TensorFlow 2.0中图形可视化的一个简单实例

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

# 创建一个简单的Sequential模型
model = Sequential()
model.add(Dense(64, activation='relu', input_dim=100))
model.add(Dense(64, activation='relu'))
model.add(Dense(10, activation='softmax'))

# 编译模型
model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

# 加载数据集
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
x_train = x_train.reshape(60000, 784).astype('float32') / 255
x_test = x_test.reshape(10000, 784).astype('float32') / 255
y_train = tf.keras.utils.to_categorical(y_train, 10)
y_test = tf.keras.utils.to_categorical(y_test, 10)

# 训练模型并使用TensorBoard进行可视化
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir='./logs')
model.fit(x_train, y_train, epochs=10, batch_size=32, callbacks=[tensorboard_callback])

# 在浏览器中打开TensorBoard可视化
# 在命令行中运行：tensorboard --logdir=./logs