python - ValueError：检查输入时出错：预期 conv2d_1_input 的形状为 (None, 50, 30, 3) 但得到的数组的形状为 (1, 3, 50, 30)

Question

我正在尝试通过 Keras 库将我用 Matlab 编写的深度学习网络转换为 Python。网络的任务是识别给定图像中的特定音符，并返回对应于特定声音名称的单个字符（'从'a'到'h'）

Matlab中的代码：

digitDatasetPath = fullfile('zbior_uczacy');
imds = imageDatastore(digitDatasetPath, ...
'IncludeSubfolders',true,'LabelSource','foldernames');

labelCount = countEachLabel(imds)

img = readimage(imds,1);
size(img)

numTrainFiles = 1200;
[imdsTrain,imdsValidation] = splitEachLabel(imds,numTrainFiles,'randomize');

layers = [
imageInputLayer([50 30 1])

convolution2dLayer(6,12,'Padding',1)
reluLayer

maxPooling2dLayer(2,'Stride',2)

convolution2dLayer(6,24,'Padding',1)
reluLayer

maxPooling2dLayer(2,'Stride',2)

convolution2dLayer(6,48,'Padding',1)
reluLayer

fullyConnectedLayer(7)
softmaxLayer
classificationLayer];


options = trainingOptions('sgdm', ...
        'LearnRateSchedule', 'piecewise', ...
        'LearnRateDropFactor', 0.3, ... 
        'LearnRateDropPeriod', 5, ... 
        'MaxEpochs', 50, ... 
        'MiniBatchSize', 300);

net = trainNetwork(imdsTrain,layers,options);

YPred = classify(net,imdsValidation);
YValidation = imdsValidation.Labels;

accuracy = sum(YPred == YValidation)/numel(YValidation)

代码已经过测试，运行良好。现在我正在尝试将此代码转换为 Keras 代码。完整代码在这里：

import numpy as np
import cv2
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten, Input
from keras.layers import Convolution2D, MaxPooling2D
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
from keras import backend as K
from keras.preprocessing import image

print("\n")
print("\n")
trained_weights = []
trained_bias = []

trained_weights = np.loadtxt('Weights.csv', delimiter=';')
trained_bias = np.loadtxt('Bias.csv', delimiter=';')
network_weights = np.array([[trained_weights]])
network_outputs = np.array([['a','c','d','e','f','g','h']])

# Load test image
img = load_img('note_a.png')
note = image.img_to_array(img)
note = np.expand_dims(note, axis=0)

# Model architecture
model = Sequential()

# Layer 1
model.add(Convolution2D(12, (6, 6), input_shape=(50, 30, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

# Layer 2
model.add(Convolution2D(24, (6, 6), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

# Layer 3
model.add(Convolution2D(48, (6, 6), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())
model.add(Dense(48))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(7, activation='softmax'))

model.set_weights([network_weights])

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics = ['accuracy'])

#model.fit((note,network_outputs), batch_size=32, nb_epoch=10, verbose=1)
score = model.evaluate(note, network_outputs)

为了避免所有的学习，我只是将学习的 matlab 网络的权重导出到 .csv 文件，然后将它们导入我的网络。所以这段代码的目标是只识别给定的图像，然后返回字符。这里没有学习。

该代码返回以下错误：

Traceback (most recent call last):
File "Train_Keras.py", line 51, in <module>
score = model.evaluate(note, network_outputs)
File "C:\Users\Karol\Anaconda3\lib\site-packages\keras\models.py", line 989, in evaluate
sample_weight=sample_weight)
File "C:\Users\Karol\Anaconda3\lib\site-packages\keras\engine\training.py", line 1723, in evaluate
batch_size=batch_size)
File "C:\Users\Karol\Anaconda3\lib\site-packages\keras\engine\training.py", line 1414, in _standardize_user_data
exception_prefix='input')
File "C:\Users\Karol\Anaconda3\lib\site-packages\keras\engine\training.py", line 153, in _standardize_input_data
str(array.shape))
ValueError: Error when checking input: expected conv2d_1_input to have shape (None, 50, 30, 3) but got array with shape (1, 3, 50, 30)

我的问题是，我是否正确进行图像到数组的转换？以及我应该如何将空行添加到数组中？发现某处，如果我将添加到第一个卷积层批量大小：

model.add(Convolution2D(12, (6, 6), batch_size=1, input_shape=(50, 30, 3), activation='relu'))

