def model(eta, batch_size):
filename = str.format("LR_BS_Trial\\loss_{0}_{1}.pkl", eta,
batch_size).replace('.', '', 1)
filepath = Path(filename)
if filepath.exists():
return filename
dataReader = MnistImageDataReader(mode="vector")
dataReader.ReadData()
dataReader.NormalizeX()
dataReader.NormalizeY(NetType.MultipleClassifier, base=0)
dataReader.Shuffle()
dataReader.GenerateValidationSet(k=12)
n_input = dataReader.num_feature
n_hidden1 = 64
n_hidden2 = 16
n_output = dataReader.num_category
eps = 0.01
max_epoch = 30
hp = HyperParameters_3_0(n_input, n_hidden1, n_hidden2, n_output, eta,
max_epoch, batch_size, eps,
NetType.MultipleClassifier, InitialMethod.Xavier)
net = NeuralNet_3_0(hp, "LR_BS_Trial")
net.train(dataReader, 0.5, True)
net.DumpLossHistory(filename)
return filename
def LoadNet():
n_input = 784
n_hidden1 = 64
n_hidden2 = 16
n_output = 10
eta = 0.2
eps = 0.01
batch_size = 128
max_epoch = 40
hp = HyperParameters_3_0(n_input, n_hidden1, n_hidden2, n_output, eta,
max_epoch, batch_size, eps,
NetType.MultipleClassifier, InitialMethod.Xavier)
net = NeuralNet_3_0(hp, "MNIST_64_16")
net.LoadResult()
return net
# Copyright (c) Microsoft. All rights reserved.
# Licensed under the MIT license. See LICENSE file in the project root for full license information.
from HelperClass2.MnistImageDataReader import *
from HelperClass2.NeuralNet_3_0 import *
if __name__ == '__main__':
dataReader = MnistImageDataReader(mode="vector")
dataReader.ReadData()
dataReader.NormalizeX()
dataReader.NormalizeY(NetType.MultipleClassifier, base=0)
dataReader.Shuffle()
dataReader.GenerateValidationSet(k=12)
n_input = dataReader.num_feature
n_hidden1 = 64
n_hidden2 = 16
n_output = dataReader.num_category
eta = 0.2
eps = 0.01
batch_size = 128
max_epoch = 40
hp = HyperParameters_3_0(n_input, n_hidden1, n_hidden2, n_output, eta,
max_epoch, batch_size, eps,
NetType.MultipleClassifier, InitialMethod.Xavier)
net = NeuralNet_3_0(hp, "MNIST_64_16")
net.train(dataReader, 0.5, True)
net.ShowTrainingHistory(xline="iteration")
if __name__ == '__main__':
n_input = 7
n_hidden1 = 16
n_hidden2 = 12
n_output = 10
eta = 0.2
eps = 0.01
batch_size = 128
max_epoch = 40
hp = HyperParameters_3_0(n_input, n_hidden1, n_hidden2, n_output, eta,
max_epoch, batch_size, eps,
NetType.MultipleClassifier, InitialMethod.Xavier)
net = NeuralNet_3_0(hp, "MNIST_gradient_check")
dict_Param = {
"W1": net.wb1.W,
"B1": net.wb1.B,
"W2": net.wb2.W,
"B2": net.wb2.B,
"W3": net.wb3.W,
"B3": net.wb3.B
}
layer_dims = [n_input, n_hidden1, n_hidden2, n_output]
n_example = 2
x = np.random.randn(n_example, n_input)
#y = np.array([1,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,1,0,0,0,0, 0,0,1,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,1,0,0, 0,0,0,0,0,0,0,0,0,1]).reshape(-1,n_example)
#y = np.array([1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]).reshape(-1,n_example)
y = np.array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0]).reshape(1, -1)