如下所示：

from keras import backend as K

from keras.models import load_model

models = load_model('models.hdf5')

image=r'image.png'

images=cv2.imread(r'image.png')

image_arr = process_image(image, (224, 224, 3))

image_arr = np.expand_dims(image_arr, axis=0)

layer_1 = K.function([base_model.get_input_at(0)], [base_model.get_layer('layer_name').output])

f1 = layer_1([image_arr])[0]

加载训练好并保存的网络模型

加载数据(图像)，并将数据处理成array形式

指定输出层

将处理后的数据输入，然后获取输出

其中，K.function有两种不同的写法：

1. 获取名为layer_name的层的输出

layer_1 = K.function([base_model.get_input_at(0)], [base_model.get_layer('layer_name').output])

#指定输出层的名称

2. 获取第n层的输出

layer_1 = K.function([model.get_input_at(0)], [model.layers[5].output])

#指定输出层的序号(层号从0开始)

另外，需要注意的是，书写不规范会导致报错：

报错：

TypeError: inputs to a TensorFlow backend function should be a list or tuple

将该句：

f1 = layer_1(image_arr)[0]

修改为：

f1 = layer_1([image_arr])[0]

补充知识：keras.backend.function()

如下所示：

def function(inputs, outputs, updates=None, **kwargs):

"""Instantiates a Keras function.

Arguments:

inputs: List of placeholder tensors.

outputs: List of output tensors.

updates: List of update ops.

**kwargs: Passed to `tf.Session.run`.

Returns:

Output values as Numpy arrays.

Raises:

ValueError: if invalid kwargs are passed in.

"""

if kwargs:

for key in kwargs:

if (key not in tf_inspect.getargspec(session_module.Session.run)[0] and

key not in tf_inspect.getargspec(Function.__init__)[0]):

msg = ('Invalid argument "%s" passed to K.function with Tensorflow '

'backend') % key

raise ValueError(msg)

return Function(inputs, outputs, updates=updates, **kwargs)

这是keras.backend.function()的源码。其中函数定义开头的注释就是官方文档对该函数的解释。

我们可以发现function()函数返回的是一个Function对象。下面是Function类的定义。

class Function(object):

"""Runs a computation graph.

Arguments:

inputs: Feed placeholders to the computation graph.

outputs: Output tensors to fetch.

updates: Additional update ops to be run at function call.

name: a name to help users identify what this function does.

"""

def __init__(self, inputs, outputs, updates=None, name=None,

**session_kwargs):

updates = updates or []

if not isinstance(inputs, (list, tuple)):

raise TypeError('`inputs` to a TensorFlow backend function '

'should be a list or tuple.')

if not isinstance(outputs, (list, tuple)):

raise TypeError('`outputs` of a TensorFlow backend function '

'should be a list or tuple.')

if not isinstance(updates, (list, tuple)):

raise TypeError('`updates` in a TensorFlow backend function '

'should be a list or tuple.')

self.inputs = list(inputs)

self.outputs = list(outputs)

with ops.control_dependencies(self.outputs):

updates_ops = []

for update in updates:

if isinstance(update, tuple):

p, new_p = update

updates_ops.append(state_ops.assign(p, new_p))

else:

# assumed already an op

updates_ops.append(update)

self.updates_op = control_flow_ops.group(*updates_ops)

self.name = name

self.session_kwargs = session_kwargs

def __call__(self, inputs):

if not isinstance(inputs, (list, tuple)):

raise TypeError('`inputs` should be a list or tuple.')

feed_dict = {}

for tensor, value in zip(self.inputs, inputs):

if is_sparse(tensor):

sparse_coo = value.tocoo()

indices = np.concatenate((np.expand_dims(sparse_coo.row, 1),

np.expand_dims(sparse_coo.col, 1)), 1)

value = (indices, sparse_coo.data, sparse_coo.shape)

feed_dict[tensor] = value

session = get_session()

updated = session.run(

self.outputs + [self.updates_op],

feed_dict=feed_dict,

**self.session_kwargs)

return updated[:len(self.outputs)]