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:

 

我们先来看跑完的结果(在Google Colab上运行

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 [==============================] - 12s 193us/step - loss: 0.2672 - acc: 0.9166 - val_loss: 0.0648 - val_acc: 0.9792
Epoch 2/12
60000/60000 [==============================] - 9s 146us/step - loss: 0.0892 - acc: 0.9731 - val_loss: 0.0433 - val_acc: 0.9866
Epoch 3/12
60000/60000 [==============================] - 9s 146us/step - loss: 0.0666 - acc: 0.9796 - val_loss: 0.0353 - val_acc: 0.9874
Epoch 4/12
60000/60000 [==============================] - 9s 146us/step - loss: 0.0578 - acc: 0.9829 - val_loss: 0.0327 - val_acc: 0.9887
Epoch 5/12
60000/60000 [==============================] - 9s 146us/step - loss: 0.0483 - acc: 0.9856 - val_loss: 0.0295 - val_acc: 0.9901
Epoch 6/12
60000/60000 [==============================] - 9s 146us/step - loss: 0.0433 - acc: 0.9869 - val_loss: 0.0313 - val_acc: 0.9895
Epoch 7/12
60000/60000 [==============================] - 9s 146us/step - loss: 0.0379 - acc: 0.9879 - val_loss: 0.0267 - val_acc: 0.9913
Epoch 8/12
60000/60000 [==============================] - 9s 147us/step - loss: 0.0353 - acc: 0.9891 - val_loss: 0.0263 - val_acc: 0.9913
Epoch 9/12
60000/60000 [==============================] - 9s 146us/step - loss: 0.0327 - acc: 0.9904 - val_loss: 0.0275 - val_acc: 0.9905
Epoch 10/12
60000/60000 [==============================] - 9s 146us/step - loss: 0.0323 - acc: 0.9898 - val_loss: 0.0260 - val_acc: 0.9914
Epoch 11/12
60000/60000 [==============================] - 9s 147us/step - loss: 0.0286 - acc: 0.9913 - val_loss: 0.0283 - val_acc: 0.9909
Epoch 12/12
60000/60000 [==============================] - 9s 147us/step - loss: 0.0267 - acc: 0.9922 - val_loss: 0.0268 - val_acc: 0.9906
Test loss: 0.026836299882206368
Test accuracy: 0.9906

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99.06%预测准确率了。

 

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:

  • minst数据集
  • 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
  • 我们都直到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变成独热码
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])

 

 

参考链接:
1、nooverfit.com/wp/keras-手把手入门1-手写数字识别-深度学习实战/

2、https://blog.csdn.net/yzh201612/article/details/69400002

转载于:https://www.cnblogs.com/lfri/p/10485597.html