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之前学习了keras和mnist，知道了如何用keras编写简单的感知器。感知器的优点是比较简单，但是缺点也很明显。训练出来的识别正确率不是很高，所以自己就想试试卷积网络。网上的卷积网络算法和代码也比较多，正好可以学习一下。
1、keras支持多种卷积核
目前keras中支持多种卷积核，有Conv1D、Conv2D、Conv3D等等。
2、cnn是图像分类的标配
对于特征提取、图像分类的场景来说，cnn基本上是标配。
3、归一化
图像输入给卷积核之前一般先归一化一下，即x_train = x_train / 255
4、池化层
卷积层一般和池化层配合使用。一个卷积神经网络可能只有一组卷积层、池化层，也可能有很多组卷积层、池化层。
5、模型大小
一般而言，卷积神经网络比感知器的模型稍大一点。
6、示例代码
#!/usr/bin/python
# -*- coding: utf-8 -*-
#
# 首次使用卷积神经网络来进行处理 20191215
#
# 参考链接：https://blog.csdn.net/weixin_41055137/article/details/81071226
# 理论上卷积神经网络可以训练几千到上万次
#
import numpy
#from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import Flatten
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.utils import np_utils
from keras import backend as k
#高版本的keras
#k.set_image_dim_ordering('th')
#低版本的keras
k.image_data_format() == 'channels_first'
seed = 7
numpy.random.seed(seed)
# load data
#(x_train, y_train), (x_test, y_test) = mnist.load_data()
#
# 原本数据从利用keras.datasets获取的
# 但是keras是从亚马逊下载，这里直接从第三方下载好，然后用numpy加载即可
#
x_train = numpy.load("./mnist/x_train.npy")
y_train = numpy.load("./mnist/y_train.npy")
x_test = numpy.load("./mnist/x_test.npy")
y_test = numpy.load("./mnist/y_test.npy")
# reshape to be [samples][pixels][width][height]
#x_train = x_train.reshape(x_train.shape[0], 1, 28, 28).astype('float32')
x_train = x_train.reshape(x_train.shape[0], 28, 28, 1).astype('float32')
#x_test = x_test.reshape(x_test.shape[0], 1, 28, 28).astype('float32')
x_test = x_test.reshape(x_test.shape[0], 28, 28, 1).astype('float32')
x_train = x_train / 255
x_test = x_test / 255
# one hot encode outputs
y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test)
num_classes = y_test.shape[1]
def baseline_model():
# create model
model = Sequential()
#model.add(Conv2D(32, (5, 5), input_shape=(1, 28, 28), activation='relu'))
#width-height=channel
model.add(Conv2D(32, (5, 5), input_shape=(28, 28, 1), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))
# Compile model
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
# build the model
model = baseline_model()
# Fit the model
model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=10, batch_size=200, verbose=2)
# Final evaluation of the model
scores = model.evaluate(x_test, y_test, verbose=0)
model.save('keras_cnn.h5')
del model
print("Baseline Error: %.2f%%" % (100-scores[1]*100))
7、输出结果
python3 keras_cnn.py
Using TensorFlow backend.
2019-12-16 21:19:30.357686: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
Train on 60000 samples, validate on 10000 samples
Epoch 1/10
- 27s - loss: 0.2235 - accuracy: 0.9364 - val_loss: 0.0746 - val_accuracy: 0.9772
Epoch 2/10
- 28s - loss: 0.0711 - accuracy: 0.9781 - val_loss: 0.0468 - val_accuracy: 0.9845
Epoch 3/10
- 27s - loss: 0.0505 - accuracy: 0.9844 - val_loss: 0.0425 - val_accuracy: 0.9859
Epoch 4/10
- 28s - loss: 0.0400 - accuracy: 0.9875 - val_loss: 0.0384 - val_accuracy: 0.9874
Epoch 5/10
- 29s - loss: 0.0319 - accuracy: 0.9901 - val_loss: 0.0343 - val_accuracy: 0.9888
Epoch 6/10
- 29s - loss: 0.0260 - accuracy: 0.9919 - val_loss: 0.0328 - val_accuracy: 0.9904
Epoch 7/10
- 28s - loss: 0.0221 - accuracy: 0.9929 - val_loss: 0.0333 - val_accuracy: 0.9891
Epoch 8/10
- 28s - loss: 0.0189 - accuracy: 0.9941 - val_loss: 0.0336 - val_accuracy: 0.9888
Epoch 9/10
- 28s - loss: 0.0163 - accuracy: 0.9947 - val_loss: 0.0306 - val_accuracy: 0.9897
Epoch 10/10
- 26s - loss: 0.0129 - accuracy: 0.9960 - val_loss: 0.0301 - val_accuracy: 0.9910
Baseline Error: 0.90%