基础_cifar10_model-阿里云开发者社区

今天进一步在cifar10数据集上解决几个问题：

1、比较一下序贯和model,为什么要分成两块；

2、同样的条件下，我去比较一下序贯和model。这个例子作为今天的晚间运行。

1、比较一下序贯和model,为什么要分成两块；

我认为比较能够说明问题的是前期那个验证码的model

input_tensor = Input((height, width, 3))
x = input_tensor
for i in range(4):
    x = Convolution2D(32*2**i, 3, 3, activation='relu')(x)
    x = Convolution2D(32*2**i, 3, 3, activation='relu')(x)
    x = MaxPooling2D((2, 2))(x)

x = Flatten()(x)
x = Dropout(0.25)(x)
x = [Dense(n_class, activation='softmax', name='c%d'%(i+1))(x) for i in range(4)]
model = Model(input=input_tensor, output=x)

一个非常显著的不同，就是最后分为了4个部分（这也是这里选择model的原因），这个 4分类直接最后导致了验证码4个部分的区分。这是验证码识别最为有技术的地方。应该说，从网络结构上来看，这个分叉，在cnn网络中，就是model和sequence最大的不同之处，也是着重要研究的地方。

model = Model(input=input_tensor, output=x)

model最大的不同就在于它首先定义了网络结构，然后最后才生成这个模型。从编码方式上来看，只是顺序的不同而已。

x = [Dense(n_class, activation='softmax', name='c%d'%(i+1))(x) for i in range(4)]

这句之所以能够将网络1分4，是因为采用的列表的形式。然后就能够进行验证？我将这个问题留给明天。另一个方面，在这个验证码的例子中，其网络就是不断地卷积-卷积-maxpooling，非常简单，是否需要进一步优化？

2、同样的条件下，我去比较一下序贯和model

'''
同样的数据集和epochs，同样的模型构造。
一个采用序贯、一个采用model方法
对生成结果进行比较
'''


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.layers import Input

from keras.models import Model

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


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)

#序贯模型
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'))

#model模型
inputs = Input(shape=(x_train.shape[1:]))
x = Conv2D(32, (3, 3), activation='relu')(inputs)
x = Conv2D(32, (3, 3), activation='relu')(x)
x = MaxPooling2D((2, 2))(x)
x = Dropout(0.25)(x)
x = Conv2D(64, (3, 3), activation='relu')(x)
x = Conv2D(64, (3, 3), activation='relu')(x)
x = MaxPooling2D((2, 2))(x)
x = Dropout(0.25)(x)
x = Flatten()(x)
x = Dense(512)(x)
x = Activation('relu')(x)
x = Dropout(0.5)(x)
x = Dense(num_classes)(x)
x = Activation('softmax')(x)
model2 = Model(inputs=inputs, outputs=x)

#显示模型
model.summary()
plot_model(model,to_file='model_sequence.png',show_shapes=True)
img = image.imread('model_sequence.png')

print(img.shape)
plt.imshow(img) # 显示图片
plt.axis('off') # 不显示坐标轴
plt.show()

model2.summary()
plot_model(model2,to_file='model_model.png',show_shapes=True)
img2 = image.imread('model_model.png')

print(img2.shape)
plt.imshow(img2) # 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'])

model2.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)
    model2.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])

模型打印出来比较：

sequence

主要是由于在

x = Conv2D(32, (3, 3), activation='relu')(inputs)

似乎是将activation合并了，所以model_model更短一些。但是层的结果似乎也是不一样的。这个等到训练结果出来后进一步研究。

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)

vz5iTQxKmTPQQhx3NMKIQ8SByGExEEIIXEQQkgch

_________________________________________________________________

Layer (type) Output Shape Param #

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

input_1 (InputLayer) (None, 32, 32, 3) 0

_________________________________________________________________

conv2d_5 (Conv2D) (None, 30, 30, 32) 896

_________________________________________________________________

conv2d_6 (Conv2D) (None, 28, 28, 32) 9248

_________________________________________________________________

max_pooling2d_3 (MaxPooling2 (None, 14, 14, 32) 0

_________________________________________________________________

dropout_4 (Dropout) (None, 14, 14, 32) 0

_________________________________________________________________

conv2d_7 (Conv2D) (None, 12, 12, 64) 18496

_________________________________________________________________

conv2d_8 (Conv2D) (None, 10, 10, 64) 36928

_________________________________________________________________

max_pooling2d_4 (MaxPooling2 (None, 5, 5, 64) 0

_________________________________________________________________

dropout_5 (Dropout) (None, 5, 5, 64) 0

_________________________________________________________________

flatten_2 (Flatten) (None, 1600) 0

_________________________________________________________________

dense_3 (Dense) (None, 512) 819712

_________________________________________________________________

activation_7 (Activation) (None, 512) 0

_________________________________________________________________

dropout_6 (Dropout) (None, 512) 0

_________________________________________________________________

dense_4 (Dense) (None, 10) 5130

_________________________________________________________________

activation_8 (Activation) (None, 10) 0

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

Total params: 890,410

Trainable params: 890,410

Non-trainable params: 0

_________________________________________________________________

(1623, 635, 4)

vhjX5vtMzwLjhCWLVuG+vp63c1lVCLR78SUX8WZk

从最后的结果上来看，model消耗的时间更少，而且正确率更高、loss更低。那么以后我优先建立model模型！

10000/10000 [==============================] - 2s 187us/step

Test loss: 0.9320432889938355

Test accuracy: 0.682

10000/10000 [==============================] - 2s 173us/step

Test loss2: 0.8202090420722962

Test accuracy2: 0.7091

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