def LoadData():
mdr = MnistImageDataReader("vector")
mdr.ReadLessData(1000)
mdr.NormalizeX()
mdr.NormalizeY(NetType.MultipleClassifier, base=0)
mdr.GenerateValidationSet(k=10)
return mdr
def LoadData(num_output):
mdr = MnistImageDataReader("image")
mdr.ReadData()
mdr.NormalizeX()
mdr.NormalizeY(NetType.MultipleClassifier, base=0)
mdr.Shuffle()
mdr.GenerateValidationSet(k=12)
return mdr
def LoadData(mode):
mdr = MnistImageDataReader(train_x, train_y, test_x, test_y, mode)
mdr.ReadData()
mdr.NormalizeX()
mdr.NormalizeY(NetType.MultipleClassifier, base=0)
mdr.Shuffle()
mdr.GenerateValidationSet(k=12)
return mdr
def load_data():
dataReader = MnistImageDataReader(mode="timestep")
dataReader.ReadLessData(10000)
dataReader.NormalizeX()
dataReader.NormalizeY(NetType.MultipleClassifier, base=0)
dataReader.Shuffle()
dataReader.GenerateValidationSet(k=12)
return dataReader
def LoadData():
print("reading data...")
dr = MnistImageDataReader(mode="vector")
dr.ReadData()
dr.NormalizeX()
dr.NormalizeY(NetType.MultipleClassifier)
dr.GenerateValidationSet(k=20)
print(dr.num_validation, dr.num_example, dr.num_test, dr.num_train)
return dr
def LoadData():
#读取时最好上锁
lock.acquire()
print("reading MNIST data...")
dr = MnistImageDataReader(mode="vector")
dr.ReadData()
dr.NormalizeX()
dr.NormalizeY(NetType.MultipleClassifier)
dr.GenerateValidationSet(k=20)
print(dr.num_validation, dr.num_example, dr.num_test, dr.num_train)
#释放锁
lock.release()
return dr
def load_data():
dataReader = MnistImageDataReader(mode="vector")
dataReader.ReadData()
dataReader.NormalizeX()
dataReader.NormalizeY(NetType.MultipleClassifier)
dataReader.GenerateValidationSet(k=20)
x_train, y_train = dataReader.XTrain, dataReader.YTrain
x_test, y_test = dataReader.XTest, dataReader.YTest
x_val, y_val = dataReader.XDev, dataReader.YDev
x_train = x_train.reshape(x_train.shape[0], 28 * 28)
x_test = x_test.reshape(x_test.shape[0], 28 * 28)
x_val = x_val.reshape(x_val.shape[0], 28 * 28)
return x_train, y_train, x_test, y_test, x_val, y_val
def load_data():
dataReader = MnistImageDataReader(mode="timestep")
dataReader.ReadData()
dataReader.NormalizeX()
dataReader.NormalizeY(NetType.MultipleClassifier, base=0)
dataReader.Shuffle()
dataReader.GenerateValidationSet(k=12)
x_train, y_train = dataReader.XTrain, dataReader.YTrain
x_test, y_test = dataReader.XTest, dataReader.YTest
x_val, y_val = dataReader.XDev, dataReader.YDev
x_train = x_train.squeeze()
x_test = x_test.squeeze()
x_val = x_val.squeeze()
x_test_raw = dataReader.XTestRaw[0:64]
y_test_raw = dataReader.YTestRaw[0:64]
return x_train, y_train, x_test, y_test, x_val, y_val, x_test_raw, y_test_raw