python神经网络slim常用函数训练保存模型

下面是关于“Python神经网络slim常用函数训练保存模型”的完整攻略。

Python神经网络slim常用函数训练保存模型

在Python神经网络中，slim是一个常用的库，它提供了许多方便的函数来训练和保存模型。以下是使用slim训练和保存模型的步骤：

步骤1：定义模型

首先需要定义模型。以下是定义模型的示例：

import tensorflow as tf
import tensorflow.contrib.slim as slim

def my_model(inputs):
    net = slim.conv2d(inputs, 32, [3, 3], scope='conv1')
    net = slim.max_pool2d(net, [2, 2], scope='pool1')
    net = slim.conv2d(net, 64, [3, 3], scope='conv2')
    net = slim.max_pool2d(net, [2, 2], scope='pool2')
    net = slim.flatten(net, scope='flatten')
    net = slim.fully_connected(net, 1024, scope='fc1')
    net = slim.dropout(net, 0.5, scope='dropout1')
    net = slim.fully_connected(net, 10, activation_fn=None, scope='fc2')
    return net

步骤2：定义损失函数和优化器

接下来需要定义损失函数和优化器。以下是定义损失函数和优化器的示例：

import tensorflow as tf
import tensorflow.contrib.slim as slim

inputs = tf.placeholder(tf.float32, [None, 28, 28, 1])
labels = tf.placeholder(tf.int64, [None])

logits = my_model(inputs)
loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
train_op = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)

步骤3：训练模型

接下来需要训练模型。以下是训练模型的示例：

import tensorflow as tf
import tensorflow.contrib.slim as slim

inputs = tf.placeholder(tf.float32, [None, 28, 28, 1])
labels = tf.placeholder(tf.int64, [None])

logits = my_model(inputs)
loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
train_op = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for i in range(1000):
        batch_inputs, batch_labels = get_batch()
        _, loss_val = sess.run([train_op, loss], feed_dict={inputs: batch_inputs, labels: batch_labels})
        if i % 100 == 0:
            print('Step %d, loss = %.2f' % (i, loss_val))

步骤4：保存模型

最后需要保存模型。以下是保存模型的示例：

import tensorflow as tf
import tensorflow.contrib.slim as slim

inputs = tf.placeholder(tf.float32, [None, 28, 28, 1])
labels = tf.placeholder(tf.int64, [None])

logits = my_model(inputs)
loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
train_op = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)

saver = tf.train.Saver()

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for i in range(1000):
        batch_inputs, batch_labels = get_batch()
        _, loss_val = sess.run([train_op, loss], feed_dict={inputs: batch_inputs, labels: batch_labels})
        if i % 100 == 0:
            print('Step %d, loss = %.2f' % (i, loss_val))
    saver.save(sess, 'my_model.ckpt')