异常:检查模型目标时出错:预期dense_3 具有形状(无,1000)但得到形状为(32、2)的数组 [英] Exception: Error when checking model target: expected dense_3 to have shape (None, 1000) but got array with shape (32, 2)
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问题描述
如何为我的数据创建 VGG-16 序列?
How do I create a VGG-16 sequence for my data?
数据如下:
model = Sequential()
model.add(ZeroPadding2D((1, 1), input_shape=(3, img_width, img_height))) model.add(Convolution2D(64, 3, 3, activation='relu', name='conv1_1')) model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(64, 3, 3, activation='relu', name='conv1_2'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(128, 3, 3, activation='relu', name='conv2_1')) model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(128, 3, 3, activation='relu', name='conv2_2'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_1')) model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_2'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_3')) model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv4_1')) model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv4_2'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv4_3')) model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_1')) model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_2'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_3')) model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(Flatten())
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1000, activation='softmax'))
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss='categorical_crossentropy')
train_datagen = ImageDataGenerator(
rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_width, img_height),
batch_size=32)
validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_width, img_height),
batch_size=32)
model.fit_generator(
train_generator,
samples_per_epoch=2000,
nb_epoch=1,
verbose=1,
validation_data=validation_generator,
nb_val_samples=800)
json_string = model.to_json()
open('my_model_architecture.json','w').write(json_string)
model.save_weights('Second_try.h5')
我有一个错误:
异常:检查模型目标时出错:预期dense_3具有形状 (None, 32) 但得到了形状为 (32, 2) 的数组
Exception: Error when checking model target: expected dense_3 to have shape (None, 32) but got array with shape (32, 2)
如何更改 Dense
以使其工作?
How do I change Dense
to make it work?
推荐答案
我有10种,
我已经通过
解决了这个问题改变:
I have 10 species,
I have solved the problem by
changing:
model.add(Dense(1000, activation='softmax'))
到:
model.add(Dense(10, activation='softmax'))
然后就可以了.
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