人类年龄预测（2）
【摘要】 加载模型首先我们将模型结构和权重加载，预训练模型位置存储在weight_file中。In [16]:from SSRNET_model import SSR_netweight_file = "./ssrnet_3_3_3_64_1.0_1.0.h5"img_size = 64stage_num = [3,3,3]lambda_local = 1lambda_d = 1model = SSR...
In [16]:
from SSRNET_model import SSR_net
weight_file = "./ssrnet_3_3_3_64_1.0_1.0.h5"
img_size = 64
stage_num = [3,3,3]
lambda_local = 1
lambda_d = 1
model = SSR_net(img_size,stage_num, lambda_local, lambda_d)()
model.load_weights(weight_file)
Using TensorFlow backend.
WARNING:tensorflow:From /home/ma-user/anaconda3/envs/TensorFlow-1.13.1/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.
Instructions for updating:
Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.
WARNING:tensorflow:From /home/ma-user/anaconda3/envs/TensorFlow-1.13.1/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.
Instructions for updating:
Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.
模型层级结构
In [17]:
model.summary()
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, 64, 64, 3) 0
__________________________________________________________________________________________________
conv2d_5 (Conv2D) (None, 62, 62, 16) 448 input_1[0][0]
__________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, 62, 62, 32) 896 input_1[0][0]
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, 62, 62, 16) 64 conv2d_5[0][0]
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 62, 62, 32) 128 conv2d_1[0][0]
__________________________________________________________________________________________________
activation_5 (Activation) (None, 62, 62, 16) 0 batch_normalization_5[0][0]
__________________________________________________________________________________________________
activation_1 (Activation) (None, 62, 62, 32) 0 batch_normalization_1[0][0]
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D) (None, 31, 31, 16) 0 activation_5[0][0]
__________________________________________________________________________________________________
average_pooling2d_1 (AveragePoo (None, 31, 31, 32) 0 activation_1[0][0]
__________________________________________________________________________________________________
conv2d_6 (Conv2D) (None, 29, 29, 16) 2320 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, 29, 29, 32) 9248 average_pooling2d_1[0][0]
__________________________________________________________________________________________________
batch_normalization_6 (BatchNor (None, 29, 29, 16) 64 conv2d_6[0][0]
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, 29, 29, 32) 128 conv2d_2[0][0]
__________________________________________________________________________________________________
activation_6 (Activation) (None, 29, 29, 16) 0 batch_normalization_6[0][0]
__________________________________________________________________________________________________
activation_2 (Activation) (None, 29, 29, 32) 0 batch_normalization_2[0][0]
__________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D) (None, 14, 14, 16) 0 activation_6[0][0]
__________________________________________________________________________________________________
average_pooling2d_2 (AveragePoo (None, 14, 14, 32) 0 activation_2[0][0]
__________________________________________________________________________________________________
conv2d_7 (Conv2D) (None, 12, 12, 16) 2320 max_pooling2d_2[0][0]
__________________________________________________________________________________________________
conv2d_3 (Conv2D) (None, 12, 12, 32) 9248 average_pooling2d_2[0][0]
__________________________________________________________________________________________________
batch_normalization_7 (BatchNor (None, 12, 12, 16) 64 conv2d_7[0][0]
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, 12, 12, 32) 128 conv2d_3[0][0]
__________________________________________________________________________________________________
activation_7 (Activation) (None, 12, 12, 16) 0 batch_normalization_7[0][0]
__________________________________________________________________________________________________
activation_3 (Activation) (None, 12, 12, 32) 0 batch_normalization_3[0][0]
__________________________________________________________________________________________________
max_pooling2d_3 (MaxPooling2D) (None, 6, 6, 16) 0 activation_7[0][0]
__________________________________________________________________________________________________
average_pooling2d_3 (AveragePoo (None, 6, 6, 32) 0 activation_3[0][0]
__________________________________________________________________________________________________
conv2d_8 (Conv2D) (None, 4, 4, 16) 2320 max_pooling2d_3[0][0]
