from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from tensorflow.python.framework import ops as tf_ops
from tensorflow.python.training import moving_averages
from tensorflow.python.ops import tensor_array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import functional_ops
from tensorflow.python.ops import ctc_ops as ctc
from tensorflow.python.client import device_lib
from tensorflow.core.protobuf import config_pb2
from collections import defaultdict
import numpy as np
import os
from .common import floatx
from .common import epsilon
from .common import normalize_data_format
from ..utils.generic_utils import has_arg
# Legacy functions
from .common import set_image_dim_ordering
from .common import image_dim_ordering
py_all = all
py_any = any
py_sum = sum
py_slice = slice
# INTERNAL UTILS
# This is the default internal TF session used by Keras.
# It can be set manually via `set_session(sess)`.
_SESSION = None
# 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 = {}
# This dictionary holds a mapping {graph: UID_DICT}.
# each UID_DICT is a dictionary mapping name prefixes to a current index,
# used for generating graph-specific string UIDs
# for various names (e.g. layer names).
_GRAPH_UID_DICTS = {}
# 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
# 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 during our lifetime.
_LOCAL_DEVICES = None
def get_uid(prefix=''):
"""Get the uid for the default graph.
# Arguments
prefix: An optional prefix of the graph.
# Returns
A unique identifier for the graph.
"""
global _GRAPH_UID_DICTS
graph = tf.get_default_graph()
if graph not in _GRAPH_UID_DICTS:
_GRAPH_UID_DICTS[graph] = defaultdict(int)
_GRAPH_UID_DICTS[graph][prefix] += 1
return _GRAPH_UID_DICTS[graph][prefix]
def reset_uids():
"""Resets graph identifiers.
"""
global _GRAPH_UID_DICTS
_GRAPH_UID_DICTS = {}
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
tf.reset_default_graph()
reset_uids()
_SESSION = None
phase = tf.placeholder_with_default(False,
shape=(),
name='keras_learning_phase')
_GRAPH_LEARNING_PHASES = {}
_GRAPH_LEARNING_PHASES[tf.get_default_graph()] = phase
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
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).
"""
graph = tf.get_default_graph()
if graph not in _GRAPH_LEARNING_PHASES:
phase = tf.placeholder_with_default(False,
shape=(),
name='keras_learning_phase')
_GRAPH_LEARNING_PHASES[graph] = phase
return _GRAPH_LEARNING_PHASES[graph]
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
if value not in {0, 1}:
raise ValueError('Expected learning phase to be '
'0 or 1.')
_GRAPH_LEARNING_PHASES[tf.get_default_graph()] = value
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.
"""
global _SESSION
default_session = tf.get_default_session()
if default_session is not None:
session = default_session
else:
if _SESSION is None:
if not os.environ.get('OMP_NUM_THREADS'):
config = tf.ConfigProto(allow_soft_placement=True)
else:
num_thread = int(os.environ.get('OMP_NUM_THREADS'))
config = tf.ConfigProto(intra_op_parallelism_threads=num_thread,
allow_soft_placement=True)
_SESSION = tf.Session(config=config)
session = _SESSION
if not _MANUAL_VAR_INIT:
with session.graph.as_default():
variables = tf.global_variables()
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(
[tf.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(tf.variables_initializer(uninitialized_vars))
# hack for list_devices() function.
# list_devices() function is not available under tensorflow r1.3.
if not hasattr(session, 'list_devices'):
session.list_devices = lambda: device_lib.list_local_devices()
return session
def set_session(session):
"""Sets the global TensorFlow session.
# Arguments
session: A TF Session.
"""
global _SESSION
_SESSION = session
# DEVICE MANIPULATION AND PROBING
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`.
"""
g = tf.get_default_graph()
op = _TfDeviceCaptureOp()
g._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.
"""
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 = len(_get_available_gpus()) > 0
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 tf.convert_to_tensor(x, dtype=dtype)
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, tf.SparseTensor)
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 tf.sparse_tensor_to_dense(tensor)
else:
return tensor
name_scope = tf.name_scope
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
>>> 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
>>> K.eval(kvar)
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 = tf.SparseTensor(indices=indices,
values=sparse_coo.data,
dense_shape=sparse_coo.shape)
v._keras_shape = sparse_coo.shape
v._uses_learning_phase = False
return v
v = tf.Variable(value, dtype=tf.as_dtype(dtype), name=name)
if isinstance(value, np.ndarray):
v._keras_shape = value.shape
elif hasattr(value, 'get_shape'):
v._keras_shape = int_shape(value)
v._uses_learning_phase = False
# TODO: move to Variable constructor when supported in public release.
try:
v.constraint = constraint
except AttributeError:
v._constraint = constraint
return v
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 tf.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
>>> 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 is_tensor(x):
raise ValueError('Unexpectedly found an instance of type `' +
str(type(x)) + '`. '
'Expected a symbolic tensor instance.')
return hasattr(x, '_keras_history')
def is_tensor(x):
return isinstance(x, tf_ops._TensorLike) or tf_ops.is_dense_tensor_like(x)
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.
