标准偏差与道路整备率

import tensorflow import keras from keras.layers import Dense, TimeDistributed, Lambda, Input import numpy as np import keras.backend as K def stdev_pooling(inputs): data, stride = inputs stride = K.cast(stride, dtype='int32') print K.dtype(stride), K.dtype(data), '---' num_windows = K.shape(data)[1] / stride idxs = K.arange(num_windows) * stride windows = K.map_fn(lambda w: data[:, w: (w + stride), :], idxs, dtype=K.floatx()) windows = K.permute_dimensions(windows, (1,0,2,3)) stds = K.map_fn(lambda w: K.std(w, axis=1), windows) return stds ipt = Input(shape=(None,10)) d = TimeDistributed(Dense(10))(ipt) out = Lambda(stdev_pooling)([d,K.variable(20, dtype='int32', name='stride_var')]) m = keras.Model(inputs=ipt, outputs=out) x = np.arange(1000).reshape(1,-1,10) m.predict(x).shape

--------------------------------------------------------------------------- ValueError Traceback (most recent call last) in () 1 ipt = Input(shape=(None,10)) 2 d = TimeDistributed(Dense(10))(ipt) ----> 3 out = Lambda(stdev_pooling)([d,K.variable(20, dtype='int32', name='stride_var')]) /home/juliano/.local/lib/python2.7/site-packages/keras/engine/base_layer.pyc in __call__(self, inputs, **kwargs) 472 if all([s is not None 473 for s in to_list(input_shape)]): --> 474 output_shape = self.compute_output_shape(input_shape) 475 else: 476 if isinstance(input_shape, list): /home/juliano/.local/lib/python2.7/site-packages/keras/layers/core.pyc in compute_output_shape(self, input_shape) 643 if isinstance(input_shape, list): 644 xs = [K.placeholder(shape=shape) for shape in input_shape] --> 645 x = self.call(xs) 646 else: 647 x = K.placeholder(shape=input_shape) /home/juliano/.local/lib/python2.7/site-packages/keras/layers/core.pyc in call(self, inputs, mask) 680 if has_arg(self.function, 'mask'): 681 arguments['mask'] = mask --> 682 return self.function(inputs, **arguments) 683 684 def compute_mask(self, inputs, mask=None): in stdev_pooling(inputs) 5 print K.dtype(stride), K.dtype(data), '---' 6 ----> 7 num_windows = K.shape(data)[1] / stride 8 9 idxs = K.arange(num_windows-1) * stride /home/juliano/.local/lib/python2.7/site-packages/tensorflow/python/ops/math_ops.pyc in binary_op_wrapper(x, y) 848 with ops.name_scope(None, op_name, [x, y]) as name: 849 if isinstance(x, ops.Tensor) and isinstance(y, ops.Tensor): --> 850 return func(x, y, name=name) 851 elif not isinstance(y, sparse_tensor.SparseTensor): 852 try: /home/juliano/.local/lib/python2.7/site-packages/tensorflow/python/ops/math_ops.pyc in _div_python2(x, y, name) 972 with ops.name_scope(name, "div", [x, y]) as name: 973 x = ops.convert_to_tensor(x, name="x") --> 974 y = ops.convert_to_tensor(y, name="y", dtype=x.dtype.base_dtype) 975 x_dtype = x.dtype.base_dtype 976 y_dtype = y.dtype.base_dtype /home/juliano/.local/lib/python2.7/site-packages/tensorflow/python/framework/ops.pyc in convert_to_tensor(value, dtype, name, preferred_dtype) 996 name=name, 997 preferred_dtype=preferred_dtype, --> 998 as_ref=False) 999 1000 /home/juliano/.local/lib/python2.7/site-packages/tensorflow/python/framework/ops.pyc in internal_convert_to_tensor(value, dtype, name, as_ref, preferred_dtype, ctx) 1092 1093 if ret is None: -> 1094 ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref) 1095 1096 if ret is NotImplemented: /home/juliano/.local/lib/python2.7/site-packages/tensorflow/python/framework/ops.pyc in _TensorTensorConversionFunction(t, dtype, name, as_ref) 929 raise ValueError( 930 "Tensor conversion requested dtype %s for Tensor with dtype %s: %r" % --> 931 (dtype.name, t.dtype.name, str(t))) 932 return t 933 ValueError: Tensor conversion requested dtype int32 for Tensor with dtype float32: 'Tensor("lambda_9/Placeholder_1:0", shape=(), dtype=float32)'

def stdev_pooling(inputs): data, stride = inputs stride = K.cast(stride, dtype='int32') print K.dtype(stride), K.dtype(data), '---' num_windows = K.shape(data)[1] / stride idxs = K.arange(num_windows) * stride windows = K.map_fn(lambda w: data[:, w: (w + stride), :], idxs, dtype=K.floatx()) windows = K.permute_dimensions(windows, (1,0,2,3)) stds = K.map_fn(lambda w: K.std(w, axis=1), windows) return stds

