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train_cnn.py
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train_cnn.py
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import tensorflow as tf
import numpy as np
import argparse
import time
import os
from cnn_3d import CNN_3D, Config
import mri_input
from test_cnn import test_cnn
import nn_utils
import sys
BATCH_SIZE = 15
MAX_STEPS = 10000
SAVE_EVERY = 10
MIN_EXAMPLES_IN_QUEUE = 1000
EARLY_STOPPING = 50
def train_cnn(config):
mode = config.mode
save_path = nn_utils.get_save_path(config)
intro_str = '==> Building 3D CNN with %d layers'
print intro_str % (config.num_layers)
if config.use_sex_labels:
print 'Debugging by training on gender labels'
fn = 'data/mri_{}train.tfrecords'.format(config.gate)
print '==> Reading examples from', fn
filename_queue = tf.train.string_input_producer([fn], num_epochs=None)
with tf.device('/cpu:0'):
image, label, sex, corr = mri_input.read_and_decode_single_example(filename_queue,
downsample_factor=config.downsample_factor, corr=config.use_correlation,
rotate=config.rotate, noise=config.noise)
image_batch, label_batch, sex_batch, corr_batch = tf.train.shuffle_batch(
[image, label, sex, corr], batch_size=BATCH_SIZE,
capacity=10000,
min_after_dequeue=MIN_EXAMPLES_IN_QUEUE
)
label_batch = sex_batch if config.use_sex_labels else label_batch
cnn = CNN_3D(
config,
image_batch,
label_batch,
corr_batch,
)
# only restore layers that were previously trained
pretrained_names = ['conv_' + str(i+1) + '/weights:0' for i in range(config.num_layers_to_restore)]
pretrained_vars = [v for v in tf.all_variables() if v.name in pretrained_names]
print '==> variables to be restored:'
for v in pretrained_vars:
print v.name
print '==> variables to be trained:'
for v in tf.trainable_variables():
print v.name
sess = tf.Session()
summary_writer = tf.train.SummaryWriter('summaries/' + config.sum_dir + '/{}/train'.format(save_path[8:]), sess.graph)
init = tf.initialize_all_variables()
sess.run(init)
if config.num_layers_to_restore > 0:
restorer = tf.train.Saver(pretrained_vars)
print '==> restoring weights'
#path = 'weights/cae_pretrain_{}.weights'.format(str(num_layers_to_restore))
path = 'weights/greedy_6layer_weights.weights'
assert os.path.exists(path)
restorer.restore(sess, path)
if config.num_layers_to_restore == -1:
restorer = tf.train.Saver()
path = 'weights/cae_supervised.weights'
assert os.path.exists(path)
restorer.restore(sess, path)
saver = tf.train.Saver()
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
train_accuracy = 0
best_val_loss = None
for step in xrange(MAX_STEPS):
start_time = time.time()
_, loss_value, image_value, output_value, accuracy, summary = sess.run([
cnn.train_op,
cnn.loss,
image_batch,
cnn.outputs,
cnn.accuracy,
cnn.merged])
#print output_value
train_accuracy += accuracy
duration = time.time() - start_time
summary_writer.add_summary(summary, step)
if step % 2 == 0:
num_examples_per_step = BATCH_SIZE
sec_per_batch = float(duration)
format_str = ('step %s, loss = %.2f (%.3f '
'sec/batch)')
s = format_str % (step, loss_value, sec_per_batch)
sys.stdout.write('\r' + s + ' ')
sys.stdout.flush()
if (step % SAVE_EVERY == 0 or (step + 1) == MAX_STEPS) and step != 0:
if mode == 'supervised':
saver.save(sess, save_path)
print '==> evaluating valid and train accuracy'
val_accuracy, val_loss = test_cnn(config, start_step=step)
print 'train accuracy:', train_accuracy/float(SAVE_EVERY)
print 'val accuracy:', val_accuracy
train_accuracy = 0
if val_loss < best_val_loss or best_val_loss is None:
early_stopping_count = 0
best_val_loss = val_loss
print '==> saving weights to', save_path + '_best'
saver.save(sess, save_path + '_best')
else:
early_stopping_count += 1
if early_stopping_count >= EARLY_STOPPING: break
#cnn._save_images(image_value, filter_val, 'new_' + str(step))
else:
saver.save(sess,
'weights/cae_' + mode + '_' + str(num_layers) + '.weights')
#cnn._save_images(image_value, output_value, 'new_' + str(step))
coord.request_stop()
coord.join(threads)
sess.close()
tf.reset_default_graph()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("-m", "--mode", default="supervised")
parser.add_argument("-l", "--num-layers", type=int, default=4)
parser.add_argument("-r", "--num-layers-to-restore", type=int, default=0)
parser.add_argument("-t", "--num-layers-to-train", type=int, default=4,
help="trains the specified number of innermost layers")
parser.add_argument("-d", "--downsample_factor", type=int, default=2)
parser.add_argument("-s", "--use_sex_labels", type=bool, default=False)
parser.add_argument("-c", "--use_correlation", type=int, default=0, help="0 indicates no use, 1 supplements, 2 trains on only correlation")
parser.add_argument('-g', '--gate', default='')
parser.add_argument('-sd', '--sum-dir', default='')
parser.add_argument('-ro', '--rotate', type=bool, default=True)
parser.add_argument('-no', '--noise', type=float, default=0.1)
args = parser.parse_args()
config = Config()
config.gate = args.gate
config.num_layers = args.num_layers
config.num_layers_to_train = args.num_layers_to_train
config.mode = args.mode
config.num_layers_to_restore = args.num_layers_to_restore
config.downsample_factor = args.downsample_factor
config.use_sex_labels = args.use_sex_labels
config.use_correlation = args.use_correlation
config.sum_dir = args.sum_dir
config.rotate = args.rotate
config.noise = args.noise
train_cnn(config)