没有为任何变量提供渐变代码在两周前就可以使用了

如何解决没有为任何变量提供渐变代码在两周前就可以使用了

我正在使用Google Colab上的flickr8k数据集实现CNN + RNN模型用于图像字幕。直到几周前，这段代码还可以正常工作，但是现在它在model.fit_generator()处引发了错误。它说

没有为任何变量提供渐变

我尝试检查文件的过去版本，只是发现以前在代码正确执行时，该模型称为model_1，但现在称为functional_1。生成器似乎正在按照模型的要求生成输入。我对深度学习还很陌生，所以我无法真正弄清楚代码是如何突然停止工作的。

EDIT_1：：我将tensorflow版本从2.3降级到2.2后，模型名称从Functional_1更改为Model_1，但是代码产生了相同的错误。所以也许那不是问题。

链接以驱动colab笔记本和数据集（向所有人开放）：https://drive.google.com/drive/folders/11ZbXrQK3YuVo76-4on4FPa236MfUdk8c?usp=sharing

不含预处理的代码：

#create input-output sequence pairs from the image description.
#data generator,used by model.fit_generator()
def data_generator(descriptions,features,tokenizer,max_length):
    while 1:
        for key,description_list in descriptions.items():
            #retrieve photo features
            feature = features[key][0]
            input_image,input_sequence,output_word = create_sequences(tokenizer,max_length,description_list,feature)
            yield [[input_image,input_sequence],output_word]

def create_sequences(tokenizer,desc_list,feature):
        X1,X2,y = list(),list(),list()
        # walk through each description for the image
        for desc in desc_list:
            # encode the sequence
            seq = tokenizer.texts_to_sequences([desc])[0]
            # split one sequence into multiple X,y pairs
            for i in range(1,len(seq)):
                # split into input and output pair
                in_seq,out_seq = seq[:i],seq[i]
                # pad input sequence
                in_seq = pad_sequences([in_seq],maxlen=max_length)[0]
                # encode output sequence
                out_seq = to_categorical([out_seq],num_classes=vocab_size)[0]
                # store
                X1.append(feature)
                X2.append(in_seq)
                y.append(out_seq)
        return np.array(X1),np.array(X2),np.array(y)
    #You can check the shape of the input and output for your model
    [a,b],c = next(data_generator(train_descriptions,max_length))
    a.shape,b.shape,c.shape
    #((47,2048),(47,32),7577))

from keras.utils import plot_model
# define the captioning model
def define_model(vocab_size,max_length):
    # features from the CNN model squeezed from 2048 to 256 nodes
    inputs1 = Input(shape=(2048,))
    fe1 = Dropout(0.5)(inputs1)
    fe2 = Dense(256,activation='relu')(fe1)
    # LSTM sequence model
    inputs2 = Input(shape=(max_length,))
    se1 = Embedding(vocab_size,256,mask_zero=True)(inputs2)
    se2 = Dropout(0.5)(se1)
    se3 = LSTM(256)(se2)
    # Merging both models
    decoder1 = add([fe2,se3])
    decoder2 = Dense(256,activation='relu')(decoder1)
    outputs = Dense(vocab_size,activation='softmax')(decoder2)
    # tie it together [image,seq] [word]
    model = Model(inputs=[inputs1,inputs2],outputs=outputs)
    model.compile(loss='categorical_crossentropy',optimizer='adam')
    # summarize model
    print(model.summary())
    plot_model(model,to_file='model.png',show_shapes=True)
    return model

# train our model
print('Dataset: ',len(train_imgs))
print('Descriptions: train=',len(train_descriptions))
print('Photos: train=',len(train_features))
print('Vocabulary Size:',vocab_size)
print('Description Length: ',max_length)
model = define_model(vocab_size,max_length)
epochs = 10
steps = len(train_descriptions)
# making a directory models to save our models
os.mkdir("models")
for i in range(epochs):
    generator = data_generator(train_descriptions,train_features,max_length)
    model.fit_generator(generator,epochs=1,steps_per_epoch= steps,verbose=1)
    model.save("models/model_" + str(i) + ".h5")

产生错误：

ValueError: in user code:

    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:806 train_function  *
        return step_function(self,iterator)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:796 step_function  **
        outputs = model.distribute_strategy.run(run_step,args=(data,))
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:1211 run
        return self._extended.call_for_each_replica(fn,args=args,kwargs=kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2585 call_for_each_replica
        return self._call_for_each_replica(fn,args,kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2945 _call_for_each_replica
        return fn(*args,**kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:789 run_step  **
        outputs = model.train_step(data)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:757 train_step
        self.trainable_variables)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:2737 _minimize
        trainable_variables))
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:562 _aggregate_gradients
        filtered_grads_and_vars = _filter_grads(grads_and_vars)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:1271 _filter_grads
        ([v.name for _,v in grads_and_vars],))

    ValueError: No gradients provided for any variable: ['embedding/embeddings:0','dense/kernel:0','dense/bias:0','lstm/lstm_cell/kernel:0','lstm/lstm_cell/recurrent_kernel:0','lstm/lstm_cell/bias:0','dense_1/kernel:0','dense_1/bias:0','dense_2/kernel:0','dense_2/bias:0'].

解决方法

对于在Google colab上遇到相同错误的任何人，对我有用的最快修复是将TensorFlow和Keras都降级。适用于我的版本是：

Tensorflow = 2.2 Keras = 2.3.1

我只是pip卸载以前的版本，并使用pip安装了所述版本。