ValueError: Filter must not be larger than the input
ValueError: Filter must not be larger than the input
我对机器学习还很陌生，所以我正在研究示例等。
代码中指定的图片大小为(28,28)
但出于某种原因，我不断收到相同的 ValueError
我不明白为什么会这样。
代码如下：
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')
所以问题出在使用的卷积尺寸上。卷积运算通常减少图像的维度。类似地 - 每个池化操作都会减少大小。您有非常小的图像，但应用了专为更大图像设计的模型架构，因此在某些时候，在 convolutions/poolings 之一之后，您实际上输出的图像小于以下过滤器尺寸，这是一种病态- 定义的操作。
暂时解决问题 - 删除第二个卷积层和最大池化层，因为这些操作（提供参数）无法在如此小的数据上执行。一般来说，您应该首先了解卷积的工作原理，而不是应用其他人的模型，因为参数对于良好的性能至关重要 - 如果您应用会大大降低分辨率的转换 - 您将无法学习任何东西。因此，一旦您对卷积的工作原理有了一些直觉，就可以返回并尝试不同的体系结构，但是没有 "magical" 方程式来计算体系结构，因此我无法为您提供 "just work" 的参数- 从删除这个额外的卷积和池化开始，然后在你更好地理解你的数据和模型后返回并尝试其他可能性。
我对机器学习还很陌生，所以我正在研究示例等。 代码中指定的图片大小为(28,28) 但出于某种原因，我不断收到相同的 ValueError 我不明白为什么会这样。
代码如下：
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')
所以问题出在使用的卷积尺寸上。卷积运算通常减少图像的维度。类似地 - 每个池化操作都会减少大小。您有非常小的图像，但应用了专为更大图像设计的模型架构，因此在某些时候，在 convolutions/poolings 之一之后，您实际上输出的图像小于以下过滤器尺寸，这是一种病态- 定义的操作。
暂时解决问题 - 删除第二个卷积层和最大池化层，因为这些操作（提供参数）无法在如此小的数据上执行。一般来说，您应该首先了解卷积的工作原理，而不是应用其他人的模型，因为参数对于良好的性能至关重要 - 如果您应用会大大降低分辨率的转换 - 您将无法学习任何东西。因此，一旦您对卷积的工作原理有了一些直觉，就可以返回并尝试不同的体系结构，但是没有 "magical" 方程式来计算体系结构，因此我无法为您提供 "just work" 的参数- 从删除这个额外的卷积和池化开始，然后在你更好地理解你的数据和模型后返回并尝试其他可能性。