In this example, we'll define and train either a two-layer model or a convolutional model in the style of LeNet 5:
from six.moves import xrange
import tensorflow as tf
import prettytensor as pt
from prettytensor.tutorial import data_utils
tf.app.flags.DEFINE_string(
'save_path', None, 'Where to save the model checkpoints.')
FLAGS = tf.app.flags.FLAGS
BATCH_SIZE = 50
EPOCH_SIZE = 60000 // BATCH_SIZE
TEST_SIZE = 10000 // BATCH_SIZE
Since we are feeding our data as numpy arrays, we need to create placeholders in the graph. These must then be fed using the feed dict.
image_placeholder = tf.placeholder\
(tf.float32, [BATCH_SIZE, 28, 28, 1])
labels_placeholder = tf.placeholder\
(tf.float32, [BATCH_SIZE, 10])
tf.app.flags.DEFINE_string('model', 'full',
'Choose one of the models...