ValueError: CorrMM: impossible output shape

Question

我需要在具有 65536 行（每行代表 1 个图像）、49 列（7x7 图像）和二进制 class（第 50 列）的数据集上构建 CNN 模型。（测试集 7800 行）

我参考了使用 mnist 数据集执行 CNN 的示例，但我未能构建训练模型。

这是我的代码：

from __future__ import print_function
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.utils import np_utils
from keras import backend as K
K.set_image_dim_ordering('th')
import pandas as pd
import numpy as np



# input image dimensions
img_rows, img_cols = 7, 7

# fix random seed for reproducibility
seed = 7
np.random.seed(seed)

x_train = pd.read_csv('palm_3x3_test.csv')
x_train.drop(['class'],axis=1,inplace=True)

x_train = x_train.as_matrix().reshape( -1,7, 7)

y_train = pd.read_csv('palm_3x3_test.csv')
y_train = y_train[['class']]


x_test = pd.read_csv('palm_3x3_data.csv')
x_test.drop(['class'],axis=1,inplace=True)
x_test = x_test.as_matrix().reshape( -1,7, 7)

y_test = pd.read_csv('palm_3x3_data.csv')
y_test = y_test[['class']]


# reshape to be [samples][pixels][width][height]
x_train_final = x_train.reshape(x_train.shape[0],1,7,7).astype('float32')
x_test_final = x_test.reshape(x_test.shape[0],1,7, 7).astype('float32')

print(x_train.shape)
print(x_test.shape)
print(x_train_final.shape)
print(x_test_final.shape)
print(y_train.shape)
print(y_test.shape)

# normalize inputs from 0-255 to 0-1
x_train_final = x_train_final / 255
x_test_final = x_test_final / 255

# one hot encode outputs
y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test)
num_classes = y_test.shape[1]

print (y_test.shape)
print(y_train.shape)

def baseline_model():
#     create model

    model = Sequential()
    model.add(Conv2D(30,(5,5), padding='valid',  activation='relu',input_shape=(1,7,7)))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Conv2D(15 ,(3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.2))
    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dense(50, activation='relu'))
    model.add(Dense(num_classes, activation='softmax'))

    # Compile model
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model

# build the model
model = baseline_model()
# Fit the model
model.fit(x_train_final,y_train, validation_data=(x_test_final,y_test), nb_epoch=10, batch_size=200,verbose=2)
# Final evaluation of the model
scores = model.evaluate(x_test,y_test, verbose=0)
print("CNN Error: %.2f%%" % (100-scores[1]*100))

我正在使用 Theano，我的数据的输出形状是：

x_train.shape = (65536L, 7L, 7L)
x_test.shape = (7800L, 7L, 7L)
x_train_final.shape = (65536L, 1L, 7L, 7L)
x_test_final.shape = (7800L, 1L, 7L, 7L)
y_train.shape = (65536L, 2L)
y_test.shape = (7800L, 2L)

而我运行它错误提示：

ValueError: CorrMM: impossible output shape
  bottom shape: 200 x 30 x 1 x 1
  weights shape: 15 x 30 x 3 x 3
  top shape: 200 x 15 x -1 x -1

Answer 1

您对第二个卷积层的输入小于您要应用的过滤器。您的输入图像是(7,7)；如果您将 valid 填充与 (5,5) 过滤器一起使用，您将得到一个过滤后的图像 (3,3) 。应用最大池后，您将得到一个 (1,1) 图像，该图像与过滤器大小为 (3,3).

的辅助卷积层不兼容

您可以在编译模型后通过运行 model.summary()查看每一层的输出形状。负形状会导致您看到 impossible output shape 错误。

在第一个卷积层中将padding='valid'改为padding='same'。这不会完全解决您的问题，您还需要更改第二个卷积层的填充以确保您的全连接层有输出。鉴于您的图像尺寸较小，您可能需要重新考虑使用最大池化。

很好地解释了有效填充和相同填充之间的区别。

ValueError: CorrMM: impossible output shape

ValueError: CorrMM: impossible output shape

python

theano

conv-neural-network

keras