python类Lambda()的实例源码

def distance_layer(x1, x2):
    """Distance and angle of two inputs.

    Compute the concatenation of element-wise subtraction and
    multiplication of two inputs.

    """
    def _distance(args):
        x1 = args[0]
        x2 = args[1]
        x = K.abs(x1 - x2)
        return x

    def _multiply(args):
        x1 = args[0]
        x2 = args[1]
        return x1 * x2

    distance = Lambda(_distance, output_shape=(K.int_shape(x1)[-1],))([x1, x2])
    multiply = Lambda(_multiply, output_shape=(K.int_shape(x1)[-1],))([x1, x2])
    return concatenate([distance, multiply])

def build_mlp(n_con,n_emb,vocabs_size,n_dis,emb_size,cluster_size):
    hidden_size = 800
    con = Sequential()
    con.add(Dense(input_dim=n_con,output_dim=emb_size))

    emb_list = []
    for i in range(n_emb):
        emb = Sequential()
        emb.add(Embedding(input_dim=vocabs_size[i],output_dim=emb_size,input_length=n_dis))
        emb.add(Flatten())
        emb_list.append(emb)

    model = Sequential()
    model.add(Merge([con] + emb_list,mode='concat'))
    model.add(BatchNormalization())
    model.add(Dense(hidden_size,activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(cluster_size,activation='softmax'))
    model.add(Lambda(caluate_point, output_shape =[2]))
    return model

def crosschannelnormalization(alpha = 1e-4, k=2, beta=0.75, n=5,**kwargs):
    """
    This is the function used for cross channel normalization in the original
    Alexnet
    """
    def f(X):
        b, ch, r, c = X.shape
        half = n // 2
        square = K.square(X)
        extra_channels = K.spatial_2d_padding(K.permute_dimensions(square, (0,2,3,1))
                                              , (0,half))
        extra_channels = K.permute_dimensions(extra_channels, (0,3,1,2))
        scale = k
        for i in range(n):
            scale += alpha * extra_channels[:,i:i+ch,:,:]
        scale = scale ** beta
        return X / scale

    return Lambda(f, output_shape=lambda input_shape:input_shape,**kwargs)

def splittensor(axis=1, ratio_split=1, id_split=0,**kwargs):
    def f(X):
        div = X.shape[axis] // ratio_split

        if axis == 0:
            output =  X[id_split*div:(id_split+1)*div,:,:,:]
        elif axis == 1:
            output =  X[:, id_split*div:(id_split+1)*div, :, :]
        elif axis == 2:
            output = X[:,:,id_split*div:(id_split+1)*div,:]
        elif axis == 3:
            output == X[:,:,:,id_split*div:(id_split+1)*div]
        else:
            raise ValueError("This axis is not possible")

        return output

    def g(input_shape):
        output_shape=list(input_shape)
        output_shape[axis] = output_shape[axis] // ratio_split
        return tuple(output_shape)

    return Lambda(f,output_shape=lambda input_shape:g(input_shape),**kwargs)

def crosschannelnormalization(alpha = 1e-4, k=2, beta=0.75, n=5,**kwargs):
    """
    This is the function used for cross channel normalization in the original
    Alexnet
    """
    def f(X):
        b, ch, r, c = X.shape
        half = n // 2
        square = K.square(X)
        extra_channels = K.spatial_2d_padding(K.permute_dimensions(square, (0,2,3,1))
                                              , (0,half))
        extra_channels = K.permute_dimensions(extra_channels, (0,3,1,2))
        scale = k
        for i in range(n):
            scale += alpha * extra_channels[:,i:i+ch,:,:]
        scale = scale ** beta
        return X / scale

    return Lambda(f, output_shape=lambda input_shape:input_shape,**kwargs)

def splittensor(axis=1, ratio_split=1, id_split=0,**kwargs):
    def f(X):
        div = X.shape[axis] // ratio_split

        if axis == 0:
            output =  X[id_split*div:(id_split+1)*div,:,:,:]
        elif axis == 1:
            output =  X[:, id_split*div:(id_split+1)*div, :, :]
        elif axis == 2:
            output = X[:,:,id_split*div:(id_split+1)*div,:]
        elif axis == 3:
            output == X[:,:,:,id_split*div:(id_split+1)*div]
        else:
            raise ValueError("This axis is not possible")