__________________________________________________________________________________________________
conv2d_4 (Conv2D) (None, 4, 4, 32) 9248 average_pooling2d_3[0][0]
__________________________________________________________________________________________________
batch_normalization_8 (BatchNor (None, 4, 4, 16) 64 conv2d_8[0][0]
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, 4, 4, 32) 128 conv2d_4[0][0]
__________________________________________________________________________________________________
activation_8 (Activation) (None, 4, 4, 16) 0 batch_normalization_8[0][0]
__________________________________________________________________________________________________
activation_4 (Activation) (None, 4, 4, 32) 0 batch_normalization_4[0][0]
__________________________________________________________________________________________________
conv2d_11 (Conv2D) (None, 14, 14, 10) 170 max_pooling2d_2[0][0]
__________________________________________________________________________________________________
conv2d_12 (Conv2D) (None, 14, 14, 10) 330 average_pooling2d_2[0][0]
__________________________________________________________________________________________________
conv2d_13 (Conv2D) (None, 31, 31, 10) 170 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
conv2d_14 (Conv2D) (None, 31, 31, 10) 330 average_pooling2d_1[0][0]
__________________________________________________________________________________________________
conv2d_9 (Conv2D) (None, 4, 4, 10) 170 activation_8[0][0]
__________________________________________________________________________________________________
conv2d_10 (Conv2D) (None, 4, 4, 10) 330 activation_4[0][0]
__________________________________________________________________________________________________
max_pooling2d_4 (MaxPooling2D) (None, 3, 3, 10) 0 conv2d_11[0][0]
__________________________________________________________________________________________________
average_pooling2d_4 (AveragePoo (None, 3, 3, 10) 0 conv2d_12[0][0]
__________________________________________________________________________________________________
max_pooling2d_5 (MaxPooling2D) (None, 3, 3, 10) 0 conv2d_13[0][0]
__________________________________________________________________________________________________
average_pooling2d_5 (AveragePoo (None, 3, 3, 10) 0 conv2d_14[0][0]
__________________________________________________________________________________________________
flatten_1 (Flatten) (None, 160) 0 conv2d_9[0][0]
__________________________________________________________________________________________________
flatten_2 (Flatten) (None, 160) 0 conv2d_10[0][0]
__________________________________________________________________________________________________
flatten_3 (Flatten) (None, 90) 0 max_pooling2d_4[0][0]
__________________________________________________________________________________________________
flatten_4 (Flatten) (None, 90) 0 average_pooling2d_4[0][0]
__________________________________________________________________________________________________
flatten_5 (Flatten) (None, 90) 0 max_pooling2d_5[0][0]
__________________________________________________________________________________________________
flatten_6 (Flatten) (None, 90) 0 average_pooling2d_5[0][0]
__________________________________________________________________________________________________
dropout_1 (Dropout) (None, 160) 0 flatten_1[0][0]
__________________________________________________________________________________________________
dropout_2 (Dropout) (None, 160) 0 flatten_2[0][0]
__________________________________________________________________________________________________
dropout_3 (Dropout) (None, 90) 0 flatten_3[0][0]
__________________________________________________________________________________________________
dropout_4 (Dropout) (None, 90) 0 flatten_4[0][0]
__________________________________________________________________________________________________
dropout_5 (Dropout) (None, 90) 0 flatten_5[0][0]
__________________________________________________________________________________________________
dropout_6 (Dropout) (None, 90) 0 flatten_6[0][0]
__________________________________________________________________________________________________
dense_1 (Dense) (None, 3) 483 dropout_1[0][0]
__________________________________________________________________________________________________
dense_2 (Dense) (None, 3) 483 dropout_2[0][0]
