pythonにおけるnnablaでのCNNでエラーが出てしまう件

Question

この記事を参考に以下のようなCNNのプログラムを書きました。
環境はmacOS X 10.15、python3.7.1です

from __future__ import absolute_import
from six.moves import range

import os

import nnabla as nn
# ①　NNabla関連モジュールのインポート
import nnabla as nn
import nnabla.functions as F
import nnabla.parametric_functions as PF
import nnabla.solvers as S
from nnabla.utils.data_iterator import data_iterator_simple
import nnabla.initializer as I

# ②　NNabla関連以外のモジュールのインポート
import numpy as np
from sklearn.datasets import load_digits
from PIL import Image


# ③　学習データを読み込み使えるようにする


def data_iterator_tiny_digits(digits, batch_size=64, shuffle=False, rng=None):
    def load_func(index):
        data_array = np.empty((0,8),float)
        target_arrry = np.array([])
        main_folder = os.listdir("./data/")
        for sub_folder in main_folder:
            data_list = os.listdir("./data/" + sub_folder)
            for data in data_list:
                img = Image.open("./data/" + sub_folder + "/" + data)
                fix_img = img.convert('L')
                data_grey = np.asarray(fix_img)
                target_arrry = np.append(target_arrry,sub_folder)
                data_array = np.append(data_array,data_grey,axis=0)
        return data_array,np.array([target_arrry]).astype(np.int32)
    return data_iterator_simple(load_func, 200, batch_size, shuffle, rng, with_file_cache=False)

# ④　損失グラフを構築する関数を定義する


def logreg_loss(y, t):
    loss_f = F.mean(F.softmax_cross_entropy(y, t))
    return loss_f

# ⑤ トレーニング関数を定義する


def training(xt, tt, data_t, loss_t, steps, learning_rate):
    solver = S.Sgd(learning_rate)
    # Set parameter variables to be updatd.
    solver.set_parameters(nn.get_parameters())
    for i in range(steps):
        xt.d, tt.d = data_t.next()
        loss_t.forward()
        solver.zero_grad()  # Initialize gradients of all parameters to zero.
        loss_t.backward()
        # Applying weight decay as an    regularization
        solver.weight_decay(1e-5)
        solver.update()
        if i % 100 == 0:  # Print for each 10 iterations
            print(str(i) + ":" + str(loss.d))

# ⑥ ニューラルネットを構築する関数を定義する


def network(x):
    initializer = I.UniformInitializer((-0.1, 0.1))
    with nn.parameter_scope("cnn"):
        with nn.parameter_scope("conv1"):
            h = F.relu(PF.batch_normalization(
                PF.convolution(x, 4, (3, 3), pad=(1, 1), stride=(2, 2),w_init=initializer, with_bias=False)))
        with nn.parameter_scope("conv2"):
            h = F.relu(PF.batch_normalization(
                PF.convolution(h, 8, (3, 3), pad=(1, 1))))
            h = F.average_pooling(h, (2, 2))
        with nn.parameter_scope("fc3"):
            h = F.relu(PF.affine(h, 32))
        with nn.parameter_scope("classifier"):
            h = PF.affine(h, 10)
    return h


# ⑦　実行開始：scikit_learnでdigits（8✕8サイズ）データを取得し、NNablaで処理可能に整形する
np.random.seed(0)
digits = 0
data = data_iterator_tiny_digits(digits,batch_size=64, shuffle=True)

# ⑧　ニューラルネットワークを構築する
nn.clear_parameters()
img, label = data.next()
x = nn.Variable(img.shape)
y = network(x)
t = nn.Variable(label.shape)
loss = logreg_loss(y, t)

# ⑨　学習する
learning_rate = 1e-1
training(x, t, data, loss, 1000, learning_rate)

# ⑩　推論し、最後に正確さを求めて表示する
x.d, t.d = data.next()
y.forward()
mch = 0
for p in range(len(t.d)):
    if t.d[p] == y.d.argmax(axis=1)[p]:
        mch += 1

print("Accuracy:{}".format(mch / len(t.d)))

しかし、実行すると以下のようなエラーが出てしまいます

2020-01-14 21:13:45,163 [nnabla][INFO]: Initializing CPU extension...
2020-01-14 21:13:48,083 [nnabla][INFO]: DataSource with shuffle(True)
2020-01-14 21:13:48,524 [nnabla][INFO]: Using DataSourceWithMemoryCache
2020-01-14 21:13:48,524 [nnabla][INFO]: DataSource with shuffle(True)
2020-01-14 21:13:48,612 [nnabla][INFO]: On-memory
2020-01-14 21:13:48,613 [nnabla][INFO]: Using DataIterator
Traceback (most recent call last):
  File "file_losd_test.py", line 94, in <module>
    y = network(x)
  File "file_losd_test.py", line 73, in network
    PF.convolution(x, 4, (3, 3), pad=(1, 1), stride=(2, 2),w_init=initializer, with_bias=False)))
  File "<string>", line 5, in convolution
  File "/Users/takabatomoki/.pyenv/versions/3.7.1/lib/python3.7/site-packages/nnabla/parametric_functions.py", line 639, in convolution
    return F.convolution(inp, w, b, base_axis, pad, stride, dilation, group, channel_last)
  File "<convolution>", line 3, in convolution
  File "/Users/takabatomoki/.pyenv/versions/3.7.1/lib/python3.7/site-packages/nnabla/function_bases.py", line 352, in convolution
    return F.Convolution(ctx, base_axis, pad, stride, dilation, group, channel_last)(*inputs, n_outputs=n_outputs, auto_forward=get_auto_forward(), outputs=outputs)
  File "function.pyx", line 292, in nnabla.function.Function.__call__
  File "function.pyx", line 271, in nnabla.function.Function._cg_call
RuntimeError: value error in setup_impl
/Users/gitlab-runner/builds/9703d983/0/nnabla/builders/all/nnabla/src/nbla/function/./generic/convolution.cpp:51
Failed `shape_weights.size() == 2 + spatial_dims_`: Weights must be a tensor more than 3D.

初心者なので疎いのですが、どうにも重みが規定値を超えなければならないそうなのですが、どうすればいいのでしょうか？
ご教授いただけると幸いです。
よろしくお願い申し上げます。