keras实现调用自己训练的模型,并去掉全连接层
更新时间：2020年06月09日 16:43:21 作者：Tom Hardy
这篇文章主要介绍了keras实现调用自己训练的模型,并去掉全连接层，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧
其实很简单
from keras.models import load_model
base_model = load_model('model_resenet.h5')#加载指定的模型
print(base_model.summary())#输出网络的结构图
这是我的网络模型的输出，其实就是它的结构图
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, 227, 227, 1) 0
__________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, 225, 225, 32) 320 input_1[0][0]
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 225, 225, 32) 128 conv2d_1[0][0]
__________________________________________________________________________________________________
activation_1 (Activation) (None, 225, 225, 32) 0 batch_normalization_1[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, 225, 225, 32) 9248 activation_1[0][0]
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, 225, 225, 32) 128 conv2d_2[0][0]
__________________________________________________________________________________________________
activation_2 (Activation) (None, 225, 225, 32) 0 batch_normalization_2[0][0]
__________________________________________________________________________________________________
conv2d_3 (Conv2D) (None, 225, 225, 32) 9248 activation_2[0][0]
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, 225, 225, 32) 128 conv2d_3[0][0]
__________________________________________________________________________________________________
merge_1 (Merge) (None, 225, 225, 32) 0 batch_normalization_3[0][0]
activation_1[0][0]
__________________________________________________________________________________________________
activation_3 (Activation) (None, 225, 225, 32) 0 merge_1[0][0]
__________________________________________________________________________________________________
conv2d_4 (Conv2D) (None, 225, 225, 32) 9248 activation_3[0][0]
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, 225, 225, 32) 128 conv2d_4[0][0]
__________________________________________________________________________________________________
activation_4 (Activation) (None, 225, 225, 32) 0 batch_normalization_4[0][0]
__________________________________________________________________________________________________
conv2d_5 (Conv2D) (None, 225, 225, 32) 9248 activation_4[0][0]
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, 225, 225, 32) 128 conv2d_5[0][0]
__________________________________________________________________________________________________
merge_2 (Merge) (None, 225, 225, 32) 0 batch_normalization_5[0][0]
activation_3[0][0]
__________________________________________________________________________________________________
activation_5 (Activation) (None, 225, 225, 32) 0 merge_2[0][0]
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D) (None, 112, 112, 32) 0 activation_5[0][0]
__________________________________________________________________________________________________
conv2d_6 (Conv2D) (None, 110, 110, 64) 18496 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
batch_normalization_6 (BatchNor (None, 110, 110, 64) 256 conv2d_6[0][0]
__________________________________________________________________________________________________
activation_6 (Activation) (None, 110, 110, 64) 0 batch_normalization_6[0][0]
__________________________________________________________________________________________________
conv2d_7 (Conv2D) (None, 110, 110, 64) 36928 activation_6[0][0]
__________________________________________________________________________________________________
batch_normalization_7 (BatchNor (None, 110, 110, 64) 256 conv2d_7[0][0]
__________________________________________________________________________________________________
activation_7 (Activation) (None, 110, 110, 64) 0 batch_normalization_7[0][0]
__________________________________________________________________________________________________
conv2d_8 (Conv2D) (None, 110, 110, 64) 36928 activation_7[0][0]
__________________________________________________________________________________________________
batch_normalization_8 (BatchNor (None, 110, 110, 64) 256 conv2d_8[0][0]
__________________________________________________________________________________________________
merge_3 (Merge) (None, 110, 110, 64) 0 batch_normalization_8[0][0]
activation_6[0][0]
__________________________________________________________________________________________________
activation_8 (Activation) (None, 110, 110, 64) 0 merge_3[0][0]
__________________________________________________________________________________________________
conv2d_9 (Conv2D) (None, 110, 110, 64) 36928 activation_8[0][0]
__________________________________________________________________________________________________
batch_normalization_9 (BatchNor (None, 110, 110, 64) 256 conv2d_9[0][0]
__________________________________________________________________________________________________
activation_9 (Activation) (None, 110, 110, 64) 0 batch_normalization_9[0][0]
__________________________________________________________________________________________________
conv2d_10 (Conv2D) (None, 110, 110, 64) 36928 activation_9[0][0]
__________________________________________________________________________________________________
batch_normalization_10 (BatchNo (None, 110, 110, 64) 256 conv2d_10[0][0]
