检查输入时出错：预期 input_19 有 4 个维度，但得到形状为 (1190, 200, 200) 的数组
Error when checking input: expected input_19 to have 4 dimensions, but got array with shape (1190, 200, 200)
我是 CNN 的新手，我不知道如何解决这个问题。
在此代码中，我正在训练一组图像以从卷积 network.the 图像中获取掩码，图像是具有形状 (200,200) 的灰度。每次我 运行 我的代码在不同 inputs.Any 处出现错误时，我都无法确定我在哪里制作 mistake.Also 将不胜感激。
生成的日志如下：
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
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。
另一个你应该检查的是你提供给模型的输入应该在 4 维中重塑，就像你提到的 (1190, 200, 200) 重塑错误的问题一样，你应该将它转换为 (1190, 200, 200, 1) '1' 代表波段数。
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)
我遇到了与灰色图像相同的情况，对图像进行整形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)
keras 将需要一个额外的维度来指定通道
格式为（no_of_images，高度，宽度，n_channels）
n_channels=1 用于灰度图像
=3 对于 RGB
我是 CNN 的新手，我不知道如何解决这个问题。 在此代码中，我正在训练一组图像以从卷积 network.the 图像中获取掩码，图像是具有形状 (200,200) 的灰度。每次我 运行 我的代码在不同 inputs.Any 处出现错误时，我都无法确定我在哪里制作 mistake.Also 将不胜感激。
生成的日志如下：
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
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。
另一个你应该检查的是你提供给模型的输入应该在 4 维中重塑，就像你提到的 (1190, 200, 200) 重塑错误的问题一样，你应该将它转换为 (1190, 200, 200, 1) '1' 代表波段数。
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)
我遇到了与灰色图像相同的情况，对图像进行整形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)
keras 将需要一个额外的维度来指定通道
格式为（no_of_images，高度，宽度，n_channels） n_channels=1 用于灰度图像 =3 对于 RGB