How to use the tflearn.layers.conv.conv_2d function in tflearn

x3 = x
    x=tflearn.layers.conv.conv_2d(x,256,(5,5),strides=2,activation='relu',weight_decay=1e-5,regularizer='L2')

    #8, 10
    x=tflearn.layers.conv.conv_2d(x,256,(3,3),strides=1,activation='relu',weight_decay=1e-5,regularizer='L2')
    x=tflearn.layers.conv.conv_2d(x,256,(3,3),strides=1,activation='relu',weight_decay=1e-5,regularizer='L2')
    x4 = x
    x=tflearn.layers.conv.conv_2d(x,512,(5,5),strides=2,activation='relu',weight_decay=1e-5,regularizer='L2')

    #4, 5
    x=tflearn.layers.conv.conv_2d(x,512,(3,3),strides=1,activation='relu',weight_decay=1e-5,regularizer='L2')
    x=tflearn.layers.conv.conv_2d(x,512,(3,3),strides=1,activation='relu',weight_decay=1e-5,regularizer='L2')
    x=tflearn.layers.conv.conv_2d_transpose(x,256,[5,5],[8,10],strides=2,activation='linear',weight_decay=1e-5,regularizer='L2')

    #8,10 
    x4=tflearn.layers.conv.conv_2d(x4,256,(3,3),strides=1,activation='linear',weight_decay=1e-5,regularizer='L2')
    x = tf.nn.relu(tf.add(x,x4))
    x=tflearn.layers.conv.conv_2d(x,512,(3,3),strides=1,activation='relu',weight_decay=1e-5,regularizer='L2')  
    x=tflearn.layers.conv.conv_2d(x,256,(3,3),strides=1,activation='relu',weight_decay=1e-5,regularizer='L2')
    x=tflearn.layers.conv.conv_2d_transpose(x,128,[5,5],[15,20],strides=2,activation='linear',weight_decay=1e-5,regularizer='L2')

    #15,20
    x3=tflearn.layers.conv.conv_2d(x3,128,(3,3),strides=1,activation='linear',weight_decay=1e-5,regularizer='L2')
    x = tf.nn.relu(tf.add(x,x3))
    x=tflearn.layers.conv.conv_2d(x,128,(3,3),strides=1,activation='relu',weight_decay=1e-5,regularizer='L2')
    x=tflearn.layers.conv.conv_2d(x,128,(3,3),strides=1,activation='relu',weight_decay=1e-5,regularizer='L2')  
    x=tflearn.layers.conv.conv_2d_transpose(x,64,[5,5],[30,40],strides=2,activation='linear',weight_decay=1e-5,regularizer='L2')
    
    #30,40
    x2=tflearn.layers.conv.conv_2d(x2,64,(3,3),strides=1,activation='linear',weight_decay=1e-5,regularizer='L2')
    x = tf.nn.relu(tf.add(x,x2))
    x=tflearn.layers.conv.conv_2d(x,128,(3,3),strides=1,activation='relu',weight_decay=1e-5,regularizer='L2')

def __create_model(self):
        # Building convolutional network
        network = input_data(shape=[None, IMG_HEIGHT, IMG_WIDTH, 3], name='input')
        network = conv_2d(network, 32, 3, activation='relu', regularizer="L2")
        network = local_response_normalization(network)
        network = conv_2d(network, 64, 3, activation='relu', regularizer="L2")
        network = local_response_normalization(network)
        network = conv_2d(network, 3, 3, activation='relu', regularizer="L2")
        network = regression(network, optimizer='adam', learning_rate=0.01,
                             loss='categorical_crossentropy', name='target')
        model = tflearn.DNN(network, tensorboard_verbose=0)
        return model

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')

# 5a
inception_5a_1_1 = conv_2d(pool4_3_3, 256, filter_size=1, activation='relu', name='inception_5a_1_1')
inception_5a_3_3_reduce = conv_2d(pool4_3_3, 160, filter_size=1, activation='relu', name='inception_5a_3_3_reduce')
inception_5a_3_3 = conv_2d(inception_5a_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_5a_3_3')
inception_5a_5_5_reduce = conv_2d(pool4_3_3, 32, filter_size=1, activation='relu', name='inception_5a_5_5_reduce')
inception_5a_5_5 = conv_2d(inception_5a_5_5_reduce, 128, filter_size=5,  activation='relu', name='inception_5a_5_5')
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)

