【分布式Tensorflow(0.11.0)问题 未解决】Segmentation fault (core dumped)
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有三个测试，主函数是基本一样的，就是模型不同，但是均以 Segmentation fault (core dumped) 出错。
在我上一篇问题记录里，是以dummy数据集测试的，只有前向计算，没有参数更新和优化等操作，因此重新写了一个脚本，使用真实的数据集。
train数据集：
960831张图片（224*224），已转换为97个tfrecords文件，如下所示：
[root@dl1 train]# lstrain_224_0.tfrecords train_224_32.tfrecords train_224_55.tfrecords train_224_78.tfrecordstrain_224_10.tfrecords train_224_33.tfrecords train_224_56.tfrecords train_224_79.tfrecordstrain_224_11.tfrecords train_224_34.tfrecords train_224_57.tfrecords train_224_7.tfrecordstrain_224_12.tfrecords train_224_35.tfrecords train_224_58.tfrecords train_224_80.tfrecordstrain_224_13.tfrecords train_224_36.tfrecords train_224_59.tfrecords train_224_81.tfrecordstrain_224_14.tfrecords train_224_37.tfrecords train_224_5.tfrecords train_224_82.tfrecordstrain_224_15.tfrecords train_224_38.tfrecords train_224_60.tfrecords train_224_83.tfrecordstrain_224_16.tfrecords train_224_39.tfrecords train_224_61.tfrecords train_224_84.tfrecordstrain_224_17.tfrecords train_224_3.tfrecords train_224_62.tfrecords train_224_85.tfrecordstrain_224_18.tfrecords train_224_40.tfrecords train_224_63.tfrecords train_224_86.tfrecordstrain_224_19.tfrecords train_224_41.tfrecords train_224_64.tfrecords train_224_87.tfrecordstrain_224_1.tfrecords train_224_42.tfrecords train_224_65.tfrecords train_224_88.tfrecordstrain_224_20.tfrecords train_224_43.tfrecords train_224_66.tfrecords train_224_89.tfrecordstrain_224_21.tfrecords train_224_44.tfrecords train_224_67.tfrecords train_224_8.tfrecordstrain_224_22.tfrecords train_224_45.tfrecords train_224_68.tfrecords train_224_90.tfrecordstrain_224_23.tfrecords train_224_46.tfrecords train_224_69.tfrecords train_224_91.tfrecordstrain_224_24.tfrecords train_224_47.tfrecords train_224_6.tfrecords train_224_92.tfrecordstrain_224_25.tfrecords train_224_48.tfrecords train_224_70.tfrecords train_224_93.tfrecordstrain_224_26.tfrecords train_224_49.tfrecords train_224_71.tfrecords train_224_94.tfrecordstrain_224_27.tfrecords train_224_4.tfrecords train_224_72.tfrecords train_224_95.tfrecordstrain_224_28.tfrecords train_224_50.tfrecords train_224_73.tfrecords train_224_96.tfrecordstrain_224_29.tfrecords train_224_51.tfrecords train_224_74.tfrecords train_224_9.tfrecordstrain_224_2.tfrecords train_224_52.tfrecords train_224_75.tfrecords train_224_image_mean.npytrain_224_30.tfrecords train_224_53.tfrecords train_224_76.tfrecordstrain_224_31.tfrecords train_224_54.tfrecords train_224_77.tfrecords
Main函数：
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) # 修改这里，调用不同的模型 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()测试1，alexnet模型，参考这里
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由于出错信息没有具体的输出，也不知道是哪一个步骤出错，因此我打印每个步骤后，发现错误是出现在这一句：
saver = tf.train.Saver()
运行到这一步的时候就突然 Segmentation fault (core dumped)
测试2：alexnet模型，参考这里，这是slim模块里的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_scdef 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
同样是 Segmentation fault (core dumped) 的错误，错误在这一句：
net = slim.conv2d(net, num_classes, [1, 1], activation_fn=None, normalizer_fn=None, biases_initializer=tf.zeros_initializer(), scope='fc8')
测试3：googlenet模型，参考这里，这是slim模块的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 scdef 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_pointsdef 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没错，还是那个错误，错误出现在 end_point = 'Mixed_3b' 部分的这一句：
branch_1 = slim.conv2d(branch_1, 128, [3, 3], scope='Conv2d_0b_3x3')
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