numpy.argmax is used to solve the confusion matrix

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 

In multi-class model training, I use:

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))