Add slim/scripts/finetune_inception_resnet_v2_on_flowers.sh

Also fixes some typos in other scripts.

slim/scripts/finetune_inception_resnet_v2_on_flowers.sh

+#!/bin/bash
+#
+# This script performs the following operations:
+# 1. Downloads the Flowers dataset
+# 2. Fine-tunes an Inception Resnet V2 model on the Flowers training set.
+# 3. Evaluates the model on the Flowers validation set.
+#
+# Usage:
+# cd slim
+# ./slim/scripts/finetune_inception_resnet_v2_on_flowers.sh
+set -e
+
+# Where the pre-trained Inception Resnet V2 checkpoint is saved to.
+PRETRAINED_CHECKPOINT_DIR=/tmp/checkpoints
+
+# Where the pre-trained Inception Resnet V2 checkpoint is saved to.
+MODEL_NAME=inception_resnet_v2
+
+# Where the training (fine-tuned) checkpoint and logs will be saved to.
+TRAIN_DIR=/tmp/flowers-models/${MODEL_NAME}
+
+# Where the dataset is saved to.
+DATASET_DIR=/tmp/flowers
+
+# Download the pre-trained checkpoint.
+if [ ! -d "$PRETRAINED_CHECKPOINT_DIR" ]; then
+  mkdir ${PRETRAINED_CHECKPOINT_DIR}
+fi
+if [ ! -f ${PRETRAINED_CHECKPOINT_DIR}/${MODEL_NAME}.ckpt ]; then
+  wget http://download.tensorflow.org/models/inception_resnet_v2_2016_08_30.tar.gz
+  tar -xvf inception_resnet_v2_2016_08_30.tar.gz
+  mv inception_resnet_v2.ckpt ${PRETRAINED_CHECKPOINT_DIR}/${MODEL_NAME}.ckpt
+  rm inception_resnet_v2_2016_08_30.tar.gz
+fi
+
+# Download the dataset
+python download_and_convert_data.py \
+  --dataset_name=flowers \
+  --dataset_dir=${DATASET_DIR}
+
+# Fine-tune only the new layers for 1000 steps.
+python train_image_classifier.py \
+  --train_dir=${TRAIN_DIR} \
+  --dataset_name=flowers \
+  --dataset_split_name=train \
+  --dataset_dir=${DATASET_DIR} \
+  --model_name=${MODEL_NAME} \
+  --checkpoint_path=${PRETRAINED_CHECKPOINT_DIR}/${MODEL_NAME}.ckpt \
+  --checkpoint_exclude_scopes=InceptionResnetV2/Logits,InceptionResnetV2/AuxLogits \
+  --trainable_scopes=InceptionResnetV2/Logits,InceptionResnetV2/AuxLogits \
+  --max_number_of_steps=1000 \
+  --batch_size=32 \
+  --learning_rate=0.01 \
+  --learning_rate_decay_type=fixed \
+  --save_interval_secs=60 \
+  --save_summaries_secs=60 \
+  --log_every_n_steps=10 \
+  --optimizer=rmsprop \
+  --weight_decay=0.00004
+
+# Run evaluation.
+python eval_image_classifier.py \
+  --checkpoint_path=${TRAIN_DIR} \
+  --eval_dir=${TRAIN_DIR} \
+  --dataset_name=flowers \
+  --dataset_split_name=validation \
+  --dataset_dir=${DATASET_DIR} \
+  --model_name=${MODEL_NAME}
+
+# Fine-tune all the new layers for 500 steps.
+python train_image_classifier.py \
+  --train_dir=${TRAIN_DIR}/all \
+  --dataset_name=flowers \
+  --dataset_split_name=train \
+  --dataset_dir=${DATASET_DIR} \
+  --model_name=${MODEL_NAME} \
+  --checkpoint_path=${TRAIN_DIR} \
+  --max_number_of_steps=500 \
+  --batch_size=32 \
+  --learning_rate=0.0001 \
+  --learning_rate_decay_type=fixed \
+  --save_interval_secs=60 \
+  --save_summaries_secs=60 \
+  --log_every_n_steps=10 \
+  --optimizer=rmsprop \
+  --weight_decay=0.00004
+
+# Run evaluation.
+python eval_image_classifier.py \
+  --checkpoint_path=${TRAIN_DIR}/all \
+  --eval_dir=${TRAIN_DIR}/all \
+  --dataset_name=flowers \
+  --dataset_split_name=validation \
+  --dataset_dir=${DATASET_DIR} \
+  --model_name=${MODEL_NAME}

slim/scripts/finetune_inception_v3_on_flowers.sh

@@ -7,7 +7,7 @@
 #
 # Usage:
 # cd slim
-# ./slim/scripts/finetune_inceptionv3_on_flowers.sh
+# ./slim/scripts/finetune_inception_v3_on_flowers.sh
 set -e
 
 # Where the pre-trained InceptionV3 checkpoint is saved to.

slim/scripts/train_cifarnet_on_cifar10.sh

@@ -7,7 +7,7 @@
 #
 # Usage:
 # cd slim
-# ./scripts/train_cifar_net_on_mnist.sh
+# ./scripts/train_cifarnet_on_cifar10.sh
 set -e
 
 # Where the checkpoint and logs will be saved to.