一些先进的网络结构：

# https://github.com/tflearn/tflearn/blob/master/examples/images/highway_dnn.py

# -*- coding: utf-8 -*-

""" Deep Neural Network for MNIST dataset classification task using

a highway network

References:

Links:

    [MNIST Dataset] http://yann.lecun.com/exdb/mnist/

    [https://arxiv.org/abs/1505.00387](https://arxiv.org/abs/1505.00387)

"""

from __future__ import division, print_function, absolute_import

import tflearn

# Data loading and preprocessing

import tflearn.datasets.mnist as mnist

X, Y, testX, testY = mnist.load_data(one_hot=True)

# Building deep neural network

input_layer = tflearn.input_data(shape=[None, 784])

dense1 = tflearn.fully_connected(input_layer, 64, activation='elu',

                                 regularizer='L2', weight_decay=0.001)

#install a deep network of highway layers

highway = dense1

for i in range(10):

    highway = tflearn.highway(highway, 64, activation='elu',

                              regularizer='L2', weight_decay=0.001, transform_dropout=0.8)

softmax = tflearn.fully_connected(highway, 10, activation='softmax')

# Regression using SGD with learning rate decay and Top-3 accuracy

sgd = tflearn.SGD(learning_rate=0.1, lr_decay=0.96, decay_step=1000)

top_k = tflearn.metrics.Top_k(3)

net = tflearn.regression(softmax, optimizer=sgd, metric=top_k,

                         loss='categorical_crossentropy')

# Training

model = tflearn.DNN(net, tensorboard_verbose=0)

model.fit(X, Y, n_epoch=20, validation_set=(testX, testY),

show_metric=True, run_id="highway_dense_model")

# https://github.com/tflearn/tflearn/blob/master/examples/images/convnet_highway_mnist.py

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 highway_conv_2d, max_pool_2d

from tflearn.layers.normalization import local_response_normalization, batch_normalization

from tflearn.layers.estimator import regression

# Data loading and preprocessing

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])

# Building convolutional network

network = input_data(shape=[None, 28, 28, 1], name='input')

#highway convolutions with pooling and dropout

for i in range(3):

    for j in [3, 2, 1]:

        network = highway_conv_2d(network, 16, j, activation='elu')

    network = max_pool_2d(network, 2)

    network = batch_normalization(network)

network = fully_connected(network, 128, activation='elu')

network = fully_connected(network, 256, activation='elu')

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=0)

model.fit(X, Y, n_epoch=20, validation_set=(testX, testY),

show_metric=True, run_id='convnet_highway_mnist')

# https://github.com/tflearn/tflearn/blob/master/examples/images/residual_network_mnist.py

from __future__ import division, print_function, absolute_import

import tflearn

import tflearn.data_utils as du

# Data loading and preprocessing

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, mean = du.featurewise_zero_center(X)

testX = du.featurewise_zero_center(testX, mean)

# Building Residual Network

net = tflearn.input_data(shape=[None, 28, 28, 1])

net = tflearn.conv_2d(net, 64, 3, activation='relu', bias=False)

# Residual blocks

net = tflearn.residual_bottleneck(net, 3, 16, 64)

net = tflearn.residual_bottleneck(net, 1, 32, 128, downsample=True)

net = tflearn.residual_bottleneck(net, 2, 32, 128)

net = tflearn.residual_bottleneck(net, 1, 64, 256, downsample=True)

net = tflearn.residual_bottleneck(net, 2, 64, 256)

net = tflearn.batch_normalization(net)

net = tflearn.activation(net, 'relu')

net = tflearn.global_avg_pool(net)

# Regression

net = tflearn.fully_connected(net, 10, activation='softmax')

net = tflearn.regression(net, optimizer='momentum',

                         loss='categorical_crossentropy',

                         learning_rate=0.1)

# Training

model = tflearn.DNN(net, checkpoint_path='model_resnet_mnist',

                    max_checkpoints=10, tensorboard_verbose=0)

model.fit(X, Y, n_epoch=100, validation_set=(testX, testY),

show_metric=True, batch_size=256, run_id='resnet_mnist')

