Machine learning 我从kaggle下载了一个DL模型，用于keras的8分类器工作

我从kaggle下载了一个DL模型，keras将其用于8-分类器的工作。显然，它在模型顶部有稠密（8）。我想将其用于2-分类器，因此我将顶部完全连接从稠密（8）修改为稠密（2）。我想这对我来说是可行的。但是，当我运行脚本时，终端会报告错误。感谢您的耐心和帮助。 下面的错误

Error when checking model target: expected dense_3 to have shape (None, 2) but got array with shape (1333L, 8L) 

这是代码，可能很长

# %load kaggle_dog_cat_classifier.py
__author__ = 'JeofuHuang: https://www.kaggle.com/jeofuhuang'
import numpy as np
np.random.seed(2016)

import os
import glob
import cv2
import datetime
import pandas as pd
import time
import warnings
warnings.filterwarnings("ignore")

from sklearn.cross_validation import KFold
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D
from keras.optimizers import SGD
from keras.callbacks import EarlyStopping
from keras.utils import np_utils
from sklearn.metrics import log_loss
from keras import __version__ as keras_version

def get_im_cv2(path):
    img = cv2.imread(path)
    resized = cv2.resize(img, (32, 32), cv2.INTER_LINEAR)
    return resized


def load_train():
    X_train = []
    X_train_id = []
    y_train = []
    start_time = time.time()

    print('Read train images')
    folders = ['dog', 'cat']
    for fld in folders:
        index = folders.index(fld)
        print('Load folder {} (Index: {})'.format(fld, index))
        path = os.path.join('.', 'input', 'train', fld, '*.jpg')
        files = glob.glob(path)
        for fl in files:
            flbase = os.path.basename(fl)
            img = get_im_cv2(fl)
            X_train.append(img)
            X_train_id.append(flbase)
            y_train.append(index)

    print('Read train data time: {} seconds'.format(round(time.time() - start_time, 2)))
    return X_train, y_train, X_train_id


def load_test():
    path = os.path.join('.', 'input', 'test', '*.jpg')
    files = sorted(glob.glob(path))

    X_test = []
    X_test_id = []
    for fl in files:
        flbase = os.path.basename(fl)
        img = get_im_cv2(fl)
        X_test.append(img)
        X_test_id.append(flbase)

    return X_test, X_test_id


def create_submission(predictions, test_id, info):
    result1 = pd.DataFrame(predictions, columns=['dog', 'cat'])
    result1.loc[:, 'image'] = pd.Series(test_id, index=result1.index)
    now = datetime.datetime.now()
    sub_file = 'submission_' + info + '_' + str(now.strftime("%Y-%m-%d-%H-%M")) + '.csv'
    result1.to_csv(sub_file, index=False)


def read_and_normalize_train_data():
    train_data, train_target, train_id = load_train()

    print('Convert to numpy...')
    train_data = np.array(train_data, dtype=np.uint8)
    train_target = np.array(train_target, dtype=np.uint8)

    print('Reshape...')
    train_data = train_data.transpose((0, 3, 1, 2))

    print('Convert to float...')
    train_data = train_data.astype('float32')
    train_data = train_data / 255
    train_target = np_utils.to_categorical(train_target, 8)

    print('Train shape:', train_data.shape)
    print(train_data.shape[0], 'train samples')
    return train_data, train_target, train_id


def read_and_normalize_test_data():
    start_time = time.time()
    test_data, test_id = load_test()

    test_data = np.array(test_data, dtype=np.uint8)
    test_data = test_data.transpose((0, 3, 1, 2))

    test_data = test_data.astype('float32')
    test_data = test_data / 255

    print('Test shape:', test_data.shape)
    print(test_data.shape[0], 'test samples')
    print('Read and process test data time: {} seconds'.format(round(time.time() - start_time, 2)))
    return test_data, test_id


def dict_to_list(d):
    ret = []
    for i in d.items():
        ret.append(i[1])
    return ret


def merge_several_folds_mean(data, nfolds):
    a = np.array(data[0])
    for i in range(1, nfolds):
        a += np.array(data[i])
    a /= nfolds
    return a.tolist()


