这是此问题Keras backend mean function: " 'float' object has no attribute 'dtype' "?的后续问题
我正在尝试为Keras创建新的正则化器。这是我的代码
import keras
from keras import initializers
from keras.models import Model, Sequential
from keras.layers import Input, Dense, Activation
from keras import regularizers
from keras import optimizers
from keras import backend as K
kullback_leibler_divergence = keras.losses.kullback_leibler_divergence
def kl_divergence_regularizer(inputs):
means = K.mean((inputs))
rho=0.05
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)))
model = Sequential([
Dense(900, input_shape=(x_train_s.shape[1],),kernel_initializer='random_uniform',kernel_regularizer=kl_divergence_regularizer),
Activation('elu'),
Dense(x_train_s.shape[1],kernel_initializer='random_uniform'),
Activation('tanh')
])
model.compile(optimizer='adam',loss='mean_squared_error')
model.fit(x_train_s, y_train_s, epochs=5)
这是错误：
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py in _create_c_op(graph, node_def, inputs, control_inputs)
1658 try:
-> 1659 c_op = c_api.TF_FinishOperation(op_desc)
1660 except errors.InvalidArgumentError as e:
InvalidArgumentError: Invalid reduction dimension -1 for input with 0 dimensions. for 'dense_3/weight_regularizer/Sum' (op: 'Sum') with input shapes: [], [] and with computed input tensors: input[1] = <-1>.
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-4-9f4dfbe34659> in <module>
39 Activation('elu'),
40 Dense(x_train_s.shape[1],kernel_initializer='random_uniform'),
---> 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)
163 # and create the node connecting the current layer
164 # to the input layer we just created.
--> 165 layer(x)
166 set_inputs = True
167 else:
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-4-9f4dfbe34659> in kl_divergence_regularizer(inputs)
15 down = 0.05 * K.ones_like(means)
16 up = (1 - 0.05) * K.ones_like(means)
---> 17 return 0.5 *(0.01 * (kullback_leibler_divergence(down, means)
18 + kullback_leibler_divergence(up, 1 - means)))
19
C:\ProgramData\Anaconda3\lib\site-packages\keras\losses.py in 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)
84
85
C:\ProgramData\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py in sum(x, axis, keepdims)
1286 A tensor with sum of `x`.
1287 """
-> 1288 return tf.reduce_sum(x, axis, keepdims)
1289
1290
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\util\deprecation.py in new_func(*args, **kwargs)
505 'in a future version' if date is None else ('after %s' % date),
506 instructions)
--> 507 return func(*args, **kwargs)
508
509 doc = _add_deprecated_arg_notice_to_docstring(
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\ops\math_ops.py in reduce_sum_v1(input_tensor, axis, keepdims, name, reduction_indices, keep_dims)
1284 keepdims = deprecation.deprecated_argument_lookup("keepdims", keepdims,
1285 "keep_dims", keep_dims)
-> 1286 return reduce_sum(input_tensor, axis, keepdims, name)
1287
1288
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\util\dispatch.py in wrapper(*args, **kwargs)
178 """Call target, and fall back on dispatchers if there is a TypeError."""
179 try:
--> 180 return target(*args, **kwargs)
181 except (TypeError, ValueError):
182 # Note: convert_to_eager_tensor currently raises a ValueError, not a
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\ops\math_ops.py in reduce_sum(input_tensor, axis, keepdims, name)
1332 gen_math_ops._sum(
1333 input_tensor, _ReductionDims(input_tensor, axis), keepdims,
-> 1334 name=name))
1335
1336
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\ops\gen_math_ops.py in _sum(input, axis, keep_dims, name)
9607 _, _, _op = _op_def_lib._apply_op_helper(
9608 "Sum", input=input, reduction_indices=axis, keep_dims=keep_dims,
-> 9609 name=name)
9610 _result = _op.outputs[:]
9611 _inputs_flat = _op.inputs
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\framework\op_def_library.py in _apply_op_helper(self, op_type_name, name, **keywords)
786 op = g.create_op(op_type_name, inputs, output_types, name=scope,
787 input_types=input_types, attrs=attr_protos,
--> 788 op_def=op_def)
789 return output_structure, op_def.is_stateful, op
790
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\util\deprecation.py in new_func(*args, **kwargs)
505 'in a future version' if date is None else ('after %s' % date),
506 instructions)
--> 507 return func(*args, **kwargs)
508
509 doc = _add_deprecated_arg_notice_to_docstring(
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py in create_op(***failed resolving arguments***)
3298 input_types=input_types,
3299 original_op=self._default_original_op,
-> 3300 op_def=op_def)
3301 self._create_op_helper(ret, compute_device=compute_device)
3302 return ret
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py in __init__(self, node_def, g, inputs, output_types, control_inputs, input_types, original_op, op_def)
1821 op_def, inputs, node_def.attr)
1822 self._c_op = _create_c_op(self._graph, node_def, grouped_inputs,
-> 1823 control_input_ops)
1824
1825 # Initialize self._outputs.
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py in _create_c_op(graph, node_def, inputs, control_inputs)
1660 except errors.InvalidArgumentError as e:
1661 # Convert to ValueError for backwards compatibility.
-> 1662 raise ValueError(str(e))
1663
1664 return c_op
ValueError: Invalid reduction dimension -1 for input with 0 dimensions. for 'dense_3/weight_regularizer/Sum' (op: 'Sum') with input shapes: [], [] and with computed input tensors: input[1] = <-1>.
我该如何解决？我需要KL差异在0.05和均值之间，对i计算以下总和：
KL = sum（0.05 * \ log（0.05 / mean [i]））
答案 0 :(得分：2)
为了打印意思，
means = K.means((input), axis=1)
...
means_ = sess.run(means, feed_dict={x: , y: })
print(means_)