visualkeras: ResNetV2 example#

An example showing the visualkeras function used by a tf.keras.Model model.

# Authors: The scikit-plots developers
# SPDX-License-Identifier: BSD-3-Clause

Force garbage collection

import gc

gc.collect()

# pip install protobuf==5.29.4
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.ResNet50V2(
    include_top=True,
    weights=None,  # "imagenet" or 'path/'
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation="softmax",
    name="resnet50v2",
)
# model.summary()

img_resnet50v2 = visualkeras.layered_view(
    model,
    legend=True,
    min_z=1,
    min_xy=1,
    max_z=4096,
    max_xy=4096,
    scale_z=0.01,
    scale_xy=10,
    font={"font_size": 99},
    # to_file="result_images/resnet50v2.png",
    save_fig=True,
    save_fig_filename="resnet50v2.png",
)

Tags: model-type: classification model-workflow: model building plot-type: visualkeras domain: neural network level: beginner purpose: showcase

# model = tf.keras.applications.ResNet101V2(
#     include_top=True,
#     weights=None,  # "imagenet" or 'path/'
#     input_tensor=None,
#     input_shape=None,
#     pooling=None,
#     classes=1000,
#     classifier_activation="softmax",
#     name="resnet101v2",
# )
# visualkeras.layered_view(
#   model,
#   legend=True,
#   show_dimension=True,
#   to_file='result_images/resnet101v2.png',
# )

# model = tf.keras.applications.ResNet152V2(
#     include_top=True,
#     weights=None,  # "imagenet" or 'path/'
#     input_tensor=None,
#     input_shape=None,
#     pooling=None,
#     classes=1000,
#     classifier_activation="softmax",
#     name="resnet152v2",
# )
# visualkeras.layered_view(
#   model,
#   legend=True,
#   show_dimension=True,
#   to_file='result_images/resnet152v2.png',
# )