How to use the tflearn.layers.conv.conv_3d function in tflearn

pool4_3_3 = max_pool_3d(inception_4e_output, kernel_size=3, strides=2, name='pool_3_3')


    inception_5a_1_1 = conv_3d(pool4_3_3, 256, filter_size=1, activation='relu', name='inception_5a_1_1')
    inception_5a_3_3_reduce = conv_3d(pool4_3_3, 160, filter_size=1, activation='relu', name='inception_5a_3_3_reduce')
    inception_5a_3_3 = conv_3d(inception_5a_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_5a_3_3')
    inception_5a_5_5_reduce = conv_3d(pool4_3_3, 32, filter_size=1, activation='relu', name='inception_5a_5_5_reduce')
    inception_5a_5_5 = conv_3d(inception_5a_5_5_reduce, 128, filter_size=5,  activation='relu', name='inception_5a_5_5')
    inception_5a_pool = max_pool_3d(pool4_3_3, kernel_size=3, strides=1,  name='inception_5a_pool')
    inception_5a_pool_1_1 = conv_3d(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=4,mode='concat')


    inception_5b_1_1 = conv_3d(inception_5a_output, 384, filter_size=1,activation='relu', name='inception_5b_1_1')
    inception_5b_3_3_reduce = conv_3d(inception_5a_output, 192, filter_size=1, activation='relu', name='inception_5b_3_3_reduce')
    inception_5b_3_3 = conv_3d(inception_5b_3_3_reduce, 384,  filter_size=3,activation='relu', name='inception_5b_3_3')
    inception_5b_5_5_reduce = conv_3d(inception_5a_output, 48, filter_size=1, activation='relu', name='inception_5b_5_5_reduce')
    inception_5b_5_5 = conv_3d(inception_5b_5_5_reduce,128, filter_size=5,  activation='relu', name='inception_5b_5_5' )
    inception_5b_pool = max_pool_3d(inception_5a_output, kernel_size=3, strides=1,  name='inception_5b_pool')
    inception_5b_pool_1_1 = conv_3d(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=4, mode='concat')

    pool5_7_7 = avg_pool_3d(inception_5b_output, kernel_size=7, strides=1)
    pool5_7_7 = dropout(pool5_7_7, 0.4)

    
    loss = fully_connected(pool5_7_7, output,activation='softmax')


    
    network = regression(loss, optimizer='momentum',

inception_3b_1_1 = conv_3d(inception_3a_output, 128,filter_size=1,activation='relu', name= 'inception_3b_1_1' )
    inception_3b_3_3_reduce = conv_3d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_3_3_reduce')
    inception_3b_3_3 = conv_3d(inception_3b_3_3_reduce, 192, filter_size=3,  activation='relu',name='inception_3b_3_3')
    inception_3b_5_5_reduce = conv_3d(inception_3a_output, 32, filter_size=1, activation='relu', name = 'inception_3b_5_5_reduce')
    inception_3b_5_5 = conv_3d(inception_3b_5_5_reduce, 96, filter_size=5,  name = 'inception_3b_5_5')
    inception_3b_pool = max_pool_3d(inception_3a_output, kernel_size=3, strides=1,  name='inception_3b_pool')
    inception_3b_pool_1_1 = conv_3d(inception_3b_pool, 64, filter_size=1,activation='relu', name='inception_3b_pool_1_1')

    #merge the inception_3b_*
    inception_3b_output = merge([inception_3b_1_1, inception_3b_3_3, inception_3b_5_5, inception_3b_pool_1_1], mode='concat',axis=4,name='inception_3b_output')

    pool3_3_3 = max_pool_3d(inception_3b_output, kernel_size=3, strides=2, name='pool3_3_3')
    inception_4a_1_1 = conv_3d(pool3_3_3, 192, filter_size=1, activation='relu', name='inception_4a_1_1')
    inception_4a_3_3_reduce = conv_3d(pool3_3_3, 96, filter_size=1, activation='relu', name='inception_4a_3_3_reduce')
    inception_4a_3_3 = conv_3d(inception_4a_3_3_reduce, 208, filter_size=3,  activation='relu', name='inception_4a_3_3')
    inception_4a_5_5_reduce = conv_3d(pool3_3_3, 16, filter_size=1, activation='relu', name='inception_4a_5_5_reduce')
    inception_4a_5_5 = conv_3d(inception_4a_5_5_reduce, 48, filter_size=5,  activation='relu', name='inception_4a_5_5')
    inception_4a_pool = max_pool_3d(pool3_3_3, kernel_size=3, strides=1,  name='inception_4a_pool')
    inception_4a_pool_1_1 = conv_3d(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=4, name='inception_4a_output')