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')
    inception_5a_3_3_reduce = conv_2d(pool4_3_3, 160, filter_size=1, activation='relu', name='inception_5a_3_3_reduce')
    inception_5a_3_3 = conv_2d(inception_5a_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_5a_3_3')
    inception_5a_5_5_reduce = conv_2d(pool4_3_3, 32, filter_size=1, activation='relu', name='inception_5a_5_5_reduce')
    inception_5a_5_5 = conv_2d(inception_5a_5_5_reduce, 128, filter_size=5,  activation='relu', name='inception_5a_5_5')
    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')


    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)

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


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

network = input_data(shape=[None, 224, 224, 3])

network = conv_2d(network, 64, 3, activation='relu')
network = conv_2d(network, 64, 3, activation='relu')
network = max_pool_2d(network, 2, strides=2)

network = conv_2d(network, 128, 3, activation='relu')
network = conv_2d(network, 128, 3, activation='relu')
network = max_pool_2d(network, 2, strides=2)

network = conv_2d(network, 256, 3, activation='relu')
network = conv_2d(network, 256, 3, activation='relu')
network = conv_2d(network, 256, 3, activation='relu')
network = max_pool_2d(network, 2, strides=2)

network = conv_2d(network, 512, 3, activation='relu')
network = conv_2d(network, 512, 3, activation='relu')
network = conv_2d(network, 512, 3, activation='relu')
network = max_pool_2d(network, 2, strides=2)

network = conv_2d(network, 512, 3, activation='relu')
network = conv_2d(network, 512, 3, activation='relu')
network = conv_2d(network, 512, 3, activation='relu')
network = max_pool_2d(network, 2, strides=2)

network = fully_connected(network, 4096, activation='relu')
network = dropout(network, 0.5)
network = fully_connected(network, 4096, activation='relu')
network = dropout(network, 0.5)
network = fully_connected(network, 17, activation='softmax')

network = regression(network, optimizer='rmsprop',

# 第一层卷积
    network = conv_2d(network, 96, 11, strides=4, activation='relu')
    network = max_pool_2d(network, 3, strides=2)
    network = local_response_normalization(network)
    # 第二层卷积
    network = conv_2d(network, 256, 5, activation='relu')
    network = max_pool_2d(network, 3, strides=2)
    network = local_response_normalization(network)
    # 第三层卷积
    network = conv_2d(network, 384, 3, activation='relu')
    network = max_pool_2d(network, 3, strides=2)
    network = local_response_normalization(network)
    # 第四层卷积
    network = conv_2d(network, 384, 3, activation='relu')
    # 第五层卷积
    network = conv_2d(network, 256, 3, activation='relu')
    network = max_pool_2d(network, 3, strides=2)
    network = local_response_normalization(network)
    # 全连接层1
    network = fully_connected(network, 4096, activation='tanh')
    network = dropout(network, 0.5)
    # 全连接层2
    network = fully_connected(network, 4096, activation='tanh')
    network = dropout(network, 0.5)
    # 输出层
    network = fully_connected(network, 17, activation='softmax')
    network = regression(network, optimizer='momentum', loss='categorical_crossentropy', learning_rate=0.001)
    return network

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')
    inception_5a_3_3_reduce = conv_2d(pool4_3_3, 160, filter_size=1, activation='relu', name='inception_5a_3_3_reduce')
    inception_5a_3_3 = conv_2d(inception_5a_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_5a_3_3')
    inception_5a_5_5_reduce = conv_2d(pool4_3_3, 32, filter_size=1, activation='relu', name='inception_5a_5_5_reduce')
    inception_5a_5_5 = conv_2d(inception_5a_5_5_reduce, 128, filter_size=5,  activation='relu', name='inception_5a_5_5')
    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')

http://arxiv.org/pdf/1409.1556
"""

import tflearn
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.estimator import regression

