diff --git a/neuralmonkey/decorators.py b/neuralmonkey/decorators.py
index 62f968009..146ac986c 100644
--- a/neuralmonkey/decorators.py
+++ b/neuralmonkey/decorators.py
@@ -1,6 +1,9 @@
 from functools import wraps
 
+import tensorflow as tf
+
 from neuralmonkey.model.model_part import ModelPart
+from neuralmonkey.tf_utils import tf_print
 
 
 def tensor(func):
@@ -12,6 +15,10 @@ def decorate(self, *args, **kwargs):
                 # jump out of the caller's scope and into the ModelPart's scope
                 with self.use_scope():
                     value = func(self, *args, **kwargs)
+                    if isinstance(value, tf.Tensor):
+                        value = tf_print(
+                            value, "<{}.{}>".format(self.name, func.__name__),
+                            "tensorval")
             else:
                 value = func(self, *args, **kwargs)
             setattr(self, attribute_name, value)
diff --git a/neuralmonkey/logging.py b/neuralmonkey/logging.py
index fd6670916..8b92861a2 100644
--- a/neuralmonkey/logging.py
+++ b/neuralmonkey/logging.py
@@ -3,7 +3,7 @@
 import os
 
 # pylint: disable=unused-import
-from typing import Any, Optional, List
+from typing import Any, List
 # pylint: enable=unused-import
 
 from termcolor import colored
@@ -11,13 +11,13 @@
 
 class Logging(object):
 
-    log_file = None  # type: Optional[Any]
+    log_file = None  # type: Any
 
     # 'all' and 'none' are special symbols,
     # others are filtered according the labels
-    debug_enabled = [
+    debug_enabled_for = [
         os.environ.get("NEURALMONKEY_DEBUG_ENABLE", "none")]  # type: List[str]
-    debug_disabled = [
+    debug_disabled_for = [
         os.environ.get("NEURALMONKEY_DEBUG_DISABLE", "")]  # type: List[str]
     strict_mode = os.environ.get("NEURALMONKEY_STRICT")  # type: str
 
@@ -76,24 +76,32 @@ def print_header(title: str, path: str) -> None:
         log_print("")
 
     @staticmethod
-    def debug(message: str, label: Optional[str] = None):
-        if "none" in Logging.debug_enabled:
-            return
-
-        if (label not in Logging.debug_enabled and
-                "all" not in Logging.debug_enabled):
-            return
-
-        if label in Logging.debug_disabled:
+    def debug(message: str, label: str = None):
+        if not debug_enabled(label):
             return
 
         if label:
-            prefix = "DEBUG ({}):".format(label)
+            prefix = "{}: DEBUG ({}): ".format(Logging._get_time(), label)
         else:
-            prefix = "DEBUG:"
+            prefix = "{}: DEBUG: ".format(Logging._get_time())
 
         log_print("{}{}".format(colored(prefix, color="cyan"), message))
 
+    @staticmethod
+    def debug_enabled(label: str = None):
+        if "none" in Logging.debug_enabled_for:
+            return False
+
+        if label is None:
+            return True
+
+        if (label in Logging.debug_disabled_for
+                or ("all" not in Logging.debug_enabled_for
+                    and label not in Logging.debug_enabled_for)):
+            return False
+
+        return True
+
 
 # pylint: disable=invalid-name
 # we want these helper functions to have this exact name
@@ -102,3 +110,4 @@ def debug(message: str, label: Optional[str] = None):
 debug = Logging.debug
 warn = Logging.warn
 notice = Logging.notice
+debug_enabled = Logging.debug_enabled
diff --git a/neuralmonkey/tf_utils.py b/neuralmonkey/tf_utils.py
index c30b9b4a9..3af3da72e 100644
--- a/neuralmonkey/tf_utils.py
+++ b/neuralmonkey/tf_utils.py
@@ -3,8 +3,11 @@
 # pylint: disable=unused-import
 from typing import Dict, Set
 # pylint: enable=unused-import
+import numpy as np
 import tensorflow as tf
 
+from neuralmonkey.logging import debug, debug_enabled
+
 
 def _get_current_experiment():
     # This is needed to avoid circular imports.
@@ -45,3 +48,40 @@ def get_variable(name: str,
         name=name, shape=shape, dtype=dtype,
         initializer=get_initializer(name, initializer),
         **kwargs)
+
+
+def tf_print(tensor: tf.Tensor,
+             message: str = None,
+             debug_label: str = None) -> tf.Tensor:
+    """Print the value of a tensor to the debug log.
+
+    Better than tf.Print, logs to console only when the "tensorval" debug
+    subject is turned on.
+
+    Idea found at: https://stackoverflow.com/a/39649614
+
+    Args:
+        tensor: The tensor whose value to print
+
+    Returns:
+        As tf.Print, this function returns a tensor identical to the input
+        tensor, with the printing side-effect added.
+    """
+    def print_tensor(x: np.ndarray) -> tf.Tensor:
+        if message is not None:
+            debug(
+                "{}, shape: {}:\n{}".format(message, x.shape, x), debug_label)
+        else:
+            debug("Shape: {}\n{}".format(x.shape, x), debug_label)
+        return x
+
+    # To save time, check if debug will print something
+    if not debug_enabled(debug_label):
+        return tensor
+
+    log_op = tf.py_func(print_tensor, [tensor], [tensor.dtype])[0]
+
+    with tf.control_dependencies([log_op]):
+        res = tf.identity(tensor)
+
+    return res