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Keras contains numerous implementations of commonly used neural-network building blocks such as layers, objectives , activation functions , optimizers , and a host of tools to make working with image and text data easier. The code is hosted on GitHub , and community support forums include the GitHub issues page, and a Slack channel.
In addition to standard neural networks, Keras has support for convolutional and recurrent neural networks. It supports other common utility layers like dropout, batch normalization, and pooling. Keras allows users to productize deep models on smartphones iOS and Android , on the web, or on the Java Virtual Machine. Keras claims over , users as of November From Wikipedia, the free encyclopedia. Neural network library. Retrieved Retrieved 30 May The first step in word embeddings is to convert the words into thier corresponding numeric indexes.
To do so, we can use the Tokenizer class from Keras.
Python for NLP: Creating Multi-Data-Type Classification Models with Keras
Sentences can have different lengths, and therefore the sequences returned by the Tokenizer class also consist of variable lengths. We specify that maximum length of the sequence will be although you can try any number. For the sentences having length less than , the remaining indexes will be padded with zeros. For the sentences having length greater than , the remaining indexes will be truncated. Next, we need to load the built-in GloVe word embeddings.
Finally, we will create an embedding matrix where rows will be equal to the number of words in the vocabulary plus 1. The number of columns will be since each word in the GloVe word embeddings that we loaded is represented as a dimensional vector. Once the word embedding step is completed, we are ready to create our model.
We will be using Keras' functional API to create our model. Though single input models like the one we are creating now can be developed using sequential API as well, but since in the next section we are going to develop a multiple input model that can only be developed using Keras functional API, we will stick to functional API in this section too. We will create a very simple model with one input layer embedding layer , one LSTM layer with neurons and one dense layer that will act as the output layer as well. Since we have 3 possible outputs, the number of neurons will be 3 and the activation function will be softmax.
If you open the image, it will look like this:. You can see that the model has 1 input layer, 1 embedding layer, 1 LSTM, and one dense layer which serves as the output layer as well. The results for the 10 epochs is as follows:. You can see that the final training accuracy of the model is The difference is very small and therefore we assume that our model is not overfitting on the training data.
You can see the lines for both training and testing accuracies and losses are pretty close to each other which means that the model is not overfitting. In this section, we will create a classification model that uses information from the useful , funny , and cool columns of the yelp reviews.
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Since the data for these columns is well structured and doesn't contain any sequential or spatial pattern, we can use simple densly connected neural networks to make predictions. Let's plot the average counts for useful , funny , and cool reviews against the review score. From the output, you can see that the average count for reviews marked as useful is the highest for the bad reviews, followed by the average reviews and the good reviews.
The output shows that again, the average count for reviews marked as funny is highest for the bad reviews.
How to Use Keras to Solve Classification Problems with a Neural Network – BMC Blogs
We expect that the average count for the cool column will be the highest for good reviews since people often mark positive or good reviews as cool:. As expected, the average cool count for the good reviews is the highest. Next, we will convert our labels into one-hot encoded values and then split our data into train and test sets:.
The next step is to create our model. Our model will consist of four layers you can try any number : the input layer, two dense hidden layers with 10 neurons and relu activation functions, and finally an output dense layer with 3 neurons and softmax activation function. From the output, you can see that our model doesn't converge and accuracy values remain between 66 and 67 accross all the epochs.
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- Python for NLP: Creating Multi-Data-Type Classification Models with Keras.
From the output, you can see that accuracy values are relatively lower. Hence, we can say that our model is underfitting. The accuracy can be increased by increasing the number of dense layers or by increasing the number of epochs, however I will leave that to you. Let's move on to the final and most important section of this article where we will use multiple inputs of different types to train our model.
In the previous sections, we saw how to train deep learning models using either textual data or meta information. What if we want to combine textual information with meta information and use that as input to our model? We can do so using the Keras functional API.
In this section we will create two submodels. The first submodel will accept textual input in the form of text reviews. This submodel will consist of an input shape layer, an embedding layer, and an LSTM layer of neurons. The second submodel will accept input in the form of meta information from the useful , funny , and cool columns. The second submodel also consist of three layers. An input layer and two dense layers. The output from the LSTM layer of the first submodel and the output from the second dense layer of the second submodel will be concatenated together and will be used as concatenated input to another dense layer with 10 neurons.
Finally, the output dense layer will have three neuorns corresponding to each review type. First we have to create two different types of inputs. To do so, we will divide our data into a feature set and label set, as shown below:. The X variable contains the feature set, where as the y variable contains label set. We need to convert our labels into one-hot encoded vectors. We will also divide our data into training and feature set.
Now our label set is in the required form. Since there will be only one output, therefore we don't need to process our label set. However, there will be multiple inputs to the model. Therefore, we need to preprocess our feature set. As a first step, we will create textual input for the training and test set. Look at the following script:. Similarly, the following script preprocess textual input data for test set:. Now we need to convert textual input for the training and test sets into numeric form using word embeddings. The following script does that:. We have preprocessed our textual input.
The second input type is the meta information in the useful , funny , and cool columns. We will filter these columns from the feature set to create meta input for training the algorithms. Let's now create our two input layers. The first input layer will be used to input the textual input and the second input layer will be used to input meta information from the three columns.
The shape size has been set to the shape of the input sentence. For the second input layer, the shape corresponds to three columns. Similarly, the following script creates a second submodel that accepts input from the second input layer:.
We now have two submodels. What we want to do is concatenate the output from the first submodel with the output from the second submodel. We can use the Concatenate class from the keras. You can see that now our model has a list of inputs with two items. The following script compiles the model and prints its summary:.
The above figure clearly explains how we have concatenated multiple inputs into one input to create our model. To evaluate our model, we wil have to pass both the test inputs to the evaluate function as shown below:. Our test accuracy is You can see that the differences for loss and accuracy values is minimal between the training and test sets, hence our model is not overfitting.
In this article, we built a very simple neural network since the purpose of the article is to explain how to create deep learning model that accepts multiple inputs of different types. Following are some of the tips that you can follow to further improve the performance of the text classification model:. Please share your results along with the neural network configuration in the comments section.
I would love to see how well did you perform.