How to use the tflearn.layers.merge_ops.merge function in tflearn

inception_5a_pool = max_pool_2d(pool4_3_3, kernel_size=3, strides=1, name='inception_5a_pool')
inception_5a_pool_1_1 = conv_2d(inception_5a_pool, 128, filter_size=1, activation='relu', name='inception_5a_pool_1_1')
inception_5a_output = merge([inception_5a_1_1, inception_5a_3_3, inception_5a_5_5, inception_5a_pool_1_1], axis=3,
                            mode='concat')

# 5b
inception_5b_1_1 = conv_2d(inception_5a_output, 384, filter_size=1, activation='relu', name='inception_5b_1_1')
inception_5b_3_3_reduce = conv_2d(inception_5a_output, 192, filter_size=1, activation='relu',
                                  name='inception_5b_3_3_reduce')
inception_5b_3_3 = conv_2d(inception_5b_3_3_reduce, 384, filter_size=3, activation='relu', name='inception_5b_3_3')
inception_5b_5_5_reduce = conv_2d(inception_5a_output, 48, filter_size=1, activation='relu',
                                  name='inception_5b_5_5_reduce')
inception_5b_5_5 = conv_2d(inception_5b_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_5b_5_5')
inception_5b_pool = max_pool_2d(inception_5a_output, kernel_size=3, strides=1, name='inception_5b_pool')
inception_5b_pool_1_1 = conv_2d(inception_5b_pool, 128, filter_size=1, activation='relu', name='inception_5b_pool_1_1')
inception_5b_output = merge([inception_5b_1_1, inception_5b_3_3, inception_5b_5_5, inception_5b_pool_1_1], axis=3,
                            mode='concat')
pool5_7_7 = avg_pool_2d(inception_5b_output, kernel_size=7, strides=1)
pool5_7_7 = dropout(pool5_7_7, 0.4)

# fc
loss = fully_connected(pool5_7_7, 17, activation='softmax')
network = regression(loss, optimizer='momentum',
                     loss='categorical_crossentropy',
                     learning_rate=0.001)

# to train
model = tflearn.DNN(network, checkpoint_path='model_googlenet',
                    max_checkpoints=1, tensorboard_verbose=2)

model.fit(X, Y, n_epoch=1000, validation_set=0.1, shuffle=True,
          show_metric=True, batch_size=64, snapshot_step=200,

inception_c_3_3=merge([inception_c_3_3_asym_1,inception_c_3_3_asym_2],mode='concat',axis=3)

    inception_c_5_5_reduce = conv_2d(input_c, 384, filter_size=1, activation='relu', name = 'inception_c_5_5_reduce')
    inception_c_5_5_asym_1 = conv_2d(inception_c_5_5_reduce, 448, filter_size=[1,3],  name = 'inception_c_5_5_asym_1')
    inception_c_5_5_asym_2 = conv_2d(inception_c_5_5_asym_1, 512, filter_size=[3,1],  activation='relu',name='inception_c_5_5_asym_2')
    inception_c_5_5_asym_3 = conv_2d(inception_c_5_5_asym_2, 256, filter_size=[1,3],  activation='relu',name='inception_c_5_5_asym_3')

    inception_c_5_5_asym_4 = conv_2d(inception_c_5_5_asym_2, 256, filter_size=[3,1],  activation='relu',name='inception_c_5_5_asym_4')
    inception_c_5_5=merge([inception_c_5_5_asym_4,inception_c_5_5_asym_3],mode='concat',axis=3)


    inception_c_pool = avg_pool_2d(input_c, kernel_size=3, strides=1 )
    inception_c_pool_1_1 = conv_2d(inception_c_pool, 256, filter_size=1, activation='relu', name='inception_c_pool_1_1')

