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))