本文介绍了Keras自定义图层-AttributeError:"Tensor"对象没有属性"_keras_history"的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!

问题描述

这么大的图片,我正在尝试制作一个keras w2v自动编码器.我试图从此官方示例中遵循CustomVariationalLayer类>.

So big picture, I'm trying to make a keras w2v auto-encoder. I tried to follow the CustomVariationalLayer class from this official example.

我的课是这样:

class custom_ae_layer(Layer):
    """custom keras layer to handle looking up wv inputs
    example from https://github.com/fchollet/keras/blob/master/examples/variational_autoencoder.py
    """
    def __init__(self, **kwargs):
        self.is_placeholder = True
        super(custom_ae_layer, self).__init__(**kwargs)
    def ae_loss(self, reconstruction,emb_lookup):
        loss = K.sum(emb_lookup - reconstruction,axis=-1)
        return K.mean(loss)

    def call(self, inputs):
        reconstruction = inputs[1]
        emb_lookup = inputs[0]
        loss = self.ae_loss(emb_lookup,reconstruction)
        self.add_loss(loss)
        return emb_lookup

无论我返回emb_lookup还是reconstruction,都会发生此错误.我的图层和官方示例之间的主要区别在于,我使用嵌入查找作为输入,这是 keras.layers.Embedding对象,而重建是

This error occurs regardless of if I return emb_lookup or reconstruction. The major difference between my layer and the official example is I use an embedding lookup as an input, which is the output of the keras.layers.Embedding object, and reconstruction is

recon_layer = Dense(outshape, activation="tanh",kernel_regularizer=l2(in_args.l2_rate))(deconv_input)
s_recon_layer = K.squeeze(recon_layer,2)

无论我返回emb_lookup还是reconstruction,都会发生此错误.

This error occurs regardless of if I return emb_lookup or reconstruction.

完整的错误消息是这样的:

Full error message is this:

Traceback (most recent call last):
      File "semi_sup_cnn_big_data_test.py", line 166, in <module>
        main()
      File "semi_sup_cnn_big_data_test.py", line 84, in main
        args,run_time,micro,macro = basic_cnn_train_val_test(args)
      File "semi_sup_cnn_big_data_test.py", line 100, in basic_cnn_train_val_test
        clf,args = init_export_network(args)
      File "/home/qqi/git/MPI_CNN/models/auto_encoder_multilayer_cnn.py", line 257, in init_export_network
        model = Model(model_input, y)
      File "/usr/local/lib/python3.5/dist-packages/keras/legacy/interfaces.py", line 88, in wrapper
        return func(*args, **kwargs)
      File "/usr/local/lib/python3.5/dist-packages/keras/engine/topology.py", line 1705, in __init__
        build_map_of_graph(x, finished_nodes, nodes_in_progress)
      File "/usr/local/lib/python3.5/dist-packages/keras/engine/topology.py", line 1695, in build_map_of_graph
        layer, node_index, tensor_index)
      File "/usr/local/lib/python3.5/dist-packages/keras/engine/topology.py", line 1665, in build_map_of_graph
        layer, node_index, tensor_index = tensor._keras_history
    AttributeError: 'Tensor' object has no attribute '_keras_history'

根据要求,这是完整的init_export_network功能:

As requested, here is the full init_export_network function:

    def init_export_network(in_args):
        import_dir = os.path.join('cv_data',
                                  in_args.data_name,
                                  in_args.label_name,
                                  in_args.this_fold)

        # set output dir as models/[model_name]/[data_name]/[label_file_name]/[this_fold]
        output_dir = os.path.join("initialized_models",
                                  in_args.model_name,
                                  in_args.data_name,
                                  in_args.label_name,
                                  in_args.this_fold)
        print("exporting to", output_dir)
        if not os.path.exists(output_dir):
            os.makedirs(output_dir)
        else:
            print(output_dir, "data dir identified but will be re-populated")
            shutil.rmtree(output_dir)
            os.makedirs(output_dir)
        "returns base cnn architecture and placeholder/untrained weights"
        # unpckl wv_matrix, class_names
        wv_matrix = unpckl(os.path.join(import_dir,'wv_matrix.pickle'))
        print("valid pre-processed data found in", import_dir)
        # define network layers ----------------------------------------------------
        input_shape = (in_args.seq_len,)
        output_shape = (in_args.seq_len,len(wv_matrix[0]),)
        emb_size = len(wv_matrix[0])
        model_input = Input(shape=input_shape)
        emb_lookup = Embedding(len(wv_matrix),
                               len(wv_matrix[0]),
                               embeddings_regularizer=l2(in_args.emb_l2_rate),
                               input_length=in_args.seq_len, name="embedding")(model_input)
        #emb_lookup = Embedding(len(wv_matrix), len(wv_matrix[0]), input_length=in_args.seq_len, name="embedding", )(model_input)
        if in_args.emb_dropout:
            emb_lookup = Dropout(in_args.emb_dropout)(emb_lookup)
        conv_blocks = []
        # conv blocks --------------------------------------------------------------
        print("emb_lookup shape!!!!",emb_lookup.shape)
        for ith_conv,sz in enumerate(in_args.filter_sizes):
            if ith_conv == 0:
                conv_input = emb_lookup
            else:
                conv_input = conv
            conv = Convolution1D(filters=in_args.feat_maps[ith_conv],
                                 kernel_size=sz,
                                 padding="valid",
                                 activation="relu",
                                 kernel_initializer = 'lecun_uniform',
                                 kernel_regularizer=l2(in_args.l2_rate),
                                 strides=1,
                                 name = "{}_conv".format(ith_conv))(conv_input)
            print("{}_conv".format(ith_conv), conv.shape)
        # deconv blocks with dimensions reverse of multilayer_cnn ------------------
        deconv_blocks = []
        deconv_filter_sizes = in_args.filter_sizes
        deconv_filter_sizes.reverse()

