神经网络模型的保存和读取(基于Mnist数据集)

最新推荐文章于 2024-06-06 21:14:17 发布

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最新推荐文章于 2024-06-06 21:14:17 发布 · 2.6k 阅读

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本文详细介绍了如何使用深度学习框架保存和加载训练好的神经网络模型，以Mnist数据集为例，从模型训练到模型的持久化，再到加载模型进行预测，帮助读者理解模型保存与读取的重要性及其实际操作。

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#Import MNIST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("data/",one_hot=True)
import tensorflow as tf

#Parameters
learning_rate = 0.001
batch_size = 100
display_step = 1
model_path = "save/model.ckpt"

#Network Parameters
n_hidden_1 = 256 #1st layer number of features
n_hidden_2 = 256 #2nd layer number of features
n_input = 784 #MNIST data input (img shape:28*28)
n_classes = 10 #MNIST total classes (0-9 digits)

#tf Graph input
x = tf.placeholder("float",[None,n_input])
y = tf.placeholder("float",[None,n_classes])

#Creat model
def mutilayer_perceptron(x,weights,biases):
    #Hidden layer with RELU activation
    layer_1 = tf.add(tf.matmul(x,weights['h1']),biases['b1'])
    layer_1 = tf.nn.relu(layer_1)
    #Hidden layer with RELU activation
    layer_2 = tf.add(tf.matmul(layer_1,weights['h2']),biases['b2'])
    layer_2 = tf.nn.relu(layer_2)
    #Output layer with linear activation