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Untitled-1.py
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Untitled-1.py
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#%% Change working directory from the workspace root to the ipynb file location. Turn this addition off with the DataScience.changeDirOnImportExport setting
# ms-python.python added
import os
try:
os.chdir(os.path.join(os.getcwd(), '..\\AI\kaggle_0424'))
print(os.getcwd())
except:
pass
#%%
##從mapping.txt取得dictionary
import re
fopen=open("/home/jovyan/CNN/mapping.txt")
text=[]
key=[]
dic=[]
for i in fopen:
text.append(i)
for i in range(len(text)):
text[i]=re.split("[\s+,]",text[i])
key.append(text[i][0])
dic.append(text[i][2])
dict_labels=dict(zip(key,dic))
print(dict_labels)
#%%
#ref.https://medium.com/%E7%94%A8%E5%8A%9B%E5%8E%BB%E6%84%9B%E4%B8%80%E5%80%8B%E4%BA%BA%E7%9A%84%E8%A9%B1-%E5%BF%83%E4%B9%9F%E6%9C%83%E7%97%9B%E7%9A%84/%E9%BB%98%E9%BB%98%E5%9C%B0%E5%AD%B8deep-learning-3-e9e90c633722
import os
import glob
import numpy as np
from keras.preprocessing.image import img_to_array, load_img
from PIL import Image
#
size = (256,256) #由於原始資料影像大小不一，因此制定一個統一值
nbofdata=10000 #從各個資料夾中抓取特定數量的檔案
#%%
for folders in glob.glob("/home/jovyan/CNN/train/*"):
print(folders)
images=[]
labels_hot=[]
labels=[]
nbofdata_i=1
for filename in os.listdir(folders):
if nbofdata_i <= nbofdata:
label = os.path.basename(folders)
className = np.asarray(label)
img=load_img(os.path.join(folders,filename))
img=img.resize(size,Image.BILINEAR)
if img is not None:
if label is not None:
labels.append(className)
labels_hot.append(dict_labels[label])
x=img_to_array(img)
images.append(x)
nbofdata_i+=1
images=np.array(images)
labels_hot=np.array(labels_hot)
print("images.shape={}, labels_hot.shape=={}".format(images.shape, labels_hot.shape))
imagesavepath='/home/jovyan/CNN/save/'
if not os.path.exists(imagesavepath):
os.makedirs(imagesavepath)
np.save(imagesavepath+'{}_images.npy'.format(label),images)
np.save(imagesavepath+'{}_label.npy'.format(label),labels)
np.save(imagesavepath+'{}_labels_hot.npy'.format(label),labels_hot)
print('{} files has been saved.'.format(label))
#%%
import numpy as np
import pandas as pd
import keras
from keras.models import Sequential
from keras.layers import Dense,Dropout,Flatten,Conv2D,MaxPooling2D
from sklearn.model_selection import train_test_split
from keras.utils import np_utils
x=np.load('/home/jovyan/CNN/traindata_images.npy')
y=np.load('/home/jovyan/CNN/traindata_labels_hot.npy')
x=x[:,:,:,0].reshape(x.shape[0],256,256,1).astype('float32')
x=x/255
x_train, x_test, y_train, y_test = train_test_split(x, y,test_size=0.2, random_state=0)
y_train=np_utils.to_categorical(y_train)
y_test=np_utils.to_categorical(y_test)
model=Sequential()
model.add(Conv2D(filters=16,
kernel_size=(3,3),
padding='same',
input_shape=(256,256,1),
activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Conv2D(filters=36,
kernel_size=(5,5),
padding='same',
activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Conv2D(filters=48,
kernel_size=(3,3),
padding='same',
activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Conv2D(filters=64,
kernel_size=(5,5),
padding='same',
activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(15,activation='softmax'))
model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
train_history=model.fit(x=x_train,
y=y_train,
validation_split=0.2,
epochs=100,
batch_size=300,
verbose=2)
model.save('/home/jovyan/CNN/mymodel.h5')
#%%
import os
import glob
import numpy as np
from keras.preprocessing.image import img_to_array, load_img
from PIL import Image
#
size = (256,256) #由於原始資料影像大小不一，因此制定一個統一值
nbofdata=2000 #從各個資料夾中抓取特定數量的檔案
#%%
folders=('/home/jovyan/CNN/testset')
images=[]
nbofdata_i=1
filelist=os.listdir(folders)
filelist.sort()
print(filelist)
for filename in filelist:
if nbofdata_i <= nbofdata:
img=load_img(os.path.join(folders,filename))
img=img.resize(size,Image.BILINEAR)
if img is not None:
x=img_to_array(img)
images.append(x)
nbofdata_i+=1
images=np.array(images)
imagesavepath='/home/jovyan/CNN/kagglehomework'
if not os.path.exists(imagesavepath):
os.makedirs(imagesavepath)
np.save(imagesavepath+'testset',images)
#%%
from keras.models import load_model
from keras.models import Sequential
import keras
import pandas as pd
model=Sequential()
model=load_model("/home/jovyan/CNN/mymodel.h5")
x=np.load("/home/jovyan/CNN/kagglehomeworktestset.npy")
x=x[:,:,:,0].reshape(x.shape[0],256,256,1).astype('float32')
x=x/255
result=model.predict(x, batch_size=None, verbose=0, steps=None)
result2=pd.DataFrame(result.argmax(axis=1), columns=['class'])
result2.to_csv('/home/jovyan/CNN/kagglefinal.csv',index=0)
#%%