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# Explain with an example how He_normal initializer work?

# Explain with an example how He_normal initializer work?

This recipe explains with an example how He_normal initializer work

He_Normal initializer

He_Normal initializer takes samples from a truncated normal distribution centered on 0 with stddev = sqrt(2 / fan_in) where fan_in is the number of input units in the weight tensor.

```
import numpy as np
import keras
from keras.models import Sequential
from keras.layers import Activation, Dense
import tensorflow as tf
```

We show you two ways to initialize the He_normal initializers.

```
initializer = tf.keras.initializers.HeNormal()
values = initializer(shape=(2, 2))
layer = tf.keras.layers.Dense(3, kernel_initializer=initializer)
```

We will be reshaping and creating the model

```
# define input data
X = np.array([17, 35, 400, 230])
# show input data for context
print(X)
# reshape input data into one sample a sample with a channel
X = X.reshape((1, 2, 2, 1))
# define model
model = Sequential()
model.add(Dense(13, input_dim=13, kernel_initializer=initializer, activation='relu'))
model.summary()
```

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