HodaFakharzade
commented
5 years ago I am trying to run some code on Deep embedding clustering on mnist with the help of Keras , however, I get the following error
from keras.datasets import mnist
import numpy as np
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
input_img = Input(shape=(dims[0],), name='input')
x = input_img
# internal layers in encoder
for i in range(n_stacks-1):
x = Dense(dims[i + 1], activation=act, kernel_initializer=init, name='encoder_%d' % i)(x)
# hidden layer
encoded = Dense(dims[-1], kernel_initializer=init, name='encoder_%d' % (n_stacks - 1))(x) # hidden layer, features are extracted from here
x = encoded
# internal layers in decoder
for i in range(n_stacks-1, 0, -1):
x = Dense(dims[i], activation=act, kernel_initializer=init, name='decoder_%d' % i)(x)
# output
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.)
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)
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)
pretrain_epochs = 300
batch_size = 256
save_dir = './results'
autoencoder, encoder = autoencoder(dims, init=init)
autoencoder.compile(optimizer=pretrain_optimizer, loss='mse')
autoencoder.fit(x, x, batch_size=batch_size, epochs=pretrain_epochs) #, callbacks=cb)
autoencoder.save_weights(save_dir + '/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', name='clusters')
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')
y_pred_last = np.copy(y_pred_kmeans)
model.get_layer(name='clustering').set_weights([kmeans.cluster_centers_])
# computing an auxiliary target distribution
def target_distribution(q):
weight = q ** 2 / q.sum(0)
return (weight.T / weight.sum(1)).T
loss = 0
index = 0
maxiter = 8000
update_interval = 140
index_array = np.arange(x.shape[0])
tol = 0.001 # tolerance threshold to stop training
for ite in range(int(maxiter)):
if ite % update_interval == 0:
q = model.predict(x, verbose=2 )
p = target_distribution(q) # update the auxiliary target distribution p
# evaluate the clustering performance
y_pred = q.argmax(1)
if y is not None:
acc = np.round(metrics.acc(y, y_pred), 5)
nmi = np.round(metrics.nmi(y, y_pred), 5)
ari = np.round(metrics.ari(y, y_pred), 5)
loss = np.round(loss, 5)
print('Iter %d: acc = %.5f, nmi = %.5f, ari = %.5f' % (ite, acc, nmi, ari), ' ; loss=', loss)
# check stop criterion - model convergence
delta_label = np.sum(y_pred != y_pred_last).astype(np.float32) / y_pred.shape[0]
y_pred_last = np.copy(y_pred)
if ite > 0 and delta_label < tol:
print('delta_label ', delta_label, '< tol ', tol)
print('Reached tolerance threshold. Stopping training.')
break
idx = index_array[index * batch_size: min((index+1) * batch_size, x.shape[0])]
loss = model.train_on_batch(x=x[idx], y=p[idx])
index = index + 1 if (index + 1) * batch_size <= x.shape[0] else 0
model.save_weights(save_dir + '/DEC_model_final.h5')
model.load_weights(save_dir + '/DEC_model_final.h5')the error:
with session.graph.as_default(): AttributeError: 'NoneType' object has no attribute 'graph'
(the problem might be in saving the model but I can't figure out why I am wrong.) my code runs perfectly in jupyter notebook but I can't run it in an editor like pycharm .please help.
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