TensorFlow定义文件：Keras后端API
2019-04-03 17:14 更新
本节提供了TensorFlow中后端函数的帮助文件：tensorflow/python/keras/backend.py，提供Keras后端API：
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
# pylint: disable=protected-access
# pylint: disable=redefined-outer-name
# pylint: disable=redefined-builtin
"""Keras backend API.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import itertools
import json
import os
import threading
import weakref
import numpy as np
from tensorflow.core.protobuf import config_pb2
from tensorflow.python.client import session as session_module
from tensorflow.python.eager import context
from tensorflow.python.eager import function as eager_function
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes as dtypes_module
from tensorflow.python.framework import func_graph
from tensorflow.python.framework import ops
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.framework import tensor_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import clip_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import ctc_ops as ctc
from tensorflow.python.ops import functional_ops
from tensorflow.python.ops import gradients as gradients_module
from tensorflow.python.ops import image_ops
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import linalg_ops
from tensorflow.python.ops import logging_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import resource_variable_ops
from tensorflow.python.ops import sparse_ops
from tensorflow.python.ops import state_ops
from tensorflow.python.ops import tensor_array_grad # pylint: disable=unused-import
from tensorflow.python.ops import tensor_array_ops
from tensorflow.python.ops import variables as variables_module
from tensorflow.python.util import nest
from tensorflow.python.util import tf_contextlib
from tensorflow.python.util import tf_inspect
from tensorflow.python.util.tf_export import tf_export
py_all = all
py_sum = sum
# INTERNAL UTILS
# The internal graph maintained by Keras and used by the symbolic Keras APIs
# while executing eagerly (such as the functional API for model-building).
_GRAPH = None
# This is a thread local object that will hold the default internal TF session
# used by Keras. It can be set manually via `set_session(sess)`.
_SESSION = threading.local()
# This dictionary holds a mapping {graph: learning_phase}.
# A learning phase is a bool tensor used to run Keras models in
# either train mode (learning_phase == 1) or test mode (learning_phase == 0).
_GRAPH_LEARNING_PHASES = weakref.WeakKeyDictionary()
# _DUMMY_EAGER_GRAPH is used as a key in _GRAPH_LEARNING_PHASES.
# We keep a separate reference to it to make sure it does not get removed from
# _GRAPH_LEARNING_PHASES. We use a dummy class instead of something like a
# string because strings are not weakly-referencable.
class _DummyEagerGraph(object):
pass
_DUMMY_EAGER_GRAPH = _DummyEagerGraph()
# This boolean flag can be set to True to leave variable initialization
# up to the user.
# Change its value via `manual_variable_initialization(value)`.
_MANUAL_VAR_INIT = False
# The type of float to use throughout a session.
_FLOATX = 'float32'
# Epsilon fuzz factor used throughout the codebase.
_EPSILON = 1e-7
# Default image data format, one of "channels_last", "channels_first".
_IMAGE_DATA_FORMAT = 'channels_last'
# This list holds the available devices.
# It is populated when `_get_available_gpus()` is called for the first time.
# We assume our devices don't change henceforth.
_LOCAL_DEVICES = None
# This dictionary holds a mapping between a graph and variables to initialize
# in the graph.
_GRAPH_VARIABLES = weakref.WeakKeyDictionary()
# This dictionary holds a mapping between a graph and TF optimizers created in
# the graph.
_GRAPH_TF_OPTIMIZERS = weakref.WeakKeyDictionary()
@tf_export('keras.backend.backend')
def backend():
"""Publicly accessible method for determining the current backend.
Only exists for API compatibility with multi-backend Keras.
Returns:
The string "tensorflow".
"""
return 'tensorflow'
@tf_export('keras.backend.epsilon')
def epsilon():
"""Returns the value of the fuzz factor used in numeric expressions.
Returns:
A float.
Example:
```python
>>> keras.backend.epsilon()
1e-07
```
"""
return _EPSILON
@tf_export('keras.backend.set_epsilon')
def set_epsilon(value):
"""Sets the value of the fuzz factor used in numeric expressions.
Arguments:
value: 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 = value
@tf_export('keras.backend.floatx')
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
@tf_export('keras.backend.set_floatx')
def set_floatx(value):
"""Sets the default float type.
Arguments:
value: String; 'float16', 'float32', or 'float64'.
Example:
```python
>>> from keras import backend as K
>>> K.floatx()
'float32'
>>> K.set_floatx('float16')
>>> K.floatx()
'float16'
```
Raises:
ValueError: In case of invalid value.
"""
global _FLOATX
if value not in {'float16', 'float32', 'float64'}:
raise ValueError('Unknown floatx type: ' + str(value))
_FLOATX = str(value)
@tf_export('keras.backend.cast_to_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)
@tf_export('keras.backend.image_data_format')
def image_data_format():
"""Returns the default image data format convention.
Returns:
A string, either `'channels_first'` or `'channels_last'`
Example:
```python
>>> keras.backend.image_data_format()
'channels_first'
```
"""
return _IMAGE_DATA_FORMAT
@tf_export('keras.backend.set_image_data_format')
def set_image_data_format(data_format):
"""Sets the value of the image 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'
```
Raises:
ValueError: In case of invalid `data_format` value.
"""
global _IMAGE_DATA_FORMAT
if data_format not in {'channels_last', 'channels_first'}:
raise ValueError('Unknown data_format: ' + str(data_format))
_IMAGE_DATA_FORMAT = str(data_format)
# A global dictionary mapping graph objects to an index of counters used
# for various layer names in each graph.
# Allows to give unique autogenerated names to layers, in a graph-specific way.
PER_GRAPH_LAYER_NAME_UIDS = weakref.WeakKeyDictionary()
@tf_export('keras.backend.get_uid')
def get_uid(prefix=''):
"""Associates a string prefix with an integer counter in a TensorFlow graph.
Arguments:
prefix: String prefix to index.
Returns:
Unique integer ID.
Example:
```
>>> get_uid('dense')
1
>>> get_uid('dense')
2
```
"""
graph = get_graph()
if graph not in PER_GRAPH_LAYER_NAME_UIDS:
PER_GRAPH_LAYER_NAME_UIDS[graph] = collections.defaultdict(int)
layer_name_uids = PER_GRAPH_LAYER_NAME_UIDS[graph]
layer_name_uids[prefix] += 1
return layer_name_uids[prefix]
@tf_export('keras.backend.reset_uids')
def reset_uids():
"""Resets graph identifiers.
"""
per_graph_layer_name_uids = PER_GRAPH_LAYER_NAME_UIDS
keys = list(per_graph_layer_name_uids.keys())
for key in keys:
del per_graph_layer_name_uids[key]
@tf_export('keras.backend.clear_session')
def clear_session():
"""Destroys the current TF graph and creates a new one.
Useful to avoid clutter from old models / layers.
"""
global _SESSION
global _GRAPH_LEARNING_PHASES # pylint: disable=global-variable-not-assigned
global _GRAPH_VARIABLES # pylint: disable=global-variable-not-assigned
global _GRAPH_TF_OPTIMIZERS # pylint: disable=global-variable-not-assigned
ops.reset_default_graph()
reset_uids()
_SESSION.session = None
graph = get_graph()
with graph.as_default():
phase = array_ops.placeholder_with_default(
False, shape=(), name='keras_learning_phase')
_GRAPH_LEARNING_PHASES = {}
_GRAPH_LEARNING_PHASES[graph] = phase
_GRAPH_VARIABLES.pop(graph, None)
_GRAPH_TF_OPTIMIZERS.pop(graph, None)
@tf_export('keras.backend.manual_variable_initialization')
def manual_variable_initialization(value):
"""Sets the manual variable initialization flag.
This boolean flag determines whether
variables should be initialized
as they are instantiated (default), or if
the user should handle the initialization
(e.g. via `tf.initialize_all_variables()`).
Arguments:
value: Python boolean.
"""
global _MANUAL_VAR_INIT
_MANUAL_VAR_INIT = value
@tf_export('keras.backend.learning_phase')
def learning_phase():
"""Returns the learning phase flag.
The learning phase flag is a bool tensor (0 = test, 1 = train)
to be passed as input to any Keras function
that uses a different behavior at train time and test time.
Returns:
Learning phase (scalar integer tensor or Python integer).
"""
if context.executing_eagerly():
if _DUMMY_EAGER_GRAPH not in _GRAPH_LEARNING_PHASES:
# Fallback to inference mode as default.
return 0
return _GRAPH_LEARNING_PHASES[_DUMMY_EAGER_GRAPH]
return symbolic_learning_phase()
def symbolic_learning_phase():
graph = get_graph()
with graph.as_default():
if graph not in _GRAPH_LEARNING_PHASES:
phase = array_ops.placeholder_with_default(
False, shape=(), name='keras_learning_phase')
_GRAPH_LEARNING_PHASES[graph] = phase
return _GRAPH_LEARNING_PHASES[graph]
@tf_export('keras.backend.set_learning_phase')
def set_learning_phase(value):
"""Sets the learning phase to a fixed value.
Arguments:
value: Learning phase value, either 0 or 1 (integers).
Raises:
ValueError: if `value` is neither `0` nor `1`.
"""
global _GRAPH_LEARNING_PHASES # pylint: disable=global-variable-not-assigned
if value not in {0, 1}:
raise ValueError('Expected learning phase to be 0 or 1.')
with ops.init_scope():
if context.executing_eagerly():
_GRAPH_LEARNING_PHASES[_DUMMY_EAGER_GRAPH] = value
else:
_GRAPH_LEARNING_PHASES[get_graph()] = value
@tf_contextlib.contextmanager
def learning_phase_scope(value):
"""Provides a scope within which the learning phase is equal to `value`.
The learning phase gets restored to its original value upon exiting the scope.
Arguments:
value: Learning phase value, either 0 or 1 (integers).
Yields:
The provided value.
Raises:
ValueError: if `value` is neither `0` nor `1`.
"""
if value not in {0, 1}:
raise ValueError('Expected learning phase to be 0 or 1.')
previous_value = learning_phase()
try:
set_learning_phase(value)
yield value
finally:
# Restore learning phase to initial value.
with ops.init_scope():
if context.executing_eagerly():
_GRAPH_LEARNING_PHASES[_DUMMY_EAGER_GRAPH] = previous_value
else:
_GRAPH_LEARNING_PHASES[get_graph()] = previous_value
def _get_session():
"""Returns the session object for the current thread."""
global _SESSION
default_session = ops.get_default_session()
if default_session is not None:
session = default_session
else:
if getattr(_SESSION, 'session', None) is None:
_SESSION.session = session_module.Session(
config=get_default_session_config())
session = _SESSION.session
return session
@tf_export(v1=['keras.backend.get_session'])
def get_session():
"""Returns the TF session to be used by the backend.
If a default TensorFlow session is available, we will return it.
Else, we will return the global Keras session.
If no global Keras session exists at this point:
we will create a new global session.
Note that you can manually set the global session
via `K.set_session(sess)`.
Returns:
A TensorFlow session.
"""
session = _get_session()
if not _MANUAL_VAR_INIT:
with session.graph.as_default():
_initialize_variables(session)
return session
def get_graph():
if context.executing_eagerly():
global _GRAPH
if _GRAPH is None:
_GRAPH = func_graph.FuncGraph('keras_graph')
return _GRAPH
else:
return ops.get_default_graph()
@tf_export('keras.backend.set_session')
def set_session(session):
"""Sets the global TensorFlow session.
Arguments:
session: A TF Session.
"""
global _SESSION
_SESSION.session = session
def get_default_session_config():
if not os.environ.get('OMP_NUM_THREADS'):
config = config_pb2.ConfigProto(allow_soft_placement=True)
else:
num_thread = int(os.environ.get('OMP_NUM_THREADS'))
config = config_pb2.ConfigProto(
intra_op_parallelism_threads=num_thread, allow_soft_placement=True)
return config
# DEVICE MANIPULATION
class _TfDeviceCaptureOp(object):
"""Class for capturing the TF device scope."""
def __init__(self):
self.device = None
def _set_device(self, device):
"""This method captures TF's explicit device scope setting."""
self.device = device
def _get_current_tf_device():
"""Return explicit device of current context, otherwise returns `None`.
Returns:
If the current device scope is explicitly set, it returns a string with
the device (`CPU` or `GPU`). If the scope is not explicitly set, it will
return `None`.
"""
graph = get_graph()
op = _TfDeviceCaptureOp()
graph._apply_device_functions(op)
return op.device
def _is_current_explicit_device(device_type):
"""Check if the current device is explicitly set on the device type specified.
Arguments:
device_type: A string containing `GPU` or `CPU` (case-insensitive).
Returns:
A boolean indicating if the current device scope is explicitly set on the
device type.
Raises:
ValueError: If the `device_type` string indicates an unsupported device.
"""
device_type = device_type.upper()
if device_type not in ['CPU', 'GPU']:
raise ValueError('`device_type` should be either "CPU" or "GPU".')
device = _get_current_tf_device()
return device is not None and device.device_type == device_type.upper()
def _get_available_gpus():
"""Get a list of available gpu devices (formatted as strings).
Returns:
A list of available GPU devices.
"""
if ops.executing_eagerly_outside_functions():
# Returns names of devices directly.
return [name for name in context.list_devices() if 'GPU' in name]
global _LOCAL_DEVICES
if _LOCAL_DEVICES is None:
_LOCAL_DEVICES = get_session().list_devices()
return [x.name for x in _LOCAL_DEVICES if x.device_type == 'GPU']
def _has_nchw_support():
"""Check whether the current scope supports NCHW ops.
TensorFlow does not support NCHW on CPU. Therefore we check if we are not
explicitly put on
CPU, and have GPUs available. In this case there will be soft-placing on the
GPU device.
Returns:
bool: if the current scope device placement would support nchw
"""
explicitly_on_cpu = _is_current_explicit_device('CPU')
gpus_available = bool(_get_available_gpus())
return not explicitly_on_cpu and gpus_available
# VARIABLE MANIPULATION
def _to_tensor(x, dtype):
"""Convert the input `x` to a tensor of type `dtype`.
Arguments:
x: An object to be converted (numpy array, list, tensors).
dtype: The destination type.
Returns:
A tensor.
"""
return ops.convert_to_tensor(x, dtype=dtype)
@tf_export('keras.backend.is_sparse')
def is_sparse(tensor):
"""Returns whether a tensor is a sparse tensor.
Arguments:
tensor: A tensor instance.
Returns:
A boolean.
Example:
```python
>>> from keras import backend as K
>>> a = K.placeholder((2, 2), sparse=False)
>>> print(K.is_sparse(a))
False
>>> b = K.placeholder((2, 2), sparse=True)
>>> print(K.is_sparse(b))
True
```
"""
return isinstance(tensor, sparse_tensor.SparseTensor)
@tf_export('keras.backend.to_dense')
def to_dense(tensor):
"""Converts a sparse tensor into a dense tensor and returns it.
Arguments:
tensor: A tensor instance (potentially sparse).
Returns:
A dense tensor.
Examples:
```python
>>> from keras import backend as K
>>> b = K.placeholder((2, 2), sparse=True)
>>> print(K.is_sparse(b))
True
>>> c = K.to_dense(b)
>>> print(K.is_sparse(c))
False
```
"""
if is_sparse(tensor):
return sparse_ops.sparse_tensor_to_dense(tensor)
else:
return tensor
name_scope = ops.name_scope
@tf_export('keras.backend.variable')
def variable(value, dtype=None, name=None, constraint=None):
"""Instantiates a variable and returns it.
Arguments:
value: Numpy array, initial value of the tensor.
dtype: Tensor type.
name: Optional name string for the tensor.
constraint: Optional projection function to be
applied to the variable after an optimizer update.
Returns:
A variable instance (with Keras metadata included).
Examples:
```python
>>> import numpy as np
>>> from keras import backend as K
>>> val = np.array([[1, 2], [3, 4]])
>>> kvar = K.variable(value=val, dtype='float64', name='example_var')
>>> K.dtype(kvar)
'float64'
>>> print(kvar)
example_var
>>> kvar.eval()
array([[ 1., 2.],
[ 3., 4.]])
```
"""
if dtype is None:
dtype = floatx()
if hasattr(value, 'tocoo'):
sparse_coo = value.tocoo()
indices = np.concatenate((np.expand_dims(sparse_coo.row, 1), np.expand_dims(
sparse_coo.col, 1)), 1)
v = sparse_tensor.SparseTensor(
indices=indices, values=sparse_coo.data, dense_shape=sparse_coo.shape)
v._keras_shape = sparse_coo.shape
return v
v = resource_variable_ops.ResourceVariable(
value,
dtype=dtypes_module.as_dtype(dtype),
name=name,
constraint=constraint)
if isinstance(value, np.ndarray):
v._keras_shape = value.shape
elif hasattr(value, 'shape'):
v._keras_shape = int_shape(value)
track_variable(v)
return v
def track_tf_optimizer(tf_optimizer):
"""Tracks the given TF optimizer for initialization of its variables."""
if context.executing_eagerly():
return
graph = get_graph()
optimizers = _GRAPH_TF_OPTIMIZERS.setdefault(graph, weakref.WeakSet())
optimizers.add(tf_optimizer)
def track_variable(v):
"""Tracks the given variable for initialization."""
if context.executing_eagerly():
return
graph = v.graph if hasattr(v, 'graph') else get_graph()
if graph not in _GRAPH_VARIABLES:
_GRAPH_VARIABLES[graph] = weakref.WeakSet()
_GRAPH_VARIABLES[graph].add(v)
def _get_variables(graph=None):
"""Returns variables corresponding to the given graph for initialization."""
assert not context.executing_eagerly()
variables = _GRAPH_VARIABLES.setdefault(graph, weakref.WeakSet())
for opt in _GRAPH_TF_OPTIMIZERS.get(graph, set()):
variables.update(opt.optimizer.variables())
return variables
def _initialize_variables(session):
"""Utility to initialize uninitialized variables on the fly."""
variables = _get_variables(get_graph())
candidate_vars = []
for v in variables:
if not getattr(v, '_keras_initialized', False):
candidate_vars.append(v)
if candidate_vars:
# This step is expensive, so we only run it on variables not already
# marked as initialized.
is_initialized = session.run(
[variables_module.is_variable_initialized(v) for v in candidate_vars])
uninitialized_vars = []
for flag, v in zip(is_initialized, candidate_vars):
if not flag:
uninitialized_vars.append(v)
v._keras_initialized = True
if uninitialized_vars:
session.run(variables_module.variables_initializer(uninitialized_vars))
@tf_export('keras.backend.constant')
def constant(value, dtype=None, shape=None, name=None):
"""Creates a constant tensor.
