Salient Features of Keras – Reasons to Learn Keras
Keras is the most useful library for Deep Learning. It runs on the top of TensorFlow and Theano. It focuses on fast experimentation and is a neural network library. Keras uses TensorFlow and Theano as its backend. It enables the user to complete their task in minimum lines of code. It is one of the most popular libraries. So, it is important to learn about the features of Keras.
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Features of Keras
Let us learn the features of Keras that make it worth learning:
Keras is modular. It considers a model in the form of a graph or a sequence. Keras allows you to save the model you are working on. Keras provides a save() method to save the current model. You can even use the model in the future.
2. Large Dataset
Keras contains a large pre-defined dataset. It provides you a variety of datasets. You can use this dataset to be directly importing and loading it.
Let’s say, IMDB DATA. It contains around 25k reviews of the movies. This dataset contains binary numbers (0 & 1) to review each movie. 0 represents negative sentiment and 1 represents positive sentiment.
You can load IMDB DATA as:
From keras.datasets import imdb (x_train, y_train), (x_test, y_test) = imdb.load_data()
3. Train from NumPy Data
Keras uses the NumPy array to train and evaluate the model. It makes use of the fit() method. The fit() method fits the model to the training data. This training process may take some time. fit() method had three arguments batch_size, validation_data and epochs.
4. Evaluation and Prediction
Keras has evaluate() and predict() methods. These methods can use the dataset of NumPy. After testing the data, the evaluation of the result is done. These methods are used to evaluate our models.
5. Pre-trained Models in Keras
Keras contains a number of pre-trained models. These models can be imported from keras.applications. These models are useful for feature extraction and fine-tuning. Keras.application is a module that contains weights for image classification like VGG16, VGG19, Xception, etc.
6. Encoding in Keras
Karas allows you encoding feature. There is one_hot() function in Keras that enables encoding. It helps you to encode integers in one step. It also enables you to tokenize the data. This function filters out the white spaces, make the text to lower case, and filter out the punctuations.
7. Layers in Keras
There are numerous layers and parameters in Keras. All Keras layers have a number of methods in them. These layers are useful to construct, train, configure the data. The dense layer is beneficial to implement operations. Flatten is used to flatten the input. Dropout allows dropout to the input. Reshape helps in reshaping the output in a certain shape. Input is used to initiate a Keras tensor.
keras.layers.Reshape(target_shape) kera.layers.Flatten(data_format=none) keras.layers.Dropout(rate, noise_shape=none, seed=none)
8. You can Obtain the Output of an Intermediate Layer
Keras is a very easy library. It enables you to obtain the output in the intermediate of a layer. To obtain output in the intermediate, you can simply create a new layer that will help you to obtain the output. Or else, you can build a Keras function that will help you to return the output of a certain layer using a certain input. Hence, Keras makes it easy for you to work with it.
9. Keras is Python-Native Library
Keras is a complete Python library. It uses all the known concepts of Python. It is a library that is written in the Python language. As Keras is Python oriented, it provides you a user-friendly environment. You can implement Keras knowing the basics of Python. So, it is very simple to work with Keras.
10. Pre-processing of Data
Keras provides you several functions for the preprocessing of data. ImageDataGenerator is one such method. You can import it in by:
from keras.preprocessing.image import ImageDataGenerator.
It helps you to resize the image, change its degree, flip the image, change the height and width of the image, etc.
Finally, we have seen the Features of Keras. Keras is an important library for Deep Learning. It enables users to save time and write code in fewer lines. It reduces the load on the user by providing many functions to deal with the datasets. Keras runs on the top of TensorFlow and Theano. It has a broad adaptation to the industry. Gaining more experience in Keras will help you to work to gain proficiency in Deep Learning.