基础_cifar10_序贯-阿里云开发者社区

今天的基础研究主要是在cifar10数据集上解决一下几个问题：

1、从头开始，从最简单的序贯开始，尝试model的构造；

2、要将模型打印出来。最好是能够打印出图片，否则也要summary;

3、尝试对例子的参数进行分析，得出初步修改意见。

1、构建模型

   '''Train a simple deep CNN on the CIFAR10 small images dataset.
  

   It gets to 75% validation accuracy in 25 epochs, and 79% after 50 epochs.
  

   (it's still underfitting at that point, though).
  

'''

 
   from 
   __future__ 
   import print_function
  

   #!apt-get -qq install -y graphviz && pip install -q pydot
  

 
   import pydot
  

 
   import keras
  

 
   import cv2
  

 
   from keras.datasets 
   import cifar10

 
   from keras.preprocessing.image 
   import ImageDataGenerator

 
   from keras.models 
   import Sequential

 
   from keras.layers 
   import Dense, Dropout, Activation, Flatten

 
   from keras.layers 
   import Conv2D, MaxPooling2D

 
   from keras.utils.vis_utils 
   import plot_model

 
   import matplotlib.image 
   as image 
   # image 用于读取图片 
  

 
   import matplotlib.pyplot 
   as plt

 
   import os
  

   %matplotlib inline
  

   %config InlineBackend.figure_format = 
   'retina' 
  

   batch_size = 
   32 
  

   num_classes = 
   10 
  

   #epochs = 100
  

   epochs = 
   3 
  

   data_augmentation = 
   True 
  

   num_predictions = 
   20 
  

   save_dir = os.path.join(os.getcwd(), 
   'saved_models')
  

   model_name = 
   'keras_cifar10_trained_model.h5' 
  

   # The data, split between train and test sets:
  

   (x_train, y_train), (x_test, y_test) = cifar10.load_data()
  

 
   print(
   'x_train shape:', x_train.shape)

 
   print(x_train.shape[
   0], 
   'train samples')
  

 
   print(x_test.shape[
   0], 
   'test samples')
  

   # Convert class vectors to binary class matrices.
  

   y_train = keras.utils.to_categorical(y_train, num_classes)
  

   y_test = keras.utils.to_categorical(y_test, num_classes)
  

   model = Sequential()
  

model.add(Conv2D(32, (3, 3), padding='same', input_shape=x_train.shape[1:]))model.add(Activation('relu'))model.add(Conv2D(32, (3, 3)))model.add(Activation('relu'))model.add(MaxPooling2D(pool_size=(2, 2)))model.add(Dropout(0.25))

   #显示模型
  

   model.summary()
  

   plot_model(model,
   to_file=
   'model1111.png',
   show_shapes=
   True)
  

   files.download(
   'model1111.png')
  

   img = image.imread(
   'model1111.png')
  

 
   print(img.shape)
  

   plt.imshow(img) 
   # 显示图片 
  

   plt.axis(
   'off') 
   # 不显示坐标轴 
  

   plt.show()
  

   # initiate RMSprop optimizer
  

   opt = keras.optimizers.rmsprop(
   lr=
   0.0001, 
   decay=
   1e-6)
  

   # Let's train the model using RMSprop
  

   model.compile(
   loss=
   'categorical_crossentropy',
  

 
   optimizer=opt,
  

 
   metrics=[
   'accuracy'])

   x_train = x_train.astype(
   'float32')
  

   x_test = x_test.astype(
   'float32')
  

   x_train /= 
   255 
  

   x_test /= 
   255 
  

 
   if 
   not data_augmentation:

 
   print(
   'Not using data augmentation.')

    model.fit(x_train, y_train,
  

 
   batch_size=batch_size,
  

 
   epochs=epochs,
  

 
   validation_data=(x_test, y_test),
  

 
   shuffle=
   True)

 
   else:
  

 
   print(
   'Using real-time data augmentation.')

 
   # This will do preprocessing and realtime data augmentation: 
  

    datagen = ImageDataGenerator(
  

 
   featurewise_center=
   False, 
   # set input mean to 0 over the dataset 
  

 
   samplewise_center=
   False, 
   # set each sample mean to 0 
  

 
   featurewise_std_normalization=
   False, 
   # divide inputs by std of the dataset 
  

 
   samplewise_std_normalization=
   False, 
   # divide each input by its std 
  

 
   zca_whitening=
   False, 
   # apply ZCA whitening 
  

 
   rotation_range=
   0, 
   # randomly rotate images in the range (degrees, 0 to 180) 
  

 
   width_shift_range=
   0.1, 
   # randomly shift images horizontally (fraction of total width) 
  

 
   height_shift_range=
   0.1, 
   # randomly shift images vertically (fraction of total height) 
  

 
   horizontal_flip=
   True, 
   # randomly flip images 
  

 
   vertical_flip=
   False) 
   # randomly flip images 
  

 
   # Compute quantities required for feature-wise normalization 
  

 
   # (std, mean, and principal components if ZCA whitening is applied). 
  

    datagen.fit(x_train)
  

 
   # Fit the model on the batches generated by datagen.flow(). 
  

    model.fit_generator(datagen.flow(x_train, y_train,
  

 
   batch_size=batch_size),
  

 
   epochs=epochs,
  

 
   validation_data=(x_test, y_test),
  

 
   workers=
   4)

   # Save model and weights
  

 
   if 
   not os.path.isdir(save_dir):

    os.makedirs(save_dir)
  

   model_path = os.path.join(save_dir, model_name)
  

   model.save(model_path)
  

 
   print(
   'Saved trained model at 
   %s
    ' % model_path)
  