然后错误更改为：'检查输入时出错：预期 conv2d_1_input 具有形状（1、50、30、3）'，但我不确定添加 batch_size 是否不会改变网络活动。

此外，我在某处发现以网络希望它的确切方式重塑图像可能会有所帮助：

note = np.reshape(note, [1, 50, 30, 3])

但是，这会返回以下错误。老实说，我完全不明白这一点：

Traceback (most recent call last):
File "Train_Keras.py", line 52, in <module>
score = model.evaluate(note, network_outputs)
File "C:\Users\Karol\Anaconda3\lib\site-packages\keras\models.py", line 989, in evaluate
sample_weight=sample_weight)
File "C:\Users\Karol\Anaconda3\lib\site-packages\keras\engine\training.py", line 1729, in evaluate
self._make_test_function()
File "C:\Users\Karol\Anaconda3\lib\site-packages\keras\engine\training.py", line 1012, in _make_test_function
**self._function_kwargs)
File "C:\Users\Karol\Anaconda3\lib\site-packages\keras\backend\theano_backend.py", line 1233, in function
return Function(inputs, outputs, updates=updates, **kwargs)
File "C:\Users\Karol\Anaconda3\lib\site-packages\keras\backend\theano_backend.py", line 1219, in __init__
**kwargs)
File "C:\Users\Karol\Anaconda3\lib\site-packages\theano\compile\function.py", line 317, in function
output_keys=output_keys)
File "C:\Users\Karol\Anaconda3\lib\site-packages\theano\compile\pfunc.py", line 486, in pfunc
output_keys=output_keys)
File "C:\Users\Karol\Anaconda3\lib\site-packages\theano\compile\function_module.py", line 1839, in orig_function
name=name)
File "C:\Users\Karol\Anaconda3\lib\site-packages\theano\compile\function_module.py", line 1519, in __init__
optimizer_profile = optimizer(fgraph)
File "C:\Users\Karol\Anaconda3\lib\site-packages\theano\gof\opt.py", line 108, in __call__
return self.optimize(fgraph)
File "C:\Users\Karol\Anaconda3\lib\site-packages\theano\gof\opt.py", line 97, in optimize
ret = self.apply(fgraph, *args, **kwargs)
File "C:\Users\Karol\Anaconda3\lib\site-packages\theano\gof\opt.py", line 251, in apply
sub_prof = optimizer.optimize(fgraph)
File "C:\Users\Karol\Anaconda3\lib\site-packages\theano\gof\opt.py", line 93, in optimize
self.add_requirements(fgraph)
File "C:\Users\Karol\Anaconda3\lib\site-packages\theano\tensor\opt.py", line 1488, in add_requirements
fgraph.attach_feature(ShapeFeature())
File "C:\Users\Karol\Anaconda3\lib\site-packages\theano\gof\fg.py", line 546, in attach_feature
attach(self)
File "C:\Users\Karol\Anaconda3\lib\site-packages\theano\tensor\opt.py", line 1305, in on_attach
self.on_import(fgraph, node, reason='on_attach')
File "C:\Users\Karol\Anaconda3\lib\site-packages\theano\tensor\opt.py", line 1368, in on_import
self.set_shape(r, s)
File "C:\Users\Karol\Anaconda3\lib\site-packages\theano\tensor\opt.py", line 1157, in set_shape
shape_vars.append(self.unpack(s[i], r))
File "C:\Users\Karol\Anaconda3\lib\site-packages\theano\tensor\opt.py", line 1079, in unpack
raise AssertionError(msg)
AssertionError: There is a negative shape in the graph!
Backtrace when that variable is created:

File "Train_Keras.py", line 30, in <module>
model.add(Convolution2D(12, (6, 6), input_shape=(50, 30, 3), activation='relu'))
File "C:\Users\Karol\Anaconda3\lib\site-packages\keras\models.py", line 464, in add
layer(x)
File "C:\Users\Karol\Anaconda3\lib\site-packages\keras\engine\topology.py", line 603, in __call__
output = self.call(inputs, **kwargs)
File "C:\Users\Karol\Anaconda3\lib\site-packages\keras\layers\convolutional.py", line 164, in call
dilation_rate=self.dilation_rate)
File "C:\Users\Karol\Anaconda3\lib\site-packages\keras\backend\theano_backend.py", line 1913, in conv2d
filter_dilation=dilation_rate)

将不胜感激任何提示如何解决这个问题。谢谢你！

标签： pythonmatlabtensorflowkerasdeep-learning