I am trying to optimize a convolutional neural network with Bayesian Optimization algorithm provided in keras tuner library. When I perform the line: tuner_cnn.search(datagen.flow(X_trainRusReshaped,Y_trainRusHot), epochs=50, batch_size=256) I encounter this error: InvalidArgumentError: Graph execution error One-Hot-Encode y_train and y_test as the following: I defined my model builder like that: perform the tuner search: I also tried to do: But it does
Tag: keras
How to hypertune input shape using keras tuner?
I am trying to hypertune the input shape of an LSTM model based on the different values of timesteps. However, I am facing an issue. While initializing the model, the default value of timesteps (which is 2) is chosen, and accordingly, the build_model.scaled_train is created of shape (4096, 2, 64). Thus the value of input_shape during initialization is (2, 64).
Tensorflow dataset, how to concatenate/repeat data within each batch?
If I have the following dataset: dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3, 4, 5, 6]) When I use a batch_size=2, I would get [[1,2], [3,4], [5,6]]. However, I would like to get the following output: [[1,2,1,2], [3,4,3,4], [5,6,5,6]] Basically, I want to repeat the batch dimension by 2x and use this as a new batch. Obviously, this is a toy example.
Why does Keras run only 5 epochs out of 25?
I have uninstalled Keras and Tensorflow and installed them both using But even after, I still have this strange thing that it’s only 5 epochs that are running: I cannot track when this situation occurred, but it used to run all of the epochs. Here is my code: I also use Please direct me. Answer Try replacing the steps_per_epoch =
Tensorflow: Issues with determining batch size in custom loss function during model fitting (batch size of “None”)
I’m trying to create a custom loss function, in which I have to slice the tensors multiple times. One example is listed below: This (and the entire loss function) works fine when testing it manually on selfmade Tensors y_true and y_pred, but when using it inside a loss function it will give an error upon model fitting (compiling goes fine).
proper input and output shape of a keras Sequential model
I am trying to run a Keras sequential model but can’t get the right shape for the model to train on. I reshaped x and y to: Currently, both the input shape and output shape are: The dataset consists of 9766 inputs and 9766 outputs respectively. Each input is a single array of 500 values and each output is also
Value error in convolutional neural network due to data shape
I am trying to predict the of number peaks in time series data by using a CNN and keep on getting a data shape error. My data looks as follows: X = list of 520 lists (each is a time series) of various lengths (shortest = 137 elements, longest = 2297 elements) y = list with 520 elements, each being
How do you add a dimension to the datapoints without using Lambda layer?
I am trying to classify the fashion_mnistdataset using the Conv2D layer and as I know it can be easily done using the following code: However, I am required to not use Lambda layer. so, the above solution is not correct. So, I am wondering, how can i classify the mnist_fashion dataset without using Lambda layer ? Update: When i add
Evaluate model on Testing Set after each epoch of training
I’m training a tensorflow model on image dataset for a classification task, we usually provide the training set and validation set to the model.fit method, we can later output model convergence graph of training and validation. I want to do the same with the testing set, in other words, I want to get the accuracy and loss of my model
How to save Keras encoder and decoder separately?
I have created an autoencoder using a separate encoder and decoder as described in this link. Split autoencoder on encoder and decoder keras I am checkpointing my autoencoder as followed. How do I save the encoder and decoder separately corresponding to the autoencoder? Alternatively, can I extract deep encoder and decoder from my save autoencoder? Answer You could try to