Answer You should be using tf.keras.Sequential() or tf.keras.models.Sequential(). Also, you need to define a valid loss function. Here is a working example:
Tag: tensorflow
How to fix failed assertion `output channels should be divisible by group’ when trying to fit the model in Keras?
I’m trying to use ImageDataGenerator() for my image datasets. Here is my image augmentation code: Then use that plug into my model: Use EarlyStopping: Compile and Fit the model: That is when the code crash, and gives this error message. I try to change the output neurons but that doesn’t work. I don’t know what to do anymore. Please help
Chronologically Propagating Data into a Keras LSTM
I had a question about using LSTMs for processing data over time. That is, how can I feed data one-by-one into an LSTM, without the LSTM forgetting about my previous inputs? I had looked through the Keras “stateful” argument a bit, but it had only made me more confused. I’m not sure whether it’s relevant or not for my purposes.
‘cannot compute Pack as input #1(zero-based) was expected to be a float tensor but is a int32 tensor [Op:Pack] name: packed’. Error with tf.squeeze
I’m trying to display images of a dataset on a plot with their predictions. But I have this error: cannot compute Pack as input #1(zero-based) was expected to be a float tensor but is a int32 tensor [Op:Pack] name: packed This is the code in which I plot: I have the error on second line, on the tf.squeeze function. I
Convert tfrecords to image
I found a training dataset which is a set of tfrecords files,im trying to convert them into images but with no results,is it possible to convert them to images ? Answer To find out what is inside a tf.record use tf.data.TFRecordDataset and tf.train.Example: To parse the records, use tf.data.TFRecordDataset with tf.io.parse_single_example and tf.io.parse_tensor: Also check the source code of Satellite
Rotate image for data augmentation using tf keras only in specific angles
In tf keras, it is possible to have a data augmentation layer that performs rotation on each given image during training, in the following way as the docs say: The factor argument indicates the value of maximum rotation if a float is given and indicates lower and upper limits if a tuple is given. For my specific application only specific
Getting error while calculating AUC ROC for keras model predictions
I have a patient data named dat and labels (0 = No Disease, 1 = Disease) named labl both in the form of array. I predicted my model and stored the predictions named pre which is also an array, and I want to calculate and plot the AUC ROC. But I am getting this error while doing so. TypeError: Singleton
splitting the data into training and testing in federated learning
I am new in federated learning I am currently experimenting with a model by following the official TFF documentation. But I am stuck with an issue and hope I find some explanation here. I am using my own dataset, the data are distributed in multiple files, each file is a single client (as I am planning to structure the model).
How to arrange tensorboard’s graphs horizontally in tensorflow 2.x?
I am using the following code, the drawing is arranged vertically, how to change it to horizontal arrangement? I didn’t find it in the homepage of tensorflow. https://www.tensorflow.org/api_docs/python/tf/summary/scalar Answer Use regex expressions as suggested here to align your plots: Start tensorboard: < Click on image to see more details>
Tensorflow error: Failed to serialize message. For multi-modal dataset
I am trying to train a model, using TPU on Colab, which will take two np.ndarray inputs, one for an image of the shape, (150, 150, 3), and the other for an audio spectrogram image of the shape, (259, 128, 1). Now I have created my dataset using NumPy arrays as follows:- here shape of each is as follows:- I