        return output

    def g(input_shape):
        output_shape=list(input_shape)
        output_shape[axis] = output_shape[axis] // ratio_split
        return tuple(output_shape)

    return Lambda(f,output_shape=lambda input_shape:g(input_shape),**kwargs)

def __call__(self, x1, x2):
        def _sub_ops(args):
            x1 = args[0]
            x2 = args[1]
            x = K.abs(x1 - x2)
            return x

        def _mult_ops(args):
            x1 = args[0]
            x2 = args[1]
            return x1 * x2

        output_shape = (self.sequence_length, self.input_dim,)
        sub = Lambda(_sub_ops, output_shape=output_shape)([x1, x2])
        mult = Lambda(_mult_ops, output_shape=output_shape)([x1, x2])
        sub = self.model(sub)
        mult = self.model(mult)
        return concatenate([sub, mult])

def adverse_model(discriminator):

    train_input = Input(shape=(None,), dtype='int32')
    hypo_input = Input(shape=(None,), dtype='int32')

    def margin_opt(inputs):
        assert len(inputs) == 2, ('Margin Output needs '
                              '2 inputs, %d given' % len(inputs))
        return K.log(inputs[0]) + K.log(1-inputs[1])

    margin = Lambda(margin_opt, output_shape=(lambda s : (None, 1)))\
               ([discriminator(train_input), discriminator(hypo_input)])
    adverserial = Model([train_input, hypo_input], margin)

    adverserial.compile(loss=minimize, optimizer='adam')
    return adverserial

def build_model(dropout):
    model = Sequential()
    model.add(Lambda(lambda x: x / 255.0 - 0.5, input_shape = INPUT_SHAPE))
    model.add(Conv2D(3, (1, 1), activation='relu'))
    model.add(Conv2D(12, (5, 5), activation='relu'))
    model.add(MaxPooling2D(pool_size = (2, 2)))
    model.add(Conv2D(16, (3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size = (2, 2)))
    model.add(Conv2D(24, (3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size = (2, 2)))
    model.add(Conv2D(48, (3, 3), activation='relu'))
    model.add(Flatten())
    model.add(Dropout(dropout))
    model.add(Dense(64, activation = 'relu'))
    model.add(Dropout(dropout))
    model.add(Dense(32, activation = 'relu'))
    model.add(Dropout(dropout))
    model.add(Dense(1))

    return model

def crosschannelnormalization(alpha = 1e-4, k=2, beta=0.75, n=5,**kwargs):
    """
    This is the function used for cross channel normalization in the original
    Alexnet
    """
    def f(X):
        b, ch, r, c = X.shape
        half = n // 2
        square = K.square(X)
        extra_channels = K.spatial_2d_padding(K.permute_dimensions(square, (0,2,3,1))
                                              , (0,half))
        extra_channels = K.permute_dimensions(extra_channels, (0,3,1,2))
        scale = k
        for i in range(n):
            scale += alpha * extra_channels[:,i:i+ch,:,:]
        scale = scale ** beta
        return X / scale

    return Lambda(f, output_shape=lambda input_shape:input_shape,**kwargs)

def splittensor(axis=1, ratio_split=1, id_split=0,**kwargs):
    def f(X):
        div = X.shape[axis] // ratio_split

        if axis == 0:
            output =  X[id_split*div:(id_split+1)*div,:,:,:]
        elif axis == 1:
            output =  X[:, id_split*div:(id_split+1)*div, :, :]
        elif axis == 2:
            output = X[:,:,id_split*div:(id_split+1)*div,:]
        elif axis == 3:
            output == X[:,:,:,id_split*div:(id_split+1)*div]
        else:
            raise ValueError("This axis is not possible")

        return output

    def g(input_shape):
        output_shape=list(input_shape)
        output_shape[axis] = output_shape[axis] // ratio_split
        return tuple(output_shape)

