我的模型是U网实现 -只能使用求和模式合并相同输出形状的图层。分层形状
from keras.layers import Input, merge, Convolution2D, MaxPooling2D,
UpSampling2D
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint, LearningRateScheduler
from keras import backend as K
from keras.models import Model
def seg_score(y_true, y_pred):
smooth = 1.0
y_true_f = K.flatten(y_true)
y_pred_f = K.flatten(y_pred)
intersection = K.sum(y_true_f * y_pred_f)
true_sum = K.sum(y_true_f); pred_sum = K.sum(y_pred_f)
if(true_sum > pred_sum):
max_sum = true_sum
else:
max_sum = pred_sum
return (intersection + smooth)/(max_sum + smooth)
def seg_score_loss(y_true, y_pred):
return -seg_score(y_true, y_pred)
def dice_coef(y_true, y_pred):
smooth = 1.
y_true_f = K.flatten(y_true)
y_pred_f = K.flatten(y_pred)
intersection = K.sum(y_true_f * y_pred_f)
return (2. * intersection + smooth)/(K.sum(y_true_f) + K.sum(y_pred_f) + smooth)
def dice_coef_loss(y_true, y_pred):
return -dice_coef(y_true, y_pred)
def get_unet(num_color_component, dimension):
img_rows = dimension; img_cols = dimension;
inputs = Input((num_color_component, img_rows, img_cols))
conv1 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(inputs)
conv1 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(pool1)
conv2 = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Convolution2D(128, 3, 3, activation='relu', border_mode='same')(pool2)
conv3 = Convolution2D(128, 3, 3, activation='relu', border_mode='same')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Convolution2D(256, 3, 3, activation='relu', border_mode='same')(pool3)
conv4 = Convolution2D(256, 3, 3, activation='relu', border_mode='same')(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)
conv5 = Convolution2D(512, 3, 3, activation='relu', border_mode='same')(pool4)
conv5 = Convolution2D(512, 3, 3, activation='relu', border_mode='same')(conv5)
up6 = merge([UpSampling2D(size=(2, 2))(conv5), conv4], mode='concat', concat_axis=1)
conv6 = Convolution2D(256, 3, 3, activation='relu', border_mode='same')(up6)
conv6 = Convolution2D(256, 3, 3, activation='relu', border_mode='same')(conv6)
up7 = merge([UpSampling2D(size=(2, 2))(conv6), conv3], mode='concat', concat_axis=1)
conv7 = Convolution2D(128, 3, 3, activation='relu', border_mode='same')(up7)
conv7 = Convolution2D(128, 3, 3, activation='relu', border_mode='same')(conv7)
up8 = merge([UpSampling2D(size=(2, 2))(conv7), conv2], mode='concat', concat_axis=1)
conv8 = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(up8)
conv8 = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(conv8)
up9 = merge([UpSampling2D(size=(2, 2))(conv8), conv1], mode='concat', concat_axis=1)
conv9 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(up9)
conv9 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(conv9)
conv10 = Convolution2D(1, 1, 1, activation='sigmoid')(conv9)
model = Model(input=inputs, output=conv10)
#model.compile(optimizer=Adam(lr=1e-5), loss=seg_score_loss, metrics=[seg_score])
model.compile(optimizer=Adam(lr=1e-5), loss=dice_coef_loss, metrics=[dice_coef])
return model
我收到错误如下 -
Traceback (most recent call last): File "/home/zaverichintan/Chintan/PycharmProjects/CNN_wbc_identification/train.py", line 60, in model = mo.get_unet(num_color_component, filter_size); File "/home/zaverichintan/Chintan/PycharmProjects/CNN_wbc_identification/models.py", line 63, in get_unet up7 = merge([UpSampling2D(size=(2, 2))(conv6), conv3], mode='concat', concat_axis=1) File "/home/zaverichintan/anaconda2/lib/python2.7/site-packages/keras/legacy/layers.py", line 456, in merge name=name) File "/home/zaverichintan/anaconda2/lib/python2.7/site-packages/keras/legacy/layers.py", line 107, in init node_indices, tensor_indices) File "/home/zaverichintan/anaconda2/lib/python2.7/site-packages/keras/legacy/layers.py", line 187, in _arguments_validation 'Layer shapes: %s' % (input_shapes)) ValueError: "concat" mode can only merge layers with matching output shapes except for the concat axis. Layer shapes: [(None, 0, 16, 256), (None, 0, 16, 128)]
改变的毗连轴3，那么我得到这个 -
File "/home/zaverichintan/Chintan/PycharmProjects/CNN_wbc_identification/train.py", line 60, in model = mo.get_unet(num_color_component, filter_size); File "/home/zaverichintan/Chintan/PycharmProjects/CNN_wbc_identification/models.py", line 71, in get_unet up8 = keras.layers.merge([UpSampling2D(size=(2, 2))(conv7), conv2], mode='concat', concat_axis=1) File "/home/zaverichintan/anaconda2/lib/python2.7/site-packages/keras/legacy/layers.py", line 456, in merge name=name) File "/home/zaverichintan/anaconda2/lib/python2.7/site-packages/keras/legacy/layers.py", line 107, in init node_indices, tensor_indices) File "/home/zaverichintan/anaconda2/lib/python2.7/site-packages/keras/legacy/layers.py", line 187, in _arguments_validation 'Layer shapes: %s' % (input_shapes)) ValueError: "concat" mode can only merge layers with matching output shapes except for the concat axis. Layer shapes: [(None, 0, 32, 128), (None, 1, 32, 64)]
您可能需要你的'conat_axis'更改为具有不同尺寸的轴，或者改变您的不同轴的尺寸相等。 – KDecker
conat_axis会是？ –
对不起，这是一个错字'concat_axis'。 – KDecker