def binarized2_bn_model():
import tensorflow as tf<EOL>bounds = (<NUM_LIT:0>, <NUM_LIT:1>)<EOL>channel_axis = <NUM_LIT:3><EOL>channels = <NUM_LIT:10>  <EOL>def mean_brightness_net(images):<EOL><INDENT>logits = tf.reduce_mean(images, axis=(<NUM_LIT:1>, <NUM_LIT:2>))<EOL>return logits<EOL><DEDENT>images = tf.placeholder(tf.float32, (None, <NUM_LIT:5>, <NUM_LIT:5>, channels))<EOL>logits = mean_brightness_net(images)<EOL>def preprocessing(x):<EOL><INDENT>x = binarize(x, (<NUM_LIT:0>, <NUM_LIT:1>), included_in='<STR_LIT>')<EOL>def backward(x):<EOL><INDENT>return x<EOL><DEDENT>return x, backward<EOL><DEDENT>with tf.Session():<EOL><INDENT>model = TensorFlowModel(<EOL>images,<EOL>logits,<EOL>bounds=bounds,<EOL>channel_axis=channel_axis,<EOL>preprocessing=preprocessing)<EOL>yield model<EOL><DEDENT>
Creates a simple brightness model that does not require training.