Can someone please guide how to fix this error? I just started on Keras:
有人能指导如何修正这个错误吗?我刚开始讲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))
Output is:
输出是:
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)
1 个解决方案
#1
12
If you print x_train.shape you will see the shape being (50000, 32, 32, 3) whereas you have given input_shape=(3, 32, 32) in the first layer. The error simply says that the expected input shape and data given are different.
如果你打印x_train。您将看到形状为(50000,32,32,3)，而您已经在第一层给出了input_shape=(3,32, 32)。错误只是说预期的输入形状和数据是不同的。
All you need to do is give input_shape=(32, 32, 3). Also if you use this shape then you must use tf as your image ordering. backend.set_image_dim_ordering('tf').
您所需要做的就是给input_shape=(32, 32, 3)。如果您使用这个形状，那么您必须使用tf作为您的图像排序。backend.set_image_dim_ordering(tf)。
Otherwise you can permute the axis of data.
否则，你就可以改变数据的轴。
x_train = x_train.transpose(0,3,1,2)
x_test = x_test.transpose(0,3,1,2)
print x_train.shape
#1
12
If you print x_train.shape you will see the shape being (50000, 32, 32, 3) whereas you have given input_shape=(3, 32, 32) in the first layer. The error simply says that the expected input shape and data given are different.
如果你打印x_train。您将看到形状为(50000,32,32,3)，而您已经在第一层给出了input_shape=(3,32, 32)。错误只是说预期的输入形状和数据是不同的。
All you need to do is give input_shape=(32, 32, 3). Also if you use this shape then you must use tf as your image ordering. backend.set_image_dim_ordering('tf').
您所需要做的就是给input_shape=(32, 32, 3)。如果您使用这个形状，那么您必须使用tf作为您的图像排序。backend.set_image_dim_ordering(tf)。
Otherwise you can permute the axis of data.
否则，你就可以改变数据的轴。
x_train = x_train.transpose(0,3,1,2)
x_test = x_test.transpose(0,3,1,2)
print x_train.shape