    return Lambda(f,output_shape=lambda input_shape:g(input_shape),**kwargs)

def getconvmodel(filter_length,nb_filter):
    model = Sequential()
    model.add(Convolution1D(nb_filter=nb_filter,
                            input_shape=(100,32),
                            filter_length=filter_length,
                            border_mode='same',
                            activation='relu',
                            subsample_length=1))
    model.add(Lambda(sum_1d, output_shape=(nb_filter,)))
    #model.add(BatchNormalization(mode=0))
    model.add(Dropout(0.5))
    return model

def splittensor(axis=1, ratio_split=1, id_split=0, **kwargs):
    def f(X):
        div = K.shape(X)[axis] // ratio_split

        if axis == 0:
            output = X[id_split*div:(id_split+1)*div, :, :, :]
        elif axis == 1:
            output = X[:, id_split*div:(id_split+1)*div, :, :]
        elif axis == 2:
            output = X[:, :, id_split*div:(id_split+1)*div, :]
        elif axis == 3:
            output = X[:, :, :, id_split*div:(id_split+1)*div]
        else:
            raise ValueError("This axis is not possible")

        return output

    def g(input_shape):
        output_shape = list(input_shape)
        output_shape[axis] = output_shape[axis] // ratio_split
        return tuple(output_shape)

    return Lambda(f, output_shape=lambda input_shape: g(input_shape), **kwargs)

def build_keras_model_sg(index_size,vector_size,
                         context_size,
                         #code_dim,
                         sub_batch_size=256,
                         learn_vectors=True,learn_hidden=True,
                         model=None):

    kerasmodel = Graph()
    kerasmodel.add_input(name='point' , input_shape=(1,), dtype=int)
    kerasmodel.add_input(name='index' , input_shape=(1,), dtype=int)
    kerasmodel.add_node(Embedding(index_size, vector_size, input_length=sub_batch_size,weights=[model.syn0]),name='embedding', input='index')
    kerasmodel.add_node(Embedding(context_size, vector_size, input_length=sub_batch_size,weights=[model.keras_syn1]),name='embedpoint', input='point')
    kerasmodel.add_node(Lambda(lambda x:x.sum(2))   , name='merge',inputs=['embedding','embedpoint'], merge_mode='mul')
    kerasmodel.add_node(Activation('sigmoid'), name='sigmoid', input='merge')
    kerasmodel.add_output(name='code',input='sigmoid')
    kerasmodel.compile('rmsprop', {'code':'mse'})
    return kerasmodel

def build_keras_model_cbow(index_size,vector_size,
                           context_size,
                           #code_dim,
                           sub_batch_size=1,
                           model=None,cbow_mean=False):

    kerasmodel = Graph()
    kerasmodel.add_input(name='point' , input_shape=(sub_batch_size,), dtype='int')
    kerasmodel.add_input(name='index' , input_shape=(1,), dtype='int')
    kerasmodel.add_node(Embedding(index_size, vector_size, weights=[model.syn0]),name='embedding', input='index')
    kerasmodel.add_node(Embedding(context_size, vector_size, input_length=sub_batch_size,weights=[model.keras_syn1]),name='embedpoint', input='point')
    if cbow_mean:
        kerasmodel.add_node(Lambda(lambda x:x.mean(1),output_shape=(vector_size,)),name='average',input='embedding')
    else:
        kerasmodel.add_node(Lambda(lambda x:x.sum(1),output_shape=(vector_size,)),name='average',input='embedding')

    kerasmodel.add_node(Activation('sigmoid'), name='sigmoid',inputs=['average','embedpoint'], merge_mode='dot',dot_axes=-1)
    kerasmodel.add_output(name='code',input='sigmoid')
    kerasmodel.compile('rmsprop', {'code':'mse'})
    return kerasmodel

def crosschannelnormalization(alpha=1e-4, k=2, beta=0.75, n=5, **kwargs):
    """
    This is the function used for cross channel normalization in the original
    Alexnet
    """

