Keras搭建经典CNN LeNet5网络进行手写体识别
1 简介
LeNet-5 模型是Yann LeCun 教授于 1998 年在论文 Gradient-based learning applied to document recognition中提出的，它是第一个成功应用于数字识别问题的卷积神经网络。在 MNIST 数据集上， LeNet-5 模型可以达到大约 **99.2%**的正确率。
LeNet-5的网络结构如下：
在minst手写体数据集基础上构建LeNet-5网络，其结构如下：
其中，T表示tanh激活函数，S代表softmax。
2 数据处理和展示
原始mnist数据格式为**[6000,28,28]，但是卷积层所需要的格式为[None,28,28,1]，None 代表任何个[28,28,1]数据，其中28为宽高**，1代表颜色通道（一般颜色通道为3，rgb，这里采用灰色).
def load_data(self):
print('loaddata')
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train.reshape(-1, 28, 28, 1) # normalize
X_test = X_test.reshape(-1, 28, 28, 1) # normalize
X_train = X_train / 255
X_test = X_test / 255
y_train = to_categorical(y_train, num_classes=10)
y_test = to_categorical(y_test, num_classes=10)
return (X_train, y_train), (X_test, y_test)
我们选取了数据中9张图进行展示：
3 模型搭建
其中LeNet5中的5代表5层，我已经在下面用彩色数字标注出来了。
4 代码实现
import tensorflow as tf
from tensorflow.keras.datasets import mnist
from tensorflow.keras.layers import Dense, Conv2D, AveragePooling2D, Flatten
from tensorflow.keras.models import Sequential
from tensorflow.keras.utils import to_categorical
class LeNet5:
def __init__(self):
self.model = Sequential()
def load_data(self):
print('loaddata')
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train.reshape(-1, 28, 28, 1) # normalize
X_test = X_test.reshape(-1, 28, 28, 1) # normalize
X_train = X_train / 255
X_test = X_test / 255
y_train = to_categorical(y_train, num_classes=10)
y_test = to_categorical(y_test, num_classes=10)
return (X_train, y_train), (X_test, y_test)
def train(self, x, y):
print("build")
self.model.add(Conv2D(input_shape=(28, 28, 1),
filters=6,
kernel_size=(5, 5),
strides=1,
activation=tf.keras.activations.tanh))
self.model.add(AveragePooling2D(pool_size=(2, 2)))
self.model.add(Conv2D(filters=6,
kernel_size=(5, 5),
strides=1,
activation=tf.keras.activations.tanh))
self.model.add(AveragePooling2D(pool_size=(2, 2)))
self.model.add(Flatten())
self.model.add(Dense(120, activation=tf.keras.activations.tanh))
self.model.add(Dense(84, activation=tf.keras.activations.tanh))
self.model.add(Dense(10, activation=tf.keras.activations.softmax))
# Compile model
self.model.compile(loss=tf.keras.losses.CategoricalCrossentropy(),
optimizer="adam",
metrics=['accuracy'])
print("train")
self.model.fit(x=x, y=y, batch_size=1, epochs=100, verbose=1)
return self.model
def predict(self, x):
print("predict")
for result in self.model.predict(x):
print(tf.argmax(result))
if __name__ == "__main__":
lenet = LeNet5()
(X_train, y_train), (X_test, y_test) = lenet.load_data()
lenet.train(X_train, y_train)
lenet.predict(X_test)
5 评估
模型结构：
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 24, 24, 6) 156
_________________________________________________________________
average_pooling2d (AveragePo (None, 12, 12, 6) 0
_________________________________________________________________
conv2d_1 (Conv2D) (None, 8, 8, 6) 906
_________________________________________________________________
average_pooling2d_1 (Average (None, 4, 4, 6) 0
_________________________________________________________________
flatten (Flatten) (None, 96) 0
_________________________________________________________________
dense (Dense) (None, 120) 11640
_________________________________________________________________
dense_1 (Dense) (None, 84) 10164
_________________________________________________________________
dense_2 (Dense) (None, 10) 850
=================================================================
Total params: 23,716
Trainable params: 23,716
Non-trainable params: 0
_________________________________________________________________
为了快速得到结果，设置epoch=1得到训练结果：
loss: 0.1207 - accuracy: 0.9625