.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/visualkeras_CNN/plot_efficientnetv2.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. or to run this example in your browser via JupyterLite or Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_visualkeras_CNN_plot_efficientnetv2.py: visualkeras EfficientNetV2 example ========================================== An example showing the :py:func:`~scikitplot.visualkeras` function used by a :py:class:`~tensorflow.keras.Model` model. .. GENERATED FROM PYTHON SOURCE LINES 8-43 .. code-block:: Python # Authors: The scikit-plots developers # SPDX-License-Identifier: BSD-3-Clause # Force garbage collection import gc; gc.collect() import tensorflow as tf # Clear any session to reset the state of TensorFlow/Keras tf.keras.backend.clear_session() from scikitplot import visualkeras model = tf.keras.applications.EfficientNetV2B0( include_top=True, weights=None, # "imagenet" or 'path/' input_tensor=None, input_shape=None, pooling=None, classes=1000, classifier_activation="softmax", name="efficientnetv2-b0", ) img_efficientnetv2 = visualkeras.layered_view( model, legend=True, show_dimension=True, to_file='../result_images/efficientnetv2-b0.png', ) try: import matplotlib.pyplot as plt plt.imshow(img_efficientnetv2) plt.axis('off') plt.show() except: pass .. image-sg:: /auto_examples/visualkeras_CNN/images/sphx_glr_plot_efficientnetv2_001.png :alt: plot efficientnetv2 :srcset: /auto_examples/visualkeras_CNN/images/sphx_glr_plot_efficientnetv2_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 44-52 .. tags:: model-type: classification model-workflow: model building plot-type: visualkeras domain: neural network level: beginner purpose: showcase .. GENERATED FROM PYTHON SOURCE LINES 53-154 .. code-block:: Python # model = tf.keras.applications.EfficientNetV2B1( # include_top=True, # weights=None, # "imagenet" or 'path/' # input_tensor=None, # input_shape=None, # pooling=None, # classes=1000, # classifier_activation="softmax", # name="efficientnetv2-b1", # ) # visualkeras.layered_view( # model, # legend=True, # show_dimension=True, # to_file='../result_images/efficientnetv2-b1.png', # ) # model = tf.keras.applications.EfficientNetV2B2( # include_top=True, # weights=None, # "imagenet" or 'path/' # input_tensor=None, # input_shape=None, # pooling=None, # classes=1000, # classifier_activation="softmax", # name="efficientnetv2-b2", # ) # visualkeras.layered_view( # model, # legend=True, # show_dimension=True, # to_file='../result_images/efficientnetv2-b2.png', # ) # model = tf.keras.applications.EfficientNetV2B3( # include_top=True, # weights=None, # "imagenet" or 'path/' # input_tensor=None, # input_shape=None, # pooling=None, # classes=1000, # classifier_activation="softmax", # name="efficientnetv2-b3", # ) # visualkeras.layered_view( # model, # legend=True, # show_dimension=True, # to_file='../result_images/efficientnetv2-b3.png', # ) # model = tf.keras.applications.EfficientNetV2S( # include_top=True, # weights=None, # "imagenet" or 'path/' # input_tensor=None, # input_shape=None, # pooling=None, # classes=1000, # classifier_activation="softmax", # name="efficientnetv2-s", # ) # visualkeras.layered_view( # model, # legend=True, # show_dimension=True, # to_file='../result_images/efficientnetv2-s.png', # ) # model = tf.keras.applications.EfficientNetV2M( # include_top=True, # weights=None, # "imagenet" or 'path/' # input_tensor=None, # input_shape=None, # pooling=None, # classes=1000, # classifier_activation="softmax", # name="efficientnetv2-m", # ) # visualkeras.layered_view( # model, # legend=True, # show_dimension=True, # to_file='../result_images/efficientnetv2-m.png', # ) # model = tf.keras.applications.EfficientNetV2L( # include_top=True, # weights=None, # "imagenet" or 'path/' # input_tensor=None, # input_shape=None, # pooling=None, # classes=1000, # classifier_activation="softmax", # name="efficientnetv2-l", # ) # visualkeras.layered_view( # model, # legend=True, # show_dimension=True, # to_file='../result_images/efficientnetv2-l.png', # ) .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 7.202 seconds) .. _sphx_glr_download_auto_examples_visualkeras_CNN_plot_efficientnetv2.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-plots/scikit-plots/0.4.X?urlpath=lab/tree/notebooks/auto_examples/visualkeras_CNN/plot_efficientnetv2.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/index.html?path=auto_examples/visualkeras_CNN/plot_efficientnetv2.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_efficientnetv2.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_efficientnetv2.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_efficientnetv2.zip ` .. include:: plot_efficientnetv2.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_