pingsutw commented on a change in pull request #487:
URL: https://github.com/apache/submarine/pull/487#discussion_r553259128
##########
File path: submarine-sdk/pysubmarine/submarine/ml/tensorflow/layers/core.py
##########
@@ -181,3 +182,72 @@ def fm_layer(inputs, **kwargs):
square_sum = tf.reduce_sum(tf.square(inputs), 1)
fm_out = 0.5 * tf.reduce_sum(tf.subtract(sum_square, square_sum), 1)
return fm_out
+
+
+class NoMask(tf.keras.layers.Layer):
+ def __init__(self, **kwargs):
+ super(NoMask, self).__init__(**kwargs)
+
+ def build(self, input_shape):
+ # Be sure to call this somewhere!
+ super(NoMask, self).build(input_shape)
+
+ def call(self, x, mask=None, **kwargs):
+ return x
+
+ def compute_mask(self, inputs, mask):
+ return None
+
+
+class KMaxPooling(Layer):
+ """K Max pooling that selects the k biggest value along the specific axis.
+ Input shape
+ - nD tensor with shape: ``(batch_size, ..., input_dim)``.
+ Output shape
+ - nD tensor with shape: ``(batch_size, ..., output_dim)``.
+ Arguments
+ - **k**: positive integer, number of top elements to look for along
the ``axis`` dimension.
+ - **axis**: positive integer, the dimension to look for elements.
+ """
+
+ def __init__(self, k=1, axis=-1, **kwargs):
+
+ self.k = k
+ self.axis = axis
+ super(KMaxPooling, self).__init__(**kwargs)
+
+ def build(self, input_shape):
+
+ if self.axis < 1 or self.axis > len(input_shape):
+ raise ValueError("axis must be 1~%d,now is %d" %
+ (len(input_shape), self.axis))
+
+ if self.k < 1 or self.k > input_shape[self.axis]:
+ raise ValueError("k must be in 1 ~ %d,now k is %d" %
+ (input_shape[self.axis], self.k))
+ self.dims = len(input_shape)
+ # Be sure to call this somewhere!
Review comment:
```suggestion
```
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