Question

keras中的model_weights和optimizer_weights之间有什么区别。运行以下代码model.summary后，显示总共9个参数，显示在1.h5文件的model_weight中。但是optimizer_weight显示总共18个参数。我只使用了1个纪元。代码如下：

from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
import numpy as np
from sklearn.model_selection import train_test_split
import tensorflow as tf
batch_size = 128
num_classes = 2
epochs = 1

# input image dimensions
img_rows, img_cols = 28, 28
(x_train, y_train), (x_test, y_test) = mnist.load_data()

#Redistributing data for only two classes
x1_train=x_train[y_train==0]; y1_train=y_train[y_train==0]
x1_test=x_test[y_test==0];y1_test=y_test[y_test==0]
x2_train=x_train[y_train==1];y2_train=y_train[y_train==1]
x2_test=x_test[y_test==1];y2_test=y_test[y_test==1]
X=np.concatenate((x1_train,x2_train,x1_test,x2_test),axis=0)
Y=np.concatenate((y1_train,y2_train,y1_test,y2_test),axis=0)
# the data, shuffled and split between train and test sets
x_train, x_test, y_train, y_test = train_test_split(X,Y)

if K.image_data_format() == 'channels_first':
    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
    input_shape = (1, img_rows, img_cols)
else:
    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
    input_shape = (img_rows, img_cols, 1)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

model = Sequential()
model.add(Conv2D(1, kernel_size=(2, 2),
                 activation='relu',
                 input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(16,16)))
model.add(Flatten())
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer=keras.optimizers.Adadelta(),
              metrics=['accuracy'])
model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=epochs,
          verbose=1,
          validation_data=(x_test, y_test))
model.summary()
model.save('1.h5')

Answer 1

模型权重是作用于实际数据的权重。它们会影响输出。

单独的模型（没有优化器）足以获取输入并生成（预测）输出。模型的权重越好，输出越好。

训练模型的整个目的是调整其权重，以便做出良好的预测。

另一方面，优化器对数据和预测没有影响优化程序的作用是决定如何在培训期间更改模型的权重。我纯粹是出于培训目的。优化器获取渐变并决定如何将这些渐变应用于模型。（考虑学习率，动力等）

优化器权重只是帮助改进模型权重的调整。一旦你认为你的模型做得很好，就可以抛弃优化器。

keras中model_weights和optimizer_weights之间的区别

1 个答案: