Keras 中的 2CNN：形状不匹配

Question

I'm trying to construct a 2D CNN neural network, looking at this code: https://kgptalkie.com/human-activity-recognition-using-accelerometer-data/ . I have four arrtibutes and nine classes

X_train[0].shape, X_test[0].shape

((200, 4), (200, 4))

X_train = X_train.reshape(3104, 200, 4, 1)
X_test = X_test.reshape(776, 200, 4, 1)
X_train[0].shape, X_test[0].shape

((200, 4, 1), (200, 4, 1))

model = Sequential()
    model.add(Conv2D(16, (2, 2), activation = 'relu', input_shape = X_train[0].shape))
    model.add(Dropout(0.1)) 
    
    model.add(Conv2D(32, (2, 2), activation='relu'))
    model.add(Dropout(0.2))
    
    model.add(Flatten())
    
    model.add(Dense(64, activation = 'relu'))
    model.add(Dropout(0.5))
    
    model.add(Dense(9, activation='softmax'))

    model.compile(optimizer=Adam(learning_rate = 0.001), loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])
    history = model.fit(X_train, y_train, epochs = 10, validation_data= (X_test, y_test), verbose=1)

I'm finding this error:

ValueError                                Traceback (most recent call last)
<ipython-input-42-d7e8ba9ba93b> in <module>()
      1 model.compile(optimizer=Adam(learning_rate = 0.001), loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])
----> 2 history = model.fit(X_train, y_train, epochs = 10, validation_data= (X_test, y_test), verbose=1)

10 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
    971           except Exception as e:  # pylint:disable=broad-except
    972             if hasattr(e, "ag_error_metadata"):
--> 973               raise e.ag_error_metadata.to_exception(e)
    974             else:
    975               raise

ValueError: in user code:

    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:806 train_function  *
        return step_function(self, iterator)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:796 step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:1211 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:2585 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:2945 _call_for_each_replica
        return fn(*args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:789 run_step  **
        outputs = model.train_step(data)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:749 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:204 __call__
        loss_value = loss_obj(y_t, y_p, sample_weight=sw)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/losses.py:149 __call__
        losses = ag_call(y_true, y_pred)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/losses.py:253 call  **
        return ag_fn(y_true, y_pred, **self._fn_kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py:201 wrapper
        return target(*args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/losses.py:1567 sparse_categorical_crossentropy
        y_true, y_pred, from_logits=from_logits, axis=axis)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py:201 wrapper
        return target(*args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend.py:4783 sparse_categorical_crossentropy
        labels=target, logits=output)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py:201 wrapper
        return target(*args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/nn_ops.py:4176 sparse_softmax_cross_entropy_with_logits_v2
        labels=labels, logits=logits, name=name)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py:201 wrapper
        return target(*args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/nn_ops.py:4091 sparse_softmax_cross_entropy_with_logits
        logits.get_shape()))

    ValueError: Shape mismatch: The shape of labels (received (288,)) should equal the shape of logits except for the last dimension (received (32, 6)).

Could you help me please?

Answer 1

我解决了将损失模式从 'sparse_categorical_crossentropy' 更改为 loss=tf.keras.losses.KLDivergence() 的问题