some of the inception v3 and v4 blocks implemented in TFLearn. I hope this helps someone!

cnn_blocks.py

import tflearn
from tflearn.layers.conv import conv_2d, max_pool_2d, avg_pool_2d
from tflearn.layers.merge_ops import merge
from tflearn.layers.normalization import local_response_normalization


def inceptionv3_block(input_block, filter_width=1, stride=1, 
                      activation='relu', regularizer='L2', normalise=False):

    # 1x1 convolutions
    conv1x1_a = conv_2d(input_block, 64*filter_width, 1, stride,
                      activation=activation, regularizer=regularizer)

    # 3x3 convolutions
    conv1x1_b = conv_2d(input_block, 96*filter_width, 1, stride, 
                      activation='linear', regularizer=regularizer)
    conv3x3_b = conv_2d(conv1x1_b, 128*filter_width, 3, stride, 
                      activation=activation, regularizer=regularizer)    

    # 5x5 convolutions
    conv1x1_c = conv_2d(input_block, 16*filter_width, 1, stride, 
                      activation='linear', regularizer=regularizer)
    conv5x5_c = conv_2d(conv1x1_c, 32*filter_width, 5, stride, 
                      activation=activation, regularizer=regularizer)    

    # maxpool layer
    max_pool_d = max_pool_2d(input_block, 3)
    conv1x1_d = conv_2d(max_pool_d, 32*filter_width, 1, stride, 
                       activation='linear', regularizer=regularizer)

    output = merge([conv1x1_a, conv3x3_b, conv5x5_c, conv1x1_d], 'concat', axis=-1)

    if normalise:
        output = local_response_normalization(output)

    return output


def inceptionv4_stem_block(x, filter_width=1, activation='relu'):
    # stem
    stem = conv_2d(x, 32*filter_width, 3, 2, 
                   padding='valid', activation=activation)
    stem = conv_2d(stem, 32*filter_width, 3, 1, 
                   padding='valid', activation=activation)
    stem = conv_2d(stem, 64*filter_width, 3, 1, activation=activation)
    
    # branch 1 a
    maxpool_1a = max_pool_2d(stem, 3, 2, padding='valid')
    
    # branch 1 b
    conv3x3_1b = conv_2d(stem, 96*filter_width, 3, 2, 
                         padding='valid', activation=activation)
    
    # merge 1
    concat_1 = merge([maxpool_1a, conv3x3_1b], 'concat', axis=-1)
    
    # branch 2 a
    conv1x1_2a = conv_2d(concat_1, 64*filter_width, 1, 1, 
                         activation=activation)
    conv3x3_2a = conv_2d(conv1x1_2a, 96*filter_width, 3, 1, 
                         padding='valid', activation=activation)
    
    # branch 2 b
    conv1x1_2b = conv_2d(concat_1, 64*filter_width, 1, 1, 
                         activation=activation)
    conv7x1_2b = conv_2d(conv1x1_2b, 64*filter_width, (7, 1), 1, 
                         activation=activation)
    conv1x7_2b = conv_2d(conv7x1_2b, 64*filter_width, (1, 7), 1, 
                         activation=activation)
    conv3x3_2b = conv_2d(conv1x7_2b, 96*filter_width, 3, 1, 
                         padding='valid', activation=activation)
    
    # merge 2
    concat_2 = merge([conv3x3_2a, conv3x3_2b], 'concat', axis=-1)
    
    # branch 3 a
    conv3x3_3a = conv_2d(stem, 192*filter_width, 3, 1, 
                         padding='valid', activation=activation)
    
    # branch 3 b
    maxpool_3b = max_pool_2d(stem, 3, 2, padding='valid')
    
    # merge 3
    concat_3 = merge([conv3x3_3a, maxpool_3b], 'concat', axis=-1)
    
    return concat_3


def inceptionv4_a_block(x, filter_width=1, activation='relu'):
    # branch a
    avgpool_a = avg_pool_2d(x, 3)
    conv1x1_a = conv_2d(avgpool_a, 96*filter_width, 1, activation=activation)
    
    # branch b
    conv1x1_b = conv_2d(x, 96*filter_width, 1, activation=activation)
    
    # branch c
    conv1x1_c = conv_2d(x, 64*filter_width, 1, activation=activation)
    conv3x3_c = conv_2d(conv1x1_c, 96*filter_width, 3, activation=activation)
    
    # branch d
    conv_1x1_d = conv_2d(x, 64*filter_width, 1, activation=activation)
    conv_3x3_d = conv_2d(conv_1x1_d, 96*filter_width, 3, activation=activation)
    conv_3x3_d = conv_2d(conv_3x3_d, 96*filter_width, 3, activation=activation)
    
    # merge
    concat_1 = merge([conv1x1_a, conv1x1_b, 
                      conv3x3_c, conv3x3_d], 'concat', axis=-1)
    
    return concat_1


def inceptionv4_b_block(x, filter_width=1, activation='relu'):
    # branch a
    avgpool_a = avg_pool_2d(x, 3)
    conv1x1_a = conv_2d(avgpool_a, 128*filter_width, 1, activation=activation)

