i am trying to run the Mnist_CNN spiking repo of hunse but got the errors “Conv2D’ object has no attribute 'subsample” .Dont know how to rectify this error. i am using keras version2.X with theano latest version and python3.6.
# number of convolutional filters to use
nb_filters = 32
# size of pooling area for max pooling
nb_pool = 2
# convolution kernel size
nb_conv = 3
# convert class vectors to binary class matrices
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)
kmodel = Sequential()
softlif_params = dict(
sigma=0.002, amplitude=0.063, tau_rc=0.022, tau_ref=0.002)
# model.add(Convolution2D(32, (3, 3), activation='relu', input_shape=(1,28,28), data_format='channels_first'))
kmodel.add(GaussianNoise(0.1, input_shape=(img_rows, img_cols,1)))
kmodel.add(Convolution2D(nb_filters, (nb_conv, nb_conv), padding='valid'))
kmodel.add(SoftLIF(**softlif_params))
kmodel.add(Convolution2D(nb_filters, nb_conv, nb_conv))
kmodel.add(SoftLIF(**softlif_params))
kmodel.add(AveragePooling2D(pool_size=(nb_pool, nb_pool)))
kmodel.add(Dropout(0.25))
kmodel.add(Flatten())
kmodel.add(Dense(128))
kmodel.add(SoftLIF(**softlif_params))
kmodel.add(Dropout(0.5))
kmodel.add(Dense(nb_classes))
kmodel.add(Activation('softmax'))
kmodel.compile(loss='categorical_crossentropy',
optimizer='adadelta',
metrics=['accuracy'])
kmodel.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch,
verbose=1, validation_data=(X_test, Y_test))
score = kmodel.evaluate(X_test, Y_test, verbose=0)
print('Test score:', score[0])
print('Test accuracy:', score[1])
save_model_pair(kmodel, filename, overwrite=True)
else:
kmodel = load_model_pair(filename)
presentation_time = 0.2
model = nengo.Network()
with model:
u = nengo.Node(nengo.processes.PresentInput(X_test, presentation_time))
knet = SequentialNetwork(kmodel, synapse=nengo.synapses.Alpha(0.005))
nengo.Connection(u, knet.input, synapse=None)
input_p = nengo.Probe(u)
output_p = nengo.Probe(knet.output)
# --- image display
image_shape = kmodel.input_shape[1:]
display_f = image_display_function(image_shape)
display_node = nengo.Node(display_f, size_in=u.size_out)
nengo.Connection(u, display_node, synapse=None)
# --- output spa display
vocab_names = ['ZERO', 'ONE', 'TWO', 'THREE', 'FOUR',
'FIVE', 'SIX', 'SEVEN', 'EIGHT', 'NINE']
vocab_vectors = np.eye(len(vocab_names))
vocab = nengo.spa.Vocabulary(len(vocab_names))
for name, vector in zip(vocab_names, vocab_vectors):
vocab.add(name, vector)
config = nengo.Config(nengo.Ensemble)
config[nengo.Ensemble].neuron_type = nengo.Direct()
with config:
output = nengo.spa.State(len(vocab_names), subdimensions=10, vocab=vocab)
nengo.Connection(knet.output, output.input)
A```
ttributeError Traceback (most recent call last)
in ()
5 with model:
6 u = nengo.Node(nengo.processes.PresentInput(X_train, presentation_time))
----> 7 knet = SequentialNetwork(kmodel,synapse=nengo.synapses.Alpha(0.001))
8 nengo.Connection(u, knet.input, synapse=None)
9
~\Anaconda3.0\lib\site-packages\nengo_extras\keras.py in init(self, model, synapse, lif_type, **kwargs)
79 self.add_data_layer(np.prod(model.input_shape[1:]))
80 for layer in model.layers:
---> 81 self._add_layer(layer)
82
83 def _add_layer(self, layer):
~\Anaconda3.0\lib\site-packages\nengo_extras\keras.py in _add_layer(self, layer)
99 for cls in type(layer).mro:
100 if cls in layer_adder:
--> 101 return layer_addercls
102
103 raise NotImplementedError("Cannot build layer type %r" %
~\Anaconda3.0\lib\site-packages\nengo_extras\keras.py in _add_conv2d_layer(self, layer)
112 filters, biases = layer.get_weights()
113 filters = filters[..., ::-1, ::-1] # flip
--> 114 strides = layer.subsample
115
116 nf, nc, ni, nj = filters.shape
AttributeError: 'Conv2D' object has no attribute 'subsample'