Question

当我适应网络时，我收到以下错误 - ValueError：检查目标时出错：期望dense_6有形状（2，）但是有形状的数组（22，）

据我所知，考虑到数据集是如何分割的，形状应该是正确的？非常感谢任何帮助，谢谢！

可在此处找到数据集：https://archive.ics.uci.edu/ml/machine-learning-databases/mushroom/agaricus-lepiota.data

from keras.layers import Dense
from keras.models import Sequential
import keras.utils
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
import numpy as np
import pandas as pd

# seed weights
np.random.seed(3)

# import dataset
data = pd.read_csv('agaricus-lepiota.csv', delimiter=',')

# encode labels as integers so the can be one-hot-encoded which takes int matrix
le = preprocessing.LabelEncoder()
data = data.apply(le.fit_transform)

# one-hot-encode string data (now type int)
ohe = preprocessing.OneHotEncoder(sparse=False)
data = ohe.fit_transform(data)

X = data[:, 1:23]
Y = data[:, 0:1]

# split into test and train set
x_train, y_train, x_test, y_test = train_test_split(X, Y, test_size=.2, random_state=5)

# create model
model = Sequential()
model.add(Dense(500, input_dim=22, activation='relu'))
model.add(Dense(300, activation='relu'))
model.add(Dense(100, activation='relu'))
model.add(Dense(50, activation='relu'))
model.add(Dense(25, activation='relu'))
model.add(Dense(2, activation='sigmoid'))

model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=1000, batch_size=25)

Answer 1

我在您的代码中发现了2个错误。

1）

x_train, y_train, x_test, y_test = train_test_split(X, Y, test_size=.2, random_state=5)

必须是

x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=.2, random_state=5)

检查this以了解有关该功能的更多信息。

2）

y_train中只有一列。但是模型中的最后一层添加了两列。而不是

model = Sequential()
model.add(Dense(500, input_dim=22, activation='relu'))
model.add(Dense(300, activation='relu'))
model.add(Dense(100, activation='relu'))
model.add(Dense(50, activation='relu'))
model.add(Dense(25, activation='relu'))
model.add(Dense(2, activation='sigmoid'))

使用它：

model = Sequential()
model.add(Dense(500, input_dim=22, activation='relu'))
model.add(Dense(300, activation='relu'))
model.add(Dense(100, activation='relu'))
model.add(Dense(50, activation='relu'))
model.add(Dense(25, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

塑造神经网络分类输出维数？

1 个答案: