ValueError：检查目标时出错：预期activation_17有2个维度，但得到的数组有形状（1,256,256,3）

我正在构建一个CNN来预测基于VGG-16的人类年龄，并提供两个RGB图像 . （filetype：jpg） . 我在Python 2.7中使用了anoronda环境中的tensorflow后端 .

但是，它总是会引发错误：

回溯（最近一次调用最后一次）：文件“train2.py”，第167行，在shuffle = True）文件“/Users/name/anaconda/lib/python2.7/sitepackages/keras/models.py",line 973 ，in fit validation_steps = validation_steps）文件“/Users/name/anaconda/lib/python2.7/sitepackages/keras/engine/training.py”，第1581行，in fit batch_size = batch_size）文件“/ Users / name / anaconda /lib/python2.7/sitepackages/keras/engine/training.py“，第1418行，_standardize_user_data exception_prefix ='target'）文件”/Users/name/anaconda/lib/python2.7/sitepackages/keras/engine/ training.py“，第141行，在_standardize_input_data str（array.shape）中）ValueError：检查目标时出错：预期activation_17有2个维度，但得到的数组有形状（1,256,256,3）

我该如何解决这个错误？这是代码：

import keras

from keras.preprocessing.image import ImageDataGenerator

from keras.models import Sequential

from keras.layers import Dense

from keras.layers import Dropout

from keras.layers import Activation

from keras.layers import Flatten

from keras.layers import Conv2D

from keras.layers import MaxPooling2D

from keras.layers import BatchNormalization

from keras.utils import np_utils

import cv2

from PIL import Image

import numpy as np

from sklearn.cross_validation import train_test_split

from sklearn.model_selection import train_test_split

name_path = ["pathname"]

new_age_list=[45,52]

img_rows=256

img_cols=256

img_array = 
np.array([np.array(Image.open(i).resize((img_rows,img_cols),Image.BILINEAR)) for i in name_path[0:2]],"f")

(X, y) = (img_array[0:2],new_age_list[0:2])

y=np.asarray(y)

X=X.reshape(2,256,256,3)

# STEP 1: split X and y into training and testing sets

train_data, train_label,test_data, test_label= train_test_split(X, y, 
test_size=0.5, random_state=4)

train_data = train_data.astype('float32')

test_data = test_data.astype('float32')

train_data = train_data / 255

test_data = test_data / 255

""" Model """

model = Sequential()

""" Block 1 """

model.add(Conv2D(64, (3,3), padding='same', 
border_mode='valid',input_shape=(256,256,3)))

model.add(BatchNormalization())

model.add(Activation('relu'))

model.add(Conv2D(64, (3,3)))

model.add(BatchNormalization())

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2,2)))

""" Block 2 """

model.add(Conv2D(128, (3,3)))

model.add(BatchNormalization())

model.add(Activation('relu'))

model.add(Conv2D(128, (3,3)))

model.add(BatchNormalization())

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2,2)))

""" Block 3 """

model.add(Conv2D(256, (3,3)))

model.add(BatchNormalization())

model.add(Activation('relu'))

model.add(Conv2D(256, (3,3)))

model.add(BatchNormalization())

model.add(Activation('relu'))

model.add(Conv2D(256, (3,3)))

model.add(BatchNormalization())

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2,2)))

""" Block 4 """
model.add(Conv2D(512, (3,3)))

model.add(BatchNormalization())

model.add(Activation('relu'))

model.add(Conv2D(512, (3,3)))

model.add(BatchNormalization())

model.add(Activation('relu'))

model.add(Conv2D(512, (3,3)))

model.add(BatchNormalization())

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2,2)))

""" Block 5 """

model.add(Conv2D(512, (3,3)))

model.add(BatchNormalization())

model.add(Activation('relu'))

model.add(Conv2D(512, (3,3)))

model.add(BatchNormalization())

model.add(Activation('relu'))

model.add(Conv2D(512, (3,3)))

model.add(BatchNormalization())

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Dropout(0.25))

""" Flatten """

model.add(Flatten())

model.add(Dense(512))

model.add(BatchNormalization())

model.add(Activation('relu'))

model.add(Dense(128))

model.add(BatchNormalization())
model.add(Activation('relu'))

model.add(Dense(32))

model.add(BatchNormalization())

model.add(Activation('relu'))

model.add(Dense(1))

model.add(BatchNormalization())

model.add(Activation('softmax'))

""" Optimizer """

opt = keras.optimizers.rmsprop(lr=config.learning_rate, 

decay=config.decay)

print model.summary()

model.compile(loss='mean_squared_error', optimizer=opt, metrics=
['accuracy'])

""" Fit Data """

batch_size = 512

epoch = 1000

learning_rate = 1e-4

decay = 1e-7

for i in range(epoch):

    model.fit(train_data, train_label,

              batch_size=batch_size,

              epochs=int(epoch/epoch),

              validation_data=(test_data, test_label),

              shuffle=True)