AttributeError:“Conv2D”对象没有属性“subsample”

Question

我正在尝试使用nengo中的Mnist dataset运行python存储库进行数字分类，但由于出现此错误而无法获得结果“AttributeError:'Conv2D'object has no attribute'subsample“我试图消除此错误，但没有任何人可以向我建议此错误的解决方案。在

from __future__ import print_function

import os
os.environ['THEANO_FLAGS'] = 'device=gpu,floatX=float32'

import nengo
import nengo_ocl
import numpy as np

import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import (
    Dense, Dropout, Activation, Flatten, Convolution2D, AveragePooling2D)
from keras.layers.noise import GaussianNoise
from keras.utils import np_utils

import nengo
from nengo_extras.keras import (
    load_model_pair, save_model_pair, SequentialNetwork, SoftLIF)
from nengo_extras.gui import image_display_function

np.random.seed(1)
filename = 'mnist_spiking_cnn'

# --- Load data
img_rows, img_cols = 28, 28
nb_classes = 10

# the data, shuffled and split between train and test sets
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols)
X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)
X_train = X_train.astype('float32')/128 - 1
X_test = X_test.astype('float32')/128 - 1

# --- Train model
if not os.path.exists(filename + '.h5'):
    batch_size = 128
    nb_epoch = 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)

    kmodel.add(GaussianNoise(0.1, input_shape=(1, img_rows, img_cols)))
    kmodel.add(Convolution2D(nb_filters, nb_conv, nb_conv, border_mode='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)


# --- Run model in Nengo
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)

错误跟踪如下

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