ValueError：检查目标时出错：期望dense_2具有形状（无，2）但是具有形状的数组（321,3）

我想使用keras创建一个图像分类器，并用一些示例图像训练它 . 然后，我将使用预先训练的模型并在最后添加几个层，但首先，我想了解keras和CNN .

我的控制台输出以下错误：

ValueError：检查目标时出错：期望dense_2有形状（None，2）但是有形状的数组（321,3）

这是我的代码：

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import sys
import time

import numpy as np
import cv2
import time
from PIL import Image

import keras
import glob
from keras.models import Sequential
from keras.models import load_model
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.optimizers import SGD
from sklearn.preprocessing import LabelBinarizer


labels = ['buena', 'mala', 'otro']

def to_one_hot(labels, ys):
    result = np.zeros((len(ys),len(labels)))
    for i in range(result.shape[0]):
        for j in range(result.shape[1]):
            result[i,j] = int(ys[i] == labels[j])
    return result

def build_dataset(labels):
    num_classes = len(labels)
    x = []
    y = []
    for label in labels:
        for filename in (glob.glob('./tf_files/papas_fotos/'+label+'/*.jpg')):
            img = cv2.imread(filename)
            img = np.resize(img,(100,100, 3))
            x.append(img)
            y.append(label)
    y = to_one_hot(labels, y)
    # y = keras.utils.to_categorical(y, num_classes=3)
    x = np.array(x)
    x_train = x[20:]
    y_train = y[20:]
    x_test = x[:19]
    y_test = y[:19]
    print (x.shape, y.shape)
    return x_train, y_train, x_test, y_test

model = Sequential()
# input: 100x100 images with 3 channels -> (100, 100, 3) tensors.
# this applies 32 convolution filters of size 3x3 each.
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(100, 100, 3)))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(3, activation='softmax'))

sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd)

x_train, y_train, x_test, y_test = build_dataset(labels)

model = load_model('thebestmodel.h5')
print (model)
model.fit(x_train, y_train, batch_size=32, epochs=20)
score = model.evaluate(x_test, y_test, batch_size=32)
model.save('thebestmodel.h5')
print (score)

我犯了什么错误？我认为这可能是我的一个热编码标签的大小，但我不能使它工作 .

谢谢！