def test(test_file):
log_dir = 'saves'
image_arr = get_one_image(test_file)
with tf.Graph().as_default():
image = tf.cast(image_arr, tf.float32)
image = tf.image.per_image_standardization(image)
image = tf.reshape(image, [1, 32, 32, 3])
print(image.shape)
p = model.mmodel(image, 1)
logits = tf.nn.softmax(p)
x = tf.placeholder(tf.float32, shape=[32, 32, 3])
saver = tf.train.Saver()
with tf.Session() as sess:
ckpt = tf.train.get_checkpoint_state(log_dir)
if ckpt and ckpt.model_checkpoint_path:
global_step = ckpt.model_checkpoint_path.split('/')[-1].split(
'-')[-1]
saver.restore(sess, ckpt.model_checkpoint_path)
print('Loading success')
prediction = sess.run(logits, feed_dict={x: image_arr})
max_index = np.argmax(prediction)
print(max_index)
return max_index
else:
print('No checkpoint')
def run_training():
data_dir = 'D:/WCsPy/data/train/'
log_dir = 'saves'
image, label = inputData.get_files(data_dir)
image_batches, label_batches = inputData.get_batches(
image, label, 32, 32, 16, 20)
print(image_batches.shape)
p = model.mmodel(image_batches, 16)
cost = model.loss(p, label_batches)
train_op = model.training(cost, 0.001)
acc = model.get_accuracy(p, label_batches)
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
saver = tf.train.Saver()
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
for step in np.arange(1000):
print(step)
if coord.should_stop():
break
_, train_acc, train_loss = sess.run([train_op, acc, cost])
print("loss:{} accuracy:{}".format(train_loss, train_acc))
if step % 100 == 0:
check = os.path.join(log_dir, "model.ckpt")
saver.save(sess, check, global_step=step)
except tf.errors.OutOfRangeError:
print("Done!!!")
finally:
coord.request_stop()
coord.join(threads)
sess.close()
def run_training(file_tfRecord):
log_dir = './log/'
image, label = rdData.read_tfRecord(file_tfRecord)
image_batches, label_batches = tf.train.batch([image, label],
batch_size=16,
capacity=20)
p = model.mmodel(image_batches, 16)
cost = model.loss(p, label_batches)
train_op = model.training(cost, 0.001)
acc = model.get_accuracy(p, label_batches)
sess = tf.Session()
init = tf.global_variables_initializer()
#merge all summary
summary_op = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(log_dir, sess.graph)
sess.run(init)
saver = tf.train.Saver()
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
for step in np.arange(1000):
if coord.should_stop():
break
_, train_acc, train_loss = sess.run([train_op, acc, cost])
print("{} step:{} loss:{} accuracy:{}".format(
datetime.now(), step, train_loss, train_acc))
if step % 250 == 0:
#record the summary
summary = sess.run(summary_op)
train_writer.add_summary(summary, step)
check = os.path.join(log_dir, "mmodel.ckpt")
saver.save(sess, check, global_step=step)
except tf.errors.OutOfRangeError:
print("Done!!!")
finally:
coord.request_stop()
coord.join(threads)
sess.close()
# Adapted from : https://www.tensorflow.org/get_started/mnist/pros
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
import model as model
import os
from tensorflow.examples.tutorials.mnist import input_data
data = input_data.read_data_sets("/tmp/data/", one_hot=True)
# Load Model
with tf.variable_scope("deep-learning"):
x = tf.placeholder(tf.float32, [None, 784])
keep_prob = tf.placeholder(tf.float32)
y, variables = model.mmodel(x, keep_prob)
# Training Model
y_ = tf.placeholder(tf.float32, [None, 10])
cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
#
save = tf.train.Saver(variables)
# Train the model
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for i in range(20000):
batch = data.train.next_batch(50)