同样是一个双层神经网络,但是最后一层要接一个Logistic二分类函数来完成二分类任务,如图14-7所示。
def model(dataReader):
num_input = 2
num_hidden = 3
num_output = 1
max_epoch = 1000
batch_size = 5
learning_rate = 0.1
params = HyperParameters_4_0(
learning_rate, max_epoch, batch_size,
net_type=NetType.BinaryClassifier,
init_method=InitialMethod.Xavier,
stopper=Stopper(StopCondition.StopLoss, 0.02))
net = NeuralNet_4_0(params, "Arc")
fc1 = FcLayer_1_0(num_input, num_hidden, params)
net.add_layer(fc1, "fc1")
sigmoid1 = ActivationLayer(Sigmoid())
net.add_layer(sigmoid1, "sigmoid1")
fc2 = FcLayer_1_0(num_hidden, num_output, params)
net.add_layer(fc2, "fc2")
logistic = ClassificationLayer(Logistic())
net.add_layer(logistic, "logistic")
net.train(dataReader, checkpoint=10, need_test=True)
return net
超参数说明:
- 输入层神经元数为2
- 隐层的神经元数为3,使用Sigmoid激活函数
- 由于是二分类任务,所以输出层只有一个神经元,用Logistic做二分类函数
- 最多训练1000轮
- 批大小=5
- 学习率=0.1
- 绝对误差停止条件=0.02
图14-8是训练记录,再看下面的打印输出结果:
......
epoch=419, total_iteration=30239
loss_train=0.010094, accuracy_train=1.000000
loss_valid=0.019141, accuracy_valid=1.000000
time used: 2.149379253387451
testing...
1.0
最后的testing...的结果是1.0,表示100%正确,这初步说明mini框架在这个基本case上工作得很好。图14-9所示的分类效果也不错。
原代码位置:ch14, Level3
个人代码:dnn_classification****
from MiniFramework.DataReader_2_0 import *
from keras.models import Sequential
from keras.layers import Dense
import matplotlib.pyplot as plt
import os
os.environ['KMP_DUPLICATE_LIB_OK']='True'
def load_data():
train_data_name = "../data/ch10.train.npz"
test_data_name = "../data/ch10.test.npz"
dataReader = DataReader_2_0(train_data_name, test_data_name)
dataReader.ReadData()
dataReader.NormalizeX()
dataReader.Shuffle()
dataReader.GenerateValidationSet()
x_train, y_train = dataReader.XTrain, dataReader.YTrain
x_test, y_test = dataReader.XTest, dataReader.YTest
x_val, y_val = dataReader.XDev, dataReader.YDev
return x_train, y_train, x_test, y_test, x_val, y_val
def build_model():
model = Sequential()
model.add(Dense(3, activation='sigmoid', input_shape=(2, )))
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='Adam',
loss='binary_crossentropy',
metrics=['accuracy'])
return model
#画出训练过程中训练和验证的精度与损失
def draw_train_history(history):
plt.figure(1)
# summarize history for accuracy
plt.subplot(211)
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper left')
# summarize history for loss
plt.subplot(212)
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper left')
plt.show()
if __name__ == '__main__':
x_train, y_train, x_test, y_test, x_val, y_val = load_data()
model = build_model()
history = model.fit(x_train, y_train, epochs=200, batch_size=5, validation_data=(x_val, y_val))
draw_train_history(history)
loss, accuracy = model.evaluate(x_test, y_test)
print("test loss: {}, test accuracy: {}".format(loss, accuracy))
weights = model.get_weights()
print("weights: ", weights)
模型输出
test loss: 0.3908280086517334, test accuracy: 0.8100000023841858
weights: [array([[-0.40774214, -0.3335594 , 0.46907774],
[-2.6843045 , 3.6533718 , -4.166602 ]], dtype=float32), array([ 1.0028745, -1.3372192, 1.7076769], dtype=float32), array([[-2.6436245],
[ 3.5234995],
[-4.228298 ]], dtype=float32), array([0.3786795], dtype=float32)]
模型损失以及准确率曲线