How to use the tflearn.layers.core.input_data function in tflearn

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),

# IMDB Dataset loading
train, test, _ = imdb.load_data(path='imdb.pkl', n_words=10000,
                                valid_portion=0.1)
trainX, trainY = train
testX, testY = test

# Data preprocessing
# Sequence padding
trainX = pad_sequences(trainX, maxlen=100, value=0.)
testX = pad_sequences(testX, maxlen=100, value=0.)
# Converting labels to binary vectors
trainY = to_categorical(trainY)
testY = to_categorical(testY)

# 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)
model.fit(trainX, trainY, n_epoch = 5, shuffle=True, validation_set=(testX, testY), show_metric=True, batch_size=32)

def construct_inceptionv3onfire(x,y, training=False):

    # build network as per architecture

    network = input_data(shape=[None, y, x, 3])

    conv1_3_3 = conv_2d(network, 32, 3, strides=2, activation='relu', name = 'conv1_3_3',padding='valid')
    conv2_3_3 = conv_2d(conv1_3_3, 32, 3, strides=1, activation='relu', name = 'conv2_3_3',padding='valid')
    conv3_3_3 = conv_2d(conv2_3_3, 64, 3, strides=2, activation='relu', name = 'conv3_3_3')

    pool1_3_3 = max_pool_2d(conv3_3_3, 3,strides=2)
    pool1_3_3 = batch_normalization(pool1_3_3)
    conv1_7_7 = conv_2d(pool1_3_3, 80,3, strides=1, activation='relu', name='conv2_7_7_s2',padding='valid')
    conv2_7_7 = conv_2d(conv1_7_7, 96,3, strides=1, activation='relu', name='conv2_7_7_s2',padding='valid')
    pool2_3_3= max_pool_2d(conv2_7_7,3,strides=2)

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

    inception_3a_3_3_reduce = conv_2d(pool2_3_3, 48, filter_size=1, activation='relu', name='inception_3a_3_3_reduce')
    inception_3a_3_3 = conv_2d(inception_3a_3_3_reduce, 64, filter_size=[5,5],  activation='relu',name='inception_3a_3_3')

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')

"""

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

import tflearn.datasets.oxflower17 as oxflower17
X, Y = oxflower17.load_data(one_hot=True, resize_pics=(227, 227))

# 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')

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

def construct_inceptionv4onfire(x,y, training=True):

    network = input_data(shape=[None, y, x, 3])
    #stem of inceptionv4

    conv1_3_3 = conv_2d(network,32,3,strides=2,activation='relu',name='conv1_3_3_s2',padding='valid')
    conv2_3_3 = conv_2d(conv1_3_3,32,3,activation='relu',name='conv2_3_3')
    conv3_3_3 = conv_2d(conv2_3_3,64,3,activation='relu',name='conv3_3_3')
    b_conv_1_pool = max_pool_2d(conv3_3_3,kernel_size=3,strides=2,padding='valid',name='b_conv_1_pool')
    b_conv_1_pool = batch_normalization(b_conv_1_pool)
    b_conv_1_conv = conv_2d(conv3_3_3,96,3,strides=2,padding='valid',activation='relu',name='b_conv_1_conv')
    b_conv_1 = merge([b_conv_1_conv,b_conv_1_pool],mode='concat',axis=3)

    b_conv4_1_1 = conv_2d(b_conv_1,64,1,activation='relu',name='conv4_3_3')
    b_conv4_3_3 = conv_2d(b_conv4_1_1,96,3,padding='valid',activation='relu',name='conv5_3_3')

    b_conv4_1_1_reduce = conv_2d(b_conv_1,64,1,activation='relu',name='b_conv4_1_1_reduce')
    b_conv4_1_7 = conv_2d(b_conv4_1_1_reduce,64,[1,7],activation='relu',name='b_conv4_1_7')
    b_conv4_7_1 = conv_2d(b_conv4_1_7,64,[7,1],activation='relu',name='b_conv4_7_1')

X, Y = shuffle(X, Y)
Y = to_categorical(Y)
Y_test = to_categorical(Y_test)

# Real-time data preprocessing
img_prep = ImagePreprocessing()
img_prep.add_featurewise_zero_center()
img_prep.add_featurewise_stdnorm()

# Real-time data augmentation
img_aug = ImageAugmentation()
img_aug.add_random_flip_leftright()
img_aug.add_random_rotation(max_angle=25.)

# Convolutional network building
network = input_data(shape=[None, 32, 32, 3],
                     data_preprocessing=img_prep,
                     data_augmentation=img_aug)
network = conv_2d(network, 32, 3, activation='relu')
network = max_pool_2d(network, 2)
network = conv_2d(network, 64, 3, activation='relu')
network = conv_2d(network, 64, 3, activation='relu')
network = max_pool_2d(network, 2)
network = fully_connected(network, 512, activation='relu')
network = dropout(network, 0.5)
network = fully_connected(network, 10, activation='softmax')
network = regression(network, optimizer='adam',
                     loss='categorical_crossentropy',
                     learning_rate=0.001)

# Train using classifier
model = tflearn.DNN(network, tensorboard_verbose=0)

def sentnet_frames(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 = 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')