# 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._keras_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)])
if sparse:
x = tf.sparse_placeholder(dtype, shape=shape, name=name)
else:
x = tf.placeholder(dtype, shape=shape, name=name)
x._keras_shape = shape
x._uses_learning_phase = False
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
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)
>>> inputs = keras.backend.placeholder(shape=(2, 4, 5))
>>> K.shape(kvar)
<tf.Tensor 'Shape_8:0' shape=(2,) dtype=int32>
>>> K.shape(inputs)
<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(inputs).eval(session=tf_session)
array([2, 4, 5], dtype=int32)
```
"""
return tf.shape(x)
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
>>> inputs = K.placeholder(shape=(2, 4, 5))
>>> K.int_shape(inputs)
(2, 4, 5)
>>> val = np.array([[1, 2], [3, 4]])
>>> kvar = K.variable(value=val)
>>> K.int_shape(kvar)
(2, 2)
```
"""
if hasattr(x, '_keras_shape'):
return x._keras_shape
try:
return tuple(x.get_shape().as_list())
except ValueError:
return None
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
>>> inputs = K.placeholder(shape=(2, 4, 5))
>>> val = np.array([[1, 2], [3, 4]])
>>> kvar = K.variable(value=val)
>>> K.ndim(inputs)
3
>>> K.ndim(kvar)
2
```
"""
dims = x.get_shape()._dims
if dims is not None:
return len(dims)
return None
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
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 to_dense(x).eval(session=get_session())
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)
```
"""
if dtype is None:
dtype = floatx()
tf_dtype = tf.as_dtype(dtype)
v = tf.zeros(shape=shape, dtype=tf_dtype, name=name)
if py_all(v.get_shape().as_list()):
return variable(v, dtype=dtype, name=name)
return v
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)
```
"""
if dtype is None:
dtype = floatx()
tf_dtype = tf.as_dtype(dtype)
v = tf.ones(shape=shape, dtype=tf_dtype, name=name)
if py_all(v.get_shape().as_list()):
return variable(v, dtype=dtype, name=name)
return v
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 = tf.as_dtype(dtype)
return variable(tf.eye(size, dtype=tf_dtype), dtype, name)
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 tf.zeros_like(x, dtype=dtype, name=name)
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 tf.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 tf.identity(x, name)
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 = tf.as_dtype(dtype)
if seed is None:
# ensure that randomness is conditioned by the Numpy RNG
seed = np.random.randint(10e8)
value = tf.random_uniform_initializer(
low, high, dtype=tf_dtype, seed=seed)(shape)
return variable(value, dtype=dtype, name=name)
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 = tf.as_dtype(dtype)
if seed is None:
# ensure that randomness is conditioned by the Numpy RNG
seed = np.random.randint(10e8)
value = tf.random_normal_initializer(
mean, scale, dtype=tf_dtype, seed=seed)(shape)
return variable(value, dtype=dtype, name=name)
def count_params(x):
"""Returns the static number of elements in a Keras variable or tensor.
# Arguments
x: Keras variable or tensor.
# Returns
Integer, the number of elements in `x`, i.e., the product of the
array's static dimensions.
# 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(int_shape(x))
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 tf.cast(x, dtype)
# UPDATES OPS
def update(x, new_x):
"""Update the value of `x` to `new_x`.
# Arguments
x: A `Variable`.
new_x: A tensor of same shape as `x`.
# Returns
The variable `x` updated.
"""
return tf.assign(x, new_x)
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 tf.assign_add(x, increment)
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 tf.assign_sub(x, decrement)
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 `x`.
momentum: The moving average momentum.
# Returns
An operation to update the variable.
