title: ValueError: Error when checking model target: expected activation_2 to have shape (None, 761, 1) but got array with shape (1, 779, 1)
body: <p>I have the following error statement...</p>
  
  <pre><code>ValueError: Error when checking model target: expected activation_2 to have shape (None, 761, 1) but got array with shape (1, 779, 1)
  </code></pre>
  
  <p>In errors, I don't know what the number 761 means, my <code>data1</code>'s shape is <code>779 * 80</code>, my <code>data3</code>'s shape is <code>779 * 1</code>.  Thank you for your help!</p>
  
  <pre><code>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])
  </code></pre>