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data_loader = mx.gluon.data.DataLoader(dataset, batch_size=5) | |
for X_batch, y_batch in data_loader: | |
print("X_batch has shape {}, and y_batch has shape {}".format(X_batch.shape, y_batch.shape)) |
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class DataSet(gluon.data.Dataset): | |
def __init__(self,root,DomainAList, DomainBList): | |
self.root = root | |
self.DomainAList = DomainAList | |
self.DomainBList = DomainBList | |
self.load_images() | |
def read_images(self, root): | |
Aroot = root + 'trainA/' #data | |
Broot = root + 'trainB/' #label |
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tensorflow/contrib/makefile/download_dependencies.sh | |
tensorflow/contrib/makefile/compile_ios_protobuf.sh | |
make -f tensorflow/contrib/makefile/Makefile TARGET=IOS IOS_ARCH=ARM64 |
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class Greeter { | |
constructor(public greeting: string) { } | |
greet() { | |
return "<h1>" + this.greeting + "</h1>"; | |
} | |
}; | |
var greeter = new Greeter("FunScript!"); | |
var str = greeter.greet(); | |
document.body.innerHTML = str; |
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#r "tools/FAKE/tools/FakeLib.dll" // include Fake lib | |
open Fake | |
Target "Test" (fun _ -> | |
trace "Testing stuff..." | |
) | |
Target "Deploy" (fun _ -> | |
trace "Heavy deploy action" |
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open Suave // always open suave | |
open Suave.Http.Successful // for OK-result | |
open Suave.Web // for config | |
web_server default_config (OK "Getting started with Suave!") |