我想将简单RNN层的输出用作其他网络中的输入。 怎么做？

Question

I have two same CRNN networks and I want to take the output from last Simple RNN layers in both the networks and input those two to another network, this is a siamese configuration. I am not able to input to input the outputs of these CRNN networks and its error: unhashable type: 'Dimension'

Full traceback error:

Traceback (most recent call last):

File "full_adda.py", line 270, in <module> model_s.fit(([in_source, in_target]), Y_train,batch_size=128,epochs=epochs)

File "/usr/lib64/python3.4/site-packages/keras/engine/training.py", line 1358, in fit batch_size=batch_size)

File "/usr/lib64/python3.4/site-packages/keras/engine/training.py", line 1246, in _standardize_user_data_
check_array_lengths(x, y, sample_weights)

File "/usr/lib64/python3.4/site-packages/keras/engine/training.py", line 222, in _check_array_lengths
    set_x = set_of_lengths(inputs)

File "/usr/lib64/python3.4/site-packages/keras/engine/training.py", line 220, in set_of_lengths
    return set([0 if y is None else y.shape[0] for y in x])

TypeError: unhashable type: 'Dimension'

-

    import numpy as np
    np.random.seed(1337)
    for run in range(0, 1):
        print ('source network..')
        print('run: ' + str(run))
        for i in range(1,nb_class+1):
            class_ind = np.where(y_all==i)
            Xi_trn, Xi_val_test, Yi_trn, Yi_val_test = train_test_split(X_all[class_ind[0],:,:], Y_all[class_ind[0],:], train_size=100, test_size=200)
            Xi_val, Xi_tst, Yi_val, Yi_tst = train_test_split(Xi_val_test, Yi_val_test, train_size=20)
            if i==1:
                X_train, Y_train, X_val, Y_val, X_test, Y_test = Xi_trn, Yi_trn, Xi_val, Yi_val, Xi_tst, Yi_tst
            else:
                X_train = np.concatenate((X_train, Xi_trn), axis=0)
                Y_train = np.concatenate((Y_train, Yi_trn), axis=0)
                X_val = np.concatenate((X_val, Xi_val), axis=0)
                Y_val = np.concatenate((Y_val, Yi_val), axis=0)
                X_test = np.concatenate((X_test, Xi_tst), axis=0)
                Y_test = np.concatenate((Y_test, Yi_tst), axis=0)

        num_epoch = 100
        batch_size = 128
        learning_rate = 1e-4
        decay_every_epochs = 1000
        decay_every = decay_every_epochs*X_train.shape[0]/batch_size
        decay_by = 5.0
        reg = 0e-4

        print('Build model...')
        model = Sequential()                                                                                                   

        model.add(Convolution1D(filters=32,kernel_size=6,padding='same',activation='relu',input_shape=X_train.shape[1:]))
        model.add(MaxPooling1D(pool_size=2))
        model.add(Convolution1D(filters=32,kernel_size=6,padding='same',activation='relu'))
        model.add(MaxPooling1D(pool_size=2))
        model.add(SimpleRNN(256, return_sequences=True))
        model.add(SimpleRNN(512, return_sequences=False))
        model.add(Dense(nb_class,activation='softmax'))

        opt = Adam(lr=learning_rate)
        model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])
        print(model.summary())

        print('Train...')
        history=model.fit(X_train, Y_train, batch_size=batch_size, epochs=num_epoch, validation_data=(X_val,Y_val))

        model.save_weights(str(run)+'.h5')
        in_source = model.layers[5].output

    #Target Network

        print('Build model...')
        model_t = Sequential()
        model_t.add(Convolution1D(filters=32,kernel_size=6,padding='same',activation='relu',input_shape=X_train.shape[1:]))
        model_t.add(MaxPooling1D(pool_size=2))
        model_t.add(Convolution1D(filters=32,kernel_size=6,padding='same',activation='relu'))
        model_t.add(MaxPooling1D(pool_size=2))
        model_t.add(SimpleRNN(256, return_sequences=True))
        model_t.add(SimpleRNN(512, return_sequences=False))
        model_t.add(Dense(nb_class,activation='softmax'))

# Loading pre-trained Weights
        model_t.load_weights(str(run)+'.h5',by_name=True)

        opt_t = Adam(lr=learning_rate)
        model_t.compile(loss='categorical_crossentropy', optimizer=opt_t, metrics=['accuracy'])
        print(model_t.summary())
        in_target = model_t.layers[5].output

    # Siamese Network

        def euclidean_distance(vects):
            x_siam, y_siam = vects
            return K.sqrt(K.maximum(K.sum(K.square(x_siam - y_siam), axis=1, keepdims=True), K.epsilon()))


        def eucl_dist_output_shape(shapes):
            shape1, shape2 = shapes
            return (shape1[0], 1)


        def contrastive_loss(y_true, y_pred):
            '''Contrastive loss from Hadsell-et-al.'06
    http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
    '''
            margin = 1
            return K.mean(y_true * K.square(y_pred) +
                  (1 - y_true) * K.square(K.maximum(margin - y_pred, 0)))


        def create_base_network(input_dim):
            '''Base network to be shared (eq. to feature extraction).
    '''
            seq = Sequential()
            seq.add(Dense(128, input_shape=(input_dim,), activation='relu'))
            seq.add(Dropout(0.1))
            seq.add(Dense(128, activation='relu'))
            seq.add(Dropout(0.1))
            seq.add(Dense(128, activation='relu'))
            return seq

        input_dim = 512

        base_network = create_base_network(input_dim)

        input_a = Input(shape=(input_dim,))
        input_b = Input(shape=(input_dim,))

        processed_a = base_network(input_a)
        processed_b = base_network(input_b)

        distance = Lambda(euclidean_distance,
                  output_shape=eucl_dist_output_shape)([processed_a, processed_b])

        model_s = Model([input_a, input_b], distance)

    # siamese training
        rms = RMSprop()
        model_s.compile(loss=contrastive_loss, optimizer=rms)
        model_s.fit([in_source, in_target], Y_train,
          batch_size = 128,
          epochs = num_epoch)

The Siamese network used here is the one which is given as example for Keras. So I am using the same loss functions too. Please help me how to solve this problem

Answer 1

The arguments you pass to the fit function need to be concrete numpy arrays, not symbolic tensors. I'm not a Keras expert but I think in_source and in_target in your code will be symbolic.

I think the solution will require quite a large change to your code: I think you need a single Sequential in the end, not three as you have right now. You need a model that whose inputs feed into the source and target stacks which are then connected symbolically to the base network stack and then the model's output will be the distance, as you have right now.