visualkeras: Spam Dense 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
14 import tensorflow as tf
15
16 # Clear any session to reset the state of TensorFlow/Keras
17 tf.keras.backend.clear_session()
18
19 import tensorflow.python as tf_python
20
21 # Clear any session to reset the state of TensorFlow/Keras
22 tf_python.keras.backend.clear_session()
25 model = tf_python.keras.models.Sequential()
26 model.add(tf_python.keras.layers.InputLayer(input_shape=(100,)))
27
28 # Add Dense layers
29 model.add(tf_python.keras.layers.Dense(64, activation="relu")) # input_shape=(100,)
30 model.add(tf_python.keras.layers.Dense(32, activation="relu"))
31 model.add(tf_python.keras.layers.Dense(1, activation="sigmoid"))
32
33 # Compile the model
34 model.compile(optimizer="rmsprop", loss="binary_crossentropy", metrics=["accuracy"])
35 model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense (Dense) (None, 64) 6464
_________________________________________________________________
dense_1 (Dense) (None, 32) 2080
_________________________________________________________________
dense_2 (Dense) (None, 1) 33
=================================================================
Total params: 8,577
Trainable params: 8,577
Non-trainable params: 0
_________________________________________________________________
38 from scikitplot import visualkeras
41 img_spam = visualkeras.graph_view(
42 model,
43 # to_file="result_images/spam_dense_x.png",
44 save_fig=True,
45 save_fig_filename="spam_dense_graph.png",
46 )
47 img_spam
<matplotlib.image.AxesImage object at 0x7f715c6b6310>
50 img_spam = visualkeras.layered_view(
51 model,
52 min_z=1,
53 min_xy=1,
54 max_z=4096,
55 max_xy=4096,
56 scale_z=1,
57 scale_xy=1,
58 font={"font_size": 7},
59 text_callable="default",
60 one_dim_orientation="x",
61 # to_file="result_images/spam_dense_x.png",
62 save_fig=True,
63 save_fig_filename="spam_dense_x.png",
64 )
65 img_spam
<matplotlib.image.AxesImage object at 0x7f7154334790>
68 img_spam = visualkeras.layered_view(
69 model,
70 min_z=1,
71 min_xy=1,
72 max_z=4096,
73 max_xy=4096,
74 scale_z=1,
75 scale_xy=1,
76 font={"font_size": 7},
77 text_callable="default",
78 one_dim_orientation="y",
79 # to_file="result_images/spam_dense_y.png",
80 save_fig=True,
81 save_fig_filename="spam_dense_y.png",
82 )
83 img_spam
<matplotlib.image.AxesImage object at 0x7f715437af90>
86 img_spam = visualkeras.layered_view(
87 model,
88 min_z=1,
89 min_xy=1,
90 max_z=4096,
91 max_xy=4096,
92 scale_z=1,
93 scale_xy=1,
94 font={"font_size": 7},
95 text_callable="default",
96 one_dim_orientation="z",
97 # to_file="result_images/spam_dense_z.png",
98 save_fig=True,
99 save_fig_filename="spam_dense_z.png",
100 )
101 img_spam
<matplotlib.image.AxesImage object at 0x7f71543caf90>
Total running time of the script: (0 minutes 2.771 seconds)
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Visualkeras: Spam Classification Conv1D Dense Example
