Tensorflow 2.1训练训练 实战实战 cifar10 完整代码 型型 Resnet SENet Inception 完整代码 准确率准确率 88.6% 模模 环境环境： tensorflow 2.1 最好用GPU 模型模型： Resnet：把前一层的数据直接加到下一层里。减少数据在传播过程中过多的丢失。 SENet： 学习每一层的通道之间的关系 Inception: 每一层都用不同的核（1×1，3×3，5×5）来学习.防止因为过小的核或者过大的核而学不到图片的特征。 用Resnet ，SENet， Inceptiont网络训练Cifar10 或者Cifar 100. 训练数据：Cifar10 或者 Cifar 100 训练集上准确率：97.11%左右 验证集上准确率：90.22%左右 测试集上准确率：88.6% 训练时间在GPU上：一小时多 权重大小：21.8 MB 训练的历程： 普通网络(65%左右)-> 数据增强(70%左右)->模型增强(进入Resnet 和SEnet) 80%左右 -> 模型的结构做了调整 模型的结构做了调整 （（86% ））- > 加加inception 模型后（ 模型后（88.6%）） 训练集与验证集上的测试结果： 347/351 [============================>.] - ETA: 0s - loss: 0.0827 - sparse_categorical_accuracy: 0.9711 348/351 [============================>.] - ETA: 0s - loss: 0.0828 - sparse_categorical_accuracy: 0.9710 349/351 [============================>.] - ETA: 0s - loss: 0.0827 - sparse_categorical_accuracy: 0.9710 350/351 [============================>.] - ETA: 0s - loss: 0.0826 - sparse_categorical_accuracy: 0.9711 351/351 [==============================] - 20s 57ms/step - loss: 0.0826 - sparse_categorical_accuracy: 0.9711 - val_loss: 0.3952 - val_sparse_categorical_accuracy: 0.9022 测试集上的结果 79/79 - 4s - loss: 0.4005 - sparse_categorical_accuracy: 0.8842 [0.40052215495630156, 0.8842] time 4.130274534225464 下面是完整的代码，运行前建一下这个目录weights4_5，不想写代码自动化建了。 如果要训练Cifar100，直接把cifar10 改成cifar100就可以了。不需要改其它地方 import tensorflow as tf import tensorflow.keras as keras import tensorflow.keras.layers as layers import image_augument.image_augment as image_augment import time as time import tensorflow.keras.preprocessing.image as image import matplotlib.pyplot as plt import os def senet_block(inputs, ratio): shape = inputs.shape channel_out = shape[-1] # print(shape) # (2, 28, 28, 32) , [1,28,28,1], [1,28,28,1] squeeze = layers.GlobalAveragePooling2D()(inputs) # [2, 1, 1, 32] # print(squeeze.shape) # 第二层，全连接层 # [2,32] # print(squeeze.shape) shape_result = layers.Flatten()(squeeze) # print(shape_result.shape) # [32,2] shape_result = layers.Dense(int(channel_out / ratio), activation='relu')(shape_result) # shape_result = layers.BatchNormalization()(shape_result) # [2,32] shape_result = layers.Dense(channel_out, activation='sigmoid')(shape_result) # shape_result = layers.BatchNormalization()(shape_result) # 第四层，点乘 # print('heres2') excitation_output = tf.reshape(shape_result, [-1, 1, 1, channel_out]) # print(excitation_output.shape) h_output = excitation_output * inputs return h_output def inception_block(input, input_filter, output_filter): 

reception_filter = output_filter print('reception_filter',reception_filter) r1 = layers.Conv2D(filters=reception_filter, kernel_size=(1, 1), activation='relu', padding='same')(input) r1 = layers.BatchNormalization()(r1 ) r1 = senet_block(r1, 8) r3 = layers.Conv2D(filters=reception_filter, kernel_size=(3, 3), activation='relu', padding='same')(input) r3 = layers.BatchNormalization()(r3 ) r3 = senet_block(r3, 8) r5 = layers.Conv2D(filters=reception_filter, kernel_size=(5, 5), activation='relu', padding='same')(input) r5 = layers.BatchNormalization()(r5 ) r5 = senet_block(r5, 8) mx = tf.keras.layers.MaxPool2D(pool_size=(3, 3), strides=1, padding="same")(input) mx = layers.Conv2D(filters=reception_filter, kernel_size=(1, 1), activation='relu', padding='same')(mx) output = tf.keras.layers.concatenate([r1, r3, r5,mx], axis=-1) output = layers.Conv2D(filters=reception_filter, kernel_size=(3, 3), activation='relu', padding='same')(output) print('output',output.shape) return output def res_block(input, input_filter, output_filter): # input = 32, output = 96 res_x = inception_block(input, input_filter, output_filter) # 96 res_x = layers.Conv2D(filters=output_filter, kernel_size=(3, 3), activation=None, padding='same')(res_x ) res_x = layers.BatchNormalization()(res_x ) res_x = senet_block(res_x, 8) if input_filter == output_filter: identity = input else: #需要升维或者降维 identity = layers.Conv2D(filters=output_filter, kernel_size=(1,1), padding='same')(input) print(identity.shape) print(res_x.shape) x = layers.Add()([identity, res_x]) output = layers.Activation('relu')(x) return output def my_model(): inputs = keras.Input(shape=(32,32,3), name='img') h1 = layers.Conv2D(filters=16, kernel_size=(3, 3), strides=(1, 1), padding='same', activation='relu')(inputs) h1 = layers.BatchNormalization()(h1) h1 = senet_block(h1, 8) block1_out = res_block(h1, 16, 32) block2_out = layers.MaxPool2D(pool_size=(2, 2))(block1_out) block2_out = res_block(block2_out, 32,64) block3_out = layers.MaxPool2D(pool_size=(2, 2))(block2_out) block3_out = res_block(block3_out, 64,128) block4_out = layers.MaxPool2D(pool_size=(2, 2))(block3_out) block4_out = res_block(block4_out, 128,256) h3 = layers.GlobalAveragePooling2D()(block4_out) h3 = layers.Flatten()(h3) h3 = layers.BatchNormalization()(h3) h3 = layers.Dense(64, activation='relu')(h3) h3 = layers.BatchNormalization()(h3) outputs = layers.Dense(10, activation='softmax')(h3) deep_model = keras.Model(inputs, outputs, name='resnet') deep_model.compile(optimizer=keras.optimizers.Adam(), loss=keras.losses.SparseCategoricalCrossentropy(), #metrics=['accuracy']) metrics=[keras.metrics.SparseCategoricalAccuracy()]) deep_model.summary() #keras.utils.plot_model(deep_model, 'my_resNet.png', show_shapes=True) return deep_model current_max_loss = 9999 weight_file='./weights4_5/model.h5' 

