I'm using the following code to load an imagenet pre-trained VGG19 model and fit to my custom dataset.
from keras.applications.vgg19 import VGG19
optim = tf.keras.optimizers.RMSprop(momentum=0.9)
vgg19 = VGG19(include_top=False, weights='imagenet', input_tensor=tf.keras.layers.Input(shape=(224, 224, 3)))
vgg19.trainable = False
# x = keras.layers.GlobalAveragePooling2D()(model_vgg19_pt.output)
x = keras.layers.Flatten()(vgg19.output)
output = keras.layers.Dense(n_classes, activation='softmax')(x)
model_vgg19_pt = keras.models.Model(inputs=[vgg19.input], outputs=[output])
model_vgg19_pt.compile(optimizer=optim,
loss='categorical_crossentropy', metrics=['categorical_accuracy'])
callback = tf.keras.callbacks.LearningRateScheduler(scheduler)
model_vgg19_pt.fit(x_train, y_train, batch_size=20,
epochs=50, callbacks=[callback]
)
on model.fit() line, I get the following error
KeyError: 'The optimizer cannot recognize variable dense_1/kernel:0. This usually means you are trying to call the optimizer to update different parts of the model separately. Please call
optimizer.build(variables)with the full list of trainable variables before the training loop or use legacy optimizer `tf.keras.optimizers.legacy.{self.class.name}.'
What does it mean and how can I fix it?
I get the same errors for
keras.applications.inception_v3
too, when using the same implementation method.
Additionally, this was working with jupyter notebook file on tensorflow cpu, but when running on a remote machine with tensorflow-gpu installed, I'm getting these errors.
This works fine with optimizer SGD, but not with RMSprop. why?
Additional Using this:
model_vgg19_pt.compile(optimizer=tf.keras.optimizers.RMSprop(momentum=0.9),
loss='categorical_crossentropy', metrics=['categorical_accuracy'])
instead as used above works. But can somebody explain why....
