Loaded Keras Model Throws Error While Predicting (Likely Issues with Masking)
I am currently developing and testing a RNN that relies upon a large amount of data for training, and so have attempted to separate my training and testing files. I have one file where I create, train, and save a tensorflow.keras model to a file 'model.keras' I then load this model in another file and predict some values, but get the following error:
Failed to convert elements of {'class_name': '__tensor__', 'config': {'dtype': 'float64', 'value': [0.0, 0.0, 0.0, 0.0]}} to Tensor. Consider casting elements to a supported type. See https://www.tensorflow.org/api_docs/python/tf/dtypes for supported TF dtypes
Why model training accuracy is increasing, but validation accuracy is not increasing but stuck?
I have to predict the sentiments Positive, Neutral and Negative on urdu text. It has 30k samples
SAMPLES DATASET
train samples = 24k, while validation sample = 6k