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!
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>