# Keras: The High-Level Library for Building Neural Networks

Keras is a popular open-source library for building and training deep learning models in Python. It is designed to be user-friendly and easy to use, allowing developers to quickly prototype and build complex neural networks without having to deal with the low-level details of building and training models.

One of the main features of Keras is its ability to build and train models using a high-level, modular approach. Users can easily define and connect the layers of their neural network, and Keras will handle the underlying details of building and training the model. This makes it easy for developers to experiment with different model architectures and hyperparameters, and to quickly iterate on their designs.

Keras also supports a wide range of model architectures, including convolutional neural networks (CNNs) for image classification and natural language processing (NLP), recurrent neural networks (RNNs) for time series analysis and NLP, and more. It also includes a number of pre-trained models that can be easily fine-tuned for specific tasks, allowing developers to get up and running quickly without having to start from scratch.

In addition to building and training models, Keras also provides a number of utilities for evaluating and optimizing the performance of models. These include functions for calculating metrics such as accuracy and loss, and for performing tasks such as model evaluation and hyperparameter tuning.

## Keras: The Basics of Simple Neural network models

Here is an example of a simple neural network using the Keras library in Python to predict the class of a handwritten digit image:

```
import tensorflow as tf
from tensorflow import keras
# Load the MNIST dataset
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
# Preprocess the data
x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)
x_train, x_test = x_train / 255.0, x_test / 255.0
# Define the model architecture
model = keras.Sequential([
keras.layers.Flatten(input_shape=(28, 28, 1)),
keras.layers.Dense(128, activation='relu'),
keras.layers.Dense(10, activation='softmax')
])
# Compile the model
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
# Train the model
model.fit(x_train, y_train, epochs=5)
# Evaluate the model on the test data
test_loss, test_acc = model.evaluate(x_test, y_test)
print('Test Accuracy: ', test_acc)
Out:
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11490434/11490434 [==============================] - 0s 0us/step
Epoch 1/5
1875/1875 [==============================] - 10s 5ms/step - loss: 0.2588 - accuracy: 0.9268
Epoch 2/5
1875/1875 [==============================] - 9s 5ms/step - loss: 0.1142 - accuracy: 0.9661
Epoch 3/5
1875/1875 [==============================] - 7s 4ms/step - loss: 0.0787 - accuracy: 0.9768
Epoch 4/5
1875/1875 [==============================] - 5s 3ms/step - loss: 0.0593 - accuracy: 0.9815
Epoch 5/5
1875/1875 [==============================] - 5s 3ms/step - loss: 0.0456 - accuracy: 0.9859
313/313 [==============================] - 1s 2ms/step - loss: 0.0792 - accuracy: 0.9756
Test Accuracy: 0.975600004196167
```

This code defines and trains a simple neural network for classifying images of handwritten digits from the MNIST dataset. The network consists of a single fully connected hidden layer with 128 units and ReLU activation, followed by an output layer with 10 units and softmax activation, one for each class (0-9). The model is then compiled with the Adam optimizer, sparse categorical crossentropy loss function, and accuracy as the evaluation metric. Finally, the model is trained for 5 epochs on the training data and evaluated on the test data.

## Keras: Sequential modeling

Here’s an example of how to build a simple multi-layer perceptron (MLP) model using the `Sequential`

class in Keras:

```
from keras.models import Sequential
from keras.layers import Dense
# Initialize the model
model = Sequential()
# Add a hidden layer with 128 units and ReLU activation
model.add(Dense(128, activation='relu', input_shape=(30,)))
# Add a second hidden layer with 64 units and ReLU activation
model.add(Dense(64, activation='relu'))
# Add an output layer with a single unit and sigmoid activation
model.add(Dense(1, activation='sigmoid'))
# Compile the model with binary crossentropy loss and the Adam optimizer
model.compile(loss='binary_crossentropy', optimizer='adam')
```

In this example, we build a model with three fully connected (`Dense`

) layers. The first layer has 128 units and takes an input shape of (30,), meaning it expects 30-dimensional vectors as input. The second and third layers have 64 and 1 units, respectively. The activation functions used are ReLU for the hidden layers and sigmoid for the output layer, as we’re solving a binary classification problem. The model is then compiled with binary crossentropy loss and the Adam optimizer.

## Keras: Convolutional Networks

Here’s an example of how to build a simple Convolutional Neural Network (CNN) model using the `Sequential`

class in Keras:

```
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
# Initialize the model
model = Sequential()
# Add a 2D convolution layer with 32 filters, a kernel size of (3,3), and ReLU activation
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
# Add a max pooling layer with a pool size of (2,2)
model.add(MaxPooling2D(pool_size=(2, 2)))
# Flatten the output from the previous layer to a 1D vector
model.add(Flatten())
# Add a dense layer with 128 units and ReLU activation
model.add(Dense(128, activation='relu'))
# Add an output layer with 10 units and soft
```

In this example, we build a model for a multi-class classification problem. The model starts with a 2D convolution layer with 32 filters, a kernel size of (3,3), and ReLU activation. This is followed by a max pooling layer with a pool size of (2,2). The output from the previous layer is then flattened to a 1D vector and passed through two fully connected (`Dense`

) layers, with 128 and 10 units, respectively. The activation functions used are ReLU for the hidden layer and softmax for the output layer. The model is then compiled with categorical crossentropy loss and the Adam optimizer, and we report accuracy as a metric.

## Keras: Recurrent Networks

Here’s an example of how to build a simple Recurrent Neural Network (RNN) model using the `Sequential`

class in Keras:

```
from keras.models import Sequential
from keras.layers import LSTM, Dense
# Initialize the model
model = Sequential()
# Add a LSTM layer with 128 units
model.add(LSTM(128, input_shape=(timesteps, input_dim)))
# Add a dense layer with a single unit and sigmoid activation
model.add(Dense(1, activation='sigmoid'))
# Compile the model with binary crossentropy loss and the Adam optimizer
model.compile(loss='binary_crossentropy', optimizer='adam')
```

In this example, we build a binary classification RNN model using a single LSTM layer with 128 units. The input shape is specified as `(timesteps, input_dim)`

, where `timesteps`

is the number of time steps in the input sequence and `input_dim`

is the number of features per time step. The model has a single dense output layer with a single unit and a sigmoid activation function, as we’re solving a binary classification problem. The model is then compiled with binary crossentropy loss and the Adam optimizer.

## Keras is an extremely powerful library.

Overall, Keras is a powerful and user-friendly library for building and training deep learning models in Python. Its high-level, modular approach and wide range of supported architectures make it a popular choice for developers working on a variety of tasks.

The notebook for this article can be found here.

The Keras documentation can be found here.

Read my other articles introducing libraries in Python here.

Here’s some links to tools that helped me learn how to code in Python.

Sign up for DataCamp today – https://www.datacamp.com/promo/

Practicum by Yandex – https://practicum.yandex.com/

Pathstream – https://www.pathstream.com