How to add LSTM layers to keras model?
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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

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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))
[[0.01069558]]

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