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Saving Keras model - UserWarning: Layer XX was passed non-serializable keyword arguments


Saving Keras model - UserWarning: Layer XX was passed non-serializable keyword arguments

By : user2185623
Date : November 22 2020, 04:01 AM
hope this fix your issue You can save the model "code", maybe a .py file just to create the model exactly as it were.
Then you load weights: model.load_weights('models/model_weights.h5').
code :


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Saving the specific layer from within a sequential Keras model

Saving the specific layer from within a sequential Keras model


By : user2171095
Date : March 29 2020, 07:55 AM
this will help After training, create a new model (model2) from your trained model (model) ending in your desired layer.
You can do so either with layer name:
code :
from keras.models import Model
model2= Model(model.input,model.get_layer('dense_5').output)
from keras.models import Model
model2= Model(model.input,model.layers[4].output)
preds=model2.predict(x)
Keras Output tensors to a Model must be the output of a Keras `Layer` (thus holding past layer metadata)

Keras Output tensors to a Model must be the output of a Keras `Layer` (thus holding past layer metadata)


By : user2525215
Date : March 29 2020, 07:55 AM
This might help you When invoking the Model API, the value for outputs argument should be tensor(or list of tensors), in this case it is a list of list of tensors, hence there is a problem. Just unpack the unpooling_masks list(*unpooling_masks) when calling Model.
code :
model = Model(inputs, [layer, *unpooling_masks], name='vgg19')
Adding an activation layer to Keras Add() layer and using this layer as output to model

Adding an activation layer to Keras Add() layer and using this layer as output to model


By : tucsonalama
Date : March 29 2020, 07:55 AM
To fix this issue It has nothing to do with the Add layer, you are using K.softmax directly on Keras tensors and this won't work, you need an actual layer. You can use the Activation layer for this:
code :
from keras.layers import Activation

predictions = Add()([x,y])
predictions = Activation("softmax")(predictions)
model = Model(inputs = model.input, outputs = predictions)
Get (input) layer of Keras model after saving and re-loading it from disk

Get (input) layer of Keras model after saving and re-loading it from disk


By : nobby
Date : March 29 2020, 07:55 AM
will help you After adding the VGG16 model to your custom model, it would have two input nodes: one is the original input node which is accessible using conv_base.get_input_at(0), and another input node which is created for the input from your custom model which would be accessible using conv_base.get_input_at(1) (this is actually the input of the model and is equivalent to model.input). The difference between a node and a layer in Keras has been explained in detail in this answer.
Keras gives 'Not JSON Serializable' error when saving the model

Keras gives 'Not JSON Serializable' error when saving the model


By : user3140266
Date : March 29 2020, 07:55 AM
this one helps. It appears that you are computing the loss in the layer of a model. It is not a good practice to accomodate the loss function as a layer. You can compute your weighted loss using custom loss function.
So your code can be rewritten as follows:
code :
def weighted_binary_loss(y_true, y_pred):
    weights = [0.5, 0.6]  # Define your weights here
    loss = binary_crossentropy(y_true, y_pred)
    weights_mask = y_true*weights[0] + (1.-y_true)*weights[1]
    loss = multiply([loss, weights_mask])
    return loss  

conv10 = Conv2D(1, 1, activation = 'sigmoid')(conv9)
model = Model(inputs = inputs, outputs = conv10)
model.compile(optimizer = Adam(lr = 1e-5), loss = weighted_binary_loss, metrics = ['accuracy'])
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