How to add LSTM layers to keras model?

How to add LSTM layers to keras model?

How to add LSTM layers to keras model?

This recipe helps you add LSTM layers to keras model


Recipe Objective

How to add LSTM layers to keras model?

LSTM stands for Long Short Term Memory comes under RNN. LSTM has mostly used the time or sequence-dependent behavior example texts, stock prices, electricity.

The LSTM model contains one or many hidden layers.

It is followed by a standard output layer.

Step-1 Importing Libraries

import keras from keras.models import Sequential from keras.layers import LSTM import numpy as np

Step 2- Defining the model.

We will define the model and Add a LSTM layer to it.

# define model where LSTM is also output layer model = Sequential() model.add(LSTM(1, input_shape=(10,1))) model.compile(optimizer='adam', loss='mse')

Step 3-Defining a sample array.

We will define a sample array to run in the model.

y = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]).reshape((1,10,1)) print(model.predict(y))

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