__________________________________________________________________________________________________
merge_4 (Merge) (None, 110, 110, 64) 0 batch_normalization_10[0][0]
activation_8[0][0]
__________________________________________________________________________________________________
activation_10 (Activation) (None, 110, 110, 64) 0 merge_4[0][0]
__________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D) (None, 55, 55, 64) 0 activation_10[0][0]
__________________________________________________________________________________________________
conv2d_11 (Conv2D) (None, 53, 53, 64) 36928 max_pooling2d_2[0][0]
__________________________________________________________________________________________________
batch_normalization_11 (BatchNo (None, 53, 53, 64) 256 conv2d_11[0][0]
__________________________________________________________________________________________________
activation_11 (Activation) (None, 53, 53, 64) 0 batch_normalization_11[0][0]
__________________________________________________________________________________________________
max_pooling2d_3 (MaxPooling2D) (None, 26, 26, 64) 0 activation_11[0][0]
__________________________________________________________________________________________________
conv2d_12 (Conv2D) (None, 26, 26, 64) 36928 max_pooling2d_3[0][0]
__________________________________________________________________________________________________
batch_normalization_12 (BatchNo (None, 26, 26, 64) 256 conv2d_12[0][0]
__________________________________________________________________________________________________
activation_12 (Activation) (None, 26, 26, 64) 0 batch_normalization_12[0][0]
__________________________________________________________________________________________________
conv2d_13 (Conv2D) (None, 26, 26, 64) 36928 activation_12[0][0]
__________________________________________________________________________________________________
batch_normalization_13 (BatchNo (None, 26, 26, 64) 256 conv2d_13[0][0]
__________________________________________________________________________________________________
merge_5 (Merge) (None, 26, 26, 64) 0 batch_normalization_13[0][0]
max_pooling2d_3[0][0]
__________________________________________________________________________________________________
activation_13 (Activation) (None, 26, 26, 64) 0 merge_5[0][0]
__________________________________________________________________________________________________
conv2d_14 (Conv2D) (None, 26, 26, 64) 36928 activation_13[0][0]
__________________________________________________________________________________________________
batch_normalization_14 (BatchNo (None, 26, 26, 64) 256 conv2d_14[0][0]
__________________________________________________________________________________________________
activation_14 (Activation) (None, 26, 26, 64) 0 batch_normalization_14[0][0]
__________________________________________________________________________________________________
conv2d_15 (Conv2D) (None, 26, 26, 64) 36928 activation_14[0][0]
__________________________________________________________________________________________________
batch_normalization_15 (BatchNo (None, 26, 26, 64) 256 conv2d_15[0][0]
__________________________________________________________________________________________________
merge_6 (Merge) (None, 26, 26, 64) 0 batch_normalization_15[0][0]
activation_13[0][0]
__________________________________________________________________________________________________
activation_15 (Activation) (None, 26, 26, 64) 0 merge_6[0][0]
__________________________________________________________________________________________________
max_pooling2d_4 (MaxPooling2D) (None, 13, 13, 64) 0 activation_15[0][0]
__________________________________________________________________________________________________
conv2d_16 (Conv2D) (None, 11, 11, 32) 18464 max_pooling2d_4[0][0]
__________________________________________________________________________________________________
batch_normalization_16 (BatchNo (None, 11, 11, 32) 128 conv2d_16[0][0]
__________________________________________________________________________________________________
activation_16 (Activation) (None, 11, 11, 32) 0 batch_normalization_16[0][0]
__________________________________________________________________________________________________
conv2d_17 (Conv2D) (None, 11, 11, 32) 9248 activation_16[0][0]
__________________________________________________________________________________________________
batch_normalization_17 (BatchNo (None, 11, 11, 32) 128 conv2d_17[0][0]
__________________________________________________________________________________________________
activation_17 (Activation) (None, 11, 11, 32) 0 batch_normalization_17[0][0]
__________________________________________________________________________________________________
conv2d_18 (Conv2D) (None, 11, 11, 32) 9248 activation_17[0][0]
__________________________________________________________________________________________________
batch_normalization_18 (BatchNo (None, 11, 11, 32) 128 conv2d_18[0][0]
__________________________________________________________________________________________________
merge_7 (Merge) (None, 11, 11, 32) 0 batch_normalization_18[0][0]