    # branch b
    conv1x1_b = conv_2d(x, 384*filter_width, 1, activation=activation)

    # branch c
    conv1x1_c = conv_2d(x, 192*filter_width, 1, 1, 
                         activation=activation)
    conv1x7_1c = conv_2d(conv1x1_c, 224*filter_width, (1, 7), 1, 
                         activation=activation)
    conv1x7_2c = conv_2d(conv1x7_1c, 256*filter_width, (1, 7), 1, 
                         activation=activation)

    # branch d
    conv1x1_d = conv_2d(x, 192*filter_width, 1, 1, 
                        activation=activation)
    conv1x7_1d = conv_2d(conv1x1_d, 192*filter_width, (1, 7), 1, 
                         activation=activation)
    conv7x1_1d = conv_2d(conv1x7_1d, 224*filter_width, (7, 1), 1, 
                         activation=activation)
    conv1x7_2d = conv_2d(conv7x1_1d, 224*filter_width, (1, 7), 1, 
                         activation=activation)
    conv7x1_2d = conv_2d(conv1x7_2d, 256*filter_width, (7, 1), 1, 
                         activation=activation)
    
    # merge
    concat_1 = merge([conv1x1_a, conv1x1_b, 
                      conv1x7_2c, conv7x1_2d], 'concat', axis=-1)

    return concat_1


def inceptionv4_c_block(x, filter_width=1, activation='relu'):
    # branch a
    avgpool_a = avg_pool_2d(x, 3)
    conv1x1_a = conv_2d(avgpool_a, 256*filter_width, 1, activation=activation)

    # branch b
    conv1x1_b = conv_2d(x, 256*filter_width, 1, activation=activation)

    # branch c
    conv1x1_c = conv_2d(x, 384*filter_width, 1, 1, 
                        activation=activation)
    # branch c a
    conv1x3_c_a = conv_2d(conv1x1_c, 256*filter_width, (1, 3), 1, 
                         activation=activation)
    # branch c b
    conv3x1_c_b = conv_2d(conv1x1_c, 256*filter_width, (3, 1), 1, 
                         activation=activation)

    # branch d
    conv1x1_d = conv_2d(x, 384*filter_width, 1, 1, 
                        activation=activation)
    conv1x3_d = conv_2d(conv1x1_d, 448*filter_width, (1, 3), 1, 
                         activation=activation)
    conv3x1_d = conv_2d(conv1x3_d, 512*filter_width, (3, 1), 1, 
                         activation=activation)
    # branch d a
    conv1x3_d_a = conv_2d(conv3x1_d, 256*filter_width, (1, 3), 1, 
                         activation=activation)
    # branch d b
    conv3x1_d_b = conv_2d(conv3x1_d, 256*filter_width, (3, 1), 1, 
                         activation=activation)

    # merge
    concat_1 = merge([conv1x1_a, conv1x1_b, conv1x3_c_a, conv3x1_c_b, 
                      conv1x3_d_a, conv3x1_d_b], 'concat', axis=-1)

    return concat_1


def inceptionv4_reduct_a_block(x, filter_width=1, activation='relu'):
    # branch a
    maxpool_a = max_pool_2d(x, 3, 2, padding='valid')
    
    # branch b
    conv3x3_b = conv_2d(x, 384*filter_width, 3, 2, padding='valid', 
                        activation=activation)
    
    # branch c
    conv1x1_c = conv_2d(x, 192*filter_width, 1, 1, 
                        activation=activation)
    conv3x3_1c = conv_2d(conv1x1_c, 224*filter_width, 3, 1, 
                         activation=activation)
    conv3x3_2c = conv_2d(conv3x3_1c, 256*filter_width, 3, 2, padding='valid', 
                         activation=activation)
    
    # merge
    concat_1 = merge([maxpool_a, conv3x3_b, conv3x3_2c], 'concat', axis=-1)
    
    return concat_1


def inceptionv4_reduct_b_block(x, filter_width=1, activation='relu'):
    # branch a
    maxpool_a = max_pool_2d(X, 3, 2, padding='valid')
    
    # branch b
    conv1x1_b = conv_2d(x, 192*filter_width, 1, 1, 
                        activation=activation)
    conv3x3_b = conv_2d(conv1x1_b, 192*filter_width, 3, 2, padding='valid', 
                        activation=activation)
    
    # branch c
    conv1x1_c = conv_2d(x, 256*filter_width, 1, 1, 
                        activation=activation)
    conv1x7_1c = conv_2d(conv1x1_c, 256*filter_width, (1, 7), 1, 
                         activation=activation)
    conv7x1_2c = conv_2d(conv1x7_1c, 320*filter_width, (7, 1), 1, 
                         activation=activation)
    conv3x3_c = conv_2d(conv7x1_2c, 320*filter_width, 3, 2, padding='valid', 
                        activation=activation)
    
    # merge
    concat_1 = merge([maxpool_a, conv3x3_b, conv3x3_c], 'concat', axis=-1)
    
    return concat_1