How to use the tflearn.layers.conv.max_pool_2d function in tflearn

import tflearn.datasets.mnist as mnist
            X, Y, testX, testY = mnist.load_data(one_hot=True)
            X = X.reshape([-1, 28, 28, 1])
            testX = testX.reshape([-1, 28, 28, 1])
            X = X[:20, :, :, :]
            Y = Y[:20, :]
            testX = testX[:10, :, :, :]
            testY = testY[:10, :]
            
            # Building convolutional network
            network = input_data(shape=[None, 28, 28, 1], name='input')
            network = conv_2d(network, 32, 3, activation='relu', regularizer="L2")
            network = max_pool_2d(network, 2)
            network = local_response_normalization(network)
            network = conv_2d(network, 64, 3, activation='relu', regularizer="L2")
            network = max_pool_2d(network, 2)
            network = local_response_normalization(network)
            network = fully_connected(network, 128, activation='tanh')
            network = dropout(network, 0.8)
            network = fully_connected(network, 256, activation='tanh')
            network = dropout(network, 0.8)
            network = fully_connected(network, 10, activation='softmax')
            network = regression(network, optimizer='adam', learning_rate=0.01,
                                 loss='categorical_crossentropy', name='target')
            
            # Training
            model = tflearn.DNN(network, tensorboard_verbose=3)
            model.fit({'input': X}, {'target': Y}, n_epoch=1,
                      batch_size=10,
                      validation_set=({'input': testX}, {'target': testY}),
                      validation_batch_size=5,
                      snapshot_step=10, show_metric=True, run_id='convnet_mnist_vbs')

def build_model(self):
        convnet = input_data(shape=[None, self.image_size, self.image_size, 3], name='input')
        convnet = conv_2d(convnet, 32, 5, activation='relu')
        convnet = max_pool_2d(convnet, 5)
        convnet = conv_2d(convnet, 64, 5, activation='relu')
        convnet = max_pool_2d(convnet, 5)
        convnet = conv_2d(convnet, 128, 5, activation='relu')
        convnet = max_pool_2d(convnet, 5)
        convnet = conv_2d(convnet, 64, 5, activation='relu')
        convnet = max_pool_2d(convnet, 5)
        convnet = conv_2d(convnet, 32, 5, activation='relu')
        convnet = max_pool_2d(convnet, 5)
        convnet = fully_connected(convnet, 1024, activation='relu')
        convnet = dropout(convnet, 0.8)
        convnet = fully_connected(convnet, len(self.classes), activation='softmax')
        convnet = regression(convnet, optimizer='adam', learning_rate=self.learning_rate, loss='categorical_crossentropy',
                             name='targets')
        model = tflearn.DNN(convnet, tensorboard_dir='log')
        return model

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')


        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)

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')
block5_conv2 = conv_2d(block5_conv1, 512, 3, activation='relu', name='block5_conv2')
block5_conv3 = conv_2d(block5_conv2, 512, 3, activation='relu', name='block5_conv3')
block5_conv4 = conv_2d(block5_conv3, 512, 3, activation='relu', name='block5_conv4')
block4_pool = max_pool_2d(block5_conv4, 2, strides=2, name='block4_pool')
flatten_layer = tflearn.layers.core.flatten(block4_pool, name='Flatten')

fc1 = fully_connected(flatten_layer, 4096, activation='relu')
dp1 = dropout(fc1, 0.5)
fc2 = fully_connected(dp1, 4096, activation='relu')
dp2 = dropout(fc2, 0.5)

network = fully_connected(dp2, 1000, activation='rmsprop')

regression = tflearn.regression(network, optimizer='adam',
                                loss='categorical_crossentropy',
                                learning_rate=0.001)

model = tflearn.DNN(regression, checkpoint_path='vgg19',
                    tensorboard_dir="./logs")

x = tf.placeholder(tf.float32, shape=[None, 1024])
        y_ = tf.placeholder(tf.float32, shape=[None, 369])
        net = tf.reshape(x, [-1, 32, 32, 1])
        net = tflearn.layers.conv.conv_2d(net,
                                          nb_filter=32,
                                          filter_size=3,
                                          activation='relu',
                                          strides=1,
                                          weight_decay=0.0)
        net = tflearn.layers.conv.conv_2d(net,
                                          nb_filter=32,
                                          filter_size=3,
                                          activation='relu',
                                          strides=1,
                                          weight_decay=0.0)
        net = tflearn.layers.conv.max_pool_2d(net,
                                              kernel_size=2,
                                              strides=2,
                                              padding='same',
                                              name='MaxPool2D')
        net = tflearn.layers.conv.conv_2d(net,
                                          nb_filter=64,
                                          filter_size=3,
                                          activation='relu',
                                          strides=1,
                                          weight_decay=0.0)
        net = tflearn.layers.conv.conv_2d(net,
                                          nb_filter=64,
                                          filter_size=3,
                                          activation='relu',
                                          strides=1,
                                          weight_decay=0.0)

from tflearn.layers.normalization import local_response_normalization
from tflearn.layers.merge_ops import merge
from tflearn.layers.estimator import regression

import tflearn.datasets.oxflower17 as oxflower17

X, Y = oxflower17.load_data(one_hot=True, resize_pics=(227, 227))

network = input_data(shape=[None, 227, 227, 3])
conv1_7_7 = conv_2d(network, 64, 7, strides=2, activation='relu', name='conv1_7_7_s2')
pool1_3_3 = max_pool_2d(conv1_7_7, 3, strides=2)
pool1_3_3 = local_response_normalization(pool1_3_3)
conv2_3_3_reduce = conv_2d(pool1_3_3, 64, 1, activation='relu', name='conv2_3_3_reduce')
conv2_3_3 = conv_2d(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_2d(conv2_3_3, kernel_size=3, strides=2, name='pool2_3_3_s2')

# 3a
inception_3a_1_1 = conv_2d(pool2_3_3, 64, 1, activation='relu', name='inception_3a_1_1')
inception_3a_3_3_reduce = conv_2d(pool2_3_3, 96, 1, activation='relu', name='inception_3a_3_3_reduce')
inception_3a_3_3 = conv_2d(inception_3a_3_3_reduce, 128, filter_size=3, activation='relu', name='inception_3a_3_3')
inception_3a_5_5_reduce = conv_2d(pool2_3_3, 16, filter_size=1, activation='relu', name='inception_3a_5_5_reduce')
inception_3a_5_5 = conv_2d(inception_3a_5_5_reduce, 32, filter_size=5, activation='relu', name='inception_3a_5_5')
inception_3a_pool = max_pool_2d(pool2_3_3, kernel_size=3, strides=1, name='inception_3a_pool')
inception_3a_pool_1_1 = conv_2d(inception_3a_pool, 32, filter_size=1, activation='relu', name='inception_3a_pool_1_1')
inception_3a_output = merge([inception_3a_1_1, inception_3a_3_3, inception_3a_5_5, inception_3a_pool_1_1],
                            mode='concat', axis=3)

# 3b
inception_3b_1_1 = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_1_1')
inception_3b_3_3_reduce = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu',
                                  name='inception_3b_3_3_reduce')

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

# Data loading and preprocessing
import tflearn.datasets.oxflower17 as oxflower17
X, Y = oxflower17.load_data(one_hot=True)

# Building 'VGG Network'
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')

def model_for_type(neural_net_type, tile_size, on_band_count):
    """The neural_net_type can be: one_layer_relu,
                                   one_layer_relu_conv,
                                   two_layer_relu_conv."""
    network = tflearn.input_data(shape=[None, tile_size, tile_size, on_band_count])

    # NN architectures mirror ch. 3 of www.cs.toronto.edu/~vmnih/docs/Mnih_Volodymyr_PhD_Thesis.pdf
    if neural_net_type == 'one_layer_relu':
        network = tflearn.fully_connected(network, 64, activation='relu')
    elif neural_net_type == 'one_layer_relu_conv':
        network = conv_2d(network, 64, 12, strides=4, activation='relu')
        network = max_pool_2d(network, 3)
    elif neural_net_type == 'two_layer_relu_conv':
        network = conv_2d(network, 64, 12, strides=4, activation='relu')
        network = max_pool_2d(network, 3)
        network = conv_2d(network, 128, 4, activation='relu')
    else:
        print("ERROR: exiting, unknown layer type for neural net")

    # classify as road or not road
    softmax = tflearn.fully_connected(network, 2, activation='softmax')

    # hyperparameters based on www.cs.toronto.edu/~vmnih/docs/Mnih_Volodymyr_PhD_Thesis.pdf
    momentum = tflearn.optimizers.Momentum(
        learning_rate=.005, momentum=0.9,
        lr_decay=0.0002, name='Momentum')

    net = tflearn.regression(softmax, optimizer=momentum, loss='categorical_crossentropy')

    return tflearn.DNN(net, tensorboard_verbose=0)

def construct_inceptionv1onfire (x,y):

