Hello Guys, How are you all? Hope You all Are Fine. Today We Are Going To learn about **How do I get the weights of a layer in Keras?** **in Python**. So Here I am Explain to you all the possible Methods here.

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

Table of Contents

## How do I get the weights of a layer in Keras?

**How do I get the weights of a layer in Keras?**Then

`dense1`

is not a layer, it's the output of a layer. The layer is`Dense(10, activation='relu')`

**get the weights of a layer in Keras**Then

`dense1`

is not a layer, it's the output of a layer. The layer is`Dense(10, activation='relu')`

## Method 1

If you write:

`dense1 = Dense(10, activation='relu')(input_x)`

Then `dense1`

is not a layer, it’s the output of a layer. The layer is `Dense(10, activation='relu')`

So it seems you meant:

dense1 = Dense(10, activation='relu') y = dense1(input_x)

Here is a full snippet:

import tensorflow as tf from tensorflow.contrib.keras import layers input_x = tf.placeholder(tf.float32, [None, 10], name='input_x') dense1 = layers.Dense(10, activation='relu') y = dense1(input_x) weights = dense1.get_weights()

## Method 2

If you want to get weights and biases of all layers, you can simply use:

for layer in model.layers: print(layer.get_config(), layer.get_weights())

This will print all information that’s relevant.

If you want the weights directly returned as numpy arrays, you can use:

first_layer_weights = model.layers[0].get_weights()[0] first_layer_biases = model.layers[0].get_weights()[1] second_layer_weights = model.layers[1].get_weights()[0] second_layer_biases = model.layers[1].get_weights()[1]

etc.

**Summery**

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

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