指定模型形状

Question

我有xtrain.shape

(60000, 28, 28)

这意味着60000个通道，图像大小为28 * 28

我想制作一个keras顺序模型。

指定模型形状

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

input_shape应该是什么样的？

model = Sequential()
model.add(Dense(64,input_shape=(1,28,28)))

当我提出这个时，我收到了以下错误

Error when checking input: expected dense_31_input to have 4 dimensions, but got array with shape (60000, 28, 28)

为什么这需要4个尺寸？以及如何通过代码修复它？

Answer 1

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

Answer 2

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

嗯，这当然不代表;它表示60000 样本，而非通道（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

你还应该将最后一层的激活更改为softmax（最有可能在最后一层之前添加一些合并和展平的图层）。

Answer 3

第一个，

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)))

确定Keras的Mnist输入形状

指定模型形状

3 个答案: