我使用python tensorflow制作了一个预测音频，当第一个上传文件成功但如果我再次重复它会出现这样的错误信息
无法将feed_dict键解释为Tensor：Tensor Tensor（＆＃34;占位符：0＆＃34;，shape =（5,5,1,32），dtype = float32）不是 该图的元素。
是否包含cookies？
def creatematrix(request):
if request.method == 'POST':
myfile = request.FILES['sound']
fs = FileSystemStorage()
filename = fs.save(myfile.name, myfile)
uploaded_file_url = fs.url(filename)
# load json and create model
json_file = open('TrainedModels/model_CNN.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
# load weights into new model
loaded_model.load_weights("TrainedModels/model_CNN.h5")
print("Model restored from disk")
sound_file_paths = myfile.name # "22347-3-3-0.wav"
parent_dir = 'learning/static/media/'
sound_names = ["air conditioner","car horn","children playing","dog bark","drilling","engine idling","gun shot","jackhammer","siren","street music"]
predict_file = parent_dir + sound_file_paths
predict_x = extract_feature_array(predict_file)
test_x_cnn = predict_x.reshape(predict_x.shape[0], 20, 41, 1).astype('float32')
loaded_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
# generate prediction, passing in just a single row of features
predictions = loaded_model.predict(test_x_cnn)
# get the indices of the top 2 predictions, invert into descending order
ind = np.argpartition(predictions[0], -2)[-9:]
ind[np.argsort(predictions[0][ind])]
ind = ind[::-1]
a = sound_names[ind[0]], 100 * round(predictions[0,ind[0]],3)
b = sound_names[ind[1]], 100 * round(predictions[0,ind[1]],3)
c = sound_names[ind[2]], 100 * round(predictions[0,ind[2]],3)
d = sound_names[ind[3]], 100 * round(predictions[0,ind[3]],3)
e = sound_names[ind[4]], 100 * round(predictions[0,ind[4]],3)
f = sound_names[ind[5]], 100 * round(predictions[0,ind[5]],3)
g = sound_names[ind[6]], 100 * round(predictions[0,ind[6]],3)
h = sound_names[ind[7]], 100 * round(predictions[0,ind[7]],3)
i = sound_names[ind[8]], 100 * round(predictions[0,ind[8]],3)
return render(request, 'base.html', {
'a': a,
'b': b,
'c': c,
'd': d,
'e': e,
'f': f,
'g': g,
'h': h,
'i': i,
})
else:
return render(request, 'base.html', {
})