我尝试使用常规分割（不是多级）在喀拉拉邦训练U-Net模型，但是出现以下错误：
ValueError：检查目标时出错：预期conv2d_19具有形状（无，320、320、1）但形状为（18、320、320、2）的数组
我知道这与输入Y_train有关，我与keras.utils.to_categorical函数有关，但是to_categorical（Y_train）和to_categorical（Y_train，2）返回此错误，而to_categorical（Y_train，1）返回IndexError：索引1超出了轴1的大小1。
我什至试图通过将Y_train重塑为（None，320，320，1）来更改它，但是它返回了
'ValueError：您正在传递形状为（18、320、320、1）的目标数组，同时将categorical_crossentropy用作损耗。 categorical_crossentropy期望目标是形状（样本，类）的二进制矩阵（1s和0s）。如果目标是整数类，则可以通过以下方式将它们转换为预期的格式： 从keras.utils导入to_categorical y_binary = to_categorical（y_int） 另外，您也可以使用损失函数
sparse_categorical_crossentropy，它确实需要整数目标。'
使用训练功能尝试拟合模型时，会返回错误。
def preprocess_images(X_train, Y_train, X_test, Y_test):
X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], X_train.shape[2], 1)
X_test = X_test.reshape(X_test.shape[0], X_train.shape[1], X_train.shape[2], 1)
X_train = X_train.astype("float32")
X_test = X_test.astype("float32")
X_train /= 255
X_test /= 255
Y_train /= 255
Y_test /= 255
#Y_train = Y_train.reshape(Y_train.shape[0], Y_train.shape[1], Y_train.shape[2], 1)
#Y_test = Y_test.reshape(Y_test.shape[0], Y_train.shape[1], Y_train.shape[2], 1)
Y_train = keras.utils.to_categorical(Y_train, 1)
Y_test = keras.utils.to_categorical(Y_test, 1)
return ((X_train, Y_train), (X_test, Y_test))
def CNNs_layers(nb_conv_pool = 1, filters = 32, kernel_size = (3, 3), activation = 'relu', pool_size = (2 ,2), dropout_rate = 0.25, input_shape = (28, 28, 1), data_format = 'channels_last'):
inputs = tf.keras.layers.Input((IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS))
s = tf.keras.layers.Lambda(lambda x: x / 255)(inputs)
c1 = tf.keras.layers.Conv2D(16, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',
padding='same')(s)
c1 = tf.keras.layers.Dropout(0.1)(c1)
c1 = tf.keras.layers.Conv2D(16, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',
padding='same')(c1)
p1 = tf.keras.layers.MaxPooling2D((2, 2))(c1)
c2 = tf.keras.layers.Conv2D(32, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',
padding='same')(p1)
c2 = tf.keras.layers.Dropout(0.1)(c2)
c2 = tf.keras.layers.Conv2D(32, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',
padding='same')(c2)
p2 = tf.keras.layers.MaxPooling2D((2, 2))(c2)
c3 = tf.keras.layers.Conv2D(64, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',
padding='same')(p2)
c3 = tf.keras.layers.Dropout(0.2)(c3)
c3 = tf.keras.layers.Conv2D(64, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',
padding='same')(c3)
p3 = tf.keras.layers.MaxPooling2D((2, 2))(c3)
c4 = tf.keras.layers.Conv2D(128, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',
padding='same')(p3)
c4 = tf.keras.layers.Dropout(0.2)(c4)
c4 = tf.keras.layers.Conv2D(128, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',
padding='same')(c4)
p4 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(c4)
c5 = tf.keras.layers.Conv2D(256, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',
padding='same')(p4)
c5 = tf.keras.layers.Dropout(0.3)(c5)
c5 = tf.keras.layers.Conv2D(256, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',
padding='same')(c5)
u6 = tf.keras.layers.Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(c5)
u6 = tf.keras.layers.concatenate([u6, c4])
c6 = tf.keras.layers.Conv2D(128, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',
padding='same')(u6)
c6 = tf.keras.layers.Dropout(0.2)(c6)
c6 = tf.keras.layers.Conv2D(128, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',
padding='same')(c6)
u7 = tf.keras.layers.Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(c6)
u7 = tf.keras.layers.concatenate([u7, c3])
c7 = tf.keras.layers.Conv2D(64, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',
padding='same')(u7)
c7 = tf.keras.layers.Dropout(0.2)(c7)
c7 = tf.keras.layers.Conv2D(64, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',
padding='same')(c7)
u8 = tf.keras.layers.Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(c7)
u8 = tf.keras.layers.concatenate([u8, c2])
c8 = tf.keras.layers.Conv2D(32, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',
padding='same')(u8)
c8 = tf.keras.layers.Dropout(0.1)(c8)
c8 = tf.keras.layers.Conv2D(32, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',
padding='same')(c8)
u9 = tf.keras.layers.Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same')(c8)
u9 = tf.keras.layers.concatenate([u9, c1], axis=3)
c9 = tf.keras.layers.Conv2D(16, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',
padding='same')(u9)
c9 = tf.keras.layers.Dropout(0.1)(c9)
c9 = tf.keras.layers.Conv2D(16, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',
padding='same')(c9)
outputs = tf.keras.layers.Conv2D(1, (1, 1), activation='sigmoid')(c9)
model = tf.keras.Model(inputs=[inputs], outputs=[outputs])
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.summary()
return (model)
def training(model, X_train, Y_train, loss = 'categorical_crossentropy', optimizer='adam', metrics=['accuracy'], batch_size = 32, nb_epoch = 100, verbose = 2, validation_split = 0.3):
model.compile(loss = loss, optimizer = optimizer, metrics = metrics)
history = model.fit(X_train, Y_train, batch_size = batch_size, epochs = nb_epoch, verbose = verbose, validation_split = validation_split)
return (history)
答案 0 :(得分：0)
假设标签是一次性编码的，则应将输出层更改为：
outputs = tf.keras.layers.Conv2D(2, (1, 1), activation='softmax')(c9)
损失函数为categorical_crossentropy。如果您想使用当前模型并且不做任何修改就损失，那么您必须不对标签进行一次热编码，因为binary_crossentropy直接获取0-1个二进制标签而无需进行一次热编码