TensorFlow 1.x Deep Learning Cookbook

How to do it...

We proceed with the recipe as follows:

1. Import a few utils and core layers for ConvNets, dropout, fully_connected, and max_pool. In addition, import a few modules useful for image processing and image augmentation. Note that TFLearn provides some already defined higher-level layers for ConvNets and this allows us to focus on the definition of our code:

from __future__ import division, print_function, absolute_import import tflearn from tflearn.data_utils import shuffle, to_categorical 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 from tflearn.data_preprocessing import ImagePreprocessing from tflearn.data_augmentation ...