keras.backend.floatx
时间: 2023-10-02 08:12:48 浏览: 40
Keras.backend.floatx is a string representing the default data type used by Keras backend (e.g., TensorFlow or Theano) for floating-point numbers. The value of this string depends on the backend used. For TensorFlow, the default is 'float32', while for Theano, the default is 'float64'. This variable can be used to ensure that all floating-point computations in a Keras model use the same precision.
相关问题
AttributeError: module 'keras.src.backend' has no attribute 'floatx'
根据您提供的引用内容,您遇到的错误是"AttributeError: module 'keras.src.backend' has no attribute 'floatx'"。这个错误通常是由于Keras版本不同造成的。解决这个问题的方法是将代码中的`keras.src.backend.floatx`改为`keras.backend.floatx`。
以下是一个示例代码,展示了如何解决这个问题:
```python
import keras
# 将代码中的keras.src.backend.floatx改为keras.backend.floatx
print(keras.backend.floatx())
```
这段代码将打印出当前Keras的默认浮点数类型。
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)
```
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