https://blog.csdn.net/u013378306/article/details/64923361 粗略讲了softmax代码意义
https://blog.csdn.net/qq_35082030/article/details/77170284 详细
下面是在我数据集上实现的代码
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.optimizers import SGD
# Generate dummy data
import numpy as np
import codecs
f = codecs.open("xlj_vec.txt", 'r', 'utf-8') # codecs包指定TXT打开方式
lines = f.readlines()
X = []
Y = []
zero = []
for i in range(0, 64):
zero.append(0)
print zero
for line in lines:
z = line.split(" ", 1)[1].replace("[", "").replace("]", "").split(",")
z.pop()
z.pop()
z.pop()
z.pop()
z = map(eval, z)
if z == zero:
# print (lines.index(line))
lines.remove(line)
# for line in lines:
# print line
for line in lines: #68维
x = line.split(" ",1)[1].replace("[","").replace("]","").split(",")
x = map(eval, x)
#x=np.array(x,dtype=float)
y = line.split(" ", 1)[0]
y = int(y)
if y==0:
y=[1,0,0,0,0]
elif y==1:
y = [0, 1, 0, 0, 0]
elif y==2:
y = [0, 0, 1, 0, 0]
elif y==3:
y = [0, 0, 0, 1, 0]
elif y==4:
y = [0, 0, 0, 0, 1]
X.append(x)
Y.append(y)
x_train = np.array(X)
y_train = np.array(Y)
print x_train
print y_train
f = codecs.open("csj_vec.txt",'r','utf-8') #codecs包指定TXT打开方式
lines=f.readlines()
X=[]
Y=[]
zero=[]
for i in range(0,64):
zero.append(0)
print zero
for line in lines:
z=line.split(" ",1)[1].replace("[","").replace("]","").split(",")
z.pop()
z.pop()
z.pop()
z.pop()
z = map(eval, z)
if z==zero:
#print (lines.index(line))
lines.remove(line)
for line in lines: #68维
x = line.split(" ",1)[1].replace("[","").replace("]","").split(",")
x = map(eval, x)
#x=np.array(x,dtype=float)
y = line.split(" ", 1)[0]
y = int(y)
if y==0:
y=[1,0,0,0,0]
elif y==1:
y = [0, 1, 0, 0, 0]
elif y==2:
y = [0, 0, 1, 0, 0]
elif y==3:
y = [0, 0, 0, 1, 0]
elif y==4:
y = [0, 0, 0, 0, 1]
X.append(x)
Y.append(y)
x_test = np.array(X)
y_test = np.array(Y)
print x_test
print y_test
# x_train = np.random.random((10, 10)) #20维度，1000个 #1000个1维 10类，
# y_train = keras.utils.to_categorical(np.random.randint(3, size=(10, 1)), num_classes=3) #10类，1000个10维
# x_test = np.random.random((10, 10))
# y_test = keras.utils.to_categorical(np.random.randint(3, size=(10, 1)), num_classes=3)
# x_train= np.array([[1.9629345734914143,3.196568012237549,0.23380190134048462],[-1.5588303009668987,0.39092262585957843,0.4524924159049988],[0.326497220993042,-5.757664465904236,1.7457123756408692],[0.326497220993042,-5.757664465904236,1.7457123756408692]])
# y_train=np.array([[1,0,0],[0,1,0],[0,0,1],[0,0,1]])
# x_test= np.array([[1.9629345734914143,3.196568012237549,0.23380190134048462],[-1.5588303009668987,0.39092262585957843,0.4524924159049988],[0.326497220993042,-5.757664465904236,1.7457123756408692],[0.326497220993042,-5.757664465904236,1.7457123756408692]])
# y_test=np.array([[1,0,0],[0,1,0],[0,0,1],[0,0,1]])
#创建一个模型
model = Sequential()
print (x_train[0])
print (len(x_train))
print (y_train[0])
print (len(y_train))
print (y_train[1])
print (x_test[0])
print (y_test[0])
# Dense(64) is a fully-connected layer with 64 hidden units.
# in the first layer, you must specify the expected input data shape:
# here, 20-dimensional vectors.
#添加神经网络层及激活函数
model.add(Dense(64, activation='relu', input_dim=68))#输入维度
model.add(Dropout(0.5))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(5, activation='softmax')) #分类类别数
#使用岁时函数和优化函数
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy',
optimizer=sgd,
metrics=['accuracy'])
#开始训练
a=model.fit(x_train, y_train,
epochs=20, #训练轮数
batch_size=128)
#损失函数和精确度
score = model.evaluate(x_test, y_test, batch_size=128)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
#预测
classes = model.predict(x_test, batch_size=128)
print classes