    # merge the inception_c__
    inception_c_output = merge([inception_c_1_1, inception_c_3_3, inception_c_5_5, inception_c_pool_1_1], mode='concat', axis=3)

    return inception_c_output

inception_4a_pool = max_pool_2d(pool3_3_3, kernel_size=3, strides=1,  name='inception_4a_pool')
        inception_4a_pool_1_1 = conv_2d(inception_4a_pool, 64, filter_size=1, activation='relu', name='inception_4a_pool_1_1')

        inception_4a_output = merge([inception_4a_1_1, inception_4a_3_3, inception_4a_5_5, inception_4a_pool_1_1], mode='concat', axis=3, name='inception_4a_output')


        inception_4b_1_1 = conv_2d(inception_4a_output, 160, filter_size=1, activation='relu', name='inception_4a_1_1')
        inception_4b_3_3_reduce = conv_2d(inception_4a_output, 112, filter_size=1, activation='relu', name='inception_4b_3_3_reduce')
        inception_4b_3_3 = conv_2d(inception_4b_3_3_reduce, 224, filter_size=3, activation='relu', name='inception_4b_3_3')
        inception_4b_5_5_reduce = conv_2d(inception_4a_output, 24, filter_size=1, activation='relu', name='inception_4b_5_5_reduce')
        inception_4b_5_5 = conv_2d(inception_4b_5_5_reduce, 64, filter_size=5,  activation='relu', name='inception_4b_5_5')

        inception_4b_pool = max_pool_2d(inception_4a_output, kernel_size=3, strides=1,  name='inception_4b_pool')
        inception_4b_pool_1_1 = conv_2d(inception_4b_pool, 64, filter_size=1, activation='relu', name='inception_4b_pool_1_1')

        inception_4b_output = merge([inception_4b_1_1, inception_4b_3_3, inception_4b_5_5, inception_4b_pool_1_1], mode='concat', axis=3, name='inception_4b_output')


        inception_4c_1_1 = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu',name='inception_4c_1_1')
        inception_4c_3_3_reduce = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu', name='inception_4c_3_3_reduce')
        inception_4c_3_3 = conv_2d(inception_4c_3_3_reduce, 256,  filter_size=3, activation='relu', name='inception_4c_3_3')
        inception_4c_5_5_reduce = conv_2d(inception_4b_output, 24, filter_size=1, activation='relu', name='inception_4c_5_5_reduce')
        inception_4c_5_5 = conv_2d(inception_4c_5_5_reduce, 64,  filter_size=5, activation='relu', name='inception_4c_5_5')

        inception_4c_pool = max_pool_2d(inception_4b_output, kernel_size=3, strides=1)
        inception_4c_pool_1_1 = conv_2d(inception_4c_pool, 64, filter_size=1, activation='relu', name='inception_4c_pool_1_1')

        inception_4c_output = merge([inception_4c_1_1, inception_4c_3_3, inception_4c_5_5, inception_4c_pool_1_1], mode='concat', axis=3,name='inception_4c_output')

        inception_4d_1_1 = conv_2d(inception_4c_output, 112, filter_size=1, activation='relu', name='inception_4d_1_1')
        inception_4d_3_3_reduce = conv_2d(inception_4c_output, 144, filter_size=1, activation='relu', name='inception_4d_3_3_reduce')
        inception_4d_3_3 = conv_2d(inception_4d_3_3_reduce, 288, filter_size=3, activation='relu', name='inception_4d_3_3')

def inception_block_c(input_c):
    inception_c_1_1 = conv_2d(input_c, 256, 1, activation='relu', name='inception_c_1_1')
    inception_c_3_3_reduce = conv_2d(input_c, 384, filter_size=1, activation='relu', name='inception_c_3_3_reduce')
    inception_c_3_3_asym_1 = conv_2d(inception_c_3_3_reduce, 256, filter_size=[1,3],  activation='relu',name='inception_c_3_3_asym_1')
    inception_c_3_3_asym_2 = conv_2d(inception_c_3_3_reduce, 256, filter_size=[3,1],  activation='relu',name='inception_c_3_3_asym_2')
    inception_c_3_3=merge([inception_c_3_3_asym_1,inception_c_3_3_asym_2],mode='concat',axis=3)

    inception_c_5_5_reduce = conv_2d(input_c, 384, filter_size=1, activation='relu', name = 'inception_c_5_5_reduce')
    inception_c_5_5_asym_1 = conv_2d(inception_c_5_5_reduce, 448, filter_size=[1,3],  name = 'inception_c_5_5_asym_1')
    inception_c_5_5_asym_2 = conv_2d(inception_c_5_5_asym_1, 512, filter_size=[3,1],  activation='relu',name='inception_c_5_5_asym_2')
    inception_c_5_5_asym_3 = conv_2d(inception_c_5_5_asym_2, 256, filter_size=[1,3],  activation='relu',name='inception_c_5_5_asym_3')

    inception_c_5_5_asym_4 = conv_2d(inception_c_5_5_asym_2, 256, filter_size=[3,1],  activation='relu',name='inception_c_5_5_asym_4')
    inception_c_5_5=merge([inception_c_5_5_asym_4,inception_c_5_5_asym_3],mode='concat',axis=3)


    inception_c_pool = avg_pool_2d(input_c, kernel_size=3, strides=1 )
    inception_c_pool_1_1 = conv_2d(inception_c_pool, 256, filter_size=1, activation='relu', name='inception_c_pool_1_1')

