title: 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)
body: <p>Can someone please guide how to fix this error? I just started on Keras:</p>
  
  <pre><code> 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))
  </code></pre>
  
  <p>Output is:</p>
  
  <pre><code>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 &lt;module&gt;
      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)
  </code></pre>