残差模型ResNet18网络架构 残差网络的核心思路 |
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残差模型ResNet18网络架构 残差网络的核心思路
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深层网络在学习任务中取得了超越人眼的准确率，但是，经过实验表明，模型的性能和模型的深度并非成正比，是由于模型的表达能力过强，反而在测试数据集中性能下降。ResNet的核心是，为了防止梯度弥散或爆炸，让信息流经快捷连接到达浅层。 目录 ResNet原理ResNet实现模型创建数据加载模型编译模型训练测试模型训练过程ResNet原理深层网络在学习任务中取得了超越人眼的准确率，但是，经过实验表明，模型的性能和模型的深度并非成正比，是由于模型的表达能力过强，反而在测试数据集中性能下降。ResNet的核心是，为了防止梯度弥散或爆炸，让信息流经快捷连接到达浅层。 更正式的讲，输入\(x\)通过卷积层,得到特征变换后的输出\(F(x)\),与输入\(x\)进行对应元素的相加运算,得到最终输出\(H(x)\): \[H(x) = x + F(x) \] VGG模块和残差模块对比如下：
为了能够满足输入\(x\)与卷积层的输出\(F(x)\)能够相加运算,需要输入\(x\)的 shape 与\(F(x)\)的shape 完全一致。当出现 shape 不一致时，一般通过Conv2D进行变换，该Conv2D的核为1×1，步幅为2。 ResNet实现使用tensorflow2.3实现ResNet 模型创建import numpy as np import tensorflow as tf from tensorflow import keras from matplotlib import pyplot as plt import os import math """ 用于控制模型层数 """ #残差块数 n = 3 depth = n * 9 + 1 def resnet_layer(inputs, num_filters=16, kernel_size=3, strides=1, activation='relu', batch_normalization=True, conv_first=True): """2D Convolution-Batch Normalization-Activation stack builder Arguments: inputs (tensor): 输入 num_filters (int): 卷积核个数 kernel_size (int): 卷积核大小 activation (string): 激活层 batch_normalization (bool): 是否使用批归一化 conv_first (bool): conv-bn-active(True) or bn-active-conv (False)层堆叠次序 Returns: x (tensor): 输出 """ conv = keras.layers.Conv2D(num_filters, kernel_size=kernel_size, strides=strides, padding='same', kernel_initializer='he_normal', kernel_regularizer=keras.regularizers.l2(1e-4)) x = inputs if conv_first: x = conv(x) if batch_normalization: x = keras.layers.BatchNormalization()(x) if activation is not None: x = keras.layers.Activation(activation)(x) else: if batch_normalization: x = keras.layers.BatchNormalization()(x) if activation is not None: x = keras.layers.Activation(activation)(x) x = conv(x) return x def resnet(input_shape,depth,num_classes=10): """ResNet Arguments: input_shape (tensor): 输入尺寸 depth (int): 网络层数 num_classes (int): 预测类别数 Return: model (Model): 模型 """ if (depth - 2) % 6 != 0: raise ValueError('depth should be 6n+2') #超参数 num_filters = 16 num_res_blocks = int((depth - 2) / 6) inputs = keras.layers.Input(shape=input_shape) x = resnet_layer(inputs=inputs) for stack in range(3): for res_block in range(num_res_blocks): strides = 1 if stack > 0 and res_block == 0: strides = 2 y = resnet_layer(inputs=x,num_filters=num_filters, strides=strides) y = resnet_layer(inputs=y,num_filters=num_filters, activation=None) if stack > 0 and res_block == 0: x = resnet_layer(inputs=x, num_filters=num_filters, kernel_size=1, strides=strides, activation=None, batch_normalization=False) x = keras.layers.add([x,y]) x = keras.layers.Activation('relu')(x) num_filters *= 2 x = keras.layers.AveragePooling2D(pool_size=8)(x) x = keras.layers.Flatten()(x) outputs = keras.layers.Dense(num_classes,activation='softmax', kernel_initializer='he_normal')(x) model = keras.Model(inputs=inputs,outputs=outputs) return model model = resnet_v1(input_shape=input_shape,depth=depth)数据加载#加载数据 (x_train,y_train),(x_test,y_test) = keras.datasets.cifar10.load_data() #计算类别数 num_labels = len(np.unique(y_train)) #转化为one-hot编码 y_train = keras.utils.to_categorical(y_train) y_test = keras.utils.to_categorical(y_test) #预处理 input_shape = x_train.shape[1:] x_train = x_train.astype('float32') / 255. x_test = x_test.astype('float32') / 255.模型编译#超参数 batch_size = 64 epochs = 200 #编译模型 model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['acc']) model.summary()模型训练model.fit(x_train,y_train, batch_size=batch_size, epochs=epochs, validation_data=(x_test,y_test), shuffle=True)测试模型scores = model.evaluate(x_test,y_test,batch_size=batch_size,verbose=0) print('Test loss: ',scores[0]) print('Test accuracy: ',scores[1])训练过程Epoch 104/200 782/782 [==============================] - ETA: 0s - loss: 0.2250 - acc: 0.9751 Epoch 00104: val_acc did not improve from 0.91140 782/782 [==============================] - 15s 19ms/step - loss: 0.2250 - acc: 0.9751 - val_loss: 0.4750 - val_acc: 0.9090 learning rate: 0.0001 Epoch 105/200 781/782 [============================>.] - ETA: 0s - loss: 0.2206 - acc: 0.9754 Epoch 00105: val_acc did not improve from 0.91140 782/782 [==============================] - 16s 20ms/step - loss: 0.2206 - acc: 0.9754 - val_loss: 0.4687 - val_acc: 0.9078 learning rate: 0.0001 Epoch 106/200 782/782 [==============================] - ETA: 0s - loss: 0.2160 - acc: 0.9769 Epoch 00106: val_acc did not improve from 0.91140 782/782 [==============================] - 15s 20ms/step - loss: 0.2160 - acc: 0.9769 - val_loss: 0.4886 - val_acc: 0.9053
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