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--- kerasを使ったcnn [2018/05/08 11:05] – suikou
+++ kerasを使ったcnn [Unknown date] (現在) – 削除 - 外部編集 (Unknown date) 127.0.0.1
@@ 行 1: / 行 1: @@
-# kerasを使ってvgg16の転移学習
-m208.sにはGTX1050のGPUがついているので、まずはm208.sにsshでログインする
-```
-sudo docker pull nvidia/cuda:9.0-cudnn7-devel
-sudo docker run --runtime=nvidia --name yoshitake03 -it nvidia/cuda:9.0-cudnn7-devel /bin/bash #yoshitake03は適当に変える
-apt-get update
-apt install -y ssh python3-pip nano git unzip screen eog
-echo "termcapinfo xterm 'is=\E[r\E[m\E[2J\E[H\E[?7h\E[?1;4;6l'
-defscrollback 100000
-screen /bin/bash" > ~/.screenrc
-python3 -m pip install --upgrade pip
-python3 -m pip install keras
-python3 -m pip install tensorflow-gpu
-python3 -m pip install ipython
-python3 -m pip install opencv-python
-python3 -m pip install tqdm
-python3 -m pip install Pillow
-python3 -m pip install matplotlib
-apt install -y libglib2.0 libsm6 libxext6 libfontconfig1 libxrender1 python3-tk
-なんとかlibcudnn7-dev_7.0.5.15-1+cuda9.0_amd64.deb とlibcudnn7_7.0.5.15-1+cuda9.0_amd64.debをコピーしてくる。
-dpkg -i libcudnn7*
-もし途中でdockerを抜けていて、再開したいときは
-sudo docker start -i yoshitake03
-byte形式の画像データの確認方法
-od -An -v -tu1 -j16 -w28 train-images-idx3-ubyte |more
-```
-下記のフォルダを作り、その中にさらにカテゴリごとにフォルダを作り画像を入れておく。(例：犬の写真の場合、ディレクトリtrain/dogを作り、その中にdog01.jpg, dog02.jpg, ... をコピーする。)
-train, validate, test
-学習用のpythonスクリプト
-実行する場合、python3 jiseki-vgg16.pyなどとする
-```
-from keras.models import Model
-from keras.layers import Dense, GlobalAveragePooling2D,Input, Flatten, Activation, Dropout
-from keras.applications.vgg16 import VGG16
-from keras.preprocessing.image import ImageDataGenerator
-from keras.optimizers import SGD
-from keras.models import Sequential
-from keras.callbacks import CSVLogger
-n_categories=132
-batch_size=32
-train_dir='jiseki/train'
-validation_dir='jiseki/validation'
-file_name='jiseki_vgg16'
-base_model=VGG16(weights='imagenet',include_top=False,
-                 input_tensor=Input(shape=(224,224,3)))
-#add new layers instead of FC networks
-x=base_model.output
-#x=GlobalAveragePooling2D()(x)
-x=Flatten()(x)
-x=Dense(1024,activation='relu')(x)
-prediction=Dense(n_categories,activation='softmax')(x)
-model=Model(inputs=base_model.input,outputs=prediction)
-#top_model = Sequential()
-#top_model.add(Flatten(input_shape=base_model.output_shape[1:]))
-#top_model.add(Dense(256))
-#top_model.add(Activation("relu"))
-#top_model.add(Dropout(0.5))
-#top_model.add(Dense(n_categories))
-#top_model.add(Activation("softmax"))
-#model = Model(input=base_model.input, output=top_model(base_model.output))
-#fix weights before VGG16 14layers
-for layer in base_model.layers[:15]:
-    layer.trainable=False
-#model.compile(optimizer=SGD(lr=0.0001,momentum=0.9),
-#              loss='categorical_crossentropy',
-#              metrics=['accuracy'])
-model.compile(loss='categorical_crossentropy',
-              optimizer=SGD(lr=1e-3, decay=1e-6, momentum=0.9, nesterov=True),
-              metrics=['accuracy'])
-model.summary()
-#save model
-json_string=model.to_json()
-open(file_name+'.json','w').write(json_string)
-train_datagen=ImageDataGenerator(
-    rescale=1.0/255,
