Question

我想手动为keras Conv2D层提供输入。

我拿MNIST数据集。

Conv2D仅接受张量，因此我使用keras的x_train命令将x_train_tensor更改为Input。

我的输入采用keras指令中给出的格式

(samples,rows, cols,channels)

示例输入：

(60000,128,128,1)

我希望输出类似于：

(None, 26, 26, 32)

我得到了：

shape=(?, 59998, 26, 32)

我做错了什么？

我的代码：

import keras
from keras.datasets import mnist
from keras.layers import Conv2D
from keras import backend as K
from keras.layers import Input
batch_size = 128
num_classes = 10
epochs = 1
# 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':
    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:
    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)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

x_train_tensor=Input(shape=(60000,28,28), name='x_train')
A=Conv2D(32, kernel_size=(3, 3),
       activation='relu',
       input_shape=input_shape)(x_train_tensor)

Answer 1

样本数量不属于input_shape，在您的情况下，您犯了两个错误。首先是输入形状错误，其次是指定两个输入形状，一次在Input构造函数中，第二个在Conv2D实例中：

x_train_tensor=Input(shape=(28, 28, 1), name='x_train')
A=Conv2D(32, kernel_size=(3, 3), activation='relu')(x_train_tensor)

Conras中的Conv2D图层输出形状

1 个答案: