ValueError:形状(无,1)和(无,2)不兼容 [英] ValueError: Shapes (None, 1) and (None, 2) are incompatible
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问题描述
我正在训练一个面部表情(愤怒与快乐)模型。最后一个密集输出层以前是1,但是当我预测图像时,其输出始终为1,准确度为64%。所以我将其更改为2 2输出。但是现在我遇到了这个错误:
I am training a facial expression (angry vs happy) model. Last dense output layer was previously 1 but when i predict an image it's output was always 1 with 64 % accuracy. So i changed it to 2 for 2 outputs. But now i am getting this error::
Epoch 1/15
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-54-9c7272c38dcb> in <module>()
11 epochs=epochs,
12 validation_data = val_data_gen,
---> 13 validation_steps = validation_steps,
14
15 )
10 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
966 except Exception as e: # pylint:disable=broad-except
967 if hasattr(e, "ag_error_metadata"):
--> 968 raise e.ag_error_metadata.to_exception(e)
969 else:
970 raise
ValueError: in user code:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:571 train_function *
outputs = self.distribute_strategy.run(
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:951 run **
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2290 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2649 _call_for_each_replica
return fn(*args, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:533 train_step **
y, y_pred, sample_weight, regularization_losses=self.losses)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/compile_utils.py:205 __call__
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/losses.py:143 __call__
losses = self.call(y_true, y_pred)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/losses.py:246 call
return self.fn(y_true, y_pred, **self._fn_kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/losses.py:1527 categorical_crossentropy
return K.categorical_crossentropy(y_true, y_pred, from_logits=from_logits)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend.py:4561 categorical_crossentropy
target.shape.assert_is_compatible_with(output.shape)
/usr/local/lib/python3.6/dist-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, 2) are incompatible
相关代码为:
model = Sequential([
Conv2D(32,3, activation='relu', input_shape=(48,48,1)),
BatchNormalization(),
MaxPooling2D(pool_size=(3, 3)),
Flatten(),
Dense(512, activation='relu'),
Dense(2,activation='softmax')
])
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
model.summary()
Model: "sequential_4"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_6 (Conv2D) (None, 46, 46, 32) 320
_________________________________________________________________
batch_normalization_4 (Batch (None, 46, 46, 32) 128
_________________________________________________________________
max_pooling2d_6 (MaxPooling2 (None, 15, 15, 32) 0
_________________________________________________________________
flatten_4 (Flatten) (None, 7200) 0
_________________________________________________________________
dense_8 (Dense) (None, 512) 3686912
_________________________________________________________________
dense_9 (Dense) (None, 2) 1026
=================================================================
Total params: 3,688,386
Trainable params: 3,688,322
Non-trainable params: 64
_________________________________________________________________
epochs = 15
steps_per_epoch = train_data_gen.n//train_data_gen.batch_size
validation_steps = val_data_gen.n//val_data_gen.batch_size
history = model.fit(
x=train_data_gen,
steps_per_epoch=steps_per_epoch,
epochs=epochs,
validation_data = val_data_gen,
validation_steps = validation_steps,
)
推荐答案
更改分类交叉熵到二进制交叉熵,因为您的输出标签是二进制的。也将Softmax更改为Sigmoid,因为Sigmoid是二进制数据的正确激活函数
Change Categorical Cross Entropy to Binary Cross Entropy since your output label is binary. Also Change Softmax to Sigmoid since Sigmoid is the proper activation function for binary data
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