Transfer learning from a custom model trained with different image sizes-CodePudding

The

Sample code:

import numpy as np import tensorflow as tf from tensorflow import keras low_res_image_size = (150, 150, 3) hig_res_image_size = (320, 240, 3) n_classes = 4 # Load your pretrained model train on low resolution images base_model = tf.keras.applications.VGG16( include_top=False, weights='imagenet', input_shape=low_res_image_size) # Freeze the pretrained model base_model.trainable = False # Unfreezed model to be trained on high resolution images model = tf.keras.applications.VGG19( include_top=False, weights='imagenet', input_shape=hig_res_image_size) model.trainable = True # Downscale images downscale_layer = tf.keras.layers.Resizing( low_res_image_size[0], low_res_image_size[1], interpolation='bilinear', crop_to_aspect_ratio=False) # Create model inputs = keras.Input(shape=hig_res_image_size) downscaled_inputs = downscale_layer(inputs) features = base_model(downscaled_inputs, training=False) features = keras.layers.GlobalAveragePooling2D()(features) x = model(inputs, training=True) x = keras.layers.GlobalAveragePooling2D()(x) concatted = tf.keras.layers.Concatenate()([features, x]) outputs = keras.layers.Dense(n_classes)(concatted) model = keras.Model(inputs, outputs) model.compile(optimizer="adam", loss='sparse_categorical_crossentropy') # Train on some random data model.fit( np.random.random((100,*hig_res_image_size)), np.random.randint(0, n_classes, 100), epochs=3)

Output:

Epoch 1/3 4/4 [==============================] - 4s 553ms/step - loss: 8.7033 Epoch 2/3 4/4 [==============================] - 2s 554ms/step - loss: 9.0746 Epoch 3/3 4/4 [==============================] - 2s 553ms/step - loss: 9.0746 <keras.callbacks.History at 0x7f559a104650>

As and added step, after the model converges you and also unfreeze all the layers and train the full model again using a very low learning rate. Just keep an eye on overfitting.

CodePudding user response：

Copy your model to the another model (transfer learning), and then update the new model in the way you want to use it. Change input size, change activation functions, whatever you wanna do.