期望conv2d_1_input具有形状（28,28,1）但是具有形状的数组（1,28,28）

Question

所以我在keras上使用mnist示例，我正在尝试预测我自己的数字。我真的很难如何匹配尺寸大小，因为我似乎无法找到一种方法来调整图像大小以使图像没有后面的行和列。我已尝试使用via numpy调整大小但是我在错误后得到错误...

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)

Answer 1

看起来你有错误的数据格式。您的数据作为channels_first传递（即每个图像为1 x 28 x 28），但Conv2D层需要channels_last（28 x 28 x 1）。

一个解决方法是将data_format=channels_first传递给Conv2D和MaxPooling图层。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

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)

总是执行else块（它重新整形为channels_last格式）。在这种情况下，不要将conzxswpoi参数包含在Conv图层中（默认为channels_last）。

Answer 2

Solution:

data_format

Reasoning:

im = cv2.resize(im,  (img_rows, img_cols)) 
im.reshape((img_rows,img_cols))
print(im.shape) # (28,28)

batch = np.expand_dims(im,axis=0)
print(batch.shape) # (1, 28, 28)
batch = np.expand_dimes(batch,axis=3)
print(batch.shape) # (1, 28, 28,1)

... # build the model
model.predict(batch)

指任何批量大小（样本号），28 * 28形状和1个通道。在您的情况下，使用1作为样本号。