[英]Adding layers to RESNET50 in order to build a JOIN CNN Model
This is my code in order to join resnet50 model with this model (that I want to train on my dataset).这是我的代码,用于将 resnet50 模型与此模型(我想在我的数据集上训练)连接起来。 I want to freeze layers of the resnet50 model ( see Trainable=false) in the code .我想在代码中冻结 resnet50 模型的层(参见 Trainable=false)。 Here I'm importing resnet 50 model我在这里导入 resnet 50 模型
``
import tensorflow.keras
import tensorflow as tf
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
resnet50_imagnet_model = tensorflow.keras.applications.resnet.ResNet50(weights = "imagenet",
include_top=False,
input_shape = (150, 150, 3),
pooling='max')
``
Here I create my model在这里我创建我的模型
```
# freeze feature layers and rebuild model
for l in resnet50_imagnet_model.layers:
l.trainable = False
#construction du model
model5 = [
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(12, activation='softmax')
]
#Jointure des deux modeles
model_using_pre_trained_resnet50 = tf.keras.Sequential(resnet50_imagnet_model.layers + model5 )
```
Last line doesn't work and I have this error : Input 0 of layer conv2_block1_3_conv is incompatible with the layer: expected axis -1 of input shape to have value 64 but received input with shape [None, 38, 38, 256最后一行不起作用,我有这个错误:层 conv2_block1_3_conv 的输入 0 与层不兼容:输入形状的预期轴 -1 具有值 64,但收到的输入形状为 [None, 38, 38, 256
Thanks for help .感谢帮助 。
You can also use keras' functional API , like below您还可以使用 keras 的功能 API ,如下所示
from tensorflow.keras.applications.resnet50 import ResNet50
import tensorflow as tf
resnet50_imagenet_model = ResNet50(include_top=False, weights='imagenet', input_shape=(150, 150, 3))
#Flatten output layer of Resnet
flattened = tf.keras.layers.Flatten()(resnet50_imagenet_model.output)
#Fully connected layer 1
fc1 = tf.keras.layers.Dense(128, activation='relu', name="AddedDense1")(flattened)
#Fully connected layer, output layer
fc2 = tf.keras.layers.Dense(12, activation='softmax', name="AddedDense2")(fc1)
model = tf.keras.models.Model(inputs=resnet50_imagenet_model.input, outputs=fc2)
Also refer this question .另请参阅此问题。
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