输出尺寸的Keras错误 [英] Keras Error with Output Dimensions
本文介绍了输出尺寸的Keras错误的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
然后我使用以下代码创建了批处理大小为64的批处理
从sklearn.preprocessing导入OneHotEncoder
def one_hot_encoder(y):
onehot_encoder = OneHotEncoder(sparse=False)
y = y.reshape(len(y), 1)
onehot_encoder = onehot_encoder.fit_transform(y)
return onehot_encoder
def data_generator(x, y, shuffle=False, batch_size=64):
# create order
while True:
index = np.arange(len(y))
if shuffle == True:
np.random.shuffle(index)
x = x[index]
y = y[index]
# generate batches
imax = int(len(index)/batch_size)
for i in range(imax):
yield x[i*batch_size: (i+1)*batch_size], y[i*batch_size: (i+1)*batch_size]
def get_batches(x, y):
x = np.array(x)
y = np.array(y)
return data_generator(x, one_hot_encoder(y))
每批 print(next(batches)[0] .shape)是(64，30，2)-30个观察值，2个变量 print(next(batches) 1 .shape)是(64，3)-每个观察对应于一个热编码的类
然后我用以下代码创建模型:
def create_model():
model = Sequential()
model.add(BatchNormalization(axis=1, input_shape=(30, 2)))
model.add(Conv1D(16, 5, activation='relu'))
model.add(BatchNormalization(axis=1))
model.add(MaxPooling1D(2))
model.add(Conv1D(16, 5, activation='relu'))
model.add(BatchNormalization(axis=1))
model.add(MaxPooling1D(3))
model.add(Conv1D(32, 3, activation='relu'))
model.add(Dense(100, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(3, activation='softmax'))
return model
model = create_model()
model.compile(RMSprop(lr=0.01), loss='categorical_crossentropy', metrics=['accuracy'])
该模型的摘要如下:
模型摘要
但是当我使用fit_generator训练模型时，出现以下错误消息.我真的很困惑我的输出尺寸是否正确?或我的代码中有任何错误.
谢谢.
model.fit_generator(batches, steps_per_epoch=30, nb_epoch=5, validation_data=None, validation_steps=None)
ValueError Traceback (most recent call last)
<ipython-input-29-c7ba2e8eddfd> in <module>()
----> 1 model.fit_generator(batches, steps_per_epoch=10, nb_epoch=5, validation_data=None, validation_steps=None)
D:\Programs\Anaconda3\lib\site-packages\keras\legacy\interfaces.py in wrapper(*args, **kwargs)
85 warnings.warn('Update your `' + object_name +
86 '` call to the Keras 2 API: ' + signature, stacklevel=2)
---> 87 return func(*args, **kwargs)
88 wrapper._original_function = func
89 return wrapper
D:\Programs\Anaconda3\lib\site-packages\keras\models.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, initial_epoch)
1119 workers=workers,
1120 use_multiprocessing=use_multiprocessing,
-> 1121 initial_epoch=initial_epoch)
1122
1123 @interfaces.legacy_generator_methods_support
D:\Programs\Anaconda3\lib\site-packages\keras\legacy\interfaces.py in wrapper(*args, **kwargs)
85 warnings.warn('Update your `' + object_name +
86 '` call to the Keras 2 API: ' + signature, stacklevel=2)
---> 87 return func(*args, **kwargs)
88 wrapper._original_function = func
89 return wrapper
D:\Programs\Anaconda3\lib\site-packages\keras\engine\training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
2040 outs = self.train_on_batch(x, y,
2041 sample_weight=sample_weight,
-> 2042 class_weight=class_weight)
2043
2044 if not isinstance(outs, list):
D:\Programs\Anaconda3\lib\site-packages\keras\engine\training.py in train_on_batch(self, x, y, sample_weight, class_weight)
1754 sample_weight=sample_weight,
1755 class_weight=class_weight,
-> 1756 check_batch_axis=True)
1757 if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
1758 ins = x + y + sample_weights + [1.]
D:\Programs\Anaconda3\lib\site-packages\keras\engine\training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_batch_axis, batch_size)
1380 output_shapes,
1381 check_batch_axis=False,
-> 1382 exception_prefix='target')
1383 sample_weights = _standardize_sample_weights(sample_weight,
1384 self._feed_output_names)
D:\Programs\Anaconda3\lib\site-packages\keras\engine\training.py in _standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
130 ' to have ' + str(len(shapes[i])) +
131 ' dimensions, but got array with shape ' +
--> 132 str(array.shape))
133 for j, (dim, ref_dim) in enumerate(zip(array.shape, shapes[i])):
134 if not j and not check_batch_axis:
ValueError: Error when checking target: expected dense_2 to have 3 dimensions, but got array with shape (64, 3)
解决方案
使用input_shape=(30,2)时，您要使用3个维度定义输入:(batchSize, 30, 2).
可以，但是可以不更改地传递到模型中，直到到达密集层为止.
密集层不会减少尺寸数量，它们将输出(batchSize, 30, denseUnits).
一种解决方案是使用平坦层，将其减少到仅(batchSize,30*someValue).然后密集对象将开始输出(batchSize,units).这将为您提供与您的2D类匹配的2D输出.
