class NetEval:
def __init__(self, num_classes, num_hidden1, num_hidden2):
self.NUM_CLASSES = num_classes
self.IMAGE_SIZE = 28
self.IMAGE_PIXELS = self.IMAGE_SIZE * self.IMAGE_SIZE
self.images_pl = tf.placeholder(tf.float32, shape=(None, self.IMAGE_PIXELS))
if FLAGS.one_hot:
self.labels_pl = tf.placeholder(tf.int32, shape=(None))
else:
self.labels_pl = tf.placeholder(tf.int32, shape=(None, self.NUM_CLASSES))
# Generate network
self.mnist = FourLayeredFFNN(self.NUM_CLASSES)
#self.mnist = FourLayeredFFCNN(self.NUM_CLASSES)
# Initialize network
self.mnist.init_net(self.images_pl, self.labels_pl, 1e-4)
def load(self, fname):
saver = tf.train.Saver()
saver.restore(self.mnist.sess, fname)
def eval(self, img):
img = cv2.resize(img, (28, 28))
imgg = cv2.cvtColor(img, cv2.cv.CV_BGR2GRAY)
imgg = imgg[:,:].reshape(imgg.size)
imgg = imgg.reshape(1,imgg.size)
imgf = imgg.astype(np.float32)
imgf = np.multiply(imgf, 1.0 / 255.0)
return self.mnist.test(feed_dict={self.images_pl: imgf,
self.labels_pl: [[0] * self.NUM_CLASSES]}).flatten()
def __init__(self, num_classes, num_hidden1, num_hidden2):
self.NUM_CLASSES = num_classes
self.IMAGE_SIZE = 28
self.IMAGE_PIXELS = self.IMAGE_SIZE * self.IMAGE_SIZE
self.images_pl = tf.placeholder(tf.float32, shape=(None, self.IMAGE_PIXELS))
if FLAGS.one_hot:
self.labels_pl = tf.placeholder(tf.int32, shape=(None))
else:
self.labels_pl = tf.placeholder(tf.int32, shape=(None, self.NUM_CLASSES))
# Generate network
self.mnist = FourLayeredFFNN(self.NUM_CLASSES)
#self.mnist = FourLayeredFFCNN(self.NUM_CLASSES)
# Initialize network
self.mnist.init_net(self.images_pl, self.labels_pl, 1e-4)
def run_training():
"""Train MNIST for a number of steps."""
# Get the sets of images and labels for training, validation, and
# test on MNIST.
data_sets = input_data.read_data_sets(FLAGS.train_dir, FLAGS.fake_data, one_hot=FLAGS.one_hot)
# Tell TensorFlow that the model will be built into the default Graph.
with tf.Graph().as_default():
# Generate network
mnist = FourLayeredFFNN()
#mnist = FourLayeredFFCNN()
# Generate placeholders for the images and labels.
if FLAGS.one_hot == True:
images_placeholder, labels_placeholder = placeholder_inputs(
FLAGS.batch_size, mnist.IMAGE_PIXELS, mnist.NUM_CLASSES)
else:
images_placeholder, labels_placeholder = placeholder_inputs(
FLAGS.batch_size, mnist.IMAGE_PIXELS)
# Initialize network
mnist.init_net(images_placeholder, labels_placeholder, FLAGS.learning_rate)
mnist.init_val()
# Build the summary operation based on the TF collection of Summaries.
summary_op = tf.merge_all_summaries()
# Create a saver for writing training checkpoints.
saver = tf.train.Saver()
# Instantiate a SummaryWriter to output summaries and the Graph.
summary_writer = tf.train.SummaryWriter(FLAGS.train_dir,
graph_def=mnist.sess.graph_def)
# And then after everything is built, start the training loop.
for step in xrange(FLAGS.max_steps):
start_time = time.time()
# Fill a feed dictionary with the actual set of images and labels
# for this particular training step.
feed_dict = fill_feed_dict(data_sets.train,
images_placeholder,
labels_placeholder)
# Run one step of the model. The return values are the activations
# from the `train_op` (which is discarded) and the `loss` Op. To
# inspect the values of your Ops or variables, you may include them
# in the list passed to sess.run() and the value tensors will be
# returned in the tuple from the call.
loss_value = mnist.train(feed_dict)
duration = time.time() - start_time
# Write the summaries and print an overview fairly often.
if step % 100 == 0:
# Print status to stdout.
print('Step %d: loss = %.2f (%.3f sec)' % (step, loss_value, duration))
# Update the events file.
summary_str = mnist.run(summary_op, feed_dict=feed_dict)
summary_writer.add_summary(summary_str, step)
# Save a checkpoint and evaluate the model periodically.
if (step + 1) % 1000 == 0 or (step + 1) == FLAGS.max_steps:
saver.save(mnist.sess, FLAGS.train_dir, global_step=step)
# Evaluate against the training set.
print('Training Data Eval:')
do_eval(mnist,
images_placeholder,
labels_placeholder,
data_sets.train)
# Evaluate against the validation set.
print('Validation Data Eval:')
do_eval(mnist,
images_placeholder,
labels_placeholder,
data_sets.validation)
# Evaluate against the test set.
print('Test Data Eval:')
do_eval(mnist,
images_placeholder,
labels_placeholder,
data_sets.test)
