InceptionNet_train.py

import tensorflow as tf
import os
import numpy as np
from matplotlib import pyplot as plt
from tensorflow.keras import Model
from tensorflow import keras
from tensorflow.keras.layers import Conv2D,BatchNormalization,Activation,MaxPool2D,Dropout,Flatten,Dense,GlobalAveragePooling2D
np.set_printoptions(threshold=np.inf)

#########载入数据集
train =np.load('F:/re_train-data/train_data.npy',allow_pickle=True)
simple =np.load('F:/re_train-data/target.npy',allow_pickle=True)
x_train =train[:-5000]
y_train=simple[:-5000]
x_test =train[-5000:]
y_test=simple[-5000:]
x_train,x_test =x_train/255.0,x_test/255.0 #归一化

###建立inceptionnet网络
class ConvBNRelu(Model):
    def __init__(self, ch, kernelsz=3, strides=1, padding='same'):
        super(ConvBNRelu, self).__init__()
        self.model = tf.keras.models.Sequential([
            Conv2D(ch, kernelsz, strides=strides, padding=padding),
            BatchNormalization(),
            Activation('relu')
        ])

    def call(self, x):
        x = self.model(x, training=False) #在training=False时，BN通过整个训练集计算均值、方差去做批归一化，training=True时，通过当前batch的均值、方差去做批归一化。推理时 training=False效果好
        return x


class InceptionBlk(Model):
    def __init__(self, ch, strides=1):
        super(InceptionBlk, self).__init__()
        self.ch = ch
        self.strides = strides
        self.c1 = ConvBNRelu(ch, kernelsz=1, strides=strides)
        self.c2_1 = ConvBNRelu(ch, kernelsz=1, strides=strides)
        self.c2_2 = ConvBNRelu(ch, kernelsz=3, strides=1)
        self.c3_1 = ConvBNRelu(ch, kernelsz=1, strides=strides)
        self.c3_2 = ConvBNRelu(ch, kernelsz=5, strides=1)
        self.p4_1 = MaxPool2D(3, strides=1, padding='same')
        self.c4_2 = ConvBNRelu(ch, kernelsz=1, strides=strides)

    def call(self, x):
        x1 = self.c1(x)
        x2_1 = self.c2_1(x)
        x2_2 = self.c2_2(x2_1)
        x3_1 = self.c3_1(x)
        x3_2 = self.c3_2(x3_1)
        x4_1 = self.p4_1(x)
        x4_2 = self.c4_2(x4_1)
        # concat along axis=channel
        x = tf.concat([x1, x2_2, x3_2, x4_2], axis=3)
        return x


class Inception10(Model):
    def __init__(self, num_blocks, num_classes, init_ch=16, **kwargs):
        super(Inception10, self).__init__(**kwargs)
        self.in_channels = init_ch
        self.out_channels = init_ch
        self.num_blocks = num_blocks
        self.init_ch = init_ch
        self.c1 = ConvBNRelu(init_ch)
        self.blocks = tf.keras.models.Sequential()
        for block_id in range(num_blocks):
            for layer_id in range(2):
                if layer_id == 0:
                    block = InceptionBlk(self.out_channels, strides=2)
                else:
                    block = InceptionBlk(self.out_channels, strides=1)
                self.blocks.add(block)
            # enlarger out_channels per block
            self.out_channels *= 2
        self.p1 = GlobalAveragePooling2D()
        self.f1 = Dense(num_classes, activation='softmax')

    def call(self, x):
        x = self.c1(x)
        x = self.blocks(x)
        x = self.p1(x)
        y = self.f1(x)
        return y


model = Inception10(num_blocks=2, num_classes=10)
model.compile(optimizer='adam',#使用adam优化器
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),#使用SparseCategoricalCrossentropy损失函数
              metrics=['sparse_categorical_accuracy'])#准确率
#加入回调函数
checkpoint_savepath = './checkpoint/inceptionNet_GTA5_01.ckpt'
if os.path.exists(checkpoint_savepath + '.index'):
    print('______启用已经训练的模型______')
    model.load_weights(checkpoint_savepath)#加载已经训练的模型
#设置回调函数参数
cp_callback =tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_savepath,save_weights_only=True,save_best_only=True)
history =model.fit(x_train,y_train,batch_size=32,epochs=20,validation_data=(x_test,y_test),validation_freq=1,callbacks=[cp_callback])
model.summary()
#保存训练数据
file =open('./inception_GTA5_weight.txt','w')
for v in model.trainable_variables:
    file.write(str(v.name)+'\n')
    file.write(str(v.shape)+'\n')
    file.write(str(v.numpy()) + '\n')
file.close()
#使用MAT显示图表
acc = history.history['sparse_categorical_accuracy']
val_acc = history.history['val_sparse_categorical_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
plt.subplot(1, 2, 1)
plt.plot(acc, label='Training Accuracy')
plt.plot(val_acc, label='Validation Accuracy')
plt.title('Training and Validation Accuracy')
plt.legend()
plt.subplot(1, 2, 2)
plt.plot(loss, label='Training Loss')
plt.plot(val_loss, label='Validation Loss')
plt.title('Training and Validation Loss')
plt.legend()
plt.show()