class CNN(object):
def __init__(self):
self.config = Config()
self.reader = Reader()
self.layer = self.setup_layer()
self.loss = self.setup_loss()
self.metrics = self.setup_metrics()
self.merged = tf.summary.merge_all()
self.train_writer = tf.summary.FileWriter(self.config.root +
'/event_detect/summary/cnn')
def setup_layer(self):
layer = dict()
layer['target'] = tf.placeholder(tf.int32, shape=[None], name='target')
layer['input'] = tf.placeholder(
tf.float32,
shape=[None, None, self.config.winsize, 3],
name='input')
layer['conv1'] = layers.conv(
layer['input'],
filter=[3, 3, 3, 8],
# strides=[1, 1, 4, 1],
strides=[1, 1, 1, 1],
padding='SAME',
wd=0.001,
bias=0.0,
name='conv1')
layer['pooling1'] = layers.pool(layer['conv1'],
ksize=[1, 1, 3, 1],
strides=[1, 1, 3, 1],
padding='SAME',
pool_func=tf.nn.max_pool,
name='pooling1')
layer['conv2'] = layers.conv(
layer['pooling1'],
filter=[3, 3, 8, 16],
# strides=[1, 1, 4, 1],
strides=[1, 1, 1, 1],
padding='SAME',
wd=0.001,
bias=0.0,
name='conv2')
layer['pooling2'] = layers.pool(layer['conv2'],
ksize=[1, 1, 3, 1],
strides=[1, 1, 3, 1],
padding='SAME',
pool_func=tf.nn.max_pool,
name='pooling2')
layer['conv3'] = layers.conv(
layer['pooling2'],
filter=[3, 3, 16, 32],
# strides=[1, 1, 4, 1],
strides=[1, 1, 1, 1],
padding='VALID',
wd=0.001,
bias=0.0,
name='conv3')
layer['pooling3'] = layers.pool(layer['conv3'],
ksize=[1, 1, 3, 1],
strides=[1, 1, 3, 1],
padding='SAME',
pool_func=tf.nn.max_pool,
name='pooling3')
layer['conv4'] = layers.conv(
layer['pooling3'],
filter=[1, 3, 32, 32],
# strides=[1, 1, 4, 1],
strides=[1, 1, 1, 1],
padding='SAME',
wd=0.001,
bias=0.0,
name='conv4')
layer['pooling4'] = layers.pool(layer['conv4'],
ksize=[1, 1, 3, 1],
strides=[1, 1, 3, 1],
padding='SAME',
pool_func=tf.nn.max_pool,
name='pooling4')
layer['conv5'] = layers.conv(
layer['pooling4'],
filter=[1, 3, 32, 64],
# strides=[1, 1, 4, 1],
strides=[1, 1, 1, 1],
padding='SAME',
wd=0.001,
bias=0.0,
name='conv5')
layer['pooling5'] = layers.pool(layer['conv5'],
ksize=[1, 1, 3, 1],
strides=[1, 1, 3, 1],
padding='SAME',
pool_func=tf.nn.max_pool,
name='pooling5')
layer['conv6'] = layers.conv(
layer['pooling5'],
filter=[1, 9, 64, 128],
# strides=[1, 1, 4, 1],
strides=[1, 1, 1, 1],
padding='VALID',
wd=0.001,
bias=0.0,
name='conv6')
layer['conv7'] = layers.conv(
layer['conv6'],
filter=[1, 1, 128, 128],
# strides=[1, 1, 4, 1],
strides=[1, 1, 1, 1],
padding='SAME',
wd=0.001,
bias=0.0,
name='conv7')
layer['conv8'] = layers.conv(
layer['conv7'],
filter=[1, 1, 128, 2],
# strides=[1, 1, 4, 1],
strides=[1, 1, 1, 1],
padding='SAME',
wd=0.001,
bias=0.0,
name='conv8')
layer['unfold'] = layers.unfold(layer['conv8'], name='unfold')
#layer['logits'] = tf.reduce_mean(layer['unfold'], 1, name='logits')
layer['class_prob'] = tf.nn.softmax(layer['unfold'], name='class_prob')
layer['class_prediction'] = tf.argmax(layer['class_prob'],
1,
name='class_pred')
return layer
def setup_loss(self):
with tf.name_scope('loss'):
raw_loss = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=self.layer['unfold'], labels=self.layer['target']))
tf.summary.scalar('raw_loss', raw_loss) #tensorboard可视化
tf.add_to_collection('losses', raw_loss) #将raw_loss添加到losses中
loss = tf.add_n(tf.get_collection('losses'),
name='total_loss') #所有元素相加
tf.summary.scalar('total_loss', loss) #tensorboard可视化
return loss
def setup_metrics(self):
metrics = dict()
with tf.variable_scope('metrics'):
metrics['accuracy'] = tf.metrics.accuracy(
labels=self.layer['target'],
predictions=self.layer['class_prediction'],
name='accuracy')[1]
tf.summary.scalar('accuracy', metrics['accuracy'])
metrics['recall'] = tf.metrics.recall(
labels=self.layer['target'],
predictions=self.layer['class_prediction'],
name='recall')[1]
tf.summary.scalar('recall', metrics['recall'])
metrics['precision'] = tf.metrics.precision(
labels=self.layer['target'],
