Merge pull request #200 from DrewNF/master

Error Fix in Read.me, Added download_and_preprocess_flowers_mac for Mac commands

inception/inception/data/download_and_preprocess_flowers_mac.sh

@@ -0,0 +1,96 @@
+#!/bin/bash
+# Copyright 2016 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+# Script to download and preprocess the flowers data set. This data set
+# provides a demonstration for how to perform fine-tuning (i.e. tranfer
+# learning) from one model to a new data set.
+#
+# This script provides a demonstration for how to prepare an arbitrary
+# data set for training an Inception v3 model.
+#
+# We demonstrate this with the flowers data set which consists of images
+# of labeled flower images from 5 classes:
+#
+# daisy, dandelion, roses, sunflowers, tulips
+#
+# The final output of this script are sharded TFRecord files containing
+# serialized Example protocol buffers. See build_image_data.py for
+# details of how the Example protocol buffer contains image data.
+#
+# usage:
+#  ./download_and_preprocess_flowers.sh [data-dir]
+set -e
+
+if [ -z "$1" ]; then
+  echo "usage download_and_preprocess_flowers.sh [data dir]"
+  exit
+fi
+
+# Create the output and temporary directories.
+DATA_DIR="${1%/}"
+SCRATCH_DIR="${DATA_DIR}/raw-data/"
+mkdir -p "${DATA_DIR}"
+mkdir -p "${SCRATCH_DIR}"
+WORK_DIR="$0.runfiles/inception"
+
+# Download the flowers data.
+DATA_URL="http://download.tensorflow.org/example_images/flower_photos.tgz"
+CURRENT_DIR=$(pwd)
+cd "${DATA_DIR}"
+TARBALL="flower_photos.tgz"
+if [ ! -f ${TARBALL} ]; then
+  echo "Downloading flower data set."
+  wget -O ${TARBALL} "${DATA_URL}"
+else
+  echo "Skipping download of flower data."
+fi
+
+# Note the locations of the train and validation data.
+TRAIN_DIRECTORY="${SCRATCH_DIR}train/"
+VALIDATION_DIRECTORY="${SCRATCH_DIR}validation/"
+
+# Expands the data into the flower_photos/ directory and rename it as the
+# train directory.
+tar xf flower_photos.tgz
+rm -rf "${TRAIN_DIRECTORY}" "${VALIDATION_DIRECTORY}"
+mv flower_photos "${TRAIN_DIRECTORY}"
+
+# Generate a list of 5 labels: daisy, dandelion, roses, sunflowers, tulips
+LABELS_FILE="${SCRATCH_DIR}/labels.txt"
+ls -1 "${TRAIN_DIRECTORY}" | grep -v 'LICENSE' | sed 's/\///' | sort > "${LABELS_FILE}"
+
+# Generate the validation data set.
+while read LABEL; do
+  VALIDATION_DIR_FOR_LABEL="${VALIDATION_DIRECTORY}${LABEL}"
+  TRAIN_DIR_FOR_LABEL="${TRAIN_DIRECTORY}${LABEL}"
+
+  # Move the first randomly selected 100 images to the validation set.
+  mkdir -p "${VALIDATION_DIR_FOR_LABEL}"
+  VALIDATION_IMAGES=$(ls -1 "${TRAIN_DIR_FOR_LABEL}" | gshuf | head -100)
+  for IMAGE in ${VALIDATION_IMAGES}; do
+    mv -f "${TRAIN_DIRECTORY}${LABEL}/${IMAGE}" "${VALIDATION_DIR_FOR_LABEL}"
+  done
+done < "${LABELS_FILE}"
+
+# Build the TFRecords version of the image data.
+cd "${CURRENT_DIR}"
+BUILD_SCRIPT="${WORK_DIR}/build_image_data"
+OUTPUT_DIRECTORY="${DATA_DIR}"
+"${BUILD_SCRIPT}" \
+  --train_directory="${TRAIN_DIRECTORY}" \
+  --validation_directory="${VALIDATION_DIRECTORY}" \
+  --output_directory="${OUTPUT_DIRECTORY}" \
+  --labels_file="${LABELS_FILE}"