    inception_4b_1_1 = conv_3d(inception_4a_output, 160, filter_size=1, activation='relu', name='inception_4a_1_1')
    inception_4b_3_3_reduce = conv_3d(inception_4a_output, 112, filter_size=1, activation='relu', name='inception_4b_3_3_reduce')
    inception_4b_3_3 = conv_3d(inception_4b_3_3_reduce, 224, filter_size=3, activation='relu', name='inception_4b_3_3')
    inception_4b_5_5_reduce = conv_3d(inception_4a_output, 24, filter_size=1, activation='relu', name='inception_4b_5_5_reduce')
    inception_4b_5_5 = conv_3d(inception_4b_5_5_reduce, 64, filter_size=5,  activation='relu', name='inception_4b_5_5')

    inception_4b_pool = max_pool_3d(inception_4a_output, kernel_size=3, strides=1,  name='inception_4b_pool')
    inception_4b_pool_1_1 = conv_3d(inception_4b_pool, 64, filter_size=1, activation='relu', name='inception_4b_pool_1_1')

def sentnet_v0(width, height, frame_count, lr, output=9):
    network = input_data(shape=[None, width, height, frame_count, 1], name='input')
    network = conv_3d(network, 96, 11, strides=4, activation='relu')
    network = max_pool_3d(network, 3, strides=2)
    
    #network = local_response_normalization(network)
    
    network = conv_3d(network, 256, 5, activation='relu')
    network = max_pool_3d(network, 3, strides=2)

    #network = local_response_normalization(network)
    
    network = conv_3d(network, 384, 3, 3, activation='relu')
    network = conv_3d(network, 384, 3, 3, activation='relu')
    network = conv_3d(network, 256, 3, 3, activation='relu')

    network = max_pool_3d(network, 3, strides=2)

    #network = local_response_normalization(network)
    
    network = fully_connected(network, 4096, activation='tanh')
    network = dropout(network, 0.5)
    network = fully_connected(network, 4096, activation='tanh')
    network = dropout(network, 0.5)

def inception_v3_3d(width, height, frame_count, lr, output=9, model_name = 'sentnet_color.model'):
    network = input_data(shape=[None, width, height,3, 1], name='input')
    conv1_7_7 = conv_3d(network, 64, 7, strides=2, activation='relu', name = 'conv1_7_7_s2')
    pool1_3_3 = max_pool_3d(conv1_7_7, 3,strides=2)
    #pool1_3_3 = local_response_normalization(pool1_3_3)
    conv2_3_3_reduce = conv_3d(pool1_3_3, 64,1, activation='relu',name = 'conv2_3_3_reduce')
    conv2_3_3 = conv_3d(conv2_3_3_reduce, 192,3, activation='relu', name='conv2_3_3')
    #conv2_3_3 = local_response_normalization(conv2_3_3)
    pool2_3_3 = max_pool_3d(conv2_3_3, kernel_size=3, strides=2, name='pool2_3_3_s2')
    inception_3a_1_1 = conv_3d(pool2_3_3, 64, 1, activation='relu', name='inception_3a_1_1')
    inception_3a_3_3_reduce = conv_3d(pool2_3_3, 96,1, activation='relu', name='inception_3a_3_3_reduce')
    inception_3a_3_3 = conv_3d(inception_3a_3_3_reduce, 128,filter_size=3,  activation='relu', name = 'inception_3a_3_3')
    inception_3a_5_5_reduce = conv_3d(pool2_3_3,16, filter_size=1,activation='relu', name ='inception_3a_5_5_reduce' )
    inception_3a_5_5 = conv_3d(inception_3a_5_5_reduce, 32, filter_size=5, activation='relu', name= 'inception_3a_5_5')
    inception_3a_pool = max_pool_3d(pool2_3_3, kernel_size=3, strides=1, )
    inception_3a_pool_1_1 = conv_3d(inception_3a_pool, 32, filter_size=1, activation='relu', name='inception_3a_pool_1_1')

