01-mnist数字识别

# mnist数字识别
# 设置GPU
import tensorflow as tf
# gpus = tf.config.list_physical_devices('GPU')

# if gpus:
#     gpu0 = gpus[0]#如果有多个GPU，仅使用第0个GPU
#     tf.config.experimental.set_memory_growth(gpu0,True)#设置GPU显存用量按需使用
#     tf.config.set_visible_devices([gpu0],'GPU')

#导入数据
import tensorflow as tf
from tensorflow.keras import datasets,layers,models
import matplotlib.pyplot as plt
(train_images,train_labels),\
(test_images,test_labels)=datasets.mnist.load_data()

#归一化
#将像素的值标准化至0-1的区间
train_images,test_images = train_images/255.0,test_images/255.0
train_images.shape,test_images.shape,train_labels.shape,test_labels.shape

((60000, 28, 28), (10000, 28, 28), (60000,), (10000,))

#可视化
plt.figure(figsize=(10,10))
for i in range(20):
    plt.subplot(5,10,i+1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    plt.imshow(train_images[i],cmap=plt.cm.binary)
    plt.xlabel(train_labels[i])
plt.show()

#调整图片格式
train_images = train_images.reshape((60000,28,28,1))
test_images = test_images.reshape((10000,28,28,1))

train_images.shape,test_images.shape,train_labels.shape,test_labels.shape

((60000, 28, 28, 1), (10000, 28, 28, 1), (60000,), (10000,))

#构建CNN网络模型
model = models.Sequential([
    layers.Conv2D(32,(3,3),activation='relu',input_shape=(28,28,1)),
    layers.MaxPooling2D((2,2)),
    layers.Conv2D(64,(3,3),activation='relu'),
    layers.MaxPool2D((2,2)),
    
    layers.Flatten(),
    layers.Dense(64,activation='relu'),
    layers.Dense(10)
])
model.summary()

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 26, 26, 32)        320       
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 13, 13, 32)        0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 11, 11, 64)        18496     
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 5, 5, 64)          0         
_________________________________________________________________
flatten (Flatten)            (None, 1600)              0         
_________________________________________________________________
dense (Dense)                (None, 64)                102464    
_________________________________________________________________
dense_1 (Dense)              (None, 10)                650       
=================================================================
Total params: 121,930
Trainable params: 121,930
Non-trainable params: 0
_________________________________________________________________


2022-01-07 23:45:52.268745: I tensorflow/core/platform/cpu_feature_guard.cc:145] This TensorFlow binary is optimized with Intel(R) MKL-DNN to use the following CPU instructions in performance critical operations:  SSE4.1 SSE4.2
To enable them in non-MKL-DNN operations, rebuild TensorFlow with the appropriate compiler flags.
2022-01-07 23:45:52.275905: I tensorflow/core/common_runtime/process_util.cc:115] Creating new thread pool with default inter op setting: 8. Tune using inter_op_parallelism_threads for best performance.

#编译模型
model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy']
)

#训练模型
history = model.fit(train_images,
                    train_labels,
                    epochs=10,
                    validation_data=(test_images,test_labels)
                   )

Train on 60000 samples, validate on 10000 samples
Epoch 1/10
60000/60000 [==============================] - 138s 2ms/sample - loss: 0.1428 - accuracy: 0.9568 - val_loss: 0.0579 - val_accuracy: 0.9809
Epoch 2/10
60000/60000 [==============================] - 134s 2ms/sample - loss: 0.0456 - accuracy: 0.9861 - val_loss: 0.0388 - val_accuracy: 0.9877
Epoch 3/10
60000/60000 [==============================] - 132s 2ms/sample - loss: 0.0328 - accuracy: 0.9897 - val_loss: 0.0304 - val_accuracy: 0.9902
Epoch 4/10
60000/60000 [==============================] - 131s 2ms/sample - loss: 0.0232 - accuracy: 0.9922 - val_loss: 0.0301 - val_accuracy: 0.9897
Epoch 5/10
60000/60000 [==============================] - 134s 2ms/sample - loss: 0.0176 - accuracy: 0.9943 - val_loss: 0.0325 - val_accuracy: 0.9894
Epoch 6/10
60000/60000 [==============================] - 134s 2ms/sample - loss: 0.0135 - accuracy: 0.9956 - val_loss: 0.0307 - val_accuracy: 0.9907
Epoch 7/10
60000/60000 [==============================] - 134s 2ms/sample - loss: 0.0119 - accuracy: 0.9961 - val_loss: 0.0319 - val_accuracy: 0.9905
Epoch 8/10
60000/60000 [==============================] - 134s 2ms/sample - loss: 0.0086 - accuracy: 0.9973 - val_loss: 0.0327 - val_accuracy: 0.9905
Epoch 9/10
60000/60000 [==============================] - 132s 2ms/sample - loss: 0.0080 - accuracy: 0.9974 - val_loss: 0.0296 - val_accuracy: 0.9918
Epoch 10/10
60000/60000 [==============================] - 130s 2ms/sample - loss: 0.0065 - accuracy: 0.9979 - val_loss: 0.0314 - val_accuracy: 0.9911

#预测
plt.imshow(test_images[1])

#输出测试集中第一张图片的预测结果
pre = model.predict(test_images)
pre[1]

array([-7.576601  ,  0.24191436, 22.48119   , -7.913903  , -6.1873503 ,
       -9.024768  , -0.274709  , -1.0671589 ,  3.3178928 , -9.065157  ],
      dtype=float32)

网络结构

各层的作用

• 输入层：用于将数据输入到训练网络
• 卷积层：使用卷积核提取图片特征
• 池化层：进行下采样，用更高层的抽象表示图像特征
• Flatten层：将多维的输入一维化，常用在卷积层到全连接层的过渡
• 全连接层：起到“特征提取器”的作用
• 输出层：输出结果