    # merge the inception_c__
    inception_c_output = merge([inception_c_1_1, inception_c_3_3, inception_c_5_5, inception_c_pool_1_1], mode='concat', axis=3)

elif "GRU_2/GRU_2/GRUCell/Gates/Linear/Bias" in v.name :
      drug_gru_1_gate_bias.append(v)
   elif "GRU_2/GRU_2/GRUCell/Candidate/Linear/Bias" in v.name :
      drug_gru_1_candidate_bias.append(v)
   elif "GRU_3/GRU_3/GRUCell/Gates/Linear/Matrix" in v.name :
      drug_gru_2_gate_matrix.append(v)
   elif "GRU_3/GRU_3/GRUCell/Candidate/Linear/Matrix" in v.name :
      drug_gru_2_candidate_matrix.append(v)
   elif "GRU_3/GRU_3/GRUCell/Gates/Linear/Bias" in v.name :
      drug_gru_2_gate_bias.append(v)
   elif "GRU_3/GRU_3/GRUCell/Candidate/Linear/Bias" in v.name :
      drug_gru_2_candidate_bias.append(v)
   elif "Embedding_1" in v.name:
      drug_embd_W.append(v)

merging =  merge([prot_reshape_6,drug_reshape_6],mode='concat',axis=1)
fc_1 = fully_connected(merging, 600, activation='leakyrelu',weights_init="xavier",name='fully1')
drop_2 = dropout(fc_1, 0.8)
fc_2 = fully_connected(drop_2, 300, activation='leakyrelu',weights_init="xavier",name='fully2')
drop_3 = dropout(fc_2, 0.8)
linear = fully_connected(drop_3, 1, activation='linear',name='fully3')
reg = regression(linear, optimizer='adam', learning_rate=0.0001,
                     loss='mean_square', name='target')

# Training
model = tflearn.DNN(reg, tensorboard_verbose=0,tensorboard_dir='./mytensor/',checkpoint_path="./checkpoints/")
model.load('checkpoints-370700')

######### Setting weights

model.set_weights(prot_gru_1_gate_matrix[0],prot_gru_1_gates_kernel_init)
model.set_weights(prot_gru_1_gate_bias[0],prot_gru_1_gates_bias_init)

def inception_block_c(input_c):
    inception_c_1_1 = conv_2d(input_c, 256, 1, activation='relu', name='inception_c_1_1')
    inception_c_3_3_reduce = conv_2d(input_c, 384, filter_size=1, activation='relu', name='inception_c_3_3_reduce')
    inception_c_3_3_asym_1 = conv_2d(inception_c_3_3_reduce, 256, filter_size=[1,3],  activation='relu',name='inception_c_3_3_asym_1')
    inception_c_3_3_asym_2 = conv_2d(inception_c_3_3_reduce, 256, filter_size=[3,1],  activation='relu',name='inception_c_3_3_asym_2')
    inception_c_3_3=merge([inception_c_3_3_asym_1,inception_c_3_3_asym_2],mode='concat',axis=3)

    inception_c_5_5_reduce = conv_2d(input_c, 384, filter_size=1, activation='relu', name = 'inception_c_5_5_reduce')
    inception_c_5_5_asym_1 = conv_2d(inception_c_5_5_reduce, 448, filter_size=[1,3],  name = 'inception_c_5_5_asym_1')
    inception_c_5_5_asym_2 = conv_2d(inception_c_5_5_asym_1, 512, filter_size=[3,1],  activation='relu',name='inception_c_5_5_asym_2')
    inception_c_5_5_asym_3 = conv_2d(inception_c_5_5_asym_2, 256, filter_size=[1,3],  activation='relu',name='inception_c_5_5_asym_3')

    inception_c_5_5_asym_4 = conv_2d(inception_c_5_5_asym_2, 256, filter_size=[3,1],  activation='relu',name='inception_c_5_5_asym_4')
    inception_c_5_5=merge([inception_c_5_5_asym_4,inception_c_5_5_asym_3],mode='concat',axis=3)


    inception_c_pool = avg_pool_2d(input_c, kernel_size=3, strides=1 )
    inception_c_pool_1_1 = conv_2d(inception_c_pool, 256, filter_size=1, activation='relu', name='inception_c_pool_1_1')