predictions=self.layer['class_prediction'],
name='precision')[1]
tf.summary.scalar('precision', metrics['precision'])
return metrics
def train(self, passes, new_training=True):
with tf.Session() as sess:
global_step = tf.Variable(0, trainable=False)
#learning_rate = tf.train.exponential_decay(0.001, global_step, 200, 0.8, staircase=True)
training = tf.train.AdamOptimizer(
self.config.learning_rate).minimize(self.loss)
if new_training:
saver, global_step = Model.start_new_session(sess)
else:
saver, global_step = Model.continue_previous_session(
sess,
model_file='cnn',
ckpt_file=self.config.root +
'/event_detect/saver/cnn/checkpoint')
sess.run(tf.local_variables_initializer())
self.train_writer.add_graph(sess.graph, global_step=global_step)
test_restlt = []
for step in range(1 + global_step, 1 + passes + global_step):
input, target = self.reader.get_cnn_batch_data('train')
#print(input.shape)
summary, _, acc = sess.run(
[self.merged, training, self.metrics['accuracy']],
feed_dict={
self.layer['input']: input,
self.layer['target']: target
})
self.train_writer.add_summary(summary, step)
if step % 10 == 0:
loss = sess.run(self.loss,
feed_dict={
self.layer['input']: input,
self.layer['target']: target
})
test_restlt.append(loss)
print(
"gobal_step {}, training_loss {}, accuracy {}".format(
step, loss, acc))
if step % 100 == 0:
test_x, text_y = self.reader.get_cnn_batch_data('test')
acc, recall, precision = sess.run([
self.metrics['accuracy'], self.metrics['recall'],
self.metrics['precision']
],
feed_dict={
self.layer['input']:
test_x,
self.layer['target']:
text_y
})
print("test: accuracy {}, recall {}, precision {}".format(
acc, recall, precision))
saver.save(sess,
self.config.root +
'/event_detect/saver/cnn/cnn',
global_step=step)
print('checkpoint saved')
#print(sess.run([self.layer['class_prob']], feed_dict={self.layer['input']: input}))
print(test_restlt)
def classify(self, sess, input_):
class_prediction, confidence = sess.run(
[self.layer['class_prediction'], self.layer['class_prob']],
feed_dict={self.layer['input']: input_})
confidence = confidence[:, 1]
if confidence > self.config.prob:
class_prediction = 1
else:
class_prediction = 0
return class_prediction, confidence
class CNN(object):
def __init__(self):
self.config = Config()
self.reader = Reader()
self.layer = self.setup_layer()
self.loss = self.setup_loss()
self.metrics = self.setup_metrics()
self.merged = tf.summary.merge_all()
self.train_writer = tf.summary.FileWriter('summary/cnn')
def setup_layer(self):
layer = dict()
layer['target'] = tf.placeholder(tf.int32, shape=[None], name='target')
layer['input'] = tf.placeholder(
tf.float32, shape=[None, 1, self.config.winsize, 3], name='input')
layer['conv1'] = layers.conv(
layer['input'],
filter=[1, 6, 3, 8],
# strides=[1, 1, 4, 1],
strides=[1, 1, 1, 1],
padding='SAME',
wd=1e-3,
bias=0.0,
name='conv1')
layer['pooling1'] = layers.pool(layer['conv1'],
ksize=[1, 1, 3, 1],
strides=[1, 1, 3, 1],
padding='SAME',
pool_func=tf.nn.max_pool,
name='pooling1')
layer['conv2'] = layers.conv(
layer['pooling1'],
filter=[1, 6, 8, 16],
# strides=[1, 1, 4, 1],
strides=[1, 1, 1, 1],
padding='SAME',
wd=1e-3,
bias=0.0,
name='conv2')
layer['pooling2'] = layers.pool(layer['conv2'],
ksize=[1, 1, 3, 1],
strides=[1, 1, 3, 1],
padding='SAME',
pool_func=tf.nn.max_pool,
name='pooling2')
layer['conv3'] = layers.conv(
layer['pooling2'],
filter=[1, 6, 16, 32],
# strides=[1, 1, 4, 1],
strides=[1, 1, 1, 1],
padding='SAME',
wd=1e-3,
bias=0.0,
name='conv3')
layer['pooling3'] = layers.pool(layer['conv3'],
ksize=[1, 1, 3, 1],
strides=[1, 1, 3, 1],
padding='SAME',
pool_func=tf.nn.max_pool,
name='pooling3')
layer['conv4'] = layers.conv(
layer['pooling3'],
filter=[1, 6, 32, 32],
# strides=[1, 1, 4, 1],
strides=[1, 1, 1, 1],
padding='SAME',
wd=1e-3,
bias=0.0,
name='conv4')