Arguments:
value: A constant value (or list)
dtype: The type of the elements of the resulting tensor.
shape: Optional dimensions of resulting tensor.
name: Optional name for the tensor.
Returns:
A Constant Tensor.
"""
if dtype is None:
dtype = floatx()
return constant_op.constant(value, dtype=dtype, shape=shape, name=name)
def is_keras_tensor(x):
"""Returns whether `x` is a Keras tensor.
A "Keras tensor" is a tensor that was returned by a Keras layer,
(`Layer` class) or by `Input`.
Arguments:
x: A candidate tensor.
Returns:
A boolean: Whether the argument is a Keras tensor.
Raises:
ValueError: In case `x` is not a symbolic tensor.
Examples:
```python
>>> import tensorflow as tf
>>> import numpy
>>> from keras import backend as K
>>> from keras.layers import Input, Dense
>>> np_var = numpy.array([1, 2])
>>> K.is_keras_tensor(np_var) # A numpy array is not a symbolic tensor.
ValueError
>>> k_var = tf.placeholder('float32', shape=(1,1))
>>> K.is_keras_tensor(k_var) # A variable indirectly created outside of
keras is not a Keras tensor.
False
>>> keras_var = K.variable(np_var)
>>> K.is_keras_tensor(keras_var) # A variable created with the keras
backend is not a Keras tensor.
False
>>> keras_placeholder = K.placeholder(shape=(2, 4, 5))
>>> K.is_keras_tensor(keras_placeholder) # A placeholder is not a Keras
tensor.
False
>>> keras_input = Input([10])
>>> K.is_keras_tensor(keras_input) # An Input is a Keras tensor.
True
>>> keras_layer_output = Dense(10)(keras_input)
>>> K.is_keras_tensor(keras_layer_output) # Any Keras layer output is a
Keras tensor.
True
```
"""
if not isinstance(x, (ops.Tensor,
variables_module.Variable,
sparse_tensor.SparseTensor)):
raise ValueError('Unexpectedly found an instance of type `' + str(type(x)) +
'`. Expected a symbolic tensor instance.')
return hasattr(x, '_keras_history')
@tf_export('keras.backend.placeholder')
def placeholder(shape=None, ndim=None, dtype=None, sparse=False, name=None):
"""Instantiates a placeholder tensor and returns it.
Arguments:
shape: Shape of the placeholder
(integer tuple, may include `None` entries).
ndim: Number of axes of the tensor.
At least one of {`shape`, `ndim`} must be specified.
If both are specified, `shape` is used.
dtype: Placeholder type.
sparse: Boolean, whether the placeholder should have a sparse type.
name: Optional name string for the placeholder.
Raises:
ValueError: If called with eager execution.
Returns:
Tensor instance (with Keras metadata included).
Examples:
```python
>>> from keras import backend as K
>>> input_ph = K.placeholder(shape=(2, 4, 5))
>>> input_ph
<tf.Tensor 'Placeholder_4:0' shape=(2, 4, 5) dtype=float32>
```
"""
if dtype is None:
dtype = floatx()
if not shape:
if ndim:
shape = tuple([None for _ in range(ndim)])
with get_graph().as_default():
if sparse:
x = array_ops.sparse_placeholder(dtype, shape=shape, name=name)
else:
x = array_ops.placeholder(dtype, shape=shape, name=name)
return x
def is_placeholder(x):
"""Returns whether `x` is a placeholder.
Arguments:
x: A candidate placeholder.
Returns:
Boolean.
"""
try:
return x.op.type == 'Placeholder'
except AttributeError:
return False
@tf_export('keras.backend.shape')
def shape(x):
"""Returns the symbolic shape of a tensor or variable.
Arguments:
x: A tensor or variable.
Returns:
A symbolic shape (which is itself a tensor).
Examples:
```python
# TensorFlow example
>>> from keras import backend as K
>>> tf_session = K.get_session()
>>> val = np.array([[1, 2], [3, 4]])
>>> kvar = K.variable(value=val)
>>> input = keras.backend.placeholder(shape=(2, 4, 5))
>>> K.shape(kvar)
<tf.Tensor 'Shape_8:0' shape=(2,) dtype=int32>
>>> K.shape(input)
<tf.Tensor 'Shape_9:0' shape=(3,) dtype=int32>
# To get integer shape (Instead, you can use K.int_shape(x))
>>> K.shape(kvar).eval(session=tf_session)
array([2, 2], dtype=int32)
>>> K.shape(input).eval(session=tf_session)
array([2, 4, 5], dtype=int32)
```
"""
return array_ops.shape(x)
@tf_export('keras.backend.int_shape')
def int_shape(x):
"""Returns the shape of tensor or variable as a tuple of int or None entries.
Arguments:
x: Tensor or variable.
Returns:
A tuple of integers (or None entries).
Examples:
```python
>>> from keras import backend as K
>>> input = K.placeholder(shape=(2, 4, 5))
>>> K.int_shape(input)
(2, 4, 5)
>>> val = np.array([[1, 2], [3, 4]])
>>> kvar = K.variable(value=val)
>>> K.int_shape(kvar)
(2, 2)
```
"""
try:
shape = x.shape
if not isinstance(shape, tuple):
shape = tuple(shape.as_list())
return shape
except ValueError:
return None
@tf_export('keras.backend.ndim')
def ndim(x):
"""Returns the number of axes in a tensor, as an integer.
Arguments:
x: Tensor or variable.
Returns:
Integer (scalar), number of axes.
Examples:
```python
>>> from keras import backend as K
>>> input = K.placeholder(shape=(2, 4, 5))
>>> val = np.array([[1, 2], [3, 4]])
>>> kvar = K.variable(value=val)
>>> K.ndim(input)
3
>>> K.ndim(kvar)
2
```
"""
dims = x.shape._dims
if dims is not None:
return len(dims)
return None
@tf_export('keras.backend.dtype')
def dtype(x):
"""Returns the dtype of a Keras tensor or variable, as a string.
Arguments:
x: Tensor or variable.
Returns:
String, dtype of `x`.
Examples:
```python
>>> from keras import backend as K
>>> K.dtype(K.placeholder(shape=(2,4,5)))
'float32'
>>> K.dtype(K.placeholder(shape=(2,4,5), dtype='float32'))
'float32'
>>> K.dtype(K.placeholder(shape=(2,4,5), dtype='float64'))
'float64'
# Keras variable
>>> kvar = K.variable(np.array([[1, 2], [3, 4]]))
>>> K.dtype(kvar)
'float32_ref'
>>> kvar = K.variable(np.array([[1, 2], [3, 4]]), dtype='float32')
>>> K.dtype(kvar)
'float32_ref'
```
"""
return x.dtype.base_dtype.name
@tf_export('keras.backend.eval')
def eval(x):
"""Evaluates the value of a variable.
Arguments:
x: A variable.
Returns:
A Numpy array.
Examples:
```python
>>> from keras import backend as K
>>> kvar = K.variable(np.array([[1, 2], [3, 4]]), dtype='float32')
>>> K.eval(kvar)
array([[ 1., 2.],
[ 3., 4.]], dtype=float32)
```
"""
return get_value(to_dense(x))
@tf_export('keras.backend.zeros')
def zeros(shape, dtype=None, name=None):
"""Instantiates an all-zeros variable and returns it.
Arguments:
shape: Tuple of integers, shape of returned Keras variable
dtype: String, data type of returned Keras variable
name: String, name of returned Keras variable
Returns:
A variable (including Keras metadata), filled with `0.0`.
Note that if `shape` was symbolic, we cannot return a variable,
and will return a dynamically-shaped tensor instead.
Example:
```python
>>> from keras import backend as K
>>> kvar = K.zeros((3,4))
>>> K.eval(kvar)
array([[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.]], dtype=float32)
```
"""
with ops.init_scope():
if dtype is None:
dtype = floatx()
tf_dtype = dtypes_module.as_dtype(dtype)
v = array_ops.zeros(shape=shape, dtype=tf_dtype, name=name)
if py_all(v.shape.as_list()):
return variable(v, dtype=dtype, name=name)
track_variable(v)
return v
@tf_export('keras.backend.ones')
def ones(shape, dtype=None, name=None):
"""Instantiates an all-ones variable and returns it.
Arguments:
shape: Tuple of integers, shape of returned Keras variable.
dtype: String, data type of returned Keras variable.
name: String, name of returned Keras variable.
Returns:
A Keras variable, filled with `1.0`.
Note that if `shape` was symbolic, we cannot return a variable,
and will return a dynamically-shaped tensor instead.
Example:
```python
>>> from keras import backend as K
>>> kvar = K.ones((3,4))
>>> K.eval(kvar)
array([[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.]], dtype=float32)
```
"""
with ops.init_scope():
if dtype is None:
dtype = floatx()
tf_dtype = dtypes_module.as_dtype(dtype)
v = array_ops.ones(shape=shape, dtype=tf_dtype, name=name)
if py_all(v.shape.as_list()):
return variable(v, dtype=dtype, name=name)
track_variable(v)
return v
@tf_export('keras.backend.eye')
def eye(size, dtype=None, name=None):
"""Instantiate an identity matrix and returns it.
Arguments:
size: Integer, number of rows/columns.
dtype: String, data type of returned Keras variable.
name: String, name of returned Keras variable.
Returns:
A Keras variable, an identity matrix.
Example:
```python
>>> from keras import backend as K
>>> kvar = K.eye(3)
>>> K.eval(kvar)
array([[ 1., 0., 0.],
[ 0., 1., 0.],
[ 0., 0., 1.]], dtype=float32)
```
"""
if dtype is None:
dtype = floatx()
tf_dtype = dtypes_module.as_dtype(dtype)
return variable(linalg_ops.eye(size, dtype=tf_dtype), dtype, name)
@tf_export('keras.backend.zeros_like')
def zeros_like(x, dtype=None, name=None):
"""Instantiates an all-zeros variable of the same shape as another tensor.
Arguments:
x: Keras variable or Keras tensor.
dtype: String, dtype of returned Keras variable.
None uses the dtype of x.
name: String, name for the variable to create.
Returns:
A Keras variable with the shape of x filled with zeros.
Example:
```python
>>> from keras import backend as K
>>> kvar = K.variable(np.random.random((2,3)))
>>> kvar_zeros = K.zeros_like(kvar)
>>> K.eval(kvar_zeros)
array([[ 0., 0., 0.],
[ 0., 0., 0.]], dtype=float32)
```
"""
return array_ops.zeros_like(x, dtype=dtype, name=name)
@tf_export('keras.backend.ones_like')
def ones_like(x, dtype=None, name=None):
"""Instantiates an all-ones variable of the same shape as another tensor.
Arguments:
x: Keras variable or tensor.
dtype: String, dtype of returned Keras variable.
None uses the dtype of x.
name: String, name for the variable to create.
Returns:
A Keras variable with the shape of x filled with ones.
Example:
```python
>>> from keras import backend as K
>>> kvar = K.variable(np.random.random((2,3)))
>>> kvar_ones = K.ones_like(kvar)
>>> K.eval(kvar_ones)
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
```
"""
return array_ops.ones_like(x, dtype=dtype, name=name)
def identity(x, name=None):
"""Returns a tensor with the same content as the input tensor.
Arguments:
x: The input tensor.
name: String, name for the variable to create.
Returns:
A tensor of the same shape, type and content.
"""
return array_ops.identity(x, name=name)
@tf_export('keras.backend.random_uniform_variable')
def random_uniform_variable(shape, low, high, dtype=None, name=None, seed=None):
"""Instantiates a variable with values drawn from a uniform distribution.
Arguments:
shape: Tuple of integers, shape of returned Keras variable.
low: Float, lower boundary of the output interval.
high: Float, upper boundary of the output interval.
dtype: String, dtype of returned Keras variable.
name: String, name of returned Keras variable.
seed: Integer, random seed.
Returns:
A Keras variable, filled with drawn samples.
Example:
```python
# TensorFlow example
>>> kvar = K.random_uniform_variable((2,3), 0, 1)
>>> kvar
<tensorflow.python.ops.variables.Variable object at 0x10ab40b10>
>>> K.eval(kvar)
array([[ 0.10940075, 0.10047495, 0.476143 ],
[ 0.66137183, 0.00869417, 0.89220798]], dtype=float32)
```
"""
if dtype is None:
dtype = floatx()
tf_dtype = dtypes_module.as_dtype(dtype)
if seed is None:
# ensure that randomness is conditioned by the Numpy RNG
seed = np.random.randint(10e8)
value = init_ops.random_uniform_initializer(
low, high, dtype=tf_dtype, seed=seed)(shape)
return variable(value, dtype=dtype, name=name)
@tf_export('keras.backend.random_normal_variable')
def random_normal_variable(shape, mean, scale, dtype=None, name=None,
seed=None):
"""Instantiates a variable with values drawn from a normal distribution.
Arguments:
shape: Tuple of integers, shape of returned Keras variable.
mean: Float, mean of the normal distribution.
scale: Float, standard deviation of the normal distribution.
dtype: String, dtype of returned Keras variable.
name: String, name of returned Keras variable.
seed: Integer, random seed.
Returns:
A Keras variable, filled with drawn samples.
Example:
```python
# TensorFlow example
>>> kvar = K.random_normal_variable((2,3), 0, 1)
>>> kvar
<tensorflow.python.ops.variables.Variable object at 0x10ab12dd0>
>>> K.eval(kvar)
array([[ 1.19591331, 0.68685907, -0.63814116],
[ 0.92629528, 0.28055015, 1.70484698]], dtype=float32)
```
"""
if dtype is None:
dtype = floatx()
tf_dtype = dtypes_module.as_dtype(dtype)
if seed is None:
# ensure that randomness is conditioned by the Numpy RNG
seed = np.random.randint(10e8)
value = init_ops.random_normal_initializer(
mean, scale, dtype=tf_dtype, seed=seed)(shape)
return variable(value, dtype=dtype, name=name)
@tf_export('keras.backend.count_params')
def count_params(x):
"""Returns the static number of elements in a variable or tensor.
Arguments:
x: Variable or tensor.
Returns:
Integer, the number of scalars in `x`.
Example:
```python
>>> kvar = K.zeros((2,3))
>>> K.count_params(kvar)
6
>>> K.eval(kvar)
array([[ 0., 0., 0.],
[ 0., 0., 0.]], dtype=float32)
```
"""
return np.prod(x.shape.as_list())
@tf_export('keras.backend.cast')
def cast(x, dtype):
"""Casts a tensor to a different dtype and returns it.
You can cast a Keras variable but it still returns a Keras tensor.
Arguments:
x: Keras tensor (or variable).
dtype: String, either (`'float16'`, `'float32'`, or `'float64'`).
Returns:
Keras tensor with dtype `dtype`.
Example:
```python
>>> 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>
```
"""
return math_ops.cast(x, dtype)
# UPDATES OPS
@tf_export('keras.backend.update')
def update(x, new_x):
return state_ops.assign(x, new_x)
@tf_export('keras.backend.update_add')
def update_add(x, increment):
"""Update the value of `x` by adding `increment`.
Arguments:
x: A Variable.
increment: A tensor of same shape as `x`.
Returns:
The variable `x` updated.
"""
return state_ops.assign_add(x, increment)
@tf_export('keras.backend.update_sub')
def update_sub(x, decrement):
"""Update the value of `x` by subtracting `decrement`.
Arguments:
x: A Variable.
decrement: A tensor of same shape as `x`.
Returns:
The variable `x` updated.
"""
return state_ops.assign_sub(x, decrement)
@tf_export('keras.backend.moving_average_update')
def moving_average_update(x, value, momentum):
"""Compute the moving average of a variable.
Arguments:
x: A Variable.
value: A tensor with the same shape as `variable`.
momentum: The moving average momentum.
Returns:
An Operation to update the variable.
"""
# `training` is higher-up than the Keras backend in the abstraction hierarchy.
# In particular, `training` depends on layers, and thus on Keras.
# moving_averages, being low-level ops, should not be part of the training
# module.
from tensorflow.python.training import moving_averages # pylint: disable=g-import-not-at-top
return moving_averages.assign_moving_average(
x, value, momentum, zero_debias=True)
# LINEAR ALGEBRA
@tf_export('keras.backend.dot')
def dot(x, y):
"""Multiplies 2 tensors (and/or variables) and returns a *tensor*.
When attempting to multiply a nD tensor
with a nD tensor, it reproduces the Theano behavior.
(e.g. `(2, 3) * (4, 3, 5) -> (2, 4, 5)`)
Arguments:
x: Tensor or variable.
y: Tensor or variable.
Returns:
A tensor, dot product of `x` and `y`.
Examples:
```python
# dot product between tensors
>>> x = K.placeholder(shape=(2, 3))
>>> y = K.placeholder(shape=(3, 4))
>>> xy = K.dot(x, y)
>>> xy
<tf.Tensor 'MatMul_9:0' shape=(2, 4) dtype=float32>
```
```python
# dot product between tensors
>>> x = K.placeholder(shape=(32, 28, 3))
>>> y = K.placeholder(shape=(3, 4))
>>> xy = K.dot(x, y)
>>> xy
<tf.Tensor 'MatMul_9:0' shape=(32, 28, 4) dtype=float32>
```
```python
# Theano-like behavior example
>>> x = K.random_uniform_variable(shape=(2, 3), low=0, high=1)
>>> y = K.ones((4, 3, 5))
>>> xy = K.dot(x, y)
>>> K.int_shape(xy)
(2, 4, 5)
```
"""
if ndim(x) is not None and (ndim(x) > 2 or ndim(y) > 2):
x_shape = []
for i, s in zip(int_shape(x), array_ops.unstack(array_ops.shape(x))):
if i is not None:
x_shape.append(i)
else:
x_shape.append(s)
x_shape = tuple(x_shape)
y_shape = []
for i, s in zip(int_shape(y), array_ops.unstack(array_ops.shape(y))):
if i is not None:
y_shape.append(i)
else:
y_shape.append(s)
y_shape = tuple(y_shape)
y_permute_dim = list(range(ndim(y)))
y_permute_dim = [y_permute_dim.pop(-2)] + y_permute_dim
xt = array_ops.reshape(x, [-1, x_shape[-1]])
yt = array_ops.reshape(
array_ops.transpose(y, perm=y_permute_dim), [y_shape[-2], -1])
return array_ops.reshape(
math_ops.matmul(xt, yt), x_shape[:-1] + y_shape[:-2] + y_shape[-1:])
if is_sparse(x):
out = sparse_ops.sparse_tensor_dense_matmul(x, y)
else:
out = math_ops.matmul(x, y)
return out
@tf_export('keras.backend.batch_dot')
def batch_dot(x, y, axes=None):
"""Batchwise dot product.
