python spyder界面认识

突然发现我连spyder界面还没有很好的认识，本可以很方便的使用的，就是一直没发现。。比如想看一下python函数的源代码实际只要选中关键词然后ctrl + G 就可以自动打开源代码了

还有上一个界面，各种运行方式和debug 文件，看来很有必要好好认识界面了。。今天看了商空间，但是感觉都是概念上的认识，还要再看。什么是拓扑空间，什么是算子空间，自伴算子空间？？

from keras.layers import Input, Dense, Reshape, Flatten, Dropout, multiply,concatenate
from keras.layers import BatchNormalization, Activation, Embedding
from keras.layers.convolutional import Conv1D,MaxPooling1D
from keras.models import Sequential, Model
#from keras.optimizers import Adam
import os
import numpy as np
from keras.utils import plot_model,np_utils
from PIL import Image

data=os.listdir('D:/zhy/python_work/data/fingerclass_test/')
all_number=125611
data1 = np.empty((2,4,32*45),dtype='float32')
i=0
j=0
dd=[]
for pictures in (data):
    srcs=os.listdir('D:/zhy/python_work/data/fingerclass_test/'+pictures)
    for src in srcs:
        picture=Image.open('D:/zhy/python_work/data/fingerclass_test/'+pictures+'/'+src)
        arr1 = np.asarray(picture,dtype='float32')
        arr1 = arr1.reshape(32*45,)
        data1[i,:]=arr1
        i=i+1
        dd.append(j)
    j=j+1
class_number=j
data1 /= 8.
data1 = data1.reshape(4,45*32,1)
data3=os.listdir('D:/zhy/python_work/data/fingerclass_test2/')
data2 = np.empty((4,32*45),dtype='float32')
i=0
j=0
dd=[]
for pictures in (data3):
    srcs=os.listdir('D:/zhy/python_work/data/fingerclass_test2/'+pictures)
    for src in srcs:
        picture=Image.open('D:/zhy/python_work/data/fingerclass_test2/'+pictures+'/'+src)
        arr= np.asarray(picture,dtype='float32')
        arr= arr1.reshape(32*45,)
        data2[i,:]=arr
        i=i+1
        dd.append(1)
    j=j+1
class_number=j
data2 /= 8.
data2 = data2.reshape(4,45*32,1)
Y_train = np_utils.to_categorical(dd,2)
sequential= Sequential()
sequential.add(Conv1D(128, 5,activation='relu'))
sequential.add(BatchNormalization(momentum=0.8))
sequential.add(Activation("relu"))
sequential.add(MaxPooling1D(5))
sequential.add(Conv1D(256,5,activation='relu'))
sequential.add(BatchNormalization(momentum=0.8))
sequential.add(Activation("relu"))
sequential.add(Flatten())
sequential.add(Dense(32, activation="relu"))
#sequential.add(BatchNormalization(momentum=0.8))
sequential.add(Activation("relu"))
picture1 = Input(shape=(32*45,1,))
picture2 = Input(shape=(32*45,1,))
feature1 = sequential(picture1)
feature2 = sequential(picture2)
features=concatenate([feature1, feature2])
features = Dense(2, name='fc6')(features)
features = Activation('softmax', name='prob')(features)
model=Model([picture1,picture2],features)
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
plot_model(model, show_shapes=True, to_file='D:/zhy/python_work/files/saved_models/x.png')
model.fit([data1,data2],Y_train,batch_size=6,nb_epoch=30,shuffle=True,verbose=1)
dat1=np.expand_dims(data1[1],axis=0)
dat2=np.expand_dims(data2[0],axis=0)
print(model.predict([dat1,dat2]))

自己瞎写的代码，思想借鉴facenet网络，不过对网络的层数都是尽量简洁的，主要是想让两张图片进去，相同类的两个输出为1，不同的为零，不过数据还整的不是很好。。

python spyder界面认识

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