一、代码
# coding=utf-8
import tensorflow as tf
import tensorflow.contrib.layers as layers
from tensorflow.examples.tutorials.mnist import input_data
# 数据集
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
# 定义模型
n_classes = 10 # MNIST类别(0-9)
n_input = 784 # MNIST尺寸(28*28)
n_hidden = 30 # 隐藏层的神经元数
batch_size = 200 # 每批训练批量大小
eta = 0.001 # 学习率
max_epoch = 10 # 最大迭代数
def multilayer_perceptron(x):
fc1 = layers.fully_connected(x, n_hidden, activation_fn=tf.nn.relu,
scope='fc1') # 全连接层,与输入相乘产生隐藏层单元的张量,隐藏层使用ReLU激活函数
# fc2 = layers.fully_connected(fc1, 256, activation_fn=tf.nn.relu, scope='fc2')
out = layers.fully_connected(fc1, n_classes, activation_fn=None, scope='out')
return out
x = tf.placeholder(tf.float32, [None, n_input], name='placeholder_x') # 输入x
y = tf.placeholder(tf.float32, [None, n_classes], name='placeholder_y') # 标签y
y_hat = multilayer_perceptron(x) # 多层感知机MLP
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=y_hat, labels=y)) # 损失函数,均方误差MSE
train = tf.train.AdamOptimizer(learning_rate=eta).minimize(loss) # Adam梯度优化算法
# 训练
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
for epoch in range(10):
epoch_loss = 0.0
batch_steps = int(mnist.train.num_examples / batch_size)
for i in range(batch_steps):
batch_x, batch_y = mnist.train.next_batch(batch_size)
_, c = sess.run([train, loss], feed_dict={x: batch_x, y: batch_y})
epoch_loss += c / batch_steps
print('Epoch %d, Loss = %.6f' % (epoch, epoch_loss))
# 评估
correct_prediction = tf.equal(tf.argmax(y_hat, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print('Accuracy%:', accuracy.eval({x: mnist.test.images, y: mnist.test.labels}) * 100)
感知机:perceptron,具有学习能力的神经网络。
多层感知机:Multi-layer Perceptron。
二、结果
Accuracy%: 95.89999914169312