`batch_dot` is used to compute dot product of `x` and `y` when
`x` and `y` are data in batch, i.e. in a shape of
`(batch_size, :)`.
`batch_dot` results in a tensor or variable with less dimensions
than the input. If the number of dimensions is reduced to 1,
we use `expand_dims` to make sure that ndim is at least 2.
Arguments:
x: Keras tensor or variable with `ndim >= 2`.
y: Keras tensor or variable with `ndim >= 2`.
axes: list of (or single) int with target dimensions.
The lengths of `axes[0]` and `axes[1]` should be the same.
Returns:
A tensor with shape equal to the concatenation of `x`'s shape
(less the dimension that was summed over) and `y`'s shape
(less the batch dimension and the dimension that was summed over).
If the final rank is 1, we reshape it to `(batch_size, 1)`.
Examples:
Assume `x = [[1, 2], [3, 4]]` and `y = [[5, 6], [7, 8]]`
`batch_dot(x, y, axes=1) = [[17, 53]]` which is the main diagonal
of `x.dot(y.T)`, although we never have to calculate the off-diagonal
elements.
Shape inference:
Let `x`'s shape be `(100, 20)` and `y`'s shape be `(100, 30, 20)`.
If `axes` is (1, 2), to find the output shape of resultant tensor,
loop through each dimension in `x`'s shape and `y`'s shape:
* `x.shape[0]` : 100 : append to output shape
* `x.shape[1]` : 20 : do not append to output shape,
dimension 1 of `x` has been summed over. (`dot_axes[0]` = 1)
* `y.shape[0]` : 100 : do not append to output shape,
always ignore first dimension of `y`
* `y.shape[1]` : 30 : append to output shape
* `y.shape[2]` : 20 : do not append to output shape,
dimension 2 of `y` has been summed over. (`dot_axes[1]` = 2)
`output_shape` = `(100, 30)`
```python
>>> x_batch = K.ones(shape=(32, 20, 1))
>>> y_batch = K.ones(shape=(32, 30, 20))
>>> xy_batch_dot = K.batch_dot(x_batch, y_batch, axes=[1, 2])
>>> K.int_shape(xy_batch_dot)
(32, 1, 30)
```
"""
if isinstance(axes, int):
axes = (axes, axes)
x_ndim = ndim(x)
y_ndim = ndim(y)
if axes is None:
# behaves like tf.batch_matmul as default
axes = [x_ndim - 1, y_ndim - 2]
if x_ndim > y_ndim:
diff = x_ndim - y_ndim
y = array_ops.reshape(y,
array_ops.concat(
[array_ops.shape(y), [1] * (diff)], axis=0))
elif y_ndim > x_ndim:
diff = y_ndim - x_ndim
x = array_ops.reshape(x,
array_ops.concat(
[array_ops.shape(x), [1] * (diff)], axis=0))
else:
diff = 0
if ndim(x) == 2 and ndim(y) == 2:
if axes[0] == axes[1]:
out = math_ops.reduce_sum(math_ops.multiply(x, y), axes[0])
else:
out = math_ops.reduce_sum(
math_ops.multiply(array_ops.transpose(x, [1, 0]), y), axes[1])
else:
adj_x = None if axes[0] == ndim(x) - 1 else True
adj_y = True if axes[1] == ndim(y) - 1 else None
out = math_ops.matmul(x, y, adjoint_a=adj_x, adjoint_b=adj_y)
if diff:
if x_ndim > y_ndim:
idx = x_ndim + y_ndim - 3
else:
idx = x_ndim - 1
out = array_ops.squeeze(out, list(range(idx, idx + diff)))
if ndim(out) == 1:
out = expand_dims(out, 1)
return out
@tf_export('keras.backend.transpose')
def transpose(x):
"""Transposes a tensor and returns it.
Arguments:
x: Tensor or variable.
Returns:
A tensor.
Examples:
```python
>>> var = K.variable([[1, 2, 3], [4, 5, 6]])
>>> K.eval(var)
array([[ 1., 2., 3.],
[ 4., 5., 6.]], dtype=float32)
>>> var_transposed = K.transpose(var)
>>> K.eval(var_transposed)
array([[ 1., 4.],
[ 2., 5.],
[ 3., 6.]], dtype=float32)
```
```python
>>> input = K.placeholder((2, 3))
>>> input
<tf.Tensor 'Placeholder_11:0' shape=(2, 3) dtype=float32>
>>> input_transposed = K.transpose(input)
>>> input_transposed
<tf.Tensor 'transpose_4:0' shape=(3, 2) dtype=float32>
```
"""
return array_ops.transpose(x)
@tf_export('keras.backend.gather')
def gather(reference, indices):
"""Retrieves the elements of indices `indices` in the tensor `reference`.
Arguments:
reference: A tensor.
indices: An integer tensor of indices.
Returns:
A tensor of same type as `reference`.
"""
return array_ops.gather(reference, indices)
# ELEMENT-WISE OPERATIONS
@tf_export('keras.backend.max')
def max(x, axis=None, keepdims=False):
"""Maximum value in a tensor.
Arguments:
x: A tensor or variable.
axis: An integer, the axis to find maximum values.
keepdims: A boolean, whether to keep the dimensions or not.
If `keepdims` is `False`, the rank of the tensor is reduced
by 1. If `keepdims` is `True`,
the reduced dimension is retained with length 1.
Returns:
A tensor with maximum values of `x`.
"""
return math_ops.reduce_max(x, axis, keepdims)
@tf_export('keras.backend.min')
def min(x, axis=None, keepdims=False):
"""Minimum value in a tensor.
Arguments:
x: A tensor or variable.
axis: An integer, the axis to find minimum values.
keepdims: A boolean, whether to keep the dimensions or not.
If `keepdims` is `False`, the rank of the tensor is reduced
by 1. If `keepdims` is `True`,
the reduced dimension is retained with length 1.
Returns:
A tensor with miminum values of `x`.
"""
return math_ops.reduce_min(x, axis, keepdims)
@tf_export('keras.backend.sum')
def sum(x, axis=None, keepdims=False):
"""Sum of the values in a tensor, alongside the specified axis.
Arguments:
x: A tensor or variable.
axis: An integer, the axis to sum over.
keepdims: A boolean, whether to keep the dimensions or not.
If `keepdims` is `False`, the rank of the tensor is reduced
by 1. If `keepdims` is `True`,
the reduced dimension is retained with length 1.
Returns:
A tensor with sum of `x`.
"""
return math_ops.reduce_sum(x, axis, keepdims)
@tf_export('keras.backend.prod')
def prod(x, axis=None, keepdims=False):
"""Multiplies the values in a tensor, alongside the specified axis.
Arguments:
x: A tensor or variable.
axis: An integer, the axis to compute the product.
keepdims: A boolean, whether to keep the dimensions or not.
If `keepdims` is `False`, the rank of the tensor is reduced
by 1. If `keepdims` is `True`,
the reduced dimension is retained with length 1.
Returns:
A tensor with the product of elements of `x`.
"""
return math_ops.reduce_prod(x, axis, keepdims)
def cumsum(x, axis=0):
"""Cumulative sum of the values in a tensor, alongside the specified axis.
Arguments:
x: A tensor or variable.
axis: An integer, the axis to compute the sum.
Returns:
A tensor of the cumulative sum of values of `x` along `axis`.
"""
return math_ops.cumsum(x, axis=axis)
def cumprod(x, axis=0):
"""Cumulative product of the values in a tensor, alongside the specified axis.
Arguments:
x: A tensor or variable.
axis: An integer, the axis to compute the product.
Returns:
A tensor of the cumulative product of values of `x` along `axis`.
"""
return math_ops.cumprod(x, axis=axis)
@tf_export('keras.backend.var')
def var(x, axis=None, keepdims=False):
"""Variance of a tensor, alongside the specified axis.
Arguments:
x: A tensor or variable.
axis: An integer, the axis to compute the variance.
keepdims: A boolean, whether to keep the dimensions or not.
If `keepdims` is `False`, the rank of the tensor is reduced
by 1. If `keepdims` is `True`,
the reduced dimension is retained with length 1.
Returns:
A tensor with the variance of elements of `x`.
"""
if x.dtype.base_dtype == dtypes_module.bool:
x = math_ops.cast(x, floatx())
return math_ops.reduce_variance(x, axis=axis, keepdims=keepdims)
@tf_export('keras.backend.std')
def std(x, axis=None, keepdims=False):
"""Standard deviation of a tensor, alongside the specified axis.
Arguments:
x: A tensor or variable.
axis: An integer, the axis to compute the standard deviation.
keepdims: A boolean, whether to keep the dimensions or not.
If `keepdims` is `False`, the rank of the tensor is reduced
by 1. If `keepdims` is `True`,
the reduced dimension is retained with length 1.
Returns:
A tensor with the standard deviation of elements of `x`.
"""
if x.dtype.base_dtype == dtypes_module.bool:
x = math_ops.cast(x, floatx())
return math_ops.reduce_std(x, axis=axis, keepdims=keepdims)
@tf_export('keras.backend.mean')
def mean(x, axis=None, keepdims=False):
"""Mean of a tensor, alongside the specified axis.
Arguments:
x: A tensor or variable.
axis: A list of integer. Axes to compute the mean.
keepdims: A boolean, whether to keep the dimensions or not.
If `keepdims` is `False`, the rank of the tensor is reduced
by 1 for each entry in `axis`. If `keepdims` is `True`,
the reduced dimensions are retained with length 1.
Returns:
A tensor with the mean of elements of `x`.
"""
if x.dtype.base_dtype == dtypes_module.bool:
x = math_ops.cast(x, floatx())
return math_ops.reduce_mean(x, axis, keepdims)
@tf_export('keras.backend.any')
def any(x, axis=None, keepdims=False):
"""Bitwise reduction (logical OR).
Arguments:
x: Tensor or variable.
axis: axis along which to perform the reduction.
keepdims: whether the drop or broadcast the reduction axes.
Returns:
A uint8 tensor (0s and 1s).
"""
x = math_ops.cast(x, dtypes_module.bool)
return math_ops.reduce_any(x, axis, keepdims)
@tf_export('keras.backend.all')
def all(x, axis=None, keepdims=False):
"""Bitwise reduction (logical AND).
Arguments:
x: Tensor or variable.
axis: axis along which to perform the reduction.
keepdims: whether the drop or broadcast the reduction axes.
Returns:
A uint8 tensor (0s and 1s).
"""
x = math_ops.cast(x, dtypes_module.bool)
return math_ops.reduce_all(x, axis, keepdims)
@tf_export('keras.backend.argmax')
def argmax(x, axis=-1):
"""Returns the index of the maximum value along an axis.
Arguments:
x: Tensor or variable.
axis: axis along which to perform the reduction.
Returns:
A tensor.
"""
return math_ops.argmax(x, axis)
@tf_export('keras.backend.argmin')
def argmin(x, axis=-1):
"""Returns the index of the minimum value along an axis.
Arguments:
x: Tensor or variable.
axis: axis along which to perform the reduction.
Returns:
A tensor.
"""
return math_ops.argmin(x, axis)
@tf_export('keras.backend.square')
def square(x):
"""Element-wise square.
Arguments:
x: Tensor or variable.
Returns:
A tensor.
"""
return math_ops.square(x)
@tf_export('keras.backend.abs')
def abs(x):
"""Element-wise absolute value.
Arguments:
x: Tensor or variable.
Returns:
A tensor.
"""
return math_ops.abs(x)
@tf_export('keras.backend.sqrt')
def sqrt(x):
"""Element-wise square root.
Arguments:
x: Tensor or variable.
Returns:
A tensor.
"""
zero = _to_tensor(0., x.dtype.base_dtype)
inf = _to_tensor(np.inf, x.dtype.base_dtype)
x = clip_ops.clip_by_value(x, zero, inf)
return math_ops.sqrt(x)
@tf_export('keras.backend.exp')
def exp(x):
"""Element-wise exponential.
Arguments:
x: Tensor or variable.
Returns:
A tensor.
"""
return math_ops.exp(x)
@tf_export('keras.backend.log')
def log(x):
"""Element-wise log.
Arguments:
x: Tensor or variable.
Returns:
A tensor.
"""
return math_ops.log(x)
def logsumexp(x, axis=None, keepdims=False):
"""Computes log(sum(exp(elements across dimensions of a tensor))).
This function is more numerically stable than log(sum(exp(x))).
It avoids overflows caused by taking the exp of large inputs and
underflows caused by taking the log of small inputs.
Arguments:
x: A tensor or variable.
axis: An integer, the axis to reduce over.
keepdims: A boolean, whether to keep the dimensions or not.
If `keepdims` is `False`, the rank of the tensor is reduced
by 1. If `keepdims` is `True`, the reduced dimension is
retained with length 1.
Returns:
The reduced tensor.
"""
return math_ops.reduce_logsumexp(x, axis, keepdims)
@tf_export('keras.backend.round')
def round(x):
"""Element-wise rounding to the closest integer.
In case of tie, the rounding mode used is "half to even".
Arguments:
x: Tensor or variable.
Returns:
A tensor.
"""
return math_ops.round(x)
@tf_export('keras.backend.sign')
def sign(x):
"""Element-wise sign.
Arguments:
x: Tensor or variable.
Returns:
A tensor.
"""
return math_ops.sign(x)
@tf_export('keras.backend.pow')
def pow(x, a):
"""Element-wise exponentiation.
Arguments:
x: Tensor or variable.
a: Python integer.
Returns:
A tensor.
"""
return math_ops.pow(x, a)
@tf_export('keras.backend.clip')
def clip(x, min_value, max_value):
"""Element-wise value clipping.
Arguments:
x: Tensor or variable.
min_value: Python float or integer.
max_value: Python float or integer.
Returns:
A tensor.
"""
if max_value is not None and max_value < min_value:
max_value = min_value
if max_value is None:
max_value = np.inf
min_value = _to_tensor(min_value, x.dtype.base_dtype)
max_value = _to_tensor(max_value, x.dtype.base_dtype)
return clip_ops.clip_by_value(x, min_value, max_value)
@tf_export('keras.backend.equal')
def equal(x, y):
"""Element-wise equality between two tensors.
Arguments:
x: Tensor or variable.
y: Tensor or variable.
Returns:
A bool tensor.
"""
return math_ops.equal(x, y)
@tf_export('keras.backend.not_equal')
def not_equal(x, y):
"""Element-wise inequality between two tensors.
Arguments:
x: Tensor or variable.
y: Tensor or variable.
Returns:
A bool tensor.
"""
return math_ops.not_equal(x, y)
@tf_export('keras.backend.greater')
def greater(x, y):
"""Element-wise truth value of (x > y).
Arguments:
x: Tensor or variable.
y: Tensor or variable.
Returns:
A bool tensor.
"""
return math_ops.greater(x, y)
@tf_export('keras.backend.greater_equal')
def greater_equal(x, y):
"""Element-wise truth value of (x >= y).
Arguments:
x: Tensor or variable.
y: Tensor or variable.
Returns:
A bool tensor.
"""
return math_ops.greater_equal(x, y)
@tf_export('keras.backend.less')
def less(x, y):
"""Element-wise truth value of (x < y).
Arguments:
x: Tensor or variable.
y: Tensor or variable.
Returns:
A bool tensor.
"""
return math_ops.less(x, y)
@tf_export('keras.backend.less_equal')
def less_equal(x, y):
"""Element-wise truth value of (x <= y).
Arguments:
x: Tensor or variable.
y: Tensor or variable.
Returns:
A bool tensor.
"""
return math_ops.less_equal(x, y)
@tf_export('keras.backend.maximum')
def maximum(x, y):
"""Element-wise maximum of two tensors.
Arguments:
x: Tensor or variable.
y: Tensor or variable.
Returns:
A tensor.
"""
return math_ops.maximum(x, y)
@tf_export('keras.backend.minimum')
def minimum(x, y):
"""Element-wise minimum of two tensors.
Arguments:
x: Tensor or variable.
y: Tensor or variable.
Returns:
A tensor.
"""
return math_ops.minimum(x, y)
@tf_export('keras.backend.sin')
def sin(x):
"""Computes sin of x element-wise.
Arguments:
x: Tensor or variable.
Returns:
A tensor.
"""
return math_ops.sin(x)
@tf_export('keras.backend.cos')
def cos(x):
"""Computes cos of x element-wise.
Arguments:
x: Tensor or variable.
Returns:
A tensor.
"""
return math_ops.cos(x)
def _regular_normalize_batch_in_training(x,
gamma,
beta,
reduction_axes,
epsilon=1e-3):
"""Non-fused version of `normalize_batch_in_training`.
Arguments:
x: Input tensor or variable.
gamma: Tensor by which to scale the input.
beta: Tensor with which to center the input.
reduction_axes: iterable of integers,
axes over which to normalize.
epsilon: Fuzz factor.
Returns:
A tuple length of 3, `(normalized_tensor, mean, variance)`.
"""
mean, var = nn.moments(x, reduction_axes, None, None, False)
normed = nn.batch_normalization(x, mean, var, beta, gamma, epsilon)
return normed, mean, var
def _broadcast_normalize_batch_in_training(x,
gamma,
beta,
reduction_axes,
epsilon=1e-3):
"""Non-fused, broadcast version of `normalize_batch_in_training`.
Arguments:
x: Input tensor or variable.
gamma: Tensor by which to scale the input.
beta: Tensor with which to center the input.
reduction_axes: iterable of integers,
axes over which to normalize.
epsilon: Fuzz factor.
Returns:
A tuple length of 3, `(normalized_tensor, mean, variance)`.
