self.index = np.arange(self.maxlen)
np.random.shuffle(self.index)
self.cnt = 0
return self
def __next__(self):
if self.cnt == self.maxlen: raise StopIteration
self.cnt += self.batch
return self.data[self.index[self.cnt - self.batch: self.cnt], :], \
self.label[self.index[self.cnt - self.batch: self.cnt], :]
def next(self):
return self.__next__()
if __name__ == "__main__":
print tf.__version__
sys.path.append("../")
import DataReader
tdata = DataReader.ImageReader("../dataset/train-images-idx3-ubyte.gz").to_tensor()
ldata = DataReader.LabelReader("../dataset/train-labels-idx1-ubyte.gz").to_tensor()
print tdata.shape
print ldata.shape
tf_mlp = TFMLP(tdata, ldata)
tf_mlp.train()
ttest = DataReader.ImageReader("../dataset/t10k-images-idx3-ubyte.gz").to_tensor()
ltest = DataReader.LabelReader("../dataset/t10k-labels-idx1-ubyte.gz").to_tensor()
tf_mlp.test(ttest, ltest)
