Model.summary()
어떻게 다른지 알수가 없다.
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print('conv_base를 동결하기 전 훈련되는 가중치의 수 : ', len(model.trainable_weights))
print(model.trainable_weights)
conv_base.trainable = False
print()
print(model.trainable_weights)
print('conv_base를 동결한 후 훈련되는 가중치의 수 : ', len(model.trainable_weights))
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<tf.Variable 'block1_conv1_1/kernel:0' shape=(3, 3, 3, 64) dtype=float32_ref>
<tf.Variable 'block1_conv1_1/bias:0' shape=(64,) dtype=float32_ref>
<tf.Variable 'block1_conv2_1/kernel:0' shape=(3, 3, 64, 64) dtype=float32_ref>
<tf.Variable 'block1_conv2_1/bias:0' shape=(64,) dtype=float32_ref>
<tf.Variable 'block2_conv1_1/kernel:0' shape=(3, 3, 64, 128) dtype=float32_ref>
<tf.Variable 'block2_conv1_1/bias:0' shape=(128,) dtype=float32_ref>
<tf.Variable 'block2_conv2_1/kernel:0' shape=(3, 3, 128, 128) dtype=float32_ref>
<tf.Variable 'block2_conv2_1/bias:0' shape=(128,) dtype=float32_ref>
<tf.Variable 'block3_conv1_1/kernel:0' shape=(3, 3, 128, 256) dtype=float32_ref>
<tf.Variable 'block3_conv1_1/bias:0' shape=(256,) dtype=float32_ref>
<tf.Variable 'block3_conv2_1/kernel:0' shape=(3, 3, 256, 256) dtype=float32_ref>
<tf.Variable 'block3_conv2_1/bias:0' shape=(256,) dtype=float32_ref>
<tf.Variable 'block3_conv3_1/kernel:0' shape=(3, 3, 256, 256) dtype=float32_ref>
<tf.Variable 'block3_conv3_1/bias:0' shape=(256,) dtype=float32_ref>
<tf.Variable 'block4_conv1_1/kernel:0' shape=(3, 3, 256, 512) dtype=float32_ref>
<tf.Variable 'block4_conv1_1/bias:0' shape=(512,) dtype=float32_ref>
<tf.Variable 'block4_conv2_1/kernel:0' shape=(3, 3, 512, 512) dtype=float32_ref>
<tf.Variable 'block4_conv2_1/bias:0' shape=(512,) dtype=float32_ref>
<tf.Variable 'block4_conv3_1/kernel:0' shape=(3, 3, 512, 512) dtype=float32_ref>
<tf.Variable 'block4_conv3_1/bias:0' shape=(512,) dtype=float32_ref>
<tf.Variable 'block5_conv1_1/kernel:0' shape=(3, 3, 512, 512) dtype=float32_ref>
<tf.Variable 'block5_conv1_1/bias:0' shape=(512,) dtype=float32_ref>
<tf.Variable 'block5_conv2_1/kernel:0' shape=(3, 3, 512, 512) dtype=float32_ref>
<tf.Variable 'block5_conv2_1/bias:0' shape=(512,) dtype=float32_ref>
<tf.Variable 'block5_conv3_1/kernel:0' shape=(3, 3, 512, 512) dtype=float32_ref>
<tf.Variable 'block5_conv3_1/bias:0' shape=(512,) dtype=float32_ref>
<tf.Variable 'dense_5/kernel:0' shape=(2048, 256) dtype=float32_ref>
<tf.Variable 'dense_5/bias:0' shape=(256,) dtype=float32_ref>
<tf.Variable 'dense_6/kernel:0' shape=(256, 13) dtype=float32_ref>
<tf.Variable 'dense_6/bias:0' shape=(13,) dtype=float32_ref>
동결 전 30개의 층
동결 후 4개의 층
<tf.Variable 'dense_5/kernel:0' shape=(2048, 256) dtype=float32_ref>
<tf.Variable 'dense_5/bias:0' shape=(256,) dtype=float32_ref>
<tf.Variable 'dense_6/kernel:0' shape=(256, 13) dtype=float32_ref>
<tf.Variable 'dense_6/bias:0' shape=(13,) dtype=float32_ref>