    # merge the inception_3a__
    inception_3a_output = merge([inception_3a_1_1, inception_3a_3_3, inception_3a_5_5, inception_3a_pool_1_1], mode='concat', axis=4)

    inception_3b_1_1 = conv_3d(inception_3a_output, 128,filter_size=1,activation='relu', name= 'inception_3b_1_1' )
    inception_3b_3_3_reduce = conv_3d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_3_3_reduce')
    inception_3b_3_3 = conv_3d(inception_3b_3_3_reduce, 192, filter_size=3,  activation='relu',name='inception_3b_3_3')
    inception_3b_5_5_reduce = conv_3d(inception_3a_output, 32, filter_size=1, activation='relu', name = 'inception_3b_5_5_reduce')
    inception_3b_5_5 = conv_3d(inception_3b_5_5_reduce, 96, filter_size=5,  name = 'inception_3b_5_5')
    inception_3b_pool = max_pool_3d(inception_3a_output, kernel_size=3, strides=1,  name='inception_3b_pool')

def sentnet2(width, height, frame_count, lr, output=9):
    network = input_data(shape=[None, width, height, frame_count, 1], name='input')
    network = conv_3d(network, 96, 11, strides=4, activation='relu')
    network = max_pool_3d(network, 3, strides=2)
    #network = local_response_normalization(network)
    network = conv_3d(network, 256, 5, activation='relu')
    network = max_pool_3d(network, 3, strides=2)
    #network = local_response_normalization(network)
    network = conv_3d(network, 384, 3, activation='relu')
    network = conv_3d(network, 384, 3, activation='relu')
    network = conv_3d(network, 256, 3, activation='relu')
    network = max_pool_3d(network, 3, strides=2)
    #network = local_response_normalization(network)
    network = fully_connected(network, 4096, activation='tanh')
    network = dropout(network, 0.5)
    network = fully_connected(network, 4096, activation='tanh')
    network = dropout(network, 0.5)
    network = fully_connected(network, 3, activation='softmax')
    network = regression(network, optimizer='momentum',

inception_3b_output = merge([inception_3b_1_1, inception_3b_3_3, inception_3b_5_5, inception_3b_pool_1_1], mode='concat',axis=4,name='inception_3b_output')

    pool3_3_3 = max_pool_3d(inception_3b_output, kernel_size=3, strides=2, name='pool3_3_3')
    inception_4a_1_1 = conv_3d(pool3_3_3, 192, filter_size=1, activation='relu', name='inception_4a_1_1')
    inception_4a_3_3_reduce = conv_3d(pool3_3_3, 96, filter_size=1, activation='relu', name='inception_4a_3_3_reduce')
    inception_4a_3_3 = conv_3d(inception_4a_3_3_reduce, 208, filter_size=3,  activation='relu', name='inception_4a_3_3')
    inception_4a_5_5_reduce = conv_3d(pool3_3_3, 16, filter_size=1, activation='relu', name='inception_4a_5_5_reduce')
    inception_4a_5_5 = conv_3d(inception_4a_5_5_reduce, 48, filter_size=5,  activation='relu', name='inception_4a_5_5')
    inception_4a_pool = max_pool_3d(pool3_3_3, kernel_size=3, strides=1,  name='inception_4a_pool')
    inception_4a_pool_1_1 = conv_3d(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=4, name='inception_4a_output')


    inception_4b_1_1 = conv_3d(inception_4a_output, 160, filter_size=1, activation='relu', name='inception_4a_1_1')
    inception_4b_3_3_reduce = conv_3d(inception_4a_output, 112, filter_size=1, activation='relu', name='inception_4b_3_3_reduce')
    inception_4b_3_3 = conv_3d(inception_4b_3_3_reduce, 224, filter_size=3, activation='relu', name='inception_4b_3_3')
    inception_4b_5_5_reduce = conv_3d(inception_4a_output, 24, filter_size=1, activation='relu', name='inception_4b_5_5_reduce')
    inception_4b_5_5 = conv_3d(inception_4b_5_5_reduce, 64, filter_size=5,  activation='relu', name='inception_4b_5_5')

    inception_4b_pool = max_pool_3d(inception_4a_output, kernel_size=3, strides=1,  name='inception_4b_pool')
    inception_4b_pool_1_1 = conv_3d(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=4, name='inception_4b_output')


    inception_4c_1_1 = conv_3d(inception_4b_output, 128, filter_size=1, activation='relu',name='inception_4c_1_1')
    inception_4c_3_3_reduce = conv_3d(inception_4b_output, 128, filter_size=1, activation='relu', name='inception_4c_3_3_reduce')
    inception_4c_3_3 = conv_3d(inception_4c_3_3_reduce, 256,  filter_size=3, activation='relu', name='inception_4c_3_3')
    inception_4c_5_5_reduce = conv_3d(inception_4b_output, 24, filter_size=1, activation='relu', name='inception_4c_5_5_reduce')
    inception_4c_5_5 = conv_3d(inception_4c_5_5_reduce, 64,  filter_size=5, activation='relu', name='inception_4c_5_5')

inception_4c_3_3 = conv_3d(inception_4c_3_3_reduce, 256,  filter_size=3, activation='relu', name='inception_4c_3_3')
    inception_4c_5_5_reduce = conv_3d(inception_4b_output, 24, filter_size=1, activation='relu', name='inception_4c_5_5_reduce')
    inception_4c_5_5 = conv_3d(inception_4c_5_5_reduce, 64,  filter_size=5, activation='relu', name='inception_4c_5_5')

    inception_4c_pool = max_pool_3d(inception_4b_output, kernel_size=3, strides=1)
    inception_4c_pool_1_1 = conv_3d(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=4,name='inception_4c_output')

    inception_4d_1_1 = conv_3d(inception_4c_output, 112, filter_size=1, activation='relu', name='inception_4d_1_1')
    inception_4d_3_3_reduce = conv_3d(inception_4c_output, 144, filter_size=1, activation='relu', name='inception_4d_3_3_reduce')
    inception_4d_3_3 = conv_3d(inception_4d_3_3_reduce, 288, filter_size=3, activation='relu', name='inception_4d_3_3')
    inception_4d_5_5_reduce = conv_3d(inception_4c_output, 32, filter_size=1, activation='relu', name='inception_4d_5_5_reduce')
    inception_4d_5_5 = conv_3d(inception_4d_5_5_reduce, 64, filter_size=5,  activation='relu', name='inception_4d_5_5')
    inception_4d_pool = max_pool_3d(inception_4c_output, kernel_size=3, strides=1,  name='inception_4d_pool')
    inception_4d_pool_1_1 = conv_3d(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=4, name='inception_4d_output')

    inception_4e_1_1 = conv_3d(inception_4d_output, 256, filter_size=1, activation='relu', name='inception_4e_1_1')
    inception_4e_3_3_reduce = conv_3d(inception_4d_output, 160, filter_size=1, activation='relu', name='inception_4e_3_3_reduce')
    inception_4e_3_3 = conv_3d(inception_4e_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_4e_3_3')
    inception_4e_5_5_reduce = conv_3d(inception_4d_output, 32, filter_size=1, activation='relu', name='inception_4e_5_5_reduce')
    inception_4e_5_5 = conv_3d(inception_4e_5_5_reduce, 128,  filter_size=5, activation='relu', name='inception_4e_5_5')
    inception_4e_pool = max_pool_3d(inception_4d_output, kernel_size=3, strides=1,  name='inception_4e_pool')
    inception_4e_pool_1_1 = conv_3d(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=4, mode='concat')

    pool4_3_3 = max_pool_3d(inception_4e_output, kernel_size=3, strides=2, name='pool_3_3')

inception_4c_pool_1_1 = conv_3d(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=4,name='inception_4c_output')

    inception_4d_1_1 = conv_3d(inception_4c_output, 112, filter_size=1, activation='relu', name='inception_4d_1_1')
    inception_4d_3_3_reduce = conv_3d(inception_4c_output, 144, filter_size=1, activation='relu', name='inception_4d_3_3_reduce')
    inception_4d_3_3 = conv_3d(inception_4d_3_3_reduce, 288, filter_size=3, activation='relu', name='inception_4d_3_3')
    inception_4d_5_5_reduce = conv_3d(inception_4c_output, 32, filter_size=1, activation='relu', name='inception_4d_5_5_reduce')
    inception_4d_5_5 = conv_3d(inception_4d_5_5_reduce, 64, filter_size=5,  activation='relu', name='inception_4d_5_5')
    inception_4d_pool = max_pool_3d(inception_4c_output, kernel_size=3, strides=1,  name='inception_4d_pool')
    inception_4d_pool_1_1 = conv_3d(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=4, name='inception_4d_output')

    inception_4e_1_1 = conv_3d(inception_4d_output, 256, filter_size=1, activation='relu', name='inception_4e_1_1')
    inception_4e_3_3_reduce = conv_3d(inception_4d_output, 160, filter_size=1, activation='relu', name='inception_4e_3_3_reduce')
    inception_4e_3_3 = conv_3d(inception_4e_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_4e_3_3')
    inception_4e_5_5_reduce = conv_3d(inception_4d_output, 32, filter_size=1, activation='relu', name='inception_4e_5_5_reduce')
    inception_4e_5_5 = conv_3d(inception_4e_5_5_reduce, 128,  filter_size=5, activation='relu', name='inception_4e_5_5')
    inception_4e_pool = max_pool_3d(inception_4d_output, kernel_size=3, strides=1,  name='inception_4e_pool')
    inception_4e_pool_1_1 = conv_3d(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=4, mode='concat')

    pool4_3_3 = max_pool_3d(inception_4e_output, kernel_size=3, strides=2, name='pool_3_3')


    inception_5a_1_1 = conv_3d(pool4_3_3, 256, filter_size=1, activation='relu', name='inception_5a_1_1')
    inception_5a_3_3_reduce = conv_3d(pool4_3_3, 160, filter_size=1, activation='relu', name='inception_5a_3_3_reduce')
    inception_5a_3_3 = conv_3d(inception_5a_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_5a_3_3')
    inception_5a_5_5_reduce = conv_3d(pool4_3_3, 32, filter_size=1, activation='relu', name='inception_5a_5_5_reduce')

def sentnet2(width, height, frame_count, lr, output=9):
    network = input_data(shape=[None, width, height, frame_count, 1], name='input')
    network = conv_3d(network, 96, 11, strides=4, activation='relu')
    network = max_pool_3d(network, 3, strides=2)
    #network = local_response_normalization(network)
    network = conv_3d(network, 256, 5, activation='relu')
    network = max_pool_3d(network, 3, strides=2)
    #network = local_response_normalization(network)
    network = conv_3d(network, 384, 3, activation='relu')
    network = conv_3d(network, 384, 3, activation='relu')
    network = conv_3d(network, 256, 3, activation='relu')
    network = max_pool_3d(network, 3, strides=2)
    #network = local_response_normalization(network)
    network = fully_connected(network, 4096, activation='tanh')
    network = dropout(network, 0.5)
    network = fully_connected(network, 4096, activation='tanh')
    network = dropout(network, 0.5)
    network = fully_connected(network, 3, activation='softmax')
    network = regression(network, optimizer='momentum',
                         loss='categorical_crossentropy',
                         learning_rate=lr, name='targets')

inception_4c_output = merge([inception_4c_1_1, inception_4c_3_3, inception_4c_5_5, inception_4c_pool_1_1], mode='concat', axis=4,name='inception_4c_output')

    inception_4d_1_1 = conv_3d(inception_4c_output, 112, filter_size=1, activation='relu', name='inception_4d_1_1')
    inception_4d_3_3_reduce = conv_3d(inception_4c_output, 144, filter_size=1, activation='relu', name='inception_4d_3_3_reduce')
    inception_4d_3_3 = conv_3d(inception_4d_3_3_reduce, 288, filter_size=3, activation='relu', name='inception_4d_3_3')
    inception_4d_5_5_reduce = conv_3d(inception_4c_output, 32, filter_size=1, activation='relu', name='inception_4d_5_5_reduce')
    inception_4d_5_5 = conv_3d(inception_4d_5_5_reduce, 64, filter_size=5,  activation='relu', name='inception_4d_5_5')
    inception_4d_pool = max_pool_3d(inception_4c_output, kernel_size=3, strides=1,  name='inception_4d_pool')
    inception_4d_pool_1_1 = conv_3d(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=4, name='inception_4d_output')

    inception_4e_1_1 = conv_3d(inception_4d_output, 256, filter_size=1, activation='relu', name='inception_4e_1_1')
    inception_4e_3_3_reduce = conv_3d(inception_4d_output, 160, filter_size=1, activation='relu', name='inception_4e_3_3_reduce')
    inception_4e_3_3 = conv_3d(inception_4e_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_4e_3_3')
    inception_4e_5_5_reduce = conv_3d(inception_4d_output, 32, filter_size=1, activation='relu', name='inception_4e_5_5_reduce')
    inception_4e_5_5 = conv_3d(inception_4e_5_5_reduce, 128,  filter_size=5, activation='relu', name='inception_4e_5_5')
    inception_4e_pool = max_pool_3d(inception_4d_output, kernel_size=3, strides=1,  name='inception_4e_pool')
    inception_4e_pool_1_1 = conv_3d(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=4, mode='concat')

    pool4_3_3 = max_pool_3d(inception_4e_output, kernel_size=3, strides=2, name='pool_3_3')


    inception_5a_1_1 = conv_3d(pool4_3_3, 256, filter_size=1, activation='relu', name='inception_5a_1_1')
    inception_5a_3_3_reduce = conv_3d(pool4_3_3, 160, filter_size=1, activation='relu', name='inception_5a_3_3_reduce')
    inception_5a_3_3 = conv_3d(inception_5a_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_5a_3_3')
    inception_5a_5_5_reduce = conv_3d(pool4_3_3, 32, filter_size=1, activation='relu', name='inception_5a_5_5_reduce')
    inception_5a_5_5 = conv_3d(inception_5a_5_5_reduce, 128, filter_size=5,  activation='relu', name='inception_5a_5_5')
    inception_5a_pool = max_pool_3d(pool4_3_3, kernel_size=3, strides=1,  name='inception_5a_pool')