在密集层之前:
model.add(Flatten())
I have a time series (2 variables), each time series has about 80000 observations (X), each observation corresponds to a class (Y). I used moving window method to segment the time series to several intervals (each has a length of 30). Then I hot encoded the Y to make it to categorical variable.
Then I created batches with batch size of 64 with the following code
from sklearn.preprocessing import OneHotEncoder
def one_hot_encoder(y):
onehot_encoder = OneHotEncoder(sparse=False)
y = y.reshape(len(y), 1)
onehot_encoder = onehot_encoder.fit_transform(y)
return onehot_encoder
def data_generator(x, y, shuffle=False, batch_size=64):
# create order
while True:
index = np.arange(len(y))
if shuffle == True:
np.random.shuffle(index)
x = x[index]
y = y[index]
# generate batches
imax = int(len(index)/batch_size)
for i in range(imax):
yield x[i*batch_size: (i+1)*batch_size], y[i*batch_size: (i+1)*batch_size]
def get_batches(x, y):
x = np.array(x)
y = np.array(y)
return data_generator(x, one_hot_encoder(y))
For each batch print(next(batches)[0].shape) is (64, 30 ,2) -- 30 observations, 2 variables print(next(batches)1.shape) is (64, 3) - each observation corresponds to a hot-encoded class
Then I create Model with the following code:
def create_model():
model = Sequential()
model.add(BatchNormalization(axis=1, input_shape=(30, 2)))
model.add(Conv1D(16, 5, activation='relu'))
model.add(BatchNormalization(axis=1))
model.add(MaxPooling1D(2))
model.add(Conv1D(16, 5, activation='relu'))
model.add(BatchNormalization(axis=1))
model.add(MaxPooling1D(3))
model.add(Conv1D(32, 3, activation='relu'))
model.add(Dense(100, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(3, activation='softmax'))
return model
model = create_model()
model.compile(RMSprop(lr=0.01), loss='categorical_crossentropy', metrics=['accuracy'])
The summary of the model is as follow:
model summary
But when I train the model using fit_generator, I get the following error message. I am really confused whether my output dimension is not correct? or is there any error in my code.
Thanks.
model.fit_generator(batches, steps_per_epoch=30, nb_epoch=5, validation_data=None, validation_steps=None)
ValueError Traceback (most recent call last)
<ipython-input-29-c7ba2e8eddfd> in <module>()
----> 1 model.fit_generator(batches, steps_per_epoch=10, nb_epoch=5, validation_data=None, validation_steps=None)
D:\Programs\Anaconda3\lib\site-packages\keras\legacy\interfaces.py in wrapper(*args, **kwargs)
85 warnings.warn('Update your `' + object_name +
86 '` call to the Keras 2 API: ' + signature, stacklevel=2)
---> 87 return func(*args, **kwargs)
88 wrapper._original_function = func
89 return wrapper
D:\Programs\Anaconda3\lib\site-packages\keras\models.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, initial_epoch)
1119 workers=workers,
1120 use_multiprocessing=use_multiprocessing,
-> 1121 initial_epoch=initial_epoch)
1122
1123 @interfaces.legacy_generator_methods_support
D:\Programs\Anaconda3\lib\site-packages\keras\legacy\interfaces.py in wrapper(*args, **kwargs)
85 warnings.warn('Update your `' + object_name +
86 '` call to the Keras 2 API: ' + signature, stacklevel=2)
---> 87 return func(*args, **kwargs)
88 wrapper._original_function = func
89 return wrapper
D:\Programs\Anaconda3\lib\site-packages\keras\engine\training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
2040 outs = self.train_on_batch(x, y,
2041 sample_weight=sample_weight,
-> 2042 class_weight=class_weight)
2043
2044 if not isinstance(outs, list):
D:\Programs\Anaconda3\lib\site-packages\keras\engine\training.py in train_on_batch(self, x, y, sample_weight, class_weight)
1754 sample_weight=sample_weight,
1755 class_weight=class_weight,
-> 1756 check_batch_axis=True)
1757 if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
1758 ins = x + y + sample_weights + [1.]
D:\Programs\Anaconda3\lib\site-packages\keras\engine\training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_batch_axis, batch_size)
1380 output_shapes,
1381 check_batch_axis=False,
-> 1382 exception_prefix='target')
1383 sample_weights = _standardize_sample_weights(sample_weight,
1384 self._feed_output_names)
D:\Programs\Anaconda3\lib\site-packages\keras\engine\training.py in _standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
130 ' to have ' + str(len(shapes[i])) +
131 ' dimensions, but got array with shape ' +
--> 132 str(array.shape))
133 for j, (dim, ref_dim) in enumerate(zip(array.shape, shapes[i])):
134 if not j and not check_batch_axis:
ValueError: Error when checking target: expected dense_2 to have 3 dimensions, but got array with shape (64, 3)
解决方案
When you use input_shape=(30,2), you are defining your input with 3 dimensions: (batchSize, 30, 2).
This is ok, but it's being passed through your model unchanged until it reaches the dense layers.
Dense layers won't reduce the number of dimensions, they will output (batchSize, 30, denseUnits).
One solution is to use a flatten layer, to reduce to only (batchSize,30*someValue). Then the dense will start outputting (batchSize,units) This will provide you a 2D output that matches your 2D classes.
Before the dense layers:
model.add(Flatten())
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