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What is the process to incorporate new layers onto a pre-trained PyTorch model, with reference to a Keras instance?

asked 2022-02-28 11:00:00 +0000

plato gravatar image

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answered 2021-10-02 07:00:00 +0000

bukephalos gravatar image

The process to incorporate new layers onto a pre-trained PyTorch model, with reference to a Keras instance, involves the following steps:

  1. Load the pre-trained PyTorch model using the torch.load method.
  2. Convert the PyTorch model to a Keras model using the keras.models.Sequential.from_config method.
  3. Freeze the layers of the pre-trained model by setting their trainable attribute to False.
  4. Add new layers to the Keras model using the keras.layers API.
  5. Train the model on the new data using the fit method.

Here's an example code snippet that demonstrates this process:

import torch
import keras

# Load the pre-trained PyTorch model
model_pt = torch.load('pretrained_model.pth')

# Convert PyTorch model to Keras model
config = model_pt['model'].config
weights = model_pt['model'].state_dict()
model_keras = keras.models.Sequential.from_config(config)
model_keras.set_weights(weights)

# Freeze the layers of the pre-trained model
for layer in model_keras.layers:
    layer.trainable = False

# Add new layers to the Keras model
model_keras.add(keras.layers.Flatten())
model_keras.add(keras.layers.Dense(256, activation='relu'))
model_keras.add(keras.layers.Dropout(0.5))
model_keras.add(keras.layers.Dense(10, activation='softmax'))

# Train the model on new data
model_keras.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model_keras.fit(x_train, y_train, batch_size=32, epochs=10, validation_data=(x_val, y_val))

Note: The example code assumes that the pre-trained PyTorch model was saved as a .pth file. If the model was saved as a .pt file, replace torch.load with torch.load_state_dict.

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Asked: 2022-02-28 11:00:00 +0000

Seen: 6 times

Last updated: Oct 02 '21