def train_my_model(deep_model): (x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data() train_datagen = image.ImageDataGenerator( rescale=1 / 255, rotation_range=40, # 角度值，0-180.表示图像随机旋转的角度范围 width_shift_range=0.2, # 平移比例，下同 height_shift_range=0.2, shear_range=0.2, # 随机错切变换角度 zoom_range=0.2, # 随即缩放比例 horizontal_flip=True, # 随机将一半图像水平翻转 fill_mode='nearest' # 填充新创建像素的方法 ) test_datagen = image.ImageDataGenerator(rescale=1 / 255) validation_datagen = image.ImageDataGenerator(rescale=1 / 255) train_generator = train_datagen.flow(x_train[:45000], y_train[:45000], batch_size=128) # train_generator = train_datagen.flow(x_train, y_train, batch_size=128) validation_generator = validation_datagen.flow(x_train[45000:], y_train[45000:], batch_size=128) test_generator = test_datagen.flow(x_test, y_test, batch_size=128) begin_time = time.time() if os.path.isfile(weight_file): print('load weight') deep_model.load_weights(weight_file) def save_weight(epoch, logs): global current_max_loss if(logs['val_loss'] is not None and logs['val_loss']< current_max_loss): current_max_loss = logs['val_loss'] print('save_weight', epoch, current_max_loss) deep_model.save_weights(weight_file) batch_print_callback = keras.callbacks.LambdaCallback( on_epoch_end=save_weight ) callbacks = [ tf.keras.callbacks.EarlyStopping(patience=4, monitor='loss'), batch_print_callback, # keras.callbacks.ModelCheckpoint('./weights/model.h5', save_best_only=True), tf.keras.callbacks.TensorBoard(log_dir='logs4_4') ] print(train_generator[0][0].shape) history = deep_model.fit_generator(train_generator, steps_per_epoch=351, epochs=200, callbacks=callbacks, validation_data=validation_generator, validation_steps=39, initial_epoch = 0) if (history.history['val_loss'] is not None and history.history['val_loss'] < current_max_loss): current_max_loss = history['val_loss'] print('save_weight', current_max_loss) deep_model.save_weights(weight_file) result = deep_model.evaluate_generator(test_generator, verbose=2) print(result) print('time', time.time() - begin_time) def show_result(history): plt.plot(history.history['loss']) plt.plot(history.history['val_loss']) plt.plot(history.history['sparse_categorical_accuracy']) plt.plot(history.history['val_sparse_categorical_accuracy']) plt.legend(['loss', 'val_loss', 'sparse_categorical_accuracy', 'val_sparse_categorical_accuracy'], loc='upper left') plt.show() print(history) show_result(history) 

def test_module(deep_model): (x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data() test_datagen = image.ImageDataGenerator(rescale=1 / 255) test_generator = test_datagen.flow(x_test, y_test, batch_size=128) begin_time = time.time() if os.path.isfile(weight_file): print('load weight') deep_model.load_weights(weight_file) result = deep_model.evaluate_generator(test_generator, verbose=2) print(result) print('time', time.time() - begin_time) def predict_module(deep_model): x_train, y_train, x_test, y_test = image_augment.get_all_train_data(False) import numpy as np if os.path.isfile(weight_file): print('load weight') deep_model.load_weights(weight_file) print(y_test[0:20]) for i in range(20): img = x_test[i][np.newaxis, :]/255 y_ = deep_model.predict(img) v = np.argmax(y_) print(v, y_test[i]) if __name__ == '__main__': deep_model = my_model() train_my_model(deep_model) #test_module(deep_model) #predict_module(deep_model) 作者：keeppractice