tensorflow2之模型加载恢复(h5)使用new_model = tf.keras.models.load_model('my_model.h5') 创建一个与 my_model相同的模型并加载权重测试。

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我是靠谱客的博主迅速小懒虫，这篇文章主要介绍tensorflow2之模型加载恢复(h5)使用new_model = tf.keras.models.load_model('my_model.h5') 创建一个与 my_model相同的模型并加载权重测试。，现在分享给大家，希望可以做个参考。

使用new_model = tf.keras.models.load_model('my_model.h5') 创建一个与 my_model相同的模型并加载权重测试。

创建 model_hand_h5_load.py

复制代码

#encoding=utf-8
# 手动创建和加载 *.h5 模型和权重值

from __future__ import absolute_import, division, print_function, unicode_literals

import os

import tensorflow as tf
from tensorflow import keras

print(tf.version.VERSION)

(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data()

train_labels = train_labels[:1000]
test_labels = test_labels[:1000]

train_images = train_images[:1000].reshape(-1, 28 * 28) / 255.0
test_images = test_images[:1000].reshape(-1, 28 * 28) / 255.0

# # 定义一个简单的序列模型
# def create_model():
#     model = tf.keras.models.Sequential([
#       keras.layers.Dense(512, activation='relu', input_shape=(784,)),
#       keras.layers.Dropout(0.2),
#       keras.layers.Dense(10, activation='softmax')
#     ]) 
#     return model
  
#   # 创建一个基本的模型实例
# model = create_model()
  
# # 显示模型的结构
# model.summary()
# 创建和原先保存的my_model一样结构的模型，并加载权重
new_model = tf.keras.models.load_model('my_model.h5')
new_model.summary()

# Evaluate the model
loss,acc = new_model.evaluate(test_images, test_labels)
print("Restored model, accuracy: {:5.2f}%".format(100*acc))

print(new_model.predict(train_images[:1]))  # [[2.5317803e-04 7.2924799e-04 1.4610562e-03 7.4771196e-02 9.9087765e-06
  # 9.1992557e-01 2.5099045e-05 9.3348324e-04 1.7478490e-03 1.4335765e-04]]

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#encoding=utf-8
# 手动创建和加载 *.h5 模型和权重值

from __future__ import absolute_import, division, print_function, unicode_literals

import os

import tensorflow as tf
from tensorflow import keras

print(tf.version.VERSION)

(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data()

train_labels = train_labels[:1000]
test_labels = test_labels[:1000]

train_images = train_images[:1000].reshape(-1, 28 * 28) / 255.0
test_images = test_images[:1000].reshape(-1, 28 * 28) / 255.0

# # 定义一个简单的序列模型
# def create_model():
#     model = tf.keras.models.Sequential([
#       keras.layers.Dense(512, activation='relu', input_shape=(784,)),
#       keras.layers.Dropout(0.2),
#       keras.layers.Dense(10, activation='softmax')
#     ]) 
#     return model
  
#   # 创建一个基本的模型实例
# model = create_model()
  
# # 显示模型的结构
# model.summary()
# 创建和原先保存的my_model一样结构的模型，并加载权重
new_model = tf.keras.models.load_model('my_model.h5')
new_model.summary()


# Evaluate the model
loss,acc = new_model.evaluate(test_images, test_labels)
print("Restored model, accuracy: {:5.2f}%".format(100*acc)) 

print(new_model.predict(train_images[:1]))  # [[2.5317803e-04 7.2924799e-04 1.4610562e-03 7.4771196e-02 9.9087765e-06
  # 9.1992557e-01 2.5099045e-05 9.3348324e-04 1.7478490e-03 1.4335765e-04]]