Function Differences with tf.nn.softmax_cross_entropy_with_logits

tf.nn.softmax_cross_entropy_with_logits

class tf.nn.softmax_cross_entropy_with_logits(
    labels,
    logits,
    axis=-1,
    name=None
)

For more information, see tf.nn.softmax_cross_entropy_with_logits.

mindspore.nn.SoftmaxCrossEntropyWithLogits

class mindspore.nn.SoftmaxCrossEntropyWithLogits(
    sparse=False,
    reduction='none'
)(logits, labels)

For more information, see mindspore.nn.SoftmaxCrossEntropyWithLogits.

Differences

TensorFlow: The shape of labels and logits must be the same, and the reduction parameter is not provided, which cannot calculate mean or sum for loss.

MindSpore：Sparse matrices for labels are supported and mean or sum for loss can be calculated through the reduction parameter.

Code Example

# The following implements SoftmaxCrossEntropyWithLogits with MindSpore.
import numpy as np
import tensorflow as tf
import mindspore
import mindspore.nn as nn
from mindspore import Tensor

loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='sum')
logits = Tensor(np.array([[3, 5, 6, 9], [42, 12, 32, 72]]), mindspore.float32)
labels_np = np.array([1, 0]).astype(np.int32)
labels = Tensor(labels_np)
output = loss(logits, labels)
print(output)
# Out：
# 34.068203


# The following implements softmax_cross_entropy_with_logits with TensorFlow.
logits = tf.constant([[3, 5, 6, 9], [42, 12, 32, 72]], dtype=tf.float32)
labels = tf.constant([[0, 1, 0, 0], [1, 0, 0, 0]], dtype=tf.float32)
output = tf.nn.softmax_cross_entropy_with_logits(labels=labels, logits=logits)
ss = tf.Session()
ss.run(output)
# out
# array([ 4.068202, 30.  ], dtype=float32)