"""
mean, var = nn.moments(x, reduction_axes, None, None, False)
target_shape = []
for axis in range(ndim(x)):
if axis in reduction_axes:
target_shape.append(1)
else:
target_shape.append(array_ops.shape(x)[axis])
target_shape = array_ops.stack(target_shape)
broadcast_mean = array_ops.reshape(mean, target_shape)
broadcast_var = array_ops.reshape(var, target_shape)
if gamma is None:
broadcast_gamma = None
else:
broadcast_gamma = array_ops.reshape(gamma, target_shape)
if beta is None:
broadcast_beta = None
else:
broadcast_beta = array_ops.reshape(beta, target_shape)
normed = nn.batch_normalization(x, broadcast_mean, broadcast_var,
broadcast_beta, broadcast_gamma, epsilon)
return normed, mean, var
def _fused_normalize_batch_in_training(x,
gamma,
beta,
reduction_axes,
epsilon=1e-3):
"""Fused version of `normalize_batch_in_training`.
Arguments:
x: Input tensor or variable.
gamma: Tensor by which to scale the input.
beta: Tensor with which to center the input.
reduction_axes: iterable of integers,
axes over which to normalize.
epsilon: Fuzz factor.
Returns:
A tuple length of 3, `(normalized_tensor, mean, variance)`.
"""
if list(reduction_axes) == [0, 1, 2]:
normalization_axis = 3
tf_data_format = 'NHWC'
else:
normalization_axis = 1
tf_data_format = 'NCHW'
if gamma is None:
gamma = constant_op.constant(
1.0, dtype=x.dtype, shape=[x.shape[normalization_axis]])
if beta is None:
beta = constant_op.constant(
0.0, dtype=x.dtype, shape=[x.shape[normalization_axis]])
return nn.fused_batch_norm(
x, gamma, beta, epsilon=epsilon, data_format=tf_data_format)
@tf_export('keras.backend.normalize_batch_in_training')
def normalize_batch_in_training(x, gamma, beta, reduction_axes, epsilon=1e-3):
"""Computes mean and std for batch then apply batch_normalization on batch.
Arguments:
x: Input tensor or variable.
gamma: Tensor by which to scale the input.
beta: Tensor with which to center the input.
reduction_axes: iterable of integers,
axes over which to normalize.
epsilon: Fuzz factor.
Returns:
A tuple length of 3, `(normalized_tensor, mean, variance)`.
"""
if ndim(x) == 4 and list(reduction_axes) in [[0, 1, 2], [0, 2, 3]]:
if not _has_nchw_support() and list(reduction_axes) == [0, 2, 3]:
return _broadcast_normalize_batch_in_training(
x, gamma, beta, reduction_axes, epsilon=epsilon)
return _fused_normalize_batch_in_training(
x, gamma, beta, reduction_axes, epsilon=epsilon)
else:
if sorted(reduction_axes) == list(range(ndim(x)))[:-1]:
return _regular_normalize_batch_in_training(
x, gamma, beta, reduction_axes, epsilon=epsilon)
else:
return _broadcast_normalize_batch_in_training(
x, gamma, beta, reduction_axes, epsilon=epsilon)
@tf_export('keras.backend.batch_normalization')
def batch_normalization(x, mean, var, beta, gamma, axis=-1, epsilon=1e-3):
"""Applies batch normalization on x given mean, var, beta and gamma.
I.e. returns:
`output = (x - mean) / (sqrt(var) + epsilon) * gamma + beta`
Arguments:
x: Input tensor or variable.
mean: Mean of batch.
var: Variance of batch.
beta: Tensor with which to center the input.
gamma: Tensor by which to scale the input.
axis: Integer, the axis that should be normalized.
(typically the features axis).
epsilon: Fuzz factor.
Returns:
A tensor.
"""
if ndim(x) == 4:
# The CPU implementation of `fused_batch_norm` only supports NHWC
if axis == 1 or axis == -3:
tf_data_format = 'NCHW'
elif axis == 3 or axis == -1:
tf_data_format = 'NHWC'
else:
tf_data_format = None
if (tf_data_format == 'NHWC' or
tf_data_format == 'NCHW' and _has_nchw_support()):
# The mean / var / beta / gamma tensors may be broadcasted
# so they may have extra axes of size 1, which should be squeezed.
if ndim(mean) > 1:
mean = array_ops.reshape(mean, [-1])
if ndim(var) > 1:
var = array_ops.reshape(var, [-1])
if beta is None:
beta = zeros_like(mean)
elif ndim(beta) > 1:
beta = array_ops.reshape(beta, [-1])
if gamma is None:
gamma = ones_like(mean)
elif ndim(gamma) > 1:
gamma = array_ops.reshape(gamma, [-1])
y, _, _ = nn.fused_batch_norm(
x,
gamma,
beta,
epsilon=epsilon,
mean=mean,
variance=var,
data_format=tf_data_format,
is_training=False
)
return y
return nn.batch_normalization(x, mean, var, beta, gamma, epsilon)
# SHAPE OPERATIONS
@tf_export('keras.backend.concatenate')
def concatenate(tensors, axis=-1):
"""Concatenates a list of tensors alongside the specified axis.
Arguments:
tensors: list of tensors to concatenate.
axis: concatenation axis.
Returns:
A tensor.
"""
if axis < 0:
rank = ndim(tensors[0])
if rank:
axis %= rank
else:
axis = 0
if py_all(is_sparse(x) for x in tensors):
return sparse_ops.sparse_concat(axis, tensors)
else:
return array_ops.concat([to_dense(x) for x in tensors], axis)
@tf_export('keras.backend.reshape')
def reshape(x, shape):
"""Reshapes a tensor to the specified shape.
Arguments:
x: Tensor or variable.
shape: Target shape tuple.
Returns:
A tensor.
"""
return array_ops.reshape(x, shape)
@tf_export('keras.backend.permute_dimensions')
def permute_dimensions(x, pattern):
"""Permutes axes in a tensor.
Arguments:
x: Tensor or variable.
pattern: A tuple of
dimension indices, e.g. `(0, 2, 1)`.
Returns:
A tensor.
"""
return array_ops.transpose(x, perm=pattern)
@tf_export('keras.backend.resize_images')
def resize_images(x, height_factor, width_factor, data_format,
interpolation='nearest'):
"""Resizes the images contained in a 4D tensor.
Arguments:
x: Tensor or variable to resize.
height_factor: Positive integer.
width_factor: Positive integer.
data_format: One of `"channels_first"`, `"channels_last"`.
interpolation: A string, one of `nearest` or `bilinear`.
Returns:
A tensor.
Raises:
ValueError: in case of incorrect value for
`data_format` or `interpolation`.
"""
if data_format == 'channels_first':
rows, cols = 2, 3
elif data_format == 'channels_last':
rows, cols = 1, 2
else:
raise ValueError('Invalid `data_format` argument: %s' % (data_format,))
original_shape = int_shape(x)
new_shape = array_ops.shape(x)[rows:cols + 1]
new_shape *= constant_op.constant(
np.array([height_factor, width_factor], dtype='int32'))
if data_format == 'channels_first':
x = permute_dimensions(x, [0, 2, 3, 1])
if interpolation == 'nearest':
x = image_ops.resize_nearest_neighbor(x, new_shape)
elif interpolation == 'bilinear':
x = image_ops.resize_bilinear(x, new_shape)
else:
raise ValueError('interpolation should be one '
'of "nearest" or "bilinear".')
if data_format == 'channels_first':
x = permute_dimensions(x, [0, 3, 1, 2])
if original_shape[rows] is None:
new_height = None
else:
new_height = original_shape[rows] * height_factor
if original_shape[cols] is None:
new_width = None
else:
new_width = original_shape[cols] * width_factor
if data_format == 'channels_first':
output_shape = (None, None, new_height, new_width)
else:
output_shape = (None, new_height, new_width, None)
x.set_shape(output_shape)
return x
@tf_export('keras.backend.resize_volumes')
def resize_volumes(x, depth_factor, height_factor, width_factor, data_format):
"""Resizes the volume contained in a 5D tensor.
Arguments:
x: Tensor or variable to resize.
depth_factor: Positive integer.
height_factor: Positive integer.
width_factor: Positive integer.
data_format: One of `"channels_first"`, `"channels_last"`.
Returns:
A tensor.
Raises:
ValueError: if `data_format` is neither
`channels_last` or `channels_first`.
"""
if data_format == 'channels_first':
output = repeat_elements(x, depth_factor, axis=2)
output = repeat_elements(output, height_factor, axis=3)
output = repeat_elements(output, width_factor, axis=4)
return output
elif data_format == 'channels_last':
output = repeat_elements(x, depth_factor, axis=1)
output = repeat_elements(output, height_factor, axis=2)
output = repeat_elements(output, width_factor, axis=3)
return output
else:
raise ValueError('Invalid data_format: ' + str(data_format))
@tf_export('keras.backend.repeat_elements')
def repeat_elements(x, rep, axis):
"""Repeats the elements of a tensor along an axis, like `np.repeat`.
If `x` has shape `(s1, s2, s3)` and `axis` is `1`, the output
will have shape `(s1, s2 * rep, s3)`.
Arguments:
x: Tensor or variable.
rep: Python integer, number of times to repeat.
axis: Axis along which to repeat.
Returns:
A tensor.
"""
x_shape = x.shape.as_list()
# For static axis
if x_shape[axis] is not None:
# slices along the repeat axis
splits = array_ops.split(value=x,
num_or_size_splits=x_shape[axis],
axis=axis)
# repeat each slice the given number of reps
x_rep = [s for s in splits for _ in range(rep)]
return concatenate(x_rep, axis)
# Here we use tf.tile to mimic behavior of np.repeat so that
# we can handle dynamic shapes (that include None).
# To do that, we need an auxiliary axis to repeat elements along
# it and then merge them along the desired axis.
# Repeating
auxiliary_axis = axis + 1
x_shape = array_ops.shape(x)
x_rep = array_ops.expand_dims(x, axis=auxiliary_axis)
reps = np.ones(len(x.shape) + 1)
reps[auxiliary_axis] = rep
x_rep = array_ops.tile(x_rep, reps)
# Merging
reps = np.delete(reps, auxiliary_axis)
reps[axis] = rep
reps = array_ops.constant(reps, dtype='int32')
x_shape *= reps
x_rep = array_ops.reshape(x_rep, x_shape)
# Fix shape representation
x_shape = x.shape.as_list()
x_rep.set_shape(x_shape)
x_rep._keras_shape = tuple(x_shape)
return x_rep
@tf_export('keras.backend.repeat')
def repeat(x, n):
"""Repeats a 2D tensor.
if `x` has shape (samples, dim) and `n` is `2`,
the output will have shape `(samples, 2, dim)`.
Arguments:
x: Tensor or variable.
n: Python integer, number of times to repeat.
Returns:
A tensor.
"""
assert ndim(x) == 2
x = array_ops.expand_dims(x, 1)
pattern = array_ops.stack([1, n, 1])
return array_ops.tile(x, pattern)
@tf_export('keras.backend.arange')
def arange(start, stop=None, step=1, dtype='int32'):
"""Creates a 1D tensor containing a sequence of integers.
The function arguments use the same convention as
Theano's arange: if only one argument is provided,
it is in fact the "stop" argument and "start" is 0.
The default type of the returned tensor is `'int32'` to
match TensorFlow's default.
Arguments:
start: Start value.
stop: Stop value.
step: Difference between two successive values.
dtype: Integer dtype to use.
Returns:
An integer tensor.
"""
# Match the behavior of numpy and Theano by returning an empty sequence.
if stop is None and start < 0:
start = 0
result = math_ops.range(start, limit=stop, delta=step, name='arange')
if dtype != 'int32':
result = cast(result, dtype)
return result
@tf_export('keras.backend.tile')
def tile(x, n):
"""Creates a tensor by tiling `x` by `n`.
Arguments:
x: A tensor or variable
n: A list of integer. The length must be the same as the number of
dimensions in `x`.
Returns:
A tiled tensor.
"""
if isinstance(n, int):
n = [n]
return array_ops.tile(x, n)
@tf_export('keras.backend.flatten')
def flatten(x):
"""Flatten a tensor.
Arguments:
x: A tensor or variable.
Returns:
A tensor, reshaped into 1-D
"""
return array_ops.reshape(x, [-1])
@tf_export('keras.backend.batch_flatten')
def batch_flatten(x):
"""Turn a nD tensor into a 2D tensor with same 0th dimension.
In other words, it flattens each data samples of a batch.
Arguments:
x: A tensor or variable.
Returns:
A tensor.
"""
x = array_ops.reshape(x, array_ops.stack([-1, prod(shape(x)[1:])]))
return x
@tf_export('keras.backend.expand_dims')
def expand_dims(x, axis=-1):
"""Adds a 1-sized dimension at index "axis".
Arguments:
x: A tensor or variable.
axis: Position where to add a new axis.
Returns:
A tensor with expanded dimensions.
"""
return array_ops.expand_dims(x, axis)
@tf_export('keras.backend.squeeze')
def squeeze(x, axis):
"""Removes a 1-dimension from the tensor at index "axis".
Arguments:
x: A tensor or variable.
axis: Axis to drop.
Returns:
A tensor with the same data as `x` but reduced dimensions.
"""
return array_ops.squeeze(x, [axis])
@tf_export('keras.backend.temporal_padding')
def temporal_padding(x, padding=(1, 1)):
"""Pads the middle dimension of a 3D tensor.
Arguments:
x: Tensor or variable.
padding: Tuple of 2 integers, how many zeros to
add at the start and end of dim 1.
Returns:
A padded 3D tensor.
"""
assert len(padding) == 2
pattern = [[0, 0], [padding[0], padding[1]], [0, 0]]
return array_ops.pad(x, pattern)
@tf_export('keras.backend.spatial_2d_padding')
def spatial_2d_padding(x, padding=((1, 1), (1, 1)), data_format=None):
"""Pads the 2nd and 3rd dimensions of a 4D tensor.
Arguments:
x: Tensor or variable.
padding: Tuple of 2 tuples, padding pattern.
data_format: One of `channels_last` or `channels_first`.
Returns:
A padded 4D tensor.
Raises:
ValueError: if `data_format` is neither
`channels_last` or `channels_first`.
"""
assert len(padding) == 2
assert len(padding[0]) == 2
assert len(padding[1]) == 2
if data_format is None:
data_format = image_data_format()
if data_format not in {'channels_first', 'channels_last'}:
raise ValueError('Unknown data_format: ' + str(data_format))
if data_format == 'channels_first':
pattern = [[0, 0], [0, 0], list(padding[0]), list(padding[1])]
else:
pattern = [[0, 0], list(padding[0]), list(padding[1]), [0, 0]]
return array_ops.pad(x, pattern)
@tf_export('keras.backend.spatial_3d_padding')
def spatial_3d_padding(x, padding=((1, 1), (1, 1), (1, 1)), data_format=None):
"""Pads 5D tensor with zeros along the depth, height, width dimensions.
Pads these dimensions with respectively
"padding[0]", "padding[1]" and "padding[2]" zeros left and right.
For 'channels_last' data_format,
the 2nd, 3rd and 4th dimension will be padded.
For 'channels_first' data_format,
the 3rd, 4th and 5th dimension will be padded.
Arguments:
x: Tensor or variable.
padding: Tuple of 3 tuples, padding pattern.
data_format: One of `channels_last` or `channels_first`.
Returns:
A padded 5D tensor.
Raises:
ValueError: if `data_format` is neither
`channels_last` or `channels_first`.
"""
assert len(padding) == 3
assert len(padding[0]) == 2
assert len(padding[1]) == 2
assert len(padding[2]) == 2
if data_format is None:
data_format = image_data_format()
if data_format not in {'channels_first', 'channels_last'}:
raise ValueError('Unknown data_format: ' + str(data_format))
if data_format == 'channels_first':
pattern = [[0, 0], [0, 0], [padding[0][0], padding[0][1]],
[padding[1][0], padding[1][1]], [padding[2][0], padding[2][1]]]
else:
pattern = [[0, 0], [padding[0][0], padding[0][1]],
[padding[1][0], padding[1][1]], [padding[2][0],
padding[2][1]], [0, 0]]
return array_ops.pad(x, pattern)
@tf_export('keras.backend.stack')
def stack(x, axis=0):
"""Stacks a list of rank `R` tensors into a rank `R+1` tensor.
Arguments:
x: List of tensors.
axis: Axis along which to perform stacking.
Returns:
A tensor.
"""
return array_ops.stack(x, axis=axis)
@tf_export('keras.backend.one_hot')
def one_hot(indices, num_classes):
"""Computes the one-hot representation of an integer tensor.
Arguments:
indices: nD integer tensor of shape
`(batch_size, dim1, dim2, ... dim(n-1))`
num_classes: Integer, number of classes to consider.
Returns:
(n + 1)D one hot representation of the input
with shape `(batch_size, dim1, dim2, ... dim(n-1), num_classes)`
Returns:
The one-hot tensor.
"""
return array_ops.one_hot(indices, depth=num_classes, axis=-1)
@tf_export('keras.backend.reverse')
def reverse(x, axes):
"""Reverse a tensor along the specified axes.
Arguments:
x: Tensor to reverse.
axes: Integer or iterable of integers.
Axes to reverse.
Returns:
A tensor.
"""
if isinstance(axes, int):
axes = [axes]
return array_ops.reverse(x, axes)
# VALUE MANIPULATION
@tf_export('keras.backend.get_value')
def get_value(x):
"""Returns the value of a variable.
Arguments:
x: input variable.
Returns:
A Numpy array.
Raises:
RuntimeError: If this method is called inside defun.
"""
if context.executing_eagerly():
return x.numpy()
elif ops.inside_function():
raise RuntimeError('Cannot get value inside Tensorflow graph function.')
return x.eval(session=get_session())
@tf_export('keras.backend.batch_get_value')
def batch_get_value(tensors):
"""Returns the value of more than one tensor variable.
Arguments:
tensors: list of ops to run.
Returns:
A list of Numpy arrays.
Raises:
RuntimeError: If this method is called inside defun.
"""
if context.executing_eagerly():
return [x.numpy() for x in tensors]
elif ops.inside_function(): # pylint: disable=protected-access
raise RuntimeError('Cannot get value inside Tensorflow graph function.')
if tensors:
return get_session().run(tensors)
else:
return []
@tf_export('keras.backend.set_value')
def set_value(x, value):
"""Sets the value of a variable, from a Numpy array.
Arguments:
x: Tensor to set to a new value.
value: Value to set the tensor to, as a Numpy array
(of the same shape).
"""
value = np.asarray(value, dtype=dtype(x))
if ops.executing_eagerly_outside_functions():
x.assign(value)
else:
with get_graph().as_default():
tf_dtype = dtypes_module.as_dtype(x.dtype.name.split('_')[0])
if hasattr(x, '_assign_placeholder'):
assign_placeholder = x._assign_placeholder
assign_op = x._assign_op
else:
assign_placeholder = array_ops.placeholder(tf_dtype, shape=value.shape)
assign_op = x.assign(assign_placeholder)
x._assign_placeholder = assign_placeholder
x._assign_op = assign_op
get_session().run(assign_op, feed_dict={assign_placeholder: value})
@tf_export('keras.backend.batch_set_value')
def batch_set_value(tuples):
"""Sets the values of many tensor variables at once.
Arguments:
tuples: a list of tuples `(tensor, value)`.
`value` should be a Numpy array.
"""
if ops.executing_eagerly_outside_functions():
for x, value in tuples:
x.assign(np.asarray(value, dtype=dtype(x)))
else:
with get_graph().as_default():
if tuples:
assign_ops = []
feed_dict = {}
for x, value in tuples:
value = np.asarray(value, dtype=dtype(x))
tf_dtype = dtypes_module.as_dtype(x.dtype.name.split('_')[0])
if hasattr(x, '_assign_placeholder'):
assign_placeholder = x._assign_placeholder
assign_op = x._assign_op
else:
assign_placeholder = array_ops.placeholder(tf_dtype,
shape=value.shape)
assign_op = x.assign(assign_placeholder)
x._assign_placeholder = assign_placeholder
x._assign_op = assign_op
assign_ops.append(assign_op)
feed_dict[assign_placeholder] = value
get_session().run(assign_ops, feed_dict=feed_dict)
@tf_export('keras.backend.print_tensor')
def print_tensor(x, message=''):
"""Prints `message` and the tensor value when evaluated.
Note that `print_tensor` returns a new tensor identical to `x`
which should be used in the following code. Otherwise the
print operation is not taken into account during evaluation.
Example:
```python
>>> x = K.print_tensor(x, message="x is: ")
```
Arguments:
x: Tensor to print.
message: Message to print jointly with the tensor.
Returns:
The same tensor `x`, unchanged.
"""
return logging_ops.Print(x, [x], message)
# GRAPH MANIPULATION
class GraphExecutionFunction(object):
"""Runs a computation graph.
It's possible to pass arguments to `tf.Session.run()` via `session_kwargs`.
In particular additional operations via `fetches` argument and additional
tensor substitutions via `feed_dict` arguments. Note that given
substitutions are merged with substitutions from `inputs`. Even though
`feed_dict` is passed once in the constructor (called in `model.compile()`)
we can modify the values in the dictionary. Through this feed_dict we can
provide additional substitutions besides Keras inputs.
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.
session_kwargs: Arguments to `tf.Session.run()`:
`fetches`, `feed_dict`, `options`, `run_metadata`.
"""
def __init__(self, inputs, outputs, updates=None, name=None,
**session_kwargs):
updates = updates or []
if not isinstance(updates, (list, tuple)):
raise TypeError('`updates` in a Keras backend function '
'should be a list or tuple.')
self.inputs = nest.flatten(inputs)
self._outputs_structure = outputs
self.outputs = nest.flatten(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
# additional tensor substitutions
self.feed_dict = session_kwargs.pop('feed_dict', None)
# additional operations
self.fetches = session_kwargs.pop('fetches', [])
if not isinstance(self.fetches, list):
self.fetches = [self.fetches]
self.run_options = session_kwargs.pop('options', None)
self.run_metadata = session_kwargs.pop('run_metadata', None)
# The main use case of `fetches` being passed to a model is the ability
# to run custom updates
# This requires us to wrap fetches in `identity` ops.
self.fetches = [array_ops.identity(x) for x in self.fetches]
self.session_kwargs = session_kwargs
# This mapping keeps track of the function that should receive the
# output from a fetch in `fetches`: { fetch: function(fetch_output) }
# A Callback can use this to register a function with access to the
# output values for a fetch it added.
self.fetch_callbacks = dict()
if session_kwargs:
raise ValueError('Some keys in session_kwargs are not supported at this '
'time: %s' % (session_kwargs.keys(),))
self._callable_fn = None
self._feed_arrays = None
self._feed_symbols = None
self._symbol_vals = None
self._fetches = None
self._session = None
def _make_callable(self, feed_arrays, feed_symbols, symbol_vals, session):
"""Generates a callable that runs the graph.
Arguments:
feed_arrays: List of input tensors to be fed Numpy arrays at runtime.
feed_symbols: List of input tensors to be fed symbolic tensors at runtime.
symbol_vals: List of symbolic tensors to be fed to `feed_symbols`.
session: Session to use to generate the callable.
Returns:
Function that runs the graph according to the above options.
"""
# Prepare callable options.
callable_opts = config_pb2.CallableOptions()
# Handle external-data feed.
for x in feed_arrays:
callable_opts.feed.append(x.name)
if self.feed_dict:
for key in sorted(self.feed_dict.keys()):
callable_opts.feed.append(key.name)
# Handle symbolic feed.
for x, y in zip(feed_symbols, symbol_vals):
connection = callable_opts.tensor_connection.add()
if x.dtype != y.dtype:
y = math_ops.cast(y, dtype=x.dtype)
from_tensor = ops._as_graph_element(y)
if from_tensor is None:
from_tensor = y
connection.from_tensor = from_tensor.name # Data tensor
connection.to_tensor = x.name # Placeholder
# Handle fetches.
for x in self.outputs + self.fetches:
callable_opts.fetch.append(x.name)
# Handle updates.
callable_opts.target.append(self.updates_op.name)
# Handle run_options.
if self.run_options:
callable_opts.run_options.CopyFrom(self.run_options)
# Create callable.
callable_fn = session._make_callable_from_options(callable_opts)
# Cache parameters corresponding to the generated callable, so that
# we can detect future mismatches and refresh the callable.
self._callable_fn = callable_fn
self._feed_arrays = feed_arrays
self._feed_symbols = feed_symbols
self._symbol_vals = symbol_vals
self._fetches = list(self.fetches)
self._session = session
def _call_fetch_callbacks(self, fetches_output):
for fetch, output in zip(self._fetches, fetches_output):
if fetch in self.fetch_callbacks:
self.fetch_callbacks[fetch](output)
def __call__(self, inputs):
inputs = nest.flatten(inputs)
session = get_session()
feed_arrays = []
array_vals = []
feed_symbols = []
symbol_vals = []
for tensor, value in zip(self.inputs, inputs):
if value is None:
continue
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)
if tensor_util.is_tensor(value):
# Case: feeding symbolic tensor.
feed_symbols.append(tensor)
symbol_vals.append(value)
else:
# Case: feeding Numpy array.
feed_arrays.append(tensor)
# We need to do array conversion and type casting at this level, since
# `callable_fn` only supports exact matches.
tensor_type = dtypes_module.as_dtype(tensor.dtype)
array_vals.append(np.asarray(value,
dtype=tensor_type.as_numpy_dtype))
if self.feed_dict:
for key in sorted(self.feed_dict.keys()):
array_vals.append(
np.asarray(self.feed_dict[key], dtype=key.dtype.base_dtype.name))
# Refresh callable if anything has changed.
if (self._callable_fn is None or feed_arrays != self._feed_arrays or
symbol_vals != self._symbol_vals or
feed_symbols != self._feed_symbols or self.fetches != self._fetches or
session != self._session):
self._make_callable(feed_arrays, feed_symbols, symbol_vals, session)
fetched = self._callable_fn(*array_vals,
run_metadata=self.run_metadata)
self._call_fetch_callbacks(fetched[-len(self._fetches):])
return nest.pack_sequence_as(self._outputs_structure,
fetched[:len(self.outputs)])
class EagerExecutionFunction(object):
"""Helper class for constructing a TF graph function from the Keras 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.
session_kwargs: Unsupported.
"""
def __init__(self, inputs, outputs, updates=None, name=None):
updates = updates or []
if not isinstance(updates, (list, tuple)):
raise TypeError('`updates` in a Keras backend function '
'should be a list or tuple.')
self.inputs = nest.flatten(inputs)
self._outputs_structure = outputs
self.outputs = nest.flatten(outputs)
self.name = name
graph = get_graph()
# Consolidate updates
with graph.as_default():
with ops.control_dependencies(self.outputs):
# In general, updates should be run after the outputs have been
# computed. However, we can only ensure this when we create
# the updates here (i.e. when updates are passed as tuples).
# We cannot modify the control dependencies of preexisting update ops.
updates_ops = []
for update in updates:
# For legacy reasons it is allowed to pass an update as a tuple
# `(variable, new_value)` (this maps to an assign op).
if isinstance(update, tuple):
p, new_p = update
updates_ops.append(state_ops.assign(p, new_p))
else:
# Assumed already an op -- we cannot control its execution order.
updates_ops.append(update)
# We set the update ops to run at the end by conditioning it on output[0]
if updates and not self.outputs:
# Edge case; never happens in practice
raise ValueError('Cannot create a Keras backend function with updates'
' but no outputs during eager execution.')
with ops.control_dependencies(updates_ops):
self.outputs[0] = array_ops.identity(self.outputs[0])
# Prepare graph function
# TODO(fchollet): can we restrict `captures` to variables actually used in
# the relevant subgraph?
graph.inputs = self.inputs + list(graph.captures.values())
graph.outputs = self.outputs
graph_fn = eager_function.Function(graph)
graph_fn._num_positional_args = len(self.inputs)
graph_fn._arg_keywords = []
self._graph_fn = graph_fn
# Handle placeholders with default
# (treated as required placeholder by graph functions)
self._placeholder_default_values = {}
with graph.as_default():
for x in self.inputs:
if x.op.type == 'PlaceholderWithDefault':
self._placeholder_default_values[x] = tensor_util.constant_value(
x.op.inputs[0])
def __call__(self, inputs):
inputs = nest.flatten(inputs)
converted_inputs = []
for tensor, value in zip(self.inputs, inputs):
if value is None:
# Assume `value` is a placeholder with default
value = self._placeholder_default_values.get(tensor, None)
if value is None:
raise ValueError(
'You must feed a value for placeholder %s' % (tensor,))
if not isinstance(value, ops.Tensor):
value = ops.convert_to_tensor(value, dtype=tensor.dtype)
if value.dtype != tensor.dtype:
# Temporary workaround due to `convert_to_tensor` not casting floats.
# See b/119637405
value = math_ops.cast(value, tensor.dtype)
converted_inputs.append(value)
outputs = self._graph_fn(*converted_inputs)
return nest.pack_sequence_as(self._outputs_structure,
[x.numpy() for x in outputs])
@tf_export('keras.backend.function')
def function(inputs, outputs, updates=None, name=None, **kwargs):
"""Instantiates a Keras function.
Arguments:
inputs: List of placeholder tensors.
outputs: List of output tensors.
updates: List of update ops.
name: String, name of function.
**kwargs: Passed to `tf.Session.run`.
Returns:
Output values as Numpy arrays.
Raises:
ValueError: if invalid kwargs are passed in or if in eager execution.
"""
if ops.executing_eagerly_outside_functions():
if kwargs:
raise ValueError('Session keyword arguments are not support during '
'eager execution. You passed: %s' % (kwargs,))
return EagerExecutionFunction(inputs, outputs, updates=updates, name=name)
if kwargs:
for key in kwargs:
if (key not in tf_inspect.getfullargspec(session_module.Session.run)[0]
and key not in ['inputs', 'outputs', 'updates', 'name']):
msg = ('Invalid argument "%s" passed to K.function with TensorFlow '
'backend') % key
raise ValueError(msg)
return GraphExecutionFunction(inputs, outputs, updates=updates, **kwargs)
@tf_export('keras.backend.gradients')
def gradients(loss, variables):
"""Returns the gradients of `loss` w.r.t. `variables`.
Arguments:
loss: Scalar tensor to minimize.
variables: List of variables.
Returns:
A gradients tensor.
"""
return gradients_module.gradients(
loss, variables, colocate_gradients_with_ops=True)
@tf_export('keras.backend.stop_gradient')
def stop_gradient(variables):
"""Returns `variables` but with zero gradient w.r.t. every other variable.
Arguments:
variables: Tensor or list of tensors to consider constant with respect
to any other variable.
Returns:
A single tensor or a list of tensors (depending on the passed argument)
that has no gradient with respect to any other variable.
"""
if isinstance(variables, (list, tuple)):
return map(array_ops.stop_gradient, variables)
return array_ops.stop_gradient(variables)
# CONTROL FLOW
@tf_export('keras.backend.rnn')
def rnn(step_function,
inputs,
initial_states,
go_backwards=False,
mask=None,
constants=None,
unroll=False,
input_length=None,
time_major=False,
zero_output_for_mask=False):
"""Iterates over the time dimension of a tensor.
Arguments:
step_function: RNN step function.
Args;
input; Tensor with shape `(samples, ...)` (no time dimension),
representing input for the batch of samples at a certain
time step.
states; List of tensors.
Returns;
output; Tensor with shape `(samples, output_dim)`
(no time dimension).
new_states; List of tensors, same length and shapes
as 'states'. The first state in the list must be the
output tensor at the previous timestep.
inputs: Tensor of temporal data of shape `(samples, time, ...)`
(at least 3D), or nested tensors, and each of which has shape
`(samples, time, ...)`.
initial_states: Tensor with shape `(samples, state_size)`
(no time dimension), containing the initial values for the states used
in the step function. In the case that state_size is in a nested
shape, the shape of initial_states will also follow the nested
structure.
go_backwards: Boolean. If True, do the iteration over the time
dimension in reverse order and return the reversed sequence.
mask: Binary tensor with shape `(samples, time, 1)`,
with a zero for every element that is masked.
constants: List of constant values passed at each step.
unroll: Whether to unroll the RNN or to use a symbolic `while_loop`.
input_length: If specified, assume time dimension is of this length.
time_major: Boolean. If true, the inputs and outputs will be in shape
`(timesteps, batch, ...)`, whereas in the False case, it will be
`(batch, timesteps, ...)`. Using `time_major = True` is a bit more
efficient because it avoids transposes at the beginning and end of the
RNN calculation. However, most TensorFlow data is batch-major, so by
default this function accepts input and emits output in batch-major
form.
zero_output_for_mask: Boolean. If True, the output for masked timestep
will be zeros, whereas in the False case, output from previous
timestep is returned.
Returns:
A tuple, `(last_output, outputs, new_states)`.
last_output: the latest output of the rnn, of shape `(samples, ...)`
outputs: tensor with shape `(samples, time, ...)` where each
entry `outputs[s, t]` is the output of the step function
at time `t` for sample `s`.
new_states: list of tensors, latest states returned by
the step function, of shape `(samples, ...)`.
Raises:
ValueError: if input dimension is less than 3.
ValueError: if `unroll` is `True` but input timestep is not a fixed
number.
ValueError: if `mask` is provided (not `None`) but states is not provided
(`len(states)` == 0).
"""
def swap_batch_timestep(input_t):
# Swap the batch and timestep dim for the incoming tensor.
axes = list(range(len(input_t.shape)))
axes[0], axes[1] = 1, 0
return array_ops.transpose(input_t, axes)
if not time_major:
inputs = nest.map_structure(swap_batch_timestep, inputs)
flatted_inputs = nest.flatten(inputs)
time_steps = flatted_inputs[0].shape[0]
batch = flatted_inputs[0].shape[1]
time_steps_t = array_ops.shape(flatted_inputs[0])[0]
for input_ in flatted_inputs:
input_.get_shape().with_rank_at_least(3)
if mask is not None:
if mask.dtype != dtypes_module.bool:
mask = math_ops.cast(mask, dtypes_module.bool)
if len(mask.shape) == 2:
mask = expand_dims(mask)
if not time_major:
mask = swap_batch_timestep(mask)
if constants is None:
constants = []
# tf.where needs its condition tensor to be the same shape as its two
# result tensors, but in our case the condition (mask) tensor is
# (nsamples, 1), and inputs are (nsamples, ndimensions) or even more.
# So we need to broadcast the mask to match the shape of inputs.
# That's what the tile call does, it just repeats the mask along its
# second dimension n times.
def _expand_mask(mask_t, input_t, fixed_dim=1):
assert not nest.is_sequence(mask_t)
assert not nest.is_sequence(input_t)
rank_diff = len(input_t.shape) - len(mask_t.shape)
for _ in range(rank_diff):
mask_t = array_ops.expand_dims(mask_t, -1)
multiples = [1] * fixed_dim + input_t.shape.as_list()[fixed_dim:]
return array_ops.tile(mask_t, multiples)
if unroll:
if not time_steps:
raise ValueError('Unrolling requires a fixed number of timesteps.')
