tensorflow技术解析与实战，v2.0构建多层感知器

小君 2023-03-01 18:12:09 396

tensorflow技术解析与实战，v2.0构建多层感知器step: 100 loss: 567.292969 accuracy: 0.136719 step: 200 loss: 398.614929 accuracy: 0.562500 step: 300 loss: 226.743774 accuracy: 0.753906 step: 400 loss: 193.384521 accuracy: 0.777344 step: 500 loss: 138.649963 accuracy: 0.886719 step: 600 loss: 109.713669 accuracy: 0.898438 step: 700 loss: 90.397217 accuracy: 0.906250 step: 800 loss: 104.545380 accuracy: 0.894531 step: 900 loss:

使用TensorFlow v2.0构建一个两层隐藏层完全连接的神经网络（多层感知器）。

这个例子使用低级方法来更好地理解构建神经网络和训练过程背后的所有机制。

神经网络概述

tensorflow技术解析与实战，v2.0构建多层感知器(1)

MNIST 数据集概述

此示例使用手写数字的MNIST数据集。该数据集包含60 000个用于训练的示例和10 000个用于测试的示例。这些数字已经过尺寸标准化并位于图像中心，图像是固定大小(28x28像素)，值为0到255。

在此示例中，每个图像将转换为float32并归一化为[0 1]，并展平为784个特征的一维数组（28 * 28）

tensorflow技术解析与实战，v2.0构建多层感知器(2)

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 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 = 3000 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) # 存储层的权重和偏置 # 随机值生成器初始化权重 random_normal = tf.initializers.RandomNormal() weights = { 'h1': tf.Variable(random_normal([num_features n_hidden_1])) 'h2': tf.Variable(random_normal([n_hidden_1 n_hidden_2])) 'out': tf.Variable(random_normal([n_hidden_2 num_classes])) } biases = { 'b1': tf.Variable(tf.zeros([n_hidden_1])) 'b2': tf.Variable(tf.zeros([n_hidden_2])) 'out': tf.Variable(tf.zeros([num_classes])) } # 创建模型 def neural_net(x): # Hidden fully connected layer with 128 neurons. # 具有128个神经元的隐含完全连接层 layer_1 = tf.add(tf.matmul(x weights['h1']) biases['b1']) # Apply sigmoid to layer_1 output for non-linearity. # 将sigmoid用于layer_1输出以获得非线性 layer_1 = tf.nn.sigmoid(layer_1) # 具有128个神经元的隐含完全连接层 layer_2 = tf.add(tf.matmul(layer_1 weights['h2']) biases['b2']) # 将sigmoid用于layer_2输出以获得非线性 layer_2 = tf.nn.sigmoid(layer_2) # 输出完全连接层，每一个神经元代表一个类别 out_layer = tf.matmul(layer_2 weights['out']) biases['out'] # 应用softmax将输出标准化为概率分布 return tf.nn.softmax(out_layer) # 交叉熵损失函数 def cross_entropy(y_pred y_true): # 将标签编码为独热向量 y_true = tf.one_hot(y_true depth=num_classes) # 将预测值限制在一个范围之内以避免log（0）错误 y_pred = tf.clip_by_value(y_pred 1e-9 1.) # 计算交叉熵 return tf.reduce_mean(-tf.reduce_sum(y_true * tf.math.log(y_pred))) # 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: pred = neural_net(x) loss = cross_entropy(pred y) # 要更新的变量，即可训练的变量 trainable_variables = weights.values() biases.values() # 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) loss = cross_entropy(pred batch_y) acc = accuracy(pred batch_y) print("step: %i loss: %f accuracy: %f" % (step loss acc))

output:

step: 100 loss: 567.292969 accuracy: 0.136719 step: 200 loss: 398.614929 accuracy: 0.562500 step: 300 loss: 226.743774 accuracy: 0.753906 step: 400 loss: 193.384521 accuracy: 0.777344 step: 500 loss: 138.649963 accuracy: 0.886719 step: 600 loss: 109.713669 accuracy: 0.898438 step: 700 loss: 90.397217 accuracy: 0.906250 step: 800 loss: 104.545380 accuracy: 0.894531 step: 900 loss: 94.204697 accuracy: 0.890625 step: 1000 loss: 81.660645 accuracy: 0.906250 step: 1100 loss: 81.237137 accuracy: 0.902344 step: 1200 loss: 65.776703 accuracy: 0.925781 step: 1300 loss: 94.195862 accuracy: 0.910156 step: 1400 loss: 79.425507 accuracy: 0.917969 step: 1500 loss: 93.508163 accuracy: 0.914062 step: 1600 loss: 88.912506 accuracy: 0.917969 step: 1700 loss: 79.033607 accuracy: 0.929688 step: 1800 loss: 65.788315 accuracy: 0.898438 step: 1900 loss: 73.462387 accuracy: 0.937500 step: 2000 loss: 59.309540 accuracy: 0.917969 step: 2100 loss: 67.014008 accuracy: 0.917969 step: 2200 loss: 48.297115 accuracy: 0.949219 step: 2300 loss: 64.523148 accuracy: 0.910156 step: 2400 loss: 72.989517 accuracy: 0.925781 step: 2500 loss: 57.588585 accuracy: 0.929688 step: 2600 loss: 44.957100 accuracy: 0.960938 step: 2700 loss: 59.788242 accuracy: 0.937500 step: 2800 loss: 63.581337 accuracy: 0.937500 step: 2900 loss: 53.471252 accuracy: 0.941406 step: 3000 loss: 43.869728 accuracy: 0.949219 # 在验证集上测试模型 pred = neural_net(x_test) 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:

tensorflow技术解析与实战，v2.0构建多层感知器(3)