network_anything_happening.py

import tflearn
from tflearn.data_preprocessing import ImagePreprocessing
from tflearn.data_augmentation import ImageAugmentation

from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.conv import conv_1d, max_pool_1d
from tflearn.layers.estimator import regression

img_prep = ImagePreprocessing()
img_prep.add_featurewise_zero_center()
img_prep.add_featurewise_stdnorm()

img_aug = ImageAugmentation()
img_aug.add_random_flip_leftright()

# Specify shape of the data, image prep
network = input_data(shape=[None, 52, 64],
                     data_preprocessing=img_prep,
                     data_augmentation=img_aug)

# Since the image position remains consistent and are fairly similar, this can be spatially aware.
# Using a fully connected network directly, no need for convolution.
network = fully_connected(network, 2048, activation='relu')
network = fully_connected(network, 2, activation='softmax')

network = regression(network, optimizer='adam',
                     loss='categorical_crossentropy',
                     learning_rate=0.00003)

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