    # merge the inception_c__
    inception_c_output = merge([inception_c_1_1, inception_c_3_3, inception_c_5_5, inception_c_pool_1_1], mode='concat', axis=3)

    return inception_c_output

# merge the inception_5a__
    inception_5a_output = merge([inception_5a_1_1, inception_5a_3_3, inception_5a_5_5, inception_5a_pool_1_1], mode='concat', axis=3)



    inception_7a_1_1 = conv_2d(inception_5a_output, 80, 1, activation='relu', name='inception_7a_1_1')
    inception_7a_3_3_reduce = conv_2d(inception_5a_output, 96, filter_size=1, activation='relu', name='inception_7a_3_3_reduce')
    inception_7a_3_3_asym_1 = conv_2d(inception_7a_3_3_reduce, 96, filter_size=[1,3],  activation='relu',name='inception_7a_3_3_asym_1')
    inception_7a_3_3_asym_2 = conv_2d(inception_7a_3_3_reduce, 96, filter_size=[3,1],  activation='relu',name='inception_7a_3_3_asym_2')
    inception_7a_3_3=merge([inception_7a_3_3_asym_1,inception_7a_3_3_asym_2],mode='concat',axis=3)

    inception_7a_5_5_reduce = conv_2d(inception_5a_output, 66, filter_size=1, activation='relu', name = 'inception_7a_5_5_reduce')
    inception_7a_5_5_asym_1 = conv_2d(inception_7a_5_5_reduce, 96, filter_size=[3,3],  name = 'inception_7a_5_5_asym_1')
    inception_7a_5_5_asym_2 = conv_2d(inception_7a_3_3_asym_1, 96, filter_size=[1,3],  activation='relu',name='inception_7a_5_5_asym_2')
    inception_7a_5_5_asym_3 = conv_2d(inception_7a_3_3_asym_1, 96, filter_size=[3,1],  activation='relu',name='inception_7a_5_5_asym_3')
    inception_7a_5_5=merge([inception_7a_5_5_asym_2,inception_7a_5_5_asym_3],mode='concat',axis=3)


    inception_7a_pool = avg_pool_2d(inception_5a_output, kernel_size=3, strides=1 )
    inception_7a_pool_1_1 = conv_2d(inception_7a_pool, 96, filter_size=1, activation='relu', name='inception_7a_pool_1_1')

    # merge the inception_7a__
    inception_7a_output = merge([inception_7a_1_1, inception_7a_3_3, inception_7a_5_5, inception_7a_pool_1_1], mode='concat', axis=3)



    pool5_7_7=global_avg_pool(inception_7a_output)
    pool5_7_7=dropout(pool5_7_7,0.4)
    loss = fully_connected(pool5_7_7, 2,activation='softmax')

    if(training):
        network = regression(loss, optimizer='rmsprop',

W_drug = tflearn.variables.variable(name="Attn_W_drug",shape=[GRU_size_drug,GRU_size_drug],initializer=tf.random_normal([GRU_size_drug,GRU_size_drug],stddev=0.1),restore=True)
b_drug = tflearn.variables.variable(name="Attn_b_drug",shape=[GRU_size_drug],initializer=tf.random_normal([GRU_size_drug],stddev=0.1),restore=True)
U_drug = tflearn.variables.variable(name="Attn_U_drug",shape=[GRU_size_drug],initializer=tf.random_normal([GRU_size_drug],stddev=0.1),restore=True)
V_drug = tf.tanh(tf.tensordot(drug_GCN_3,W_drug,axes=[[2],[0]])+b_drug)
VU_drug = tf.tensordot(V_drug,U_drug,axes=[[2],[0]])
alphas_drug = tf.nn.softmax(VU_drug,name='alphas')
Attn_drug = tf.reduce_sum(drug_GCN_3 *tf.expand_dims(alphas_drug,-1),1)
drug_reshape_6 = tflearn.reshape(Attn_drug, [-1, GRU_size_drug])




