keras CNN解读

2022-09-10 22:39:46

loss是训练集损失值. acc是训练集准确率。val_loss是测试集上的损失值，val_acc是测试集上的准确率。

用深度学习框架跑过实际问题的朋友一定有这样的感觉: 太神奇了，它竟然能自己学习重要的特征 ! 下一步我们改教会计算机什么？莫非是教会他们寻找新的未知特征？

对于卷积神经网络cnn，其中每个卷积核就是一个cnn习得的特征，详见David 9之前的关于cnn博客。

今天我们的主角是keras，其简洁性和易用性简直出乎David 9我的预期。大家都知道keras是在TensorFlow上又包装了一层，向简洁易用的深度学习又迈出了坚实的一步。

所以，今天就来带大家写keras中的Hello World ，做一个手写数字识别的cnn。回顾cnn架构：

我们要处理的是这样的灰度像素图：

识别0-9的手写数字，60来行代码就能搞定，简单易懂，全程无尿点。我们先来看单机版跑完的结果：

(python3_env) yanchao727@yanchao727-VirtualBox:~$ python keras_mnist_cnn.py
Using TensorFlow backend.
x_train shape: (60000, 28, 28, 1)
60000 train samples
10000 test samples
Train on 60000 samples, validate on 10000 samples
Epoch 1/12
60000/60000 [==============================] - 446s - loss: 0.3450 - acc: 0.8933 - val_loss: 0.0865 - val_acc: 0.9735
Epoch 2/12
60000/60000 [==============================] - 415s - loss: 0.1246 - acc: 0.9627 - val_loss: 0.0565 - val_acc: 0.9819
Epoch 3/12
60000/60000 [==============================] - 389s - loss: 0.0957 - acc: 0.9717 - val_loss: 0.0493 - val_acc: 0.9842
Epoch 4/12
60000/60000 [==============================] - 385s - loss: 0.0805 - acc: 0.9761 - val_loss: 0.0413 - val_acc: 0.9865
Epoch 5/12
60000/60000 [==============================] - 366s - loss: 0.0708 - acc: 0.9788 - val_loss: 0.0361 - val_acc: 0.9874
Epoch 6/12
60000/60000 [==============================] - 368s - loss: 0.0619 - acc: 0.9824 - val_loss: 0.0355 - val_acc: 0.9875
Epoch 7/12
60000/60000 [==============================] - 363s - loss: 0.0585 - acc: 0.9826 - val_loss: 0.0345 - val_acc: 0.9875
Epoch 8/12
60000/60000 [==============================] - 502s - loss: 0.0527 - acc: 0.9837 - val_loss: 0.0339 - val_acc: 0.9883
Epoch 9/12
60000/60000 [==============================] - 444s - loss: 0.0501 - acc: 0.9852 - val_loss: 0.0309 - val_acc: 0.9891
Epoch 10/12
60000/60000 [==============================] - 358s - loss: 0.0472 - acc: 0.9861 - val_loss: 0.0308 - val_acc: 0.9897
Epoch 11/12
60000/60000 [==============================] - 358s - loss: 0.0441 - acc: 0.9872 - val_loss: 0.0330 - val_acc: 0.9893
Epoch 12/12
60000/60000 [==============================] - 381s - loss: 0.0431 - acc: 0.9871 - val_loss: 0.0304 - val_acc: 0.9901
Test loss: 0.0303807387119
Test accuracy: 0.9901

所以我们跑的是keras_mnist_cnn.py。最后达到99%的预测准确率。首先来解释一下输出：

测试样本格式是28*28像素的1通道，灰度图，数量为60000个样本。

测试集是10000个样本。

一次epoch是一次完整迭代（所有样本都训练过），这里我们用了12次迭代，最后一次迭代就可以收敛到99.01%预测准确率了。

loss是训练集损失值. acc是训练集准确率。val_loss是测试集上的损失值，val_acc是测试集上的准确率。

接下来我们看代码：

from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K

一开始我们导入一些基本库，包括：

mnist数据源
Sequential类，可以封装各种神经网络层，包括Dense全连接层，Dropout层，Cov2D 卷积层，等等。
我们都知道keras支持两个后端TensorFlow和Theano，可以在$HOME/.keras/keras.json中配置。