-    rotation_range=180,
-#    width_shift_range=0.1,
-    height_shift_range=0.1,
-#    shear_range=0.2,
-#    zoom_range=0.2,
-    horizontal_flip=True)
-validation_datagen=ImageDataGenerator(rescale=1.0/255)
-train_generator=train_datagen.flow_from_directory(
-    train_dir,
-    target_size=(224,224),
-    batch_size=batch_size,
-    class_mode='categorical',
-    shuffle=True
-)
-validation_generator=validation_datagen.flow_from_directory(
-    validation_dir,
-    target_size=(224,224),
-    batch_size=batch_size,
-    class_mode='categorical',
-    shuffle=True
-)
-hist=model.fit_generator(train_generator,
-                         epochs=500,
-                         verbose=1,
-                         validation_data=validation_generator,
-                         callbacks=[CSVLogger(file_name+'.csv')])
-#save weights
-model.save(file_name+'.h5')
-```
-判定用のスクリプト
-使用方法は
-python3 jiseki_predict.py xxx.jpg
-```
-from keras.models import model_from_json
-import matplotlib.pyplot as plt
-import numpy as np
-import os,random
-from keras.preprocessing.image import img_to_array, load_img
-from keras.preprocessing.image import ImageDataGenerator
-from keras.optimizers import SGD
-import sys
-batch_size=32
-file_name='jiseki_vgg16'
-test_dir='jiseki/test'
-display_dir='jiseki/display'
-label=['Albulidae-Albula','Albulidae-Pterothrissus','Alepocephalidae-Alepocephalus','Alepocephalidae-Xenodermichthys','Anguillidae-Anguilla','Antennariidae-Antennarius','Antennariidae-His
-trio','Argentinidae-Argentina','Argentinidae-Glossanodon','Ariidae-Arius','Ariidae-Netuma','Ariidae-Plicofollis','Ateleopodidae-Ateleopus','Ateleopodidae-Ijimaia','Atherinidae-Atherinomor
-us','Atherinidae-Hypoatherina','Aulopidae-Hime','Bagridae-Pseudobagrus','Bregmacerotidae-Bregmaceros','Carapidae-Pyramodon','Chanidae-Chanos','Chaunacidae-Chaunax','Chlorophthalmidae-Chlo
-rophthalmus','Clupeidae-Amblygaster','Clupeidae-Etrumeus','Clupeidae-Herklotsichthys','Clupeidae-Nematalosa','Clupeidae-Sardinella','Congridae-Bathymyrus','Congridae-Conger','Congridae-Ma
-crocephenchelys','Congridae-Rhynchoconger','Congridae-Uroconger','Cyprinidae-Acrossocheilus','Cyprinidae-Aristichthys','Cyprinidae-Candidia','Cyprinidae-Carassius','Cyprinidae-Chanodichth
-ys','Cyprinidae-Clarias','Cyprinidae-Culter','Cyprinidae-Cyprinus','Cyprinidae-Gobiobotia','Cyprinidae-Hemiculter','Cyprinidae-Hypsibarbus','Cyprinidae-Mylopharyngodon','Cyprinidae-Onycho
-stoma','Cyprinidae-Opsariichthys','Cyprinidae-Pseudorasbora','Cyprinidae-Puntius','Cyprinidae-Rhodeus','Cyprinidae-Spinibarbus','Cyprinidae-Tanakia','Elopidae-Elops','Engraulidae-Engrauli
-s','Engraulidae-Setipinna','Engraulidae-Stolephorus','Engraulidae-Thryssa','Exocoetidae-Cheilopogon','Exocoetidae-Cypselurus','Exocoetidae-Exocoetus','Exocoetidae-Hirundichthys','Exocoeti
-dae-Oxyporhamphus','Gigantactinidae-Gigantactis','Gonorynchidae-Gonorynchus','Gonostomatidae-Gonostoma','Gonostomatidae-Sigmops','Lophiidae-Lophiodes','Lophiidae-Lophiomus','Lophiidae-Lop
-hius','Lophotidae-Lophotus','Macrouridae-Coelorinchus','Macrouridae-Coelorinclus','Macrouridae-Coryphaenoides','Macrouridae-Hymenocephalus','Macrouridae-Lucigadus','Macrouridae-Malacoceph
