other values will raise an exception when this op is run on CPU, and return `NaN` for corresponding corresponding loss and gradient rows on GPU. + logits: Unscaled log probabilities of rank `r` and shape + `[d_0, d_1, ..., d_{r-2}, num_classes]` and dtype `float32` or `float64`. name: A name for the operation (optional). Returns: @@ -1658,6 +1681,9 @@ def sparse_softmax_cross_entropy_with_logits(logits, labels, name=None): ValueError: If logits are scalars (need to have rank >= 1) or if the rank of the labels is not equal to the rank of the labels minus one. """ + _ensure_xent_args("sparse_softmax_cross_entropy_with_logits", _sentinel, + labels, logits) + # TODO(pcmurray) Raise an error when the label is not an index in # [0, num_classes). Note: This could break users who call this with bad # labels, but disregard the bad results. @@ -1679,8 +1705,8 @@ def sparse_softmax_cross_entropy_with_logits(logits, labels, name=None): if logits.get_shape().ndims is not None and ( labels_static_shape.ndims is not None and labels_static_shape.ndims != logits.get_shape().ndims - 1): - raise ValueError("Rank mismatch: Rank of labels (received %s) should equal " - "rank of logits minus 1 (received %s)." % + raise ValueError("Rank mismatch: Rank of labels (received %s) should " + "equal rank of logits minus 1 (received %s)." % (labels_static_shape.ndims, logits.get_shape().ndims)) # Check if no reshapes are required. if logits.get_shape().ndims == 2: @@ -1857,8 +1883,7 @@ def xw_plus_b_v1(x, weights, biases, name=None): # pylint: disable=invalid-name return bias_add_v1(mm, biases, name=name) -# pylint: disable=invalid-name -def dropout(x, keep_prob, noise_shape=None, seed=None, name=None): +def dropout(x, keep_prob, noise_shape=None, seed=None, name=None): # pylint: disable=invalid-name """Computes dropout. With probability `keep_prob`, outputs the input element scaled up by @@ -2082,5 +2107,3 @@ def erosion2d(value, kernel, strides, rates, padding, name=None): rates=rates, padding=padding, name=name)) - -# pylint: enable=invalid-name diff --git a/tensorflow/python/ops/nn_xent_test.py b/tensorflow/python/ops/nn_xent_test.py index 3d0157fb89..90f4b40770 100644 --- a/tensorflow/python/ops/nn_xent_test.py +++ b/tensorflow/python/ops/nn_xent_test.py @@ -57,7 +57,7 @@ class SigmoidCrossEntropyWithLogitsTest(test.TestCase): with self.test_session(): logits, targets, _ = self._Inputs() loss = nn_impl.sigmoid_cross_entropy_with_logits( - logits, targets, name="mylogistic") + labels=targets, logits=logits, name="mylogistic") self.assertEqual("mylogistic", loss.op.name) def testLogisticOutput(self): @@ -65,7 +65,8 @@ class SigmoidCrossEntropyWithLogitsTest(test.TestCase): for dtype in [dtypes.float32, dtypes.float16]: with self.test_session(use_gpu=use_gpu): logits, targets, losses = self._Inputs(dtype=dtype) - loss = nn_impl.sigmoid_cross_entropy_with_logits(logits, targets) + loss = nn_impl.sigmoid_cross_entropy_with_logits( + labels=targets, logits=logits) np_loss = np.array(losses).astype(np.float32) tf_loss = loss.eval() self.assertAllClose(np_loss, tf_loss, atol=0.001) @@ -75,7 +76,8 @@ class SigmoidCrossEntropyWithLogitsTest(test.TestCase): for dtype in [dtypes.float32, dtypes.float16]: with self.test_session(use_gpu=use_gpu): logits, targets, losses = self._Inputs(dtype=dtype, sizes=[2, 2, 2]) - loss = nn_impl.sigmoid_cross_entropy_with_logits(logits, targets) + loss = nn_impl.sigmoid_cross_entropy_with_logits( + labels=targets, logits=logits) np_loss = np.array(losses).astype(np.float32) tf_loss = loss.eval() self.assertAllClose(np_loss, tf_loss, atol=0.001) @@ -84,7 +86,8 @@ class SigmoidCrossEntropyWithLogitsTest(test.TestCase): sizes = [4, 2] with self.test_session(): logits, targets, _ = self._Inputs(sizes=sizes) - loss = nn_impl.sigmoid_cross_entropy_with_logits(logits, targets) + loss = nn_impl.sigmoid_cross_entropy_with_logits( + labels=targets, logits=logits) err = gradient_checker.compute_gradient_error(logits, sizes, loss, sizes) print("logistic loss gradient err = ", err) self.assertLess(err, 1e-7) @@ -93,13 +96,15 @@ class SigmoidCrossEntropyWithLogitsTest(test.TestCase): with self.test_session(): logits = constant_op.constant([0.0, 0.0], dtype=dtypes.float64) targets = constant_op.constant([0.0, 1.0], dtype=dtypes.float64) - loss = nn_impl.sigmoid_cross_entropy_with_logits(logits, targets) + loss = nn_impl.sigmoid_cross_entropy_with_logits( + labels=targets, logits=logits) grads = gradients_impl.gradients(loss, logits)[0].eval() self.assertAllClose(grads, [0.5, -0.5]) def testShapeError(self): with