多层神经网络实现

多层神经网络

张量方式实现

初始化各层调用梯度记录器（自动求导）搭建各层 import tensorflow as tf from tensorflow.keras import layers x = tf.random.normal([3,784])#模拟2个样本，50个特征 w1 = tf.Variable(tf.random.truncated_normal([784,256],stddev=0.5))#第1层初始化 b1 = tf.zeros([256]) w2 = tf.Variable(tf.random.truncated_normal([256,128],stddev=0.5))#第2层初始化 b2 = tf.zeros([128]) w3 = tf.Variable(tf.random.truncated_normal([128,64],stddev=0.5))#第3层初始化 b3 = tf.zeros([64]) w4 = tf.Variable(tf.random.truncated_normal([64,10],stddev=0.5))#第4层初始化 b4 = tf.zeros([10]) with tf.GradientTape() as tape: # 梯度记录器 o1 = tf.matmul(x,w1) + b1 #也可使用广播机制拓展b1, tf.broadcast_to(b1, [x.shape[0], 256]) o1 = tf.nn.relu(o1) o2 = tf.matmul(o1,w2) + b2 o2 = tf.nn.relu(o2) o3 =tf.matmul(o2,w3) + b3 o3 = tf.nn.relu(o3) o4 =tf.matmul(o3,w4) + b4 print(o4) out: tf.Tensor( [[-184.37228 250.41742 790.16876 -711.15125 -268.63834 246.24713 -434.43478 547.58203 591.3756 1196.3761 ] [-841.72205 -416.0834 166.62344 -616.033 -439.455 59.845196 318.67456 60.858128 383.62747 1009.4866 ] [ -9.59348 297.99423 -370.27063 -913.0671 -73.28527 449.61548 -586.05334 -529.05145 674.3345 610.58124 ]], shape=(3, 10), dtype=float32)

层方式实现

简单调用

import tensorflow as tf from tensorflow.keras import layers fc1 = layers.Dense(256,activation=tf.nn.relu) fc2 = layers.Dense(128,activation=tf.nn.relu) fc3 = layers.Dense(64,activation=tf.nn.relu) fc4 = layers.Dense(10,activation=None) x = tf.random.normal([3,256]) o1 = fc1(x) o2 = fc2(o1) o3 = fc3(o2) o4 = fc4(o3) print(o4) out: tf.Tensor( [[-0.49023932 -0.04196656 -0.5316457 -0.22760229 -0.64782906 0.4603924 -0.46672398 0.54325604 -0.7902754 -0.15966915] [-0.17537238 0.15008762 -0.3496042 -0.17603163 0.50083745 -0.62207043 0.01685877 0.796759 -0.34656522 1.0465541 ] [-0.01338768 -0.16533731 0.11743313 -0.1506098 -0.65836793 0.3003255 0.21182871 0.22682111 0.33577356 0.33805916]], shape=(3, 10), dtype=float32)

封装调用

使用Sequential作为容器 import tensorflow as tf from tensorflow.keras import layers from tensorflow.keras import Sequential modle = Sequential([ layers.Dense(256,activation=tf.nn.relu), layers.Dense(128,activation=tf.nn.relu), layers.Dense(64,activation=tf.nn.relu), layers.Dense(10,activation=None)]) x = tf.random.normal([3,256]) a = modle(x) print(a) out: tf.Tensor( [[-0.48306665 0.3618398 -0.27131498 -0.80744046 -0.6388896 -0.26316196 0.8380004 0.341826 0.15942936 -0.19762628] [ 0.03107908 0.07116681 -0.49450177 -0.48394847 -0.40307134 -0.2677082 0.4005356 0.29835856 0.0189686 -0.2250806 ] [ 0.55381507 0.6946548 -0.7538996 -1.324675 -0.59704345 0.39796948 0.6455777 0.02428103 -0.31168345 -0.51364326]], shape=(3, 10), dtype=float32)