merging =  merge([prot_reshape_6,drug_reshape_6],mode='concat',axis=1)
fc_1 = fully_connected(merging, 600, activation='leakyrelu',weights_init="xavier",name='fully1')
drop_2 = dropout(fc_1, 0.8)
fc_2 = fully_connected(drop_2, 300, activation='leakyrelu',weights_init="xavier",name='fully2')
drop_3 = dropout(fc_2, 0.8)
linear = fully_connected(drop_3, 1, activation='linear',name='fully3')
reg = regression(linear, optimizer='adam', learning_rate=0.001,
                     loss='mean_square', name='target')

# Training
model = tflearn.DNN(reg, tensorboard_verbose=0,tensorboard_dir='./mytensor/',checkpoint_path="./checkpoints/")


######### Setting weights

model.set_weights(prot_embd_W[0],prot_embd_init) 
model.set_weights(prot_gru_1_gate_matrix[0],prot_gru_1_gates_kernel_init)

inception_4c_5_5 = conv_2d(inception_4c_5_5_reduce, 64,  filter_size=5, activation='relu', name='inception_4c_5_5')

    inception_4c_pool = max_pool_2d(inception_4b_output, kernel_size=3, strides=1)
    inception_4c_pool_1_1 = conv_2d(inception_4c_pool, 64, filter_size=1, activation='relu', name='inception_4c_pool_1_1')

    inception_4c_output = merge([inception_4c_1_1, inception_4c_3_3, inception_4c_5_5, inception_4c_pool_1_1], mode='concat', axis=3,name='inception_4c_output')

    inception_4d_1_1 = conv_2d(inception_4c_output, 112, filter_size=1, activation='relu', name='inception_4d_1_1')
    inception_4d_3_3_reduce = conv_2d(inception_4c_output, 144, filter_size=1, activation='relu', name='inception_4d_3_3_reduce')
    inception_4d_3_3 = conv_2d(inception_4d_3_3_reduce, 288, filter_size=3, activation='relu', name='inception_4d_3_3')
    inception_4d_5_5_reduce = conv_2d(inception_4c_output, 32, filter_size=1, activation='relu', name='inception_4d_5_5_reduce')
    inception_4d_5_5 = conv_2d(inception_4d_5_5_reduce, 64, filter_size=5,  activation='relu', name='inception_4d_5_5')
    inception_4d_pool = max_pool_2d(inception_4c_output, kernel_size=3, strides=1,  name='inception_4d_pool')
    inception_4d_pool_1_1 = conv_2d(inception_4d_pool, 64, filter_size=1, activation='relu', name='inception_4d_pool_1_1')

    inception_4d_output = merge([inception_4d_1_1, inception_4d_3_3, inception_4d_5_5, inception_4d_pool_1_1], mode='concat', axis=3, name='inception_4d_output')

    inception_4e_1_1 = conv_2d(inception_4d_output, 256, filter_size=1, activation='relu', name='inception_4e_1_1')
    inception_4e_3_3_reduce = conv_2d(inception_4d_output, 160, filter_size=1, activation='relu', name='inception_4e_3_3_reduce')
    inception_4e_3_3 = conv_2d(inception_4e_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_4e_3_3')
    inception_4e_5_5_reduce = conv_2d(inception_4d_output, 32, filter_size=1, activation='relu', name='inception_4e_5_5_reduce')
    inception_4e_5_5 = conv_2d(inception_4e_5_5_reduce, 128,  filter_size=5, activation='relu', name='inception_4e_5_5')
    inception_4e_pool = max_pool_2d(inception_4d_output, kernel_size=3, strides=1,  name='inception_4e_pool')
    inception_4e_pool_1_1 = conv_2d(inception_4e_pool, 128, filter_size=1, activation='relu', name='inception_4e_pool_1_1')


    inception_4e_output = merge([inception_4e_1_1, inception_4e_3_3, inception_4e_5_5,inception_4e_pool_1_1],axis=3, mode='concat')

    pool4_3_3 = max_pool_2d(inception_4e_output, kernel_size=3, strides=2, name='pool_3_3')


    inception_5a_1_1 = conv_2d(pool4_3_3, 256, filter_size=1, activation='relu', name='inception_5a_1_1')