x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
时间: 2023-05-11 20:06:45 浏览: 187
这是将 x_train 数组的形状从 (n, 784) 转换为 (n, 28, 28, 1) 的代码,其中 n 是样本数。这个操作可以将每个样本的像素值重新排列成一个 28x28 的矩阵,并添加一个额外的维度,以便于在卷积神经网络中使用。
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x_train = np.reshape(x_train, (x_train.shape[0], 12, 1))
这行代码是用于将 `x_train` 数组的形状进行重塑的,其中 `x_train` 是一个NumPy数组,其形状为 `(n_samples, n_features)`,即包含 `n_samples` 个样本,每个样本有 `n_features` 个特征。这行代码将 `x_train` 的形状重塑为 `(n_samples, 12, 1)`,即每个样本有12个特征,每个特征只有1个值。
具体来说,可以按照以下步骤进行操作:
1. 使用NumPy中的 `reshape()` 方法将 `x_train` 数组的形状进行重塑
2. 将重塑后的数组赋值给 `x_train`,以更新 `x_train` 数组的值
下面是一个示例代码:
```python
# 假设 x_train 的形状为 (n_samples, n_features)
import numpy as np
# 将 x_train 的形状重塑为 (n_samples, 12, 1)
x_train = np.reshape(x_train, (x_train.shape[0], 12, 1))
```
需要注意的是,重塑后的数组的总元素个数必须与原数组的总元素个数相等,否则会抛出异常。在本例中,由于每个样本中有12个特征,因此新数组中的每个子数组都需要有12个元素,每个元素只有1个值。
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)
```