from keras.layers import Input, Conv3D, MaxPooling3D, concatenate, Conv3DTranspose, BatchNormalization, Dropout, Lambda
from keras.optimizers import Adam
我的影像的形狀是（36,128,128,1）。如何更改 u7 的形狀以便我可以執行連接？如何修改？
def simple_unet_model(IMG_HEIGHT, IMG_WIDTH, IMG_DEPTH, IMG_CHANNELS, num_classes):
#Build the model
inputs = Input((IMG_HEIGHT, IMG_WIDTH, IMG_DEPTH, IMG_CHANNELS))
#s = Lambda(lambda x: x / 255)(inputs) #No need for this if we normalize our inputs beforehand
s = inputs
inputs = Input(shape=(36,128,128),name='input')
#Contraction path
c1 = Conv3D(16, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(s)
c1 = Dropout(0.1)(c1)
c1 = Conv3D(16, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c1)
p1 = MaxPooling3D((2, 2, 2))(c1)
c2 = Conv3D(32, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(p1)
c2 = Dropout(0.1)(c2)
c2 = Conv3D(32, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c2)
p2 = MaxPooling3D((2, 2, 2))(c2)
c3 = Conv3D(64, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(p2)
c3 = Dropout(0.2)(c3)
c3 = Conv3D(64, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c3)
p3 = MaxPooling3D((2, 2, 2))(c3)
c4 = Conv3D(128, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(p3)
c4 = Dropout(0.2)(c4)
c4 = Conv3D(128, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c4)
p4 = MaxPooling3D(pool_size=(2, 2, 2))(c4)
c5 = Conv3D(256, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(p4)
c5 = Dropout(0.3)(c5)
c5 = Conv3D(256, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c5)
#Expansive path
u6 = Conv3DTranspose(128, (2, 2, 2), strides=(2, 2, 2), padding='same')(c5)
u6 = concatenate([u6, c4])
c6 = Conv3D(128, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(u6)
c6 = Dropout(0.2)(c6)
c6 = Conv3D(128, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c6)
u7 = Conv3DTranspose(64, (2, 2, 2), strides=(2, 2, 2), padding='same')(c6)
u7 = concatenate([u7, c3])
c7 = Conv3D(64, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(u7)
c7 = Dropout(0.2)(c7)
c7 = Conv3D(64, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c7)
u8 = Conv3DTranspose(32, (2, 2, 2), strides=(2, 2, 2), padding='same')(c7)
u8 = concatenate([u8, c2])
c8 = Conv3D(32, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(u8)
c8 = Dropout(0.1)(c8)
c8 = Conv3D(32, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c8)
u9 = Conv3DTranspose(16, (2, 2, 2), strides=(2, 2, 2), padding='same')(c8)
u9 = concatenate([u9, c1])
c9 = Conv3D(16, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(u9)
c9 = Dropout(0.1)(c9)
c9 = Conv3D(16, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c9)
outputs = Conv3D(num_classes, (1, 1, 1), activation='softmax')(c9)
model = Model(inputs=[inputs], outputs=[outputs])
#compile model outside of this function to make it flexible.
model.summary()
return model
我在 u7 = concatenate([u7, c3]) 行中遇到錯誤
層需要具有匹配形狀的Concatenate輸入，除了 concat 軸。得到輸入形狀：[(None, 32, 32, 8, 64), (None, 32, 32, 9, 64)] 但是如果我的影像的形狀是 (64,128,128,1)。它可以正常作業。但是如果我將深度從 36 增加到 64；影像發生變化
構建模型
epochs = 10
model.fit(
X_train,y_train,
validation_data=(X_test,y_test),
epochs=epochs,
shuffle=True,
verbose=2,
callbacks = callbacks_list)
我收到錯誤 ValueError: Input 0 is incompatible with layer model: expected shape=(None, 36, 128, 128, 1), found shape=(None, 64, 128, 128, 1)
uj5u.com熱心網友回復：
您可以嘗試連接axis=1并洗掉Input兩層之一。你只需要一個。這是一個作業示例（盡管我不確定您的目標是什么）：
from keras.layers import Input, Conv3D, MaxPooling3D, concatenate, Conv3DTranspose, BatchNormalization, Dropout, Lambda
import tensorflow as tf
def simple_unet_model(IMG_HEIGHT, IMG_WIDTH, IMG_DEPTH, IMG_CHANNELS, num_classes):
#Build the model
kernel_initializer = tf.keras.initializers.GlorotNormal()
inputs = Input((IMG_HEIGHT, IMG_WIDTH, IMG_DEPTH, IMG_CHANNELS), name='input')
#s = Lambda(lambda x: x / 255)(inputs) #No need for this if we normalize our inputs beforehand
s = inputs
#Contraction path
c1 = Conv3D(16, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(s)
c1 = Dropout(0.1)(c1)
c1 = Conv3D(16, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c1)
p1 = MaxPooling3D((2, 2, 2))(c1)
c2 = Conv3D(32, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(p1)
c2 = Dropout(0.1)(c2)
c2 = Conv3D(32, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c2)
p2 = MaxPooling3D((2, 2, 2))(c2)
c3 = Conv3D(64, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(p2)
c3 = Dropout(0.2)(c3)
c3 = Conv3D(64, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c3)
p3 = MaxPooling3D((2, 2, 2))(c3)
c4 = Conv3D(128, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(p3)
c4 = Dropout(0.2)(c4)
c4 = Conv3D(128, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c4)
p4 = MaxPooling3D(pool_size=(2, 2, 2))(c4)
c5 = Conv3D(256, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(p4)
c5 = Dropout(0.3)(c5)
c5 = Conv3D(256, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c5)
#Expansive path
u6 = Conv3DTranspose(128, (2, 2, 2), strides=(2, 2, 2), padding='same')(c5)
u6 = concatenate([u6, c4])
c6 = Conv3D(128, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(u6)
c6 = Dropout(0.2)(c6)
c6 = Conv3D(128, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c6)
u7 = Conv3DTranspose(64, (2, 2, 2), strides=(2, 2, 2), padding='same')(c6)
u7 = concatenate([u7, c3], axis=1)
c7 = Conv3D(64, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(u7)
c7 = Dropout(0.2)(c7)
c7 = Conv3D(64, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c7)
u8 = Conv3DTranspose(32, (2, 2, 2), strides=(2, 2, 2), padding='same')(c7)
u8 = concatenate([u8, c2], axis=1)
c8 = Conv3D(32, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(u8)
c8 = Dropout(0.1)(c8)
c8 = Conv3D(32, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c8)
u9 = Conv3DTranspose(16, (2, 2, 2), strides=(2, 2, 2), padding='same')(c8)
u9 = concatenate([u9, c1], axis=1)
c9 = Conv3D(16, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(u9)
c9 = Dropout(0.1)(c9)
c9 = Conv3D(16, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c9)
outputs = Conv3D(num_classes, (1, 1, 1), activation='softmax')(c9)
model = tf.keras.Model(inputs=[inputs], outputs=[outputs])
#compile model outside of this function to make it flexible.
model.summary()
return model
simple_unet_model(36,128,128, 1, 5)
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