keras 二分类预测结果几乎全是一个值

程序是用来对蜜蜂(bee)和黄蜂(wasp)分类的，用的模型是在vgg16上拼接的，代码如下

from keras.applications.vgg16 import VGG16
from keras.layers import Dense, Flatten, Activation, Dropout
from keras.models import Sequential
from keras.preprocessing.image import ImageDataGenerator
import keras
import shutil
import os

def creatDataGenerator(train_dir, test_dir):
    train_data_generator = ImageDataGenerator(rescale=.1/255)
    test_data_generator = ImageDataGenerator(rescale=.1/255)

    train_generator = train_data_generator.flow_from_directory(train_dir,
                                                            target_size=(150,150),
                                                            batch_size=32,
                                                            class_mode='binary')
    test_generator = test_data_generator.flow_from_directory(test_dir,
                                                            target_size=(150,150),
                                                            batch_size=32,
                                                            class_mode='binary')
    return train_generator, test_generator

vgg_model = VGG16(weights='imagenet', include_top=False, input_shape=(150,150,3))

cla_model = Sequential()
cla_model.add(Flatten())
cla_model.add(Dense(512, activation='relu'))
cla_model.add(Dropout(0.5))
cla_model.add(Dense(1, activation='sigmoid'))

model = Sequential()
model.add(vgg_model)
model.add(cla_model)

model.compile(loss='binary_crossentropy', optimizer='RMSprop', metrics=['accuracy'])

train_generator, test_generator = creatDataGenerator(train_dir=r'C:\Users\ayana\.keras\datasets\bee-vs-wasp\train',
                                                    test_dir=r'C:\Users\ayana\.keras\datasets\bee-vs-wasp\test')
H = model.fit(train_generator,
              steps_per_epoch=50,
              epochs=30,
              validation_data=test_generator,
              validation_steps=50)

然后训练以后进行预测，选择的是黄蜂的10张图（蜜蜂预测出来也是同样的结果）

顺便训练的准确率也比较低，不到0.6，也一直不知道怎么能高一些

from keras.preprocessing.image import load_img, img_to_array
import numpy as np

def predict(i):
    img_path = os.listdir(r'C:\Users\ayana\.keras\datasets\bee-vs-wasp\test\wasp')[i]
    img = load_img(path='C:\\Users\\ayana\\.keras\\datasets\\bee-vs-wasp\\test\\wasp\\'+img_path, 
                    target_size=(150,150))
    img = np.expand_dims(img, axis=0)/255
    prediction = model.predict(img)
    return prediction

for i in range(10):
    print(predict(i))

#>>>[[0.4714901]]
#    [[0.4714901]]
#    [[0.4714901]]
#    [[0.4714901]]
#    [[0.4714901]]
#    [[0.4714901]]
#    [[0.4714901]]
#    [[0.4714901]]
#    [[0.4714901]]
#    [[0.4714901]]

再用np.argmax()的话就都是0了

被困了一天了，#求救

应该是这个吧，你训练之后得到的模型是H，prediction = H.predict(img)  

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