import numpy as np
from collections import defaultdict
# the type of float to use throughout the session.
_FLOATX = 'float32'
_EPSILON = 10e-8
_UID_PREFIXES = defaultdict(int)
_IMAGE_DIM_ORDERING = 'tf'
_LEGACY_WEIGHT_ORDERING = False
def epsilon():
'''Returns the value of the fuzz
factor used in numeric expressions.
# Returns
A float.
# Example
```python
>>> keras.backend.epsilon()
1e-08
```
'''
return _EPSILON
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-08
>>> K.set_epsilon(1e-05)
>>> K.epsilon()
1e-05
```
'''
global _EPSILON
_EPSILON = e
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
def set_floatx(floatx):
'''Sets the default float type.
# Arguments
String: 'float16', 'float32', or 'float64'.
# Example
```python
>>> from keras import backend as K
>>> K.floatx()
'float32'
>>> K.set_floatx('float16')
>>> K.floatx()
'float16'
```
'''
global _FLOATX
if floatx not in {'float16', 'float32', 'float64'}:
raise ValueError('Unknown floatx type: ' + str(floatx))
_FLOATX = str(floatx)
def cast_to_floatx(x):
'''Cast a Numpy array to the default Keras float type.
# 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)
def image_dim_ordering():
'''Returns the default image dimension ordering
convention ('th' or 'tf').
# Returns
A string, either `'th'` or `'tf'`
# Example
```python
>>> keras.backend.image_dim_ordering()
'th'
```
'''
return _IMAGE_DIM_ORDERING
def set_image_dim_ordering(dim_ordering):
'''Sets the value of the image dimension
ordering convention ('th' or 'tf').
# Arguments
dim_ordering: string. `'th'` or `'tf'`.
# Example
```python
>>> from keras import backend as K
>>> K.image_dim_ordering()
'th'
>>> K.set_image_dim_ordering('tf')
>>> K.image_dim_ordering()
'tf'
```
'''
global _IMAGE_DIM_ORDERING
if dim_ordering not in {'tf', 'th'}:
raise ValueError('Unknown dim_ordering:', dim_ordering)
_IMAGE_DIM_ORDERING = str(dim_ordering)
def get_uid(prefix=''):
'''Provides a unique UID given a string prefix.
# Arguments
prefix: string.
# Returns
An integer.
# Example
```
>>> keras.backend.get_uid('dense')
>>> 1
>>> keras.backend.get_uid('dense')
>>> 2
```
'''
_UID_PREFIXES[prefix] += 1
return _UID_PREFIXES[prefix]
def reset_uids():
global _UID_PREFIXES
_UID_PREFIXES = defaultdict(int)
def is_keras_tensor(x):
'''Returns whether `x` is a Keras tensor.
# Arguments
x: a potential tensor.
# Returns
A boolean: whether the argument is a Keras tensor.
# Examples
```python
>>> from keras import backend as K
>>> np_var = numpy.array([1, 2])
>>> K.is_keras_tensor(np_var)
False
>>> keras_var = K.variable(np_var)
>>> K.is_keras_tensor(keras_var) # A variable is not a Tensor.
False
>>> keras_placeholder = K.placeholder(shape=(2, 4, 5))
>>> K.is_keras_tensor(keras_placeholder) # A placeholder is a Tensor.
True
```
'''
if hasattr(x, '_keras_shape'):
return True
else:
return False
def set_legacy_weight_ordering(value):
global _LEGACY_WEIGHT_ORDERING
assert value in {True, False}
_LEGACY_WEIGHT_ORDERING = value
def legacy_weight_ordering():
return _LEGACY_WEIGHT_ORDERING