self.assertRaisesRegexp(ValueError, "must have the same shape"): - nn_impl.sigmoid_cross_entropy_with_logits([[2, 1]], [1, 2, 3]) + nn_impl.sigmoid_cross_entropy_with_logits(labels=[1, 2, 3], + logits=[[2, 1]]) class WeightedCrossEntropyTest(test.TestCase): @@ -128,15 +133,15 @@ class WeightedCrossEntropyTest(test.TestCase): with self.test_session(): logits, targets, pos_weight, _ = self._Inputs() loss = nn_impl.weighted_cross_entropy_with_logits( - targets, logits, pos_weight, name="mybce") + targets=targets, logits=logits, pos_weight=pos_weight, name="mybce") self.assertEqual("mybce", loss.op.name) def testOutput(self): for use_gpu in [True, False]: with self.test_session(use_gpu=use_gpu): logits, targets, pos_weight, losses = self._Inputs(dtype=dtypes.float32) - loss = nn_impl.weighted_cross_entropy_with_logits(targets, logits, - pos_weight) + loss = nn_impl.weighted_cross_entropy_with_logits( + targets=targets, logits=logits, pos_weight=pos_weight) np_loss = np.array(losses).astype(np.float32) tf_loss = loss.eval() self.assertAllClose(np_loss, tf_loss, atol=0.001) @@ -146,8 +151,8 @@ class WeightedCrossEntropyTest(test.TestCase): with self.test_session(use_gpu=use_gpu): logits, targets, pos_weight, losses = self._Inputs( dtype=dtypes.float32, sizes=[2, 2, 2]) - loss = nn_impl.weighted_cross_entropy_with_logits(targets, logits, - pos_weight) + loss = nn_impl.weighted_cross_entropy_with_logits( + targets=targets, logits=logits, pos_weight=pos_weight) np_loss = np.array(losses).astype(np.float32) tf_loss = loss.eval() self.assertAllClose(np_loss, tf_loss, atol=0.001) @@ -156,15 +161,16 @@ class WeightedCrossEntropyTest(test.TestCase): sizes = [4, 2] with self.test_session(): logits, targets, pos_weight, _ = self._Inputs(sizes=sizes) - loss = nn_impl.weighted_cross_entropy_with_logits(targets, logits, - pos_weight) + loss = nn_impl.weighted_cross_entropy_with_logits( + targets=targets, logits=logits, pos_weight=pos_weight) err = gradient_checker.compute_gradient_error(logits, sizes, loss, sizes) print("logistic loss gradient err = ", err) self.assertLess(err, 1e-7) def testShapeError(self): with self.assertRaisesRegexp(ValueError, "must have the same shape"): - nn_impl.weighted_cross_entropy_with_logits([1, 2, 3], [[2, 1]], 2.0) + nn_impl.weighted_cross_entropy_with_logits( + targets=[1, 2, 3], logits=[[2, 1]], pos_weight=2.0) if __name__ == "__main__": diff --git a/tensorflow/python/training/saver_test.py b/tensorflow/python/training/saver_test.py index 68e6bb5e63..2bde726b45 100644 --- a/tensorflow/python/training/saver_test.py +++ b/tensorflow/python/training/saver_test.py @@ -1614,7 +1614,7 @@ class MetaGraphTest(test.TestCase): concated, array_ops.stack([batch_size, 10]), 1.0, 0.0) logits = ops_lib.get_collection("logits")[0] cross_entropy = nn_ops.softmax_cross_entropy_with_logits( - logits, onehot_labels, name="xentropy") + labels=onehot_labels, logits=logits, name="xentropy") loss = math_ops.reduce_mean(cross_entropy, name="xentropy_mean") summary.scalar("loss", loss) @@ -1698,7 +1698,8 @@ class MetaGraphTest(test.TestCase): bias = variables.Variable(array_ops.zeros([10]), name="bias") logit = nn_ops.relu(math_ops.matmul(image, weights) + bias, name="logits") nn_ops.softmax(logit, name="prediction") - cost = nn_ops.softmax_cross_entropy_with_logits(logit, label, name="cost") + cost = nn_ops.softmax_cross_entropy_with_logits(labels=label, + logits=logit, name="cost") adam.AdamOptimizer().minimize(cost, name="optimize") saver = saver_module.Saver() sess.run(variables.global_variables_initializer()) @@ -1726,7 +1727,8 @@ class MetaGraphTest(test.TestCase): bias = variables.Variable(array_ops.zeros([10]), name="bias") logit = nn_ops.relu(math_ops.matmul(image, weights) + bias) nn_ops.softmax(logit, name="prediction") - cost = nn_ops.softmax_cross_entropy_with_logits(logit, label) + cost = nn_ops.softmax_cross_entropy_with_logits(labels=label, + logits=logit) adam.AdamOptimizer().minimize(cost, name="optimize") meta_graph_def = saver_module.export_meta_graph() @@ -1758,7 +1760,8 @@ class MetaGraphTest(test.TestCase): bias = variables.Variable(array_ops.zeros([10]), name="bias") logit = nn_ops.relu(math_ops.matmul(image, weights) + bias) nn_ops.softmax(logit, name="prediction") - cost = nn_ops.softmax_cross_entropy_with_logits(logit, label) + cost = nn_ops.softmax_cross_entropy_with_logits(labels=label, + logits=logit) adam.AdamOptimizer().minimize(cost, name="optimize") meta_graph_def = saver_module.export_meta_graph(clear_devices=True) graph_io.write_graph(meta_graph_def, "/tmp", "meta_graph.pbtxt") |