Keras【极简】CNN
文章目录
- 1、极简示例
- 2、详细示例
- 2.1、内置数据集
- 2.2、图像生成器
- 2.3、模型可视化
- 2.4、损失曲线和精度曲线
- 3、EarlyStopping设置自动结束
1、极简示例
from keras.datasets import cifar10 # 小图像10分类
from keras.utils import to_categorical # 独热码
from keras.models import Sequential # 顺序模型
from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D
"""数据读取、预处理"""
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
x_train, x_test = x_train / 255, x_test / 255 # 像素值→[0,1]
y_train = to_categorical(y_train, 10)
y_test = to_categorical(y_test, 10)
"""创建顺序模型"""
model = Sequential()
# 卷积层：30个3x3的卷积滤波器、ReLU激活
model.add(Conv2D(30, (3, 3), padding='same', activation='relu'))
# 池化层
model.add(MaxPooling2D(pool_size=(2, 2)))
# 平坦化
model.add(Flatten())
# 全连接层：神经元个数、ReLU激活
model.add(Dense(100, activation='relu'))
# 输出层（10分类）
model.add(Dense(10, activation='softmax'))
"""模型编译"""
# 优化器【随机梯度下降】；损失函数【分类交叉熵】；添加度量【精准度】
model.compile('sgd', 'categorical_crossentropy', ['accuracy'])
"""训练"""
model.fit(x_train, y_train, 1000, 2) # 批量、训练次数
"""模型评估"""
loss, acc = model.evaluate(x_test, y_test, 1000)
print('loss %g acc %g' % (loss, acc))
2、详细示例
from keras.datasets import cifar10
from keras.utils import to_categorical
from keras.models import Sequential
from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D
from keras.utils import plot_model # conda install pydot
from keras.optimizers import RMSprop # 优化器
from keras.preprocessing.image import ImageDataGenerator # 图像生成器
import matplotlib.pyplot as mp
"""数据读取、预处理"""
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
input_shape = x_train.shape[1:]
y_train = to_categorical(y_train, 10)
y_test = to_categorical(y_test, 10)
# 数据增强
batch_size = 100
steps_per_epoch = len(y_train) / batch_size
epochs = 2
idg_train = ImageDataGenerator(
horizontal_flip=True,
rescale=1/255).flow(x_train, y_train, batch_size)
idg_test = ImageDataGenerator(
rescale=1/255).flow(x_test, y_test, batch_size)
del x_train, y_train, x_test, y_test # 释放内存
"""建模"""
model = Sequential()
model.add(Conv2D(30, (3, 3), activation='relu',
input_shape=input_shape)) # 输入尺寸使编译成功))
model.add(MaxPooling2D(pool_size=(2, 2), name='MaxPooling2D'))
model.add(Flatten())
model.add(Dense(100, activation='relu'))
model.add(Dense(10, activation='softmax'))
optimizer = RMSprop(lr=1e-3, decay=1e-6)
model.compile(optimizer, 'categorical_crossentropy', ['acc'])
"""训练"""
history = model.fit_generator(
idg_train, steps_per_epoch, epochs,
validation_data=idg_test,
validation_steps=batch_size)
"""模型可视化"""
model.summary() # 网络维度
plot_model(model, 'model.png', show_shapes=True,
show_layer_names=False) # 网络结构（存图片）
"""损失曲线和精度曲线"""
ls = ['loss', 'val_loss', 'acc', 'val_acc']
hs = [history.history[i] for i in ls]
for i in (0, 1):
mp.subplot(1, 2, i + 1)
mp.plot(range(1, epochs + 1), hs[i * 2], 'g', label=ls[i * 2])
mp.plot(range(1, epochs + 1), hs[i * 2 + 1], label=ls[i * 2 + 1])
mp.legend()
mp.show()
2.1、内置数据集
from keras.datasets import cifar10 # 10分类小图像
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
print(x_train.shape) # (50000, 32, 32, 3)
print(y_train.shape) # (50000, 1)
print(x_test.shape) # (10000, 32, 32, 3)
print(y_test.shape) # (10000, 1)
import matplotlib.pyplot as mp, numpy as np
images = np.concatenate([x_train[i] for i in range(9)], axis=1)
