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]

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)

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)]