From 3d83156c756518fae4a57d73c33473cf20829c7e Mon Sep 17 00:00:00 2001
From: Yuwei Hu <huyuwei1995@gmail.com>
Date: Mon, 7 Jan 2019 02:03:41 +0800
Subject: [PATCH] [Relay][Keras] force const dtype to be float32 (#2376)
* [Relay][Keras] force const dtype to be float32
* fix pylint
---
python/tvm/relay/frontend/keras.py | 12 ++++++------
1 file changed, 6 insertions(+), 6 deletions(-)
diff --git a/python/tvm/relay/frontend/keras.py b/python/tvm/relay/frontend/keras.py
index 0c55c7595..868b46b74 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]
--
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