我正在构建一个仅通过使用
来检测汽车的模型:- 方法:Tensorflow 2.0 with transfer-learning
- 预训练模型:ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8
- 数据集:coco-2017(仅限class car)
训练完成后，我运行模型，得到准确率约为50%，total_loss规模减少但不收敛。您可以查看2张检测到的汽车和tensorboard图片。
很高兴看到total_loss减少，但如果模型更收敛，我可以获得更高的准确性。我已经尝试在管道中使用num_epochs(从1更改为4)。配置，但它不工作。我认为修改训练历元数的参数是不正确的。
问题:如何修改Tensorflow 2.0中训练模型的epoch数?
你可以查看我的文件管道。下面的配置。
model {
ssd {
num_classes: 1
image_resizer {
fixed_shape_resizer {
height: 320
width: 320
}
}
feature_extractor {
type: "ssd_mobilenet_v2_fpn_keras"
depth_multiplier: 1.0
min_depth: 16
conv_hyperparams {
regularizer {
l2_regularizer {
weight: 4e-05
}
}
initializer {
random_normal_initializer {
mean: 0.0
stddev: 0.01
}
}
activation: RELU_6
batch_norm {
decay: 0.997
scale: true
epsilon: 0.001
}
}
use_depthwise: true
override_base_feature_extractor_hyperparams: true
fpn {
min_level: 3
max_level: 7
additional_layer_depth: 128
}
}
box_coder {
faster_rcnn_box_coder {
y_scale: 10.0
x_scale: 10.0
height_scale: 5.0
width_scale: 5.0
}
}
matcher {
argmax_matcher {
matched_threshold: 0.5
unmatched_threshold: 0.5
ignore_thresholds: false
negatives_lower_than_unmatched: true
force_match_for_each_row: true
use_matmul_gather: true
}
}
similarity_calculator {
iou_similarity {
}
}
box_predictor {
weight_shared_convolutional_box_predictor {
conv_hyperparams {
regularizer {
l2_regularizer {
weight: 4e-05
}
}
initializer {
random_normal_initializer {
mean: 0.0
stddev: 0.01
}
}
activation: RELU_6
batch_norm {
decay: 0.997
scale: true
epsilon: 0.001
}
}
depth: 128
num_layers_before_predictor: 4
kernel_size: 3
class_prediction_bias_init: -4.6
share_prediction_tower: true
use_depthwise: true
}
}
anchor_generator {
multiscale_anchor_generator {
min_level: 3
max_level: 7
anchor_scale: 4.0
aspect_ratios: 1.0
aspect_ratios: 2.0
aspect_ratios: 0.5
scales_per_octave: 2
}
}
post_processing {
batch_non_max_suppression {
score_threshold: 1e-08
iou_threshold: 0.6
max_detections_per_class: 100
max_total_detections: 100
use_static_shapes: false
}
score_converter: SIGMOID
}
normalize_loss_by_num_matches: true
loss {
localization_loss {
weighted_smooth_l1 {
}
}
classification_loss {
weighted_sigmoid_focal {
gamma: 2.0
alpha: 0.25
}
}
classification_weight: 1.0
localization_weight: 1.0
}
encode_background_as_zeros: true
normalize_loc_loss_by_codesize: true
inplace_batchnorm_update: true
freeze_batchnorm: false
}
}
train_config {
batch_size: 4
data_augmentation_options {
random_horizontal_flip {
}
}
data_augmentation_options {
random_crop_image {
min_object_covered: 0.0
min_aspect_ratio: 0.75
max_aspect_ratio: 3.0
min_area: 0.75
max_area: 1.0
overlap_thresh: 0.0
}
}
sync_replicas: true
optimizer {
momentum_optimizer {
learning_rate {
cosine_decay_learning_rate {
learning_rate_base: 0.0008
total_steps: 50000
warmup_learning_rate: 0.00026666
warmup_steps: 1000
}
}
momentum_optimizer_value: 0.9
}
use_moving_average: false
}
fine_tune_checkpoint: "Tensorflow/workspace/pre-trained-models/ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/checkpoint/ckpt-0"
num_steps: 50000
startup_delay_steps: 0.0
replicas_to_aggregate: 8
max_number_of_boxes: 100
unpad_groundtruth_tensors: false
fine_tune_checkpoint_type: "detection"
fine_tune_checkpoint_version: V2
}
train_input_reader {
label_map_path: "Tensorflow/workspace/annotations/label_map.pbtxt"
tf_record_input_reader {
input_path: "Tensorflow/workspace/annotations/train.record"
}
}
eval_config {
metrics_set: "coco_detection_metrics"
use_moving_averages: false
}
eval_input_reader {
label_map_path: "Tensorflow/workspace/annotations/label_map.pbtxt"
shuffle: false
num_epochs: 4
tf_record_input_reader {
input_path: "Tensorflow/workspace/annotations/test.record"
}
}
我的老师说我需要增加epoch以获得更好的结果。我试图找到epoch的参数，但在tensorflow(没有keras)中，我找不到它。因此，我的老师解释说，我可以修改num_steps并计算epoch，如下所示:
-
图片总数= 12000张(用于训练的图片)
-
batch_size = 4
=比;1 epoch = 12000/4 = 3000步
所以使用num_steps = 10000，我有大约3个epoch(10000/3000)。因此，如果我想修改epoch，我只需要修改num_steps(总图像和batch_size是固定的)。