ValueError异常：过滤器必须不大于输入

Question

大我非常新的机器学习，所以我用的例子和这样的玩弄。 代码中指定的图像尺寸为（28,28） 但由于某些原因我不断收到同样的ValueError异常 我无法弄清楚为什么发生这种情况。

下面的代码：

import pandas as pd 
import numpy as np 
np.random.seed(1337) # for reproducibility 

from keras.models import Sequential 
from keras.layers.core import Dense, Dropout, Activation, Flatten 
from keras.layers.convolutional import Convolution2D, MaxPooling2D 
from keras.utils import np_utils 

# input image dimensions 
img_rows, img_cols = 28, 28 

batch_size = 128 # Number of images used in each optimization step 
nb_classes = 10 # One class per digit 
nb_epoch = 35 # Number of times the whole data is used to learn 

# Read the train and test datasets 
train = pd.read_csv("../input/train.csv").values 
test = pd.read_csv("../input/test.csv").values 

# Reshape the data to be used by a Theano CNN. Shape is 
# (nb_of_samples, nb_of_color_channels, img_width, img_heigh) 
X_train = train[:, 1:].reshape(train.shape[0], 1, img_rows, img_cols) 
X_test = test.reshape(test.shape[0], 1, img_rows, img_cols) 
y_train = train[:, 0] # First data is label (already removed from X_train) 

# Make the value floats in [0;1] instead of int in [0;255] 
X_train = X_train.astype('float32') 
X_test = X_test.astype('float32') 
X_train /= 255 
X_test /= 255 

# convert class vectors to binary class matrices (ie one-hot vectors) 
Y_train = np_utils.to_categorical(y_train, nb_classes) 

#Display the shapes to check if everything's ok 
print('X_train shape:', X_train.shape) 
print('Y_train shape:', Y_train.shape) 
print('X_test shape:', X_test.shape) 

model = Sequential() 
# For an explanation on conv layers see http://cs231n.github.io/convolutional-networks/#conv 
# By default the stride/subsample is 1 
# border_mode "valid" means no zero-padding. 
# If you want zero-padding add a ZeroPadding layer or, if stride is 1 use border_mode="same" 
model.add(Convolution2D(12, 5, 5, border_mode='valid',input_shape=(1,img_rows, img_cols))) 
model.add(Activation('relu')) 

# For an explanation on pooling layers see http://cs231n.github.io/convolutional-networks/#pool 
model.add(MaxPooling2D(pool_size=(2, 2))) 

model.add(Dropout(0.15)) 

model.add(Convolution2D(24, 5, 5)) 
model.add(Activation('relu')) 

model.add(MaxPooling2D(pool_size=(2, 2))) 

model.add(Dropout(0.15)) 

# Flatten the 3D output to 1D tensor for a fully connected layer to accept the input 
model.add(Flatten()) 
model.add(Dense(180)) 
model.add(Activation('relu')) 
model.add(Dropout(0.5)) 
model.add(Dense(100)) 
model.add(Activation('relu')) 
model.add(Dropout(0.5)) 
model.add(Dense(nb_classes)) #Last layer with one output per class 
model.add(Activation('softmax')) #We want a score simlar to a probability for each class 

# The function to optimize is the cross entropy between the true label and the output (softmax) of the model 
# We will use adadelta to do the gradient descent see http://cs231n.github.io/neural-networks-3/#ada 
model.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=["accuracy"]) 

# Make the model learn 
model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch, verbose=1) 

# Predict the label for X_test 
yPred = model.predict_classes(X_test) 

# Save prediction in file for Kaggle submission 
np.savetxt('mnist-pred.csv', np.c_[range(1,len(yPred)+1),yPred], delimiter=',', header = 'ImageId,Label', comments = '', fmt='%d') 

Answer 1

所以，问题是所使用的卷积大小。卷积操作通常是减少图像的尺寸。同样 - 每个池操作都会减小大小。你有非常小的图像，但应用了模型架构，它被设计为一个更大的模型，因此在某一点上，在一个卷积/汇集之后，实际上有一个较小的输出图像比一个后续的滤波器大小，这是一个不明确的操作。

为了即暂时解决这个问题 - 除去第二卷积和maxpooling层，因为这些操作（与设置参数）不能在这样小的数据进行的。一般来说，首先应该了解卷积是如何工作的，而不应用别人的模型，因为这些参数对于提高性能至关重要 - 如果应用可将分辨率降低到很低的转换，您将无法学习任何东西。因此，一旦你有直觉知道卷积如何工作，你可以回去尝试不同的架构，但是没有人能够找出这个架构的“神奇”方程，因此我无法为你提供“正常工作”的参数 - 从删除这个额外的卷积和池，并且一旦你更好地理解你的数据和模型，就回去尝试其他的可能性。