Hello Guys, How are you all? Hope You all Are Fine. Today I get the following error** Keras: change learning rate** in Python. So Here I am Explain to you all the possible solutions here.

Without wasting your time, Let’s start This Article to Solve This Error.

Table of Contents

## How Keras: change learning rate Error Occurs?

## How To Solve Keras: change learning rate Error ?

**How To Solve Keras: change learning rate Error ?**To Solve Keras: change learning rate Error optimizer = tf.keras.optimizers.Adam(0.001)

optimizer.learning_rate.assign(0.01) print(optimizer.learning_rate)

## Solution 1

You can change the learning rate as follows:

from keras import backend as K K.set_value(model.optimizer.learning_rate, 0.001)

Included into your complete example it looks as follows:

from keras.models import Sequential from keras.layers import Dense from keras import backend as K import keras import numpy as np model = Sequential() model.add(Dense(1, input_shape=(10,))) optimizer = keras.optimizers.Adam(lr=0.01) model.compile(loss='mse', optimizer=optimizer) print("Learning rate before first fit:", model.optimizer.learning_rate.numpy()) model.fit(np.random.randn(50,10), np.random.randn(50), epochs=50, verbose=0) # Change learning rate to 0.001 and train for 50 more epochs K.set_value(model.optimizer.learning_rate, 0.001) print("Learning rate before second fit:", model.optimizer.learning_rate.numpy()) model.fit(np.random.randn(50,10), np.random.randn(50), initial_epoch=50, epochs=50, verbose=0)

I’ve just tested this with keras 2.3.1. Not sure why the approach didn’t seem to work for you.

## Solution 2

There is another way, you have to find the variable that holds the learning rate and assign it another value.

optimizer = tf.keras.optimizers.Adam(0.001) optimizer.learning_rate.assign(0.01) print(optimizer.learning_rate)

output:

<tf.Variable 'learning_rate:0' shape=() dtype=float32, numpy=0.01>

**Summery**

It’s all About this issue. Hope all solution helped you a lot. Comment below Your thoughts and your queries. Also, Comment below which solution worked for you? Thank You.

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