Tensorflow学习-MNIST数据集CNN

Tensorflow学习-MNIST数据集CNN

CNN

①数据集导入，keras自带的下载或者从某盘提取点击获取数据集，提取码：45yf

#加载MNIST数据集
from keras.datasets import mnist
(x_train,y_train),(x_test,y_test)=mnist.load_data('E:/TensorFlow_mnist/MNIST_data/mnist.npz')

print(x_train.shape,type(x_train))  #60000张28*28的图片
print(y_train.shape,type(y_train))  #60000个标签

②图像和数据类型的转化
这里使用通道的方式进行数据类型转化，channels_first(batch,channels,height,width)

#数据处理：规范化
from keras import backend as K

img_rows,img_cols=28,28;

#channels_first(batch,channels,height,width)

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.shap[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)

print(x_train.shape,type(x_train))
print(x_test.shape,type(x_test))

#将数据类型转换为float32
X_train = x_train.astype('float32')
X_test = x_test.astype('float32')
#数据归一化
X_train /=255
X_test /=255

print(X_train.shape[0],'train samples')
print(X_test.shape[0],'test samples')

③统计训练数据集中各标签的数量并可视化展示

# 统计训练数据中的各标签数量
import numpy as np
import matplotlib.pyplot as plt

label, count = np.unique(y_train, return_counts=True)
print(label, count)
# lable的可视化输出
fig = plt.figure()
plt.bar(label, count, width=0.7, align='center')
plt.title("Label Distribution")
plt.xlabel("Label")
plt.ylabel("Count")
plt.xticks(label)
plt.ylim(0, 7500)
for a, b in zip(label, count):
    plt.text(a, b, '%d' % b, ha='center', va='bottom', fontsize=10)

plt.show()

④标签编码
one-hot编码的实现

#one-hot编码
from keras.utils import  np_utils

n_classes=10
print("Shape before one-hot encoding: ",y_train.shape)
Y_train=np_utils.to_categorical(y_train,n_classes)
print("Shape after one-hot encoding: ",Y_train.shape)
Y_test = np_utils.to_categorical(y_test,n_classes)

print(y_train[1])
print(Y_train[1])

结果显示:

0  #编码前的标签
[1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] #编码后0的标签

⑤使用Kears sequential model定义MNIST CNN网络

model=Sequential()
##Feature Extraction 特征提取
# 第1层卷积，32个3x3的卷积层，**函数使用relu
model.add(Conv2D(filters=32,kernel_size=(3,3),activation='relu',input_shape=input_shape))
# 第2层卷积，64个3x3的卷积层，**函数使用relu
model.add(Conv2D(filters=64,kernel_size=(3,3),activation='relu'))
#最大池化层，池化窗口2x2
model.add(MaxPooling2D(pool_size=(2,2)))
#Dropout 25% 的输入神经元
model.add(Dropout(0.25))

#Pooled featuremap 摊平后输入全连接网络
model.add(Flatten())

##Classification
#全连接层
model.add(Dense(128,activation='relu'))

#Dropout 50% 的输入神经元
model.add(Dropout(0.5))

#使用softmax**函数做多分类，输出各数字的概率
model.add(Dense(n_classes,activation='softmax'))
#查看模型结构
model.summary()

查看MNIST CNN网络结构：

每个网络层输出的形状

for layer in model.layers:
    print(layer.get_output_at(0).get_shape().as_list())

结果：

⑥编译模型

#编译模型
model.compile(loss='categorical_crossentropy',metrics=['accuracy'],optimizer='adam')

#训练模型，并将指标保存到history中
history = model.fit(X_train,
                    Y_train,
                    batch_size=128,
                    epochs=5,
                    verbose=2,
                    validation_data=(X_test,Y_test))

训练结果：

⑥保存模型并加载

#保存模型
import os
import tensorflow.gfile as gfile

save_dir='./mnist/model/'

if gfile.Exists(save_dir):
    gfile.DeleteRecursively(save_dir)
gfile.MakeDirs(save_dir)

model_name = 'keras_mnist.h5'
model_path = os.path.join(save_dir,model_name)
model.save(model_path)
print('Saved trained model at %s' % model_path)

#加载模型
from keras.models import load_model
mnist_model = load_model(model_path)

#统计模型在测试集上的分类结果
loss_and_metrics = mnist_model.evaluate(X_test,Y_test,verbose=2)

print("Test Loss:{}".format(loss_and_metrics[0]))
print("Test Accuracy:{}%".format(loss_and_metrics[1]*100))

predicted_classes=mnist_model.predict_classes(X_test)

correct_indices=np.nonzero(predicted_classes==y_test)[0]
incorrect_indices = np.nonzero(predicted_classes!=y_test)[0]
print("Classified correctly count:{}".format(len(correct_indices)))
print("Classified incorrectly count:{}".format(len(incorrect_indices)))

测试结果：