    # Build network
    # 227 x 227 original size
    network = input_data(shape=[None, y, x, 3])

    conv1_7_7 = conv_2d(network, 64, 5, strides=2, activation='relu', name = 'conv1_7_7_s2')

    pool1_3_3 = max_pool_2d(conv1_7_7, 3,strides=2)
    pool1_3_3 = local_response_normalization(pool1_3_3)

    conv2_3_3_reduce = conv_2d(pool1_3_3, 64,1, activation='relu',name = 'conv2_3_3_reduce')
    conv2_3_3 = conv_2d(conv2_3_3_reduce, 128,3, activation='relu', name='conv2_3_3')

    conv2_3_3 = local_response_normalization(conv2_3_3)
    pool2_3_3 = max_pool_2d(conv2_3_3, kernel_size=3, strides=2, name='pool2_3_3_s2')

    inception_3a_1_1 = conv_2d(pool2_3_3, 64, 1, activation='relu', name='inception_3a_1_1')

    inception_3a_3_3_reduce = conv_2d(pool2_3_3, 96,1, activation='relu', name='inception_3a_3_3_reduce')
    inception_3a_3_3 = conv_2d(inception_3a_3_3_reduce, 128,filter_size=3,  activation='relu', name = 'inception_3a_3_3')
    inception_3a_5_5_reduce = conv_2d(pool2_3_3,16, filter_size=1,activation='relu', name ='inception_3a_5_5_reduce' )
    inception_3a_5_5 = conv_2d(inception_3a_5_5_reduce, 32, filter_size=5, activation='relu', name= 'inception_3a_5_5')
    inception_3a_pool = max_pool_2d(pool2_3_3, kernel_size=3, strides=1, )
    inception_3a_pool_1_1 = conv_2d(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=3)

    inception_3b_1_1 = conv_2d(inception_3a_output, 128,filter_size=1,activation='relu', name= 'inception_3b_1_1' )
    inception_3b_3_3_reduce = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_3_3_reduce')
    inception_3b_3_3 = conv_2d(inception_3b_3_3_reduce, 192, filter_size=3,  activation='relu',name='inception_3b_3_3')

from __future__ import division, print_function, absolute_import

import tflearn
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.conv import conv_2d, max_pool_2d, avg_pool_2d
from tflearn.layers.normalization import local_response_normalization
from tflearn.layers.merge_ops import merge
from tflearn.layers.estimator import regression

import tflearn.datasets.oxflower17 as oxflower17

X, Y = oxflower17.load_data(one_hot=True, resize_pics=(227, 227))

network = input_data(shape=[None, 227, 227, 3])
conv1_7_7 = conv_2d(network, 64, 7, strides=2, activation='relu', name='conv1_7_7_s2')
pool1_3_3 = max_pool_2d(conv1_7_7, 3, strides=2)
pool1_3_3 = local_response_normalization(pool1_3_3)
conv2_3_3_reduce = conv_2d(pool1_3_3, 64, 1, activation='relu', name='conv2_3_3_reduce')
conv2_3_3 = conv_2d(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_2d(conv2_3_3, kernel_size=3, strides=2, name='pool2_3_3_s2')

# 3a
inception_3a_1_1 = conv_2d(pool2_3_3, 64, 1, activation='relu', name='inception_3a_1_1')
inception_3a_3_3_reduce = conv_2d(pool2_3_3, 96, 1, activation='relu', name='inception_3a_3_3_reduce')
inception_3a_3_3 = conv_2d(inception_3a_3_3_reduce, 128, filter_size=3, activation='relu', name='inception_3a_3_3')
inception_3a_5_5_reduce = conv_2d(pool2_3_3, 16, filter_size=1, activation='relu', name='inception_3a_5_5_reduce')
inception_3a_5_5 = conv_2d(inception_3a_5_5_reduce, 32, filter_size=5, activation='relu', name='inception_3a_5_5')
inception_3a_pool = max_pool_2d(pool2_3_3, kernel_size=3, strides=1, name='inception_3a_pool')
inception_3a_pool_1_1 = conv_2d(inception_3a_pool, 32, filter_size=1, activation='relu', name='inception_3a_pool_1_1')
inception_3a_output = merge([inception_3a_1_1, inception_3a_3_3, inception_3a_5_5, inception_3a_pool_1_1],
                            mode='concat', axis=3)