作者 | Aymeric Damien
编辑 | 奇予纪
出品 | 磐创AI团队
原项目 | https://github.com/aymericdamien/TensorFlow-Examples/
AI学习路线之TensorFlow篇
更多信息请查看链接: http://yann.lecun.com/exdb/mnist/
from __future__ import absolute_import, division, print_function
import tensorflow as tf
from tensorflow.keras import Model, layers
import numpy as np
# MNIST 数据集参数
num_classes = 10 # 所有类别(数字 0-9)
num_features = 784 # 数据特征数目 (图像形状: 28*28)
# 训练参数
learning_rate = 0.001
training_steps = 2000
batch_size = 256
display_step = 100
# 网络参数
n_hidden_1 = 128 # 第一层隐含层神经元的数目
n_hidden_2 = 256 # 第二层隐含层神经元的数目
# 准备MNIST数据
from tensorflow.keras.datasets import mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# 转化为float32
x_train, x_test = np.array(x_train, np.float32), np.array(x_test, np.float32)
# 将每张图像展平为具有784个特征的一维向量(28 * 28)
x_train, x_test = x_train.reshape([-1, num_features]), x_test.reshape([-1, num_features])
# 将图像值从[0,255]归一化到[0,1]
x_train, x_test = x_train / 255., x_test / 255.
# 使用tf.data API对数据进行随机排序和批处理
train_data = tf.data.Dataset.from_tensor_slices((x_train, y_train))
train_data = train_data.repeat().shuffle(5000).batch(batch_size).prefetch(1)
# 创建 TF 模型
class NeuralNet(Model):
# 设置层
def __init__(self):
super(NeuralNet, self).__init__()
# 第一层全连接隐含层
self.fc1 = layers.Dense(n_hidden_1, activation=tf.nn.relu)
# 第二层全连接隐含层
self.fc2 = layers.Dense(n_hidden_2, activation=tf.nn.relu)
# 全连接输出层
self.out = layers.Dense(num_classes, activation=tf.nn.softmax)
# 设置前向传播
def call(self, x, is_training=False):
x = self.fc1(x)
x = self.out(x)
if not is_training:
# tf交叉熵期望输出没有softmax,所以只有
#不训练时使用softmax。
x = tf.nn.softmax(x)
return x
# 构建神经网络模型
neural_net = NeuralNet()
# 交叉熵损失
# 注意这将会对输使用'softmax'
def cross_entropy_loss(x, y):
# 将标签转化为int64以使用交叉熵函数
y = tf.cast(y, tf.int64)
#将softmax应用于输出并计算交叉熵。
loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=x)
# 批的平均损失。
return tf.reduce_mean(loss)
# 准确率评估
def accuracy(y_pred, y_true):
# 预测类是预测向量中最高分的索引(即argmax)
correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.cast(y_true, tf.int64))
return tf.reduce_mean(tf.cast(correct_prediction, tf.float32), axis=-1)
# 随机梯度下降优化器
optimizer = tf.optimizers.SGD(learning_rate)
# 优化过程
def run_optimization(x, y):
# 将计算封装在GradientTape中以实现自动微分
with tf.GradientTape() as g:
# 前向传播
pred = neural_net(x, is_training=True)
# 计算损失
loss = cross_entropy_loss(pred, y)
# 要更新的变量,即可训练的变量
trainable_variables = neural_net.trainable_variables
# 计算梯度
gradients = g.gradient(loss, trainable_variables)
# 按gradients更新 W 和 b
optimizer.apply_gradients(zip(gradients, trainable_variables))
# 针对给定步骤数进行训练
for step, (batch_x, batch_y) in enumerate(train_data.take(training_steps), 1):
# 运行优化以更新W和b值
run_optimization(batch_x, batch_y)
if step % display_step == 0:
pred = neural_net(batch_x, is_training=True)
loss = cross_entrop
···y_loss(pred, batch_y)
acc = accuracy(pred, batch_y)
print("step: %i, loss: %f, accuracy: %f" % (step, loss, acc))
step: 100, loss: 2.031049, accuracy: 0.535156
step: 200, loss: 1.821917, accuracy: 0.722656
step: 300, loss: 1.764789, accuracy: 0.753906
step: 400, loss: 1.677593, accuracy: 0.859375
step: 500, loss: 1.643402, accuracy: 0.867188
step: 600, loss: 1.645116, accuracy: 0.859375
step: 700, loss: 1.618012, accuracy: 0.878906
step: 800, loss: 1.618097, accuracy: 0.878906
step: 900, loss: 1.616565, accuracy: 0.875000
step: 1000, loss: 1.599962, accuracy: 0.894531
step: 1100, loss: 1.593849, accuracy: 0.910156
step: 1200, loss: 1.594491, accuracy: 0.886719
step: 1300, loss: 1.622147, accuracy: 0.859375
step: 1400, loss: 1.547483, accuracy: 0.937500
step: 1500, loss: 1.581775, accuracy: 0.898438
step: 1600, loss: 1.555893, accuracy: 0.929688
step: 1700, loss: 1.578076, accuracy: 0.898438
step: 1800, loss: 1.584776, accuracy: 0.882812
step: 1900, loss: 1.563029, accuracy: 0.921875
step: 2000, loss: 1.569637, accuracy: 0.902344
# 在验证集上测试模型
pred = neural_net(x_test, is_training=False)
print("Test Accuracy: %f" % accuracy(pred, y_test)
# 可视化预测
import matplotlib.pyplot as plt
# 从验证集中预测5张图像
n_images = 5
test_images = x_test[:n_images]
predictions = neural_net(test_images)
# 显示图片和模型预测结果
for i in range(n_images):
plt.imshow(np.reshape(test_images[i], [28, 28]), cmap='gray')
plt.show()
print("Model prediction: %i" % np.argmax(predictions.numpy()[i]))
output:
还想看更多TensorFlow2.0专栏文章?可在公众号底部菜单栏子菜单“独家原创”中找到TensorFlow2.0系列文章(如下图),同步更新中,关注公众号了解更多吧~
或点击下方“阅读原文”,进入TensorFlow2.0专栏,即可查看往期文章。
嗨,你还在看吗?