我正在做一个Unet来进行分割，在这个文件中https://github.com/erickfmm/ML-experiments/blob/master/test/butterfly.py
xtrain 和 ytrain 的形状为 x_train 形状 (557, 100, 100, 3) y_火车形状 (557, 100, 100, 3)
并且没有 None 值
我的模型的摘要（）是：
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 100, 100, 3)] 0 []
conv2d (Conv2D) (None, 100, 100, 32) 2432 ['input_1[0][0]']
dropout (Dropout) (None, 100, 100, 32) 0 ['conv2d[0][0]']
max_pooling2d (MaxPooling2 (None, 50, 50, 32) 0 ['dropout[0][0]']
D)
conv2d_1 (Conv2D) (None, 50, 50, 32) 25632 ['max_pooling2d[0][0]']
dropout_1 (Dropout) (None, 50, 50, 32) 0 ['conv2d_1[0][0]']
max_pooling2d_1 (MaxPoolin (None, 25, 25, 32) 0 ['dropout_1[0][0]']
g2D)
conv2d_2 (Conv2D) (None, 25, 25, 32) 9248 ['max_pooling2d_1[0][0]']
dropout_2 (Dropout) (None, 25, 25, 32) 0 ['conv2d_2[0][0]']
max_pooling2d_2 (MaxPoolin (None, 9, 9, 32) 0 ['dropout_2[0][0]']
g2D)
conv2d_3 (Conv2D) (None, 9, 9, 256) 73984 ['max_pooling2d_2[0][0]']
up_sampling2d (UpSampling2 (None, 27, 27, 256) 0 ['conv2d_3[0][0]']
D)
zero_padding2d (ZeroPaddin (None, 27, 27, 32) 0 ['conv2d_2[0][0]']
g2D)
concatenate (Concatenate) (None, 27, 27, 288) 0 ['up_sampling2d[0][0]',
'zero_padding2d[0][0]']
conv2d_4 (Conv2D) (None, 25, 25, 32) 82976 ['concatenate[0][0]']
dropout_3 (Dropout) (None, 25, 25, 32) 0 ['conv2d_4[0][0]']
up_sampling2d_1 (UpSamplin (None, 50, 50, 32) 0 ['dropout_3[0][0]']
g2D)
concatenate_1 (Concatenate (None, 50, 50, 64) 0 ['up_sampling2d_1[0][0]',
) 'conv2d_1[0][0]']
conv2d_5 (Conv2D) (None, 46, 46, 32) 51232 ['concatenate_1[0][0]']
dropout_4 (Dropout) (None, 46, 46, 32) 0 ['conv2d_5[0][0]']
up_sampling2d_2 (UpSamplin (None, 92, 92, 32) 0 ['dropout_4[0][0]']
g2D)
zero_padding2d_1 (ZeroPadd (None, 100, 100, 32) 0 ['up_sampling2d_2[0][0]']
ing2D)
concatenate_2 (Concatenate (None, 100, 100, 64) 0 ['zero_padding2d_1[0][0]',
) 'conv2d[0][0]']
conv2d_6 (Conv2D) (None, 100, 100, 3) 1731 ['concatenate_2[0][0]']
==================================================================================================
我的代码是
```
input = keras.Input(shape=(100,100,3))
x1 = layers.Conv2D(32, 5, activation="relu", padding="same")(input)
x = layers.Dropout(0.1)(x1)
x = layers.MaxPooling2D((2,2), padding="same")(x)
x2 = layers.Conv2D(32, 5, activation="relu", padding="same")(x)
x = layers.Dropout(0.1)(x2)
x = layers.MaxPooling2D((2,2), padding="same")(x)
x3 = layers.Conv2D(32, 3, activation="relu", padding="same")(x)
x = layers.Dropout(0.1)(x3)
x = layers.MaxPooling2D((3,3), padding="same")(x)
x = layers.Conv2D(256, 3, activation="relu", padding="same")(x)
x = layers.UpSampling2D(size=(3,3))(x)
x3 = layers.ZeroPadding2D(padding=(1, 1))(x3)
x = layers.concatenate([x, x3], axis=-1)
x = layers.Conv2D(32, 3, activation="relu", padding="valid")(x)
x = layers.Dropout(0.1)(x)
x = layers.UpSampling2D(size=(2,2))(x)
#x2 = layers.ZeroPadding2D(2)(x2)
x = layers.concatenate([x, x2], axis=-1)
x = layers.Conv2D(32, 5, activation="relu", padding="valid")(x)
x = layers.Dropout(0.1)(x)
x = layers.UpSampling2D(size=(2,2))(x)
x = layers.ZeroPadding2D(padding=(4, 4))(x)
x = layers.concatenate([x, x1], axis=-1)
output = layers.Conv2D(3, 3, activation="relu", padding="same")(x)
model = keras.Model(inputs=input, outputs=output)
model.summary() #only for printing purposes
model.compile(
optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy']
)
model.fit(x, y, epochs=100, batch_size=16)
```
我将图层的形状修复为正常，我确保没有 None 值和所有 int。 我不知道为什么输入形状输出有 [] 但我认为它很好，不知道为什么它给我这个错误
错误信息
WARNING:tensorflow:Keras is training/fitting/evaluating on array-like data. Keras may not be optimized for this format, so if your input data format is supported by TensorFlow I/O (https://github.com/tensorflow/io) we recommend using that to load a Dataset instead.
Traceback (most recent call last):
File "D:\<mifolder>\ML-experiments\test\butterfly.py", line 189, in <module>
model = simple_segmentation(x_train, y_train)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\<mifolder>\ML-experiments\test\butterfly.py", line 167, in simple_segmentation
model.fit(x, y, epochs=100, batch_size=16)
File "D:\<mifolder>\venv\Lib\site-packages\keras\src\utils\traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "D:\<mifolder>\venv\Lib\site-packages\keras\src\engine\data_adapter.py", line 266, in <genexpr>
int(i.shape[0]) for i in tf.nest.flatten(inputs)
^^^^^^^^^^^^^^^
TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
```
解决了，哈哈 我用“x”变量层替换了函数参数中的“x”变量。 我用 def simple_segmentation(x_input, y_input) 替换了 def simple_segmentation(x, y) 并解决了XD 我现在也使用binary_crossentropy作为损失函数