The answer to your needs is called "bucketing". It consists of creating batches of sequences with similar length, to minimize the needed padding.
In tensorflow, you can do it with tf.data.experimental.bucket_by_sequence_length. Take into account that previously it was in a different python package (tf.contrib.data.bucket_by_sequence_length), so the ...
It looks like the inverse reinforcement learning problem defined by Stuart Russell as
measurements of an agent’s behaviour over time, in a variety
measurements of the sensory inputs to that agent;
a model of the physical environment (including the agent’s
Determine the reward function that the agent is optimizing.
It is ...
Found a solution, which is to pass a custom batch generator of type keras.utils.Sequence to the model.fit function (where one can write any logic to construct batches and to modify/augment training data) instead of passing the entire dataset in one go. Relevant code for reference:
# Must implement the __len__ function returning the number
# of batches in ...
First, you need to understand what Sequential Modeling is?
There are two categories in which Sequential Modeling falls in
Suppose the data itself is a Sequence of the stream like audio, time-series data, textual data.
Other is if you have the model who is working in sequential manner, what it means? Suppose you are giving a model training data but the model ...
You can do this very easily using python sets as follows:
with open("file_a.txt", "r") as f:
data_a = f.read().splitlines()
with open("file_b.txt", "r") as f:
data_b = f.read().splitlines()
set(data_a) - set(data_b)
Can machine learning algorithms predict random number
No, absolutely not.
ML is not a magical process, it works by identifying patterns in the data. In the case of supervised learning (i.e. with training) it means finding in the features the most relevant indications to help predict the target variable.
Randomness is the complete opposite of "patterns&...
Setting return_sequences to True or False depends entirely on which one is appropriate with respect to how you are going to make predictions at inference time:
If you want your model to make a prediction only when it has consumed a whole sequence as input, then you use return_sequences=False, so that your loss is only based on the output after ingesting the ...
One option is a model comparison approach. Start with simpler models then progressively trying more complex models. Along the way, checking if the additional complexity is resulting in increased predictive ability.
If you take a simplifying representation with a bag-of-words model, you can fit a Naive Bayes classifier.
If you make the Markov assumption, ...
In tf.keras.preprocessing.text (docs) you have utilities to process discrete token sequences, normally used to represent text.
In tf.keras.preprocessing.sequence (docs) you have utilities to process both continuous value sequences (normally used to represent time series) like TimeSeriesGenerator, and discrete token sequences (i.e. text), like the skipgrams ...
I think you should treat this problem as a binary classification problem. For each word in the changed sentence, you will have a binary label: correct or incorrect. I would recommend relabeling so that "correct" words will have a label of 0 and "incorrect" words will have a label of 1. In your example you would have:
correct_sentence = ...