【分散式Tensorflow(0.11.0)問題 未解決】Segmentation fault (core dumped)
有三個測試，主函式是基本一樣的，就是模型不同，但是均以 Segmentation fault (core dumped) 出錯。
在我上一篇問題記錄裡，是以dummy資料集測試的，只有前向計算，沒有引數更新和優化等操作，因此重新寫了一個指令碼，使用真實的資料集。
train資料集：
960831張圖片（224*224），已轉換為97個tfrecords檔案，如下所示：
[root@dl1 train]# ls
train_224_0.tfrecords train_224_32.tfrecords train_224_55.tfrecords train_224_78.tfrecords
train_224_10.tfrecords train_224_33.tfrecords train_224_56.tfrecords train_224_79.tfrecords
train_224_11.tfrecords train_224_34.tfrecords train_224_57.tfrecords train_224_7.tfrecords
train_224_12.tfrecords train_224_35.tfrecords train_224_58.tfrecords train_224_80.tfrecords
train_224_13.tfrecords train_224_36.tfrecords train_224_59.tfrecords train_224_81.tfrecords
train_224_14.tfrecords train_224_37.tfrecords train_224_5.tfrecords train_224_82.tfrecords
train_224_15.tfrecords train_224_38.tfrecords train_224_60.tfrecords train_224_83.tfrecords
train_224_16.tfrecords train_224_39.tfrecords train_224_61.tfrecords train_224_84.tfrecords
train_224_17.tfrecords train_224_3.tfrecords train_224_62.tfrecords train_224_85.tfrecords
train_224_18.tfrecords train_224_40.tfrecords train_224_63.tfrecords train_224_86.tfrecords
train_224_19.tfrecords train_224_41.tfrecords train_224_64.tfrecords train_224_87.tfrecords
train_224_1.tfrecords train_224_42.tfrecords train_224_65.tfrecords train_224_88.tfrecords
train_224_20.tfrecords train_224_43.tfrecords train_224_66.tfrecords train_224_89.tfrecords
train_224_21.tfrecords train_224_44.tfrecords train_224_67.tfrecords train_224_8.tfrecords
train_224_22.tfrecords train_224_45.tfrecords train_224_68.tfrecords train_224_90.tfrecords
train_224_23.tfrecords train_224_46.tfrecords train_224_69.tfrecords train_224_91.tfrecords
train_224_24.tfrecords train_224_47.tfrecords train_224_6.tfrecords train_224_92.tfrecords
train_224_25.tfrecords train_224_48.tfrecords train_224_70.tfrecords train_224_93.tfrecords
train_224_26.tfrecords train_224_49.tfrecords train_224_71.tfrecords train_224_94.tfrecords
train_224_27.tfrecords train_224_4.tfrecords train_224_72.tfrecords train_224_95.tfrecords
train_224_28.tfrecords train_224_50.tfrecords train_224_73.tfrecords train_224_96.tfrecords
train_224_29.tfrecords train_224_51.tfrecords train_224_74.tfrecords train_224_9.tfrecords
train_224_2.tfrecords train_224_52.tfrecords train_224_75.tfrecords train_224_image_mean.npy
train_224_30.tfrecords train_224_53.tfrecords train_224_76.tfrecords
train_224_31.tfrecords train_224_54.tfrecords train_224_77.tfrecords
Main函式：
def main(_):
ps_hosts = FLAGS.ps_hosts.split(",")
worker_hosts = FLAGS.worker_hosts.split(",")
cluster = tf.train.ClusterSpec({"ps": ps_hosts, "worker": worker_hosts})
server = tf.train.Server(cluster,job_name=FLAGS.job_name,task_index=FLAGS.task_index)
issync = FLAGS.issync
if FLAGS.job_name == "ps":
server.join()
elif FLAGS.job_name == "worker":
images, labels = ...
