def run(self, step_limit):
self.train()
with tf.Session() as sess:
tf.global_variables_initializer().run()
#cifar-10 데이터(32*32*3)을 받아오고, fc로 할 수 있도록 형태를 변환한다.(32*32*3 => 3072)
dataset = cifar.Cifar()
train_data, train_label, test_data, test_label = dataset.getdata()
train_data = train_data.reshape(-1, 3072)
test_data = test_data.reshape(-1, 3072)
test_indices = np.arange(len(test_data))
np.random.shuffle(test_indices)
test_indices = test_indices[0:1000]
name = self.info()
path = "cifar/" + str(step_limit) + name
saver = NNutils.save(path, sess)
writer, writer_test, merged = NNutils.graph(path, sess)
step = sess.run(self.global_step)
while step < step_limit:
print("step :", step)
for start, end in zip(
range(0, len(train_data), self.batch_size),
range(self.batch_size, len(train_data),
self.batch_size)):
summary, \
_, loss, \
step = sess.run([merged,
self.training, self.cost,
self.global_step],
feed_dict={self.x: train_data[start:end],
self.y: train_label[start:end],
self.dropout_conv: 1.0,
self.dropout_normal: 1.0})
if step % 50 == 0:
writer.add_summary(summary, step)
print(step, datetime.now(), loss)
summary, \
loss, \
accuracy = sess.run([merged, self.cost, self.accuracy],
feed_dict={self.x: test_data,
self.y: test_label,
self.dropout_conv: 1.0,
self.dropout_normal: 1.0})
writer_test.add_summary(summary, step)
print("test results : ", accuracy, loss)
saver.save(sess, path + "/" + name + ".ckpt", step)
def run(self, step_limit):
self.train()
with tf.Session() as sess:
tf.global_variables_initializer().run()
dataset = cifar.Cifar()
train_data, train_label, test_data, test_label = dataset.getdata()
test_indices = np.arange(len(test_data))
np.random.shuffle(test_indices)
test_indices = test_indices[0:1000]
path = "ConvAE_2dim/50000"
saver = NNutils.save(path, sess)
writer, writer_test, merged = NNutils.graph(path, sess)
step = sess.run(self.global_step)
while step < step_limit:
print("step :", step)
for start, end in zip(
range(0, len(train_data), self.batch_size),
range(self.batch_size, len(train_data),
self.batch_size)):
summary, \
_, \
loss, \
step = sess.run([merged,
self.training,
self.cost,
self.global_step],
feed_dict={self.x: train_data[start:end],
self.y: train_label[start:end],
self.dropout_conv: 0.8,
self.dropout_normal: 0.5})
if step % 50 == 0:
writer.add_summary(summary, step)
print(step, datetime.now(), loss)
summary, loss = sess.run(
[merged, self.cost],
feed_dict={
self.x: test_data[test_indices],
self.y: test_label[test_indices],
self.dropout_conv: 1.0,
self.dropout_normal: 1.0
})
writer_test.add_summary(summary, step)
print("test results : ", loss)
saver.save(sess, path + "/model.ckpt", step)
import cifar
from matplotlib import pyplot as plt
num_train_images = 10
num_test_images = 10
width = 32
height = 32
channels = 3
c = cifar.Cifar(num_train_images, num_test_images)
((train_images, train_labels), (test_images, test_labels)) = c.load_data()
print('train_labels = %s' % \
(','.join([c.labels[i] for i in c.train_labels])))
plt.imshow(train_images[0])
plt.show()
print('test_labels = %s' % \
(','.join([c.labels[i] for i in c.test_labels])))
plt.imshow(test_images[0])
plt.show()
self.training,
self.cost,
self.global_step],
feed_dict={self.x: train_data[start:end],
self.y: train_label[start:end],
self.dropout_conv: 0.8,
self.dropout_normal: 0.5})
if step % 50 == 0:
writer.add_summary(summary, step)
print(step, datetime.now(), loss)
summary, loss = sess.run(
[merged, self.cost],
feed_dict={
self.x: test_data[test_indices],
self.y: test_label[test_indices],
self.dropout_conv: 1.0,
self.dropout_normal: 1.0
})
writer_test.add_summary(summary, step)
print("test results : ", loss)
saver.save(sess, path + "/model.ckpt", step)
dataset = cifar.Cifar()
train_data, train_label, test_data, test_label = dataset.getdata()
model = Network()
model.run(50000)