Adding data_augmentation layer causes errors with model_1 in 05_transfer_learning #683
Description
Discussed in #680
Originally posted by MikaATuomaala December 17, 2024
Hi,
I have problems with "05_transfer_learning". Following code used for model_1:
input_shape = (224, 224, 3)
base_model = tf.keras.applications.EfficientNetV2B0(include_top=False)
base_model.trainable = False
inputs = layers.Input(shape=input_shape, name="input_layer")
x = data_augmentation(inputs, training=True)
where data_augmentation is defined as follows:
data_augmentation = keras.Sequential([
layers.RandomFlip("horizontal"),
layers.RandomRotation(0.2),
layers.RandomZoom(0.2),
layers.RandomHeight(0.2),
layers.RandomHeight(0.2),
# preprocessing.rescasle(1./255) # Resnet50v2 may need this
], name = "data_augmentation")
produces following error:
Input 0 of layer "functional_1" is incompatible with the layer: expected shape=(None, 384, 512, 3), found shape=(None, 224, 224, 3)
Arguments received by Sequential.call():
• args=('<KerasTensor shape=(None, 224, 224, 3), dtype=float32, sparse=False, name=input_layer>',)
• kwargs={'training': 'True', 'mask': 'None'}
As far as I can tell, the code is same as in the updated course notes. And I have tried to copy-paste from the updated notes, but that doesn't seem to make any difference.
Tensorflow version is 2.17.1 and the runtime type is T4 GPU
Any ideas what could be causing this? And better yet, how to fix it?
Thanks,
-Mika