<jupyter_start><jupyter_text>**Keras Visualizer Example 1**  install libraries<jupyter_code>!pip install keras
!apt-get -qq install -y graphviz && pip install graphviz
!pip install keras-visualizer<jupyter_output><empty_output><jupyter_text>Visualize Keras Model<jupyter_code>from keras import models, layers
from keras_visualizer import visualizer
model = models.Sequential([
    layers.Dense(64, activation='relu', input_shape=(8,)),
    layers.Dense(6, activation='softmax'),
    layers.Dense(32),
    layers.Dense(9, activation='sigmoid')])

visualizer(model, format='png', view=True)<jupyter_output><empty_output><jupyter_text>View Image in Notebook<jupyter_code>from IPython.display import Image
Image("graph.png")<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.12266666666666666, 'fraction_numerical': 0.010666666666666666, 'mean_word_length': 8.3875, 'pattern_counts': {'":': 0, '<': 13, '<?xml version=': 0, '>': 13, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '423257', 'license': 'MIT', 'n_tokens_mistral': 267, 'n_tokens_neox': 257, 'n_words': 49, 'path': 'examples/example1.ipynb', 'repo_name': 'lordmahyar/Keras-Visualizer', 'size': '750'}