Question

在Keras中声明输入层时收到此错误消息。

回溯（最近通话最近一次）：

createModel model.add（Conv3D（16，（22，5，5），中的文件“ E：/physionet/CNN_onemodel.py”，第150行， strides =（1、2、2），padding ='valid'，activation ='relu'，data_format =“ channels_last”，input_shape = input_shape））

ValueError：由引起的负尺寸大小对于输入形状为[？，1,22,5,3844]，[22,5,5,3844,16]的'conv3d_3 / convolution'（运算符：'Conv3D'），从1中减去22。

感谢您的帮助。

代码：

    input_shape=(1, 22, 5, 3844)
    model = Sequential()
    #C1
    model.add(Conv3D(16, (22, 5, 5), strides=(1, 2, 2), padding='valid',activation='relu',data_format= "channels_first", input_shape=input_shape))
    model.add(keras.layers.MaxPooling3D(pool_size=(1, 2, 2),data_format= "channels_first",  padding='same'))
    model.add(BatchNormalization())
    #C2
    model.add(Conv3D(32, (1, 3, 3), strides=(1, 1,1), padding='valid',data_format= "channels_first",  activation='relu'))#incertezza se togliere padding
    model.add(keras.layers.MaxPooling3D(pool_size=(1,2, 2),data_format= "channels_first", ))
    model.add(BatchNormalization())

     #C3
    model.add(Conv3D(64, (1,3, 3), strides=(1, 1,1), padding='valid',data_format= "channels_first",  activation='relu'))#incertezza se togliere padding
    model.add(keras.layers.MaxPooling3D(pool_size=(1,2, 2),data_format= "channels_first", ))
    model.add(BatchNormalization())

    model.add(Flatten())
    model.add(Dropout(0.5))
    model.add(Dense(256, activation='sigmoid'))
    model.add(Dropout(0.5))
    model.add(Dense(2, activation='softmax'))

    opt_adam = keras.optimizers.Adam(lr=0.00001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
    model.compile(loss='categorical_crossentropy', optimizer=opt_adam, metrics=['accuracy'])

Answer 1

如果设置padding = "valid"（默认行为），则意味着在卷积/最大合并过程中会自动减少维数，并且会得到负数维。为确保在执行卷积/最大合并之后获得与您在指定Conv3D和MaxPooling3D图层时设置padding=same相同的尺寸。

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten
from tensorflow.keras.layers import Conv3D, MaxPooling3D, BatchNormalization
import numpy as np

input_shape=(1, 22, 5, 3844)
model = Sequential()
    #C1
model.add(Conv3D(16, (22, 5, 5), strides=(1, 2, 2), padding='same',activation='relu',data_format= "channels_first", input_shape=input_shape))
model.add(MaxPooling3D(pool_size=(1, 2, 2),data_format= "channels_first", padding='same'))
model.add(BatchNormalization())
    #C2
model.add(Conv3D(32, (1, 3, 3), strides=(1, 1, 1), padding='same',data_format= "channels_first",  activation='relu'))#incertezza se togliere padding
model.add(MaxPooling3D(pool_size=(1, 2, 2),data_format= "channels_first", padding='same'))
model.add(BatchNormalization())
     #C3
model.add(Conv3D(64, (1, 3, 3), strides=(1, 1, 1), padding='same',data_format= "channels_first",  activation='relu'))#incertezza se togliere padding
model.add(MaxPooling3D(pool_size=(1, 2, 2), data_format= "channels_first", padding='same'))
model.add(BatchNormalization())

model.add(Flatten())
model.add(Dropout(0.5))
model.add(Dense(256, activation='sigmoid'))
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax'))

opt_adam = tf.keras.optimizers.Adam(lr=0.00001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
model.compile(loss='categorical_crossentropy', optimizer=opt_adam, metrics=['accuracy'])

print(model.summary())

输出：

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv3d (Conv3D)              (None, 16, 22, 3, 1922)   8816      
_________________________________________________________________
max_pooling3d (MaxPooling3D) (None, 16, 22, 2, 961)    0         
_________________________________________________________________
batch_normalization (BatchNo (None, 16, 22, 2, 961)    3844      
_________________________________________________________________
conv3d_1 (Conv3D)            (None, 32, 22, 2, 961)    4640      
_________________________________________________________________
max_pooling3d_1 (MaxPooling3 (None, 32, 22, 1, 481)    0         
_________________________________________________________________
batch_normalization_1 (Batch (None, 32, 22, 1, 481)    1924      
_________________________________________________________________
conv3d_2 (Conv3D)            (None, 64, 22, 1, 481)    18496     
_________________________________________________________________
max_pooling3d_2 (MaxPooling3 (None, 64, 22, 1, 241)    0         
_________________________________________________________________
batch_normalization_2 (Batch (None, 64, 22, 1, 241)    964       
_________________________________________________________________
flatten (Flatten)            (None, 339328)            0         
_________________________________________________________________
dropout (Dropout)            (None, 339328)            0         
_________________________________________________________________
dense (Dense)                (None, 256)               86868224  
_________________________________________________________________
dropout_1 (Dropout)          (None, 256)               0         
_________________________________________________________________
dense_1 (Dense)              (None, 2)                 514       
=================================================================
Total params: 86,907,422
Trainable params: 86,904,056
Non-trainable params: 3,366
_________________________________________________________________

ValueError：“ conv3d_3 / convolution”从1中减去22导致的负尺寸大小（运算符：“ Conv3D”）

1 个答案: