预期conv2d_1_input具有形状(28、28、1)，但具有形状为(1、28、28)的数组 [英] expected conv2d_1_input to have shape (28, 28, 1) but got array with shape (1, 28, 28)

问题描述

所以我在keras上使用mnist示例，我试图预测自己的数字.我真的在如何匹配尺寸尺寸方面苦苦挣扎，因为似乎无法找到一种方法来调整图像的大小以在图像编号之后添加行和列.我已经尝试通过numpy调整大小，但是在出错之后我只会得到错误...

So i'm using the mnist example on keras and I am trying to predict a digit of my own. I'm really struggling with how I can match the dimension sizes as I cant seem to find a way to resize my image to have the rows and columns after the image no. I've tried resizing with via numpy however I just get error after error...

代码

from __future__ import print_function
import keras
from keras.datasets import mnist
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 numpy as np
import cv2

batch_size = 20
num_classes = 10
epochs = 1
img_rows, img_cols = 28, 28

(x_train, y_train), (x_test, y_test) = mnist.load_data()

print("Processing image")   
im = cv2.imread('C:/Users/Luke/pic4.png', 0) #loading the image
print(im.shape) #28*28
im = cv2.resize(im,  (img_rows, img_cols)) 



list = [im]


batch = np.array([list for i in range(1)])   
print(batch.shape)#1*28*28
batch = batch.astype('float32')
batch /= 255

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)

#print("x_train shape")     

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255

y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

def base_model():
    model = Sequential()
    model.add(Conv2D(32, kernel_size=(3, 3),
                 activation='relu',
                 input_shape=input_shape))
    model.add(Conv2D(64, (3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))
    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(num_classes, activation='softmax'))

    model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer=keras.optimizers.Adadelta(),
              metrics=['accuracy'])
return model

cnn_m = base_model()
cnn_m.summary()


print("Predicting image")
cnn_m.predict(batch)
print("Predicted image")

错误

$ python mnist_cnn_test.py
Using TensorFlow backend.

x_train shape: (60000, 28, 28, 1)
60000 train samples
10000 test samples
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
conv2d_1 (Conv2D)            (None, 26, 26, 32)        320
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 24, 24, 64)        18496
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 12, 12, 64)        0
_________________________________________________________________
dropout_1 (Dropout)          (None, 12, 12, 64)        0
_________________________________________________________________
flatten_1 (Flatten)          (None, 9216)              0
_________________________________________________________________
dense_1 (Dense)              (None, 128)               1179776
_________________________________________________________________
dropout_2 (Dropout)          (None, 128)               0
_________________________________________________________________
dense_2 (Dense)              (None, 10)                1290
=================================================================
Total params: 1,199,882
Trainable params: 1,199,882
Non-trainable params: 0
_________________________________________________________________
Predicting image
Traceback (most recent call last):
  File "mnist_cnn_test.py", line 100, in <module>
    cnn_m.predict(batch)
  File "C:\Python35\lib\site-packages\keras\models.py", line 1027, in predict
steps=steps)
  File "C:\Python35\lib\site-packages\keras\engine\training.py", line 1782, in predict
    check_batch_axis=False)
  File "C:\Python35\lib\site-packages\keras\engine\training.py", line 120, in _standardize_input_data
str(data_shape))
ValueError: Error when checking : expected conv2d_1_input to have shape (28, 28, 1) but got array with shape (1, 28, 28)

推荐答案

好像您使用了错误的数据格式.您的数据以"channels_first"(即每张图片为1 x 28 x 28)的形式传递，但Conv2D图层期望使用"channels_last"(28 x 28 x 1).

Looks like you have the wrong data format. Your data is passed as channels_first (i.e. each image is 1 x 28 x 28) but the Conv2D layers expect channels_last (28 x 28 x 1).

一种解决方法是将data_format=channels_first传递给Conv2D和MaxPooling层.但是，如果您在CPU上运行，则可能不支持此功能.或者，更改此部分

One fix would be to pass data_format=channels_first to the Conv2D and MaxPooling layers. However this might not be supported if you are running on the CPU. Alternatively, change this part

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)

始终执行else块(它将重塑为channels_last格式).在这种情况下，请不要在Conv图层中包含data_format参数(默认为channels_last).

to always execute the else block (which does reshaping to a channels_last format). In that case, don't include the data_format argument to the Conv layers (it defaults to channels_last).

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