Python Keras-检查目标时出错

给定以下代码：

import matplotlib.pyplot as plt
import numpy
from keras import callbacks
from keras import optimizers
from keras.layers import Dense, Dropout
from keras.models import Sequential
from keras.callbacks import ModelCheckpoint
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import ExtraTreesClassifier

from sklearn.utils import shuffle

# Early stopping - Stop training before overfitting
early_stop = callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=3, verbose=1, mode='auto')

# fix random seed for reproducibility
seed = 42
numpy.random.seed(seed)
# load pima indians dataset
dataset = numpy.loadtxt("./data/poc.csv",skiprows=1, delimiter=",")
# split into input (X) and output (Y) variables
X = dataset[:, 0:14]
Y = dataset[:, 14:18]

# # Standardize features by removing the mean and scaling to unit variance
scaler = StandardScaler()
X = scaler.fit_transform(X)


#ADAM Optimizer with learning rate decay
opt = optimizers.Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0001)

## Create our model
model = Sequential()

model.add(Dense(200, input_dim=14, kernel_initializer='uniform', activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(100, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(60, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(30, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(5, activation='sigmoid'))


model.summary()
# Compile the model using binary crossentropy since we are predicting 0/1
model.compile(loss='categorical_crossentropy',
              optimizer=opt,
              metrics=['accuracy'])

# checkpoint
filepath="./checkpoints/weights.best.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')

# Fit the model
history = model.fit(X, Y, validation_split=0.33, epochs=10000, batch_size=10, verbose=0, callbacks=[early_stop,checkpoint])

数据如下：

17.6,1,1,0,1,0,0,0,0,0,0,3.9,9.2,20.29,0,1,0,0,0
12.9,1,0,1,0,0,0,0,0,0,0,4.1,13.5,0.08,0,0,0,1,0
3.2,1,0,1,0,0,0,0,0,0,0,4.122031746,13.8,0.01,0,0,0,0,0
...

我得到以下输出/错误：

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_1 (Dense)              (None, 200)               3000      
_________________________________________________________________
dropout_1 (Dropout)          (None, 200)               0         
_________________________________________________________________
dense_2 (Dense)              (None, 100)               20100     
_________________________________________________________________
dropout_2 (Dropout)          (None, 100)               0         
_________________________________________________________________
dense_3 (Dense)              (None, 60)                6060      
_________________________________________________________________
dropout_3 (Dropout)          (None, 60)                0         
_________________________________________________________________
dense_4 (Dense)              (None, 30)                1830      
_________________________________________________________________
dropout_4 (Dropout)          (None, 30)                0         
_________________________________________________________________
dense_5 (Dense)              (None, 5)                 155       
=================================================================
Total params: 31,145
Trainable params: 31,145
Non-trainable params: 0
_________________________________________________________________

检查目标时出错：预期密集_5具有形状（无，1），但获得具有形状（716，4）的数组

---

我遗漏了什么？

您的最后一层，

dense_5

，大小为5，而目标层的大小为4

为了工作，每个targetr的大小必须是您想要预测的类的数量。请记住，他们必须在一个热的重唱代表。你可以用Keras的