diff --git a/python/tvm/relay/frontend/keras.py b/python/tvm/relay/frontend/keras.py
index 0c55c7595002..868b46b74d4f 100644
--- a/python/tvm/relay/frontend/keras.py
+++ b/python/tvm/relay/frontend/keras.py
@@ -28,7 +28,7 @@ def _get_pad_pair(input1d, kernel1d, stride1d):
 
 def _get_elu(inexpr, alpha):
     """A helper method for elu."""
-    return _op.negative(alpha) * _op.nn.relu(_expr.const(1.) - \
+    return _op.negative(alpha) * _op.nn.relu(_expr.const(1., dtype='float32') - \
         _op.exp(inexpr)) + _op.nn.relu(inexpr)
 
 
@@ -69,7 +69,7 @@ def _convert_activation(inexpr, keras_layer, _):
     elif act_type == 'relu':
         return _op.nn.relu(inexpr)
     elif act_type == 'softplus':
-        return _op.log(_op.add(_op.exp(inexpr), _expr.const(1.)))
+        return _op.log(_op.add(_op.exp(inexpr), _expr.const(1., dtype='float32')))
     elif act_type == 'elu':
         alpha = keras_layer.alpha if hasattr(keras_layer, 'alpha') else 1.
         alpha = _expr.const(alpha, dtype='float32')
@@ -86,10 +86,10 @@ def _convert_activation(inexpr, keras_layer, _):
     elif act_type == 'relu6':
         return _op.clip(inexpr, a_min=0., a_max=6.)
     elif act_type == 'softsign':
-        return inexpr / (_expr.const(1.) + _op.abs(inexpr))
+        return inexpr / (_expr.const(1., dtype='float32') + _op.abs(inexpr))
     elif act_type == 'hard_sigmoid':
-        transformX = (_expr.const(0.2) * inexpr) + _expr.const(0.5)
-        return _op.clip(transformX, a_min=0., a_max=1.)
+        x = (_expr.const(0.2, dtype='float32') * inexpr) + _expr.const(0.5, dtype='float32')
+        return _op.clip(x, a_min=0., a_max=1.)
     else:
         raise TypeError("Unsupported activation type : {}".format(act_type))
 
@@ -522,7 +522,7 @@ def _convert_gru(inexpr, keras_layer, etab):
     recurrent_h = _op.nn.dense(rec_act_r * h_tm1_op, rec_weights[1], units=units)
     act_hh = _convert_activation(x_h + recurrent_h, keras_layer, None)
     # previous and candidate state mixed by update gate
-    output = rec_act_z * h_tm1_op + (_expr.const(1.) - rec_act_z) * act_hh
+    output = rec_act_z * h_tm1_op + (_expr.const(1., dtype='float32') - rec_act_z) * act_hh
     out_shape = tuple(dim if dim else 1 for dim in _as_list(keras_layer.output_shape)[0])
     output = _op.reshape(output, newshape=out_shape)
     return [output, output]