__________________________________________________________________________________________________
dense_4 (Dense) (None, 3) 273 dropout_3[0][0]
__________________________________________________________________________________________________
dense_5 (Dense) (None, 3) 273 dropout_4[0][0]
__________________________________________________________________________________________________
dense_7 (Dense) (None, 3) 273 dropout_5[0][0]
__________________________________________________________________________________________________
dense_8 (Dense) (None, 3) 273 dropout_6[0][0]
__________________________________________________________________________________________________
multiply_2 (Multiply) (None, 3) 0 dense_1[0][0]
dense_2[0][0]
__________________________________________________________________________________________________
multiply_4 (Multiply) (None, 3) 0 dense_4[0][0]
dense_5[0][0]
__________________________________________________________________________________________________
multiply_6 (Multiply) (None, 3) 0 dense_7[0][0]
dense_8[0][0]
__________________________________________________________________________________________________
dense_3 (Dense) (None, 6) 24 multiply_2[0][0]
__________________________________________________________________________________________________
dense_6 (Dense) (None, 6) 24 multiply_4[0][0]
__________________________________________________________________________________________________
dense_9 (Dense) (None, 6) 24 multiply_6[0][0]
__________________________________________________________________________________________________
multiply_1 (Multiply) (None, 160) 0 flatten_1[0][0]
flatten_2[0][0]
__________________________________________________________________________________________________
multiply_3 (Multiply) (None, 90) 0 flatten_3[0][0]
flatten_4[0][0]
__________________________________________________________________________________________________
multiply_5 (Multiply) (None, 90) 0 flatten_5[0][0]
flatten_6[0][0]
__________________________________________________________________________________________________
pred_age_stage1 (Dense) (None, 3) 21 dense_3[0][0]
__________________________________________________________________________________________________
pred_age_stage2 (Dense) (None, 3) 21 dense_6[0][0]
__________________________________________________________________________________________________
pred_age_stage3 (Dense) (None, 3) 21 dense_9[0][0]
__________________________________________________________________________________________________
delta_s1 (Dense) (None, 1) 161 multiply_1[0][0]
__________________________________________________________________________________________________
delta_s2 (Dense) (None, 1) 91 multiply_3[0][0]
__________________________________________________________________________________________________
delta_s3 (Dense) (None, 1) 91 multiply_5[0][0]
__________________________________________________________________________________________________
local_delta_stage1 (Dense) (None, 3) 21 dense_3[0][0]
__________________________________________________________________________________________________
local_delta_stage2 (Dense) (None, 3) 21 dense_6[0][0]
__________________________________________________________________________________________________
local_delta_stage3 (Dense) (None, 3) 21 dense_9[0][0]
__________________________________________________________________________________________________
pred_a (Lambda) (None, 1) 0 pred_age_stage1[0][0]
pred_age_stage2[0][0]
pred_age_stage3[0][0]
delta_s1[0][0]
delta_s2[0][0]
delta_s3[0][0]
local_delta_stage1[0][0]
local_delta_stage2[0][0]
local_delta_stage3[0][0]
==================================================================================================
Total params: 40,915
Trainable params: 40,531
Non-trainable params: 384
__________________________________________________________________________________________________
准备输入数据
In [18]:
faces = np.empty((len(detected), img_size, img_size, 3))
faces.shape
Out[18]:
(1, 64, 64, 3)
In [19]:
ad = 0.4
img_h, img_w, _ = np.shape(img)
for i,d in enumerate(detected):
if d['confidence'] >=0.95 :
x1,y1,w,h = d['box']
x2 = x1 + w
y2 = y1 + h
xw1 = max(int(x1 - ad * w), 0)
yw1 = max(int(y1 - ad * h), 0)
xw2 = min(int(x2 + ad * w), img_w - 1)
yw2 = min(int(y2 + ad * h), img_h - 1)
img = cv2.resize(img[yw1:yw2+1, xw1:xw2+1, :], (img_size, img_size))
faces[i,:,:,:] = img
res_img = Image.fromarray(img)
res_img
Out[19]:
In [20]:
res = model.predict(faces)
In [21]:
print("预测年龄为："+str(int(res[0])))
预测年龄为：27
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