Tensorflow 2.1 keras 训练 实战 cifar10 cifar100 准确率 可达86%-87% 模型 Resnet Senet

Manda ·
更新时间:2024-11-15
· 813 次阅读

环境:
tensorflow 2.1
最好用GPU

模型:
Resnet
SENet
用Resnet 和SENet网络训练Cifar10 或者Cifar 100.

训练数据:Cifar10 或者 Cifar 100
准确率可达86%-87%
训练时间在GPU上:一小时多
权重大小:5.08 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(86%)
开始的时候我也用tensorlfow 1.4训练过Cifar10. 但是没有跑出理想的准确率,总是在70%左右。后来也没有想过模型上增强直接跳到tensorflow2.1了。

下一步准备加入inception网络试一试结果如何

下面是测试集上的结果

#79/79 - 2s - loss: 0.4225 - sparse_categorical_accuracy: 0.8708 #[0.42247119277149814, 0.8708]

下面是完整的代码,运行前建一下这个目录weights3_8,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 res_block(input, input_filter, output_filter): res_x = layers.Conv2D(filters=output_filter, kernel_size=(3, 3), activation='relu', padding='same')(input) res_x = layers.BatchNormalization()(res_x ) res_x = senet_block(res_x, 8) 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) 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) block1_out = layers.MaxPool2D(pool_size=(2, 2))(block1_out) # Resnet block block2_out = res_block(block1_out, 32,64) block2_out = layers.MaxPool2D(pool_size=(2, 2))(block2_out) block3_out = res_block(block2_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 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('./weights3_8/model.h5'): print('load weight') deep_model.load_weights('./weights3_8/model.h5') def save_weight(epoch, logs): global current_max_loss if(logs['val_loss']< current_max_loss): current_max_loss = logs['val_loss'] print('save_weight', epoch, current_max_loss) deep_model.save_weights('./weights3_8/model.h5') 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='logs3_8') ] 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) 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 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('./weights3_8/model.h5'): print('load weight') deep_model.load_weights('./weights3_8/model.h5') #test_datagen = image.ImageDataGenerator(rescale=1 / 255) # test_generator = test_datagen.flow(x_test[0:20], y_test[0:20], batch_size=20) # result = deep_model.evaluate_generator(test_generator, verbose=2) #print(result) 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) #predict_module(deep_model)
作者:keeppractice



实战 训练 准确率 模型 tensorflow keras resnet

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