[英]“ValueError: Shapes (None, 1) and (None, 6) are incompatible”
I want to classify 6 different categories of x-ray scans, What's wrong with the code?我想对 6 种不同类别的 X 射线扫描进行分类,代码有什么问题?
model = Sequential()
model.add(Conv2D(256, (3, 3), input_shape=X.shape[1:]))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(256, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(64))
model.add(Dense(6))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.fit(X, y, batch_size=32, epochs=3, validation_split=0.1)
The shapes of input are: (50, 50, 1)输入的形状是:(50, 50, 1)
Should I remove one of the MaxPooling layers?我应该删除 MaxPooling 层之一吗?
I've seen it's good manners here to post the traceback aswell, so here it is:我已经看到在这里发布回溯也很有礼貌,所以这里是:
Epoch 1/3
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
(...)
ValueError: in user code:
C:\Python38\lib\site-packages\tensorflow\python\keras\engine\training.py:571 train_function *
outputs = self.distribute_strategy.run(
C:\Python38\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:951 run **
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
C:\Python38\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2290 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
C:\Python38\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2649 _call_for_each_replica
return fn(*args, **kwargs)
C:\Python38\lib\site-packages\tensorflow\python\keras\engine\training.py:532 train_step **
loss = self.compiled_loss(
C:\Python38\lib\site-packages\tensorflow\python\keras\engine\compile_utils.py:205 __call__
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
C:\Python38\lib\site-packages\tensorflow\python\keras\losses.py:143 __call__
losses = self.call(y_true, y_pred)
C:\Python38\lib\site-packages\tensorflow\python\keras\losses.py:246 call
return self.fn(y_true, y_pred, **self._fn_kwargs)
C:\Python38\lib\site-packages\tensorflow\python\keras\losses.py:1527 categorical_crossentropy
return K.categorical_crossentropy(y_true, y_pred, from_logits=from_logits)
C:\Python38\lib\site-packages\tensorflow\python\keras\backend.py:4561 categorical_crossentropy
target.shape.assert_is_compatible_with(output.shape)
C:\Python38\lib\site-packages\tensorflow\python\framework\tensor_shape.py:1117 assert_is_compatible_with
raise ValueError("Shapes %s and %s are incompatible" % (self, other))
ValueError: Shapes (None, 1) and (None, 6) are incompatible
to avoid misunderstandings and possible error I suggest you to reshape your target from (586,1) to (586,).为避免误解和可能的错误,我建议您将目标从 (586,1) 重塑为 (586,)。 you can simply do y = y.ravel()
你可以简单地做y = y.ravel()
you have to simply manage the correct loss你必须简单地管理正确的损失
if you have 1D integer encoded target you can use sparse_categorical_crossentropy as loss function如果您有 1D integer 编码目标,您可以使用 sparse_categorical_crossentropy 作为损失 function
X = np.random.randint(0,10, (1000,100))
y = np.random.randint(0,3, 1000)
model = Sequential([
Dense(128, input_dim = 100),
Dense(3, activation='softmax'),
])
model.summary()
model.compile(loss='sparse_categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
history = model.fit(X, y, epochs=3)
Otherwise, if you have one-hot encoded your target in order to have 2D shape (n_samples, n_class) you can use categorical_crossentropy否则,如果您对目标进行一次性编码以获得 2D 形状 (n_samples, n_class),则可以使用 categorical_crossentropy
X = np.random.randint(0,10, (1000,100))
y = pd.get_dummies(np.random.randint(0,3, 1000)).values
model = Sequential([
Dense(128, input_dim = 100),
Dense(3, activation='softmax'),
])
model.summary()
model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
history = model.fit(X, y, epochs=3)
For my case in the one-hot coding scenario, I use the following method.对于我在 one-hot 编码场景中的情况,我使用以下方法。
1.Build the DNN 1.构建DNN
model.add(layers.Dense(1, activation='sigmoid'))
2.Configure the model 2.配置model
model.compile(optimizer=optimizers.RMSprop(lr=1e-4),
loss='binary_crossentropy',
metrics=['acc'])
It can successfully solves the above-mentioned issue.它可以成功解决上述问题。
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.