Keras 深度聚类自定义层中的未定义错误
Keras deep clustering undefined errors in clustering custom layer
我正在按照本指南将聚类实施到深度模型中
https://ai-mrkogao.github.io/reinforcement%20learning/clusteringkeras/ 但我有两个错误：
第一个，这一行
clustering_layer = ClusteringLayer(n_clusters, name='clustering')(encoder.output)
它说
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.6/dist-packages/keras/engine/base_layer.py", line 463, in __call__
self.build(unpack_singleton(input_shapes))
File "<stdin>", line 14, in build
TypeError: add_weight() got multiple values for argument 'name'
所以我找到了
self.clusters = self.add_weight((self.n_clusters, input_dim), initializer='glorot_uniform', name='clusters')
从 class ClusteringLayer 中删除了 name='clusters'，如下所示
self.clusters = self.add_weight((self.n_clusters, input_dim), initializer='glorot_uniform')
但是现在一直报错
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.6/dist-packages/keras/engine/base_layer.py", line 463, in __call__
self.build(unpack_singleton(input_shapes))
File "<stdin>", line 14, in build
File "/usr/local/lib/python3.6/dist-packages/keras/engine/base_layer.py", line 282, in add_weight
constraint=constraint)
File "/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py", line 620, in variable
value, dtype=dtype, name=name, constraint=constraint)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend.py", line 782, in variable
constraint=constraint)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/variables.py", line 263, in __call__
return super(VariableMetaclass, cls).__call__(*args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py", line 460, in __init__
shape=shape)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py", line 582, in _init_from_args
if init_from_fn else [initial_value]) as name:
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py", line 6513, in __enter__
return self._name_scope.__enter__()
File "/usr/lib/python3.6/contextlib.py", line 81, in __enter__
return next(self.gen)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py", line 4306, in name_scope
if not _VALID_SCOPE_NAME_REGEX.match(name):
TypeError: expected string or bytes-like object
这是完整且可重现的代码
import keras.backend as K
from keras.engine.topology import Layer, InputSpec
from keras.layers import Dense, Input
from keras.models import Model
from keras.optimizers import SGD
from keras import callbacks
from keras.initializers import VarianceScaling
from sklearn.cluster import KMeans
def autoencoder(dims, act='relu', init='glorot_uniform'):
n_stacks = len(dims) - 1
input_img = Input(shape=(dims[0],), name='input')
x = input_img
for i in range(n_stacks-1):
x = Dense(dims[i + 1], activation=act, kernel_initializer=init, name='encoder_%d' % i)(x)
encoded = Dense(dims[-1], kernel_initializer=init, name='encoder_%d' % (n_stacks - 1))(x) # hidden layer, features are extracted from here
x = encoded
for i in range(n_stacks-1, 0, -1):
x = Dense(dims[i], activation=act, kernel_initializer=init, name='decoder_%d' % i)(x)
x = Dense(dims[0], kernel_initializer=init, name='decoder_0')(x)
decoded = x
return Model(inputs=input_img, outputs=decoded, name='AE'), Model(inputs=input_img, outputs=encoded, name='encoder')
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x = np.concatenate((x_train, x_test))
y = np.concatenate((y_train, y_test))
x = x.reshape((x.shape[0], -1))
x = np.divide(x, 255.)