activation_16[0][0]
__________________________________________________________________________________________________
activation_18 (Activation) (None, 11, 11, 32) 0 merge_7[0][0]
__________________________________________________________________________________________________
conv2d_19 (Conv2D) (None, 11, 11, 32) 9248 activation_18[0][0]
__________________________________________________________________________________________________
batch_normalization_19 (BatchNo (None, 11, 11, 32) 128 conv2d_19[0][0]
__________________________________________________________________________________________________
activation_19 (Activation) (None, 11, 11, 32) 0 batch_normalization_19[0][0]
__________________________________________________________________________________________________
conv2d_20 (Conv2D) (None, 11, 11, 32) 9248 activation_19[0][0]
__________________________________________________________________________________________________
batch_normalization_20 (BatchNo (None, 11, 11, 32) 128 conv2d_20[0][0]
__________________________________________________________________________________________________
merge_8 (Merge) (None, 11, 11, 32) 0 batch_normalization_20[0][0]
activation_18[0][0]
__________________________________________________________________________________________________
activation_20 (Activation) (None, 11, 11, 32) 0 merge_8[0][0]
__________________________________________________________________________________________________
max_pooling2d_5 (MaxPooling2D) (None, 5, 5, 32) 0 activation_20[0][0]
__________________________________________________________________________________________________
conv2d_21 (Conv2D) (None, 3, 3, 64) 18496 max_pooling2d_5[0][0]
__________________________________________________________________________________________________
batch_normalization_21 (BatchNo (None, 3, 3, 64) 256 conv2d_21[0][0]
__________________________________________________________________________________________________
activation_21 (Activation) (None, 3, 3, 64) 0 batch_normalization_21[0][0]
__________________________________________________________________________________________________
conv2d_22 (Conv2D) (None, 3, 3, 64) 36928 activation_21[0][0]
__________________________________________________________________________________________________
batch_normalization_22 (BatchNo (None, 3, 3, 64) 256 conv2d_22[0][0]
__________________________________________________________________________________________________
activation_22 (Activation) (None, 3, 3, 64) 0 batch_normalization_22[0][0]
__________________________________________________________________________________________________
conv2d_23 (Conv2D) (None, 3, 3, 64) 36928 activation_22[0][0]
__________________________________________________________________________________________________
batch_normalization_23 (BatchNo (None, 3, 3, 64) 256 conv2d_23[0][0]
__________________________________________________________________________________________________
merge_9 (Merge) (None, 3, 3, 64) 0 batch_normalization_23[0][0]
activation_21[0][0]
__________________________________________________________________________________________________
activation_23 (Activation) (None, 3, 3, 64) 0 merge_9[0][0]
__________________________________________________________________________________________________
conv2d_24 (Conv2D) (None, 3, 3, 64) 36928 activation_23[0][0]
__________________________________________________________________________________________________
batch_normalization_24 (BatchNo (None, 3, 3, 64) 256 conv2d_24[0][0]
__________________________________________________________________________________________________
activation_24 (Activation) (None, 3, 3, 64) 0 batch_normalization_24[0][0]
__________________________________________________________________________________________________
conv2d_25 (Conv2D) (None, 3, 3, 64) 36928 activation_24[0][0]
__________________________________________________________________________________________________
batch_normalization_25 (BatchNo (None, 3, 3, 64) 256 conv2d_25[0][0]
__________________________________________________________________________________________________
merge_10 (Merge) (None, 3, 3, 64) 0 batch_normalization_25[0][0]
activation_23[0][0]
__________________________________________________________________________________________________
activation_25 (Activation) (None, 3, 3, 64) 0 merge_10[0][0]
__________________________________________________________________________________________________
max_pooling2d_6 (MaxPooling2D) (None, 1, 1, 64) 0 activation_25[0][0]
__________________________________________________________________________________________________
flatten_1 (Flatten) (None, 64) 0 max_pooling2d_6[0][0]
__________________________________________________________________________________________________
dense_1 (Dense) (None, 256) 16640 flatten_1[0][0]
__________________________________________________________________________________________________
dropout_1 (Dropout) (None, 256) 0 dense_1[0][0]
__________________________________________________________________________________________________
dense_2 (Dense) (None, 2) 514 dropout_1[0][0]
==================================================================================================
