Updated March 15, 2023
Definition of Keras Embedding
Keras embedding refers to embedding a layer over the neural network used for the text data that will be part of this neural network. It needs data as input where encoding is needed for the text to decode and manipulate with all text. Each word present as part of the text is a unique integer as it is present in an encoded format. It also includes pre-trained words as part of a neural network that will help embed the text or word layers. It is a type of transfer of words that will be saved for use in another model.
What is Keras embedding?
Word embedding within a text strongly impacts the over-representation of words and their associated meanings. Therefore, it is an improvement over representation that is sparse and contains a handful of words. It comprises the Keras library that will embed a layer over the neural network as per its requirement. Once the embedding with a layer is done, the main idea is to fit it in the proper Keras model.
How to use keras embedding?
- Keras embedding, as mentioned, gels well with the word embedding, and word embedding provides ample scope for representation of words with some relative meaning, where an improvement with sparse representation can be used for deep learning with Keras.
- The embedding layer is one of Keras’s available and important layers.
- This embedding layer is mainly used in Natural language processing applications such as modeling language, dealing with NLP-related problems, and using pre-trained word embeddings like GloVe.
- It Is also possible to train and prepare your custom embeddings using the keras embedding layer.
- Keras is the library in python, built on top of TensorFlow; it has its advantage like it is used for most deep learning networks.
- Keras supports two types of API: Sequential API and Functional API. Similarly, To implement word embeddings, the keras library provides or contains a layer called Embedding().
- The embedding layer is a class used as a first layer in the sequential model for NLP tasks.
- The embedding layer has certain requisites where there is a need for glove embedding for many words that might be useful over the sequential way of calling Keras API.
The embedding layer can be used to carry out three important tasks:
– It can be used for learning word embedding and saving the total resulting model.
– The embedding layer can be used to load some pretrained data or word embeddings in a new model per requirement.
The embedding layer can also be used to learn the word embedding and perform some NLP tasks like sentiment analysis, text classification, and many more NLP-relevant tasks.
Parameters Keras embedding
Parameters as keras embedding are as follows:
- embedding_layer = Embedding(120, 12, input_lenth=25)
The first layer in the embedding layer refers to the size of the entire vocabulary, or in other terms, the total number of unique words in a corpus.
The second parameter refers to the number of dimensions for each word vector. For example length of each vector is 32; the same is the case with others. Finally, the third parameter includes the length of the input sentence.
- The expected output to the input of the embedding layer is a 2D vector where words get represented along a row and their corresponding dimensions in the form of columns.
- Coming to the class representation of the Embedding layer comprises of following parameters or arguments:
tf.keras.layers.Embedding(
input_dimnsion,
output_dimnsion,
embeddings_initializer_0="uniform",
embeddings_regularizer_0=None,
activity_regularizer_0=None,
embeddings_constraint_0=None,
mask_zero_0=False,
input_length_with=None,
**kwargs
)
Where,
- Input_dimnsion: It is an integer type, and there is the size of vocabulary type with maximum integer index +1
- Output_dimnsion: It is also an integer type where dense embedding has some other dimension.
- Embeddings_initializer_0: It represents keras initializer which is used for embeddings matrix as an initializer.
- embeddings_regularizer_0: It is a regulizer function applied for embedding matrix with keras regularizers.
- Embedding_constraints: These are some constraints used for constraint function as part of embedding class as part of keras constraints.
- Mask_zero_0: It mainly represents any boolean value where the value can be either 0 or 1, where the input value as 0 has the special padding value masked out. It is useful for recurrent layers that might consider some input variable.
- Input_layer: It is a constant that comprises length with certain parameters that have to either connect it with flattened or dense layer upstreams for manipulation.
Keras Embedding Example
Example 1: This code snippet tells us to create a document with a label with a different set of arrays for work, as shown.
docs_def = ['Pleasent_weather!',
'chilled_wind',
'Autmn_break',
'winter_fall',
'Excellent!',
'Storm',
'Snowfall!',
'Night',
'time_would_have_been_better.']
labels_def = array([1,1,0,1,0,1,0,0,0,1])
Example 2: This code snippet is an extension to example 1, where one hot() function is used for creating a hash of each word with an integer encoding.
vocab_sz = 60
encoded_docms = [one_hot(dt, vocab_sz) for dt in docs_def]
print(encoded_docms)
Example 3: This code snippet represents the padding of the document with a maximum length of 6 using the pad_seuences function as part of the keras library function.
max_lenth = 6
padded_docms = pad_sequences(encoded_docms, maxlen=max_lenth, padding='post')
print(padded_docms)
Example 4: Once all the prerequisites are completed in the previous step, it is now required to design the model where a sequential API is called with a final summary of a defined model as represented below.
model = Sequential()
model.add(Embedding(vocab_sz, 12, input_lenth=max_lenth))
model.add(Flatten())
model.add(Dense(2, activation='sigmoid'))
model.compile(optimizer='ame', loss='binary_catestrotropy', metrics=['accurate'])
print(model.summary())
Example 5: This code snippet represents the fit and evaluates the method of any classification model based on the accuracy value as shown.
model.fit(padded_docms, labels, epochs=60, verbose=1)
loss, accurate = model.evaluate(padded_docms, labels, verbose=1)
print('Accurate: %f' % (accurate*200))
Conclusion
It is one of the efficient ways to deal with words and text as it provides an embedding layer in advance to handle it. All the neural network models and machine learning algorithms use Keras embedding regarding words and text level training and manipulation, which is truly powerful when incorporated with neural network analysis.
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