visualkeras: ResNetV2 example#
An example showing the visualkeras function
used by a tf.keras.Model model.
9 # Authors: The scikit-plots developers
10 # SPDX-License-Identifier: BSD-3-Clause
pip install protobuf==5.29.4
15 import tensorflow as tf
16
17 # Clear any session to reset the state of TensorFlow/Keras
18 tf.keras.backend.clear_session()
19
20 from scikitplot import visualkeras
23 model = tf.keras.applications.ResNet50V2(
24 include_top=True,
25 weights=None, # "imagenet" or 'path/'
26 input_tensor=None,
27 input_shape=None,
28 pooling=None,
29 classes=1000,
30 classifier_activation="softmax",
31 name="resnet50v2",
32 )
33 # model.summary()
36 img_resnet50v2 = visualkeras.layered_view(
37 model,
38 legend=True,
39 min_z=1,
40 min_xy=1,
41 max_z=4096,
42 max_xy=4096,
43 scale_z=0.01,
44 scale_xy=10,
45 font={"font_size": 99},
46 # to_file="result_images/resnet50v2.png",
47 save_fig=True,
48 save_fig_filename="resnet50v2.png",
49 )
50 img_resnet50v2
<matplotlib.image.AxesImage object at 0x7fa0dc35d7d0>
65 # model = tf.keras.applications.ResNet101V2(
66 # include_top=True,
67 # weights=None, # "imagenet" or 'path/'
68 # input_tensor=None,
69 # input_shape=None,
70 # pooling=None,
71 # classes=1000,
72 # classifier_activation="softmax",
73 # name="resnet101v2",
74 # )
75 # visualkeras.layered_view(
76 # model,
77 # legend=True,
78 # show_dimension=True,
79 # to_file='result_images/resnet101v2.png',
80 # )
81
82 # model = tf.keras.applications.ResNet152V2(
83 # include_top=True,
84 # weights=None, # "imagenet" or 'path/'
85 # input_tensor=None,
86 # input_shape=None,
87 # pooling=None,
88 # classes=1000,
89 # classifier_activation="softmax",
90 # name="resnet152v2",
91 # )
92 # visualkeras.layered_view(
93 # model,
94 # legend=True,
95 # show_dimension=True,
96 # to_file='result_images/resnet152v2.png',
97 # )
Total running time of the script: (0 minutes 3.802 seconds)
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