一、K.prod

prod

keras.backend.prod(x,axis=None,keepdims=False)

功能：在某一指定軸，計算張量中的值的乘積。

引數

x: 張量或變數。

axis: 一個整數需要計算乘積的軸。

keepdims: 布林值，是否保留原尺寸。 如果 keepdims 為 False，則張量的秩減 1。 如果 keepdims 為 True，縮小的維度保留為長度 1。

返回

x 的元素的乘積的張量。

Numpy 實現

def prod(x,keepdims=False):
  if isinstance(axis,list):
    axis = tuple(axis)
  return np.prod(x,axis=axis,keepdims=keepdims)

具體例子：

import numpy as np
x=np.array([[2,4,6],[2,6]])
 
scaling = np.prod(x,axis=1,keepdims=False)
print(x)
print(scaling)

【執行結果】

二、K.cast

cast

keras.backend.cast(x,dtype)

功能：將張量轉換到不同的 dtype 並返回。

你可以轉換一個 Keras 變數，但它仍然返回一個 Keras 張量。

引數

x: Keras 張量（或變數）。

dtype: 字串， ('float16','float32' 或 'float64')。

返回

Keras 張量，型別為 dtype。

例子

>>> from keras import backend as K
>>> input = K.placeholder((2,3),dtype='float32')
>>> input
<tf.Tensor 'Placeholder_2:0' shape=(2,3) dtype=float32>
# It doesn't work in-place as below.
>>> K.cast(input,dtype='float16')
<tf.Tensor 'Cast_1:0' shape=(2,3) dtype=float16>
>>> input
<tf.Tensor 'Placeholder_2:0' shape=(2,3) dtype=float32>
# you need to assign it.
>>> input = K.cast(input,dtype='float16')
>>> input
<tf.Tensor 'Cast_2:0' shape=(2,3) dtype=float16>

補充知識：keras原始碼之backend庫目錄

backend庫目錄

先看common.py

一上來是一些說明

# the type of float to use throughout the session. 整個模組都是用浮點型資料
_FLOATX = 'float32' # 資料型別為32位浮點型
_EPSILON = 1e-7 # 很小的常數
_IMAGE_DATA_FORMAT = 'channels_last' # 影象資料格式 最後顯示通道，tensorflow格式

接下來看裡面的一些函式

def epsilon():
  """Returns the value of the fuzz factor used in numeric expressions. 
    返回數值表示式中使用的模糊因子的值
    
  # Returns
    A float.
  # Example
  ```python
    >>> keras.backend.epsilon()
    1e-07
  ```
  """
  return _EPSILON

該函式定義了一個常量，值為1e-07，在終端可以直接輸出，如下：

def set_epsilon(e):
  """Sets the value of the fuzz factor used in numeric expressions.
  # Arguments
    e: float. New value of epsilon.
  # Example
  ```python
    >>> from keras import backend as K
    >>> K.epsilon()
    1e-07
    >>> K.set_epsilon(1e-05)
    >>> K.epsilon()
    1e-05
  ```
  """
  global _EPSILON
  _EPSILON = e

該函式允許自定義值

以string的形式返回預設的浮點型別：

def floatx():
  """Returns the default float type,as a string.
  (e.g. 'float16','float32','float64').
  # Returns
    String,the current default float type.
  # Example
  ```python
    >>> keras.backend.floatx()
    'float32'
  ```
  """
  return _FLOATX

把numpy陣列投影到預設的浮點型別：

def cast_to_floatx(x):
  """Cast a Numpy array to the default Keras float type.把numpy陣列投影到預設的浮點型別
  # Arguments
    x: Numpy array.
  # Returns
    The same Numpy array,cast to its new type.
  # Example
  ```python
    >>> from keras import backend as K
    >>> K.floatx()
    'float32'
    >>> arr = numpy.array([1.0,2.0],dtype='float64')
    >>> arr.dtype
    dtype('float64')
    >>> new_arr = K.cast_to_floatx(arr)
    >>> new_arr
    array([ 1.,2.],dtype=float32)
    >>> new_arr.dtype
    dtype('float32')
  ```
  """
  return np.asarray(x,dtype=_FLOATX)

