Visualkeras: Spam Classification Conv1D Dense Example#
An example showing Spam the visualkeras function
used by a tf.keras.Model model.
9 # Authors: The scikit-plots developers
10 # SPDX-License-Identifier: BSD-3-Clause
Installing dependencies https://sphinx-gallery.github.io/stable/configuration.html#using-multiple-code-blocks-to-create-a-single-figure
%%bash # (e.g. %%bash or %%writefile) will be turned into a runnable code block. # pip install -q tensorflow # apt-get -qq install curl
pip install protobuf==5.29.4
26 import tensorflow as tf
27
28 # Clear any session to reset the state of TensorFlow/Keras
29 tf.keras.backend.clear_session()
32 model = tf.keras.models.Sequential()
33 model.add(tf.keras.layers.InputLayer(input_shape=(100,)))
34
35 # To convert 2D of input data into a 3D input
36 # Reshape to a compatible shape for Conv1D as [batch_size, time_steps, input_dimension]
37 # The Conv1D layer expects a 3D input: (batch_size, steps, channels).
38 # The Reshape layer now reshapes the input to (n_timesteps,n_features) like (100, 1),
39 # which matches the expected input of Conv1D.
40 model.add(
41 tf.keras.layers.Reshape((100, 1))
42 ) # Shape: (batch_size, 100, 1), input_shape=(100,)
43
44 # Add Conv1D and other layers
45 model.add(tf.keras.layers.Conv1D(32, 1, strides=1, activation="relu"))
46 model.add(tf.keras.layers.Dropout(0.5))
47 model.add(tf.keras.layers.MaxPooling1D(pool_size=2))
48
49 # Flatten and add Dense layers
50 model.add(tf.keras.layers.Flatten())
51 model.add(tf.keras.layers.Dense(64, activation="relu"))
52 model.add(tf.keras.layers.Dense(32, activation="relu"))
53 model.add(tf.keras.layers.Dense(1, activation="sigmoid"))
54
55 # Compile the model
56 model.compile(optimizer="rmsprop", loss="binary_crossentropy", metrics=["accuracy"])
57 model.summary()
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type) ┃ Output Shape ┃ Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ reshape (Reshape) │ (None, 100, 1) │ 0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d (Conv1D) │ (None, 100, 32) │ 64 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout) │ (None, 100, 32) │ 0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ max_pooling1d (MaxPooling1D) │ (None, 50, 32) │ 0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ flatten (Flatten) │ (None, 1600) │ 0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense) │ (None, 64) │ 102,464 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_1 (Dense) │ (None, 32) │ 2,080 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_2 (Dense) │ (None, 1) │ 33 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
Total params: 104,641 (408.75 KB)
Trainable params: 104,641 (408.75 KB)
Non-trainable params: 0 (0.00 B)
60 import matplotlib.pyplot as plt
61 from scikitplot import visualkeras
64 img_spam = visualkeras.layered_view(
65 model,
66 min_z=1,
67 min_xy=1,
68 max_z=4096,
69 max_xy=4096,
70 scale_z=6,
71 scale_xy=0.2,
72 font={"font_size": 14},
73 text_callable="default",
74 one_dim_orientation="x",
75 # to_file="./spam_conv_x.png",
76 save_fig=True,
77 save_fig_filename="spam_conv_x.png",
78 show_fig=True,
79 )
80 img_spam
<matplotlib.image.AxesImage object at 0x7fe0b638d690>
83 img_spam = visualkeras.layered_view(
84 model,
85 min_z=1,
86 min_xy=1,
87 max_z=4096,
88 max_xy=4096,
89 scale_z=6,
90 scale_xy=0.2,
91 font={"font_size": 14},
92 text_callable="default",
93 one_dim_orientation="y",
94 # to_file="./spam_conv_y.png",
95 save_fig=True,
96 save_fig_filename="spam_conv_y.png",
97 )
98 img_spam
<matplotlib.image.AxesImage object at 0x7fe0b88ea890>
101 img_spam = visualkeras.layered_view(
102 model,
103 min_z=1,
104 min_xy=1,
105 max_z=4096,
106 max_xy=4096,
107 scale_z=0.2,
108 scale_xy=1,
109 font={"font_size": 9},
110 text_callable="default",
111 one_dim_orientation="z",
112 # to_file="./spam_conv_z.png",
113 save_fig=True,
114 save_fig_filename="spam_conv_z.png",
115 overwrite=False,
116 add_timestamp=True,
117 verbose=True,
118 )
119 img_spam
[INFO] Saving path to: /home/circleci/repo/galleries/examples/visualkeras_ANN/result_images/spam_conv_z_20250627_090928Z.png
<matplotlib.image.AxesImage object at 0x7fe0ac3e3f50>
Total running time of the script: (0 minutes 2.935 seconds)
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