对CIFAR-10数据集的分类是机器学习中一个公开的基准测试问题，.zip

# Cifar-10 对CIFAR-10数据集的分类是机器学习中一个公开的基准测试问题，其任务是对一组32x32RGB的图像进行分类，这些图像涵盖了10个类别：飞机，汽车，鸟，猫，鹿，狗，青蛙，马，船以及卡车。  #### 为了熟悉掌握经典的DeepLearning Model以及Tensorflow的使用，我构建了多种模型对cifar10数据集进行分类。<br> #### 在终端运行步骤如下：python Vgg19.py (以Vgg19模型为例） ``` !python Vgg19.py Using TensorFlow backend. ======Loading data====== Loading ../input0/data_batch_1 : 10000. Loading ../input0/data_batch_2 : 10000. Loading ../input0/data_batch_3 : 10000. Loading ../input0/data_batch_4 : 10000. Loading ../input0/data_batch_5 : 10000. Loading ../input0/test_batch : 10000. Train data: (50000, 32, 32, 3) (50000, 10) Test data : (10000, 32, 32, 3) (10000, 10) ======Load finished====== ======Shuffling data====== ======Prepare Finished====== _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= block1_conv1 (Conv2D) (None, 32, 32, 64) 1792 _________________________________________________________________ batch_normalization_1 (Batch (None, 32, 32, 64) 256 _________________________________________________________________ activation_1 (Activation) (None, 32, 32, 64) 0 _________________________________________________________________ block1_conv2 (Conv2D) (None, 32, 32, 64) 36928 _________________________________________________________________ batch_normalization_2 (Batch (None, 32, 32, 64) 256 _________________________________________________________________ activation_2 (Activation) (None, 32, 32, 64) 0 _________________________________________________________________ block1_pool (MaxPooling2D) (None, 16, 16, 64) 0 _________________________________________________________________ block2_conv1 (Conv2D) (None, 16, 16, 128) 73856 _________________________________________________________________ batch_normalization_3 (Batch (None, 16, 16, 128) 512 _________________________________________________________________ activation_3 (Activation) (None, 16, 16, 128) 0 _________________________________________________________________ block2_conv2 (Conv2D) (None, 16, 16, 128) 147584 _________________________________________________________________ batch_normalization_4 (Batch (None, 16, 16, 128) 512 _________________________________________________________________ activation_4 (Activation) (None, 16, 16, 128) 0 _________________________________________________________________ block2_pool (MaxPooling2D) (None, 8, 8, 128) 0 _________________________________________________________________ block3_conv1 (Conv2D) (None, 8, 8, 256) 295168 _________________________________________________________________ batch_normalization_5 (Batch (None, 8, 8, 256) 1024 _________________________________________________________________ activation_5 (Activation) (None, 8, 8, 256) 0 _________________________________________________________________ block3_conv2 (Conv2D) (None, 8, 8, 256) 590080 _________________________________________________________________ batch_normalization_6 (Batch (None, 8, 8, 256) 1024 _________________________________________________________________ activation_6 (Activation) (None, 8, 8, 256) 0 _________________________________________________________________ block3_conv3 (Conv2D) (None, 8, 8, 256) 590080 _________________________________________________________________ batch_normalization_7 (Batch (None, 8, 8, 256) 1024 _________________________________________________________________ activation_7 (Activation) (None, 8, 8, 256) 0 _________________________________________________________________ block3_conv4 (Conv2D) (None, 8, 8, 256) 590080 _________________________________________________________________ batch_normalization_8 (Batch (None, 8, 8, 256) 1024 _________________________________________________________________ activation_8 (Activation) (None, 8, 8, 256) 0 _________________________________________________________________ block3_pool (MaxPooling2D) (None, 4, 4, 256) 0 _________________________________________________________________ block4_conv1 (Conv2D) (None, 4, 4, 512) 1180160 _________________________________________________________________ batch_normalization_9 (Batch (None, 4, 4, 512) 2048 _________________________________________________________________ activation_9 (Activation) (None, 4, 4, 512) 0 _________________________________________________________________ block4_conv2 (Conv2D) (None, 4, 4, 512) 2359808 _________________________________________________________________ batch_normalization_10 (Batc (None, 4, 4, 512) 2048 _________________________________________________________________ activation_10 (Activation) (None, 4, 4, 512) 0 _________________________________________________________________ block4_conv3 (Conv2D) (None, 4, 4, 512) 2359808 _________________________________________________________________ batch_normalization_11 (Batc (None, 4, 4, 512) 2048 _________________________________________________________________ activation_11 (Activation) (None, 4, 4, 512) 0 _________________________________________________________________ block4_conv4 (Conv2D) (None, 4, 4, 512) 2359808 _________________________________________________________________ batch_normalization_12 (Batc (None, 4, 4, 512) 2048 _________________________________________________________________ activation_12 (Activation) (None, 4, 4, 512) 0 _________________________________________________________________ block4_pool (MaxPooling2D) (None, 2, 2, 512) 0 _________________________________________________________________ block5_conv1 (Conv2D) (None, 2, 2, 512) 2359808 _________________________________________________________________ batch_normalization_13 (Batc (None, 2, 2, 512) 2048 _________________________________________________________________ activation_13 (Activation) (None, 2, 2, 512) 0 _________________________________________________________________ block5_conv2 (Conv2D) (None, 2, 2, 512) 2359808 _________________________________________________________________ batch_normalization_14 (Batc (None, 2, 2, 512) 2048 _________________________________________________________________ activation_14 (Activation) (None, 2, 2, 512) 0 _________________________________________________________________ block5_conv3 (Conv2D) (None, 2, 2, 512) 2359808 _________________________________________________________________ batch_normalization_15 (Batc (None, 2, 2, 512) 2048 _________________________________________________________________ activation_15 (Activation) (None, 2, 2, 512) 0 _________________________________________________________________ block5_conv4 (Conv2D) (None, 2, 2, 512) 2359808 _________________________________________________________________ batch_normalization_16 (Batc (None, 2, 2, 512) 2048 _________________________________________________________________ activation_16 (Activation) (None, 2, 2, 512) 0 __________________________