Remove top layer from pre-trained model, transfer learning, tensorflow (load_model)

Question

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. I have loaded the pre-trained model into the new training script:

base_model = tf.keras.models.load_model("base_model_path")

How can I remove the top/head layer (a conv1D layer)?

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?

(I have tried something like this:

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)

Thanks for any help!

Answer 1

You could try something like this:

The base model is made up of a simple Conv1D network with an output layer with two classes:

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. It also contains all the layers of the base_model except the first Conv1D layer and the last output layer:

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>