我是python和tensorflow的初学者。 我在维度问题上有错误。 有没有人解决这个问题? 我的代码如下,错误来自'aux = Convolution2D'line。 错误消息是“ValueError:由于输入形状为[?,10,10,512],[10,512,512,1]而为'conv2d_15 / convolution'(op:'Conv2D')从10减去512而导致的负尺寸大小。
这是张量流后端。
def _conv_bn_relu(nb_filter, nb_row, nb_col, subsample=(1, 1)):
def f(input):
conv = Convolution2D(nb_filter=nb_filter, nb_row=nb_row, nb_col=nb_col,
subsample=subsample, init="he_normal",
border_mode="same")(input)
norm = BatchNormalization()(conv)
return ELU()(norm)
return f
def get_unet():
inputs = Input((img_rows, img_cols, 1), name='main_input')
conv1 = _conv_bn_relu(32, 7, 7)(inputs)
conv1 = _conv_bn_relu(32, 3, 3)(conv1)
pool1 = _conv_bn_relu(32, 2, 2, subsample=(2, 2))(conv1)
drop1 = Dropout(0.5)(pool1)
conv2 = _conv_bn_relu(64, 3, 3)(drop1)
conv2 = _conv_bn_relu(64, 3, 3)(conv2)
pool2 = _conv_bn_relu(64, 2, 2, subsample=(2, 2))(conv2)
drop2 = Dropout(0.5)(pool2)
conv3 = _conv_bn_relu(128, 3, 3)(drop2)
conv3 = _conv_bn_relu(128, 3, 3)(conv3)
pool3 = _conv_bn_relu(128, 2, 2, subsample=(2, 2))(conv3)
drop3 = Dropout(0.5)(pool3)
conv4 = _conv_bn_relu(256, 3, 3)(drop3)
conv4 = _conv_bn_relu(256, 3, 3)(conv4)
pool4 = _conv_bn_relu(256, 2, 2, subsample=(2, 2))(conv4)
drop4 = Dropout(0.5)(pool4)
conv5 = _conv_bn_relu(512, 3, 3)(drop4)
conv5 = _conv_bn_relu(512, 3, 3)(conv5)
drop5 = Dropout(0.5)(conv5)
print(drop5.shape)
# Using conv to mimic fully connected layer.
aux = Convolution2D(nb_filter=1, nb_row=drop5._keras_shape[2], nb_col=drop5._keras_shape[3],
subsample=(1, 1), init="he_normal", activation='sigmoid')(drop5)
aux = Flatten(name='aux_output')(aux)
# up6 = concatenate([Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same')(drop5), conv4], axis=3)
up6 = merge([UpSampling2D()(drop5), conv4], mode='concat', concat_axis=1)
conv6 = _conv_bn_relu(256, 3, 3)(up6)
conv6 = _conv_bn_relu(256, 3, 3)(conv6)
drop6 = Dropout(0.5)(conv6)
# up7 = concatenate([Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(drop6), conv3], axis=3)
up7 = merge([UpSampling2D()(drop6), conv3], mode='concat', concat_axis=1)
conv7 = _conv_bn_relu(128, 3, 3)(up7)
conv7 = _conv_bn_relu(128, 3, 3)(conv7)
drop7 = Dropout(0.5)(conv7)
# up8 = concatenate([Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(drop7), conv2], axis=3)
up8 = merge([UpSampling2D()(drop7), conv2], mode='concat', concat_axis=1)
conv8 = _conv_bn_relu(64, 3, 3)(up8)
conv8 = _conv_bn_relu(64, 3, 3)(conv8)
drop8 = Dropout(0.5)(conv8)
# up9 = concatenate([Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(drop8), conv1], axis=3)
up9 = merge([UpSampling2D()(drop8), conv1], mode='concat', concat_axis=1)
conv9 = _conv_bn_relu(32, 3, 3)(up9)
conv9 = _conv_bn_relu(32, 3, 3)(conv9)
drop9 = Dropout(0.5)(conv9)
conv10 = Convolution2D(1, 1, 1, activation='sigmoid', init="he_normal", name='main_output')(drop9)
# model = Model(inputs=[inputs], outputs=[conv10])
model = Model(inputs=[inputs], outputs=[conv10, aux])
# model.compile(optimizer=Adam(lr=1e-5), loss={'main_output': dice_loss},
# metrics={'main_output': dice},
# loss_weights={'main_output': 1})
model.compile(optimizer=Adam(lr=1e-5), loss={'main_output': dice_loss, 'aux_output': 'binary_crossentropy'},
metrics={'main_output': dice, 'aux_output': 'acc'},
loss_weights={'main_output': 1, 'aux_output': 0.5})
return model
答案 0 :(得分:1)
我认为你应该改变这一行:
aux = Convolution2D(nb_filter=1, nb_row=drop5._keras_shape[2], nb_col=drop5._keras_shape[3],
subsample=(1, 1), init="he_normal", activation='sigmoid')(drop5)
为:
aux = Convolution2D(nb_filter=1, nb_row=drop5._keras_shape[1], nb_col=drop5._keras_shape[2],
subsample=(1, 1), init="he_normal", activation='sigmoid')(drop5)
答案 1 :(得分:0)
我没有使用Keras,但我相信您的代码中的问题在于您输入的过滤器大小
aux = Convolution2D(nb_filter=1, nb_row=drop5._keras_shape[2], nb_col=drop5._keras_shape[3], subsample=(1, 1), init="he_normal", activation='sigmoid')(drop5)
很难推断出张量的尺寸,但在阅读了Keras documentation of Convolution2D之后,以及分析了张量的尺寸后,我假设drop5输出了一个形状的张量(samples, new_rows, new_cols, nb_filter)
([?,10,10,512] in your error message)
。换句话说,您的drop5
输出尺寸为10 x 10 x 512
的图片,或等效地说出512 10 x 10
张图片(this is a great read if you want to learn more about CNNs)。
现在设置nb_row=drop5._keras_shape[2]
和nb_col=drop5._keras_shape[3]
时,您可以将过滤器的尺寸设置为nb_row=10
和nb_col=512
。这意味着您将尝试使用10 x 10
形状的过滤器对512 10 x 512
张图片执行卷积。为了查看过滤器是否适合图像,我假设TensorFlow减去图像和过滤器尺寸。 [10, 10] - [10, 512] = [0, -502]
表示过滤器比图像大得多,因此无法执行卷积,从而导致出现错误消息。
此问题的解决方案是更改nb_row
和nb_col
维度。如果您希望过滤器大小超过10 x 10
,则可以从drop5
调整输出图像的大小。