如何在Keras 2.0中使用InceptionV3瓶颈作为输入

我想在Keras中使用InceptionV3来利用瓶颈进行转移学习 . 我已经使用了一些关于创建，加载和使用瓶颈的技巧来自https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html

我的问题是我不知道如何使用瓶颈（numpy数组）作为具有新顶层的InceptionV3的输入 .

I get the following error:

ValueError：检查输入时出错：期望input_3有形状（None，None，None，3）但是有形状的数组（248,8,8,2048）

248指的是这种情况下的图像总数 .

我知道这行是错的，但我不知道如何纠正它：

model = Model（inputs = base_model.input，outputs = predictions）

将瓶颈输入InceptionV3的正确方法是什么？

Creating the InceptionV3 bottlenecks:

def create_bottlenecks():
datagen = ImageDataGenerator(rescale=1. / 255)

model = InceptionV3(include_top=False, weights='imagenet')

# Generate bottlenecks for all training images
generator = datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode=None,
    shuffle=False)

nb_train_samples = len(generator.filenames)
bottlenecks_train = model.predict_generator(generator, int(math.ceil(nb_train_samples / float(batch_size))), verbose=1)
np.save(open(train_bottlenecks_file, 'w'), bottlenecks_train)

# Generate bottlenecks for all validation images
generator = datagen.flow_from_directory(
    validation_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode=None,
    shuffle=False)

nb_validation_samples = len(generator.filenames)

bottlenecks_validation = model.predict_generator(generator, int(math.ceil(nb_validation_samples / float(batch_size))), verbose=1)
np.save(open(validation_bottlenecks_file, 'w'), bottlenecks_validation)

Loading the bottlenecks:

def load_bottlenecks(src_dir, bottleneck_file):
    datagen = ImageDataGenerator(rescale=1. / 255)
    generator = datagen.flow_from_directory(
        src_dir,
        target_size=(img_width, img_height),
        batch_size=batch_size,
        class_mode='categorical',
        shuffle=False)

    num_classes = len(generator.class_indices)

    # load the bottleneck features saved earlier
    bottleneck_data = np.load(bottleneck_file)

    # get the class lebels for the training data, in the original order
    bottleneck_class_labels = generator.classes

    # convert the training labels to categorical vectors
    bottleneck_class_labels = to_categorical(bottleneck_class_labels, num_classes=num_classes)

    return bottleneck_data, bottleneck_class_labels

Starting training:

def start_training():
global nb_train_samples, nb_validation_samples

create_bottlenecks()

train_data, train_labels = load_bottlenecks(train_data_dir, train_bottlenecks_file)
validation_data, validation_labels = load_bottlenecks(validation_data_dir, validation_bottlenecks_file)

nb_train_samples = len(train_data)
nb_validation_samples = len(validation_data)

base_model = InceptionV3(weights='imagenet', include_top=False)

# add a global spatial average pooling layer
x = base_model.output
x = GlobalAveragePooling2D()(x)

# let's add a fully-connected layer
x = Dense(1024, activation='relu')(x)

# and a logistic layer -- let's say we have 2 classes
predictions = Dense(2, activation='softmax')(x)

# What is the correct input? Obviously not base_model.input.
model = Model(inputs=base_model.input, outputs=predictions)

# first: train only the top layers (which were randomly initialized)
# i.e. freeze all convolutional InceptionV3 layers
for layer in base_model.layers:
    layer.trainable = False

model.compile(optimizer=optimizers.SGD(lr=0.01, momentum=0.9), loss='categorical_crossentropy', metrics=['accuracy'])

# train the model on the new data for a few epochs
history = model.fit(train_data, train_labels,
                    epochs=epochs,
                    batch_size=batch_size,
                    validation_data=(validation_data, validation_labels),
)

任何帮助，将不胜感激！