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我正在尝试在 Python 中学习机器学习 - 并且想运行 lasagne / nolearn 包。我已经安装了所有软件包 - 并且正在使用下面的脚本（来自http://semantive.com/deep-learning-examples/），它给出了以下错误。如果有人知道如何解决此错误，请告诉我。

该脚本仅在其中一个千层面模块中给出了初始错误：

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

ImportError: cannot import name visualize

随后 - pad 参数周围有一个错误：

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'

编码

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)
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1 回答 1

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您的 nolearn 版本似乎使用了不兼容的 Lasagne 版本。

该pad功能于Conv2DLayer2015 年 7 月和 8 月添加到 Lasagne 的课程中​​（请参阅此处和此处）。您的 nolearn 版本显然希望使用该版本或更高版本。

有两种可能：

  1. 您（可能是不小心）在您的系统上有两个版本的千层面，但它是 Python 最先发现的旧版本。如果这是真的，请删除旧版本和/或确保 Python （首先）找到新版本。

  2. 你只是有一个过时的千层面。解决方法：更新吧！你如何做到这一点可能取决于你最初是如何安装它的。不过，最终你需要从 Github 获取最新版本。

于 2015-11-06T07:19:33.080 回答