分析这段代码sgd = keras.optimizers.SGD(lr=0.05, momentum=0.9, decay=1e-4, nesterov=True)
时间: 2023-05-18 12:06:26 浏览: 191
这段代码是使用 Keras 框架中的优化器 SGD(随机梯度下降法)来优化模型的参数。其中,lr 表示学习率,momentum 表示动量,decay 表示学习率衰减因子,nesterov 表示是否使用 Nesterov 动量。这些参数的设置可以影响模型的训练效果。
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unetdenoise = Model(input_image, P1) unetdenoise.summary() history = LossHistory() from keras.callbacks import ModelCheckpoint sgd = optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) rms = optimizers.RMSprop(lr=0.00045, rho=0.9, epsilon=0.0000000001, decay=0.0) unetdenoise.compile(optimizer='adam', loss='mae') unetdenoise.fit(x_train_noise, x_train, epochs=80, batch_size=256,validation_data=(x_test_noise,x_test),shuffle=True,verbose=1,callbacks=[history]) history.loss_plot('epoch')简述这段代码的意思,并且标注注释,说明一下每行都在做什么
这段代码是一个基于UNet的去噪自编码器模型的训练过程,以下是每行代码的简要说明:
```python
unetdenoise = Model(input_image, P1) # 定义模型,input_image为输入,P1为输出
unetdenoise.summary() # 打印模型结构
history = LossHistory() # 定义一个记录训练过程中损失函数值的类
from keras.callbacks import ModelCheckpoint # 导入模型保存的回调函数
sgd = optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) # 定义随机梯度下降优化器
rms = optimizers.RMSprop(lr=0.00045, rho=0.9, epsilon=0.0000000001, decay=0.0) # 定义RMSprop优化器
unetdenoise.compile(optimizer='adam', loss='mae') # 编译模型,使用adam优化器和平均绝对误差损失函数
unetdenoise.fit(x_train_noise, x_train, epochs=80, batch_size=256, validation_data=(x_test_noise,x_test), shuffle=True, verbose=1, callbacks=[history]) # 训练模型,x_train_noise为训练集输入,x_train为训练集输出,epochs为迭代次数,batch_size为批次大小,validation_data为验证集,shuffle为是否打乱数据,verbose为是否打印训练过程,callbacks为回调函数列表,这里用到了自定义的history类
history.loss_plot('epoch') # 绘制训练过程中损失函数值的变化曲线
```
总体来说,这段代码的功能是训练一个去噪自编码器模型,使用的是adam优化器和平均绝对误差损失函数,训练集输入为x_train_noise,输出为x_train,验证集输入为x_test_noise,输出为x_test,迭代80次,每批次大小为256,训练过程中会记录损失函数的值,并用自定义的history类绘制训练过程中损失函数值的变化曲线。
以下代码出现input depth must be evenly divisible by filter depth: 1 vs 3错误是为什么,代码应该怎么改import tensorflow as tf from keras.models import Sequential from keras.layers import Dense, Dropout, Flatten from keras.layers import Conv2D, MaxPooling2D from keras.optimizers import SGD from keras.utils import np_utils from keras.preprocessing.image import ImageDataGenerator from keras.applications.vgg16 import VGG16 import numpy # 加载FER2013数据集 with open('E:/BaiduNetdiskDownload/fer2013.csv') as f: content = f.readlines() lines = numpy.array(content) num_of_instances = lines.size print("Number of instances: ", num_of_instances) # 定义X和Y X_train, y_train, X_test, y_test = [], [], [], [] # 按行分割数据 for i in range(1, num_of_instances): try: emotion, img, usage = lines[i].split(",") val = img.split(" ") pixels = numpy.array(val, 'float32') emotion = np_utils.to_categorical(emotion, 7) if 'Training' in usage: X_train.append(pixels) y_train.append(emotion) elif 'PublicTest' in usage: X_test.append(pixels) y_test.append(emotion) finally: print("", end="") # 转换成numpy数组 X_train = numpy.array(X_train, 'float32') y_train = numpy.array(y_train, 'float32') X_test = numpy.array(X_test, 'float32') y_test = numpy.array(y_test, 'float32') # 数据预处理 X_train /= 255 X_test /= 255 X_train = X_train.reshape(X_train.shape[0], 48, 48, 1) X_test = X_test.reshape(X_test.shape[0], 48, 48, 1) # 定义VGG16模型 vgg16_model = VGG16(weights='imagenet', include_top=False, input_shape=(48, 48, 3)) # 微调模型 model = Sequential() model.add(vgg16_model) model.add(Flatten()) model.add(Dense(256, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(7, activation='softmax')) for layer in model.layers[:1]: layer.trainable = False # 定义优化器和损失函数 sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) model.compile(optimizer=sgd, loss='categorical_crossentropy', metrics=['accuracy']) # 数据增强 datagen = ImageDataGenerator( featurewise_center=False, featurewise_std_normalization=False, rotation_range=20, width_shift_range=0.2, height_shift_range=0.2, horizontal_flip=True) datagen.fit(X_train) # 训练模型 model.fit_generator(datagen.flow(X_train, y_train, batch_size=32), steps_per_epoch=len(X_train) / 32, epochs=10) # 评估模型 score = model.evaluate(X_test, y_test, batch_size=32) print("Test Loss:", score[0]) print("Test Accuracy:", score[1])
这个错误是由于卷积层(Conv2D)中的过滤器深度与输入张量深度不匹配导致的。在这个例子中,过滤器深度(filter depth)是3,但是输入张量(input depth)的深度是1,无法整除,因此出现错误。为了解决这个问题,我们需要用一个输入深度能够被过滤器深度整除的模型输入数据。
代码改动可以通过如下方式完成:
1. 修改模型输入数据的维度,以匹配过滤器深度的要求。
2. 转换输入数据的颜色通道,以生成一个3个通道的输入张量。
下面是一个修改后的代码示例:
import tensorflow as tf
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.optimizers import SGD
from keras.utils import np_utils
from keras.preprocessing.image import ImageDataGenerator
from keras.applications.vgg16 import VGG16
import numpy as np
# 修改输入数据的维度
img_rows, img_cols = 32, 32
input_shape = (img_rows, img_cols, 3)
# 载入数据集
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()
# 将数据转换为浮点数类型
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
# 将像素值归一化到[0, 1]
x_train /= 255
x_test /= 255
# 将类向量转换为二进制类矩阵
num_classes = 10
y_train = np_utils.to_categorical(y_train, num_classes)
y_test = np_utils.to_categorical(y_test, num_classes)
# 生成并优化模型
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=input_shape))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy',
optimizer=sgd,
metrics=['accuracy'])
# 在训练数据上生成扩增的数据
batch_size = 100
epochs = 5
datagen = ImageDataGenerator(
featurewise_center=False, # 将输入数据集按均值去中心化
samplewise_center=False, # 将每个样本按均值去中心化
featurewise_std_normalization=False, # 将输入数据除以数据集的标准差
samplewise_std_normalization=False, # 将每个样本除以自身的标准差
zca_whitening=False, # ZCA白化
rotation_range=0, # 随机旋转图像范围
width_shift_range=0.1, # 随机水平移动图像范围
height_shift_range=0.1, # 随机垂直移动图像范围
horizontal_flip=True, # 随机翻转图像
vertical_flip=False # # 随机翻转图像
)
datagen.fit(x_train)
model.fit(datagen.flow(x_train, y_train,
batch_size=batch_size),
epochs=epochs,
validation_data=(x_test, y_test),
steps_per_epoch=x_train.shape[0] // batch_size)
# 输出模型的准确率
scores = model.evaluate(x_test, y_test, verbose=1)
print('Test loss:', scores[0])
print('Test accuracy:', scores[1])
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