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我是 CNN 的新手，我无法确定如何解决这个问题。在这段代码中，我正在训练一组图像以从卷积网络中获取掩码。图像是灰度的，形状为 (200,200)。我无法确定我在哪里犯了错误。此外，每次我运行我的代码时，不同的输入都会出现错误。任何帮助将不胜感激。

以下是生成的日志：

Creating training images...
Saving to .npy files done.
Creating test images...
Saving to .npy files done.
------------------------------
Loading and preprocessing train data...
------------------------------
------------------------------
Creating and compiling model...
------------------------------
C:/Users/Asus/Desktop/training.py:101: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(25, (3, 3), activation="relu", padding="same", data_format="channels_last")`
  conv2 = Conv2D(25, (3, 3), activation='relu', padding='same',dim_ordering="th")(inputs)
C:/Users/Asus/Desktop/training.py:102: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(25, (3, 3), activation="relu", padding="same", data_format="channels_first")`
  conv2 = Conv2D(25, (3, 3), activation='relu', padding='same',dim_ordering="th")(conv2)
C:/Users/Asus/Desktop/training.py:103: UserWarning: Update your `MaxPooling2D` call to the Keras 2 API: `MaxPooling2D(pool_size=(2, 2), data_format="channels_last")`
  pool2 = MaxPooling2D(pool_size=(2, 2), dim_ordering="tf")(conv2)
C:/Users/Asus/Desktop/training.py:105: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(50, (3, 3), activation="relu", padding="same", data_format="channels_first")`
  conv3 = Conv2D(50, (3, 3), activation='relu', padding='same',dim_ordering="th")(pool2)
C:/Users/Asus/Desktop/training.py:106: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(50, (3, 3), activation="relu", padding="same", data_format="channels_first")`
  conv3 = Conv2D(50, (3, 3), activation='relu', padding='same',dim_ordering="th")(conv3)
C:/Users/Asus/Desktop/training.py:107: UserWarning: Update your `MaxPooling2D` call to the Keras 2 API: `MaxPooling2D(pool_size=(2, 2), data_format="channels_last")`
  pool3 = MaxPooling2D(pool_size=(2, 2),dim_ordering="tf")(conv3)
C:/Users/Asus/Desktop/training.py:109: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(100, (3, 3), activation="relu", padding="same", data_format="channels_first")`
  conv4 = Conv2D(100, (3, 3), activation='relu', padding='same',dim_ordering="th")(pool3)
C:/Users/Asus/Desktop/training.py:110: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(100, (3, 3), activation="relu", padding="same", data_format="channels_first")`
  conv4 = Conv2D(100, (3, 3), activation='relu', padding='same',dim_ordering="th")(conv4)
C:/Users/Asus/Desktop/training.py:111: UserWarning: Update your `MaxPooling2D` call to the Keras 2 API: `MaxPooling2D(pool_size=(2, 2), data_format="channels_last")`
  pool4 = MaxPooling2D(pool_size=(2, 2), dim_ordering="tf")(conv4)
C:/Users/Asus/Desktop/training.py:113: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(200, (3, 3), activation="relu", padding="same", data_format="channels_first")`
  conv5 = Conv2D(200, (3, 3), activation='relu', padding='same',dim_ordering="th")(pool4)
C:/Users/Asus/Desktop/training.py:114: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(200, (3, 3), activation="relu", padding="same", data_format="channels_first")`
  conv5 = Conv2D(200, (3, 3), activation='relu', padding='same',dim_ordering="th")(conv5)
C:/Users/Asus/Desktop/training.py:116: UserWarning: Update your `Conv2DTranspose` call to the Keras 2 API: `Conv2DTranspose(200, (2, 2), strides=(2, 2), padding="same", data_format="channels_first")`
  up6 = concatenate([Conv2DTranspose(200, (2, 2), strides=(2, 2), padding='same',dim_ordering="th")(conv5), conv4], axis=3)
Traceback (most recent call last):

  File "<ipython-input-25-4b34507d9da0>", line 1, in <module>
    runfile('C:/Users/Asus/Desktop/training.py', wdir='C:/Users/Asus/Desktop')

  File "C:\Users\Asus\AppData\Local\Continuum\anaconda3\envs\tensorflow\lib\site-packages\spyder\utils\site\sitecustomize.py", line 705, in runfile
    execfile(filename, namespace)

  File "C:\Users\Asus\AppData\Local\Continuum\anaconda3\envs\tensorflow\lib\site-packages\spyder\utils\site\sitecustomize.py", line 102, in execfile
    exec(compile(f.read(), filename, 'exec'), namespace)

  File "C:/Users/Asus/Desktop/training.py", line 205, in <module>
    train_and_predict()

  File "C:/Users/Asus/Desktop/training.py", line 163, in train_and_predict
    model = get_unet()

  File "C:/Users/Asus/Desktop/training.py", line 116, in get_unet
    up6 = concatenate([Conv2DTranspose(200, (2, 2), strides=(2, 2), padding='same',dim_ordering="th")(conv5), conv4], axis=3)

  File "C:\Users\Asus\AppData\Local\Continuum\anaconda3\envs\tensorflow\lib\site-packages\keras\layers\merge.py", line 641, in concatenate
    return Concatenate(axis=axis, **kwargs)(inputs)

  File "C:\Users\Asus\AppData\Local\Continuum\anaconda3\envs\tensorflow\lib\site-packages\keras\engine\topology.py", line 594, in __call__
    self.build(input_shapes)

  File "C:\Users\Asus\AppData\Local\Continuum\anaconda3\envs\tensorflow\lib\site-packages\keras\layers\merge.py", line 354, in build
    'Got inputs shapes: %s' % (input_shape))

ValueError: A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got inputs shapes: [(None, 200, 50, 50), (None, 100, 50, 25)]

这是我的代码：

#load dataset
import h5py
h5f = h5py.File('liver_augmented_dataset.h5', 'r')
X = h5f['ct_scans'][:]
Y = h5f['seg_mask'][:]
h5f.close()

X_ax = X[1310:2500]
Y_ax = Y[1310:2500]

X_t=X[2501:2619]
Y_t=Y[2501:2619]

image_rows = 200
image_cols = 200


def get_unet():
    inputs = Input(shape=(img_rows, img_cols,1))
#    conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(inputs)
#    conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv1)
#    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)

    conv2 = Conv2D(25, (3, 3), activation='relu', padding='same',dim_ordering="tf")(inputs)
    conv2 = Conv2D(25, (3, 3), activation='relu', padding='same',dim_ordering="tf")(conv2)
    pool2 = MaxPooling2D(pool_size=(2, 2), dim_ordering="tf")(conv2)

    conv3 = Conv2D(50, (3, 3), activation='relu', padding='same',dim_ordering="tf")(pool2)
    conv3 = Conv2D(50, (3, 3), activation='relu', padding='same',dim_ordering="tf")(conv3)
    pool3 = MaxPooling2D(pool_size=(2, 2),dim_ordering="tf")(conv3)

    conv4 = Conv2D(100, (3, 3), activation='relu', padding='same',dim_ordering="tf")(pool3)
    conv4 = Conv2D(100, (3, 3), activation='relu', padding='same',dim_ordering="tf")(conv4)
    pool4 = MaxPooling2D(pool_size=(2, 2), dim_ordering="tf")(conv4)

    conv5 = Conv2D(200, (3, 3), activation='relu', padding='same',dim_ordering="tf")(pool4)
    conv5 = Conv2D(200, (3, 3), activation='relu', padding='same',dim_ordering="tf")(conv5)

    up6 = concatenate([Conv2DTranspose(200, (2, 2), strides=(2, 2), padding='same',dim_ordering="tf")(conv5), conv4], axis=3)
    conv6 = Conv2D(100, (3, 3), activation='relu', padding='same',dim_ordering="tf")(up6)
    conv6 = Conv2D(100, (3, 3), activation='relu', padding='same',dim_ordering="tf")(conv6)

    up7 = concatenate([Conv2DTranspose(100, (2, 2), strides=(2, 2), padding='same',dim_ordering="tf")(conv6), conv3], axis=3)
    conv7 = Conv2D(50, (3, 3), activation='relu', padding='same',dim_ordering="tf")(up7)
    conv7 = Conv2D(50, (3, 3), activation='relu', padding='same',dim_ordering="tf")(conv7)

    up8 = concatenate([Conv2DTranspose(50, (2, 2), strides=(2, 2), padding='same',dim_ordering="tf")(conv7), conv2], axis=3)
    conv8 = Conv2D(25, (3, 3), activation='relu', padding='same',dim_ordering="tf")(up8)
    conv8 = Conv2D(25, (3, 3), activation='relu', padding='same',dim_ordering="tf")(conv8)
#
#    up9 = concatenate([Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(conv8), conv1], axis=3)
#    conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(up9)
#    conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv9)

    conv10 = Conv2D(1, (1, 1), activation='sigmoid')(conv8)

    model = Model(inputs=[inputs], outputs=[conv10])

    model.compile(optimizer=Adam(lr=1e-5), loss=dice_coef_loss, metrics=[dice_coef])

    return model
4

3 回答 3

1

我能够成功编译模型。我无法重新创建日志中提到的连接错误。

您应该检查的另一个是您提供给模型的输入应该在 4 维中重新整形，如您提到的 (1190, 200, 200) 的整形错误问题中，您应该将其转换为 (1190, 200, 200, 1) '1' 表示频段数。

所以基本上你应该为你的灰度图像添加一个额外的维度并将其转换为 (img_rows,img_cols,bands)

于 2018-04-10T10:38:12.887 回答
0

我在灰色图像上遇到了同样的情况，图像上的重塑将通过为灰度通道添加额外的维度来解决它。

train_images_reshape = train_images.reshape(no_images_train, h,w,1)
test_images_reshape = test_images.reshape(no_images_test, h,w,1)
于 2019-05-25T03:47:18.920 回答
0

keras 需要一个额外的维度来指定通道

格式为 (no_of_images, height, width, n_channels) n_channels=1 用于灰度图像 =3 用于 RGB

于 2020-07-17T06:06:49.373 回答