        #print("conv_shape!!!", conv.shape)
        conv_input = conv
        print("conv_upsampling_shape!!!", conv_input.shape)

        #unpool_shape = ((conv[1],-1,conv[2]))
        #conv_input = Reshape((1,conv_input[1],conv_input[2]))(conv_input)
        #print("conv_input_shape!!!", conv_input.shape)

        #conv_input = Reshape(unpool_shape),conv_input
        #conv_input = Reshape(unpool_shape)(conv_input)
        deconv_input=K.expand_dims(conv_input,2)

        print("conv_reshape_shape!!!", conv_input)
        for ith_conv,sz in enumerate(deconv_filter_sizes):
            print("{}_deconv input shape!!!".format(ith_conv), deconv_input)
            deconv = Conv2DTranspose(filters=in_args.feat_maps[ith_conv],
                                 kernel_size=(sz,1),
                                 #kernel_size=sz,
                                 padding="valid",
                                 activation="relu",
                                 kernel_initializer = 'lecun_uniform',
                                 kernel_regularizer=l2(in_args.l2_rate),
                                 strides=(1,1),
                                 name = "{}_deconv".format(ith_conv))(deconv_input)
            deconv_input = deconv
        print("{}_deconv input shape!!!".format(ith_conv), deconv_input)
        print("deconv_output shape",deconv)
        #z = Flatten()(conv)
        #deconv_out = Flatten(deconv)
        #outshape = (in_args.seq_len,len(wv_matrix[0]))
        outshape = len(wv_matrix[0])
        recon_layer = Dense(outshape, activation="tanh",kernel_regularizer=l2(in_args.l2_rate))(deconv_input)
        print("recon_layer shape",recon_layer)
        #s_recon_layer = K.squeeze(recon_layer,2)
        s_recon_layer = Lambda(lambda x: K.squeeze(x, 2))(recon_layer)
        print("squeezed recon_layer shape",s_recon_layer)
        #print("conv_reshape_shape!!!", conv_input.shape)(conv)
        # end define network layers ------------------------------------------------
        #model_output = Dense(outshape, activation="elu",kernel_regularizer=l2(in_args.l2_rate))(z)
        y = custom_ae_layer()([model_input,emb_lookup,s_recon_layer])
        model = Model(model_input, y)
        # finished network layers definition - compile network
        opt = optimizers.Adamax()

        model.compile(loss=None, optimizer='adamax')
        embedding_layer = model.get_layer("embedding")
        embedding_layer.set_weights([wv_matrix])
        # load wv_matrix into embedidng layer
        print("Initializing embedding layer with word2vec weights, shape", wv_matrix.shape)


        # save model architecture as json
        open(os.path.join(output_dir,"structure.json"),"w").write(model.to_json())
        # save initialized model weights as .hdf5fmacro
        model.save_weights(os.path.join(output_dir, "weights"+".hdf5"))
        print("multilayer network/initial weights successfully saved in", output_dir)
        print(in_args)
        #print(model.summary())
        return model,in_args

推荐答案

错误消息看起来与以下问题非常相似: https: //stackoverflow.com/a/45309816/1531463

The error message looks pretty much similar to this question: https://stackoverflow.com/a/45309816/1531463

简而言之,我认为您需要换行:

In short, I think you need to wrap this line:

s_recon_layer = K.squeeze(recon_layer,2)

(或任何其他后端函数调用)进入Lambda层.

(or any other backend function calls) into a Lambda layer.

具体地说,

s_recon_layer = Lambda(lambda x: K.squeeze(x, 2))(recon_layer)

这篇关于Keras自定义图层-AttributeError:"Tensor"对象没有属性"_keras_history"的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持!

09-03 08:13