标题`#LeNet-5 完成 cifar2
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.keras import layers, losses, Model
1)定义一个有参有返回值的函数用于加载图片
def load_img(file_path):
img = tf.io.read_file(file_path)
img = tf.image.decode_jpeg(img)
img = tf.image.resize(img, [32, 32]) / 255.
label = tf.constant(1,tf.int32) if tf.strings.regex_full_match(file_path, ‘.airplane.’) else tf.constant(0,tf.int32)
return img, label
2)合理定义相关参数
batch_num = 100
epochs = 15
3)使用通道和自定义函数加载cifar2数据集
train_data = tf.data.Dataset.list_files(‘cifar2/train//.jpg’).map(load_img,tf.data.experimental.AUTOTUNE).shuffle(buffer_size=1000).batch(100).prefetch(tf.data.experimental.AUTOTUNE)
test_data = tf.data.Dataset.list_files(‘cifar2/test//.jpg’).map(load_img,tf.data.experimental.AUTOTUNE).shuffle(buffer_size=1000).batch(100).prefetch(tf.data.experimental.AUTOTUNE)
②模型搭建
class LeNet(Model):
def init(self):
super(LeNet, self).init()
self.c1 = layers.Conv2D(6, 5)
self.s2 = layers.MaxPooling2D()
self.c3 = layers.Conv2D(16, 5)
self.s4 = layers.MaxPooling2D()
self.f5 = layers.Flatten()
self.d6 = layers.Dense(120, activation=‘relu’)
self.d7 = layers.Dense(84, activation=‘relu’)
self.d8 = layers.Dense(2, activation=‘softmax’)
# 3)进行正向传播
@tf.function
def call(self, inputs):
out = self.c1(inputs)
out = self.s2(out)
out = self.c3(out)
out = self.s4(out)
out = self.f5(out)
out = self.d6(out)
out = self.d7(out)
out = self.d8(out)
return out
③模型预测
model = LeNet()
1)查看每层的参数数量
model.build((None, 32, 32, 3))
model.summary()
2)进行训练，选择Adam优化器，合理选择损失函数和迭代次数
model.compile(‘adam’, losses.sparse_categorical_crossentropy, ‘accuracy’)
history = model.fit(train_data, epochs=epochs, validation_data=test_data)
3)绘制训练集与测试集准确率对比图
plt.plot(history.history[‘val_accuracy’], label=‘test_accuracy’)
plt.plot(history.history[‘accuracy’], label=‘train_accuracy’)
plt.legend()
plt.show()
‘’’
Model: “le_net”
Layer (type) Output Shape Param #
conv2d (Conv2D) multiple 456
max_pooling2d (MaxPooling2D) multiple 0
conv2d_1 (Conv2D) multiple 2416
max_pooling2d_1 (MaxPooling2 multiple 0
flatten (Flatten) multiple 0
dense (Dense) multiple 48120
dense_1 (Dense) multiple 10164
dense_2 (Dense) multiple 170
Total params: 61,326
Trainable params: 61,326
Non-trainable params: 0
Epoch 1/15
100/100 [] - 4s 40ms/step - loss: 0.4533 - accuracy: 0.7902 - val_loss: 0.3372 - val_accuracy: 0.8515
Epoch 2/15
100/100 [] - 4s 40ms/step - loss: 0.3327 - accuracy: 0.8545 - val_loss: 0.2771 - val_accuracy: 0.8810
Epoch 3/15
100/100 [] - 4s 43ms/step - loss: 0.2565 - accuracy: 0.8940 - val_loss: 0.2434 - val_accuracy: 0.9025
Epoch 4/15
100/100 [] - 5s 45ms/step - loss: 0.2159 - accuracy: 0.9110 - val_loss: 0.2283 - val_accuracy: 0.9115
Epoch 5/15
100/100 [] - 5s 46ms/step - loss: 0.1786 - accuracy: 0.9289 - val_loss: 0.2228 - val_accuracy: 0.9030
Epoch 6/15
