預期conv2d具有4個維度，但具有形狀的數組

Question

我想在具有nifti格式的一些醫學影像，使用Keras進行卷積網絡。 當我嘗試像這樣擬合模型時：

model.fit(X_train, Y_train, 
      batch_size=batch_size, 
      epochs = n_epoch, 
      validation_data=(X_test, Y_test))

我收到此錯誤：

預期conv2d_171具有4個維度，但數組的形狀為（1240、240、240）

但是，當我將輸入大小從img_channels = 4更改為此時：

img_channels = 3
img_rows = 240
img_cols = 240

我收到另一個錯誤：

預期input_8的形狀為（240，240，3），但數組的形狀為（240，240，4）

圖片的大小是這樣的：

我應該調整圖像大小嗎？ 或翻轉圖像元素的順序？

這是該模型的代碼：

    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)

Answer 1

我看到您正在使用Unet進行細分。 問題出在您的標簽上，每個標簽都應該是3D (width, height, num_classes)例如(width, height, num_classes) 。 因此，對於樣本長度，應該為(sample_length, width, height, num_classes) 。

在您的情況下，將其轉換為(310, 240, 240, num_classes) 。 如果標簽/目標圖像是二進制的，則num_classes=1 。 否則，您可能需要對帶注釋的圖像進行一些預處理，然后將它們num_classes編碼為num_classes 。

另外，請查看是否要多次構建計算圖，因為conv2d_171似乎是一個很高的數字。 我看不到您的體系結構中的太多層。