    def f(X):
        b, ch, r, c = X.shape
        half = n // 2
        square = K.square(X)
        extra_channels = K.spatial_2d_padding(K.permute_dimensions(square, (0, 2, 3, 1))
                                              , (0, half))
        extra_channels = K.permute_dimensions(extra_channels, (0, 3, 1, 2))
        scale = k
        for i in range(n):
            scale += alpha * extra_channels[:, i:i + ch, :, :]
        scale = scale ** beta
        return X / scale

    return Lambda(f, output_shape=lambda input_shape: input_shape, **kwargs)

def splittensor(axis=1, ratio_split=1, id_split=0, **kwargs):
    def f(X):
        div = X.shape[axis] // ratio_split

        if axis == 0:
            output = X[id_split * div:(id_split + 1) * div, :, :, :]
        elif axis == 1:
            output = X[:, id_split * div:(id_split + 1) * div, :, :]
        elif axis == 2:
            output = X[:, :, id_split * div:(id_split + 1) * div, :]
        elif axis == 3:
            output = X[:, :, :, id_split * div:(id_split + 1) * div]
        else:
            raise ValueError('This axis is not possible')

        return output

    def g(input_shape):
        output_shape = list(input_shape)
        output_shape[axis] = output_shape[axis] // ratio_split
        return tuple(output_shape)

    return Lambda(f, output_shape=lambda input_shape: g(input_shape), **kwargs)

def make_parallel(model, gpu_count):
    def get_slice(data, idx, parts):
        shape = tf.shape(data)
        size = tf.concat([shape[:1] // parts, shape[1:]], axis=0)
        stride = tf.concat([shape[:1] // parts, shape[1:] * 0], axis=0)
        start = stride * idx
        return tf.slice(data, start, size)

    outputs_all = []
    for i in range(len(model.outputs)):
        outputs_all.append([])

    # Place a copy of the model on each GPU, each getting a slice of the batch
    for i in range(gpu_count):
        with tf.device('/gpu:%d' % i):
            with tf.name_scope('tower_%d' % i) as scope:

                inputs = []
                # Slice each input into a piece for processing on this GPU
                for x in model.inputs:
                    input_shape = tuple(x.get_shape().as_list())[1:]
                    slice_n = Lambda(get_slice, output_shape=input_shape, arguments={'idx': i, 'parts': gpu_count})(x)
                    inputs.append(slice_n)

                outputs = model(inputs)

                if not isinstance(outputs, list):
                    outputs = [outputs]

                # Save all the outputs for merging back together later
                for l in range(len(outputs)):
                    outputs_all[l].append(outputs[l])

    # merge outputs on CPU
    with tf.device('/cpu:0'):
        merged = []
        for outputs in outputs_all:
            merged.append(Concatenate(axis=0)(outputs))

        return Model(model.inputs, merged)

def __init__(self, sequence_length, nb_chars, nb_per_word,
                 embedding_dim, rnn_dim, rnn_unit='gru', dropout=0.0):
        def _collapse_input(x, nb_per_word=0):
            x = K.reshape(x, (-1, nb_per_word))
            return x

        def _unroll_input(x, sequence_length=0, rnn_dim=0):
            x = K.reshape(x, (-1, sequence_length, rnn_dim))
            return x

        if rnn_unit == 'gru':
            rnn = GRU
        else:
            rnn = LSTM
        self.model = Sequential()
        self.model.add(Lambda(_collapse_input,
                              arguments={'nb_per_word': nb_per_word},
                              output_shape=(nb_per_word,),
                              input_shape=(sequence_length, nb_per_word,)))
        self.model.add(Embedding(nb_chars,
                                 embedding_dim,
                                 input_length=nb_per_word,
                                 trainable=True))
        self.model.add(rnn(rnn_dim,
                           dropout=dropout,
                           recurrent_dropout=dropout))
        self.model.add(Lambda(_unroll_input,
                              arguments={'sequence_length': sequence_length,
                                         'rnn_dim': rnn_dim},
                              output_shape=(sequence_length, rnn_dim)))

def test_lambda():
    from keras.utils.layer_utils import layer_from_config
    Lambda = core.Lambda

    layer_test(Lambda,
               kwargs={'function': lambda x: x + 1},
               input_shape=(3, 2))

    layer_test(Lambda,
               kwargs={'function': lambda x, a, b: x * a + b,
                       'arguments': {'a': 0.6, 'b': 0.4}},
               input_shape=(3, 2))

