import sys from upload import Upload argvs = sys.argv test = Upload() print(test.upload(str(argvs[1])))
from entry import login
import json
def start():
print("Please Enter Your Name")
name = input("> ")
print("Welcome, {}".format(name))
key = ""
if name.lower() == "toby":
path = input("Enter full key path: ")
key = login(name.lower(), path)
else:
key = login(name)
return key
if __name__ == "__main__":
key = start()
a = Upload(key)
path = input("Enter file path> ")
b,c= a.upload(path)
print(b)
print()
print(c)
print("prediction value: ")
print(prediction_value)
print("target value: ")
print(y_value_raw[example])
# generate greyscale image data from prediction data
prediction_imgdata = prediction * 255
prediction_imgdata = prediction_imgdata.astype(np.uint8)
# generate greyscale image of target data
target_imgdata = y_policy_raw[example]
# merge image data in color channels
merged_imgdata = np.stack([input_imgdata, prediction_imgdata, target_imgdata],
axis=2)
#create image
img = Image.fromarray(merged_imgdata, 'RGB')
img = img.resize(size=(img.size[0] * 10, img.size[1] * 10))
imgpath = logdest + "/" + os.path.basename(target_model).replace(
'.h5', '') + "." + str(iteration) + ".png"
img.save(imgpath)
# upload results
if upload:
uploader = Upload()
uploader.upload(modelPath, logpath, imgpath)
from upload import Upload
uploader = Upload()
uploader.upload('model/alphaZeroV1.h5', 'logs/alphaZeroV10.0.log',
'logs/alphaZeroV10.0.png')
