问题
I am trying to introduce a new kernel regularize for a network using Keras. But, it gives me the error: 'float' object has no attribute 'dtype' How can I fix it?
I found the code here: KL divergence in keras(tensorflow backend)
Here is my code:
from keras import backend as K
kullback_leibler_divergence = keras.losses.kullback_leibler_divergence
def kl_divergence_regularizer(inputs):
means = K.mean((inputs))
return 0.5 *(0.01 * (kullback_leibler_divergence(0.05, means)
+ kullback_leibler_divergence(1 - 0.05, 1 - means)))
model = Sequential([
Dense(100, input_shape=(x_train_s.shape[1],),kernel_initializer='random_uniform'),
Activation('elu'),
Dense(x_train_s.shape[1],kernel_initializer='random_uniform', kernel_regularizer=kl_divergence_regularizer),
Activation('tanh')
])
model.compile(optimizer='adam',loss='mean_squared_error')
model.fit(x_train_s, x_train_s, epochs=5,validation_split=0.1, shuffle=True, verbose=1,batch_size=np.uint(x_train_s.shape[0]/100))
The full error trace back is presented here:
AttributeError Traceback (most recent call last)
<ipython-input-39-59bc90c687de> in <module>
39 Activation('elu'),
40 Dense(x_train_s.shape[1],kernel_initializer='random_uniform', kernel_regularizer=kl_divergence_regularizer),
---> 41 Activation('tanh')
42 ])
43
C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\sequential.py in __init__(self, layers, name)
91 if layers:
92 for layer in layers:
---> 93 self.add(layer)
94
95 @property
C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\sequential.py in add(self, layer)
179 self.inputs = network.get_source_inputs(self.outputs[0])
180 elif self.outputs:
--> 181 output_tensor = layer(self.outputs[0])
182 if isinstance(output_tensor, list):
183 raise TypeError('All layers in a Sequential model '
C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\base_layer.py in __call__(self, inputs, **kwargs)
429 'You can build it manually via: '
430 '`layer.build(batch_input_shape)`')
--> 431 self.build(unpack_singleton(input_shapes))
432 self.built = True
433
C:\ProgramData\Anaconda3\lib\site-packages\keras\layers\core.py in build(self, input_shape)
864 name='kernel',
865 regularizer=self.kernel_regularizer,
--> 866 constraint=self.kernel_constraint)
867 if self.use_bias:
868 self.bias = self.add_weight(shape=(self.units,),
C:\ProgramData\Anaconda3\lib\site-packages\keras\legacy\interfaces.py in wrapper(*args, **kwargs)
89 warnings.warn('Update your `' + object_name + '` call to the ' +
90 'Keras 2 API: ' + signature, stacklevel=2)
---> 91 return func(*args, **kwargs)
92 wrapper._original_function = func
93 return wrapper
C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\base_layer.py in add_weight(self, name, shape, dtype, initializer, regularizer, trainable, constraint)
253 if regularizer is not None:
254 with K.name_scope('weight_regularizer'):
--> 255 self.add_loss(regularizer(weight))
256 if trainable:
257 self._trainable_weights.append(weight)
<ipython-input-39-59bc90c687de> in kl_divergence_regularizer(inputs)
14 means = K.mean((inputs))
15 # means=1e-6
---> 16 return 0.5 *(0.01 * (kullback_leibler_divergence(0.05, means)
17 + kullback_leibler_divergence(1 - 0.05, 1 - means)))
18 # return 1e-10
C:\ProgramData\Anaconda3\lib\site-packages\keras\losses.py in kullback_leibler_divergence(y_true, y_pred)
79
80 def kullback_leibler_divergence(y_true, y_pred):
---> 81 y_true = K.clip(y_true, K.epsilon(), 1)
82 y_pred = K.clip(y_pred, K.epsilon(), 1)
83 return K.sum(y_true * K.log(y_true / y_pred), axis=-1)
C:\ProgramData\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py in clip(x, min_value, max_value)
1599 if max_value is None:
1600 max_value = np.inf
-> 1601 min_value = _to_tensor(min_value, x.dtype.base_dtype)
1602 max_value = _to_tensor(max_value, x.dtype.base_dtype)
1603 return tf.clip_by_value(x, min_value, max_value)
AttributeError: 'float' object has no attribute 'dtype'
I need the KL divergence between 0.05 and mean calculate the following sum over i:
KL=sum(0.05*\log(0.05/mean[i]))
回答1:
You should change your KL divergence regularizer to:
def kl_divergence_regularizer(inputs):
means = K.mean((inputs))
down = 0.05 * K.ones_like(means)
up = (1 - 0.05) * K.ones_like(means)
return 0.5 *(0.01 * (kullback_leibler_divergence(down, means)
+ kullback_leibler_divergence(up, 1 - means)))
This is because the KL divergence takes two vectors/tensors, the KL between a scalar and a vector cannot be computed (its not defined).
回答2:
kullback_leibler_divergence(0.05, means)
This is a loss function. It is expecting y_true, y_pred, both as tensors.
You are passing a float (0.05), and the system is trying to get the properties of this supposed tensor, but it's not a tensor.
The same problem will happen in the second kullback_leibler_divergence call where you pass again a float 1 - 0.05 instead of a tensor.
A simple solution (but you must check whether this is mathematically reasonable -- I don't know what KL_diff is supposed to do) is use a K.ones_like(means) * 0,05 and in the second call K.ones_like(means)*(1-0.05).
来源：https://stackoverflow.com/questions/57530823/keras-backend-mean-function-float-object-has-no-attribute-dtype