Total params: 632,098
Trainable params: 629,538
Non-trainable params: 2,560
__________________________________________________________________________________________________
去掉模型的全连接层
from keras.models import load_model
base_model = load_model('model_resenet.h5')
resnet_model = Model(inputs=base_model.input, outputs=base_model.get_layer('max_pooling2d_6').output)
#'max_pooling2d_6'其实就是上述网络中全连接层的前面一层，当然这里你也可以选取其它层，把该层的名称代替'max_pooling2d_6'即可，这样其实就是截取网络，输出网络结构就是方便读取每层的名字。
print(resnet_model.summary())
新输出的网络结构：
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, 227, 227, 1) 0
__________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, 225, 225, 32) 320 input_1[0][0]
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 225, 225, 32) 128 conv2d_1[0][0]
__________________________________________________________________________________________________
activation_1 (Activation) (None, 225, 225, 32) 0 batch_normalization_1[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, 225, 225, 32) 9248 activation_1[0][0]
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, 225, 225, 32) 128 conv2d_2[0][0]
__________________________________________________________________________________________________
activation_2 (Activation) (None, 225, 225, 32) 0 batch_normalization_2[0][0]
__________________________________________________________________________________________________
conv2d_3 (Conv2D) (None, 225, 225, 32) 9248 activation_2[0][0]
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, 225, 225, 32) 128 conv2d_3[0][0]
__________________________________________________________________________________________________
merge_1 (Merge) (None, 225, 225, 32) 0 batch_normalization_3[0][0]
activation_1[0][0]
__________________________________________________________________________________________________
activation_3 (Activation) (None, 225, 225, 32) 0 merge_1[0][0]
__________________________________________________________________________________________________
conv2d_4 (Conv2D) (None, 225, 225, 32) 9248 activation_3[0][0]
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, 225, 225, 32) 128 conv2d_4[0][0]
__________________________________________________________________________________________________
activation_4 (Activation) (None, 225, 225, 32) 0 batch_normalization_4[0][0]
__________________________________________________________________________________________________
conv2d_5 (Conv2D) (None, 225, 225, 32) 9248 activation_4[0][0]
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, 225, 225, 32) 128 conv2d_5[0][0]
__________________________________________________________________________________________________
merge_2 (Merge) (None, 225, 225, 32) 0 batch_normalization_5[0][0]
activation_3[0][0]
__________________________________________________________________________________________________
activation_5 (Activation) (None, 225, 225, 32) 0 merge_2[0][0]
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D) (None, 112, 112, 32) 0 activation_5[0][0]
__________________________________________________________________________________________________
conv2d_6 (Conv2D) (None, 110, 110, 64) 18496 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
batch_normalization_6 (BatchNor (None, 110, 110, 64) 256 conv2d_6[0][0]
__________________________________________________________________________________________________
activation_6 (Activation) (None, 110, 110, 64) 0 batch_normalization_6[0][0]
__________________________________________________________________________________________________
conv2d_7 (Conv2D) (None, 110, 110, 64) 36928 activation_6[0][0]
__________________________________________________________________________________________________
batch_normalization_7 (BatchNor (None, 110, 110, 64) 256 conv2d_7[0][0]
__________________________________________________________________________________________________
activation_7 (Activation) (None, 110, 110, 64) 0 batch_normalization_7[0][0]
__________________________________________________________________________________________________
conv2d_8 (Conv2D) (None, 110, 110, 64) 36928 activation_7[0][0]
__________________________________________________________________________________________________
batch_normalization_8 (BatchNor (None, 110, 110, 64) 256 conv2d_8[0][0]
__________________________________________________________________________________________________
merge_3 (Merge) (None, 110, 110, 64) 0 batch_normalization_8[0][0]
activation_6[0][0]
__________________________________________________________________________________________________
activation_8 (Activation) (None, 110, 110, 64) 0 merge_3[0][0]
__________________________________________________________________________________________________
conv2d_9 (Conv2D) (None, 110, 110, 64) 36928 activation_8[0][0]
__________________________________________________________________________________________________
batch_normalization_9 (BatchNor (None, 110, 110, 64) 256 conv2d_9[0][0]
__________________________________________________________________________________________________
activation_9 (Activation) (None, 110, 110, 64) 0 batch_normalization_9[0][0]
