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', ...
