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  • numpy.argmax 用在求解混淆矩阵用

    numpy.argmax

    numpy.argmax(a, axis=None, out=None)[source]

    Returns the indices of the maximum values along an axis.

    Parameters:

    a : array_like

    Input array.

    axis : int, optional

    By default, the index is into the flattened array, otherwise along the specified axis.

    out : array, optional

    If provided, the result will be inserted into this array. It should be of the appropriate shape and dtype.

    Returns:

    index_array : ndarray of ints

    Array of indices into the array. It has the same shape as a.shape with the dimension along axis removed.

    See also

    ndarray.argmax, argmin

    amax
    The maximum value along a given axis.
    unravel_index
    Convert a flat index into an index tuple.

    Notes

    In case of multiple occurrences of the maximum values, the indices corresponding to the first occurrence are returned.

    Examples

    >>> a = np.arange(6).reshape(2,3)
    >>> a
    array([[0, 1, 2],
           [3, 4, 5]])
    >>> np.argmax(a)
    5
    >>> np.argmax(a, axis=0)
    array([1, 1, 1])
    >>> np.argmax(a, axis=1)
    array([2, 2])
    
    >>> b = np.arange(6)
    >>> b[1] = 5
    >>> b
    array([0, 5, 2, 3, 4, 5])
    >>> np.argmax(b) # Only the first occurrence is returned.
    1

    在多分类模型训练中,我的使用:org_labels = [0,1,2,....max_label] 从0开始的标记类别
    if __name__ == "__main__":
        width, height = 32, 32
        X, Y, org_labels = load_data(dirname="data", resize_pics=(width, height))
        trainX, testX, trainY, testY = train_test_split(X, Y, test_size=0.2, random_state=666)
        print("sample data:")
        print(trainX[0])
        print(trainY[0])
        print(testX[-1])
        print(testY[-1])
    
        model = get_model(width, height, classes=100)
    
        filename = 'cnn_handwrite-acc0.8.tflearn'
        # try to load model and resume training
        #try:
        #    model.load(filename)
        #    print("Model loaded OK. Resume training!")
        #except:
        #    pass
    
        # Initialize our callback with desired accuracy threshold.
        early_stopping_cb = EarlyStoppingCallback(val_acc_thresh=0.6)
        try:
            model.fit(trainX, trainY, validation_set=(testX, testY), n_epoch=500, shuffle=True,
                      snapshot_epoch=True, # Snapshot (save & evaluate) model every epoch.
                      show_metric=True, batch_size=32, callbacks=early_stopping_cb, run_id='cnn_handwrite')
        except StopIteration as e:
            print("OK, stop iterate!Good!")
    
        model.save(filename)
    
        # predict all data and calculate confusion_matrix
        model.load(filename)
    
        pro_arr =model.predict(X)
        predict_labels = np.argmax(pro_arr, axis=1)
        print(classification_report(org_labels, predict_labels))
        print(confusion_matrix(org_labels, predict_labels))
    
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  • 原文地址:https://www.cnblogs.com/bonelee/p/8976380.html
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