SeqLenMaskLayer might have inf in output

TFNetwork.py

@@ -472,7 +472,7 @@ def _create_layer(self, name, layer_class, **layer_desc):
         layer.output.placeholder = tf.Print(
           layer.output.placeholder, [layer_class.cls_get_tf_scope_name(name), "shape:", tf.shape(layer.output.placeholder)],
           summarize=10, name="debug_print_layer_output_shape")
-      if debug_add_check_numerics_on_output and layer.output.dtype.startswith("float"):
+      if debug_add_check_numerics_on_output and layer.output.dtype.startswith("float") and not layer.allow_inf_in_output:
         print("debug_add_check_numerics_on_output: add for layer %r: %r" % (name, layer.output.placeholder))
         from TFUtil import identity_with_check_numerics
         layer.output.placeholder = identity_with_check_numerics(

TFNetworkLayer.py

@@ -43,6 +43,7 @@ def get_out_data_from_opts(cls, **kwargs):
 
   layer_class = None  # type: str|None  # for get_layer_class()
   recurrent = False  # if the order in the time-dimension is relevant
+  allow_inf_in_output = False
 
   def __init__(self, name, network, output=None, n_out=None, out_type=None, sources=(),
                target=None, loss=None, size_target=None,
@@ -2165,6 +2166,8 @@ def __init__(self, mask_value, axis="T", seq_len_source=None, **kwargs):
     x_ = tf.where(energy_mask, x.placeholder, mask_value * tf.ones_like(x.placeholder), "energy_masked")
     self.output.placeholder = x_
     self.output.size_placeholder = x.size_placeholder.copy()
+    if mask_value in [float("-inf"), float("inf")]:
+      self.allow_inf_in_output = True
 
   @classmethod
   def transform_config_dict(cls, d, network, get_layer):