diff --git a/keras/backend/cntk_backend.py b/keras/backend/cntk_backend.py
index c2807d2d813..d416c4e18c1 100644
--- a/keras/backend/cntk_backend.py
+++ b/keras/backend/cntk_backend.py
@@ -1718,7 +1718,7 @@ def _is_input_shape_compatible(input, placeholder):
 
     def __call__(self, inputs):
         global _LEARNING_PHASE
-        assert type(inputs) in {list, tuple}
+        assert isinstance(inputs, (list, tuple))
         feed_dict = {}
         for tensor, value in zip(self.placeholders, inputs):
             # cntk only support calculate on float, do auto cast here
diff --git a/keras/layers/core.py b/keras/layers/core.py
index 54c8fa9df89..a89ed39f79a 100644
--- a/keras/layers/core.py
+++ b/keras/layers/core.py
@@ -639,7 +639,7 @@ def compute_output_shape(self, input_shape):
             if not isinstance(shape, (list, tuple)):
                 raise ValueError('`output_shape` function must return a tuple or a list of tuples.')
             if isinstance(shape, list):
-                if type(shape[0]) == int or shape[0] is None:
+                if isinstance(shape[0], int) or shape[0] is None:
                     shape = tuple(shape)
             return shape
 
diff --git a/tests/keras/backend/backend_test.py b/tests/keras/backend/backend_test.py
index 0a22844a24f..f54d7c54328 100644
--- a/tests/keras/backend/backend_test.py
+++ b/tests/keras/backend/backend_test.py
@@ -26,7 +26,7 @@ def cntk_func_single_tensor(function_name, x_shape, **kwargs):
 
 
 def cntk_func_two_tensor(function_name, x_shape, y, **kwargs):
-    if type(y).__name__ == 'ndarray':
+    if isinstance(y, (np.generic, np.ndarray)):
         xc = KC.placeholder(x_shape)
         output_cntk = getattr(KC, function_name)(xc, KC.variable(y), **kwargs)
         return KC.function([xc], [output_cntk])
diff --git a/tests/keras/engine/test_topology.py b/tests/keras/engine/test_topology.py
index 78d990f5a4f..6e3229d2d8e 100644
--- a/tests/keras/engine/test_topology.py
+++ b/tests/keras/engine/test_topology.py
@@ -151,20 +151,20 @@ def test_node_construction():
     node = a_layer.inbound_nodes[a_node_index]
     assert node.outbound_layer == a_layer
 
-    assert type(node.inbound_layers) is list
+    assert isinstance(node.inbound_layers, list)
     assert node.inbound_layers == []
-    assert type(node.input_tensors) is list
+    assert isinstance(node.input_tensors, list)
     assert node.input_tensors == [a]
-    assert type(node.input_masks) is list
+    assert isinstance(node.input_masks, list)
     assert node.input_masks == [None]
-    assert type(node.input_shapes) is list
+    assert isinstance(node.input_shapes, list)
     assert node.input_shapes == [(None, 32)]
 
-    assert type(node.output_tensors) is list
+    assert isinstance(node.output_tensors, list)
     assert node.output_tensors == [a]
-    assert type(node.output_shapes) is list
+    assert isinstance(node.output_shapes, list)
     assert node.output_shapes == [(None, 32)]
-    assert type(node.output_masks) is list
+    assert isinstance(node.output_masks, list)
     assert node.output_masks == [None]
 
     dense = Dense(16, name='dense_1')