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.
Table of Contents
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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Abhinav Agarwal
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