layer['pooling4'] = layers.pool(layer['conv4'],
ksize=[1, 1, 3, 1],
strides=[1, 1, 3, 1],
padding='SAME',
pool_func=tf.nn.max_pool,
name='pooling4')
layer['conv5'] = layers.conv(
layer['pooling4'],
filter=[1, 6, 32, 32],
# strides=[1, 1, 4, 1],
strides=[1, 1, 1, 1],
padding='SAME',
wd=1e-3,
bias=0.0,
name='conv5')
layer['pooling5'] = layers.pool(layer['conv5'],
ksize=[1, 1, 3, 1],
strides=[1, 1, 3, 1],
padding='SAME',
pool_func=tf.nn.max_pool,
name='pooling5')
layer['conv6'] = layers.conv(
layer['pooling5'],
filter=[1, 6, 32, 32],
# strides=[1, 1, 4, 1],
strides=[1, 1, 1, 1],
padding='SAME',
wd=1e-3,
bias=0.0,
name='conv6')
layer['pooling6'] = layers.pool(layer['conv6'],
ksize=[1, 1, 3, 1],
strides=[1, 1, 3, 1],
padding='SAME',
pool_func=tf.nn.max_pool,
name='pooling6')
layer['unfold'] = layers.Unfold(layer['pooling6'], name='unfold')
layer['logits'] = layers.fc(layer['unfold'], 2, wd=1e-3, name='logits')
layer['class_prob'] = tf.nn.softmax(layer['logits'], name='class_prob')
layer['class_prediction'] = tf.argmax(layer['class_prob'],
1,
name='class_pred')
return layer
def setup_loss(self):
with tf.name_scope('loss'):
raw_loss = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=self.layer['logits'], labels=self.layer['target']))
tf.summary.scalar('raw_loss', raw_loss)
tf.add_to_collection('losses', raw_loss)
loss = tf.add_n(tf.get_collection('losses'), name='total_loss')
tf.summary.scalar('total_loss', loss)
return loss
def setup_metrics(self):
metrics = dict()
with tf.variable_scope('metrics'):
metrics['accuracy'] = tf.metrics.accuracy(
labels=self.layer['target'],
predictions=self.layer['class_prediction'],
name='accuracy')[1]
tf.summary.scalar('accuracy', metrics['accuracy'])
metrics['recall'] = tf.metrics.recall(
labels=self.layer['target'],
predictions=self.layer['class_prediction'],
name='recall')[1]
tf.summary.scalar('recall', metrics['recall'])
metrics['precision'] = tf.metrics.precision(
labels=self.layer['target'],
predictions=self.layer['class_prediction'],
name='precision')[1]
tf.summary.scalar('precision', metrics['precision'])
return metrics
def train(self, passes, new_training=True):
with tf.Session() as sess:
training = tf.train.AdamOptimizer(1e-3).minimize(self.loss)
if new_training:
saver, global_step = Model.start_new_session(sess)
else:
saver, global_step = Model.continue_previous_session(
sess, model_file='cnn', ckpt_file='saver/cnn/checkpoint')
sess.run(tf.local_variables_initializer())
self.train_writer.add_graph(sess.graph, global_step=global_step)
for step in range(1 + global_step, 1 + passes + global_step):
input, target = self.reader.get_cnn_batch_data('train')
summary, _, acc = sess.run(
[self.merged, training, self.metrics['accuracy']],
feed_dict={
self.layer['input']: input,
self.layer['target']: target
})
self.train_writer.add_summary(summary, step)
if step % 10 == 0:
loss = sess.run(self.loss,
feed_dict={
self.layer['input']: input,
self.layer['target']: target
})
print(
"gobal_step {}, training_loss {}, accuracy {}".format(
step, loss, acc))
if step % 100 == 0:
test_x, text_y = self.reader.get_cnn_batch_data('test')
acc, recall, precision = sess.run([
self.metrics['accuracy'], self.metrics['recall'],
self.metrics['precision']
],
feed_dict={
self.layer['input']:
test_x,
self.layer['target']:
text_y
})
print("test: accuracy {}, recall {}, precision {}".format(
acc, recall, precision))
saver.save(sess, 'saver/cnn/cnn', global_step=step)
print('checkpoint saved')
def classify(self, sess, input_):
# with tf.Session() as sess:
# saver, global_step = Model.continue_previous_session(sess,
# model_file='cnn',
# ckpt_file='saver/cnn/checkpoint')
class_prediction, confidence = sess.run(
[self.layer['class_prediction'], self.layer['class_prob']],
feed_dict={self.layer['input']: input_})
confidence = confidence[:, 1]
return class_prediction, confidence