小编Chr*_*ris的帖子

我正在尝试遵循Deep Autoencoder Keras 示例.我得到了一个维度不匹配异常,但对于我的生活,我无法弄清楚为什么.当我只使用一个编码维度时它可以工作,但是当我堆叠它们时却不行.

例外:输入0与图层dense_18不兼容:
expected shape =(None,128),found shape =(None,32)*

错误就行了 decoder = Model(input=encoded_input, output=decoder_layer(encoded_input))

from keras.layers import Dense,Input
from keras.models import Model

import numpy as np

# this is the size of the encoded representations
encoding_dim = 32

#NPUT LAYER
input_img = Input(shape=(784,))

#ENCODE LAYER
# "encoded" is the encoded representation of the input
encoded = Dense(encoding_dim*4, activation='relu')(input_img)
encoded = Dense(encoding_dim*2, activation='relu')(encoded)
encoded = Dense(encoding_dim, activation='relu')(encoded)

#DECODED LAYER
# "decoded" is the lossy reconstruction of the input
decoded …

我正在尝试从Pandas中的多索引数据框创建一个自举样本.下面是一些生成我需要的数据的代码.

from itertools import product
import pandas as pd
import numpy as np

df = pd.DataFrame({'group1': [1, 1, 1, 2, 2, 3],
                       'group2': [13, 18, 20, 77, 109, 123],
                       'value1': [1.1, 2, 3, 4, 5, 6],
                       'value2': [7.1, 8, 9, 10, 11, 12]
                       })
df = df.set_index(['group1', 'group2'])

print df

df数据框如下所示:

                   value1  value2
group1 group2                
1      13         1.1     7.1
       18         2.0     8.0
       20         3.0     9.0
2      77         4.0    10.0
       109        5.0    11.0
3      123        6.0    12.0

我想从第一个索引中获取一个随机样本.例如,假设随机值np.random.randint(3,size=3)产生[3,2,2].我希望结果数据框看起来像:

                   value1 …