__________________________________________________________________________________________________
conv2d_10 (Conv2D) (None, 110, 110, 64) 36928 activation_9[0][0]
__________________________________________________________________________________________________
batch_normalization_10 (BatchNo (None, 110, 110, 64) 256 conv2d_10[0][0]
__________________________________________________________________________________________________
merge_4 (Merge) (None, 110, 110, 64) 0 batch_normalization_10[0][0]
activation_8[0][0]
__________________________________________________________________________________________________
activation_10 (Activation) (None, 110, 110, 64) 0 merge_4[0][0]
__________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D) (None, 55, 55, 64) 0 activation_10[0][0]
__________________________________________________________________________________________________
conv2d_11 (Conv2D) (None, 53, 53, 64) 36928 max_pooling2d_2[0][0]
__________________________________________________________________________________________________
batch_normalization_11 (BatchNo (None, 53, 53, 64) 256 conv2d_11[0][0]
__________________________________________________________________________________________________
activation_11 (Activation) (None, 53, 53, 64) 0 batch_normalization_11[0][0]
__________________________________________________________________________________________________
max_pooling2d_3 (MaxPooling2D) (None, 26, 26, 64) 0 activation_11[0][0]
__________________________________________________________________________________________________
conv2d_12 (Conv2D) (None, 26, 26, 64) 36928 max_pooling2d_3[0][0]
__________________________________________________________________________________________________
batch_normalization_12 (BatchNo (None, 26, 26, 64) 256 conv2d_12[0][0]
__________________________________________________________________________________________________
activation_12 (Activation) (None, 26, 26, 64) 0 batch_normalization_12[0][0]
__________________________________________________________________________________________________
conv2d_13 (Conv2D) (None, 26, 26, 64) 36928 activation_12[0][0]
__________________________________________________________________________________________________
batch_normalization_13 (BatchNo (None, 26, 26, 64) 256 conv2d_13[0][0]
__________________________________________________________________________________________________
merge_5 (Merge) (None, 26, 26, 64) 0 batch_normalization_13[0][0]
max_pooling2d_3[0][0]
__________________________________________________________________________________________________
activation_13 (Activation) (None, 26, 26, 64) 0 merge_5[0][0]
__________________________________________________________________________________________________
conv2d_14 (Conv2D) (None, 26, 26, 64) 36928 activation_13[0][0]
__________________________________________________________________________________________________
batch_normalization_14 (BatchNo (None, 26, 26, 64) 256 conv2d_14[0][0]
__________________________________________________________________________________________________
activation_14 (Activation) (None, 26, 26, 64) 0 batch_normalization_14[0][0]
__________________________________________________________________________________________________
conv2d_15 (Conv2D) (None, 26, 26, 64) 36928 activation_14[0][0]
__________________________________________________________________________________________________
batch_normalization_15 (BatchNo (None, 26, 26, 64) 256 conv2d_15[0][0]
__________________________________________________________________________________________________
merge_6 (Merge) (None, 26, 26, 64) 0 batch_normalization_15[0][0]
activation_13[0][0]
__________________________________________________________________________________________________
activation_15 (Activation) (None, 26, 26, 64) 0 merge_6[0][0]
__________________________________________________________________________________________________
max_pooling2d_4 (MaxPooling2D) (None, 13, 13, 64) 0 activation_15[0][0]
__________________________________________________________________________________________________
conv2d_16 (Conv2D) (None, 11, 11, 32) 18464 max_pooling2d_4[0][0]
__________________________________________________________________________________________________
batch_normalization_16 (BatchNo (None, 11, 11, 32) 128 conv2d_16[0][0]
__________________________________________________________________________________________________
activation_16 (Activation) (None, 11, 11, 32) 0 batch_normalization_16[0][0]
__________________________________________________________________________________________________
conv2d_17 (Conv2D) (None, 11, 11, 32) 9248 activation_16[0][0]
__________________________________________________________________________________________________
batch_normalization_17 (BatchNo (None, 11, 11, 32) 128 conv2d_17[0][0]
__________________________________________________________________________________________________
activation_17 (Activation) (None, 11, 11, 32) 0 batch_normalization_17[0][0]
__________________________________________________________________________________________________
conv2d_18 (Conv2D) (None, 11, 11, 32) 9248 activation_17[0][0]
