2、读入自己的数据到keras
<pre><code>
# coding: utf-8
# In[1]:
import numpy as np
from PIL import Image
import os
import pandas as pd
from sklearn.model_selection import train_test_split
from tensorflow.python.keras import backend as K
from tensorflow import keras
from tensorflow.python.keras.layers import Conv2D, MaxPooling2D
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import Dense, Dropout, Flatten
from tensorflow.python.keras.optimizers import RMSprop
# In[2]:
def read_image(img_name):
im = Image.open(img_name).convert('L')
data = np.array(im)
return data
# In[3]:
images = []
# In[4]:
def read_imgs_labels():
# 照片目录
file_dir="D:\\work\\jupyterNotebook\\baidu_dianshi\\data\\micro_height_data"
# 读入标签数据
df = pd.DataFrame(pd.read_csv('./data/micro_height_data_lable.csv',header=0))
# 获取lables 和img_name
lables = df['lables']
img_name = df['img_name']
'''
#输出TFRecord文件的地址
filename="./output.tfrecords"
#创建一个writer来写TFRecord文件
writer=tf.python_io.TFRecordWriter(filename)
'''
# 遍历文件夹内所有图片，读取图片存到img，遍历到名字，在img_name中查找
# 查找到后，获取当前行数，取出对应的label,并替换为1-6的整数存入label_name
for file in os.listdir(file_dir):
# print(file)
img_path=file_dir+'\\'+file #每个图片的地址
# img=Image.open(img_path)
i = 0
for file_name in img_name:
if file_name == file:
label = lables[i]
# print(label)
if label == 'MOUNTAIN':
label_name = 1
elif label == 'OCEAN':
label_name = 2
elif label == 'DESERT': #沙漠
label_name = 3
elif label == 'LAKE':
label_name = 4
elif label == 'FARMLAND': #农田
label_name = 5
elif label == 'CITY':
label_name = 6
# 把标签存入txt文件
label = label_name
# print (label)#ord():查看字符asc码
doc= open('label.txt','a')
print(label,file=doc)
break
else:
i = i+1
images.append(read_image(img_path))
doc.close()
X_img = np.array(images)
Y_label = np.loadtxt('label.txt')
return X_img,Y_label
# In[5]:
images = []
(X_img,Y_label) = read_imgs_labels()
# if label == 'MOUNTAIN': label_name = 1
#
# elif label == 'OCEAN': label_name = 2
#
# elif label == 'DESERT': label_name = 3 #沙漠
#
# elif label == 'LAKE': label_name = 4
#
# elif label == 'FARMLAND': label_name = 5 #农田
#
# elif label == 'CITY': label_name = 6
# In[6]:
print(X_img.shape)
print(Y_label.shape)
# In[7]:
x_train, x_test, y_train, y_test = train_test_split(X_img, Y_label, test_size = 0.2, random_state= 30)
print (x_train.shape)
print (x_test.shape)
print (y_train.shape)
print (y_test.shape)
# In[8]:
batch_size = 5
num_classes = 6
epochs = 3
# input image dimensions
img_rows, img_cols = 256, 256
if K.image_data_format() == 'channels_first':
x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
input_shape = (1, img_rows, img_cols)
else:
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
# In[9]:
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# In[10]:
# convert class vectors to binary class matrices
# 由于之前分六类写为了1-6 这里需要减1 变为 0-5
y_train = keras.utils.to_categorical(y_train-1, num_classes)
y_test = keras.utils.to_categorical(y_test-1, num_classes)
print(y_test[0])
# In[11]:
model = Sequential()
model.add(Conv2D(8, kernel_size=(3, 3),
activation='relu',
input_shape=input_shape))
model.add(Conv2D(16, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(32, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
# In[12]:
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adadelta(),
metrics=['accuracy'])
# In[13]:
history = model.fit(x_train, y_train,
batch_size=5,
epochs=epochs,
verbose=1,
validation_data=(x_test, y_test))
# In[16]:
model.save('./model.hdf5')
# In[17]:
model.summary()
# In[18]:
from tensorflow.python.keras.utils import plot_model
from tensorflow.python.keras.utils.vis_utils import model_to_dot
from IPython.display import SVG
SVG(model_to_dot(model,show_shapes=True).create(prog='dot', format='svg'))
# In[20]:
import matplotlib.pyplot as plt
image_arr = np.expand_dims(x_train[1], axis=0)
layer_1 = K.function([model.layers[0].input], [model.layers[0].output])
f1 = layer_1([image_arr])[0]
for _ in range(8):
show_img = f1[:, :, :, _]
show_img.shape = [254, 254]
#plt.subplot(4, 8, _ + 1)
plt.subplot(1, 8, _ + 1)
plt.imshow(show_img, cmap='gray')
plt.axis('off')
plt.show()
</code></pre>