100/100 [] - 4s 45ms/step - loss: 0.1574 - accuracy: 0.9384 - val_loss: 0.2079 - val_accuracy: 0.9175
Epoch 7/15
100/100 [] - 4s 45ms/step - loss: 0.1290 - accuracy: 0.9529 - val_loss: 0.2092 - val_accuracy: 0.9205
Epoch 8/15
100/100 [] - 4s 41ms/step - loss: 0.1022 - accuracy: 0.9603 - val_loss: 0.2297 - val_accuracy: 0.9095
Epoch 9/15
100/100 [] - 4s 43ms/step - loss: 0.0907 - accuracy: 0.9671 - val_loss: 0.2313 - val_accuracy: 0.9200
Epoch 10/15
100/100 [] - 4s 44ms/step - loss: 0.0670 - accuracy: 0.9744 - val_loss: 0.2353 - val_accuracy: 0.9230
Epoch 11/15
100/100 [] - 4s 40ms/step - loss: 0.0501 - accuracy: 0.9817 - val_loss: 0.2627 - val_accuracy: 0.9160
Epoch 12/15
100/100 [] - 4s 39ms/step - loss: 0.0366 - accuracy: 0.9888 - val_loss: 0.2789 - val_accuracy: 0.9250
Epoch 13/15
100/100 [] - 4s 39ms/step - loss: 0.0293 - accuracy: 0.9901 - val_loss: 0.2958 - val_accuracy: 0.9115
Epoch 14/15
100/100 [] - 4s 39ms/step - loss: 0.0335 - accuracy: 0.9860 - val_loss: 0.3240 - val_accuracy: 0.9090
Epoch 15/15
100/100 [==============================] - 4s 40ms/step - loss: 0.0201 - accuracy: 0.9939 - val_loss: 0.3261 - val_accuracy: 0.9235
Process finished with exit code 0
‘’’
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.keras import layers, losses, Model
def load_img(file_path):
img = tf.io.read_file(file_path)
img = tf.image.decode_jpeg(img)
img = tf.image.resize(img, [32, 32]) / 255.
label = tf.constant(1,tf.int32) if tf.strings.regex_full_match(file_path, '.*airplane.*') else tf.constant(0,tf.int32)
return img, label
batch_num = 100
epochs = 15
train_data = tf.data.Dataset.list_files('cifar2/train/*/*.jpg')./
map(load_img,tf.data.experimental.AUTOTUNE)./
shuffle(buffer_size=1000).batch(100)./
prefetch(tf.data.experimental.AUTOTUNE)
test_data = tf.data.Dataset.list_files('cifar2/test/*/*.jpg')./
map(load_img,tf.data.experimental.AUTOTUNE)./
shuffle(buffer_size=1000).batch(100)./
prefetch(tf.data.experimental.AUTOTUNE)
class LeNet(Model):
def __init__(self):
super(LeNet, self).__init__()
self.c1 = layers.Conv2D(6, 5)
self.s2 = layers.MaxPooling2D()
self.c3 = layers.Conv2D(16, 5)
self.s4 = layers.MaxPooling2D()
self.f5 = layers.Flatten()
self.d6 = layers.Dense(120, activation='relu')
self.d7 = layers.Dense(84, activation='relu')
self.d8 = layers.Dense(2, activation='softmax')
@tf.function
def call(self, inputs):
out = self.c1(inputs)
out = self.s2(out)
out = self.c3(out)
out = self.s4(out)
out = self.f5(out)
out = self.d6(out)
out = self.d7(out)
out = self.d8(out)
return out
model = LeNet()
model.build((None, 32, 32, 3))
model.summary()
model.compile('adam', losses.sparse_categorical_crossentropy, 'accuracy')
history = model.fit(train_data, epochs=epochs, validation_data=test_data)
plt.plot(history.history['val_accuracy'], label='test_accuracy')
plt.plot(history.history['accuracy'], label='train_accuracy')
plt.legend()
plt.show()