experiments/STT-tensorflow
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Linter fixes

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Martin Wicke 2018-02-15 15:42:02 -08:00 committed by GitHub
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commit f0a9686511
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1 changed files with 17 additions and 13 deletions
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20
tensorflow/examples/image_retraining/retrain.py
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@ -41,7 +41,6 @@ The subfolder names are important, since they define what label is applied to
each image, but the filenames themselves don't matter. Once your images are
prepared, you can run the training with a command like this:
```bash
bazel build tensorflow/examples/image_retraining:retrain && \
bazel-bin/tensorflow/examples/image_retraining/retrain \
@ -70,12 +69,14 @@ on resource-limited platforms, you can try the `--architecture` flag with a
Mobilenet model. For example:
Run floating-point version of mobilenet:
```bash
python tensorflow/examples/image_retraining/retrain.py \
--image_dir ~/flower_photos --architecture mobilenet_1.0_224
```
Run quantized version of mobilenet:
```bash
python tensorflow/examples/image_retraining/retrain.py \
--image_dir ~/flower_photos/ --architecture mobilenet_1.0_224_quantized
@ -98,8 +99,10 @@ tensorboard --logdir /tmp/retrain_logs
To use with Tensorflow Serving:
tensorflow_model_server --port=9000 --model_name=inception --model_base_path=/tmp/saved_models/
```bash
tensorflow_model_server --port=9000 --model_name=inception \
--model_base_path=/tmp/saved_models/
```
"""
from __future__ import absolute_import
from __future__ import division
@ -1026,13 +1029,13 @@ def export_model(sess, architecture, saved_model_dir):
inputs = {'image': tf.saved_model.utils.build_tensor_info(in_image)}
out_classes = sess.graph.get_tensor_by_name('final_result:0')
outputs = {'prediction': tf.saved_model.utils.build_tensor_info(out_classes)}
outputs = {'prediction':
tf.saved_model.utils.build_tensor_info(out_classes)}
signature = tf.saved_model.signature_def_utils.build_signature_def(
inputs=inputs,
outputs=outputs,
method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME
)
method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME)
legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op')
@ -1040,8 +1043,9 @@ def export_model(sess, architecture, saved_model_dir):
builder = tf.saved_model.builder.SavedModelBuilder(saved_model_dir)
builder.add_meta_graph_and_variables(
sess, [tf.saved_model.tag_constants.SERVING],
signature_def_map={
tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: signature
signature_def_map = {
tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
signature
},
legacy_init_op=legacy_init_op)
builder.save()