x_train.shape
x.shape
n_clusters = len(np.unique(y))
kmeans = KMeans(n_clusters=n_clusters, n_init=20, n_jobs=4)
y_pred_kmeans = kmeans.fit_predict(x)
y_pred_kmeans[:10]
dims = [x.shape[-1], 500, 500, 2000, 10]
init = VarianceScaling(scale=1. / 3., mode='fan_in',distribution='uniform')
pretrain_optimizer = SGD(lr=1, momentum=0.9)
autoencoder, encoder = autoencoder(dims, init=init)
autoencoder.compile(optimizer=pretrain_optimizer, loss='mse')
autoencoder.fit(x, x, batch_size=250, epochs=2) #, callbacks=cb)
autoencoder.save_weights( 'ae_weights.h5')
class ClusteringLayer(Layer):
def __init__(self, n_clusters, weights=None, alpha=1.0, **kwargs):
if 'input_shape' not in kwargs and 'input_dim' in kwargs:
kwargs['input_shape'] = (kwargs.pop('input_dim'),)
super(ClusteringLayer, self).__init__(**kwargs)
self.n_clusters = n_clusters
self.alpha = alpha
self.initial_weights = weights
self.input_spec = InputSpec(ndim=2)
def build(self, input_shape):
assert len(input_shape) == 2
input_dim = input_shape[1]
self.input_spec = InputSpec(dtype=K.floatx(), shape=(None, input_dim))
self.clusters = self.add_weight((self.n_clusters, input_dim), initializer='glorot_uniform')
if self.initial_weights is not None:
self.set_weights(self.initial_weights)
del self.initial_weights
self.built = True
def call(self, inputs, **kwargs):
q = 1.0 / (1.0 + (K.sum(K.square(K.expand_dims(inputs, axis=1) - self.clusters), axis=2) / self.alpha))
q **= (self.alpha + 1.0) / 2.0
q = K.transpose(K.transpose(q) / K.sum(q, axis=1)) # Make sure each sample's 10 values add up to 1.
return q
def compute_output_shape(self, input_shape):
assert input_shape and len(input_shape) == 2
return input_shape[0], self.n_clusters
def get_config(self):
config = {'n_clusters': self.n_clusters}
base_config = super(ClusteringLayer, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
clustering_layer = ClusteringLayer(n_clusters, name='clustering')(encoder.output)
model = Model(inputs=encoder.input, outputs=clustering_layer)
model.compile(optimizer=SGD(0.01, 0.9), loss='kld')
model.get_layer(name='clustering').set_weights([kmeans.cluster_centers_])
encoder.output
clustering_layer
784 image input -> 10 classification
的结果
from tensorflow.python import keras
print(keras.__version__)
是
2.2.4-tf
您甚至可以在 repbioinfo/autoencoderforclustering
下载带有 keras 安装的 docker
我已经解决了错误，只需更正这些行
self.clusters = self.add_weight(shape = (self.n_clusters, input_dim),
initializer='glorot_uniform', name='clusters')
而不是
self.clusters = self.add_weight((self.n_clusters, input_dim), initializer='glorot_uniform')
我正在按照本指南将聚类实施到深度模型中 https://ai-mrkogao.github.io/reinforcement%20learning/clusteringkeras/ 但我有两个错误：
第一个，这一行
clustering_layer = ClusteringLayer(n_clusters, name='clustering')(encoder.output)
它说
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.6/dist-packages/keras/engine/base_layer.py", line 463, in __call__
self.build(unpack_singleton(input_shapes))
File "<stdin>", line 14, in build
TypeError: add_weight() got multiple values for argument 'name'
所以我找到了
self.clusters = self.add_weight((self.n_clusters, input_dim), initializer='glorot_uniform', name='clusters')
从 class ClusteringLayer 中删除了 name='clusters'，如下所示
self.clusters = self.add_weight((self.n_clusters, input_dim), initializer='glorot_uniform')
但是现在一直报错
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.6/dist-packages/keras/engine/base_layer.py", line 463, in __call__
self.build(unpack_singleton(input_shapes))
File "<stdin>", line 14, in build
File "/usr/local/lib/python3.6/dist-packages/keras/engine/base_layer.py", line 282, in add_weight
constraint=constraint)
File "/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py", line 620, in variable
value, dtype=dtype, name=name, constraint=constraint)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend.py", line 782, in variable
constraint=constraint)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/variables.py", line 263, in __call__
return super(VariableMetaclass, cls).__call__(*args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py", line 460, in __init__
shape=shape)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py", line 582, in _init_from_args
if init_from_fn else [initial_value]) as name:
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py", line 6513, in __enter__
return self._name_scope.__enter__()
File "/usr/lib/python3.6/contextlib.py", line 81, in __enter__
return next(self.gen)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py", line 4306, in name_scope
if not _VALID_SCOPE_NAME_REGEX.match(name):
TypeError: expected string or bytes-like object
这是完整且可重现的代码
import keras.backend as K
from keras.engine.topology import Layer, InputSpec
from keras.layers import Dense, Input
from keras.models import Model
from keras.optimizers import SGD
from keras import callbacks
from keras.initializers import VarianceScaling
from sklearn.cluster import KMeans
def autoencoder(dims, act='relu', init='glorot_uniform'):
n_stacks = len(dims) - 1
input_img = Input(shape=(dims[0],), name='input')
x = input_img
for i in range(n_stacks-1):
x = Dense(dims[i + 1], activation=act, kernel_initializer=init, name='encoder_%d' % i)(x)
encoded = Dense(dims[-1], kernel_initializer=init, name='encoder_%d' % (n_stacks - 1))(x) # hidden layer, features are extracted from here
x = encoded
for i in range(n_stacks-1, 0, -1):
x = Dense(dims[i], activation=act, kernel_initializer=init, name='decoder_%d' % i)(x)
x = Dense(dims[0], kernel_initializer=init, name='decoder_0')(x)
decoded = x
return Model(inputs=input_img, outputs=decoded, name='AE'), Model(inputs=input_img, outputs=encoded, name='encoder')
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x = np.concatenate((x_train, x_test))
y = np.concatenate((y_train, y_test))
x = x.reshape((x.shape[0], -1))
x = np.divide(x, 255.)
