2020-09-28

一、莫烦Keras cnn网络代码详解

本人觉得弄清楚cnn跟rnn两个代码模型，对keras就有了基本的理解，可以入门了。

代码如下：

"""
To know more or get code samples, please visit my website:
https://morvanzhou.github.io/tutorials/
Or search: 莫烦Python
Thank you for supporting!
"""

# please note, all tutorial code are running under python3.5.
# If you use the version like python2.7, please modify the code accordingly

# 6 - CNN example

# to try tensorflow, un-comment following two lines
# import os
# os.environ['KERAS_BACKEND']='tensorflow'

import numpy as np
np.random.seed(1337)  # for reproducibility
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense, Activation, Convolution2D, MaxPooling2D, Flatten
from keras.optimizers import Adam

# download the mnist to the path '~/.keras/datasets/' if it is the first time to be called
# training X shape (60000, 28x28), Y shape (60000, ). test X shape (10000, 28x28), Y shape (10000, )
(X_train, y_train), (X_test, y_test) = mnist.load_data()

# data pre-processing
X_train = X_train.reshape(-1, 1,28, 28)/255.
X_test = X_test.reshape(-1, 1,28, 28)/255.
y_train = np_utils.to_categorical(y_train, num_classes=10)#np_utilss.to_categorical将整型的类别标签转为onehot编码。y为int数组，num_classes为标签类别总数
y_test = np_utils.to_categorical(y_test, num_classes=10)

# Another way to build your CNN
model = Sequential()

# Conv layer 1 output shape (32, 28, 28)    conv是卷积的意思，卷积不压缩图片了，压缩的工作交给了池化
model.add(Convolution2D(
    batch_input_shape=(None, 1, 28, 28),#输入的数量不限，高度为1即黑白照片，长28，宽28
    filters=32,#32个卷积核，每个卷积核提取一个特征
    kernel_size=5,#卷积核的大小为5x5
    strides=1,#步长为1
    padding='same',     # Padding method Padding为same即不改变数据的长度和宽度
    data_format='channels_first',#data_format默认返回图片的维度，这里默认返回一维
))
model.add(Activation('relu'))

# Pooling layer 1 (max pooling) output shape (32, 14, 14)  pooling是池化
model.add(MaxPooling2D(
    pool_size=2,
    strides=2,
    padding='same',    # Padding method
    data_format='channels_first',
))

# Conv layer 2 output shape (64, 14, 14)
model.add(Convolution2D(64, 5, strides=1, padding='same', data_format='channels_first'))
model.add(Activation('relu'))

# Pooling layer 2 (max pooling) output shape (64, 7, 7)
model.add(MaxPooling2D(2, 2, 'same', data_format='channels_first'))

# Fully connected layer 1 input shape (64 * 7 * 7) = (3136), output shape (1024) Fully connected是全连接层
model.add(Flatten())
model.add(Dense(1024))
model.add(Activation('relu'))

# Fully connected layer 2 to shape (10) for 10 classes
model.add(Dense(10))
model.add(Activation('softmax'))

# Another way to define your optimizer
adam = Adam(lr=1e-4)

# We add metrics to get more results you want to see
model.compile(optimizer=adam,
              loss='categorical_crossentropy',
              metrics=['accuracy'])

print('Training ------------')
# Another way to train the model
model.fit(X_train, y_train, epochs=1, batch_size=64,)
#Epoch（时期）：当一个完整的数据集通过了神经网络一次并且返回了一次，这个过程称为一次>epoch。（也就是说，所有训练样本在神经网络中都 进行了一次正向传播 和一次反向传播 ）
#再通俗一点，一个Epoch就是将所有训练样本训练一次的过程。
#然而，当一个Epoch的样本（也就是所有的训练样本）数量可能太过庞大（对于计算机而言），就需要把它分成多个小块，也就是就是分成多个Batch 来进行训练。**

#Batch（批 / 一批样本）：
#将整个训练样本分成若干个Batch。

#Batch_Size（批大小）：
#每批样本的大小。

#Iteration（一次迭代）：
#训练一个Batch就是一次Iteration（这个概念跟程序语言中的迭代器相似）。
print('\nTesting ------------')
# Evaluate the model with the metrics we defined earlier
loss, accuracy = model.evaluate(X_test, y_test)

print('\ntest loss: ', loss)
print('\ntest accuracy: ', accuracy)

总结

这就是cnn代码模型详解，下期咋们聊聊rnn。