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[英]“ValueError: Shapes (None, 1) and (None, 6) are incompatible”
[英]ValueError: Shapes (None, 2) and (None, 3) are incompatible
所以这是我试图运行的代码
from sklearn.datasets import make_blobs
from tensorflow.keras.utils import to_categorical
from keras.layers import *
from keras import metrics
from tensorflow.keras.optimizers import SGD
from keras.models import Sequential
from keras.layers import Dense
import numpy as np
def prepa ():
X, y = make_blobs(n_samples=1000,centers=3, n_features=2,random_state=2)
testX=X[:499]
trainX=X[500:999]
testy=y[:499]
trainy=y[500:999]
return trainX,trainy,testX,testy
trainX, testX, trainy,testy=prepa()
#define the model
model = Sequential() # Création d'un réseau de neurones vide
model.add(Dense(50,input_dim=2,activation="relu",kernel_initializer='he_uniform'))
model.add(Dense(3,activation="Softmax"))
#compile the model
opt = SGD(learning_rate=0.001)
model.compile(loss='categorical_crossentropy',optimizer=opt,metrics=['accuracy'])
fit the model
history=model.fit(trainX, trainy, validation_data=(testX, testy), epochs=200, verbose=0)
但我收到此错误:
ValueError 回溯(最近一次调用)
in () 1 #拟合模型----> 2 history=model.fit(trainX, trainy, validation_data=(testX, testy), epochs=200,verbose=0)
9帧
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs) 992 除了异常为 e: # pylint:disable=broad-except 993 if hasattr(e, "ag_error_metadata"): --> 994 raise e.ag_error_metadata.to_exception(e) 995 else: 996 raise
值错误:在用户代码中:
/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:853 train_function *
return step_function(self, iterator)
/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:842 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
/usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:1286 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:2849 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:3632 _call_for_each_replica
return fn(*args, **kwargs)
/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:835 run_step **
outputs = model.train_step(data)
/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:789 train_step
y, y_pred, sample_weight, regularization_losses=self.losses)
/usr/local/lib/python3.7/dist-packages/keras/engine/compile_utils.py:201 __call__
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
/usr/local/lib/python3.7/dist-packages/keras/losses.py:141 __call__
losses = call_fn(y_true, y_pred)
/usr/local/lib/python3.7/dist-packages/keras/losses.py:245 call **
return ag_fn(y_true, y_pred, **self._fn_kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:206 wrapper
return target(*args, **kwargs)
/usr/local/lib/python3.7/dist-packages/keras/losses.py:1666 categorical_crossentropy
y_true, y_pred, from_logits=from_logits, axis=axis)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:206 wrapper
return target(*args, **kwargs)
/usr/local/lib/python3.7/dist-packages/keras/backend.py:4839 categorical_crossentropy
target.shape.assert_is_compatible_with(output.shape)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_shape.py:1161 assert_is_compatible_with
raise ValueError("Shapes %s and %s are incompatible" % (self, other))
ValueError: Shapes (None, 2) and (None, 3) are incompatible
正如您的错误所描述的那样,形状有问题。 在训练模型之前,您应该始终检查数据形状,以避免出现任何不一致问题。
因此,当我检查时, testX
的形状是(501,)
,它应该是(501,2)
,这是因为testX
在您的代码中被分配给trainy
。
代替
trainX,testX,trainy,testy = prepa()
和
trainX,trainy,testX,testy = prepa()
因为这是您从prepa()
函数返回的内容,并且解包应该以合适/相似的顺序来维护分配。
此外,您应该在prepa()
函数中使用以下索引来获取整个数据集。
testX = X[:499]
trainX = X[499:]
testy = y[:499]
trainy = y[499:]
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