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# Visualize Neural Network Architecture

## Preliminaries

```
/* Load libraries */
from keras import models
from keras import layers
from IPython.display import SVG
from keras.utils.vis_utils import model_to_dot
from keras.utils import plot_model
```

```
Using TensorFlow backend.
```

## Construct Neural Network Architecture

```
/* Start neural network */
network = models.Sequential()
/* Add fully connected layer with a ReLU activation function */
network.add(layers.Dense(units=16, activation='relu', input_shape=(10,)))
/* Add fully connected layer with a ReLU activation function */
network.add(layers.Dense(units=16, activation='relu'))
/* Add fully connected layer with a sigmoid activation function */
network.add(layers.Dense(units=1, activation='sigmoid'))
```

## Visualize Network Architecture

```
/* Visualize network architecture */
SVG(model_to_dot(network, show_shapes=True).create(prog='dot', format='svg'))
```

## Save To File

```
/* Save the visualization as a file */
plot_model(network, show_shapes=True, to_file='network.png')
```

# Python Example for Beginners

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