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import tensorflow as tf
import os
import pickle
import numpy as np
CIFAR_DIR = "cifar-10-batches-py"
def load_data(filename):
with open(filename, 'rb') as f:
data = pickle.load(f, encoding='iso-8859-1')
return data['data'], data['labels']
class CifarData:
def __init__(self, filenames, need_shuffle):
all_data = []
all_labels = []
for filename in filenames:
data, labels = load_data(filename)
for item, label in zip(data, labels):
all_data.append(item)
all_labels.append(label)
self._data = np.vstack(all_data)
self._labels = np.hstack(all_labels)
self._num_examples = self._data.shape[0]
self._need_shuffle = need_shuffle
self._indicator = 0
if self._need_shuffle:
self._shuffle_data()
def _shuffle_data(self):
p = np.random.permutation(self._num_examples)
self._data = self._data[p]
self._labels = self._labels[p]
def next_batch(self, batch_size):
end_indicator = self._indicator + batch_size
if end_indicator > self._num_examples:
if self._need_shuffle:
self._shuffle_data()
self._indicator = 0
end_indicator = batch_size
else:
raise Exception("have no more examples")
if end_indicator > self._num_examples:
raise Exception("batch size is larger than all examples")
batch_data = self._data[self._indicator: end_indicator]
batch_labels = self._labels[self._indicator: end_indicator]
self._indicator = end_indicator
return batch_data, batch_labels
batch_size = 40
x = tf.placeholder(tf.float32, [batch_size, 3072])
y = tf.placeholder(tf.int64, [batch_size])
is_training =tf.placeholder(tf.bool, [])
x_image = tf.reshape(x, [-1, 3, 32, 32])
x_image = tf.transpose(x_image, perm=[0, 2, 3, 1])
x_image_list = tf.split(x_image, num_or_size_splits=batch_size, axis=0)
result_x_image_list = []
for x_single_image in x_image_list:
x_single_image = tf.reshape(x_single_image, [32, 32, 3])
data_aug_1 = tf.image.random_flip_left_right(x_single_image)
data_aug_2 = tf.image.random_brightness(data_aug_1, max_delta=63)
data_aug_3 = tf.image.random_contrast(data_aug_2, lower=0.2, upper=1.8)
data_aug_3 = tf.reshape(data_aug_3, [1, 32, 32, 3])
result_x_image_list.append(data_aug_3)
result_x_images = tf.concat(result_x_image_list, axis= 0)
result_x_image_normal = result_x_images /127.5 -1
def Myconv2d(input,name,is_training,output_channel= 32,kernel_size=(3,3),padding='same',activation= tf.nn.relu):
conv_result = tf.layers.conv2d(input, output_channel, kernel_size, padding=padding, activation=None, name=name)
batch_normalization_result = tf.layers.batch_normalization(conv_result, training = is_training)
activation_result = activation(batch_normalization_result)
return activation_result
def Mypooling2d(input, name):
return tf.layers.max_pooling2d(input, (2,2), (2,2), name = name)
conv1_1 = Myconv2d(result_x_image_normal, 'conv1_1', is_training)
conv1_2 = Myconv2d(conv1_1, 'conv1_2', is_training)
conv1_3 = Myconv2d(conv1_2, 'conv1_3', is_training)
pooling1 = Mypooling2d(conv1_3, 'pool1')
conv2_1 = Myconv2d(pooling1, 'conv2_1', is_training)
conv2_2 = Myconv2d(conv2_1, 'conv2_2', is_training)
conv2_3 = Myconv2d(conv2_2, 'conv2_3', is_training)
pooling2 = Mypooling2d(conv2_3, 'pool2')
conv3_1 = Myconv2d(pooling2,'conv3_1', is_training)
conv3_2 = Myconv2d(conv3_1, 'conv3_2', is_training)
conv3_3 = Myconv2d(conv3_2, 'conv3_3', is_training)
pooling3 = Mypooling2d(conv3_3, 'pool3')
flatten = tf.layers.flatten(pooling3)
y_ = tf.layers.dense(flatten, 10)
loss = tf.losses.sparse_softmax_cross_entropy(labels=y, logits=y_)
predict = tf.math.argmax(y_, 1)
correct_prediction = tf.equal(predict, y)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float64))
with tf.name_scope('train_op'):
train_op = tf.train.GradientDescentOptimizer(0.01
).minimize(loss)
train_filenames = []
for i in range(1, 6):
train_filenames.append(os.path.join(CIFAR_DIR, 'data_batch_%d' % i))
test_filenames = [os.path.join(CIFAR_DIR, 'test_batch')]
train_data = CifarData(train_filenames, True)
test_data = CifarData(test_filenames, False)
init = tf.global_variables_initializer()
train_steps = 10000
test_steps = 50
with tf.Session() as sess:
sess.run(init)
for i in range(train_steps):
batch_data, batch_labels = train_data.next_batch(batch_size)
loss_val, acc_val, _ = sess.run(
[loss, accuracy, train_op],
feed_dict={
x: batch_data,
y: batch_labels,
is_training: True
}
)
if (i + 1) % 50 == 0:
print('[Train] Step : %d, loss %4.5f, acc: %4.5f' % (i, loss_val, acc_val))
all_test_acc_val = []
if (i + 1) % 500 == 0:
test_batch_data, test_batch_labels = test_data.next_batch(batch_size)
test_acc_val = sess.run(
[accuracy],
feed_dict={
x: test_batch_data,
y: test_batch_labels,
is_training: False
}
)
all_test_acc_val.append(test_acc_val)
test_acc = np.mean(all_test_acc_val)
print('[Test] Step: %d, acc: %4.5f' % (i + 1, test_acc))
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