# https://github.com/tflearn/tflearn/blob/master/examples/images/resnext_cifar10.py

from __future__ import division, print_function, absolute_import

import tflearn

# Residual blocks

# 32 layers: n=5, 56 layers: n=9, 110 layers: n=18

n = 5

# Data loading

from tflearn.datasets import cifar10

(X, Y), (testX, testY) = cifar10.load_data()

Y = tflearn.data_utils.to_categorical(Y)

testY = tflearn.data_utils.to_categorical(testY)

# Real-time data preprocessing

img_prep = tflearn.ImagePreprocessing()

img_prep.add_featurewise_zero_center(per_channel=True)

# Real-time data augmentation

img_aug = tflearn.ImageAugmentation()

img_aug.add_random_flip_leftright()

img_aug.add_random_crop([32, 32], padding=4)

# Building Residual Network

net = tflearn.input_data(shape=[None, 32, 32, 3],

                         data_preprocessing=img_prep,

                         data_augmentation=img_aug)

net = tflearn.conv_2d(net, 16, 3, regularizer='L2', weight_decay=0.0001)

net = tflearn.resnext_block(net, n, 16, 32)

net = tflearn.resnext_block(net, 1, 32, 32, downsample=True)

net = tflearn.resnext_block(net, n-1, 32, 32)

net = tflearn.resnext_block(net, 1, 64, 32, downsample=True)

net = tflearn.resnext_block(net, n-1, 64, 32)

net = tflearn.batch_normalization(net)

net = tflearn.activation(net, 'relu')

net = tflearn.global_avg_pool(net)

# Regression

net = tflearn.fully_connected(net, 10, activation='softmax')

opt = tflearn.Momentum(0.1, lr_decay=0.1, decay_step=32000, staircase=True)

net = tflearn.regression(net, optimizer=opt,

                         loss='categorical_crossentropy')

# Training

model = tflearn.DNN(net, checkpoint_path='model_resnext_cifar10',

                    max_checkpoints=10, tensorboard_verbose=0,

                    clip_gradients=0.)

model.fit(X, Y, n_epoch=200, validation_set=(testX, testY),

          snapshot_epoch=False, snapshot_step=500,

          show_metric=True, batch_size=128, shuffle=True,

run_id='resnext_cifar10')

一维的，后续优化网络结构使用：

# 关于一维CNN的网络，例子较少

# https://github.com/tflearn/tflearn/blob/master/examples/nlp/cnn_sentence_classification.py

# Building convolutional network

network = input_data(shape=[None, 100], name='input')

network = tflearn.embedding(network, input_dim=10000, output_dim=128)

branch1 = conv_1d(network, 128, 3, padding='valid', activation='relu', regularizer="L2")

branch2 = conv_1d(network, 128, 4, padding='valid', activation='relu', regularizer="L2")

branch3 = conv_1d(network, 128, 5, padding='valid', activation='relu', regularizer="L2")

network = merge([branch1, branch2, branch3], mode='concat', axis=1)

network = tf.expand_dims(network, 2)

network = global_max_pool(network)

network = dropout(network, 0.5)

network = fully_connected(network, 2, activation='softmax')

network = regression(network, optimizer='adam', learning_rate=0.001,

                     loss='categorical_crossentropy', name='target')

# Training

model = tflearn.DNN(network, tensorboard_verbose=0)

# http://codegists.com/snippet/python/specificpy_lastzactionhero_python

# Specify shape of the data, image prep

network = input_data(shape=[None, 52, 64],

                     data_preprocessing=img_prep,

                     data_augmentation=img_aug)

# conv_2d incoming, nb_filter, filter_size

# incoming: Tensor. Incoming 4-D Tensor.

# nb_filter: int. The number of convolutional filters. # WHAT IS THIS?

# filter_size: 'intor list ofints`. Size of filters.   # WHAT IS THIS?

network = conv_1d(network, 512, 3, activation='relu')

