Tensorflow2.0笔记26——参数提取，写至文本

Tensorflow2.0笔记

本博客为Tensorflow2.0学习笔记，感谢北京大学微电子学院曹建老师

目录

• Tensorflow2.0笔记
  • 2.4 参数提取，写至文本
    • 1.提取可训练参数
    • 2.设置print输出格式

2.4 参数提取，写至文本

1.提取可训练参数

model.trainable_variables 模型中可训练的参数

2.设置print输出格式

np.set_printoptions(precision=小数点后按四舍五入保留几位,threshold=数组元素数量少于或等于门槛值，打印全部元素；否则打印门槛值+1 个元素，中间用省略号补充)

>>> np.set_printoptions(precision=5)

>>> print(np.array([1.123456789]))

[1.12346]

>>> np.set_printoptions(threshold=5)

>>> print(np.arange(10))

[0 1 2 … , 7 8 9]

注：precision=np.inf 打印完整小数位；threshold=np.nan 打印全部数组元素。

import tensorflow as tf
import os
import numpy as np
np.set_printoptions(threshold=np.inf)
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
              metrics=['sparse_categorical_accuracy'])
checkpoint_save_path = "./checkpoint/mnist.ckpt"
if os.path.exists(checkpoint_save_path + '.index'):
    print('-------------load the model-----------------')
    model.load_weights(checkpoint_save_path)
cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_save_path,
                                                 save_weights_only=True,
                                                 save_best_only=True)
history = model.fit(x_train, y_train, batch_size=32, epochs=5, validation_data=(x_test, y_test), validation_freq=1,
                    callbacks=[cp_callback])
model.summary()
print(model.trainable_variables)
file = open('./weights.txt', 'w')
for v in model.trainable_variables:
    file.write(str(v.name) + '
')
    file.write(str(v.shape) + '
')
    file.write(str(v.numpy()) + '
')
file.close()