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我有以下错误的语句...ValueError异常：错误检查时模型的目标：预计activation_2有形状（无，761，1），但得到阵列形状（1，779，1）
ValueError: Error when checking model target: expected activation_2 to have shape (None, 761, 1) but got array with shape (1, 779, 1)
出现错误时，我不知道761是什么意思，我的data1的形状是779 * 80，我的data3的形状是779 * 1。感谢您的帮助！
from __future__ import print_function
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, \
Dropout, \
Activation, \
Flatten
from keras.layers import Convolution1D, \
MaxPooling2D, \
Convolution2D
from keras.utils import np_utils
import scipy.io as sio
import numpy as np
matfn = 'LIVE_data.mat'
data = sio.loadmat(matfn)
data0 = data['data']
data1 = np.ones((1, 779, 80))
data1[0, :, :] = data0
data00 = data['label']
data2 = np.ones((1,779,1))
data2[0, :, :] = data00
data000 = data['ref_ind_live']
data3 = np.ones((1, 779, 1))
data3[0, :, :] = data000
batch_size = 64
nb_classes = 30
nb_epoch = 50
X_train = data1
y_train = data3
X_test = data1[0, :]
y_test = data3[0, :]
X_train = X_train.astype('double')
X_test = X_test.astype('double')
X_train /= 255
X_test /= 255
# Convert class vectors to binary class matrices.
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)
model = Sequential()
model.add(Convolution1D(32, \
10, \
border_mode = 'same', \
input_shape = (779, \
80)))
model.add(Activation('relu'))
model.add(Convolution1D(64, \
10, \
activation='relu'))
model.add(Dropout(0.25))
model.add(Convolution1D(128, \
10, \
activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('softmax'))
# Let's train the model using RMSprop
model.compile(loss = 'categorical_crossentropy', \
optimizer = 'rmsprop', \
metrics=['accuracy'])
print("start train")
model.fit(X_train, \
Y_train, \
batch_size = batch_size, \
nb_epoch = nb_epoch, \
shuffle = True)
print("end")
score = model.evaluate(X_test, \
Y_test, \
batch_size = 32)
print('Test score:', \
score[0])
print('Test accuracy:', \
score[1])
我已经修改了你的布局，使其更容易调试。你的旧布局是好的，但不一致的应用。另外，你的价值观，经营者等常常被混淆在一起。这并不是最佳选择，因为不分离的元素可能会让某些人阅读您的代码，特别是如果他们不熟悉您的布局风格或某些使用的技术，有时甚至是他们时。要做到这一点，你应该像我用'data1 = np.ones（（1,779,80））'和下面的25行一样分开它们，或者如我已经完成的其余代码一样。 – toonice
有关如何在Python中执行间距的更多信息，请参阅http://stackoverflow.com/questions/9714161/spaces-in-python-coding-style和http://stackoverflow.com/questions/4172448/is-it - 可能致断-A-长的线路到多线式-蟒。 – toonice