ValueError：检查输入时出错：预期density_9_input具有形状（9，），但数组的形状为（1，）

Question

首先，我将数据设置为随机，如图所示：

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from itertools import combinations as comb
import math
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Flatten

dataset = pd.read_csv('Partial_quantarize.csv') #My dataset
print(dataset.columns.values)

pick = np.random.rand(len(dataset)) < 0.7
train = dataset[pick]
test = dataset[~pick]

#ingredient for training/testing the algorithm
coord = ['ra','dec']
cmodel_mags = ['Mag_u','Mag_g','Mag_r','Mag_i','Mag_z']
rad = ['rad_u', 'rad_g', 'rad_r', 'rad_i', 'rad_z']
dered = ['ext_u','ext_g','ext_r','ext_i','ext_z']
dered_color_indices = ['ext_ug','ext_gr','ext_ri','ext_iz']
coindex = ['coindex_u','coindex_g','coindex_r','coindex_i','coindex_z']
cmodel_color_indices = ['ug','gr','ri','iz']
prad50 = ['petroR50_u','petroR50_g','petroR50_r','petroR50_i','petroR50_z']
prad90 = ['petroR90_u','petroR90_g','petroR90_r','petroR90_i','petroR90_z']
#rad = ['petroRad_u','petroRad_g','petroRad_r','petroRad_i','petroRad_z']
#petro_color_indices = ['p_ug','p_gr','p_ri','p_iz']

#training models
model1 = cmodel_mags + cmodel_color_indices
model2 = cmodel_mags + cmodel_color_indices + rad
model3 = cmodel_mags + cmodel_color_indices + rad + coindex
model4 = dered + dered_color_indices
model5 = dered + dered_color_indices + rad
model6 = dered + dered_color_indices + rad + coindex
model7 = cmodel_mags + cmodel_color_indices + dered + dered_color_indices + rad + coindex
fullparms = coord + cmodel_mags + cmodel_color_indices + dered + dered_color_indices + rad + prad50 + prad90 + coindex

print(train[model4].shape,test[model4].shape) #this gives me (70061,9) (29939,9)

def nn_mlp(test, train, labels, k=7):
    ylabel = train['redshift']
    prediction = []
    batch=1
    no_bins = k*100 if k*100 < 1000 else 1000
    max_z = np.max(train['redshift'].values)
    min_z = np.min(train['redshift'].values)
    model = Sequential()
    model.add(Dense(len(labels), input_dim=len(labels), kernel_initializer='normal', use_bias=True, activation='relu'))
    model.add(Dense(1, kernel_initializer='normal', use_bias=True))
    model.compile(loss='mean_squared_error', optimizer='adam')
    edges = np.histogram(train['redshift'].values[::batch], bins=no_bins, range=(min_z,max_z))[1]
    edges_with_overflow = np.histogram(train['redshift'].values[::batch], bins=no_bins+1, range=(min_z, max_z))[1]
    model.fit(train[labels].values[::batch], edges_with_overflow[np.digitize(train['redshift'].values[::batch], edges)], epochs=1)
    for point in test[labels].values:
        prediction.append(model.predict([point])[0])
    return np.array(prediction)

pred_4 = nn_mlp(test, train, model4)

无论我设置了哪个纪元，我的代码实际上都可以运行，但我不知道为什么我总是不断获得最终输出，如[

“ ValueError：检查输入时出错：预期density_9_input具有形状（9，），但数组的形状为（1，）“

Answer 1

model.add(Dense(1, kernel_initializer='normal', use_bias=True))

Dense层的输出尺寸为1（更具体地说为(batch_size,1)），而不是匹配9（或(batch_size,9)）的标签形状。因此，您需要将上述代码行更改为：

model.add(Dense(9, kernel_initializer='normal', use_bias=True))

希望这会有所帮助！ ：）