"""
return moving_averages.assign_moving_average(
x, value, momentum, zero_debias=True)
# LINEAR ALGEBRA
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), tf.unstack(tf.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), tf.unstack(tf.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 = tf.reshape(x, [-1, x_shape[-1]])
yt = tf.reshape(tf.transpose(y, perm=y_permute_dim), [y_shape[-2], -1])
return tf.reshape(tf.matmul(xt, yt),
x_shape[:-1] + y_shape[:-2] + y_shape[-1:])
if is_sparse(x):
out = tf.sparse_tensor_dense_matmul(x, y)
else:
out = tf.matmul(x, y)
return out
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 py_any([isinstance(a, (list, tuple)) for a in axes]):
raise ValueError('Multiple target dimensions are not supported. ' +
'Expected: None, int, (int, int), ' +
'Provided: ' + str(axes))
if x_ndim > y_ndim:
diff = x_ndim - y_ndim
y = tf.reshape(y, tf.concat([tf.shape(y), [1] * (diff)], axis=0))
elif y_ndim > x_ndim:
diff = y_ndim - x_ndim
x = tf.reshape(x, tf.concat([tf.shape(x), [1] * (diff)], axis=0))
else:
diff = 0
if ndim(x) == 2 and ndim(y) == 2:
if axes[0] == axes[1]:
out = tf.reduce_sum(tf.multiply(x, y), axes[0])
else:
out = tf.reduce_sum(tf.multiply(tf.transpose(x, [1, 0]), y), axes[1])
else:
if axes is not None:
adj_x = None if axes[0] == ndim(x) - 1 else True
adj_y = True if axes[1] == ndim(y) - 1 else None
else:
adj_x = None
adj_y = None
out = tf.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 = tf.squeeze(out, list(range(idx, idx + diff)))
if ndim(out) == 1:
out = expand_dims(out, 1)
return out
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
>>> inputs = K.placeholder((2, 3))
>>> inputs
<tf.Tensor 'Placeholder_11:0' shape=(2, 3) dtype=float32>
>>> input_transposed = K.transpose(inputs)
>>> input_transposed
<tf.Tensor 'transpose_4:0' shape=(3, 2) dtype=float32>
```
"""
return tf.transpose(x)
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 tf.nn.embedding_lookup(reference, indices)
# ELEMENT-WISE OPERATIONS
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 tf.reduce_max(x, axis, keepdims)
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 tf.reduce_min(x, axis, keepdims)
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 tf.reduce_sum(x, axis, keepdims)
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 tf.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 tf.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 tf.cumprod(x, axis=axis)
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 == tf.bool:
x = tf.cast(x, floatx())
m = tf.reduce_mean(x, axis, True)
devs_squared = tf.square(x - m)
return tf.reduce_mean(devs_squared,
axis,
keepdims)
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`.
"""
return tf.sqrt(var(x, axis=axis, keepdims=keepdims))
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 == tf.bool:
x = tf.cast(x, floatx())
return tf.reduce_mean(x, axis, keepdims)
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 = tf.cast(x, tf.bool)
return tf.reduce_any(x, axis, keepdims)
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 = tf.cast(x, tf.bool)
return tf.reduce_all(x, axis, keepdims)
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 tf.argmax(x, axis)
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 tf.argmin(x, axis)
def square(x):
"""Element-wise square.
# Arguments
x: Tensor or variable.
# Returns
A tensor.
"""
return tf.square(x)
def abs(x):
"""Element-wise absolute value.
# Arguments
x: Tensor or variable.
# Returns
A tensor.
"""
return tf.abs(x)
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 = tf.clip_by_value(x, zero, inf)
return tf.sqrt(x)
def exp(x):
"""Element-wise exponential.
# Arguments
x: Tensor or variable.
# Returns
A tensor.
"""
return tf.exp(x)
def log(x):
"""Element-wise log.
# Arguments
x: Tensor or variable.
# Returns
A tensor.
"""
return tf.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 tf.reduce_logsumexp(x, axis, keepdims)
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 tf.round(x)
def sign(x):
"""Element-wise sign.
# Arguments
x: Tensor or variable.
# Returns
A tensor.
"""
return tf.sign(x)
def pow(x, a):
"""Element-wise exponentiation.
# Arguments
x: Tensor or variable.
a: Python integer.
# Returns
A tensor.
"""
return tf.pow(x, a)
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 tf.clip_by_value(x, min_value, max_value)
def equal(x, y):
"""Element-wise equality between two tensors.
# Arguments
x: Tensor or variable.
y: Tensor or variable.
# Returns
A bool tensor.
"""
return tf.equal(x, y)
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 tf.not_equal(x, y)
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 tf.greater(x, y)
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 tf.greater_equal(x, y)
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 tf.less(x, y)
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 tf.less_equal(x, y)
def maximum(x, y):
"""Element-wise maximum of two tensors.
# Arguments
x: Tensor or variable.
y: Tensor or variable.
