在keras中对单一输入图像进行预测并返回预测结果操作

import numpy as np
from keras.applications.imagenet_utils import decode_predictions
from keras.preprocessing import image
from keras.applications import *
 
import os
 
# 忽略硬件加速的警告信息
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
 
file_path = 'images/0a70f64352edfef4c82c22015f0e3a20.jpg'
 
img = image.load_img(file_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
 
model = ResNet50(weights='imagenet')
y = model.predict(x)
# print(np.argmax(y))
print('Predicted:', decode_predictions(y, top=3)[0])

x = Convolution2D(8, 5, 5, subsample=(1, 1))(image_input)
x = Activation('relu')(x)
x = Flatten()(x)
x = Dense(50, W_regularizer=l2(0.0001))(x)
x = Activation('relu')(x)

output1 = Dense(1, activation='linear', name='output1')(x)
output2 = Dense(1, activation='linear', name='output2')(x)

model = Model(input=image_input, output=[output1, output2])
model.compile(optimizer='adam', loss={'output1': 'mean_squared_error', 'output2': 'mean_squared_error'})

batch_generator(x, y, batch_size):
  ....transform images
  ....generate batch batch of size: batch_size 
  yield(X_batch, {'output1': y1, 'output2': y2} ))