data_format=backend.image_data_format()
时间: 2024-05-28 21:14:09 浏览: 19
这是一个 Python 代码片段,用于获取当前 Keras 后端的图像数据格式。在 Keras 中,有两种可用的图像数据格式,分别是 "channels_last" 和 "channels_first"。该代码片段中,使用了 backend.image_data_format() 函数来获取当前使用的数据格式,并将结果赋值给变量 data_format。
相关问题
下面的代码哪里有问题,帮我改一下from __future__ import print_function import numpy as np import tensorflow import keras from keras.models import Sequential from keras.layers import Dense,Dropout,Flatten from keras.layers import Conv2D,MaxPooling2D from keras import backend as K import tensorflow as tf import datetime import os np.random.seed(0) from sklearn.model_selection import train_test_split from PIL import Image import matplotlib.pyplot as plt from keras.datasets import mnist images = [] labels = [] (x_train,y_train),(x_test,y_test)=mnist.load_data() X = np.array(images) print (X.shape) y = np.array(list(map(int, labels))) print (y.shape) x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=0) print (x_train.shape) print (x_test.shape) print (y_train.shape) print (y_test.shape) ############################ ########## batch_size = 20 num_classes = 4 learning_rate = 0.0001 epochs = 10 img_rows,img_cols = 32 , 32 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')
代码中存在以下问题:
1. 变量 images 和 labels 没有被赋值,而后面却使用了它们来构造 X 和 y,导致 X 和 y 的值都为空。
2. 导入的 mnist 数据集没有被使用,而是使用了未定义的 images 和 labels,需要将其替换为 mnist 数据集。
3. input_shape 的值应该是三元组 (img_rows, img_cols, 1),而代码中缺少了最后一个 1。
4. 在 if K.image_data_format() =='channels_first': 分支中,x_train 和 x_test 被改变了形状,但 y_train 和 y_test 却没有被改变,需要将其同步修改。
下面是修改后的代码:
```
from __future__ import print_function
import numpy as np
import tensorflow
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
import tensorflow as tf
import datetime
import os
np.random.seed(0)
from sklearn.model_selection import train_test_split
from PIL import Image
import matplotlib.pyplot as plt
from keras.datasets import mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
print(x_train.shape)
print(y_train.shape)
############################
##########
batch_size = 20
num_classes = 10
learning_rate = 0.0001
epochs = 10
img_rows, img_cols = 32, 32
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 = (img_rows, img_cols, 1)
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)
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
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')
print('input_shape:', input_shape)
```
channel_axis = 1 if K.image_data_format() == "channels_first" else -1 中K是什么
在深度学习中,K通常是指Keras库。Keras是一个高级神经网络API,它是用Python编写的,可以在TensorFlow、Theano和CNTK等后端上运行。
Keras提供了一系列高层次的API,可以更方便地定义、训练和评估深度学习模型,同时也支持多种常见的深度学习模型和层类型。在Keras中,K是指Keras的backend模块,它提供了一些通用的、与后端无关的函数,以方便用户在不同的后端上进行开发。
在这个代码中,K.image_data_format()是Keras的backend模块中的一个函数,用于获取当前Keras后端所使用的图像数据格式。通过这个函数的返回值,我们可以确定通道轴的位置,从而正确地处理输入数据。
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