Python keras.utils 模块，to_categorical() 实例源码

我们从Python开源项目中，提取了以下29个代码示例，用于说明如何使用keras.utils.to_categorical()。

项目：Controller-Hand 作者：ardamavi | 项目源码 | 文件源码

def get_dataset(dataset_path='Data/Train_Data'):
    # Getting all data from data path:
    try:
        X = np.load('Data/npy_train_data/X.npy')
        Y = np.load('Data/npy_train_data/Y.npy')
    except:
        labels = listdir(dataset_path) # Geting labels
        X = []
        Y = []
        for label in labels:
            datas_path = dataset_path+'/'+label
            for data in listdir(datas_path):
                img = get_img(datas_path+'/'+data)
                X.append(img)
                Y.append(int(label))
        # Create dateset:
        X = np.array(X).astype('float32')/255.
        Y = np.array(Y).astype('float32')
        Y = to_categorical(Y, 2)
        if not os.path.exists('Data/npy_train_data/'):
            os.makedirs('Data/npy_train_data/')
        np.save('Data/npy_train_data/X.npy', X)
        np.save('Data/npy_train_data/Y.npy', Y)
    X, X_test, Y, Y_test = train_test_split(X, Y, test_size=0.1, random_state=42)
    return X, X_test, Y, Y_test

项目：awesome-text-classification 作者：Hironsan | 项目源码 | 文件源码

def transform(self, X, y=None):
        chars = []
        for doc in X:
            char_ids = []
            for char in doc[:self.maxlen]:
                if char in self.vocab:
                    char_ids.append(self.vocab[char])
                else:
                    char_ids.append(self.vocab[UNK])
            char_ids += [self.vocab[PAD]] * (self.maxlen - len(char_ids))  # padding
            chars.append(char_ids)
        chars = dense_to_one_hot(chars, len(self.vocab))

        if y is not None:
            y = [self.classes[t] for t in y]
            y = to_categorical(y, len(self.classes))

        return (chars, y) if y is not None else chars

项目：autonomio 作者：autonomio | 项目源码 | 文件源码

def onehot(data):

    '''One Hot Encoding

    WHAT: Transform a feature in to binary columns.

    HOW: onehot(df.col)

    INPUT: An array, series, or list

    OUTPUT: Multiple columns of binary values that
    represent the input values.

    '''

    if type(data) == list:
        data = pd.Series(data)

    data = data.astype(int)

    data = array(data)
    encoded = to_categorical(data)

    encoded = pd.DataFrame(encoded)

    return encoded

项目：TF_MemN2N-tableQA 作者：vendi12 | 项目源码 | 文件源码

def one_hot_encode(array, len_dic):
    return np.array([to_categorical(vector, num_classes=len_dic) for vector in array])

项目：TF_MemN2N-tableQA 作者：vendi12 | 项目源码 | 文件源码

def check_model(path=MODEL_PATH, file=SAMPLE_CSV_FILE, nsamples=2):
    '''
    see predictions generated for the training dataset
    '''

    # load model
    model = load_model(path)

    # load data
    data, dic = get_data(file)
    rows, questions, true_answers = encode_data(data, dic)

    # visualize model graph
    # plot_model(model, to_file='tableqa_model.png')

    # predict answers
    prediction = model.predict([rows[:nsamples], questions[:nsamples]])
    print prediction
    predicted_answers = [[np.argmax(character) for character in sample] for sample in prediction]
    print predicted_answers
    print true_answers[:nsamples]

    # one hot encode answers
    # true_answers = [to_categorical(answer, num_classes=len(dic)) for answer in answers[:nsamples]]

    # decode chars from char ids int
    inv_dic = {v: k for k, v in dic.iteritems()}
    for i in xrange(nsamples):
        print '\n'
        # print 'Predicted answer: ' + ''.join([dic[char] for char in sample])
        print 'Table: ' + ''.join([inv_dic[char_id] for char_id in rows[i] if char_id != 0])
        print 'Question: ' + ''.join([inv_dic[char_id] for char_id in questions[i] if char_id != 0])
        print 'Answer(correct): ' + ''.join([inv_dic[char_id] for char_id in true_answers[i] if char_id != 0])
        print 'Answer(predicted): ' + ''.join([inv_dic[char_id] for char_id in predicted_answers[i] if char_id != 0])

项目：five-video-classification-methods 作者：harvitronix | 项目源码 | 文件源码

def get_class_one_hot(self, class_str):
        """Given a class as a string, return its number in the classes
        list. This lets us encode and one-hot it for training."""
        # Encode it first.
        label_encoded = self.classes.index(class_str)

        # Now one-hot it.
        label_hot = to_categorical(label_encoded, len(self.classes))

        assert len(label_hot) == len(self.classes)

        return label_hot