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)]
所以，function函数利用我们之前已经创建好的comuptation graph。遵循计算图，从输入到定义的输出。这也是为什么该函数经常用于提取中间层结果。
以上这篇keras K.function获取某层的输出操作就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。