From 2dea42976f0f6324876b770c28b4f943ce393114 Mon Sep 17 00:00:00 2001
From: Yuwei Hu <huyuwei1995@gmail.com>
Date: Mon, 10 Sep 2018 14:46:09 -0400
Subject: [PATCH] [TEST][Keras] use pretrained model to avoid small error
caused by random weights (#1701)
---
nnvm/python/nnvm/frontend/keras.py | 2 ++
nnvm/tests/python/frontend/keras/test_forward.py | 9 +++++----
2 files changed, 7 insertions(+), 4 deletions(-)
diff --git a/nnvm/python/nnvm/frontend/keras.py b/nnvm/python/nnvm/frontend/keras.py
index bb2ad7830..eb3bb0d01 100644
--- a/nnvm/python/nnvm/frontend/keras.py
+++ b/nnvm/python/nnvm/frontend/keras.py
@@ -75,6 +75,8 @@ def _convert_activation(insym, keras_layer, _):
def _convert_advanced_activation(insym, keras_layer, symtab):
act_type = type(keras_layer).__name__
if act_type == 'ReLU':
+ if keras_layer.max_value:
+ return _sym.clip(insym, a_min=0, a_max=keras_layer.max_value)
return _sym.relu(insym)
elif act_type == 'LeakyReLU':
return _sym.leaky_relu(insym, alpha=keras_layer.alpha)
diff --git a/nnvm/tests/python/frontend/keras/test_forward.py b/nnvm/tests/python/frontend/keras/test_forward.py
index c8c9b2c78..a07e69c75 100644
--- a/nnvm/tests/python/frontend/keras/test_forward.py
+++ b/nnvm/tests/python/frontend/keras/test_forward.py
@@ -141,25 +141,25 @@ def test_forward_crop():
def test_forward_vgg16():
- keras_model = keras.applications.vgg16.VGG16(include_top=True, weights=None,
+ keras_model = keras.applications.vgg16.VGG16(include_top=True, weights='imagenet',
input_shape=(224,224,3), classes=1000)
verify_keras_frontend(keras_model)
def test_forward_xception():
- keras_model = keras.applications.xception.Xception(include_top=True, weights=None,
+ keras_model = keras.applications.xception.Xception(include_top=True, weights='imagenet',
input_shape=(299,299,3), classes=1000)
verify_keras_frontend(keras_model)
def test_forward_resnet50():
- keras_model = keras.applications.resnet50.ResNet50(include_top=True, weights=None,
+ keras_model = keras.applications.resnet50.ResNet50(include_top=True, weights='imagenet',
input_shape=(224,224,3), classes=1000)
verify_keras_frontend(keras_model)
def test_forward_mobilenet():
- keras_model = keras.applications.mobilenet.MobileNet(include_top=True, weights=None,
+ keras_model = keras.applications.mobilenet.MobileNet(include_top=True, weights='imagenet',
input_shape=(224,224,3), classes=1000)
verify_keras_frontend(keras_model)
@@ -169,6 +169,7 @@ def test_forward_activations():
act_funcs = [keras.layers.Activation('softmax'),
keras.layers.Activation('softplus'),
keras.layers.ReLU(),
+ keras.layers.ReLU(max_value=6.),
keras.layers.LeakyReLU(alpha=0.3),
keras.layers.PReLU(weights=weights, alpha_initializer="zero"),
keras.layers.ELU(alpha=0.5),
--
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