接下来，我们准备训练集和测试集，以及一些重要参数：

# batch_size 太小会导致训练慢，过拟合等问题，太大会导致欠拟合。所以要适当选择
batch_size = 128
# 0-9手写数字一个有10个类别
num_classes = 10
# 12次完整迭代，差不多够了
epochs = 12
# 输入的图片是28*28像素的灰度图
img_rows, img_cols = 28, 28
# 训练集，测试集收集非常方便
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# keras输入数据有两种格式，一种是通道数放在前面，一种是通道数放在后面，
# 其实就是格式差别而已
if K.image_data_format() == 'channels_first':
x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
input_shape = (1, img_rows, img_cols)
else:
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
# 把数据变成float32更精确
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# 把类别0-9变成2进制，方便训练
y_train = keras.utils.np_utils.to_categorical(y_train, num_classes)
y_test = keras.utils.np_utils.to_categorical(y_test, num_classes)

然后，是令人兴奋而且简洁得令人吃鲸的训练构造cnn和训练过程：

# 牛逼的Sequential类可以让我们灵活地插入不同的神经网络层
model = Sequential()
# 加上一个2D卷积层， 32个输出（也就是卷积通道），激活函数选用relu，
# 卷积核的窗口选用3*3像素窗口
model.add(Conv2D(32,
activation='relu',
input_shape=input_shape,
nb_row=3,
nb_col=3))
# 64个通道的卷积层
model.add(Conv2D(64, activation='relu',
nb_row=3,
nb_col=3))
# 池化层是2*2像素的
model.add(MaxPooling2D(pool_size=(2, 2)))
# 对于池化层的输出，采用0.35概率的Dropout
model.add(Dropout(0.35))
# 展平所有像素，比如[28*28] -> [784]
model.add(Flatten())
# 对所有像素使用全连接层，输出为128，激活函数选用relu
model.add(Dense(128, activation='relu'))
# 对输入采用0.5概率的Dropout
model.add(Dropout(0.5))
# 对刚才Dropout的输出采用softmax激活函数，得到最后结果0-9
model.add(Dense(num_classes, activation='softmax'))
# 模型我们使用交叉熵损失函数，最优化方法选用Adadelta
model.compile(loss=keras.metrics.categorical_crossentropy,
optimizer=keras.optimizers.Adadelta(),
metrics=['accuracy'])
# 令人兴奋的训练过程
model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs,
verbose=1, validation_data=(x_test, y_test))

完整地训练完毕之后，可以计算一下预测准确率：

score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

整个cnn的MNIST手写数字识别就训练完毕了，是不是非常简单，如果觉得注释还不够详尽，请自己试试源码感受下，或者看看我们文章的底部参考文献。

keras_mnist_cnn.py 完整源码：

'''Trains a simple convnet on the MNIST dataset.
Gets to 99.25% test accuracy after 12 epochs
(there is still a lot of margin for parameter tuning).
16 seconds per epoch on a GRID K520 GPU.
'''
from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
batch_size = 128
num_classes = 10
epochs = 12
# input image dimensions
img_rows, img_cols = 28, 28
# the data, shuffled and split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()
if K.image_data_format() == 'channels_first':
x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
input_shape = (1, img_rows, img_cols)
else:
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
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.np_utils.to_categorical(y_train, num_classes)
y_test = keras.utils.np_utils.to_categorical(y_test, num_classes)
model = Sequential()
model.add(Conv2D(32,
activation='relu',
input_shape=input_shape,
nb_row=3,
nb_col=3))
model.add(Conv2D(64, activation='relu',
nb_row=3,
nb_col=3))
# model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.35))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss=keras.metrics.categorical_crossentropy,
optimizer=keras.optimizers.Adadelta(),
metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs,
verbose=1, validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

参考文献：

码农公寓

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