如何在Tensorboard中可视化图神经网络的模型图

pip install tensorboard

import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Dense

# 定义输入层
input_layer = Input(shape=(input_shape,))

# 定义隐藏层
hidden_layer = Dense(units=hidden_units, activation='relu')(input_layer)

# 定义输出层
output_layer = Dense(units=output_units, activation='softmax')(hidden_layer)

# 创建模型
model = Model(inputs=input_layer, outputs=output_layer)

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(x_val, y_val))

# 创建Tensorboard回调
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)

# 训练模型并将Tensorboard回调传递给fit函数
model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(x_val, y_val), callbacks=[tensorboard_callback])

tensorboard --logdir=log_dir

http://localhost:6006