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========== SHIPSTATION ORDERS ========== | |
Found 66454 shipped orders in ShipStation... | |
============== DEAR SALES ============== | |
Found 66442 sales in DEAR systems... | |
===== DUPLICATE SHIPSTATION ORDERS ===== | |
+-----------+--------------+---------------------+---------+ | |
| Order ID | Order Number | Order Date | Status | | |
+-----------+--------------+---------------------+---------+ |
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{ | |
"orderId": 686882714, | |
"orderNumber": "9999616949", | |
"orderKey": "1849507", | |
"orderDate": "2018-04-16T00:00:00.0000000", | |
"createDate": "2022-11-09T15:15:09.9970000", | |
"modifyDate": "2022-11-09T15:15:10.7400000", | |
"paymentDate": "2018-04-16T00:00:00.0000000", | |
"shipByDate": null, | |
"orderStatus": "awaiting_shipment", |
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------------------------------------- | |
Translated Report (Full Report Below) | |
------------------------------------- | |
Process: Electron [28492] | |
Path: /Users/USER/*/Electron.app/Contents/MacOS/Electron | |
Identifier: com.github.Electron | |
Version: 17.1.0 (17.1.0) | |
Code Type: X86-64 (Native) | |
Parent Process: node [28489] |
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58266 dog | |
58432 not_dog |
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n04507155 1300 umbrella | |
n03633091 1300 ladle | |
n03982430 1300 pool table, billiard table, snooker table | |
n01877812 1300 wallaby, brush kangaroo | |
n04486054 1300 triumphal arch | |
n02840245 1300 binder, ring-binder | |
n04065272 1300 recreational vehicle, RV, R.V. | |
n01843065 1300 jacamar | |
n03028079 1300 church, church building | |
n04330267 1300 stove |
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bazel-bin/tensorflow/contrib/lite/toco/toco \ | |
--input_file=/home/ubuntu/pps_output/output_graph.pb \ | |
--output_file=/home/ubuntu/pps_output/output_graph.tflite \ | |
--input_arrays=Placeholder \ | |
--output_arrays=final_result \ | |
--inference_type=FLOAT \ | |
--input_shapes=1,224,224,3 \ | |
--input_format=TENSORFLOW_GRAPHDEF \ | |
--output_format=TFLITE |
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import os | |
import tfcoreml as tf_converter | |
tf_model_path = os.path.join(os.getcwd(), 'output_graph.pb') | |
mlmodel_path = os.path.join(os.getcwd(), 'output_graph.mlmodel') | |
mlmodel = tf_converter.convert( | |
tf_model_path = tf_model_path, | |
mlmodel_path = mlmodel_path, | |
output_feature_names = ['final_result:0'], | |
input_name_shape_dict = {'input:0':[1,224,224,3]}, |
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# Train | |
cd ~/tensorflow/ | |
source ./bin/activate | |
python tensorflow/tensorflow/examples/image_retraining/retrain.py \ | |
--image_dir $(pwd)/datasets/oxford-iiit-pet/images/ \ | |
--learning_rate=0.001 \ | |
--testing_percentage=20 \ | |
--validation_percentage=20 \ | |
--train_batch_size=32 \ | |
--validation_batch_size=-1 \ |
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from PIL import Image | |
import os | |
path = os.getcwd() | |
for folder in os.listdir(path): | |
if os.path.isdir(os.path.join(path, folder)): | |
for file in os.listdir(os.path.join(path, folder)): | |
extension = file.split('.')[-1] | |
if extension == 'jpg': | |
fileLoc = os.path.join(path, folder)+'/'+file | |
img = Image.open(fileLoc) |
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# /bin/sh | |
# this script categorize image files by moving them into subfolders | |
# using the name of animals. | |
for f in *.jpg; do | |
name=`echo "$f"|sed 's/ -.*//'` | |
dir=`echo "${name%_*}"|tr '_' ' '| tr '[A-Z]' '[a-z]'` | |
mkdir -p "$dir" | |
mv "$f" "$dir" | |
done |