比较与tf.keras.backend.batch_dot的功能差异

tf.keras.backend.batch_dot(x, y, axes=None)

mindspore.ops.batch_dot(x1, x2, axes=None)

# TensorFlow
import keras.backend as K
import tensorflow as tf
import numpy as np

x = K.variable(np.random.randint(10,size=(10,12,4,5)), dtype=tf.float32)
y = K.variable(np.random.randint(10,size=(10,12,5,8)), dtype=tf.float32)
output = K.batch_dot(x, y)
print(output.shape)
# (10, 12, 4, 12, 8)

# MindSpore
import numpy as np
import mindspore
import mindspore.ops as ops
from mindspore import Tensor

x1 = Tensor(np.random.randint(10,size=(10,12,4,5)), mindspore.float32)
x2 = Tensor(np.random.randint(10,size=(10,12,5,8)), mindspore.float32)
output = ops.batch_dot(x1, x2)
print(output.shape)
# (10, 12, 4, 12, 8)

# TensorFlow
import keras.backend as K
import tensorflow as tf
import numpy as np

x = K.variable(np.ones(shape=[2, 2]), dtype=tf.float32)
y = K.variable(np.ones(shape=[2, 3, 2]), dtype=tf.float32)
axes = (1, 2)
output = K.batch_dot(x, y, axes)
print(output.shape)
# (2, 3)

# MindSpore
import numpy as np
import mindspore
import mindspore.ops as ops
from mindspore import Tensor

x1 = Tensor(np.ones(shape=[2, 2]), mindspore.float32)
x2 = Tensor(np.ones(shape=[2, 3, 2]), mindspore.float32)
axes = (1, 2)
output = ops.batch_dot(x1, x2, axes)
print(output.shape)
# (2, 3)