x_train.shape
x.shape
n_clusters = len(np.unique(y))
kmeans = KMeans(n_clusters=n_clusters, n_init=20, n_jobs=4)
y_pred_kmeans = kmeans.fit_predict(x)
y_pred_kmeans[:10]
dims = [x.shape[-1], 500, 500, 2000, 10]
init = VarianceScaling(scale=1. / 3., mode='fan_in',distribution='uniform')
pretrain_optimizer = SGD(lr=1, momentum=0.9)
autoencoder, encoder = autoencoder(dims, init=init)
autoencoder.compile(optimizer=pretrain_optimizer, loss='mse')
autoencoder.fit(x, x, batch_size=250, epochs=2) #, callbacks=cb)
autoencoder.save_weights( 'ae_weights.h5')
class ClusteringLayer(Layer):
def __init__(self, n_clusters, weights=None, alpha=1.0, **kwargs):
if 'input_shape' not in kwargs and 'input_dim' in kwargs:
kwargs['input_shape'] = (kwargs.pop('input_dim'),)
super(ClusteringLayer, self).__init__(**kwargs)
self.n_clusters = n_clusters
self.alpha = alpha
self.initial_weights = weights
self.input_spec = InputSpec(ndim=2)
def build(self, input_shape):
assert len(input_shape) == 2
input_dim = input_shape[1]
self.input_spec = InputSpec(dtype=K.floatx(), shape=(None, input_dim))
self.clusters = self.add_weight((self.n_clusters, input_dim), initializer='glorot_uniform')
if self.initial_weights is not None:
self.set_weights(self.initial_weights)
del self.initial_weights
self.built = True
def call(self, inputs, **kwargs):
q = 1.0 / (1.0 + (K.sum(K.square(K.expand_dims(inputs, axis=1) - self.clusters), axis=2) / self.alpha))
q **= (self.alpha + 1.0) / 2.0
q = K.transpose(K.transpose(q) / K.sum(q, axis=1)) # Make sure each sample's 10 values add up to 1.
return q
def compute_output_shape(self, input_shape):
assert input_shape and len(input_shape) == 2
return input_shape[0], self.n_clusters
def get_config(self):
config = {'n_clusters': self.n_clusters}
base_config = super(ClusteringLayer, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
clustering_layer = ClusteringLayer(n_clusters, name='clustering')(encoder.output)
model = Model(inputs=encoder.input, outputs=clustering_layer)
model.compile(optimizer=SGD(0.01, 0.9), loss='kld')
model.get_layer(name='clustering').set_weights([kmeans.cluster_centers_])
encoder.output
clustering_layer
784 image input -> 10 classification
from tensorflow.python import keras
print(keras.__version__)
是
2.2.4-tf
您甚至可以在 repbioinfo/autoencoderforclustering
下载带有 keras 安装的 docker我已经解决了错误，只需更正这些行
self.clusters = self.add_weight(shape = (self.n_clusters, input_dim),
initializer='glorot_uniform', name='clusters')
而不是
self.clusters = self.add_weight((self.n_clusters, input_dim), initializer='glorot_uniform')