with tf.device(tf.train.replica_device_setter(
worker_device="/job:worker/task:%d" % FLAGS.task_index,
cluster=cluster)):
global_step = tf.Variable(0, name='global_step', trainable=False)
# 修改這裡，呼叫不同的模型
logits, parameters = inference(images)
logits = tf.contrib.layers.flatten(logits)
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels, logits=logits, name='xentropy')
loss_value = tf.reduce_mean(cross_entropy, name='xentropy_mean')
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
grads_and_vars = optimizer.compute_gradients(loss_value)
if issync == 1:
# Synchronous mode
rep_op = tf.train.SyncReplicasOptimizer(optimizer,
replicas_to_aggregate=len(
worker_hosts),
replica_id=FLAGS.task_index,
total_num_replicas=len(
worker_hosts),
use_locking=True)
train_op = rep_op.apply_gradients(grads_and_vars, global_step=global_step)
init_token_op = rep_op.get_init_tokens_op()
chief_queue_runner = rep_op.get_chief_queue_runner()
else:
# Asynchronous mode
train_op = optimizer.apply_gradients(grads_and_vars, global_step=global_step)
init_op = tf.initialize_all_variables()
saver = tf.train.Saver()
tf.summary.scalar('cost', loss_value)
summary_op = tf.summary.merge_all()
sv = tf.train.Supervisor(is_chief=(FLAGS.task_index == 0),
logdir="./alexnet_checkpoint",
init_op=init_op,
summary_op=None,
saver=saver,
global_step=global_step,
save_model_secs=60)
with sv.prepare_or_wait_for_session(server.target) as sess:
# Sync
if FLAGS.task_index == 0 and issync == 1:
sv.start_queue_runners(sess, [chief_queue_runner])
sess.run(init_token_op)
step = 0
while not sv.should_stop():
try:
start_time = time.time()
_, loss_v, step = sess.run([train_op, loss_value, global_step])
if step > 1000:
break
duration = time.time() - start_time
if step >= 10:
if not step % 10:
print ('%s: step %d, duration = %.3f' % (datetime.now(), step, duration))
except tf.errors.OutOfRangeError:
print('Done training -- epoch limit reached')
sv.stop()測試1，alexnet模型，參考這裡
def print_activations(t):
print(t.op.name, ' ', t.get_shape().as_list())
def inference(images):
"""Build the AlexNet model.
Args:
images: Images Tensor
Returns:
pool5: the last Tensor in the convolutional component of AlexNet.
parameters: a list of Tensors corresponding to the weights and biases of the
AlexNet model.
"""
parameters = []
# conv1
with tf.name_scope('conv1') as scope:
kernel = tf.Variable(tf.truncated_normal([11, 11, 3, 64], dtype=tf.float32,
stddev=1e-1), name='weights')
conv = tf.nn.conv2d(images, kernel, [1, 4, 4, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[64], dtype=tf.float32),
trainable=True, name='biases')
bias = tf.nn.bias_add(conv, biases)
conv1 = tf.nn.relu(bias, name=scope)
print_activations(conv1)
parameters += [kernel, biases]
# lrn1
# TODO(shlens, jiayq): Add a GPU version of local response normalization.
# pool1
pool1 = tf.nn.max_pool(conv1,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='VALID',
name='pool1')
print_activations(pool1)
# conv2
with tf.name_scope('conv2') as scope:
kernel = tf.Variable(tf.truncated_normal([5, 5, 64, 192], dtype=tf.float32,
stddev=1e-1), name='weights')
conv = tf.nn.conv2d(pool1, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[192], dtype=tf.float32),
trainable=True, name='biases')
bias = tf.nn.bias_add(conv, biases)
conv2 = tf.nn.relu(bias, name=scope)
parameters += [kernel, biases]
print_activations(conv2)
# pool2
pool2 = tf.nn.max_pool(conv2,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='VALID',
name='pool2')
print_activations(pool2)
# conv3
with tf.name_scope('conv3') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 192, 384],
dtype=tf.float32,
stddev=1e-1), name='weights')
conv = tf.nn.conv2d(pool2, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[384], dtype=tf.float32),
trainable=True, name='biases')
bias = tf.nn.bias_add(conv, biases)
conv3 = tf.nn.relu(bias, name=scope)
parameters += [kernel, biases]
print_activations(conv3)
# conv4
with tf.name_scope('conv4') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 384, 256],
dtype=tf.float32,
stddev=1e-1), name='weights')
conv = tf.nn.conv2d(conv3, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32),
trainable=True, name='biases')
bias = tf.nn.bias_add(conv, biases)
conv4 = tf.nn.relu(bias, name=scope)
parameters += [kernel, biases]
print_activations(conv4)
# conv5
with tf.name_scope('conv5') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 256, 256],
dtype=tf.float32,
stddev=1e-1), name='weights')
conv = tf.nn.conv2d(conv4, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32),
trainable=True, name='biases')
bias = tf.nn.bias_add(conv, biases)
conv5 = tf.nn.relu(bias, name=scope)
parameters += [kernel, biases]
print_activations(conv5)
# pool5
pool5 = tf.nn.max_pool(conv5,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='VALID',
name='pool5')
print_activations(pool5)
return pool5, parameters
由於出錯資訊沒有具體的輸出，也不知道是哪一個步驟出錯，因此我列印每個步驟後，發現錯誤是出現在這一句：
saver = tf.train.Saver()執行到這一步的時候就突然 Segmentation fault (core dumped)
測試2：alexnet模型，參考這裡，這是slim模組裡的alexnet_v2模型。
def alexnet_v2_arg_scope(weight_decay=0.0005):
with slim.arg_scope([slim.conv2d, slim.fully_connected],
activation_fn=tf.nn.relu,
biases_initializer=tf.constant_initializer(0.1),
weights_regularizer=slim.l2_regularizer(weight_decay)):
with slim.arg_scope([slim.conv2d], padding='SAME'):
with slim.arg_scope([slim.max_pool2d], padding='VALID') as arg_sc:
return arg_sc
def alexnet_v2(inputs,
num_classes=1000,
is_training=True,
dropout_keep_prob=0.5,
spatial_squeeze=True,
scope='alexnet_v2'):
with tf.variable_scope(scope, 'alexnet_v2', [inputs]) as sc:
end_points_collection = sc.name + '_end_points'