    # test serialization with function
    def f(x):
        return x + 1

    ld = Lambda(f)
    config = ld.get_config()
    ld = layer_from_config({'class_name': 'Lambda', 'config': config})

    ld = Lambda(lambda x: K.concatenate([K.square(x), x]),
                output_shape=lambda s: tuple(list(s)[:-1] + [2 * s[-1]]))
    config = ld.get_config()
    ld = Lambda.from_config(config)

    # test serialization with output_shape function
    def f(x):
        return K.concatenate([K.square(x), x])

    def f_shape(s):
        return tuple(list(s)[:-1] + [2 * s[-1]])

    ld = Lambda(f, output_shape=f_shape)
    config = ld.get_config()
    ld = layer_from_config({'class_name': 'Lambda', 'config': config})

def _build_multi_gpu_model(blueprint, devices):
    import tensorflow as tf
    model = _build_single_device_model(blueprint, cpu_device())
    gpu_devices = [d for d in devices if is_gpu_device(d)]
    gpu_count = len(gpu_devices)

    def get_input(data, idx, parts):
        shape = tf.shape(data)
        size = tf.concat([ shape[:1] // parts, shape[1:] ], 0)
        stride = tf.concat([ shape[:1] // parts, shape[1:]*0 ], 0)
        start = stride * idx
        return tf.slice(data, start, size)

    outputs = []
    for i, device in enumerate(gpu_devices):
        with tf.device(device):
            x = model.inputs[0]
            input_shape = tuple(x.get_shape().as_list())[1:]
            model_input = Lambda(
                get_input, 
                output_shape=input_shape, 
                arguments={'idx':i,'parts':gpu_count})(x)
            outputs.append(model(model_input))
    with tf.device(cpu_device()):
        output = merge(outputs, mode='concat', concat_axis=0)
        return MultiGpuModel(
            model, 
            model_input=model.inputs, 
            model_output=output)

def __init__(self, sequence_length, nb_chars, nb_per_word,
                 embedding_dim, rnn_dim, rnn_unit='gru', dropout=0.0):
        def _collapse_input(x, nb_per_word=0):
            x = K.reshape(x, (-1, nb_per_word))
            return x

        def _unroll_input(x, sequence_length=0, rnn_dim=0):
            x = K.reshape(x, (-1, sequence_length, rnn_dim))
            return x

        if rnn_unit == 'gru':
            rnn = GRU
        else:
            rnn = LSTM
        self.model = Sequential()
        self.model.add(Lambda(_collapse_input,
                              arguments={'nb_per_word': nb_per_word},
                              output_shape=(nb_per_word,),
                              input_shape=(sequence_length, nb_per_word,)))
        self.model.add(Embedding(nb_chars,
                                 embedding_dim,
                                 input_length=nb_per_word,
                                 trainable=True))
        self.model.add(rnn(rnn_dim,
                           dropout=dropout,
                           recurrent_dropout=dropout))
        self.model.add(Lambda(_unroll_input,
                              arguments={'sequence_length': sequence_length,
                                         'rnn_dim': rnn_dim},
                              output_shape=(sequence_length, rnn_dim)))

def __call__(self, x1, x2):
        def _dot_product(args):
            x = args[0]
            y = args[1]
            return K.batch_dot(x, K.permute_dimensions(y, (0, 2, 1)))

        def _normalize(args, transpose=False):
            att_w = args[0]
            x = args[1]
            if transpose:
                att_w = K.permute_dimensions(att_w, (0, 2, 1))
            e = K.exp(att_w - K.max(att_w, axis=-1, keepdims=True))
            sum_e = K.sum(e, axis=-1, keepdims=True)
            nor_e = e / sum_e
            return K.batch_dot(nor_e, x)

