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LeNet.py
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#模型定义:LeNet5
#yang, 2020/2/6
import tensorflow as tf
import numpy as np
import datetime
import matplotlib.pyplot as plt
#------------------------------【MNIST数据集】---------------------------------
#内置数据集查看
#print(dir(tf.keras.datasets))
#1.加载网络数据
#mnist = tf.keras.datasets.mnist
#(x_train, y_train), (x_test, y_test) = mnist.load_data()
#1.加载本地数据
f = np.load("mnist.npz")
x_train, y_train = f['x_train'],f['y_train']
x_test, y_test = f['x_test'],f['y_test']
f.close()
print(x_train.shape, y_train.shape)
print(x_test.shape, y_test.shape)
#2.MNIST数据集可视化
#image_index = 123
#print(y_train[image_index]) #查看随机一张图片的label
#plt.imshow(x_train[image_index], cmap='Greys') #图片显示
#plt.show()
#3.数据集格式转换
def DataFormat(x):
x = np.pad(x, ((0, 0), (2, 2), (2, 2)), 'constant', constant_values=0) #将图片从28*28扩展成32*32
x = x.astype('float32') #数据类型转换
x /= 255 # 数据正则化
x = x.reshape(x.shape[0], 32, 32, 1) # 数据维度转换
return x
# x_train = np.pad(x_train, ((0, 0), (2, 2), (2, 2)), 'constant', constant_values=0) #将图片从28*28扩展成32*32
# x_train = x_train.astype('float32') #数据类型转换
# x_train /= 255 #数据正则化
# x_train = x_train.reshape(x_train.shape[0], 32, 32, 1) #数据维度转换
x_train = DataFormat(x_train)
print(x_train.shape)
#------------------------------【LeNet模型】---------------------------------
#方法一：Model类方式
class LeNet(tf.keras.Model):
def __init__(self):
super().__init__()
#模型
self.conv_layer_1 = tf.keras.layers.Conv2D(
filters=6,
kernel_size=(5, 5),
padding='valid',
activation=tf.nn.relu)
self.pool_layer_1 = tf.keras.layers.MaxPool2D(
pool_size=(2, 2),
padding='same')
self.conv_layer_2 = tf.keras.layers.Conv2D(
filters=16,
kernel_size=(5, 5),
padding='valid',
activation=tf.nn.relu)
self.pool_layer_2 = tf.keras.layers.MaxPool2D(
pool_size=(2, 2),
padding='same')
self.flatten = tf.keras.layers.Flatten()
self.fc_layer_1 = tf.keras.layers.Dense(
units=120,
activation=tf.nn.relu)
self.fc_layer_2 = tf.keras.layers.Dense(
units=84,
activation=tf.nn.relu)
self.output_layer = tf.keras.layers.Dense(
units=10,
activation=tf.nn.relu)
def call(self, inputs):
x = self.conv_layer_1(inputs)
x = self.pool_layer_1(x)
x = self.conv_layer_2(x)
x = self.pool_layer_2(x)
x = self.flatten(x)
x = self.fc_layer_1(x)
x = self.fc_layer_2(x)
output = self.output_layer(x)
return output
#model = LeNet()
#方法二：Sequential类方式
model = tf.keras.models.Sequential([
tf.keras.layers.Conv2D(filters=6, kernel_size=(5, 5), padding='valid', activation=tf.nn.relu, input_shape=(32, 32, 1)),
tf.keras.layers.MaxPool2D(pool_size=(2, 2), padding='same'),
tf.keras.layers.Conv2D(filters=6, kernel_size=(5, 5), padding='valid', activation=tf.nn.relu),
tf.keras.layers.MaxPool2D(pool_size=(2, 2), padding='same'),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(units=120, activation=tf.nn.relu),
tf.keras.layers.Dense(units=84, activation=tf.nn.relu),
tf.keras.layers.Dense(units=10, activation=tf.nn.relu)
])
#model.summary()
#------------------------------【训练】---------------------------------
#超参数设置
num_epochs = 1
batch_size = 64
learning_rate = 0.01
#优化器
adam_optimizer = tf.keras.optimizers.Adam(learning_rate)
#编译
model.compile(optimizer=adam_optimizer,
loss=tf.keras.losses.sparse_categorical_crossentropy,
metrics=['accuracy'])
#训练
start_time = datetime.datetime.now()
model.fit(x=x_train,
y=y_train,
batch_size=batch_size,
epochs=num_epochs)
endtime = datetime.datetime.now()
time_cost = endtime - start_time
print('time_cost = ', time_cost)
#保存/加载模型
model.save('lenet.h5')
#model = tf.keras.models.load_model('lenet.h5')
#------------------------------【评估】---------------------------------
x_test = DataFormat(x_test)
print(x_test.shape)
print(model.evaluate(x_test, y_test))
#------------------------------【预测】---------------------------------
image_index = 2333
print(x_test[image_index].shape)
plt.imshow(x_test[image_index].reshape(32, 32), cmap='Greys')
plt.show()
pred = model.predict(x_test[image_index].reshape(1, 32, 32, 1))
print(pred.argmax())