Keras 模型输入错误

以下是完整的错误：

异常：检查模型输入时出错：预期卷积2D\u输入\u 1具有形状（无，3，224，224），但获得具有形状（20，3，244，244）的数组。

在最终的

模型之前，一切都正常工作。安装生成器（…）

代码块。我使用的是theano后端

我是keras的新手，所以我不知道该怎么做。查看文档，我可以看到

层中的
None
？替换
input\u shape=（20,3244244）
产生以下错误
异常：input 0与层conv1\u 1不兼容：预期ndim=4，发现ndim=5
。使用23000而不是20产生了相同的错误

感谢您的帮助

下面是我的代码：

# ======================
# load data
# ======================

# Set relevant paths for dir structure
current_dir = "/home/ubuntu/nbs/"
DATA_HOME_DIR = current_dir + 'lesson1/data/redux'
path = DATA_HOME_DIR + '/'
train_path = DATA_HOME_DIR + '/train/'
valid_path = DATA_HOME_DIR + '/valid/'
test_path = DATA_HOME_DIR + '/test/'

nb_train_samples = 23000
nb_validation_samples = 2000
nb_epoch = 4

# ======================
# import stuff
# ======================
import numpy as np 
from keras.utils.data_utils import get_file 
from keras import backend as K 
from keras.layers.normalization import BatchNormalization
from keras.models import Sequential
from keras.layers.core import Flatten, Dense, Dropout, Lambda
from keras.layers.convolutional import Convolution2D, MaxPooling2D,         ZeroPadding2D
from keras.layers.pooling import GlobalAveragePooling2D
from keras.optimizers import SGD, RMSprop, Adam
from keras.preprocessing import image
from keras.preprocessing.image  import  ImageDataGenerator



# ======================
# define model
# ======================

def vgg():
model = Sequential()
model.add(Convolution2D(64, 3, 3,input_shape=(3,224,224), 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'))
return model 


model = vgg()

print model.summary()

#### load weights
   fname = 'vgg16.h5'
model.load_weights(get_file(fname, 'http://www.platform.ai/models/'+fname, cache_subdir='models'))

print "successfully created model and loaded weights"







#### Finetune model
model.pop()
for layer in model.layers: layer.trainable=False
    model.add(Dense(batches.nb_class, activation='softmax'))

#### Compile model
model.compile(optimizer=Adam(lr=0.01),
                loss='categorical_crossentropy', metrics=['accuracy'])






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_path, 
    target_size=(244,244),
    batch_size = 20,
    class_mode='categorical')

validation_generator = test_datagen.flow_from_directory(
    valid_path,
    target_size=(244,244),
    batch_size=20,
    class_mode='categorical')



model.fit_generator(
    train_generator,
    samples_per_epoch=nb_train_samples,
    nb_epoch=nb_epoch,
    validation_data=validation_generator,
    nb_val_samples=nb_validation_samples)

图像的预期大小与实际大小不匹配。您的型号需要大小为
224x224
的图像，根据随附的错误消息，实际大小为
244x244