我想保持vgg 16的前4层并添加最后一层。我有这个例子：

vgg16_model = VGG16(weights="imagenet", include_top=True)

# (2) remove the top layer
base_model = Model(input=vgg16_model.input, 
                   output=vgg16_model.get_layer("block5_pool").output) #I wanna cut all layers after 'block1_pool'

# (3) attach a new top layer
base_out = base_model.output
base_out = Reshape(25088,)(base_out) 
top_fc1 = Dropout(0.5)(base_out)
top_preds = Dense(1, activation="sigmoid")(top_fc1)

# (4) freeze weights until the last but one convolution layer (block4_pool)
for layer in base_model.layers[0:4]:
    layer.trainable = False

# (5) create new hybrid model
model = Model(input=base_model.input, output=top_preds)

所以在这个例子中，他从“block5_pool”中删除，我想从“block1_pool”中删除，但如果我只更改为block1_pool，它会抛出这个错误：

data_format = value.lower()

AttributeError: 'int' object has no attribute 'lower'

那么我怎么能改变它来切入block1_pool，然后添加我自己的密集层呢？

完整代码

#import tensorflow as tf
import cv2
import os
import numpy as np

from keras.layers.core import Flatten, Dense, Dropout, Reshape
from keras.models import Model
from keras.layers import Input, ZeroPadding2D, Dropout
from keras import optimizers
from keras.optimizers import SGD
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import EarlyStopping

from keras.applications.vgg16 import VGG16

TRAIN_DIR = 'train/'
TEST_DIR = 'test/'
v = 'v/'
BATCH_SIZE = 32
NUM_EPOCHS = 5

def crop_img(img, h, w):
    h_margin = (img.shape[0] - h) // 2 if img.shape[0] > h else 0
    w_margin = (img.shape[1] - w) // 2 if img.shape[1] > w else 0

    crop_img = img[h_margin:h + h_margin,w_margin:w + w_margin,:]

    return crop_img

def subtract_gaussian_blur(img):

    return cv2.addWeighted(img, 4, cv2.GaussianBlur(img, (0, 0), 5), -4, 128)

def ReadImages(Path):
    LabelList = list()
    ImageCV = list()
    classes = ["nonPdr", "pdr"]

    # Get all subdirectories
    FolderList = [f for f in os.listdir(Path) if not f.startswith('.')]
    
    # Loop over each directory
    for File in FolderList:
        for index, Image in enumerate(os.listdir(os.path.join(Path, File))):
            # Convert the path into a file
            ImageCV.append(cv2.resize(cv2.imread(os.path.join(Path, File) + os.path.sep + Image), (224,224)))
            #ImageCV[index]= np.array(ImageCV[index]) / 255.0
            LabelList.append(classes.index(os.path.splitext(File)[0])) 
            
            img_crop = crop_img(ImageCV[index].copy(), 224, 224)
            
            ImageCV[index] = subtract_gaussian_blur(img_crop.copy())
            
    return ImageCV, LabelList


data, labels = ReadImages(TRAIN_DIR)
valid, vlabels = ReadImages(TEST_DIR)

vgg16_model = VGG16(weights="imagenet", include_top=True)

# (2) remove the top layer
base_model = Model(input=vgg16_model.input, 
                   output=vgg16_model.get_layer("block1_pool").output)
print(base_model)
# (3) attach a new top layer
base_out = base_model.output
base_out = Reshape(25088,)(base_out)
top_fc1 = Dropout(0.5)(base_out)
# output layer: (None, 5)
top_preds = Dense(1, activation="sigmoid")(top_fc1)

# (4) freeze weights until the last but one convolution layer (block4_pool)
for layer in base_model.layers[0:4]:
    layer.trainable = False

# (5) create new hybrid model
model = Model(input=base_model.input, output=top_preds)
    
# (6) compile and train the model
sgd = SGD(lr=1e-4, momentum=0.9)
model.compile(optimizer=sgd, loss="binary_crossentropy", metrics=["accuracy"])

data = np.asarray(data)
valid = np.asarray(valid)

data = data.astype('float32')
valid = valid.astype('float32')

data /= 255
valid /= 255
labels = np.array(labels)

datagen = ImageDataGenerator(
    featurewise_center=True,
    featurewise_std_normalization=True,
    rotation_range=20,
    width_shift_range=0.2,
    height_shift_range=0.2,
    horizontal_flip=True)

# compute quantities required for featurewise normalization
# (std, mean, and principal components if ZCA whitening is applied)
datagen.fit(data)
mean = datagen.mean  
std = datagen.std

print(mean, "mean")
print(std, "std")

es = EarlyStopping(monitor='val_loss', verbose=1)

# fits the model on batches with real-time data augmentation:
model.fit_generator(datagen.flow(data, np.array(labels), batch_size=32), 
                    steps_per_epoch=len(data) / 32, epochs=15,
                    validation_data=(valid, np.array(vlabels)),
                    nb_val_samples=72, callbacks=[es])


model.save('model.h5')

完全错误

    base_out = Reshape(25088,)(base_out)

    self.target_shape = tuple(target_shape)

TypeError: 'int' object is not iterable
```