Question

我有以下错误陈述......

ValueError: Error when checking model target: expected activation_2 to have shape (None, 761, 1) but got array with shape (1, 779, 1)

如果出现错误，我不知道761号码的含义，data1的形状为779 * 80，data3的形状为779 * 1。谢谢你的帮助！

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])

Answer 1

您的模型输出形状为(779, 1)，并且在最终层中预期相同的形状，但由于2卷积运算减少到761.因此，通过在其他2卷积层中添加border_mode = 'same'将解决问题。

您可以登记模型摘要：

图层（类型）输出形状参数＃

conv1d_1（Conv1D）（无，779,32）25632

activation_1（激活）（无，779,32）0

conv1d_2（Conv1D）（无，770,64）20544

dropout_1（Dropout）（无，770,64）0

conv1d_3（Conv1D）（无，761,128）82048

dropout_2（Dropout）（无，761,128）0

dense_13（密集）（无，761,1）129

activation_2（激活）（无，761,1）0

ValueError：检查模型目标时出错：预期activation_2具有形状（无，761,1）但是具有形状的数组（1,779,1）

1 个答案: