检查模型输入时出错：预期 convolution2d_input_1 具有形状 (None, 3, 32, 32) 但得到形状为 (50000, 32, 32, 3) 的数组
Error when checking model input: expected convolution2d_input_1 to have shape (None, 3, 32, 32) but got array with shape (50000, 32, 32, 3)
有人可以指导如何修复此错误吗？我刚开始使用 Keras：
1 from keras.datasets import cifar10
2 from matplotlib import pyplot
3 from scipy.misc import toimage
4
5 (x_train, y_train), (x_test, y_test) = cifar10.load_data()
6 for i in range(0, 9):
7 pyplot.subplot(330 + 1 + i)
8 pyplot.imshow(toimage(x_train[i]))
9 pyplot.show()
10
11 import numpy
12 from keras.models import Sequential
13 from keras.layers import Dense
14 from keras.layers import Dropout
15 from keras.layers import Flatten
16 from keras.constraints import maxnorm
17 from keras.optimizers import SGD
18 from keras.layers.convolutional import Convolution2D
19 from keras.layers.convolutional import MaxPooling2D
20 from keras.utils import np_utils
21 from keras import backend
22 backend.set_image_dim_ordering('th')
23
24 seed = 7
25 numpy.random.seed(seed)
26
27 x_train = x_train.astype('float32')
28 x_test = x_test.astype('float32')
29 x_train = x_train / 255.0
30 x_test = x_test / 255.0
31
32 y_train = np_utils.to_categorical(y_train)
33 y_test = np_utils.to_categorical(y_test)
34 num_classes = y_test.shape[1]
35
36 model = Sequential()
37 model.add(Convolution2D(32, 3, 3, input_shape=(3, 32, 32), border_mode='same', activation='relu', W_constraint=maxnorm(3)))
38 model.add(Dropout(0.2))
39 model.add(Convolution2D(32, 3, 3, activation='relu', border_mode='same', W_constraint=maxnorm(3)))
40 model.add(Flatten())
41 model.add(Dense(512, activation='relu', W_constraint=maxnorm(3)))
42 model.add(Dropout(0.5))
43 model.add(Dense(num_classes, activation='softmax'))
44
45 epochs = 25
46 learning_rate = 0.01
47 learning_rate_decay = learning_rate/epochs
48 sgd = SGD(lr=learning_rate, momentum=0.9, decay=learning_rate_decay, nesterov=False)
49 model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
50 print(model.summary())
51
52 model.fit(x_train, y_train, validation_data=(x_test, y_test), nb_epoch=epochs, batch_size=32)
53 scores = model.evaluate(x_test, y_test, verbose=0)
54 print("Accuracy: %.2f%%" % (scores[1]*100))
输出为：
mona@pascal:/data/wd1$ python test_keras.py
Using TensorFlow backend.
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcublas.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcudnn.so.5.0 locally
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcufft.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcurand.so.8.0 locally
____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
convolution2d_1 (Convolution2D) (None, 32, 32, 32) 896 convolution2d_input_1[0][0]
____________________________________________________________________________________________________
dropout_1 (Dropout) (None, 32, 32, 32) 0 convolution2d_1[0][0]
____________________________________________________________________________________________________
convolution2d_2 (Convolution2D) (None, 32, 32, 32) 9248 dropout_1[0][0]
____________________________________________________________________________________________________
flatten_1 (Flatten) (None, 32768) 0 convolution2d_2[0][0]
____________________________________________________________________________________________________
dense_1 (Dense) (None, 512) 16777728 flatten_1[0][0]
____________________________________________________________________________________________________
dropout_2 (Dropout) (None, 512) 0 dense_1[0][0]
____________________________________________________________________________________________________
dense_2 (Dense) (None, 10) 5130 dropout_2[0][0]
====================================================================================================
Total params: 16,793,002
Trainable params: 16,793,002
Non-trainable params: 0
____________________________________________________________________________________________________
None
Traceback (most recent call last):
File "test_keras.py", line 52, in <module>
model.fit(x_train, y_train, validation_data=(x_test, y_test), nb_epoch=epochs, batch_size=32)
File "/usr/local/lib/python2.7/dist-packages/keras/models.py", line 664, in fit
sample_weight=sample_weight)
File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 1068, in fit
batch_size=batch_size)
File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 981, in _standardize_user_data
exception_prefix='model input')
File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 113, in standardize_input_data
str(array.shape))
ValueError: Error when checking model input: expected convolution2d_input_1 to have shape (None, 3, 32, 32) but got array with shape (50000, 32, 32, 3)
如果你打印 x_train.shape 你会看到形状是 (50000, 32, 32, 3) 而你在第一层给出了 input_shape=(3, 32, 32)。该错误只是说预期的输入形状和给定的数据不同。
你需要做的就是给input_shape=(32, 32, 3)。此外，如果您使用此形状，则必须使用 tf 作为图像排序。 backend.set_image_dim_ordering('tf')。
否则你可以置换数据轴。
x_train = x_train.transpose(0,3,1,2)
x_test = x_test.transpose(0,3,1,2)
print x_train.shape
有人可以指导如何修复此错误吗？我刚开始使用 Keras：
1 from keras.datasets import cifar10
2 from matplotlib import pyplot
3 from scipy.misc import toimage
4
5 (x_train, y_train), (x_test, y_test) = cifar10.load_data()
6 for i in range(0, 9):
7 pyplot.subplot(330 + 1 + i)
8 pyplot.imshow(toimage(x_train[i]))
9 pyplot.show()
10
11 import numpy
12 from keras.models import Sequential
13 from keras.layers import Dense
14 from keras.layers import Dropout
15 from keras.layers import Flatten
16 from keras.constraints import maxnorm
17 from keras.optimizers import SGD
18 from keras.layers.convolutional import Convolution2D
19 from keras.layers.convolutional import MaxPooling2D
20 from keras.utils import np_utils
21 from keras import backend
22 backend.set_image_dim_ordering('th')
23
24 seed = 7
25 numpy.random.seed(seed)
26
27 x_train = x_train.astype('float32')
28 x_test = x_test.astype('float32')
29 x_train = x_train / 255.0
30 x_test = x_test / 255.0
31
32 y_train = np_utils.to_categorical(y_train)
33 y_test = np_utils.to_categorical(y_test)
34 num_classes = y_test.shape[1]
35
36 model = Sequential()
37 model.add(Convolution2D(32, 3, 3, input_shape=(3, 32, 32), border_mode='same', activation='relu', W_constraint=maxnorm(3)))
38 model.add(Dropout(0.2))
39 model.add(Convolution2D(32, 3, 3, activation='relu', border_mode='same', W_constraint=maxnorm(3)))
40 model.add(Flatten())
41 model.add(Dense(512, activation='relu', W_constraint=maxnorm(3)))
42 model.add(Dropout(0.5))
43 model.add(Dense(num_classes, activation='softmax'))
44
45 epochs = 25
46 learning_rate = 0.01
47 learning_rate_decay = learning_rate/epochs
48 sgd = SGD(lr=learning_rate, momentum=0.9, decay=learning_rate_decay, nesterov=False)
49 model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
50 print(model.summary())
51
52 model.fit(x_train, y_train, validation_data=(x_test, y_test), nb_epoch=epochs, batch_size=32)
53 scores = model.evaluate(x_test, y_test, verbose=0)
54 print("Accuracy: %.2f%%" % (scores[1]*100))
输出为：
mona@pascal:/data/wd1$ python test_keras.py
Using TensorFlow backend.
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcublas.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcudnn.so.5.0 locally
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcufft.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcurand.so.8.0 locally
____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
convolution2d_1 (Convolution2D) (None, 32, 32, 32) 896 convolution2d_input_1[0][0]
____________________________________________________________________________________________________
dropout_1 (Dropout) (None, 32, 32, 32) 0 convolution2d_1[0][0]
____________________________________________________________________________________________________
convolution2d_2 (Convolution2D) (None, 32, 32, 32) 9248 dropout_1[0][0]
____________________________________________________________________________________________________
flatten_1 (Flatten) (None, 32768) 0 convolution2d_2[0][0]
____________________________________________________________________________________________________
dense_1 (Dense) (None, 512) 16777728 flatten_1[0][0]
____________________________________________________________________________________________________
dropout_2 (Dropout) (None, 512) 0 dense_1[0][0]
____________________________________________________________________________________________________
dense_2 (Dense) (None, 10) 5130 dropout_2[0][0]
====================================================================================================
Total params: 16,793,002
Trainable params: 16,793,002
Non-trainable params: 0
____________________________________________________________________________________________________
None
Traceback (most recent call last):
File "test_keras.py", line 52, in <module>
model.fit(x_train, y_train, validation_data=(x_test, y_test), nb_epoch=epochs, batch_size=32)
File "/usr/local/lib/python2.7/dist-packages/keras/models.py", line 664, in fit
sample_weight=sample_weight)
File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 1068, in fit
batch_size=batch_size)
File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 981, in _standardize_user_data
exception_prefix='model input')
File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 113, in standardize_input_data
str(array.shape))
ValueError: Error when checking model input: expected convolution2d_input_1 to have shape (None, 3, 32, 32) but got array with shape (50000, 32, 32, 3)
如果你打印 x_train.shape 你会看到形状是 (50000, 32, 32, 3) 而你在第一层给出了 input_shape=(3, 32, 32)。该错误只是说预期的输入形状和给定的数据不同。
你需要做的就是给input_shape=(32, 32, 3)。此外，如果您使用此形状，则必须使用 tf 作为图像排序。 backend.set_image_dim_ordering('tf')。
否则你可以置换数据轴。
x_train = x_train.transpose(0,3,1,2)
x_test = x_test.transpose(0,3,1,2)
print x_train.shape