預設資料格式、自定義資料格式和檢查資料格式：

 def image_data_format():
  """Returns the default image data format convention ('channels_first' or 'channels_last').
  # Returns
    A string,either `'channels_first'` or `'channels_last'`
  # Example
  ```python
    >>> keras.backend.image_data_format()
    'channels_first'
  ```
  """
  return _IMAGE_DATA_FORMAT
 
 
def set_image_data_format(data_format):
  """Sets the value of the data format convention.
  # Arguments
    data_format: string. `'channels_first'` or `'channels_last'`.
  # Example
  ```python
    >>> from keras import backend as K
    >>> K.image_data_format()
    'channels_first'
    >>> K.set_image_data_format('channels_last')
    >>> K.image_data_format()
    'channels_last'
  ```
  """
  global _IMAGE_DATA_FORMAT
  if data_format not in {'channels_last','channels_first'}:
    raise ValueError('Unknown data_format:',data_format)
  _IMAGE_DATA_FORMAT = str(data_format)
 
def normalize_data_format(value):
  """Checks that the value correspond to a valid data format.
  # Arguments
    value: String or None. `'channels_first'` or `'channels_last'`.
  # Returns
    A string,either `'channels_first'` or `'channels_last'`
  # Example
  ```python
    >>> from keras import backend as K
    >>> K.normalize_data_format(None)
    'channels_first'
    >>> K.normalize_data_format('channels_last')
    'channels_last'
  ```
  # Raises
    ValueError: if `value` or the global `data_format` invalid.
  """
  if value is None:
    value = image_data_format()
  data_format = value.lower()
  if data_format not in {'channels_first','channels_last'}:
    raise ValueError('The `data_format` argument must be one of '
             '"channels_first","channels_last". Received: ' +
             str(value))
  return data_format

剩餘的關於維度順序和資料格式的方法：

def set_image_dim_ordering(dim_ordering):
  """Legacy setter for `image_data_format`.
  # Arguments
    dim_ordering: string. `tf` or `th`.
  # Example
  ```python
    >>> from keras import backend as K
    >>> K.image_data_format()
    'channels_first'
    >>> K.set_image_data_format('channels_last')
    >>> K.image_data_format()
    'channels_last'
  ```
  # Raises
    ValueError: if `dim_ordering` is invalid.
  """
  global _IMAGE_DATA_FORMAT
  if dim_ordering not in {'tf','th'}:
    raise ValueError('Unknown dim_ordering:',dim_ordering)
  if dim_ordering == 'th':
    data_format = 'channels_first'
  else:
    data_format = 'channels_last'
  _IMAGE_DATA_FORMAT = data_format
 
 
def image_dim_ordering():
  """Legacy getter for `image_data_format`.
  # Returns
    string,one of `'th'`,`'tf'`
  """
  if _IMAGE_DATA_FORMAT == 'channels_first':
    return 'th'
  else:
    return 'tf'

在common.py之後有三個backend，分別是cntk,tensorflow和theano。

__init__.py

首先從common.py中引入了所有需要的東西

from .common import epsilon
from .common import floatx
from .common import set_epsilon
from .common import set_floatx
from .common import cast_to_floatx
from .common import image_data_format
from .common import set_image_data_format
from .common import normalize_data_format

接下來是檢查環境變數與配置檔案，設定backend和format，預設的backend是tensorflow。

# Set Keras base dir path given KERAS_HOME env variable,if applicable.
# Otherwise either ~/.keras or /tmp.
if 'KERAS_HOME' in os.environ: # 環境變數
  _keras_dir = os.environ.get('KERAS_HOME')
else:
  _keras_base_dir = os.path.expanduser('~')
  if not os.access(_keras_base_dir,os.W_OK):
    _keras_base_dir = '/tmp'
  _keras_dir = os.path.join(_keras_base_dir,'.keras')
 
# Default backend: TensorFlow. 預設後臺是TensorFlow
_BACKEND = 'tensorflow'
 
# Attempt to read Keras config file.讀取keras配置檔案
_config_path = os.path.expanduser(os.path.join(_keras_dir,'keras.json'))
if os.path.exists(_config_path):
  try:
    with open(_config_path) as f:
      _config = json.load(f)
  except ValueError:
    _config = {}
  _floatx = _config.get('floatx',floatx())
  assert _floatx in {'float16','float64'}
  _epsilon = _config.get('epsilon',epsilon())
  assert isinstance(_epsilon,float)
  _backend = _config.get('backend',_BACKEND)
  _image_data_format = _config.get('image_data_format',image_data_format())
  assert _image_data_format in {'channels_last','channels_first'}
 
  set_floatx(_floatx)
  set_epsilon(_epsilon)
  set_image_data_format(_image_data_format)
  _BACKEND = _backend

之後的tensorflow_backend.py檔案是一些tensorflow中的函式說明，詳細內容請參考tensorflow有關資料。

以上這篇淺談keras中的後端backend及其相關函式(K.prod,K.cast)就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支援我們。