python-kerasHow do I use Python Keras to create a Recurrent Neural Network (RNN) example?

A Recurrent Neural Network (RNN) example in Python Keras can be created by following these steps:

1. Import the necessary libraries, such as keras and numpy:

import keras
import numpy as np

2. Define the model:

model = keras.Sequential()
model.add(keras.layers.Embedding(input_dim=1000, output_dim=64))
model.add(keras.layers.LSTM(64))
model.add(keras.layers.Dense(1, activation='sigmoid'))

3. Compile the model:

model.compile(optimizer='rmsprop',
              loss='binary_crossentropy',
              metrics=['acc'])

4. Generate dummy training data:

x_train = np.random.random((1000, 10))
y_train = np.random.randint(2, size=(1000, 1))

5. Train the model:

model.fit(x_train, y_train, epochs=10, batch_size=32)

6. Generate dummy test data:

x_test = np.random.random((100, 10))
y_test = np.random.randint(2, size=(100, 1))

7. Evaluate the model:

model.evaluate(x_test, y_test)

The output of the last command should be a list of two numbers, the first being the loss and the second being the accuracy.

Helpful links

• Keras Documentation
• Tutorial: Deep Learning in Python with Keras

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