mp.imshow(images)
mp.show()
2.2、图像生成器
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as mp, numpy as np
# 数据读取
(x_train, y_train), _ = cifar10.load_data()
imgs = x_train[:3]
# 图像生成器
idg = ImageDataGenerator(
rescale=1./255, # 像素值→[0,1]
rotation_range=30, # 旋转角度
horizontal_flip=True, # 水平翻转
width_shift_range=.3, # 水平偏移
height_shift_range=.3) # 垂直偏移
# 可视化
for e, i in enumerate(idg.flow(imgs, shuffle=False), 1):
if e > 9: break # 打断死循环
mp.subplot(1, 9, e)
mp.axis('off')
img = np.concatenate(i)
mp.imshow(img)
mp.show()
2.3、模型可视化
.summary
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 30, 30, 30) 840
_________________________________________________________________
MaxPooling2D (MaxPooling2D) (None, 15, 15, 30) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 6750) 0
_________________________________________________________________
dense_1 (Dense) (None, 100) 675100
_________________________________________________________________
dense_2 (Dense) (None, 10) 1010
=================================================================
Total params: 676,950
Trainable params: 676,950
Non-trainable params: 0
plot_model(model, show_shapes=True, show_layer_names=False)
2.4、损失曲线和精度曲线
3、EarlyStopping设置自动结束
from keras.datasets import cifar10
from keras.utils import to_categorical
from keras.models import Sequential
from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Dropout
from keras import optimizers
from keras.callbacks import EarlyStopping # 提早停止训练
import matplotlib.pyplot as mp
"""训练批量和轮数"""
batch_size = 500
epochs = 50 # 总训练轮数
min_delta = .01 # 最小进步，低于该值将被认为没有进步
patience = 5 # 没有进步的训练轮数
"""读数据、预处理"""
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
x_train, x_test = x_train / 255, x_test / 255
y_train = to_categorical(y_train, 10)
y_test = to_categorical(y_test, 10)
"""建模"""
model = Sequential()
for i in (32, 64, 128):
model.add(Conv2D(i, (3, 3), padding='same', activation='relu'))
model.add(Conv2D(i, (3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(.25))
model.add(Flatten())
model.add(Dense(1024, activation='relu'))
model.add(Dropout(.5))
model.add(Dense(10, activation='softmax'))
optimizer = optimizers.RMSprop(lr=1e-3, decay=1e-6)
# 模型编译
model.compile(optimizer, 'categorical_crossentropy', ['acc'])
# 拟合（取1%样本用于验证）、自动结束
callback = EarlyStopping('val_acc', min_delta, patience)
history = model.fit(x_train, y_train, batch_size, epochs, verbose=2,
callbacks=[callback], validation_split=.01)
"""评估"""
loss, acc = model.evaluate(x_test, y_test, batch_size, verbose=2)
print('loss %g acc %g' % (loss, acc))
acc = history.history['acc']
val_acc = history.history['val_acc']
mp.plot(range(len(acc)), acc, 'g', label='acc')
mp.plot(range(len(val_acc)), val_acc, label='val_acc')
mp.legend()
mp.show()
| en | cn | en | cn |
|---|---|---|---|
| convolution | 卷积；盘旋 | Conv2D | 二维卷积 |
| util | 跑龙套；工具 | regularizer | 正则化矩阵 |
| dense | 稠密的 | flatten | 铲平vt；变平vi |
| optimize | 优化 | epoch | 新纪元；时间上的一点 |
| categorical | 绝对的；分类的 | sequential | 连续的 |
| pooling | [贸易]联营；池化 | drop out | 退学；脱离 |
| validation | 确认；批准；生效 | summary | 摘要 |
| rotation | 旋转 | flip | 筋斗 |
| rescale | 重新调节 | shift | 移动 |
| patience | 耐性 | monitor | 监视器；显示屏；监测 |