Question

我需要在数据集上构建CNN模型，该数据集包含65536行（每行代表1个图像），49列（7x7图像）和二进制类（第50列）。（测试集7800行）

我正在引用使用mnist数据集执行CNN的示例，但我未能构建列车模型。

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)

当我运行它时出现错误提示：

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

您对第二个卷积图层的输入小于您要应用的滤镜。您的输入图片为(7,7);如果您对valid过滤器使用(5,5)填充，则会生成(3,3)的过滤图像。应用最大合并后，您将留下(1,1)图片，该图片与您的辅助卷积图层不兼容，其过滤尺寸为(3,3)。

在编译模型后，您可以通过运行model.summary()来查看每个图层的输出形状。负面形状会导致您看到impossible output shape错误。

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

This question是对有效填充和相同填充之间差异的一个很好的解释。

ValueError：CorrMM：不可能的输出形状

1 个答案: