[英]Remove top layer from pre-trained model, transfer learning, tensorflow (load_model)
I have pre-trained a model (my own saved model) with two classes, which I want to use for transfer learning to train a model with six classes.我已经预训练了一个带有两个类的 model(我自己保存的模型),我想用它来进行迁移学习来训练一个有六个类的 model。 I have loaded the pre-trained model into the new training script:
我已将预训练的 model 加载到新的训练脚本中:
base_model = tf.keras.models.load_model("base_model_path")
How can I remove the top/head layer (a conv1D layer)?如何删除顶层/头层(conv1D 层)?
I see that in keras one can use base_model.pop(), and for tf.keras.applications one can simply use include_top=false
but is there something similar when using tf.keras and load_model?我看到在 keras 中可以使用 base_model.pop(),而对于 tf.keras.applications 可以简单地使用
include_top=false
但是在使用 tf.Z063009BB15C8272BD0C701CF0 和 load_ZDF 时有类似的东西吗?
(I have tried something like this: (我尝试过这样的事情:
for layer in base_model.layers[:-1]:
layer.trainable = False`
and then add it to a new model (?) but I am not sure on how to continue)然后将其添加到新的 model (?)但我不确定如何继续)
Thanks for any help!谢谢你的帮助!
You could try something like this:你可以尝试这样的事情:
The base model is made up of a simple Conv1D
network with an output layer with two classes:基础 model 由一个简单的
Conv1D
网络组成,该网络具有一个 output 层和两个类:
import tensorflow as tf
samples = 100
timesteps = 5
features = 2
classes = 2
dummy_x, dummy_y = tf.random.normal((100, 5, 2)), tf.random.uniform((100, 1), maxval=2, dtype=tf.int32)
base_model = tf.keras.Sequential()
base_model.add(tf.keras.layers.Conv1D(32, 3, activation='relu', input_shape=(5, 2)))
base_model.add(tf.keras.layers.GlobalMaxPool1D())
base_model.add(tf.keras.layers.Dense(32, activation='relu'))
base_model.add( tf.keras.layers.Dense(classes, activation='softmax'))
base_model.compile(optimizer='adam', loss = tf.keras.losses.SparseCategoricalCrossentropy())
print(base_model.summary())
base_model.fit(dummy_x, dummy_y, batch_size=16, epochs=1)
base_model.save("base_model")
base_model = tf.keras.models.load_model("base_model")
Model: "sequential_8"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv1d_31 (Conv1D) (None, 3, 32) 224
global_max_pooling1d_13 (Gl (None, 32) 0
obalMaxPooling1D)
dense_17 (Dense) (None, 32) 1056
dense_18 (Dense) (None, 2) 66
=================================================================
Total params: 1,346
Trainable params: 1,346
Non-trainable params: 0
_________________________________________________________________
None
7/7 [==============================] - 0s 3ms/step - loss: 0.6973
INFO:tensorflow:Assets written to: base_model/assets
The new model is also is made up of a simple Conv1D
network, but with an output layer with six classes.新的 model 也是由一个简单的
Conv1D
网络组成,但有一个包含六个类的 output 层。 It also contains all the layers of the base_model
except the first Conv1D
layer and the last output layer:它还包含
base_model
的所有层,除了第一个Conv1D
层和最后一个 output 层:
classes = 6
dummy_x, dummy_y = tf.random.normal((100, 5, 2)), tf.random.uniform((100, 1), maxval=6, dtype=tf.int32)
model = tf.keras.Sequential()
model.add(tf.keras.layers.Conv1D(64, 3, activation='relu', input_shape=(5, 2)))
model.add(tf.keras.layers.Conv1D(32, 2, activation='relu'))
for layer in base_model.layers[1:-1]: # Skip first and last layer
model.add(layer)
model.add(tf.keras.layers.Dense(classes, activation='softmax'))
model.compile(optimizer='adam', loss = tf.keras.losses.SparseCategoricalCrossentropy())
print(model.summary())
model.fit(dummy_x, dummy_y, batch_size=16, epochs=1)
Model: "sequential_9"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv1d_32 (Conv1D) (None, 3, 64) 448
conv1d_33 (Conv1D) (None, 2, 32) 4128
global_max_pooling1d_13 (Gl (None, 32) 0
obalMaxPooling1D)
dense_17 (Dense) (None, 32) 1056
dense_19 (Dense) (None, 6) 198
=================================================================
Total params: 5,830
Trainable params: 5,830
Non-trainable params: 0
_________________________________________________________________
None
7/7 [==============================] - 0s 3ms/step - loss: 1.8069
<keras.callbacks.History at 0x7f90c87a3c50>
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