# (incoming, kernel_size)

# incoming: Tensor. Incoming 4-D Layer.

# kernel_size: 'intor list ofints`. Pooling kernel size.

network = max_pool_1d(network, 2)

network = conv_1d(network, 64, 3, activation='relu')

network = conv_1d(network, 64, 3, activation='relu')

network = max_pool_1d(network, 2)

network = fully_connected(network, 512, activation='relu')

network = dropout(network, 0.5)

network = fully_connected(network, 4, activation='softmax')

network = regression(network, optimizer='adam',

                     loss='categorical_crossentropy',

                     learning_rate=0.0003)

model = tflearn.DNN(network, tensorboard_verbose=0)

# https://github.com/gonzalolc/CharacterLevel-CNN-TFLearn/blob/master/train.py

# Building convolutional network

network = input_data(shape=[None, 1024], name='input')

network = tflearn.embedding(network, input_dim=71, output_dim=256)

network = conv_1d(network, 256, 7, padding='valid', scope='conv1', activation='relu')

network = max_pool_1d(network, 3, strides=3, name='Maxpool1D_1')

network = conv_1d(network, 256, 7, padding='valid', scope='conv2', activation='relu')

network = max_pool_1d(network, 3, strides=3, name='Maxpool1D_2')

network = conv_1d(network, 256, 3, padding='valid', scope='conv3', activation='relu')

network = conv_1d(network, 256, 3, padding='valid', scope='conv4', activation='relu')

network = conv_1d(network, 256, 3, padding='valid', scope='conv5', activation='relu')

network = conv_1d(network, 256, 3, padding='valid', scope='conv6', activation='relu')

network = max_pool_1d(network, 3, strides=3, name='Maxpool1D_Last')

network = tflearn.fully_connected(network, 1024, name='Fullyconected_0')

network = dropout(network, 0.5)

network = tflearn.fully_connected(network, 1024, name='Fullyconected_1')

network = dropout(network, 0.5)

network = fully_connected(network, 14, activation='softmax', name='FullyConected_Last')

network = regression(network, optimizer='adam', loss='categorical_crossentropy', name='target')

# Training

model = tflearn.DNN(network,tensorboard_dir='runs', checkpoint_path='checkpoints/Checkpoints', best_checkpoint_path='bestcheckpoints/BestCheckpoint', best_val_accuracy=0.94, tensorboard_verbose=2)

# https://github.com/jrzaurin/Text-Classification-with-Tensorflow/blob/master/pretrained_word_embedding_TF_tflearn.py

    net = input_data(shape=[None,MAX_SEQUENCE_LENGTH], name='input')

    net = embedding(net, input_dim=MAX_NB_WORDS, output_dim=EMBEDDING_DIM, trainable=False, name="EmbeddingLayer")

    net = conv_1d(net, 128, 5, 1, activation='relu', padding="valid")

    # one could add regularization as:

    # net = conv_1d(net, 128, 5, 1, activation='relu', regularizer="L2", padding="valid")

    net = max_pool_1d(net, 5, padding="valid")

    net = batch_normalization(net)

    net = conv_1d(net, 128, 5, activation='relu', padding="valid")

    net = max_pool_1d(net, 5, padding="valid")

    net = batch_normalization(net)

    net = conv_1d(net, 128, 5, activation='relu', padding="valid")

    net = max_pool_1d(net, 35)

    net = batch_normalization(net)

    net = fully_connected(net, 128, activation='relu')

    net = dropout(net, 0.5)

    net = fully_connected(net, y_train.shape[1], activation='softmax')

    net = regression(net, optimizer='adam', learning_rate=0.01,

                         loss='categorical_crossentropy', name='target')

    model = tflearn.DNN(net, tensorboard_verbose=0)

二维的tflearn官方的例子就非常多，到时候一维的可以借鉴他们的结构设计：

#https://github.com/tflearn/tflearn/blob/master/examples/basics/logical.py

    # Building a network with 2 optimizers

    g = tflearn.input_data(shape=[None, 2])

    # Nand operator definition

    g_nand = tflearn.fully_connected(g, 32, activation='linear')

    g_nand = tflearn.fully_connected(g_nand, 32, activation='linear')

    g_nand = tflearn.fully_connected(g_nand, 1, activation='sigmoid')

    g_nand = tflearn.regression(g_nand, optimizer='sgd',

                                learning_rate=2.,

                                loss='binary_crossentropy')

