预期 conv2d 有 4 个维度,但得到了形状数组
expected conv2d to have 4 dimensions, but got array with shape
我正在尝试使用 Keras 在一些具有 nifti 格式的医学图像上执行卷积网络。当我尝试像这样拟合模型时:
model.fit(X_train, Y_train,
batch_size=batch_size,
epochs = n_epoch,
validation_data=(X_test, Y_test))
我收到这个错误:
expected conv2d_171 to have 4 dimensions, but got array with shape
(1240, 240, 240)
但是当我将输入大小从 img_channels = 4
更改为:
img_channels = 3
img_rows = 240
img_cols = 240
我收到另一个错误:
expected input_8 to have shape (240, 240, 3) but got array with shape (240, 240, 4)
图片大小是这样的:
我应该调整图像大小吗?或翻转图像元素的顺序?
这是模型的代码:
inputs = Input((img_rows, img_cols, img_channels))
s = Lambda(lambda x: x / 255) (inputs)
c1 = Conv2D(16, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (s)
c1 = Dropout(0.1) (c1)
c1 = Conv2D(16, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c1)
p1 = MaxPooling2D((2, 2)) (c1)
c2 = Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (p1)
c2 = Dropout(0.1) (c2)
c2 = Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c2)
p2 = MaxPooling2D((2, 2)) (c2)
c3 = Conv2D(64, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (p2)
c3 = Dropout(0.2) (c3)
c3 = Conv2D(64, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c3)
p3 = MaxPooling2D((2, 2)) (c3)
c4 = Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (p3)
c4 = Dropout(0.2) (c4)
c4 = Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c4)
p4 = MaxPooling2D(pool_size=(2, 2)) (c4)
c5 = Conv2D(256, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (p4)
c5 = Dropout(0.3) (c5)
c5 = Conv2D(256, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c5)
u6 = Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same') (c5)
u6 = concatenate([u6, c4])
c6 = Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (u6)
c6 = Dropout(0.2) (c6)
c6 = Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c6)
u7 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same') (c6)
u7 = concatenate([u7, c3])
c7 = Conv2D(64, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (u7)
c7 = Dropout(0.2) (c7)
c7 = Conv2D(64, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c7)
u8 = Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same') (c7)
u8 = concatenate([u8, c2])
c8 = Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (u8)
c8 = Dropout(0.1) (c8)
c8 = Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c8)
u9 = Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same') (c8)
u9 = concatenate([u9, c1], axis=3)
c9 = Conv2D(16, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (u9)
c9 = Dropout(0.1) (c9)
c9 = Conv2D(16, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c9)
outputs = Conv2D(1, (1, 1), activation='sigmoid') (c9)
我看到您正在使用 Unet 进行分段。问题出在您的标签上,每个标签都应该是像 (width, height, num_classes)
这样的 3D 矩阵。所以加上样本长度,应该是(sample_length, width, height, num_classes)
。
在您的情况下,将它们转换为 (310, 240, 240, num_classes)
。如果 label/target 个图像是二进制的,则 num_classes=1
。否则,您可能需要对带注释的图像进行一些预处理并将它们一次性编码为 num_classes
.
此外,请查看您是否多次构建计算图,因为 conv2d_171
似乎是一个相当高的数字。我在您的架构中看不到那么多层次。
我正在尝试使用 Keras 在一些具有 nifti 格式的医学图像上执行卷积网络。当我尝试像这样拟合模型时:
model.fit(X_train, Y_train,
batch_size=batch_size,
epochs = n_epoch,
validation_data=(X_test, Y_test))
我收到这个错误:
expected conv2d_171 to have 4 dimensions, but got array with shape (1240, 240, 240)
但是当我将输入大小从 img_channels = 4
更改为:
img_channels = 3
img_rows = 240
img_cols = 240
我收到另一个错误:
expected input_8 to have shape (240, 240, 3) but got array with shape (240, 240, 4)
图片大小是这样的:
我应该调整图像大小吗?或翻转图像元素的顺序?
这是模型的代码:
inputs = Input((img_rows, img_cols, img_channels))
s = Lambda(lambda x: x / 255) (inputs)
c1 = Conv2D(16, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (s)
c1 = Dropout(0.1) (c1)
c1 = Conv2D(16, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c1)
p1 = MaxPooling2D((2, 2)) (c1)
c2 = Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (p1)
c2 = Dropout(0.1) (c2)
c2 = Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c2)
p2 = MaxPooling2D((2, 2)) (c2)
c3 = Conv2D(64, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (p2)
c3 = Dropout(0.2) (c3)
c3 = Conv2D(64, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c3)
p3 = MaxPooling2D((2, 2)) (c3)
c4 = Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (p3)
c4 = Dropout(0.2) (c4)
c4 = Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c4)
p4 = MaxPooling2D(pool_size=(2, 2)) (c4)
c5 = Conv2D(256, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (p4)
c5 = Dropout(0.3) (c5)
c5 = Conv2D(256, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c5)
u6 = Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same') (c5)
u6 = concatenate([u6, c4])
c6 = Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (u6)
c6 = Dropout(0.2) (c6)
c6 = Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c6)
u7 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same') (c6)
u7 = concatenate([u7, c3])
c7 = Conv2D(64, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (u7)
c7 = Dropout(0.2) (c7)
c7 = Conv2D(64, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c7)
u8 = Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same') (c7)
u8 = concatenate([u8, c2])
c8 = Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (u8)
c8 = Dropout(0.1) (c8)
c8 = Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c8)
u9 = Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same') (c8)
u9 = concatenate([u9, c1], axis=3)
c9 = Conv2D(16, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (u9)
c9 = Dropout(0.1) (c9)
c9 = Conv2D(16, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c9)
outputs = Conv2D(1, (1, 1), activation='sigmoid') (c9)
我看到您正在使用 Unet 进行分段。问题出在您的标签上,每个标签都应该是像 (width, height, num_classes)
这样的 3D 矩阵。所以加上样本长度,应该是(sample_length, width, height, num_classes)
。
在您的情况下,将它们转换为 (310, 240, 240, num_classes)
。如果 label/target 个图像是二进制的,则 num_classes=1
。否则,您可能需要对带注释的图像进行一些预处理并将它们一次性编码为 num_classes
.
此外,请查看您是否多次构建计算图,因为 conv2d_171
似乎是一个相当高的数字。我在您的架构中看不到那么多层次。