states = initial_states
successive_states = []
successive_outputs = []
# Process the input tensors. The input tensor need to be split on the
# time_step dim, and reverse if go_backwards is True. In the case of nested
# input, the input is flattened and then transformed individually.
# The result of this will be a tuple of lists, each of the item in tuple is
# list of the tensor with shape (batch, feature)
def _process_single_input_t(input_t):
input_t = array_ops.unstack(input_t) # unstack for time_step dim
if go_backwards:
input_t.reverse()
return input_t
if nest.is_sequence(inputs):
processed_input = nest.map_structure(_process_single_input_t, inputs)
else:
processed_input = (_process_single_input_t(inputs),)
def _get_input_tensor(time):
inp = [t_[time] for t_ in processed_input]
return nest.pack_sequence_as(inputs, inp)
if mask is not None:
mask_list = array_ops.unstack(mask)
if go_backwards:
mask_list.reverse()
for i in range(time_steps):
inp = _get_input_tensor(i)
mask_t = mask_list[i]
output, new_states = step_function(inp, states + constants)
tiled_mask_t = _expand_mask(mask_t, output)
if not successive_outputs:
prev_output = zeros_like(output)
else:
prev_output = successive_outputs[-1]
output = array_ops.where(tiled_mask_t, output, prev_output)
return_states = []
for state, new_state in zip(states, new_states):
# (see earlier comment for tile explanation)
tiled_mask_t = _expand_mask(mask_t, new_state)
return_states.append(array_ops.where(tiled_mask_t, new_state, state))
states = return_states
successive_outputs.append(output)
successive_states.append(states)
last_output = successive_outputs[-1]
new_states = successive_states[-1]
outputs = array_ops.stack(successive_outputs)
if zero_output_for_mask:
last_output = array_ops.where(
_expand_mask(mask_list[-1], last_output),
last_output,
zeros_like(last_output))
outputs = array_ops.where(
_expand_mask(mask, outputs, fixed_dim=2),
outputs,
zeros_like(outputs))
else:
for i in range(time_steps):
inp = _get_input_tensor(i)
output, states = step_function(inp, states + constants)
successive_outputs.append(output)
successive_states.append(states)
last_output = successive_outputs[-1]
new_states = successive_states[-1]
outputs = array_ops.stack(successive_outputs)
else:
states = tuple(initial_states)
# Create input tensor array, if the inputs is nested tensors, then it will
# be flattened first, and tensor array will be created one per flattened
# tensor.
input_ta = tuple(
tensor_array_ops.TensorArray(
dtype=inp.dtype,
size=time_steps_t,
tensor_array_name='input_ta_%s' % i)
for i, inp in enumerate(flatted_inputs))
input_ta = tuple(
ta.unstack(input_) if not go_backwards else ta
.unstack(reverse(input_, 0))
for ta, input_ in zip(input_ta, flatted_inputs))
# Get the time(0) input and compute the output for that, the output will be
# used to determine the dtype of output tensor array. Don't read from
# input_ta due to TensorArray clear_after_read default to True.
input_time_zero = nest.pack_sequence_as(inputs,
[inp[0] for inp in flatted_inputs])
# output_time_zero is used to determine the cell output shape and its dtype.
# the value is discarded.
output_time_zero, _ = step_function(input_time_zero,
initial_states + constants)
output_ta = tuple(
tensor_array_ops.TensorArray(
dtype=out.dtype,
size=time_steps_t,
tensor_array_name='output_ta_%s' % i)
for i, out in enumerate(nest.flatten(output_time_zero)))
time = constant_op.constant(0, dtype='int32', name='time')
while_loop_kwargs = {
'cond': lambda time, *_: time < time_steps_t,
'maximum_iterations': input_length,
'parallel_iterations': 32,
'swap_memory': True,
}
if mask is not None:
if not states:
raise ValueError('No initial states provided! '
'When using masking in an RNN, you should '
'provide initial states '
'(and your step function should return '
'as its first state at time `t` '
'the output at time `t-1`).')
if go_backwards:
mask = reverse(mask, 0)
mask_ta = tensor_array_ops.TensorArray(
dtype=dtypes_module.bool,
size=time_steps_t,
tensor_array_name='mask_ta')
mask_ta = mask_ta.unstack(mask)
# Mask for the T output will be base on the output of T - 1. In the case
# T = 0, a zero filled tensor will be used.
flat_zero_output = tuple(array_ops.zeros_like(o)
for o in nest.flatten(output_time_zero))
def _step(time, output_ta_t, prev_output, *states):
"""RNN step function.
Arguments:
time: Current timestep value.
output_ta_t: TensorArray.
prev_output: tuple of outputs from time - 1.
*states: List of states.
Returns:
Tuple: `(time + 1, output_ta_t, output) + tuple(new_states)`
"""
current_input = tuple(ta.read(time) for ta in input_ta)
# maybe set shape.
current_input = nest.pack_sequence_as(inputs, current_input)
mask_t = mask_ta.read(time)
output, new_states = step_function(current_input,
tuple(states) + tuple(constants))
# mask output
flat_output = nest.flatten(output)
flat_mask_output = (flat_zero_output if zero_output_for_mask
else nest.flatten(prev_output))
tiled_mask_t = tuple(_expand_mask(mask_t, o) for o in flat_output)
flat_new_output = tuple(
array_ops.where(m, o, zo) for m, o, zo in zip(
tiled_mask_t, flat_output, flat_mask_output))
# mask states
flat_state = nest.flatten(states)
flat_new_state = nest.flatten(new_states)
for state, new_state in zip(flat_state, flat_new_state):
new_state.set_shape(state.shape)
tiled_mask_t = tuple(_expand_mask(mask_t, s) for s in flat_state)
flat_final_state = tuple(
array_ops.where(m, s, ps)
for m, s, ps in zip(tiled_mask_t, flat_new_state, flat_state))
new_states = nest.pack_sequence_as(new_states, flat_final_state)
output_ta_t = tuple(
ta.write(time, out)
for ta, out in zip(output_ta_t, flat_new_output))
return (time + 1, output_ta_t,
tuple(flat_new_output)) + tuple(new_states)
final_outputs = control_flow_ops.while_loop(
body=_step,
loop_vars=(time, output_ta, flat_zero_output) + states,
**while_loop_kwargs)
# Skip final_outputs[2] which is the output for final timestep.
new_states = final_outputs[3:]
else:
def _step(time, output_ta_t, *states):
"""RNN step function.
Arguments:
time: Current timestep value.
output_ta_t: TensorArray.
*states: List of states.
Returns:
Tuple: `(time + 1,output_ta_t) + tuple(new_states)`
"""
current_input = tuple(ta.read(time) for ta in input_ta)
current_input = nest.pack_sequence_as(inputs, current_input)
output, new_states = step_function(current_input,
tuple(states) + tuple(constants))
flat_state = nest.flatten(states)
flat_new_state = nest.flatten(new_states)
for state, new_state in zip(flat_state, flat_new_state):
new_state.set_shape(state.shape)
flat_output = nest.flatten(output)
output_ta_t = tuple(
ta.write(time, out) for ta, out in zip(output_ta_t, flat_output))
new_states = nest.pack_sequence_as(initial_states, flat_new_state)
return (time + 1, output_ta_t) + tuple(new_states)
final_outputs = control_flow_ops.while_loop(
body=_step,
loop_vars=(time, output_ta) + states,
**while_loop_kwargs)
new_states = final_outputs[2:]
output_ta = final_outputs[1]
outputs = tuple(o.stack() for o in output_ta)
last_output = tuple(o[-1] for o in outputs)
outputs = nest.pack_sequence_as(output_time_zero, outputs)
last_output = nest.pack_sequence_as(output_time_zero, last_output)
# static shape inference
def set_shape(output_):
shape = output_.shape.as_list()
shape[0] = time_steps
shape[1] = batch
output_.set_shape(shape)
return output_
outputs = nest.map_structure(set_shape, outputs)
if not time_major:
outputs = nest.map_structure(swap_batch_timestep, outputs)
return last_output, outputs, new_states
@tf_export('keras.backend.switch')
def switch(condition, then_expression, else_expression):
"""Switches between two operations depending on a scalar value.
Note that both `then_expression` and `else_expression`
should be symbolic tensors of the *same shape*.
Arguments:
condition: tensor (`int` or `bool`).
then_expression: either a tensor, or a callable that returns a tensor.
else_expression: either a tensor, or a callable that returns a tensor.
Returns:
The selected tensor.
Raises:
ValueError: If rank of `condition` is greater than rank of expressions.
"""
if condition.dtype != dtypes_module.bool:
condition = math_ops.cast(condition, 'bool')
cond_ndim = ndim(condition)
if not cond_ndim:
if not callable(then_expression):
def then_expression_fn():
return then_expression
else:
then_expression_fn = then_expression
if not callable(else_expression):
def else_expression_fn():
return else_expression
else:
else_expression_fn = else_expression
x = control_flow_ops.cond(condition, then_expression_fn, else_expression_fn)
else:
# tf.where needs its condition tensor
# to be the same shape as its two
# result tensors
if callable(then_expression):
then_expression = then_expression()
if callable(else_expression):
else_expression = else_expression()
expr_ndim = ndim(then_expression)
if cond_ndim > expr_ndim:
raise ValueError('Rank of `condition` should be less than or'
' equal to rank of `then_expression` and '
'`else_expression`. ndim(condition)=' + str(cond_ndim) +
', ndim(then_expression)'
'=' + str(expr_ndim))
if cond_ndim > 1:
ndim_diff = expr_ndim - cond_ndim
cond_shape = array_ops.concat(
[array_ops.shape(condition), [1] * ndim_diff], axis=0)
condition = array_ops.reshape(condition, cond_shape)
expr_shape = array_ops.shape(then_expression)
shape_diff = expr_shape - cond_shape
tile_shape = array_ops.where(shape_diff > 0, expr_shape,
array_ops.ones_like(expr_shape))
condition = array_ops.tile(condition, tile_shape)
x = array_ops.where(condition, then_expression, else_expression)
return x
@tf_export('keras.backend.in_train_phase')
def in_train_phase(x, alt, training=None):
"""Selects `x` in train phase, and `alt` otherwise.
Note that `alt` should have the *same shape* as `x`.
Arguments:
x: What to return in train phase
(tensor or callable that returns a tensor).
alt: What to return otherwise
(tensor or callable that returns a tensor).
training: Optional scalar tensor
(or Python boolean, or Python integer)
specifying the learning phase.
Returns:
Either `x` or `alt` based on the `training` flag.
the `training` flag defaults to `K.learning_phase()`.
"""
if training is None:
training = learning_phase()
if training == 1 or training is True:
if callable(x):
return x()
else:
return x
elif training == 0 or training is False:
if callable(alt):
return alt()
else:
return alt
# else: assume learning phase is a placeholder tensor.
x = switch(training, x, alt)
return x
@tf_export('keras.backend.in_test_phase')
def in_test_phase(x, alt, training=None):
"""Selects `x` in test phase, and `alt` otherwise.
Note that `alt` should have the *same shape* as `x`.
Arguments:
x: What to return in test phase
(tensor or callable that returns a tensor).
alt: What to return otherwise
(tensor or callable that returns a tensor).
training: Optional scalar tensor
(or Python boolean, or Python integer)
specifying the learning phase.
Returns:
Either `x` or `alt` based on `K.learning_phase`.
"""
return in_train_phase(alt, x, training=training)
# NN OPERATIONS
@tf_export('keras.backend.relu')
def relu(x, alpha=0., max_value=None, threshold=0):
"""Rectified linear unit.
With default values, it returns element-wise `max(x, 0)`.
Otherwise, it follows:
`f(x) = max_value` for `x >= max_value`,
`f(x) = x` for `threshold <= x < max_value`,
`f(x) = alpha * (x - threshold)` otherwise.
Arguments:
x: A tensor or variable.
alpha: A scalar, slope of negative section (default=`0.`).
max_value: float. Saturation threshold.
threshold: float. Threshold value for thresholded activation.
Returns:
A tensor.
"""
if alpha != 0.:
if max_value is None and threshold == 0:
return nn.leaky_relu(x, alpha=alpha)
if threshold != 0:
negative_part = nn.relu(-x + threshold)
else:
negative_part = nn.relu(-x)
clip_max = max_value is not None
if threshold != 0:
# computes x for x > threshold else 0
x = x * math_ops.cast(math_ops.greater(x, threshold), floatx())
elif max_value == 6:
# if no threshold, then can use nn.relu6 native TF op for performance
x = nn.relu6(x)
clip_max = False
else:
x = nn.relu(x)
if clip_max:
max_value = _to_tensor(max_value, x.dtype.base_dtype)
zero = _to_tensor(0., x.dtype.base_dtype)
x = clip_ops.clip_by_value(x, zero, max_value)
if alpha != 0.:
alpha = _to_tensor(alpha, x.dtype.base_dtype)
x -= alpha * negative_part
return x
@tf_export('keras.backend.elu')
def elu(x, alpha=1.):
"""Exponential linear unit.
Arguments:
x: A tensor or variable to compute the activation function for.
alpha: A scalar, slope of negative section.
Returns:
A tensor.
"""
res = nn.elu(x)
if alpha == 1:
return res
else:
return array_ops.where(x > 0, res, alpha * res)
@tf_export('keras.backend.softmax')
def softmax(x, axis=-1):
"""Softmax of a tensor.
Arguments:
x: A tensor or variable.
axis: The dimension softmax would be performed on.
The default is -1 which indicates the last dimension.
Returns:
A tensor.
"""
return nn.softmax(x, axis=axis)
@tf_export('keras.backend.softplus')
def softplus(x):
"""Softplus of a tensor.
Arguments:
x: A tensor or variable.
Returns:
A tensor.
"""
return nn.softplus(x)
@tf_export('keras.backend.softsign')
def softsign(x):
"""Softsign of a tensor.
Arguments:
x: A tensor or variable.
Returns:
A tensor.
"""
return nn.softsign(x)
@tf_export('keras.backend.categorical_crossentropy')
def categorical_crossentropy(target, output, from_logits=False, axis=-1):
"""Categorical crossentropy between an output tensor and a target tensor.
Arguments:
target: A tensor of the same shape as `output`.
output: A tensor resulting from a softmax
(unless `from_logits` is True, in which
case `output` is expected to be the logits).
from_logits: Boolean, whether `output` is the
result of a softmax, or is a tensor of logits.
axis: Int specifying the channels axis. `axis=-1` corresponds to data
format `channels_last', and `axis=1` corresponds to data format
`channels_first`.
Returns:
Output tensor.
Raises:
ValueError: if `axis` is neither -1 nor one of the axes of `output`.
"""
rank = len(output.shape)
axis = axis % rank
# Note: nn.softmax_cross_entropy_with_logits_v2
# expects logits, Keras expects probabilities.
if not from_logits:
# scale preds so that the class probas of each sample sum to 1
output = output / math_ops.reduce_sum(output, axis, True)
# manual computation of crossentropy
epsilon_ = _to_tensor(epsilon(), output.dtype.base_dtype)
output = clip_ops.clip_by_value(output, epsilon_, 1. - epsilon_)
return -math_ops.reduce_sum(target * math_ops.log(output), axis)
else:
return nn.softmax_cross_entropy_with_logits_v2(labels=target, logits=output)
@tf_export('keras.backend.sparse_categorical_crossentropy')
def sparse_categorical_crossentropy(target, output, from_logits=False, axis=-1):
"""Categorical crossentropy with integer targets.
Arguments:
target: An integer tensor.
output: A tensor resulting from a softmax
(unless `from_logits` is True, in which
case `output` is expected to be the logits).
from_logits: Boolean, whether `output` is the
result of a softmax, or is a tensor of logits.
axis: Int specifying the channels axis. `axis=-1` corresponds to data
format `channels_last', and `axis=1` corresponds to data format
`channels_first`.
Returns:
Output tensor.
Raises:
ValueError: if `axis` is neither -1 nor one of the axes of `output`.
"""
rank = len(output.shape)
axis = axis % rank
if axis != rank - 1:
permutation = list(range(axis)) + list(range(axis + 1, rank)) + [axis]
output = array_ops.transpose(output, perm=permutation)
# Note: nn.sparse_softmax_cross_entropy_with_logits
# expects logits, Keras expects probabilities.
if not from_logits:
epsilon_ = _to_tensor(epsilon(), output.dtype.base_dtype)
output = clip_ops.clip_by_value(output, epsilon_, 1 - epsilon_)
output = math_ops.log(output)
output_shape = output.shape
targets = cast(flatten(target), 'int64')
logits = array_ops.reshape(output, [-1, int(output_shape[-1])])
res = nn.sparse_softmax_cross_entropy_with_logits(
labels=targets, logits=logits)
if len(output_shape) >= 3:
# If our output includes timesteps or spatial dimensions we need to reshape
return array_ops.reshape(res, array_ops.shape(output)[:-1])
else:
return res
@tf_export('keras.backend.binary_crossentropy')
def binary_crossentropy(target, output, from_logits=False):
"""Binary crossentropy between an output tensor and a target tensor.
Arguments:
target: A tensor with the same shape as `output`.
output: A tensor.
from_logits: Whether `output` is expected to be a logits tensor.
By default, we consider that `output`
encodes a probability distribution.
Returns:
A tensor.
"""
# Note: nn.sigmoid_cross_entropy_with_logits
# expects logits, Keras expects probabilities.
if not from_logits:
# transform back to logits
epsilon_ = _to_tensor(epsilon(), output.dtype.base_dtype)
output = clip_ops.clip_by_value(output, epsilon_, 1 - epsilon_)
output = math_ops.log(output / (1 - output))
return nn.sigmoid_cross_entropy_with_logits(labels=target, logits=output)
@tf_export('keras.backend.sigmoid')
def sigmoid(x):
"""Element-wise sigmoid.
Arguments:
x: A tensor or variable.
Returns:
A tensor.
"""
return nn.sigmoid(x)
@tf_export('keras.backend.hard_sigmoid')
def hard_sigmoid(x):
"""Segment-wise linear approximation of sigmoid.
Faster than sigmoid.
Returns `0.` if `x < -2.5`, `1.` if `x > 2.5`.
In `-2.5 <= x <= 2.5`, returns `0.2 * x + 0.5`.
Arguments:
x: A tensor or variable.
Returns:
A tensor.
"""
x = (0.2 * x) + 0.5
zero = _to_tensor(0., x.dtype.base_dtype)
one = _to_tensor(1., x.dtype.base_dtype)
x = clip_ops.clip_by_value(x, zero, one)
return x
@tf_export('keras.backend.tanh')
def tanh(x):
"""Element-wise tanh.
Arguments:
x: A tensor or variable.
Returns:
A tensor.
"""
return nn.tanh(x)
@tf_export('keras.backend.dropout')
def dropout(x, level, noise_shape=None, seed=None):
"""Sets entries in `x` to zero at random, while scaling the entire tensor.
Arguments:
x: tensor
level: fraction of the entries in the tensor
that will be set to 0.
noise_shape: shape for randomly generated keep/drop flags,
must be broadcastable to the shape of `x`
seed: random seed to ensure determinism.
Returns:
A tensor.
"""
retain_prob = 1. - level
if seed is None:
seed = np.random.randint(10e6)
# the dummy 1. works around a TF bug
# (float32_ref vs. float32 incompatibility)
return nn.dropout(x * 1., retain_prob, noise_shape, seed=seed)
@tf_export('keras.backend.l2_normalize')
def l2_normalize(x, axis=None):
"""Normalizes a tensor wrt the L2 norm alongside the specified axis.
Arguments:
x: Tensor or variable.
axis: axis along which to perform normalization.
Returns:
A tensor.
"""
return nn.l2_normalize(x, axis=axis)
@tf_export('keras.backend.in_top_k')
def in_top_k(predictions, targets, k):
"""Returns whether the `targets` are in the top `k` `predictions`.
Arguments:
predictions: A tensor of shape `(batch_size, classes)` and type `float32`.
targets: A 1D tensor of length `batch_size` and type `int32` or `int64`.
k: An `int`, number of top elements to consider.
Returns:
A 1D tensor of length `batch_size` and type `bool`.
`output[i]` is `True` if `predictions[i, targets[i]]` is within top-`k`
values of `predictions[i]`.
"""
return nn.in_top_k(predictions, targets, k)
# CONVOLUTIONS
def _preprocess_conv1d_input(x, data_format):
"""Transpose and cast the input before the conv1d.
Arguments:
x: input tensor.
data_format: string, `"channels_last"` or `"channels_first"`.
Returns:
A tensor.
"""
tf_data_format = 'NWC' # to pass TF Conv2dNative operations
if data_format == 'channels_first':
if not _has_nchw_support():
x = array_ops.transpose(x, (0, 2, 1)) # NCW -> NWC
else:
tf_data_format = 'NCW'
return x, tf_data_format
def _preprocess_conv2d_input(x, data_format, force_transpose=False):
"""Transpose and cast the input before the conv2d.
Arguments:
x: input tensor.
data_format: string, `"channels_last"` or `"channels_first"`.
force_transpose: Boolean. If True, the input will always be transposed
from NCHW to NHWC if `data_format` is `"channels_first"`.
If False, the transposition only occurs on CPU (GPU ops are
assumed to support NCHW).
Returns:
A tensor.
"""
tf_data_format = 'NHWC'
if data_format == 'channels_first':
if not _has_nchw_support() or force_transpose:
x = array_ops.transpose(x, (0, 2, 3, 1)) # NCHW -> NHWC
else:
tf_data_format = 'NCHW'
return x, tf_data_format
def _preprocess_conv3d_input(x, data_format):
"""Transpose and cast the input before the conv3d.
Arguments:
x: input tensor.
data_format: string, `"channels_last"` or `"channels_first"`.
Returns:
A tensor.
"""
tf_data_format = 'NDHWC'
if data_format == 'channels_first':
if not _has_nchw_support():
x = array_ops.transpose(x, (0, 2, 3, 4, 1))
else:
tf_data_format = 'NCDHW'
return x, tf_data_format
def _preprocess_padding(padding):
"""Convert keras' padding to TensorFlow's padding.
Arguments:
padding: string, one of 'same' , 'valid'
Returns:
a string, one of 'SAME', 'VALID'.
Raises:
ValueError: if invalid `padding'`
"""
if padding == 'same':
padding = 'SAME'
elif padding == 'valid':
padding = 'VALID'
else:
raise ValueError('Invalid padding: ' + str(padding))
return padding
@tf_export('keras.backend.conv1d')
def conv1d(x,
kernel,
strides=1,
padding='valid',
data_format=None,
dilation_rate=1):
"""1D convolution.
Arguments:
x: Tensor or variable.
kernel: kernel tensor.
strides: stride integer.
padding: string, `"same"`, `"causal"` or `"valid"`.
data_format: string, one of "channels_last", "channels_first".
dilation_rate: integer dilate rate.
Returns:
A tensor, result of 1D convolution.
Raises:
ValueError: if `data_format` is neither `channels_last` or
`channels_first`.
"""
if data_format is None:
data_format = image_data_format()
if data_format not in {'channels_first', 'channels_last'}:
raise ValueError('Unknown data_format: ' + str(data_format))
kernel_shape = kernel.shape.as_list()
if padding == 'causal':
# causal (dilated) convolution:
left_pad = dilation_rate * (kernel_shape[0] - 1)
x = temporal_padding(x, (left_pad, 0))
padding = 'valid'
padding = _preprocess_padding(padding)
x, tf_data_format = _preprocess_conv1d_input(x, data_format)
x = nn.convolution(
input=x,
filter=kernel,
dilation_rate=(dilation_rate,),
strides=(strides,),
padding=padding,
data_format=tf_data_format)
if data_format == 'channels_first' and tf_data_format == 'NWC':
x = array_ops.transpose(x, (0, 2, 1)) # NWC -> NCW
return x
@tf_export('keras.backend.conv2d')
def conv2d(x,
kernel,
strides=(1, 1),
padding='valid',
data_format=None,
dilation_rate=(1, 1)):
"""2D convolution.
Arguments:
x: Tensor or variable.
kernel: kernel tensor.
strides: strides tuple.
padding: string, `"same"` or `"valid"`.
data_format: `"channels_last"` or `"channels_first"`.
Whether to use Theano or TensorFlow data format
for inputs/kernels/outputs.
dilation_rate: tuple of 2 integers.
Returns:
A tensor, result of 2D convolution.
Raises:
ValueError: if `data_format` is neither `channels_last` or
`channels_first`.
"""
if data_format is None:
data_format = image_data_format()
if data_format not in {'channels_first', 'channels_last'}:
raise ValueError('Unknown data_format: ' + str(data_format))
x, tf_data_format = _preprocess_conv2d_input(x, data_format)
padding = _preprocess_padding(padding)
x = nn.convolution(
input=x,
filter=kernel,
dilation_rate=dilation_rate,
strides=strides,
padding=padding,
data_format=tf_data_format)
if data_format == 'channels_first' and tf_data_format == 'NHWC':
x = array_ops.transpose(x, (0, 3, 1, 2)) # NHWC -> NCHW
return x
@tf_export('keras.backend.conv2d_transpose')
def conv2d_transpose(x,
kernel,
output_shape,
strides=(1, 1),
padding='valid',
data_format=None,
dilation_rate=(1, 1)):
"""2D deconvolution (i.e.
transposed convolution).
Arguments:
x: Tensor or variable.
kernel: kernel tensor.
output_shape: 1D int tensor for the output shape.
strides: strides tuple.
padding: string, `"same"` or `"valid"`.
data_format: string, `"channels_last"` or `"channels_first"`.
Whether to use Theano or TensorFlow/CNTK data format
for inputs/kernels/outputs.
dilation_rate: Tuple of 2 integers.
Returns:
A tensor, result of transposed 2D convolution.
Raises:
ValueError: if `data_format` is neither `channels_last` or
`channels_first`.
"""
if data_format is None:
data_format = image_data_format()
if data_format not in {'channels_first', 'channels_last'}:
raise ValueError('Unknown data_format: ' + str(data_format))
if isinstance(output_shape, (tuple, list)):
output_shape = array_ops.stack(output_shape)
# `atrous_conv2d_transpose` only supports NHWC format, even on GPU.
if data_format == 'channels_first' and dilation_rate != (1, 1):
force_transpose = True
else:
force_transpose = False
x, tf_data_format = _preprocess_conv2d_input(x, data_format, force_transpose)
if data_format == 'channels_first' and tf_data_format == 'NHWC':
output_shape = (output_shape[0], output_shape[2], output_shape[3],
output_shape[1])
if output_shape[0] is None:
output_shape = (array_ops.shape(x)[0],) + tuple(output_shape[1:])
output_shape = array_ops.stack(list(output_shape))
padding = _preprocess_padding(padding)
if tf_data_format == 'NHWC':
strides = (1,) + strides + (1,)
else:
strides = (1, 1) + strides
if dilation_rate == (1, 1):
x = nn.conv2d_transpose(x, kernel, output_shape, strides,
padding=padding,
data_format=tf_data_format)
else:
assert dilation_rate[0] == dilation_rate[1]
x = nn.atrous_conv2d_transpose(
x,
kernel,
output_shape,
rate=dilation_rate[0],
padding=padding)
if data_format == 'channels_first' and tf_data_format == 'NHWC':
x = array_ops.transpose(x, (0, 3, 1, 2)) # NHWC -> NCHW
return x
def separable_conv1d(x,
depthwise_kernel,
pointwise_kernel,
strides=1,
padding='valid',
data_format=None,
dilation_rate=1):
"""1D convolution with separable filters.
Arguments:
x: input tensor
depthwise_kernel: convolution kernel for the depthwise convolution.
pointwise_kernel: kernel for the 1x1 convolution.
strides: stride integer.
padding: string, `"same"` or `"valid"`.
data_format: string, `"channels_last"` or `"channels_first"`.
dilation_rate: integer dilation rate.
Returns:
Output tensor.
Raises:
ValueError: if `data_format` is neither `channels_last` or
`channels_first`.
"""
if data_format is None:
data_format = image_data_format()
if data_format not in {'channels_first', 'channels_last'}:
raise ValueError('Unknown data_format: ' + str(data_format))
if isinstance(strides, int):
strides = (strides,)
if isinstance(dilation_rate, int):
dilation_rate = (dilation_rate,)
x, tf_data_format = _preprocess_conv1d_input(x, data_format)
padding = _preprocess_padding(padding)
if not isinstance(strides, tuple):
strides = tuple(strides)
if tf_data_format == 'NWC':
spatial_start_dim = 1
strides = (1,) + strides * 2 + (1,)
else:
spatial_start_dim = 2
strides = (1, 1) + strides * 2
x = array_ops.expand_dims(x, spatial_start_dim)
depthwise_kernel = array_ops.expand_dims(depthwise_kernel, 0)
pointwise_kernel = array_ops.expand_dims(pointwise_kernel, 0)
dilation_rate = (1,) + dilation_rate
x = nn.separable_conv2d(
x,
depthwise_kernel,
pointwise_kernel,
strides=strides,
padding=padding,
rate=dilation_rate,
data_format=tf_data_format)
x = array_ops.squeeze(x, [spatial_start_dim])
if data_format == 'channels_first' and tf_data_format == 'NWC':
x = array_ops.transpose(x, (0, 2, 1)) # NWC -> NCW
return x
@tf_export('keras.backend.separable_conv2d')
def separable_conv2d(x,
depthwise_kernel,
pointwise_kernel,
strides=(1, 1),
padding='valid',
data_format=None,
dilation_rate=(1, 1)):
"""2D convolution with separable filters.
Arguments:
x: input tensor
depthwise_kernel: convolution kernel for the depthwise convolution.
pointwise_kernel: kernel for the 1x1 convolution.
strides: strides tuple (length 2).
padding: string, `"same"` or `"valid"`.
data_format: string, `"channels_last"` or `"channels_first"`.
dilation_rate: tuple of integers,
dilation rates for the separable convolution.
Returns:
Output tensor.
Raises:
ValueError: if `data_format` is neither `channels_last` or
`channels_first`.
"""
if data_format is None:
data_format = image_data_format()
if data_format not in {'channels_first', 'channels_last'}:
raise ValueError('Unknown data_format: ' + str(data_format))
if len(strides) != 2:
raise ValueError('`strides` must be a tuple of 2 integers.')
x, tf_data_format = _preprocess_conv2d_input(x, data_format)
padding = _preprocess_padding(padding)
if not isinstance(strides, tuple):
strides = tuple(strides)
if tf_data_format == 'NHWC':
strides = (1,) + strides + (1,)
else:
strides = (1, 1) + strides
x = nn.separable_conv2d(
x,
depthwise_kernel,
pointwise_kernel,
strides=strides,
padding=padding,
rate=dilation_rate,
data_format=tf_data_format)
if data_format == 'channels_first' and tf_data_format == 'NHWC':
x = array_ops.transpose(x, (0, 3, 1, 2)) # NHWC -> NCHW
return x
def depthwise_conv2d(x,
depthwise_kernel,
strides=(1, 1),
padding='valid',
data_format=None,
dilation_rate=(1, 1)):
"""2D convolution with separable filters.
Arguments:
x: input tensor
depthwise_kernel: convolution kernel for the depthwise convolution.
strides: strides tuple (length 2).
padding: string, `"same"` or `"valid"`.
data_format: string, `"channels_last"` or `"channels_first"`.
dilation_rate: tuple of integers,
dilation rates for the separable convolution.
Returns:
Output tensor.
Raises:
ValueError: if `data_format` is neither `channels_last` or
`channels_first`.
"""
if data_format is None:
data_format = image_data_format()
if data_format not in {'channels_first', 'channels_last'}:
raise ValueError('Unknown data_format: ' + str(data_format))
x, tf_data_format = _preprocess_conv2d_input(x, data_format)
padding = _preprocess_padding(padding)
if tf_data_format == 'NHWC':
strides = (1,) + strides + (1,)
else:
strides = (1, 1) + strides
x = nn.depthwise_conv2d(
x,
depthwise_kernel,
strides=strides,
padding=padding,
rate=dilation_rate,
data_format=tf_data_format)
if data_format == 'channels_first' and tf_data_format == 'NHWC':
x = array_ops.transpose(x, (0, 3, 1, 2)) # NHWC -> NCHW
return x
@tf_export('keras.backend.conv3d')
def conv3d(x,
kernel,
strides=(1, 1, 1),
padding='valid',
data_format=None,
dilation_rate=(1, 1, 1)):
"""3D convolution.
Arguments:
x: Tensor or variable.
kernel: kernel tensor.
strides: strides tuple.
padding: string, `"same"` or `"valid"`.
data_format: string, `"channels_last"` or `"channels_first"`.
Whether to use Theano or TensorFlow/CNTK data format
for inputs/kernels/outputs.
dilation_rate: tuple of 3 integers.
Returns:
A tensor, result of 3D convolution.
Raises:
ValueError: if `data_format` is neither `channels_last` or
`channels_first`.
"""
if data_format is None:
data_format = image_data_format()
if data_format not in {'channels_first', 'channels_last'}:
raise ValueError('Unknown data_format: ' + str(data_format))
x, tf_data_format = _preprocess_conv3d_input(x, data_format)
padding = _preprocess_padding(padding)
x = nn.convolution(
input=x,
filter=kernel,
dilation_rate=dilation_rate,
strides=strides,
padding=padding,
data_format=tf_data_format)
if data_format == 'channels_first' and tf_data_format == 'NDHWC':
x = array_ops.transpose(x, (0, 4, 1, 2, 3))
return x
def conv3d_transpose(x,
kernel,
output_shape,
strides=(1, 1, 1),
padding='valid',
data_format=None):
"""3D deconvolution (i.e.
transposed convolution).
Arguments:
x: input tensor.
kernel: kernel tensor.
output_shape: 1D int tensor for the output shape.
strides: strides tuple.
padding: string, "same" or "valid".
data_format: string, `"channels_last"` or `"channels_first"`.
Whether to use Theano or TensorFlow/CNTK data format
for inputs/kernels/outputs.
Returns:
A tensor, result of transposed 3D convolution.
Raises:
ValueError: if `data_format` is neither `channels_last` or
`channels_first`.
"""
if data_format is None:
data_format = image_data_format()
if data_format not in {'channels_first', 'channels_last'}:
raise ValueError('Unknown data_format: ' + str(data_format))
if isinstance(output_shape, (tuple, list)):
output_shape = array_ops.stack(output_shape)
x, tf_data_format = _preprocess_conv3d_input(x, data_format)
if data_format == 'channels_first' and tf_data_format == 'NDHWC':
output_shape = (output_shape[0], output_shape[2], output_shape[3],
output_shape[4], output_shape[1])
if output_shape[0] is None:
output_shape = (array_ops.shape(x)[0],) + tuple(output_shape[1:])
output_shape = array_ops.stack(list(output_shape))
padding = _preprocess_padding(padding)
if tf_data_format == 'NDHWC':
strides = (1,) + strides + (1,)
else:
strides = (1, 1) + strides
x = nn.conv3d_transpose(
x,
kernel,
output_shape,
strides,
padding=padding,
data_format=tf_data_format)
if data_format == 'channels_first' and tf_data_format == 'NDHWC':
x = array_ops.transpose(x, (0, 4, 1, 2, 3))
return x
@tf_export('keras.backend.pool2d')
def pool2d(x,
pool_size,
strides=(1, 1),
padding='valid',
data_format=None,
pool_mode='max'):
"""2D Pooling.
Arguments:
x: Tensor or variable.
pool_size: tuple of 2 integers.
strides: tuple of 2 integers.
padding: string, `"same"` or `"valid"`.
data_format: string, `"channels_last"` or `"channels_first"`.
pool_mode: string, `"max"` or `"avg"`.
Returns:
A tensor, result of 2D pooling.
Raises:
ValueError: if `data_format` is neither `"channels_last"` or
`"channels_first"`.
ValueError: if `pool_mode` is neither `"max"` or `"avg"`.
"""
if data_format is None:
data_format = image_data_format()
if data_format not in {'channels_first', 'channels_last'}:
raise ValueError('Unknown data_format: ' + str(data_format))
if len(pool_size) != 2:
raise ValueError('`pool_size` must be a tuple of 2 integers.')
if len(strides) != 2:
raise ValueError('`strides` must be a tuple of 2 integers.')
x, tf_data_format = _preprocess_conv2d_input(x, data_format)
padding = _preprocess_padding(padding)
if tf_data_format == 'NHWC':
strides = (1,) + strides + (1,)
pool_size = (1,) + pool_size + (1,)
else:
strides = (1, 1) + strides
pool_size = (1, 1) + pool_size
if pool_mode == 'max':
x = nn.max_pool(
x, pool_size, strides, padding=padding, data_format=tf_data_format)
elif pool_mode == 'avg':
x = nn.avg_pool(
x, pool_size, strides, padding=padding, data_format=tf_data_format)
else:
raise ValueError('Invalid pooling mode: ' + str(pool_mode))
if data_format == 'channels_first' and tf_data_format == 'NHWC':
x = array_ops.transpose(x, (0, 3, 1, 2)) # NHWC -> NCHW
return x
@tf_export('keras.backend.pool3d')
def pool3d(x,
pool_size,
strides=(1, 1, 1),
padding='valid',
data_format=None,
pool_mode='max'):
"""3D Pooling.
Arguments:
x: Tensor or variable.
pool_size: tuple of 3 integers.
strides: tuple of 3 integers.
padding: string, `"same"` or `"valid"`.
data_format: string, `"channels_last"` or `"channels_first"`.
pool_mode: string, `"max"` or `"avg"`.
Returns:
A tensor, result of 3D pooling.
Raises:
ValueError: if `data_format` is neither `"channels_last"` or
`"channels_first"`.
ValueError: if `pool_mode` is neither `"max"` or `"avg"`.