# Collect outputs for conv2d, fully_connected and max_pool2d.
with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d],
outputs_collections=[end_points_collection]):
net = slim.conv2d(inputs, 64, [11, 11], 4, padding='VALID',
scope='conv1')
net = slim.max_pool2d(net, [3, 3], 2, scope='pool1')
net = slim.conv2d(net, 192, [5, 5], scope='conv2')
net = slim.max_pool2d(net, [3, 3], 2, scope='pool2')
net = slim.conv2d(net, 384, [3, 3], scope='conv3')
net = slim.conv2d(net, 384, [3, 3], scope='conv4')
net = slim.conv2d(net, 256, [3, 3], scope='conv5')
net = slim.max_pool2d(net, [3, 3], 2, scope='pool5')
# Use conv2d instead of fully_connected layers.
with slim.arg_scope([slim.conv2d],
weights_initializer=trunc_normal(0.005),
biases_initializer=tf.constant_initializer(0.1)):
net = slim.conv2d(net, 4096, [5, 5], padding='VALID',
scope='fc6')
net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
scope='dropout6')
net = slim.conv2d(net, 4096, [1, 1], scope='fc7')
net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
scope='dropout7')
net = slim.conv2d(net, num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
biases_initializer=tf.zeros_initializer,
scope='fc8')
# Convert end_points_collection into a end_point dict.
end_points = slim.utils.convert_collection_to_dict(end_points_collection)
if spatial_squeeze:
net = tf.squeeze(net, [1, 2], name='fc8/squeezed')
end_points[sc.name + '/fc8'] = net
return net, end_points
同樣是 Segmentation fault (core dumped) 的錯誤，錯誤在這一句：
net = slim.conv2d(net, num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
biases_initializer=tf.zeros_initializer(),
scope='fc8')測試3：googlenet模型，參考這裡，這是slim模組的inception_v1模型
def incepton_v1_arg_scope(weight_decay = 0.00004, stddev = 0.1, batch_norm_var_collection = 'moving_vars'):
batch_norm_params = {
'decay': 0.9997,
'epsilon': 0.001,
'updates_collections': tf.GraphKeys.UPDATE_OPS,
'variables_collections': {
'beta': None,
'gamma': None,
'moving_mean': [batch_norm_var_collection],
'moving_variance': [batch_norm_var_collection],
}
}
with slim.arg_scope([slim.conv2d, slim.fully_connected], weights_regularizer = slim.l2_regularizer(weight_decay)):
with slim.arg_scope([slim.conv2d],
weights_initializer = tf.truncated_normal_initializer(stddev = stddev),
activation_fn = tf.nn.relu,
normalizer_fn = slim.batch_norm,
normalizer_params = batch_norm_params) as sc:
return sc
def inception_v1_base(inputs,
final_endpoint='Mixed_5c',
scope='InceptionV1'):
end_points = {}
with tf.variable_scope(scope, 'InceptionV1', [inputs]):
with slim.arg_scope([slim.conv2d, slim.fully_connected],
weights_initializer=trunc_normal(0.01)):
with slim.arg_scope([slim.conv2d, slim.max_pool2d],
stride=1, padding='SAME'):
end_point = 'Conv2d_1a_7x7'
net = slim.conv2d(inputs, 64, [7, 7], stride=2, scope=end_point)
end_points[end_point] = net
if final_endpoint == end_point: return net, end_points
end_point = 'MaxPool_2a_3x3'
net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
end_points[end_point] = net
if final_endpoint == end_point: return net, end_points
end_point = 'Conv2d_2b_1x1'
net = slim.conv2d(net, 64, [1, 1], scope=end_point)
end_points[end_point] = net
if final_endpoint == end_point: return net, end_points
end_point = 'Conv2d_2c_3x3'
net = slim.conv2d(net, 192, [3, 3], scope=end_point)
end_points[end_point] = net
if final_endpoint == end_point: return net, end_points
end_point = 'MaxPool_3a_3x3'
net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
end_points[end_point] = net
if final_endpoint == end_point: return net, end_points
end_point = 'Mixed_3b'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, 96, [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, 128, [3, 3], scope='Conv2d_0b_3x3')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, 16, [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, 32, [3, 3], scope='Conv2d_0b_3x3')
with tf.variable_scope('Branch_3'):
branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, 32, [1, 1], scope='Conv2d_0b_1x1')
net = tf.concat(3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if final_endpoint == end_point: return net, end_points
end_point = 'Mixed_3c'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, 192, [3, 3], scope='Conv2d_0b_3x3')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_3x3')
with tf.variable_scope('Branch_3'):
branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
net = tf.concat(3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if final_endpoint == end_point: return net, end_points
end_point = 'MaxPool_4a_3x3'
net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
end_points[end_point] = net
if final_endpoint == end_point: return net, end_points
