3 回答

• 1

  尝试将数据重塑为（60000,28,28,1）或（60000,1,28,28） .

  回复于 2024-04-25T06:42:25+08:00
• 0

  我有xtrain.shape（60000,28,28）
  它意味着60000个通道，图像大小为28 * 28

  嗯，这当然不代表;它意味着60000个样本，而不是通道（MNIST是单通道数据集） .

  在这种情况下无需重新发明轮子 - 看看Keras的MNIST CNN example：

  from keras import backend as K
  
  # input image dimensions
  img_rows, img_cols = 28, 28
  
  # the data, shuffled and split between train and test sets
  (x_train, y_train), (x_test, y_test) = mnist.load_data()
  
  if K.image_data_format() == 'channels_first': # Theano backend
      x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
      x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
      input_shape = (1, img_rows, img_cols)
  else:                                         # Tensorflow backend
      x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
      x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
      input_shape = (img_rows, img_cols, 1)
  
  # normalise:
  x_train = x_train.astype('float32')
  x_test = x_test.astype('float32')
  x_train /= 255
  x_test /= 255
  
  # convert class vectors to binary class matrices
  y_train = keras.utils.to_categorical(y_train, num_classes)
  y_test = keras.utils.to_categorical(y_test, num_classes)
  
  # your model:
  model = Sequential()
  model.add(Convolution2D(32,3,activation='relu',input_shape=input_shape))
  model.add(Dense(10, activation='softmax'))  # change to softmax in the final layer
  

  您还应该将最终图层的激活更改为 softmax （并且最可能在最终密集图层之前添加一些合并和展平图层） .

  回复于 2024-04-25T06:42:25+08:00
• 1

  第一，

  model = Sequential()
  model.add(Convolution2D(32,3,activation='relu',input_shape=(60000,28,28)))
  model.add(Dense(10, activation='relu'))
  model.summary()
  

  第二个，

  model = Sequential()
  model.add(Dense(64,input_shape=(None,60000,28,28)))
  

  回复于 2024-04-25T06:42:25+08:00