raise ValueError("Shapes %s and %s are incompatible" % (self, other)) ValueError: Shapes (None, 15) and (None, 14) are incompatible

Question

我在做语音情感识别项目。它在一周前工作，我升级了 anaconda，但我以前使用的代码运行不再有效。我找不到问题它给出了标题中的错误。我的代码是：

# New model

model = Sequential()
model.add(Conv1D(256, 8, padding='same',input_shape=(X_train.shape[1],1)))  # X_train.shape[1] = No. of Columns
model.add(Activation('relu'))
model.add(Conv1D(256, 8, padding='same'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dropout(0.25))
model.add(MaxPooling1D(pool_size=(8)))
model.add(Conv1D(128, 8, padding='same'))
model.add(Activation('relu'))
model.add(Conv1D(128, 8, padding='same'))
model.add(Activation('relu'))
model.add(Conv1D(128, 8, padding='same'))
model.add(Activation('relu'))
model.add(Conv1D(128, 8, padding='same'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dropout(0.25))
model.add(MaxPooling1D(pool_size=(8)))
model.add(Conv1D(64, 8, padding='same'))
model.add(Activation('relu'))
model.add(Conv1D(64, 8, padding='same'))
model.add(Activation('relu'))
model.add(Flatten())
model.add(Dense(14)) # Target class number
model.add(Activation('softmax'))
# opt = keras.optimizers.SGD(lr=0.0001, momentum=0.0, decay=0.0, nesterov=False)
# opt = keras.optimizers.Adam(lr=0.0001)
opt = tf.keras.optimizers.RMSprop(lr=0.00001, decay=1e-6)
model.summary()

# %%
model.compile(loss='categorical_crossentropy', optimizer=opt,metrics=['accuracy'])
model_history=model.fit(X_train, y_train, batch_size=16, epochs=100, validation_data=(X_test, y_test))

我收到这个错误

ValueError: in user code:

    C:\Users\oguz_\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:806 train_function  *
        return step_function(self, iterator)
    C:\Users\oguz_\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:796 step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    C:\Users\oguz_\anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:1211 run
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    C:\Users\oguz_\anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2585 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    C:\Users\oguz_\anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2945 _call_for_each_replica
        return fn(*args, **kwargs)
    C:\Users\oguz_\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:789 run_step  **
        outputs = model.train_step(data)
    C:\Users\oguz_\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:748 train_step
        loss = self.compiled_loss(
    C:\Users\oguz_\anaconda3\lib\site-packages\tensorflow\python\keras\engine\compile_utils.py:204 __call__
        loss_value = loss_obj(y_t, y_p, sample_weight=sw)
    C:\Users\oguz_\anaconda3\lib\site-packages\tensorflow\python\keras\losses.py:149 __call__
        losses = ag_call(y_true, y_pred)
    C:\Users\oguz_\anaconda3\lib\site-packages\tensorflow\python\keras\losses.py:253 call  **
        return ag_fn(y_true, y_pred, **self._fn_kwargs)
    C:\Users\oguz_\anaconda3\lib\site-packages\tensorflow\python\util\dispatch.py:201 wrapper
        return target(*args, **kwargs)
    C:\Users\oguz_\anaconda3\lib\site-packages\tensorflow\python\keras\losses.py:1535 categorical_crossentropy
        return K.categorical_crossentropy(y_true, y_pred, from_logits=from_logits)
    C:\Users\oguz_\anaconda3\lib\site-packages\tensorflow\python\util\dispatch.py:201 wrapper
        return target(*args, **kwargs)
    C:\Users\oguz_\anaconda3\lib\site-packages\tensorflow\python\keras\backend.py:4687 categorical_crossentropy
        target.shape.assert_is_compatible_with(output.shape)
    C:\Users\oguz_\anaconda3\lib\site-packages\tensorflow\python\framework\tensor_shape.py:1134 assert_is_compatible_with
        raise ValueError("Shapes %s and %s are incompatible" % (self, other))

    ValueError: Shapes (None, 15) and (None, 14) are incompatible

我的值错误代码是

def print_confusion_matrix(confusion_matrix, class_names, figsize = (10,7), fontsize=15):
 
    df_cm = pd.DataFrame(
        confusion_matrix, index=class_names, columns=class_names, 
    )
    fig = plt.figure(figsize=figsize)
    try:
        heatmap = sns.heatmap(df_cm, annot=True, fmt="d")
    except ValueError:
        raise ValueError("Confusion matrix values must be integers.")
        
    heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha='right', fontsize=fontsize)
    heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=45, ha='right', fontsize=fontsize)
    plt.ylabel('Doğru')
    plt.xlabel('Tahmin Edilen')

# Gender recode function
def gender(row):
    if row == 'kadin_igrenme' or 'kadin_korku' or 'kadin_mutlu' or 'kadin_uzgun' or 'kadin_saskin' or 'kadin_sakin':
        return 'kadin'
    elif row == 'erkek_kizgin' or 'erkek_korku' or 'erkek_mutlu' or 'erkek_uzgun' or 'erkek_saskin' or 'erkek_sakin' or 'erkek_igrenme':
        return 'erkek'

谁能帮帮我编辑我添加了 x 列 y 列形状

print(X_train.shape)
print(y_train.shape)
print(y_test.shape)
print(X_test.shape)
print(lb.classes_)
#print(y_train[0:10])
#print(y_test[0:10])

(9031, 216, 1) (9031, 15) (3011, 15) (3011, 216, 1) [1]: https://i.stack.imgur.com/LKZDB.png

Answer 1

问题在于网络输出形状。由于标签具有形状

(b, 15) where b = 9031 for train and 3011 for test

网络中最后的密集层也应该有 15 个神经元。将最后一层更新为

model.add(Dense(15)

它应该可以正常工作。

raise ValueError("Shapes %s and %s are incompatible" % (self, other)) ValueError: Shapes (None, 15) and (None, 14) are incompatible

raise ValueError("Shapes %s and %s are incompatible" % (self, other)) ValueError: Shapes (None, 15) and (None, 14) are incompatible

machine-learning

python-3.x

deep-learning

tensorflow

librosa