        # (batch_size, timesteps1, dim)
        f1 = self.model(x1)
        # (batch_size, timesteps2, dim)
        f2 = self.model(x2)
        output_shape = (self.sequence_length, self.sequence_length,)
        # attention weights, (batch_size, timesteps1, timesteps2)
        att_w = Lambda(
            _dot_product,
            output_shape=output_shape)([f1, f2])
        output_shape = (self.sequence_length, self.input_dim,)
        # (batch_size, timesteps1, dim)
        att1 = Lambda(
            _normalize, arguments={'transpose': False},
            output_shape=output_shape)([att_w, x2])
        # (batch_size, timestep2, dim)
        att2 = Lambda(
            _normalize, arguments={'transpose': True},
            output_shape=output_shape)([att_w, x1])
        return att1, att2

def get_simple_cnn(height, width):
    """ A simple CNN that has the same input/output shapes as the VGG16 model.

    Args:
        height: input height
        width: input width
    Return: Keras model

    """
    model = Sequential()
    model.add(ZeroPadding2D((1, 1), input_shape=(3, height, width)))
    model.add(Convolution2D(64, 3, 3, activation='relu'))
    model.add(MaxPooling2D((4, 4), strides=(2, 2)))

    model.add(ZeroPadding2D((1, 1)))
    model.add(Convolution2D(64, 3, 3, activation='relu'))
    model.add(MaxPooling2D((4, 4), strides=(2, 2)))

    #model.add(ZeroPadding2D((1, 1)))
    #model.add(Convolution2D(64, 3, 3, activation='relu'))
    #model.add(MaxPooling2D((4, 4), strides=(2, 2)))

    #model.add(ZeroPadding2D((1, 1)))
    #model.add(Convolution2D(512, 3, 3, activation='relu'))
    #model.add(MaxPooling2D((2, 2), strides=(2, 2)))

    model.add(ZeroPadding2D((1, 1)))
    model.add(Convolution2D(64, 3, 3, activation='relu'))
    model.add(Lambda(global_average_pooling,
                     output_shape=global_average_pooling_shape))
    model.add(Dense(2, activation="softmax", init="uniform"))
    return model

def cbow_base_model(dict_size, emb_size=100, context_window_size=4):
    model = keras.models.Sequential()
    model.add(Embedding(dict_size, emb_size, 
        input_length=context_window_size,
        embeddings_initializer=keras.initializers.TruncatedNormal(mean=0.0, stddev=0.2),
        ))
    model.add(Lambda(lambda x: K.mean(x, axis=1), output_shape=(emb_size,)))
    model.add(Dense(dict_size))
    model.add(Activation('softmax')) # TODO: use nce

    sgd = optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
    model.compile(optimizer=sgd,
            loss='categorical_crossentropy',)
    return model

def baseline_train(noise_examples, hidden_size, noise_dim, glove, hypo_len, version):
    prem_input = Input(shape=(None,), dtype='int32', name='prem_input')
    hypo_input = Input(shape=(hypo_len + 1,), dtype='int32', name='hypo_input')
    noise_input = Input(shape=(1,), dtype='int32', name='noise_input')
    train_input = Input(shape=(None,), dtype='int32', name='train_input')
    class_input = Input(shape=(3,), name='class_input')
    concat_dim = hidden_size + noise_dim + 3
    prem_embeddings = make_fixed_embeddings(glove, None)(prem_input)
    hypo_embeddings = make_fixed_embeddings(glove, hypo_len + 1)(hypo_input)

    premise_layer = LSTM(output_dim=hidden_size, return_sequences=False,
                            inner_activation='sigmoid', name='premise')(prem_embeddings)

    noise_layer = Embedding(noise_examples, noise_dim,
                            input_length = 1, name='noise_embeddings')(noise_input)
    flat_noise = Flatten(name='noise_flatten')(noise_layer)    
    merged = merge([premise_layer, class_input, flat_noise], mode='concat')
    creative = Dense(concat_dim, name = 'cmerge')(merged)
    fake_merge = Lambda(lambda x:x[0], output_shape=lambda x:x[0])([hypo_embeddings, creative])
    hypo_layer = FeedLSTM(output_dim=concat_dim, return_sequences=True,
                         feed_layer = creative, inner_activation='sigmoid', 
                         name='attention')([fake_merge])

