检查目标时出错：预期 dense_3 具有形状 (3,) 但得到形状为 (1,) 的数组

Question

新手上路，请多包涵

我正在 Keras 中训练一个类似 VGG16 的模型，在 Places205 的一个 3 类子集上，遇到了以下错误：

 ValueError: Error when checking target: expected dense_3 to have shape (3,) but got array with shape (1,)

我阅读了多个类似的问题，但到目前为止没有一个对我有帮助。错误出现在最后一层，我在其中放置了 3，因为这是我现在正在尝试的类数。

代码如下：

 import keras from keras.datasets
import cifar10 from keras.preprocessing.image
import ImageDataGenerator from keras.models
import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K import os

# Constants used
img_width, img_height = 224, 224
train_data_dir='places\\train'
validation_data_dir='places\\validation'
save_filename = 'vgg_trained_model.h5'
training_samples = 15
validation_samples = 5
batch_size = 5
epochs = 5

if K.image_data_format() == 'channels_first':
    input_shape = (3, img_width, img_height) else:
    input_shape = (img_width, img_height, 3)

model = Sequential([
    # Block 1
    Conv2D(64, (3, 3), activation='relu', input_shape=input_shape, padding='same'),
    Conv2D(64, (3, 3), activation='relu', padding='same'),
    MaxPooling2D(pool_size=(2, 2), strides=(2, 2)),
    # Block 2
    Conv2D(128, (3, 3), activation='relu', padding='same'),
    Conv2D(128, (3, 3), activation='relu', padding='same'),
    MaxPooling2D(pool_size=(2, 2), strides=(2, 2)),
    # Block 3
    Conv2D(256, (3, 3), activation='relu', padding='same'),
    Conv2D(256, (3, 3), activation='relu', padding='same'),
    Conv2D(256, (3, 3), activation='relu', padding='same'),
    MaxPooling2D(pool_size=(2, 2), strides=(2, 2)),
    # Block 4
    Conv2D(512, (3, 3), activation='relu', padding='same'),
    Conv2D(512, (3, 3), activation='relu', padding='same'),
    Conv2D(512, (3, 3), activation='relu', padding='same'),
    MaxPooling2D(pool_size=(2, 2), strides=(2, 2)),
    # Block 5
    Conv2D(512, (3, 3), activation='relu', padding='same',),
    Conv2D(512, (3, 3), activation='relu', padding='same',),
    Conv2D(512, (3, 3), activation='relu', padding='same',),
    MaxPooling2D(pool_size=(2, 2), strides=(2, 2)),
    # Top
    Flatten(),
    Dense(4096, activation='relu'),
    Dense(4096, activation='relu'),
    Dense(3, activation='softmax') ])

model.summary()

model.compile(loss='binary_crossentropy',
              optimizer='rmsprop',
              metrics=['accuracy'])

# no augmentation config train_datagen = ImageDataGenerator() validation_datagen = ImageDataGenerator()
     train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='binary')

validation_generator = validation_datagen.flow_from_directory(
    validation_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='binary')

model.fit_generator(
    train_generator,
    steps_per_epoch=training_samples // batch_size,
    epochs=epochs,
    validation_data=validation_generator,
    validation_steps=validation_samples // batch_size)

model.save_weights(save_filename)