"""
if data_format is None:
data_format = image_data_format()
if data_format not in {'channels_first', 'channels_last'}:
raise ValueError('Unknown data_format: ' + str(data_format))
x, tf_data_format = _preprocess_conv3d_input(x, data_format)
padding = _preprocess_padding(padding)
if tf_data_format == 'NDHWC':
strides = (1,) + strides + (1,)
pool_size = (1,) + pool_size + (1,)
else:
strides = (1, 1) + strides
pool_size = (1, 1) + pool_size
if pool_mode == 'max':
x = nn.max_pool3d(
x, pool_size, strides, padding=padding, data_format=tf_data_format)
elif pool_mode == 'avg':
x = nn.avg_pool3d(
x, pool_size, strides, padding=padding, data_format=tf_data_format)
else:
raise ValueError('Invalid pooling mode: ' + str(pool_mode))
if data_format == 'channels_first' and tf_data_format == 'NDHWC':
x = array_ops.transpose(x, (0, 4, 1, 2, 3))
return x
def local_conv(inputs,
kernel,
kernel_size,
strides,
output_shape,
data_format=None):
"""Apply N-D convolution with un-shared weights.
Arguments:
inputs: (N+2)-D tensor with shape
(batch_size, channels_in, d_in1, ..., d_inN)
if data_format='channels_first', or
(batch_size, d_in1, ..., d_inN, channels_in)
if data_format='channels_last'.
kernel: the unshared weight for N-D convolution,
with shape (output_items, feature_dim, channels_out), where
feature_dim = np.prod(kernel_size) * channels_in,
output_items = np.prod(output_shape).
kernel_size: a tuple of N integers, specifying the
spatial dimensions of the N-D convolution window.
strides: a tuple of N integers, specifying the strides
of the convolution along the spatial dimensions.
output_shape: a tuple of (d_out1, ..., d_outN) specifying the spatial
dimensionality of the output.
data_format: string, "channels_first" or "channels_last".
Returns:
An (N+2)-D tensor with shape:
(batch_size, channels_out) + output_shape
if data_format='channels_first', or:
(batch_size,) + output_shape + (channels_out,)
if data_format='channels_last'.
Raises:
ValueError: if `data_format` is neither
`channels_last` nor `channels_first`.
"""
if data_format is None:
data_format = image_data_format()
if data_format not in {'channels_first', 'channels_last'}:
raise ValueError('Unknown data_format: ' + str(data_format))
kernel_shape = int_shape(kernel)
feature_dim = kernel_shape[1]
channels_out = kernel_shape[-1]
ndims = len(output_shape)
spatial_dimensions = list(range(ndims))
xs = []
output_axes_ticks = [range(axis_max) for axis_max in output_shape]
for position in itertools.product(*output_axes_ticks):
slices = [slice(None)]
if data_format == 'channels_first':
slices.append(slice(None))
slices.extend([slice(position[d] * strides[d],
position[d] * strides[d] + kernel_size[d])
for d in spatial_dimensions])
if data_format == 'channels_last':
slices.append(slice(None))
xs.append(reshape(inputs[slices], (1, -1, feature_dim)))
x_aggregate = concatenate(xs, axis=0)
output = batch_dot(x_aggregate, kernel)
output = reshape(output, output_shape + (-1, channels_out))
if data_format == 'channels_first':
permutation = [ndims, ndims + 1] + spatial_dimensions
else:
permutation = [ndims] + spatial_dimensions + [ndims + 1]
return permute_dimensions(output, permutation)
def local_conv1d(inputs, kernel, kernel_size, strides, data_format=None):
"""Apply 1D conv with un-shared weights.
Arguments:
inputs: 3D tensor with shape:
(batch_size, steps, input_dim)
if data_format is "channels_last" or
(batch_size, input_dim, steps)
if data_format is "channels_first".
kernel: the unshared weight for convolution,
with shape (output_length, feature_dim, filters).
kernel_size: a tuple of a single integer,
specifying the length of the 1D convolution window.
strides: a tuple of a single integer,
specifying the stride length of the convolution.
data_format: the data format, channels_first or channels_last.
Returns:
A 3d tensor with shape:
(batch_size, output_length, filters)
if data_format='channels_first'
or 3D tensor with shape:
(batch_size, filters, output_length)
if data_format='channels_last'.
"""
output_shape = (kernel.shape[0],)
return local_conv(inputs,
kernel,
kernel_size,
strides,
output_shape,
data_format)
def local_conv2d(inputs,
kernel,
kernel_size,
strides,
output_shape,
data_format=None):
"""Apply 2D conv with un-shared weights.
Arguments:
inputs: 4D tensor with shape:
(batch_size, filters, new_rows, new_cols)
if data_format='channels_first'
or 4D tensor with shape:
(batch_size, new_rows, new_cols, filters)
if data_format='channels_last'.
kernel: the unshared weight for convolution,
with shape (output_items, feature_dim, filters).
kernel_size: a tuple of 2 integers, specifying the
width and height of the 2D convolution window.
strides: a tuple of 2 integers, specifying the strides
of the convolution along the width and height.
output_shape: a tuple with (output_row, output_col).
data_format: the data format, channels_first or channels_last.
Returns:
A 4D tensor with shape:
(batch_size, filters, new_rows, new_cols)
if data_format='channels_first'
or 4D tensor with shape:
(batch_size, new_rows, new_cols, filters)
if data_format='channels_last'.
"""
return local_conv(inputs,
kernel,
kernel_size,
strides,
output_shape,
data_format)
@tf_export('keras.backend.bias_add')
def bias_add(x, bias, data_format=None):
"""Adds a bias vector to a tensor.
Arguments:
x: Tensor or variable.
bias: Bias tensor to add.
data_format: string, `"channels_last"` or `"channels_first"`.
Returns:
Output tensor.
Raises:
ValueError: In one of the two cases below:
1. invalid `data_format` argument.
2. invalid bias shape.
the bias should be either a vector or
a tensor with ndim(x) - 1 dimension
"""
if data_format is None:
data_format = image_data_format()
if data_format not in {'channels_first', 'channels_last'}:
raise ValueError('Unknown data_format: ' + str(data_format))
bias_shape = int_shape(bias)
if len(bias_shape) != 1 and len(bias_shape) != ndim(x) - 1:
raise ValueError(
'Unexpected bias dimensions %d, expect to be 1 or %d dimensions' %
(len(bias_shape), ndim(x)))
# pylint: disable=g-no-augmented-assignment
if ndim(x) == 5:
if data_format == 'channels_first':
if len(bias_shape) == 1:
x = x + reshape(bias, (1, bias_shape[0], 1, 1, 1))
else:
x = x + reshape(bias, (1, bias_shape[3]) + bias_shape[:3])
elif data_format == 'channels_last':
if len(bias_shape) == 1:
x = x + reshape(bias, (1, 1, 1, bias_shape[0]))
else:
x = x + reshape(bias, (1,) + bias_shape)
elif ndim(x) == 4:
if data_format == 'channels_first':
if len(bias_shape) == 1:
if _has_nchw_support():
x = nn.bias_add(x, bias, data_format='NCHW')
else:
x = x + reshape(bias, (1, bias_shape[0], 1, 1))
else:
x = x + reshape(bias, (1, bias_shape[2]) + bias_shape[:2])
elif data_format == 'channels_last':
if len(bias_shape) == 1:
x = nn.bias_add(x, bias, data_format='NHWC')
else:
x = x + reshape(bias, (1,) + bias_shape)
elif ndim(x) == 3:
if data_format == 'channels_first':
if len(bias_shape) == 1:
x = x + reshape(bias, (1, bias_shape[0], 1))
else:
x = x + reshape(bias, (1, bias_shape[1], bias_shape[0]))
elif data_format == 'channels_last':
if len(bias_shape) == 1:
x = x + reshape(bias, (1, 1, bias_shape[0]))
else:
x = x + reshape(bias, (1,) + bias_shape)
else:
x = nn.bias_add(x, bias)
# pylint: enable=g-no-augmented-assignment
return x
# RANDOMNESS
@tf_export('keras.backend.random_normal')
def random_normal(shape, mean=0.0, stddev=1.0, dtype=None, seed=None):
"""Returns a tensor with normal distribution of values.
Arguments:
shape: A tuple of integers, the shape of tensor to create.
mean: A float, mean of the normal distribution to draw samples.
stddev: A float, standard deviation of the normal distribution
to draw samples.
dtype: String, dtype of returned tensor.
seed: Integer, random seed.
Returns:
A tensor.
"""
if dtype is None:
dtype = floatx()
if seed is None:
seed = np.random.randint(10e6)
return random_ops.random_normal(
shape, mean=mean, stddev=stddev, dtype=dtype, seed=seed)
@tf_export('keras.backend.random_uniform')
def random_uniform(shape, minval=0.0, maxval=1.0, dtype=None, seed=None):
"""Returns a tensor with uniform distribution of values.
Arguments:
shape: A tuple of integers, the shape of tensor to create.
minval: A float, lower boundary of the uniform distribution
to draw samples.
maxval: A float, upper boundary of the uniform distribution
to draw samples.
dtype: String, dtype of returned tensor.
seed: Integer, random seed.
Returns:
A tensor.
"""
if dtype is None:
dtype = floatx()
if seed is None:
seed = np.random.randint(10e6)
return random_ops.random_uniform(
shape, minval=minval, maxval=maxval, dtype=dtype, seed=seed)
@tf_export('keras.backend.random_binomial')
def random_binomial(shape, p=0.0, dtype=None, seed=None):
"""Returns a tensor with random binomial distribution of values.
Arguments:
shape: A tuple of integers, the shape of tensor to create.
p: A float, `0. <= p <= 1`, probability of binomial distribution.
dtype: String, dtype of returned tensor.
seed: Integer, random seed.
Returns:
A tensor.
"""
if dtype is None:
dtype = floatx()
if seed is None:
seed = np.random.randint(10e6)
return array_ops.where(
random_ops.random_uniform(shape, dtype=dtype, seed=seed) <= p,
array_ops.ones(shape, dtype=dtype), array_ops.zeros(shape, dtype=dtype))
@tf_export('keras.backend.truncated_normal')
def truncated_normal(shape, mean=0.0, stddev=1.0, dtype=None, seed=None):
"""Returns a tensor with truncated random normal distribution of values.
The generated values follow a normal distribution
with specified mean and standard deviation,
except that values whose magnitude is more than
two standard deviations from the mean are dropped and re-picked.
Arguments:
shape: A tuple of integers, the shape of tensor to create.
mean: Mean of the values.
stddev: Standard deviation of the values.
dtype: String, dtype of returned tensor.
seed: Integer, random seed.
Returns:
A tensor.
"""
if dtype is None:
dtype = floatx()
if seed is None:
seed = np.random.randint(10e6)
return random_ops.truncated_normal(
shape, mean, stddev, dtype=dtype, seed=seed)
# CTC
# TensorFlow has a native implementation, but it uses sparse tensors
# and therefore requires a wrapper for Keras. The functions below convert
# dense to sparse tensors and also wraps up the beam search code that is
# in TensorFlow's CTC implementation
@tf_export('keras.backend.ctc_label_dense_to_sparse')
def ctc_label_dense_to_sparse(labels, label_lengths):
"""Converts CTC labels from dense to sparse.
Arguments:
labels: dense CTC labels.
label_lengths: length of the labels.
Returns:
A sparse tensor representation of the labels.
"""
label_shape = array_ops.shape(labels)
num_batches_tns = array_ops.stack([label_shape[0]])
max_num_labels_tns = array_ops.stack([label_shape[1]])
def range_less_than(_, current_input):
return array_ops.expand_dims(
math_ops.range(label_shape[1]), 0) < array_ops.fill(
max_num_labels_tns, current_input)
init = math_ops.cast(
array_ops.fill([1, label_shape[1]], 0), dtypes_module.bool)
dense_mask = functional_ops.scan(
range_less_than, label_lengths, initializer=init, parallel_iterations=1)
dense_mask = dense_mask[:, 0, :]
label_array = array_ops.reshape(
array_ops.tile(math_ops.range(0, label_shape[1]), num_batches_tns),
label_shape)
label_ind = array_ops.boolean_mask(label_array, dense_mask)
batch_array = array_ops.transpose(
array_ops.reshape(
array_ops.tile(math_ops.range(0, label_shape[0]), max_num_labels_tns),
reverse(label_shape, 0)))
batch_ind = array_ops.boolean_mask(batch_array, dense_mask)
indices = array_ops.transpose(
array_ops.reshape(concatenate([batch_ind, label_ind], axis=0), [2, -1]))
vals_sparse = array_ops.gather_nd(labels, indices)
return sparse_tensor.SparseTensor(
math_ops.to_int64(indices), vals_sparse, math_ops.to_int64(label_shape))
@tf_export('keras.backend.ctc_batch_cost')
def ctc_batch_cost(y_true, y_pred, input_length, label_length):
"""Runs CTC loss algorithm on each batch element.
Arguments:
y_true: tensor `(samples, max_string_length)`
containing the truth labels.
y_pred: tensor `(samples, time_steps, num_categories)`
containing the prediction, or output of the softmax.
input_length: tensor `(samples, 1)` containing the sequence length for
each batch item in `y_pred`.
label_length: tensor `(samples, 1)` containing the sequence length for
each batch item in `y_true`.
Returns:
Tensor with shape (samples,1) containing the
CTC loss of each element.
"""
label_length = math_ops.to_int32(array_ops.squeeze(label_length, axis=-1))
input_length = math_ops.to_int32(array_ops.squeeze(input_length, axis=-1))
sparse_labels = math_ops.to_int32(
ctc_label_dense_to_sparse(y_true, label_length))
y_pred = math_ops.log(array_ops.transpose(y_pred, perm=[1, 0, 2]) + epsilon())
return array_ops.expand_dims(
ctc.ctc_loss(
inputs=y_pred, labels=sparse_labels, sequence_length=input_length), 1)
@tf_export('keras.backend.ctc_decode')
def ctc_decode(y_pred, input_length, greedy=True, beam_width=100, top_paths=1):
"""Decodes the output of a softmax.
Can use either greedy search (also known as best path)
or a constrained dictionary search.
Arguments:
y_pred: tensor `(samples, time_steps, num_categories)`
containing the prediction, or output of the softmax.
input_length: tensor `(samples, )` containing the sequence length for
each batch item in `y_pred`.
greedy: perform much faster best-path search if `true`.
This does not use a dictionary.
beam_width: if `greedy` is `false`: a beam search decoder will be used
with a beam of this width.
top_paths: if `greedy` is `false`,
how many of the most probable paths will be returned.
Returns:
Tuple:
List: if `greedy` is `true`, returns a list of one element that
contains the decoded sequence.
If `false`, returns the `top_paths` most probable
decoded sequences.
Important: blank labels are returned as `-1`.
Tensor `(top_paths, )` that contains
the log probability of each decoded sequence.
"""
y_pred = math_ops.log(array_ops.transpose(y_pred, perm=[1, 0, 2]) + epsilon())
input_length = math_ops.to_int32(input_length)
if greedy:
(decoded, log_prob) = ctc.ctc_greedy_decoder(
inputs=y_pred, sequence_length=input_length)
else:
(decoded, log_prob) = ctc.ctc_beam_search_decoder(
inputs=y_pred,
sequence_length=input_length,
beam_width=beam_width,
top_paths=top_paths)
decoded_dense = [
sparse_ops.sparse_to_dense(
st.indices, st.dense_shape, st.values, default_value=-1)
for st in decoded
]
return (decoded_dense, log_prob)
# HIGH ORDER FUNCTIONS
@tf_export('keras.backend.map_fn')
def map_fn(fn, elems, name=None, dtype=None):
"""Map the function fn over the elements elems and return the outputs.
Arguments:
fn: Callable that will be called upon each element in elems
elems: tensor
name: A string name for the map node in the graph
dtype: Output data type.
Returns:
Tensor with dtype `dtype`.
"""
return functional_ops.map_fn(fn, elems, name=name, dtype=dtype)
@tf_export('keras.backend.foldl')
def foldl(fn, elems, initializer=None, name=None):
"""Reduce elems using fn to combine them from left to right.
Arguments:
fn: Callable that will be called upon each element in elems and an
accumulator, for instance `lambda acc, x: acc + x`
elems: tensor
initializer: The first value used (`elems[0]` in case of None)
name: A string name for the foldl node in the graph
Returns:
Tensor with same type and shape as `initializer`.
"""
return functional_ops.foldl(fn, elems, initializer=initializer, name=name)
@tf_export('keras.backend.foldr')
def foldr(fn, elems, initializer=None, name=None):
"""Reduce elems using fn to combine them from right to left.
Arguments:
fn: Callable that will be called upon each element in elems and an
accumulator, for instance `lambda acc, x: acc + x`
elems: tensor
initializer: The first value used (`elems[-1]` in case of None)
name: A string name for the foldr node in the graph
Returns:
Same type and shape as initializer
"""
return functional_ops.foldr(fn, elems, initializer=initializer, name=name)
# Load Keras default configuration from config file if present.
# Set Keras base dir path given KERAS_HOME env variable, if applicable.
# Otherwise either ~/.keras or /tmp.
if 'KERAS_HOME' in os.environ:
_keras_dir = os.environ.get('KERAS_HOME')
else:
_keras_base_dir = os.path.expanduser('~')
_keras_dir = os.path.join(_keras_base_dir, '.keras')
_config_path = os.path.expanduser(os.path.join(_keras_dir, 'keras.json'))
if os.path.exists(_config_path):
try:
_config = json.load(open(_config_path))
except ValueError:
_config = {}
_floatx = _config.get('floatx', floatx())
assert _floatx in {'float16', 'float32', 'float64'}
_epsilon = _config.get('epsilon', epsilon())
assert isinstance(_epsilon, float)
_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)
# Save config file.
if not os.path.exists(_keras_dir):
try:
os.makedirs(_keras_dir)
except OSError:
# Except permission denied and potential race conditions
# in multi-threaded environments.
pass
if not os.path.exists(_config_path):
_config = {
'floatx': floatx(),
'epsilon': epsilon(),
'backend': 'tensorflow',
'image_data_format': image_data_format()
}
try:
with open(_config_path, 'w') as f:
f.write(json.dumps(_config, indent=4))
except IOError:
# Except permission denied.
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