Keras is an open source software library used to build and train deep learning models easily and efficiently. Keras was developed as a high-level API that is easy to use and flexible, allowing developers to develop models quickly without requiring in-depth knowledge of the details of mathematical operations.
What is Keras?
Keras is a deep learning library that aims to be easy to use and fast to develop. Developed as part of the TensorFlow project, Keras provides a detail-agnostic API that enables developers to build deep models quickly and easily.
Keras features:
1.Ease of use: Keras features a simple and easy-to-understand API, making it easy for beginners and professionals alike to build deep models.
2. Flexibility: Keras can be used with different machine learning libraries such as TensorFlow, Theano, and CNTK, allowing developers to choose the tool that best suits their needs.
3. Multi-layer: Keras provides many built-in layers that can be used to build different types of deep models such as artificial neural networks, rescue networks, and selective neural convolutions.
4. Support for distributed training: Keras allows users to distribute training runs across multiple processors or multiple machines while supporting load balancing and memory allocation.
5. Active Community: Keras has a large and active community of developers and researchers, which means there are many resources and support available to users.
How to use Keras:
1. Define the model: Start by defining the model using the appropriate layers for your project.
2. Compile the model: Use the Keras interface to compile the model and define the loss function and adjustment algorithm.
3. Train the model: Use the fit data to train the model using the fit() function.
4. Evaluate the model: Use the evaluate() function to evaluate the model's performance on new test data.
5. Using the Model: Use the trained model to predict new data using the predict() function.
### Simple example:
```python
import tensorflow as tf
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense
# Define the model
model = Sequential([
Dense(64, activation='relu', input_shape=(784,)),
Dense(64, activation='relu'),
Dense(10, activation='softmax')
])
# Assemble the model
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
# Train the model
model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_val, y_val))
# Evaluate the model
test_loss, test_acc = model.evaluate(X_test, y_test)
print('Test accuracy:', test_acc)
```
Keras is a powerful and easy-to-use tool for building deep models, and represents the ideal starting point for developers who want to explore and apply artificial intelligence in their projects. With Keras, you can quickly and easily build and train deep models without the need to
To in-depth knowledge of the internal details of neural networks.