    # Or operator definition

    g_or = tflearn.fully_connected(g, 32, activation='linear')

    g_or = tflearn.fully_connected(g_or, 32, activation='linear')

    g_or = tflearn.fully_connected(g_or, 1, activation='sigmoid')

    g_or = tflearn.regression(g_or, optimizer='sgd',

                              learning_rate=2.,

                              loss='binary_crossentropy')

    # XOR merging Nand and Or operators

    g_xor = tflearn.merge([g_nand, g_or], mode='elemwise_mul')

    # Training

m = tflearn.DNN(g_xor)

#https://github.com/tflearn/tflearn/blob/master/examples/images/dnn.py

# Building deep neural network

input_layer = tflearn.input_data(shape=[None, 784])

dense1 = tflearn.fully_connected(input_layer, 64, activation='tanh',

                                 regularizer='L2', weight_decay=0.001)

dropout1 = tflearn.dropout(dense1, 0.8)

dense2 = tflearn.fully_connected(dropout1, 64, activation='tanh',

                                 regularizer='L2', weight_decay=0.001)

dropout2 = tflearn.dropout(dense2, 0.8)

softmax = tflearn.fully_connected(dropout2, 10, activation='softmax')

# Regression using SGD with learning rate decay and Top-3 accuracy

sgd = tflearn.SGD(learning_rate=0.1, lr_decay=0.96, decay_step=1000)

top_k = tflearn.metrics.Top_k(3)

net = tflearn.regression(softmax, optimizer=sgd, metric=top_k,

                         loss='categorical_crossentropy')

# Training

model = tflearn.DNN(net, tensorboard_verbose=0)

# https://github.com/tflearn/tflearn/blob/master/examples/basics/finetuning.py

# Redefinition of convnet_cifar10 network

network = input_data(shape=[None, 32, 32, 3])

network = conv_2d(network, 32, 3, activation='relu')

network = max_pool_2d(network, 2)

network = dropout(network, 0.75)

network = conv_2d(network, 64, 3, activation='relu')

network = conv_2d(network, 64, 3, activation='relu')

network = max_pool_2d(network, 2)

network = dropout(network, 0.5)

network = fully_connected(network, 512, activation='relu')

network = dropout(network, 0.5)

# Finetuning Softmax layer (Setting restore=False to not restore its weights)

softmax = fully_connected(network, num_classes, activation='softmax', restore=False)

regression = regression(softmax, optimizer='adam',

                        loss='categorical_crossentropy',

                        learning_rate=0.001)

model = tflearn.DNN(regression, checkpoint_path='model_finetuning',

                    max_checkpoints=3, tensorboard_verbose=0)

# Load pre-existing model, restoring all weights, except softmax layer ones

# https://github.com/tflearn/tflearn/blob/master/examples/nlp/dynamic_lstm.py

# Network building

net = tflearn.input_data([None, 100])

# Masking is not required for embedding, sequence length is computed prior to

# the embedding op and assigned as 'seq_length' attribute to the returned Tensor.

net = tflearn.embedding(net, input_dim=10000, output_dim=128)

net = tflearn.lstm(net, 128, dropout=0.8, dynamic=True)

net = tflearn.fully_connected(net, 2, activation='softmax')

net = tflearn.regression(net, optimizer='adam', learning_rate=0.001,

                         loss='categorical_crossentropy')

# Training

model = tflearn.DNN(net, tensorboard_verbose=0)