__________________________________________________________________________________________________
batch_normalization_18 (BatchNo (None, 11, 11, 32) 128 conv2d_18[0][0]
__________________________________________________________________________________________________
merge_7 (Merge) (None, 11, 11, 32) 0 batch_normalization_18[0][0]
activation_16[0][0]
__________________________________________________________________________________________________
activation_18 (Activation) (None, 11, 11, 32) 0 merge_7[0][0]
__________________________________________________________________________________________________
conv2d_19 (Conv2D) (None, 11, 11, 32) 9248 activation_18[0][0]
__________________________________________________________________________________________________
batch_normalization_19 (BatchNo (None, 11, 11, 32) 128 conv2d_19[0][0]
__________________________________________________________________________________________________
activation_19 (Activation) (None, 11, 11, 32) 0 batch_normalization_19[0][0]
__________________________________________________________________________________________________
conv2d_20 (Conv2D) (None, 11, 11, 32) 9248 activation_19[0][0]
__________________________________________________________________________________________________
batch_normalization_20 (BatchNo (None, 11, 11, 32) 128 conv2d_20[0][0]
__________________________________________________________________________________________________
merge_8 (Merge) (None, 11, 11, 32) 0 batch_normalization_20[0][0]
activation_18[0][0]
__________________________________________________________________________________________________
activation_20 (Activation) (None, 11, 11, 32) 0 merge_8[0][0]
__________________________________________________________________________________________________
max_pooling2d_5 (MaxPooling2D) (None, 5, 5, 32) 0 activation_20[0][0]
__________________________________________________________________________________________________
conv2d_21 (Conv2D) (None, 3, 3, 64) 18496 max_pooling2d_5[0][0]
__________________________________________________________________________________________________
batch_normalization_21 (BatchNo (None, 3, 3, 64) 256 conv2d_21[0][0]
__________________________________________________________________________________________________
activation_21 (Activation) (None, 3, 3, 64) 0 batch_normalization_21[0][0]
__________________________________________________________________________________________________
conv2d_22 (Conv2D) (None, 3, 3, 64) 36928 activation_21[0][0]
__________________________________________________________________________________________________
batch_normalization_22 (BatchNo (None, 3, 3, 64) 256 conv2d_22[0][0]
__________________________________________________________________________________________________
activation_22 (Activation) (None, 3, 3, 64) 0 batch_normalization_22[0][0]
__________________________________________________________________________________________________
conv2d_23 (Conv2D) (None, 3, 3, 64) 36928 activation_22[0][0]
__________________________________________________________________________________________________
batch_normalization_23 (BatchNo (None, 3, 3, 64) 256 conv2d_23[0][0]
__________________________________________________________________________________________________
merge_9 (Merge) (None, 3, 3, 64) 0 batch_normalization_23[0][0]
activation_21[0][0]
__________________________________________________________________________________________________
activation_23 (Activation) (None, 3, 3, 64) 0 merge_9[0][0]
__________________________________________________________________________________________________
conv2d_24 (Conv2D) (None, 3, 3, 64) 36928 activation_23[0][0]
__________________________________________________________________________________________________
batch_normalization_24 (BatchNo (None, 3, 3, 64) 256 conv2d_24[0][0]
__________________________________________________________________________________________________
activation_24 (Activation) (None, 3, 3, 64) 0 batch_normalization_24[0][0]
__________________________________________________________________________________________________
conv2d_25 (Conv2D) (None, 3, 3, 64) 36928 activation_24[0][0]
__________________________________________________________________________________________________
batch_normalization_25 (BatchNo (None, 3, 3, 64) 256 conv2d_25[0][0]
__________________________________________________________________________________________________
merge_10 (Merge) (None, 3, 3, 64) 0 batch_normalization_25[0][0]
activation_23[0][0]
__________________________________________________________________________________________________
activation_25 (Activation) (None, 3, 3, 64) 0 merge_10[0][0]
__________________________________________________________________________________________________
max_pooling2d_6 (MaxPooling2D) (None, 1, 1, 64) 0 activation_25[0][0]
==================================================================================================
Total params: 614,944
Trainable params: 612,384
Non-trainable params: 2,560
__________________________________________________________________________________________________
以上这篇keras实现调用自己训练的模型,并去掉全连接层就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持脚本之家。
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