Let image retraining use inception_v3 by default.

PiperOrigin-RevId: 189724287

examples/image_retraining/retrain.py

@@ -20,14 +20,14 @@
 
 This example shows how to train an image classifier based on any
 TensorFlow Hub module that computes image feature vectors. By default,
-it uses the feature vectors computed by NASNet Large trained on ImageNet.
+it uses the feature vectors computed by Inception V3 trained on ImageNet.
 See https://github.com/tensorflow/hub/blob/master/docs/modules/image.md
 for more options.
 
-The top layer receives as input a 4032-dimensional vector (assuming
-NASNet Large) for each image. We train a softmax layer on top of this
+The top layer receives as input a 2048-dimensional vector (assuming
+Inception V3) for each image. We train a softmax layer on top of this
 representation. If the softmax layer contains N labels, this corresponds
-to learning N + 4032*N model parameters for the biases and weights.
+to learning N + 2048*N model parameters for the biases and weights.
 
 Here's an example, which assumes you have a folder containing class-named
 subfolders, each full of images for each label. The example folder flower_photos
@@ -57,7 +57,7 @@
 program, for example the tensorflow/examples/label_image sample code.
 
 By default this script will use the highly accurate, but comparatively large and
-slow NASNet Large model architecture. It's recommended that you start with this
+slow Inception V3 model architecture. It's recommended that you start with this
 to validate that you have gathered good training data, but if you want to deploy
 on resource-limited platforms, you can try the `--tfhub_module` flag with a
 Mobilenet model. For more information on Mobilenet, see
@@ -1311,7 +1311,7 @@ def main(_):
       '--tfhub_module',
       type=str,
       default=('https://storage.googleapis.com/tfhub-test-modules/'
-               'google/image/imagenet/nasnet_large/feature_vector/1.tar.gz'),
+               'google/image/imagenet/inception_v3/feature_vector/1.tar.gz'),
       help="""\
       Which TensorFlow Hub module to use.
       See https://github.com/tensorflow/hub/blob/master/docs/modules/image.md