-alus','Macrouridae-Nezumia','Macrouridae-Ventrifossa','Megalopidae-Megalops','Melanocetidae-Melanocetus','Moridae-Gadella','Moridae-Laemonema','Moridae-Physiculus','Moringuidae-Moringua',
-'Mugilidae-Chelon','Mugilidae-Moolgarda','Mugilidae-Mugil','Muraenesocidae-Gavialiceps','Muraenesocidae-Muraenesox','Muraenidae-Echidna','Muraenidae-Enchelycore','Muraenidae-Gymmothorax',
-'Myctophidae-Benthosema','Myctophidae-Diaphus','Myctophidae-Electrona','Myctophidae-Myctophum','Nemichthyidae-Nemichthys','Neoscopelidae-Neoscopelus','Notacanthidae-Notacanthus','Ogcoceph
-alildae-Halieutaea','Ogcocephalildae-Malthopsis','Ogcocephalildae-Solocisquama','Ophidiidae-Glyptophidium','Ophidiidae-Hoplobrotula','Ophidiidae-Neobythites','Ophidiidae-Ophidion','Osmeri
-dae-Plecoglossus','Paralepididae-Lestidium','Paralepididae-Lestrolepis','Plotosidae-Plotosus','Polymixiidae-Polymixia','Pristigasteridae-Ilisha','Regalecidae-Regalecus','Salmonidae-Oncorh
-ynchus','Sternoptychidae-Argyropelecus','Sternoptychidae-Maurolicus','Sternoptychidae-Polyipnus','Stomiidae-Astronesthes','Stomiidae-Chauliodus','Stomiidae-Photonectes','Stomiidae-Stomias
-','Synaphobranchidae-Dysomma','Synaphobranchidae-Echelus','Synaphobranchidae-Meadia','Synaphobranchidae-Neenchelys','Synaphobranchidae-Ophichthus','Synaphobranchidae-Pisodonophis','Synodo
-ntidae-Harpadon','Synodontidae-Saurida','Synodontidae-Synodus','Synodontidae-Trachinocephalus','Trachipteridae-Trachipterus']
-#load model and weights
-json_string=open(file_name+'.json').read()
-model=model_from_json(json_string)
-model.load_weights(file_name+'.h5')
-model.compile(optimizer=SGD(lr=0.0001,momentum=0.9),
-              loss='categorical_crossentropy',
-              metrics=['accuracy'])
-#data generate
-test_datagen=ImageDataGenerator(rescale=1.0/255)
-#test_generator=test_datagen.flow_from_directory(
-#    test_dir,
-#    target_size=(224,224),
-#    batch_size=batch_size,
-#    class_mode='categorical',
-#    shuffle=True
-#)
-#evaluate model
-#score=model.evaluate_generator(test_generator)
-#print('\n test loss:',score[0])
-#print('\n test_acc:',score[1])
-#predict model and display images
-#files=os.listdir(display_dir)
-#img=random.sample(files,2)
-for i in sys.argv[1:] :
-  temp_img=load_img(sys.argv[1],target_size=(224,224))
-#  plt.imshow(temp_img)
-  temp_img_array=img_to_array(temp_img)
-  temp_img_array=temp_img_array.astype('float32')/255.0
-  temp_img_array=temp_img_array.reshape((1,224,224,3))
-  img_pred=model.predict(temp_img_array)
-  print(i+": "+label[np.argmax(img_pred)])
-  temp_res_list={}
-  k=0
-  for j in img_pred[0]:
-    temp_res_list[k]=j
-    k=k+1
-  temp_res_sort=sorted(temp_res_list.items(), key=lambda x:x[1], reverse=True)
-  print("1: "+label[temp_res_sort[0][0]]+" "+str(temp_res_sort[0][1]))
-  print("2: "+label[temp_res_sort[1][0]]+" "+str(temp_res_sort[1][1]))
-  print("3: "+label[temp_res_sort[2][0]]+" "+str(temp_res_sort[2][1]))
-  print("4: "+label[temp_res_sort[3][0]]+" "+str(temp_res_sort[3][1]))
-  print("5: "+label[temp_res_sort[4][0]]+" "+str(temp_res_sort[4][1]))
-  del temp_img
-  del img_pred
-```