--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) in () 3 x = np.arange(2000).reshape(2,-1,10) 4 ----> 5 m = keras.Model(inputs=ipt, outputs=out) 6 7 m.predict(x).shape /home/juliano/.local/lib/python2.7/site-packages/keras/legacy/interfaces.pyc in wrapper(*args, **kwargs) 89 warnings.warn('Update your `' + object_name + 90 '` call to the Keras 2 API: ' + signature, stacklevel=2) ---> 91 return func(*args, **kwargs) 92 wrapper._original_function = func 93 return wrapper /home/juliano/.local/lib/python2.7/site-packages/keras/engine/network.pyc in __init__(self, *args, **kwargs) 91 'inputs' in kwargs and 'outputs' in kwargs): 92 # Graph network ---> 93 self._init_graph_network(*args, **kwargs) 94 else: 95 # Subclassed network /home/juliano/.local/lib/python2.7/site-packages/keras/engine/network.pyc in _init_graph_network(self, inputs, outputs, name) 235 # Keep track of the network's nodes and layers. 236 nodes, nodes_by_depth, layers, layers_by_depth = _map_graph_network( --> 237 self.inputs, self.outputs) 238 self._network_nodes = nodes 239 self._nodes_by_depth = nodes_by_depth /home/juliano/.local/lib/python2.7/site-packages/keras/engine/network.pyc in _map_graph_network(inputs, outputs) 1351 layer=layer, 1352 node_index=node_index, -> 1353 tensor_index=tensor_index) 1354 1355 for node in reversed(nodes_in_decreasing_depth): /home/juliano/.local/lib/python2.7/site-packages/keras/engine/network.pyc in build_map(tensor, finished_nodes, nodes_in_progress, layer, node_index, tensor_index) 1338 tensor_index = node.tensor_indices[i] 1339 build_map(x, finished_nodes, nodes_in_progress, layer, -> 1340 node_index, tensor_index) 1341 1342 finished_nodes.add(node) /home/juliano/.local/lib/python2.7/site-packages/keras/engine/network.pyc in build_map(tensor, finished_nodes, nodes_in_progress, layer, node_index, tensor_index) 1310 ValueError: if a cycle is detected. 1311 """ -> 1312 node = layer._inbound_nodes[node_index] 1313 1314 # Prevent cycles. AttributeError: 'NoneType' object has no attribute '_inbound_nodes'

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1楼 · 发布于 2024-09-25 08:42:18

在进一步修改代码并阅读了keras如何与tensorflow交互（在许多不同的地方，包括tensorflow和keras的源代码）之后，我发现了问题所在。在

首先，这里有一个我想做的最简单的工作示例：

import tensorflow
import keras
from keras.layers import Dense, TimeDistributed, Lambda, Input
import numpy as np
import keras.backend as K


def stdev_pooling(inputs, stride):

    data = inputs

    padding = K.shape(data)[1] % stride

    data = K.switch(padding > 0, K.temporal_padding(data, padding=(0,stride-padding)), data )

    num_windows = K.shape(data)[1] / stride

    idxs = K.arange(num_windows) * stride

    windows = K.map_fn(lambda w: data[:, w: (w + stride), :], idxs, dtype=K.floatx())

    windows = K.permute_dimensions(windows, (1,0,2,3))

    stds = K.map_fn(lambda w: K.std(w, axis=1), windows)

    return stds

ipt = Input(shape=(None,10))
d = TimeDistributed(Dense(10))(ipt)
#stride is an argument to stdev_pooling, not a signal coming from
#a previous layer. Thus it must be passed in the `arguments`
#dictionary of the `Lambda` layer.
out = Lambda(stdev_pooling, arguments={'stride':15})(d)

x = np.arange(2000).reshape(2,-1,10)    
m = keras.Model(inputs=ipt, outputs=out)    
y = m.predict(x)
print y
print y.shape

问题源于前面代码中的out = Lambda(stdev_pooling)([d,K.variable(20, dtype='int32', name='stride_var')])行。在

当一个信号（例如K.variable（…）作为输入提供给Lambda层时，keras希望它连接到输入层。因此，错误AttributeError: 'NoneType' object has no attribute '_inbound_nodes'。在

解决方案只是通过Lambda层构造函数的arguments字典提供stride参数：

out = Lambda(stdev_pooling, arguments={'stride':15})(d)

我希望这段代码能帮助所有试图在keras中构建某种池层的人。当我有时间的时候，我会把它写成一个合适的池层。现在，这个Lambda版本应该可以。在

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