报错为:Error when checking target: expected activation_1 to have 3 dimensions, but got array with shape (32, 10)
keras+tensorflow后端
代码如下
# coding=utf-8
import matplotlib
from PIL import Image
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import argparse
import numpy as np
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, UpSampling2D, BatchNormalization, Reshape, Permute, Activation, Flatten
# from keras.utils.np_utils import to_categorical
# from keras.preprocessing.image import img_to_array
from keras.models import Model
from keras.layers import Input
from keras.callbacks import ModelCheckpoint
# from sklearn.preprocessing import LabelBinarizer
# from sklearn.model_selection import train_test_split
# import pickle
import matplotlib.pyplot as plt
import os
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(
rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1./255)
path = '/tmp/2'
os.chdir(path)
training_set = train_datagen.flow_from_directory(
'trainset',
target_size=(64,64),
batch_size=32,
class_mode='categorical',
shuffle=True)
test_set = test_datagen.flow_from_directory(
'testset',
target_size=(64,64),
batch_size=32,
class_mode='categorical',
shuffle=True)
def SegNet():
model = Sequential()
# encoder
model.add(Conv2D(64, (3, 3), strides=(1, 1), input_shape=(64, 64, 3), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(64, (3, 3), strides=(1, 1), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
# (128,128)
model.add(Conv2D(128, (3, 3), strides=(1, 1), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(128, (3, 3), strides=(1, 1), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
# (64,64)
model.add(Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
# (32,32)
model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
# (16,16)
model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
# (8,8)
# decoder
model.add(UpSampling2D(size=(2, 2)))
# (16,16)
model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(UpSampling2D(size=(2, 2)))
# (32,32)
model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(UpSampling2D(size=(2, 2)))
# (64,64)
model.add(Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(UpSampling2D(size=(2, 2)))
# (128,128)
model.add(Conv2D(128, (3, 3), strides=(1, 1), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(128, (3, 3), strides=(1, 1), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(UpSampling2D(size=(2, 2)))
# (256,256)
model.add(Conv2D(64, (3, 3), strides=(1, 1), input_shape=(64, 64, 3), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(64, (3, 3), strides=(1, 1), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(10, (1, 1), strides=(1, 1), padding='valid', activation='relu'))
model.add(BatchNormalization())
model.add(Reshape((64*64, 10)))
# axis=1和axis=2互换位置,等同于np.swapaxes(layer,1,2)
model.add(Permute((2, 1)))
#model.add(Flatten())
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
model.summary()
return model
def main():
model = SegNet()
filepath = "/tmp/2/weights.best.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')
callbacks_list = [checkpoint]
history = model.fit_generator(
training_set,
steps_per_epoch=(training_set.samples / 32),
epochs=20,
callbacks=callbacks_list,
validation_data=test_set,
validation_steps=(test_set.samples / 32))
# Plotting the Loss and Classification Accuracy
model.metrics_names
print(history.history.keys())
# "Accuracy"
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('Model Accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
# "Loss"
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
if __name__ == '__main__':
main()
主要是这里,segnet没有全连接层,最后输出的应该是一个和输入图像同等大小的有判别标签的shape吗。。。求教怎么改。
输入图像是64 64的,3通道,总共10类,分别放在testset和trainset两个文件夹里