Question

以下是完整错误：

Exception: Error when checking model input: expected convolution2d_input_1 to have shape (None, 3, 224, 224) but got array with shape (20, 3, 244, 244)

一切都有效，直到最后的model.fit_generator(...)代码块。我正在使用theano后端。

我对keras很新，所以我不确定如何继续。检查文档我可以看到None中的layers.convolutional.Convolution2D对应于批次（或样本）的数量？替换input_shape=(20,3,244,244)会产生以下错误Exception: Input 0 is incompatible with layer conv1_1: expected ndim=4, found ndim=5。使用23000而不是20产生相同的错误。

感谢任何帮助。

以下是我的代码：

# ======================
# 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)

Answer 1

图像的预期大小与实际图像之间存在不匹配。您的模型需要大小为224 x 224的图片，并且根据附带的错误消息，实际尺寸为244 x 244。

模型输入错误

1 个答案: