Building a simple template of neural network with keras (3) -- CNN convolutional neural network (handwritten numeral picture recognition)

# -*- coding: utf-8 -*-
import numpy as np
np.random.seed(1337) #for reproducibility Reproducibility 
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential# By layer 
from keras.layers import Dense, Activation,Convolution2D, MaxPooling2D, Flatten
import matplotlib.pyplot as plt
from keras.optimizers import RMSprop
from keras.optimizers import Adam
#dowmload the mnisst the path '~/.keras/datasets/' if it is the first time to be called
#x shape (60000 28*28),y shape(10000,)
(x_train,y_train),(x_test,y_test) = mnist.load_data()#0-9 Image data set 
#data pre-processing
x_train = x_train.reshape(-1,1,28,28)#-1 There is no limit to the number of Representatives ,1 For height , The height of black-and-white photos is 1
x_test = x_test.reshape(-1,1,28,28)
y_train = np_utils.to_categorical(y_train, num_classes=10) # Change the label to 10 Length , if 1, It's in 1 Place for 1, The rest are marked 0
y_test = np_utils.to_categorical(y_test,num_classes=10)
#Another way to build CNN
model = Sequential()
#Conv layer 1 output shape (32,28,28)
model.add(Convolution2D(
nb_filter =32,# The filter is installed 32 individual , Each filter will scan the picture , You'll get another whole picture , So what I got later was 32 layer 
nb_row=5,
nb_col=5,
border_mode='same', #padding method
input_shape=(1, #channels The channel number 
28,28), #height & width Length and width 
))
model.add(Activation('relu'))
#Pooling layer 1 (max pooling) output shape (32,14,14)
model.add(MaxPooling2D(
pool_size=(2,2), #2*2
strides=(2,2), # Jump both long and wide, and then pool once 
border_mode='same', #paddingmethod
))
#Conv layers 2 output shape (64,14,14)
model.add(Convolution2D(64,5,5,border_mode='same'))
model.add(Activation('relu'))
#Pooling layers 2 (max pooling) output shape (64,7,7)
model.add(MaxPooling2D(pool_size=(2,2), border_mode='same'))
#Fully connected layer 1 input shape (64*7*7) = (3136)
#Flatten Wipe three dimensions into one dimension , Full connection 
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)) # Output 10 A unit of 
model.add(Activation('softmax')) #softmax Used to classify 
#Another way to define optimizer
adam = Adam(lr=1e-4)
# We add metrics to get more results you want to see
model.compile( # compile 
optimizer = adam,
loss = 'categorical_crossentropy',
metrics=['accuracy'], # Calculate at the same time when updating accuracy
)
print("Training~~~~~~~~")
#Another way to train the model
model.fit(x_train,y_train, epochs=1, batch_size=32) # Training 2 Large numbers , Each batch 32 individual 
print("\nTesting~~~~~~~~~~")
#Evalute the model with the metrics we define earlier
loss,accuracy = model.evaluate(x_test,y_test)
print('\ntest loss:',loss)
print('\ntest accuracy:', accuracy)

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