def create_model():
    model = Sequential()
    model.add(ZeroPadding2D((1, 1), input_shape=(3, 32, 32), dim_ordering='th'))
    model.add(Convolution2D(4, 3, 3, activation='relu', dim_ordering='th'))
    model.add(ZeroPadding2D((1, 1), dim_ordering='th'))
    model.add(Convolution2D(4, 3, 3, activation='relu', dim_ordering='th'))
    model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), dim_ordering='th'))

    model.add(ZeroPadding2D((1, 1), dim_ordering='th'))
    model.add(Convolution2D(8, 3, 3, activation='relu', dim_ordering='th'))
    model.add(ZeroPadding2D((1, 1), dim_ordering='th'))
    model.add(Convolution2D(8, 3, 3, activation='relu', dim_ordering='th'))
    model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), dim_ordering='th'))

    model.add(Flatten())
    model.add(Dense(32, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(32, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(2, activation='softmax'))

    sgd = SGD(lr=1e-2, decay=1e-6, momentum=0.9, nesterov=True)
    model.compile(optimizer=sgd, loss='categorical_crossentropy')

    return model


def get_validation_predictions(train_data, predictions_valid):
    pv = []
    for i in range(len(train_data)):
        pv.append(predictions_valid[i])
    return pv


def run_cross_validation_create_models(nfolds=10):
    # input image dimensions
    batch_size = 16
    nb_epoch = 30
    random_state = 51

    train_data, train_target, train_id = read_and_normalize_train_data()

    yfull_train = dict()
    kf = KFold(len(train_id), n_folds=nfolds, shuffle=True, 
random_state=random_state)
    num_fold = 0
    sum_score = 0
    models = []
    for train_index, test_index in kf:
        model = create_model()
        X_train = train_data[train_index]
        Y_train = train_target[train_index]
        X_valid = train_data[test_index]
        Y_valid = train_target[test_index]

        num_fold += 1
        print('Start KFold number {} from {}'.format(num_fold, nfolds))
        print('Split train: ', len(X_train), len(Y_train))
        print('Split valid: ', len(X_valid), len(Y_valid))

        callbacks = [
            EarlyStopping(monitor='val_loss', patience=3, verbose=0),
        ]
        model.fit(X_train, Y_train, batch_size=batch_size, 
            nb_epoch=nb_epoch,shuffle=True, verbose=2, validation_data=
            (X_valid,  Y_valid), callbacks=callbacks)

        predictions_valid = model.predict(X_valid.astype('float32'), 
            batch_size=batch_size, verbose=2)
        score = log_loss(Y_valid, predictions_valid)
        print('Score log_loss: ', score)
        sum_score += score*len(test_index)

        # Store valid predictions
        for i in range(len(test_index)):
            yfull_train[test_index[i]] = predictions_valid[i]

        models.append(model)

    score = sum_score/len(train_data)
    print("Log_loss train independent avg: ", score)

    info_string = 'loss_' + str(score) + '_folds_' + str(nfolds) + '_ep_' + 
str(nb_epoch)
    return info_string, models


def run_cross_validation_process_test(info_string, models):
    batch_size = 16
    num_fold = 0
    yfull_test = []
    test_id = []
    nfolds = len(models)

    for i in range(nfolds):
        model = models[i]
        num_fold += 1
        print('Start KFold number {} from {}'.format(num_fold, nfolds))
        test_data, test_id = read_and_normalize_test_data()
        test_prediction = model.predict(test_data, batch_size=batch_size, 
verbose=2)
        yfull_test.append(test_prediction)

    test_res = merge_several_folds_mean(yfull_test, nfolds)
    info_string = 'loss_' + info_string \ + '_folds_' + str(nfolds)
    create_submission(test_res, test_id, info_string)


if __name__ == '__main__':
    print('Keras version: {}'.format(keras_version))
    num_folds = 3
    info_string, models = run_cross_validation_create_models(num_folds)
    run_cross_validation_process_test(info_string, models)

---

错误在读取和规范化列数据（）的功能中 修改

到

然后它就起作用了

train_target = np_utils.to_categorical(train_target, 8)

train_target = np_utils.to_categorical(train_target, 2)