# https://github.com/tflearn/tflearn/blob/master/examples/images/googlenet.py

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')

inception_3b_3_3 = conv_2d(inception_3b_3_3_reduce, 192, filter_size=3, activation='relu', name='inception_3b_3_3')

inception_3b_5_5_reduce = conv_2d(inception_3a_output, 32, filter_size=1, activation='relu', name='inception_3b_5_5_reduce')

inception_3b_5_5 = conv_2d(inception_3b_5_5_reduce, 96, filter_size=5,  name='inception_3b_5_5')

inception_3b_pool = max_pool_2d(inception_3a_output, kernel_size=3, strides=1,  name='inception_3b_pool')

inception_3b_pool_1_1 = conv_2d(inception_3b_pool, 64, filter_size=1, activation='relu', name='inception_3b_pool_1_1')

inception_3b_output = merge([inception_3b_1_1, inception_3b_3_3, inception_3b_5_5, inception_3b_pool_1_1], mode='concat', axis=3, name='inception_3b_output')

pool3_3_3 = max_pool_2d(inception_3b_output, kernel_size=3, strides=2, name='pool3_3_3')

# 4a

inception_4a_1_1 = conv_2d(pool3_3_3, 192, filter_size=1, activation='relu', name='inception_4a_1_1')

inception_4a_3_3_reduce = conv_2d(pool3_3_3, 96, filter_size=1, activation='relu', name='inception_4a_3_3_reduce')

inception_4a_3_3 = conv_2d(inception_4a_3_3_reduce, 208, filter_size=3,  activation='relu', name='inception_4a_3_3')

inception_4a_5_5_reduce = conv_2d(pool3_3_3, 16, filter_size=1, activation='relu', name='inception_4a_5_5_reduce')

inception_4a_5_5 = conv_2d(inception_4a_5_5_reduce, 48, filter_size=5,  activation='relu', name='inception_4a_5_5')

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')

# 4b

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')

# 4c

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')

# 4d

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')

# 4e

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')

# 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)

# to train

model = tflearn.DNN(network, checkpoint_path='model_googlenet',

max_checkpoints=1, tensorboard_verbose=2)

# https://github.com/tflearn/tflearn/blob/master/examples/images/densenet.py

# Building Residual Network

net = tflearn.input_data(shape=[None, 32, 32, 3],

                         data_preprocessing=img_prep,

                         data_augmentation=img_aug)

net = tflearn.conv_2d(net, 16, 3, regularizer='L2', weight_decay=0.0001)

net = tflearn.densenet_block(net, nb_layers, k)

net = tflearn.densenet_block(net, nb_layers, k)

net = tflearn.densenet_block(net, nb_layers, k)

net = tflearn.global_avg_pool(net)

# Regression

net = tflearn.fully_connected(net, 10, activation='softmax')

opt = tflearn.Nesterov(0.1, lr_decay=0.1, decay_step=32000, staircase=True)

net = tflearn.regression(net, optimizer=opt,

                         loss='categorical_crossentropy')

# Training

model = tflearn.DNN(net, checkpoint_path='model_densenet_cifar10',

                    max_checkpoints=10, tensorboard_verbose=0,

clip_gradients=0.)

# https://github.com/AhmetHamzaEmra/tflearn/blob/master/examples/images/VGG19.py

# 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')

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")

#https://github.com/tflearn/tflearn/blob/master/examples/images/vgg_network.py

# 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')

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',

                     loss='categorical_crossentropy',

                     learning_rate=0.0001)

# Training

model = tflearn.DNN(network, checkpoint_path='model_vgg',

max_checkpoints=1, tensorboard_verbose=0)

#https://github.com/tflearn/tflearn/blob/master/examples/images/alexnet.py

# Building 'AlexNet'

network = input_data(shape=[None, 227, 227, 3])

network = conv_2d(network, 96, 11, strides=4, activation='relu')

network = max_pool_2d(network, 3, strides=2)

network = local_response_normalization(network)

network = conv_2d(network, 256, 5, activation='relu')

network = max_pool_2d(network, 3, strides=2)

network = local_response_normalization(network)

network = conv_2d(network, 384, 3, activation='relu')

network = conv_2d(network, 384, 3, activation='relu')

network = conv_2d(network, 256, 3, activation='relu')

network = max_pool_2d(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, 17, activation='softmax')

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

                     loss='categorical_crossentropy',

                     learning_rate=0.001)

# Training

model = tflearn.DNN(network, checkpoint_path='model_alexnet',

max_checkpoints=1, tensorboard_verbose=2)

#https://github.com/tflearn/tflearn/blob/master/examples/images/convnet_mnist.py

# 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=0)

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