Python RNN sample code

python
# Import necessary libraries
from keras.models import Sequential
from keras.layers import LSTM, Dense, Dropout

# Define the model
model = Sequential()

# Add the LSTM layer
model.add(LSTM(units=45, return_sequences=True, input_shape=(x_training_data.shape[1], 1)))

# Add dropout regularization
model.add(Dropout(0.2))

# Add additional LSTM layers with dropout
for i in [True, True, False]:
    model.add(LSTM(units=45, return_sequences=i))
    model.add(Dropout(0.2))

# Add the output layer
model.add(Dense(units=1))

# Compile the model
model.compile(optimizer='adam', loss='mean_squared_error')

# Train the model
model.fit(x_training_data, y_training_data, epochs=100, batch_size=32)

# Load test data
test_data = pd.read_csv('FB_test_data.csv')
test_data = test_data.iloc[:, 1].values

# Reshape the test data
test_data = np.reshape(test_data, (-1, 1))

# Scale the test data
test_data = scaler.transform(test_data)

# Make predictions
predictions = model.predict(test_data)

# Plot the predictions
plt.plot(predictions)

This code defines a simple RNN model using Keras, trains it on training data, and then makes predictions on test data. The model includes several LSTM layers with dropout regularization to prevent overfitting. The output layer is a dense layer with a single unit. The model is compiled with the Adam optimizer and mean squared error loss function. The training process involves fitting the model to the training data for 100 epochs with a batch size of 32. The test data is then scaled and reshaped before making predictions. Finally, the predictions are plotted[2][3].