   # Score trained model.
  

   scores = model.evaluate(x_test, y_test, 
   verbose=
   1)
  

 
   print(
   'Test loss:', scores[
   0])
  

 
   print(
   'Test accuracy:', scores[
   1])
  

2、要将模型打印出来，目前只有本地才有图片。这个图片也可以本地看。

Using TensorFlow backend.

x_train shape: (50000, 32, 32, 3)

50000 train samples

10000 test samples

_________________________________________________________________

Layer (type) Output Shape Param #

=================================================================

conv2d_1 (Conv2D) (None, 32, 32, 32) 896

_________________________________________________________________

activation_1 (Activation) (None, 32, 32, 32) 0

_________________________________________________________________

conv2d_2 (Conv2D) (None, 30, 30, 32) 9248

_________________________________________________________________

activation_2 (Activation) (None, 30, 30, 32) 0

_________________________________________________________________

max_pooling2d_1 (MaxPooling2 (None, 15, 15, 32) 0

_________________________________________________________________

dropout_1 (Dropout) (None, 15, 15, 32) 0

_________________________________________________________________

conv2d_3 (Conv2D) (None, 15, 15, 64) 18496

_________________________________________________________________

activation_3 (Activation) (None, 15, 15, 64) 0

_________________________________________________________________

conv2d_4 (Conv2D) (None, 13, 13, 64) 36928

_________________________________________________________________

activation_4 (Activation) (None, 13, 13, 64) 0

_________________________________________________________________

max_pooling2d_2 (MaxPooling2 (None, 6, 6, 64) 0

_________________________________________________________________

dropout_2 (Dropout) (None, 6, 6, 64) 0

_________________________________________________________________

flatten_1 (Flatten) (None, 2304) 0

_________________________________________________________________

dense_1 (Dense) (None, 512) 1180160

_________________________________________________________________

activation_5 (Activation) (None, 512) 0

_________________________________________________________________

dropout_3 (Dropout) (None, 512) 0

_________________________________________________________________

dense_2 (Dense) (None, 10) 5130

_________________________________________________________________

activation_6 (Activation) (None, 10) 0

=================================================================

Total params: 1,250,858

Trainable params: 1,250,858

Non-trainable params: 0

_________________________________________________________________

(2065, 635, 4)

Using real-time data augmentation.

WARNING:tensorflow:Variable *= will be deprecated. Use variable.assign_mul if you want assignment to the variable value or 'x = x * y' if you want a new python Tensor object.

Epoch 1/3

138/1563 [=>........

大图：

3、尝试对例子的参数进行分析，得出初步修改意见。

从这个序贯模型的建立过程中，其模型大概是这样的：

第一段是

model.add(Conv2D(32, (3, 3), padding='same', input_shape=x_train.shape[1:]))

model.add(Activation('relu'))

model.add(Conv2D(32, (3, 3)))

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Dropout(0.25))

基本上相当于卷积->激活->卷积->激活->maxPooling->dropout

然后

model.add(Conv2D(64, (3, 3), padding='same'))

model.add(Activation('relu'))

model.add(Conv2D(64, (3, 3)))

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Dropout(0.25))

几乎是原样的来了一遍，唯一不同的是变成了64个一组。

model.add(Flatten())

model.add(Dense(512))

model.add(Activation('relu'))

model.add(Dropout(0.5))

model.add(Dense(num_classes))

model.add(Activation('softmax'))

最后，到输出阶段了，应该是要准备输出了。

在这个地方，应该触及DL这门技术的核心了，就是我应该构造增益的网络？又怎样根据生成的结果来调整网络。迁移我在图像处理方面的知识，我首先是知道了基础的工具，然后有了很多实际的经验，这样才能够在拿到问题的第一时间，有初步的设想。

更简单的网络代表可以更快地训练，在我的研究过程中，需要寻找的并不是我们的网络能够复杂到什么程度—而是怎样简单的网络就可以完成目标，达到既定的acc。首先可能是90%到95%，逐渐地去接触更多东西。在cifar-10上要起码达到这个结果。

当然我知道增加epoch，一般时候能够提高准确率，当然也会过拟合；另一个方向，如果我缩小数据，比如在上面的例子中，不添加64位层，结果是这样：

    model = Sequential()
model.add(Conv2D(32, (3, 3), padding='same',
                 input_shape=x_train.shape[1:]))
model.add(Activation('relu'))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Conv2D(64, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes))
model.add(Activation('softmax'))

model2 = Sequential()
model2.add(Conv2D(32, (3, 3), padding='same',
                 input_shape=x_train.shape[1:]))
model2.add(Activation('relu'))
model2.add(Conv2D(32, (3, 3)))
model2.add(Activation('relu'))
model2.add(MaxPooling2D(pool_size=(2, 2)))
model2.add(Dropout(0.25))

model2.add(Flatten())
model2.add(Dense(512))
model2.add(Activation('relu'))
model2.add(Dropout(0.5))
model2.add(Dense(num_classes))
model2.add(Activation('softmax'))
   

Test loss: 0.8056231224060059

Test accuracy: 0.7182

10000/10000 [==============================] - 2s 161us/step

Test loss2: 0.9484411451339722

Test accuracy2: 0.6764

最后，在《NN&DL》中反复被提及的一点，我也实际体会到了：训练需要时间，你可以先去做其它的事情。
到此，我认为《基础_cifar10_序贯》可以结束。

来自为知笔记(Wiz)

目前方向：图像拼接融合、图像识别联系方式：jsxyhelu@foxmail.com

基础_cifar10_序贯

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