    hs = HierarchicalSoftmax(len(glove), trainable = True, name='hs')([hypo_layer, train_input])
    inputs = [prem_input, hypo_input, noise_input, train_input, class_input]


    model_name = 'version' + str(version)
    model = Model(input=inputs, output=hs, name = model_name)
    model.compile(loss=hs_categorical_crossentropy, optimizer='adam')

    return model

def OCR():
    model = Sequential()
    model.add(Lambda(vgg_preprocess, input_shape=(3,32,32)))
    print ("in ocr")
    ConvBlock(2, model, 16)
    ConvBlock(2, model, 16)
    model.add(Flatten())
    model.add(Dense(192, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(62, activation='softmax'))
    print ("outside OCR")
    return model

def buildDecomposition(self):
        q_embedding = self.tensors['q-embedding']
        a_embedding = self.tensors['a-embedding']
        q_match = self.tensors['q-match']
        a_match = self.tensors['a-match']
        # compute q+, q-, a+, a-
        # ?????????????BATCH_SIZE????????????
        # ??Lambda???Lambda?ouput_shape?????BATCH_SIZE??
        # ??????Lambda????????BATCH_SIZE???????
        # ????BATCH_SIZE?????????????????????
        # ????Merge??BATCH_SIZE????Lambda??
        q_channels = Merge(
            mode=lambda x: decomposite(*x),
            output_shape=(self.params['batch_size'], 2, self.q_length, self.wdim),
            name='q-channels'
        )([q_embedding, q_match])

        a_channels = Merge(
            mode=lambda x: decomposite(*x),
            output_shape=(self.params['batch_size'], 2, self.a_length, self.wdim),
            name='a-channels',
        )([a_embedding, a_match])
        print('q_channels', q_channels._keras_shape, K.ndim(q_channels))
        print('a_channels', a_channels._keras_shape, K.ndim(a_channels))
        self.tensors['q-channels'] = q_channels
        self.tensors['a-channels'] = a_channels

def linkFeature(self, input_name, conv_name, activation='tanh'):
        print('Am I called')
        filters = self.params.get('filters')
        nb_filter = self.params.get('nb_filter')
        convs = self.layers.get(conv_name)
        assert filters
        assert convs
        features = []
        for fsz, conv in zip(filters, convs):
            conv_output = conv(self.tensors[input_name])
            if type(activation) == type(''):
                act = Activation(
                    activation, name='%s-act-%d' % (input_name, fsz)
                )(conv_output)
            else:
                act = activation(
                    name='%s-advanced-act-%d' % (input_name, fsz)
                )(conv_output)
            maxpool = Lambda(
                lambda x: K.max(x[:,:,:,0], axis=2),
                output_shape=(nb_filter,),
                name='%s-maxpool-%d' % (input_name, fsz)
            )(act)
            features.append(maxpool)
        if len(features) > 1:
            return Merge(mode='concat', name='%s-feature' % input_name)(features)
        else:
            return features[0]

def doubleFeature(self, pos, neg, conv_name, activation='tanh'):
        name = '%s+%s' % (pos, neg)
        filters = self.params['filters']
        nb_filter = self.params['nb_filter']
        convs = self.layers[conv_name]
        features = []
        pos = self.tensors[pos]
        neg = self.tensors[neg]
        for fsz, conv in zip(filters, convs):
            sum = Merge(
                mode='sum',
            )([conv(pos), conv(neg)])
            if type(activation) == type(''):
                act = Activation(
                    activation, name='%s-act-%d' % ('+'.join(input_names), fsz)
                )(sum)
            else:
                act = activation(
                    name='%s-advanced-act-%d' % (name, fsz)
                )(sum)
            maxpool = Lambda(
                lambda x: K.max(x, axis=1),
                output_shape=(nb_filter,),
                name='%s-maxpool-%d' % (name, fsz)
            )(act)
            print('maxpool', maxpool._keras_shape)
            features.append(maxpool)
        if len(features) > 1:
            return Merge(
                mode='concat', 
                name='%s-feature' % name,
            )(features)
        else:
            return features[0]