Tensorflow 2.1训练 实战 cifar10 完整代码 准确率 88.6% 模型 Resnet SENet Inception
环境：
tensorflow 2.1
最好用GPU
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（1x1，3x3，5x5）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（86% ）- > 加inception 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
