I’m trying to run the following code but I got an error. Did I miss something in the codes? and here is the error message: Answer This error indicates that, you have defined an activation function that is not interpretable. In your definition of a dense layer you have passed two argument as layers[i] and layers[i+1]. Based on the docs
Tag: keras
How to make a custom activation function with trainable parameters in Tensorflow [closed]
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How can I see the model as visualized?
I am trying to do some sample code of GAN, here comes the generator. I want to see the visualized model but, this is not the model. Model.summary() is not the function of tensorflow but it is keras?? if so how can I see visualized model?? My function is here. Answer One possible solution (or an idea) is to wrap
Last layer in a RNN – Dense, LSTM, GRU…?
I know you can use different types of layers in an RNN architecture in Keras, depending on the type of problem you have. What I’m referring to is for example layers.SimpleRNN, layers.LSTM or layers.GRU. So let’s say we have (with the functional API in Keras): Where lstm_3 is the last layer. Does it make sense to have it as an
Keras does not load because cannot find TensorFlow
Running Anaconda and installed: Keras = 2.4.3 TensorFlow = 2.4.0 However, when importing Keras – I get “Keras requires TensorFlow 2.2 or higher”. Tried uninstalling/installing – did not help. Any idea? Answer You can use to install an upgraded and compatible TensorFlow version in your system.
Plot confusion matrix with Keras data generator using sklearn
Sklearn clearly defines how to plot a confusion matrix using its own classification model with plot_confusion_matrix. But what about using it with Keras model using data generators? Let’s have a look at an example code: First we need to train the model. Now after the model is trained let’s build a confusion matrix. Now this works fine so far. But
How to remove first N layers from a Keras Model?
I would like to remove the first N layers from the pretrained Keras model. For example, an EfficientNetB0, whose first 3 layers are responsible only for preprocessing: As M.Innat mentioned, the first layer is an Input Layer, which should be either spared or re-attached. I would like to remove those layers, but simple approach like this throws error: This will
how do I fit a time-series multi head model?
I try to create a model by concatenating 2 models together. The models I want to use, shall handle time series, and I’m experimenting with Conv1D layers. As these have an 3D input shape batch_shape + (steps, input_dim) and the Keras TimeseriesGenerator is providing such, I’m happy being able to make use of it when handling single head models. This
VGG16 Network for Multiple Inputs Images
I am trying to use the VGG16 network for multiple input images. Training this model using a simple CNN with 2 inputs gave me an acc. of about 50 %, which is why I wanted to try it using an established model like VGG16. Here is what I have tried out: I get this error while calling the Model function.
tf.train.Checkpoint is restoring or not?
I am running tensorflow 2.4 on colab. I tried to save the model using tf.train.Checkpoint() since it includes model subclassing, but after restoration I saw It didn’t restored any weights of my model. Here are few snippets: When I later restored it I didn’t get any gru weights: I also tried checkpoint.restore(manager.latest_checkpoint) but nothing changed. Is there any thing wrong