# Returns
A tensor.
"""
return tf.maximum(x, y)
def minimum(x, y):
"""Element-wise minimum of two tensors.
# Arguments
x: Tensor or variable.
y: Tensor or variable.
# Returns
A tensor.
"""
return tf.minimum(x, y)
def sin(x):
"""Computes sin of x element-wise.
# Arguments
x: Tensor or variable.
# Returns
A tensor.
"""
return tf.sin(x)
def cos(x):
"""Computes cos of x element-wise.
# Arguments
x: Tensor or variable.
# Returns
A tensor.
"""
return tf.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 = tf.nn.moments(x, reduction_axes,
None, None, False)
normed = tf.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 = tf.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(tf.shape(x)[axis])
target_shape = tf.stack(target_shape)
broadcast_mean = tf.reshape(mean, target_shape)
broadcast_var = tf.reshape(var, target_shape)
if gamma is None:
broadcast_gamma = None
else:
broadcast_gamma = tf.reshape(gamma, target_shape)
if beta is None:
broadcast_beta = None
else:
broadcast_beta = tf.reshape(beta, target_shape)
normed = tf.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 = tf.constant(1.0,
dtype=x.dtype,
shape=[x.get_shape()[normalization_axis]])
if beta is None:
beta = tf.constant(0.0,
dtype=x.dtype,
shape=[x.get_shape()[normalization_axis]])
return tf.nn.fused_batch_norm(
x,
gamma,
beta,
epsilon=epsilon,
data_format=tf_data_format)
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)
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 FusedBatchNorm only support 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 may be processed by broadcast
# so it may have extra axes with 1, it is not needed and should be removed
if ndim(mean) > 1:
mean = tf.squeeze(mean)
if ndim(var) > 1:
var = tf.squeeze(var)
if beta is None:
beta = zeros_like(mean)
elif ndim(beta) > 1:
beta = tf.squeeze(beta)
if gamma is None:
gamma = ones_like(mean)
elif ndim(gamma) > 1:
gamma = tf.squeeze(gamma)
y, _, _ = tf.nn.fused_batch_norm(
x,
gamma,
beta,
epsilon=epsilon,
mean=mean,
variance=var,
data_format=tf_data_format,
is_training=False
)
return y
# default
return tf.nn.batch_normalization(x, mean, var, beta, gamma, epsilon)
# SHAPE OPERATIONS
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 tf.sparse_concat(axis, tensors)
else:
return tf.concat([to_dense(x) for x in tensors], axis)
def reshape(x, shape):
"""Reshapes a tensor to the specified shape.
# Arguments
x: Tensor or variable.
shape: Target shape tuple.
# Returns
A tensor.
"""
return tf.reshape(x, shape)
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 tf.transpose(x, perm=pattern)
def resize_images(x, height_factor, width_factor, data_format):
"""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: string, `"channels_last"` or `"channels_first"`.
# Returns
A tensor.
# Raises
ValueError: if `data_format` is neither `"channels_last"` or `"channels_first"`.
"""
if data_format == 'channels_first':
original_shape = int_shape(x)
new_shape = tf.shape(x)[2:]
new_shape *= tf.constant(np.array([height_factor, width_factor]).astype('int32'))
x = permute_dimensions(x, [0, 2, 3, 1])
x = tf.image.resize_nearest_neighbor(x, new_shape)
x = permute_dimensions(x, [0, 3, 1, 2])
x.set_shape((None, None, original_shape[2] * height_factor if original_shape[2] is not None else None,
original_shape[3] * width_factor if original_shape[3] is not None else None))
return x
elif data_format == 'channels_last':
original_shape = int_shape(x)
new_shape = tf.shape(x)[1:3]
new_shape *= tf.constant(np.array([height_factor, width_factor]).astype('int32'))
x = tf.image.resize_nearest_neighbor(x, new_shape)
x.set_shape((None, original_shape[1] * height_factor if original_shape[1] is not None else None,
original_shape[2] * width_factor if original_shape[2] is not None else None, None))
return x
else:
raise ValueError('Unknown data_format: ' + str(data_format))
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: string, `"channels_last"` or `"channels_first"`.
