I am new to machine learning. I was following this tutorial on fine-tuning VGG16 models.
The model loaded fine with this code:
vgg_model = tensorflow.keras.applications.vgg16.VGG16()
but gets this ERROR:
TypeError: The added layer must be an instance of class Layer. Found: <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x000001FA104CBB70>
When running this code:
model = Sequential() for layer in vgg_model.layers[:-1]: model.add(layer)
Dependencies:
I am following this blog but instead, I want to use VGG16.
Any help to fix this would be appreciated. Thank you so much.
This won't work because a tensorflow.keras layer is getting added to a keras Model.
vgg_model = tensorflow.keras.applications.vgg16.VGG16() model = keras.Sequential() model.add(vgg_model.layers[0])
Instantiate tensorflow.keras.Sequential(). This will work.
model = tensorflow.keras.Sequential() model.add(vgg_model.layers[0])
Adding to @Manoj Mohan's answer, you can add an input_layer
to your model
using input_layer
from Keras
layers
as below:
import keras from keras.models import Sequential from keras.layers import InputLayer model = Sequential() model.add(InputLayer(input_shape=shape, name=name)) ....
if you are using the TensorFlow
builtin Keras
then import is different other things are still the same
import tensorflow as tf import tensorflow.keras as keras from tensorflow.keras.models import Sequential from tensorflow.keras.layers import InputLayer model = Sequential() model.add(InputLayer(input_shape=shape, name=name)) ....
Coming to the main part, if you want to import layers to your sequential model, you can use the following syntax.
import keras from keras.models import Sequential, load_model from keras import optimizers from keras.applications.vgg16 import VGG16 from keras.applications.vgg19 import VGG19 # For VGG16 loading to sequential model model = Sequential(VGG16().layers) # For VGG19 loading to sequential model model = Sequential(VGG19().layers)
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