end_point = 'Mixed_4b'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, 96, [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, 208, [3, 3], scope='Conv2d_0b_3x3')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, 16, [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, 48, [3, 3], scope='Conv2d_0b_3x3')
with tf.variable_scope('Branch_3'):
branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
net = tf.concat(3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if final_endpoint == end_point: return net, end_points
end_point = 'Mixed_4c'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, 112, [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, 224, [3, 3], scope='Conv2d_0b_3x3')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, 24, [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, 64, [3, 3], scope='Conv2d_0b_3x3')
with tf.variable_scope('Branch_3'):
branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
net = tf.concat(3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if final_endpoint == end_point: return net, end_points
end_point = 'Mixed_4d'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, 256, [3, 3], scope='Conv2d_0b_3x3')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, 24, [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, 64, [3, 3], scope='Conv2d_0b_3x3')
with tf.variable_scope('Branch_3'):
branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
net = tf.concat(3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if final_endpoint == end_point: return net, end_points
end_point = 'Mixed_4e'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, 112, [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, 144, [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, 288, [3, 3], scope='Conv2d_0b_3x3')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, 64, [3, 3], scope='Conv2d_0b_3x3')
with tf.variable_scope('Branch_3'):
branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
net = tf.concat(3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if final_endpoint == end_point: return net, end_points
end_point = 'Mixed_4f'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, 256, [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, 320, [3, 3], scope='Conv2d_0b_3x3')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, 128, [3, 3], scope='Conv2d_0b_3x3')
with tf.variable_scope('Branch_3'):
branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1')
net = tf.concat(3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if final_endpoint == end_point: return net, end_points
end_point = 'MaxPool_5a_2x2'
net = slim.max_pool2d(net, [2, 2], stride=2, scope=end_point)
end_points[end_point] = net
if final_endpoint == end_point: return net, end_points
end_point = 'Mixed_5b'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, 256, [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, 320, [3, 3], scope='Conv2d_0b_3x3')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, 128, [3, 3], scope='Conv2d_0a_3x3')
with tf.variable_scope('Branch_3'):
branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1')
net = tf.concat(3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if final_endpoint == end_point: return net, end_points
end_point = 'Mixed_5c'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, 384, [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, 384, [3, 3], scope='Conv2d_0b_3x3')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, 48, [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, 128, [3, 3], scope='Conv2d_0b_3x3')
with tf.variable_scope('Branch_3'):
branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1')
net = tf.concat(3, values=[branch_0, branch_1, branch_2, branch_3])
return net, end_points
def inception_v1(inputs,
num_classes=1000,
is_training=True,
dropout_keep_prob=0.8,
prediction_fn=slim.softmax,
spatial_squeeze=True,
reuse=None,
scope='InceptionV1'):
with tf.variable_scope(scope, 'InceptionV1', [inputs, num_classes],
reuse=reuse) as scope:
with slim.arg_scope([slim.batch_norm, slim.dropout],
is_training=is_training):
net, end_points = inception_v1_base(inputs, scope=scope)
with tf.variable_scope('Logits'):
net = slim.avg_pool2d(net, [7, 7], stride=1, scope='AvgPool_0a_7x7')
net = slim.dropout(net,
dropout_keep_prob, scope='Dropout_0b')
logits = slim.conv2d(net, num_classes, [1, 1], activation_fn=None,
normalizer_fn=None, scope='Conv2d_0c_1x1')
if spatial_squeeze:
logits = tf.squeeze(logits, [1, 2], name='SpatialSqueeze')
end_points['Logits'] = logits
end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
return logits, end_points
沒錯，還是那個錯誤，錯誤出現在 end_point = 'Mixed_3b' 部分的這一句：
branch_1 = slim.conv2d(branch_1, 128, [3, 3], scope='Conv2d_0b_3x3')相關文章
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