# Building 'VGG Network'
input_layer = input_data(shape=[None, 224, 224, 3])

block1_conv1 = conv_2d(input_layer, 64, 3, activation='relu', name='block1_conv1')
block1_conv2 = conv_2d(block1_conv1, 64, 3, activation='relu', name='block1_conv2')
block1_pool = max_pool_2d(block1_conv2, 2, strides=2, name='block1_pool')

block2_conv1 = conv_2d(block1_pool, 128, 3, activation='relu', name='block2_conv1')
block2_conv2 = conv_2d(block2_conv1, 128, 3, activation='relu', name='block2_conv2')
block2_pool = max_pool_2d(block2_conv2, 2, strides=2, name='block2_pool')

block3_conv1 = conv_2d(block2_pool, 256, 3, activation='relu', name='block3_conv1')
block3_conv2 = conv_2d(block3_conv1, 256, 3, activation='relu', name='block3_conv2')
block3_conv3 = conv_2d(block3_conv2, 256, 3, activation='relu', name='block3_conv3')
block3_conv4 = conv_2d(block3_conv3, 256, 3, activation='relu', name='block3_conv4')
block3_pool = max_pool_2d(block3_conv4, 2, strides=2, name='block3_pool')

block4_conv1 = conv_2d(block3_pool, 512, 3, activation='relu', name='block4_conv1')
block4_conv2 = conv_2d(block4_conv1, 512, 3, activation='relu', name='block4_conv2')
block4_conv3 = conv_2d(block4_conv2, 512, 3, activation='relu', name='block4_conv3')
block4_conv4 = conv_2d(block4_conv3, 512, 3, activation='relu', name='block4_conv4')
block4_pool = max_pool_2d(block4_conv4, 2, strides=2, name='block4_pool')

block5_conv1 = conv_2d(block4_pool, 512, 3, activation='relu', name='block5_conv1')

inception_5a_3_3 = conv_2d(inception_5a_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_5a_3_3')
    inception_5a_5_5_reduce = conv_2d(pool4_3_3, 32, filter_size=1, activation='relu', name='inception_5a_5_5_reduce')
    inception_5a_5_5 = conv_2d(inception_5a_5_5_reduce, 128, filter_size=5,  activation='relu', name='inception_5a_5_5')
    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')


    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)

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


    
    network = regression(loss, optimizer='momentum',
                         loss='categorical_crossentropy',
                         learning_rate=lr, name='targets')
    
    model = tflearn.DNN(network,
                        max_checkpoints=0, tensorboard_verbose=0,tensorboard_dir='log')

x2, y2, z2  = tf.unstack(frame2_xyz, axis=-1)

  wm =         - x1 * x2 - y1 * y2 - z1 * z2
  xm = w1 * x2           + y1 * z2 - z1 * y2
  ym = w1 * y2           + z1 * x2 - x1 * z2
  zm = w1 * z2           + x1 * y2 - y1 * x2

  x = -wm * x1 + xm * w1 - ym * z1 + zm * y1
  y = -wm * y1 + ym * w1 - zm * x1 + xm * z1
  z = -wm * z1 + zm * w1 - xm * y1 + ym * x1

  x_flow = tf.stack((x,y,z),axis=-1)
  x_flow = x_flow + x_transl - frame2_xyz

  x_center = tflearn.layers.conv.conv_2d(x_s,3,(3,3),strides=1,activation='linear',weight_decay=1e-3,regularizer='L2')
  x_score = tflearn.layers.conv.conv_2d(x_s,2,(3,3),strides=1,activation='linear',weight_decay=1e-3,regularizer='L2')
  x_mask = tflearn.layers.conv.conv_2d(x_s,2,(3,3),strides=1,activation='linear',weight_decay=1e-3,regularizer='L2')
  x_boundary = tflearn.layers.conv.conv_2d(x_s,2,(3,3),strides=1,activation='linear',weight_decay=1e-3,regularizer='L2')
 
  x_center = tf.add(x_center,frame2_xyz)
  xc, yc, zc  = tf.unstack(x_center, axis=-1)

  wmc =         - x1 * xc - y1 * yc - z1 * zc
  xmc = w1 * xc           + y1 * zc - z1 * yc
  ymc = w1 * yc           + z1 * xc - x1 * zc
  zmc = w1 * zc           + x1 * yc - y1 * xc

  xc = -wmc * x1 + xmc * w1 - ymc * z1 + zmc * y1
  yc = -wmc * y1 + ymc * w1 - zmc * x1 + xmc * z1
  zc = -wmc * z1 + zmc * w1 - xmc * y1 + ymc * x1

  x_center_p = tf.stack((xc,yc,zc),axis=-1)