我在尝试编写数字识别程序。我有一个数据集，包含60000张图像的像素数据，每张图像的尺寸为28×28像素。

import numpy as npimport matplotlib.pyplot as pltimport pandas as pdfrom keras.datasets import mnist(x_train, y_train), (x_test, y_test) = mnist.load_data()x_train= x_train.reshape(60000, 28, 28, 1).astype('float32')x_test= x_test.reshape(10000, 28, 28, 1).astype('float32')from keras.models import Sequentialfrom keras.layers import Convolution2Dfrom keras.layers import MaxPooling2Dfrom keras.layers import Flattenfrom keras.layers import Denseclassifier= Sequential()classifier.add(Convolution2D(32, 3, 3, input_shape= (28, 28, 1), activation= 'relu'))classifier.add(MaxPooling2D(pool_size= (2, 2)))classifier.add(Flatten())classifier.add(Dense(output_dim = 128, activation= 'relu'))classifier.add(Dense(output_dim = 10, activation= 'softmax'))classifier.compile(optimizer= 'adam', loss='binary_crossentropy', metrics = ['accuracy'])classifier.fit(x_train, y_train, validation_data= (x_test, y_test), nb_epoch= 15, verbose= 2, batch_size= 100)

我遇到了以下错误。

classifier.fit(x_train, y_train, validation_data= (x_test, y_test), nb_epoch= 15, verbose= 2, batch_size= 100)Traceback (most recent call last):  File "<ipython-input-4-9425b6d029dc>", line 1, in <module>    classifier.fit(x_train, y_train, validation_data= (x_test, y_test), nb_epoch= 15, verbose= 2, batch_size= 100)  File "C:\Users\SHUBHAM\Anaconda3\lib\site-packages\keras\models.py", line 672, in fit    initial_epoch=initial_epoch)  File "C:\Users\SHUBHAM\Anaconda3\lib\site-packages\keras\engine\training.py", line 1117, in fit    batch_size=batch_size)  File "C:\Users\SHUBHAM\Anaconda3\lib\site-packages\keras\engine\training.py", line 1034, in _standardize_user_data    exception_prefix='model target')  File "C:\Users\SHUBHAM\Anaconda3\lib\site-packages\keras\engine\training.py", line 124, in standardize_input_data    str(array.shape))ValueError: Error when checking model target: expected dense_2 to have shape (None, 10) but got array with shape (60000, 1)

我不明白问题出在哪里。请帮助我。

回答：

看起来是与输出形状相关的错误。从我的神经网络代码 classifier.add(Dense(output_dim = 10, activation= 'softmax'))来看，输出应该具有形状 [recordCount, 10]。但当我在Python控制台中运行并输入以下命令时，我看到 y_train 的形状不对

>>> from keras.datasets import mnistUsing Theano backend.Using gpu device 0: GeForce GT 730 (CNMeM is enabled with initial size: 70.0% of memory, cuDNN not available)>>> (x_train, y_train), (x_test, y_test) = mnist.load_data()Downloading data from https://s3.amazonaws.com/img-datasets/mnist.pkl.gz15253504/15296311 [============================>.] - ETA: 0s>>>>>> x_train.shape(60000, 28, 28)>>> y_train.shape(60000,)

y_train 的值在0到9的范围内。因此，我建议你进行以下转换：

>>> import numpy>>> y_train_new = numpy.zeros([60000, 10])>>> for i in range(0, 10):...     y_train_new[:, i] = (y_train == i).astype(numpy.int32)