python - ラザニア - 予期しないキーワード エラー

Question

私は Python で機械学習を学ぼうとしています - そして lasagne / nolearn パッケージを実行したいと思っています。すべてのパッケージをインストールしました。以下のスクリプトを使用しています ( http://semantive.com/deep-learning-examples/から)。次のエラーが発生します。このエラーを解決する方法を知っている人がいたら教えてください。

スクリプトは、ラザニア モジュールの 1 つのみで初期エラーを返します。

  File "<ipython-input-89-2752ae2387c3>", line 11, in <module>
    from nolearn.lasagne import visualize

ImportError: cannot import name visualize

その後 - パッド引数の周りにエラーがあります:

Traceback (most recent call last):

  File "<ipython-input-90-7a7b6ee7a652>", line 66, in <module>
    network = net.fit(x_train, y_train)

  File "C:\Users\Anaconda\lib\site-packages\nolearn\lasagne.py", line 138, in fit
    out = self._output_layer = self.initialize_layers()

  File "C:\Users\Anaconda\lib\site-packages\nolearn\lasagne.py", line 369, in initialize_layers
    layer = layer_factory(layer, **layer_params)

  File "C:\Users\src\lasagne\lasagne\layers\conv.py", line 368, in __init__
    super(Conv2DLayer, self).__init__(incoming, **kwargs)

TypeError: __init__() got an unexpected keyword argument 'pad'

コード

import cPickle as pickle
    import os
    import numpy as np

    import matplotlib.pyplot as plt
    import matplotlib.cm as cm
    import lasagne
    from lasagne import layers
    from lasagne.updates import nesterov_momentum
    from nolearn.lasagne import NeuralNet
    from nolearn.lasagne import visualize
    from sklearn.metrics import confusion_matrix, classification_report, accuracy_score


    def load_data(path):
        x_train = np.zeros((50000, 3, 32, 32), dtype='uint8')
        y_train = np.zeros((50000,), dtype="uint8")

        for i in range(1, 6):
            data = unpickle(os.path.join(path, 'data_batch_' + str(i)))
            images = data['data'].reshape(10000, 3, 32, 32)
            labels = data['labels']
            x_train[(i - 1) * 10000:i * 10000, :, :, :] = images
            y_train[(i - 1) * 10000:i * 10000] = labels

        test_data = unpickle(os.path.join(path, 'test_batch'))
        x_test = test_data['data'].reshape(10000, 3, 32, 32)
        y_test = np.array(test_data['labels'])

        return x_train, y_train, x_test, y_test


    def unpickle(file):
        f = open(file, 'rb')
        dict = pickle.load(f)
        f.close()
        return dict


    net = NeuralNet(
        layers=[('input', layers.InputLayer),
                ('conv2d1', layers.Conv2DLayer),
                ('maxpool1', layers.MaxPool2DLayer),
                ('conv2d2', layers.Conv2DLayer),
                ('maxpool2', layers.MaxPool2DLayer),
                ('dense', layers.DenseLayer),
                ('output', layers.DenseLayer),
                ],
        input_shape=(None, 3, 32, 32),
        conv2d1_num_filters=20,
        conv2d1_filter_size=(5, 5),
        conv2d1_stride=(1, 1),
        conv2d1_pad=(2, 2),
        conv2d1_nonlinearity=lasagne.nonlinearities.rectify,
        maxpool1_pool_size=(2, 2),
        conv2d2_num_filters=20,
        conv2d2_filter_size=(5, 5),
        conv2d2_stride=(1, 1),
        conv2d2_pad=(2, 2),
        conv2d2_nonlinearity=lasagne.nonlinearities.rectify,
        maxpool2_pool_size=(2, 2),
        dense_num_units=1000,
        dense_nonlinearity=lasagne.nonlinearities.rectify,
        output_nonlinearity=lasagne.nonlinearities.softmax,
        output_num_units=10,
        update=nesterov_momentum,
        update_momentum=0.9,
        update_learning_rate=0.0001,
        max_epochs=100,
        verbose=True
    )

    x_train, y_train, x_test, y_test = load_data(os.path.expanduser('~/Dropbox/Python/cifar-10-python.tar/cifar-10-python/cifar-10-batches-py/'))

    network = net.fit(x_train, y_train)
    predictions = network.predict(x_test)

    print classification_report(y_test, predictions)
    print accuracy_score(y_test, predictions)