@@ -86,7 +86,8 @@ you will not need to interact with the script again.
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DATA_DIR=$HOME /imagenet-data
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# build the preprocessing script.
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- bazel build inception/download_and_preprocess_imagenet
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+ cd tensorflow-models/inception
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+ bazel build //inception:download_and_preprocess_imagenet
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# run it
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bazel-bin/inception/download_and_preprocess_imagenet " ${DATA_DIR} "
@@ -153,7 +154,8 @@ To train this model, you simply need to specify the following:
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``` shell
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# Build the model. Note that we need to make sure the TensorFlow is ready to
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# use before this as this command will not build TensorFlow.
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- bazel build inception/imagenet_train
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+ cd tensorflow-models/inception
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+ bazel build //inception:imagenet_train
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# run it
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bazel-bin/inception/imagenet_train --num_gpus=1 --batch_size=32 --train_dir=/tmp/imagenet_train --data_dir=/tmp/imagenet_data
@@ -189,7 +191,8 @@ GPU cards.
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``` shell
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# Build the model. Note that we need to make sure the TensorFlow is ready to
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# use before this as this command will not build TensorFlow.
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- bazel build inception/imagenet_train
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+ cd tensorflow-models/inception
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+ bazel build //inception:imagenet_train
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# run it
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bazel-bin/inception/imagenet_train --num_gpus=2 --batch_size=64 --train_dir=/tmp/imagenet_train
@@ -288,7 +291,8 @@ running. Several things to note here:
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``` shell
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# Build the model. Note that we need to make sure the TensorFlow is ready to
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# use before this as this command will not build TensorFlow.
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- bazel build inception/imagenet_distributed_train
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+ cd tensorflow-models/inception
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+ bazel build //inception:imagenet_distributed_train
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# To start worker 0, go to the worker0 host and run the following (Note that
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# task_id should be in the range [0, num_worker_tasks):
@@ -395,7 +399,8 @@ Briefly, one can evaluate the model by running:
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``` shell
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# Build the model. Note that we need to make sure the TensorFlow is ready to
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# use before this as this command will not build TensorFlow.
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- bazel build inception/imagenet_eval
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+ cd tensorflow-models/inception
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+ bazel build //inception:imagenet_eval
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# run it
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bazel-bin/inception/imagenet_eval --checkpoint_dir=/tmp/imagenet_train --eval_dir=/tmp/imagenet_eval
@@ -450,7 +455,8 @@ but feel free to edit accordingly.
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FLOWERS_DATA_DIR=/tmp/flowers-data/
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# build the preprocessing script.
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- bazel build inception/download_and_preprocess_flowers
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+ cd tensorflow-models/inception
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+ bazel build //inception:download_and_preprocess_flowers
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# run it
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bazel-bin/inception/download_and_preprocess_flowers " ${FLOWERS_DATA_DIR} "
@@ -530,7 +536,8 @@ the flowers data set with the following command.
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``` shell
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# Build the model. Note that we need to make sure the TensorFlow is ready to
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# use before this as this command will not build TensorFlow.
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- bazel build inception/flowers_train
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+ cd tensorflow-models/inception
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+ bazel build //inception:flowers_train
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# Path to the downloaded Inception-v3 model.
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MODEL_PATH=" ${INCEPTION_MODEL_DIR} /inception-v3/model.ckpt-157585"
@@ -566,7 +573,8 @@ fine-tuned model, you will need to run `flowers_eval`:
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``` shell
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# Build the model. Note that we need to make sure the TensorFlow is ready to
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# use before this as this command will not build TensorFlow.
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- bazel build inception/flowers_eval
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+ cd tensorflow-models/inception
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+ bazel build //inception:flowers_eval
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# Directory where we saved the fine-tuned checkpoint and events files.
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TRAIN_DIR=/tmp/flowers_train/
@@ -654,7 +662,8 @@ To run `build_image_data.py`, you can run the following command line:
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OUTPUT_DIRECTORY=$HOME /my-custom-data/
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# build the preprocessing script.
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- bazel build inception/build_image_data
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+ cd tensorflow-models/inception
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+ bazel build //inception:build_image_data
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# convert the data.
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bazel-bin/inception/build_image_data \
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