# 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('Unknown data_format: ' + str(data_format))
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.get_shape().as_list()
# For static axis
if x_shape[axis] is not None:
# slices along the repeat axis
splits = tf.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 = tf.shape(x)
x_rep = tf.expand_dims(x, axis=auxiliary_axis)
reps = np.ones(len(x.get_shape()) + 1)
reps[auxiliary_axis] = rep
x_rep = tf.tile(x_rep, reps)
# Merging
reps = np.delete(reps, auxiliary_axis)
reps[axis] = rep
reps = tf.constant(reps, dtype='int32')
x_shape = x_shape * reps
x_rep = tf.reshape(x_rep, x_shape)
# Fix shape representation
x_shape = x.get_shape().as_list()
x_rep.set_shape(x_shape)
x_rep._keras_shape = tuple(x_shape)
return x_rep
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 = tf.expand_dims(x, 1)
pattern = tf.stack([1, n, 1])
return tf.tile(x, pattern)
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:
try:
if start < 0:
start = 0
except TypeError:
# Handle case where start is a tensor
start = tf.cond(start < 0,
true_fn=lambda: tf.constant(0, dtype=start.dtype),
false_fn=lambda: start)
result = tf.range(start, limit=stop, delta=step, name='arange')
if dtype != 'int32':
result = cast(result, dtype)
return result
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 tf.tile(x, n)
def flatten(x):
"""Flatten a tensor.
# Arguments
x: A tensor or variable.
# Returns
A tensor, reshaped into 1-D
"""
return tf.reshape(x, [-1])
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 = tf.reshape(x, tf.stack([-1, prod(shape(x)[1:])]))
return x
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 tf.expand_dims(x, axis)
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 tf.squeeze(x, [axis])
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 tf.pad(x, pattern)
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: string, `"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
data_format = normalize_data_format(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 tf.pad(x, pattern)
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: string, `"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
data_format = normalize_data_format(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 tf.pad(x, pattern)
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 tf.stack(x, axis=axis)
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)`
"""
return tf.one_hot(indices, depth=num_classes, axis=-1)
def reverse(x, axes):
"""Reverses 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 tf.reverse(x, axes)
def slice(x, start, size):
"""Extracts a slice from a tensor.
# Arguments
x: Input tensor.
start: Integer list/tuple or tensor
indicating the start indices of the slice
along each axis.
size: Integer list/tuple or tensor
indicating how many dimensions to slice
along each axis.
# Returns
Tensor `x[start[0]: start[0] + size[0],
...,
start[-1]: start[-1] + size[-1]]`
"""
return tf.slice(x, start, size)
# VALUE MANIPULATION
def get_value(x):
"""Returns the value of a variable.
# Arguments
x: input variable.
# Returns
A Numpy array.
"""
return x.eval(session=get_session())
def batch_get_value(ops):
"""Returns the value of more than one tensor variable.
# Arguments
ops: list of ops to run.
# Returns
A list of Numpy arrays.
"""
if ops:
return get_session().run(ops)
else:
return []
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))
tf_dtype = tf.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 = tf.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})
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 tuples:
assign_ops = []
feed_dict = {}
for x, value in tuples:
value = np.asarray(value, dtype=dtype(x))
tf_dtype = tf.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 = tf.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)
def get_variable_shape(x):
"""Returns the shape of a variable.
# Arguments
x: A variable.
# Returns
A tuple of integers.
"""
return int_shape(x)
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 tf.Print(x, [x], message)
# GRAPH MANIPULATION
class Function(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(inputs, (list, tuple)):
raise TypeError('`inputs` to a TensorFlow backend function '
'should be a list or tuple.')
if not isinstance(outputs, (list, tuple)):
raise TypeError('`outputs` of a TensorFlow backend function '
'should be a list or tuple.')
if not isinstance(updates, (list, tuple)):
raise TypeError('`updates` in a TensorFlow backend function '
'should be a list or tuple.')
self.inputs = list(inputs)
self.outputs = list(outputs)
with tf.control_dependencies(self.outputs):
updates_ops = []
for update in updates:
if isinstance(update, tuple):
p, new_p = update
updates_ops.append(tf.assign(p, new_p))
else:
# assumed already an op
updates_ops.append(update)
self.updates_op = tf.group(*updates_ops)
self.name = name
# additional tensor substitutions
self.feed_dict = session_kwargs.pop('feed_dict', {})
# additional operations
self.fetches = session_kwargs.pop('fetches', [])
if not isinstance(self.fetches, list):
self.fetches = [self.fetches]
# The main use case of `fetches` being passed to a model is the ability
# to run custom updates
# (since the outputs of fetches are never returned).
# This requires us to wrap fetches in `identity` ops.
self.fetches = [tf.identity(x) for x in self.fetches]
self.session_kwargs = session_kwargs
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._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 = tf.cast(y, dtype=x.dtype)
from_tensor = tf_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)
# 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._session = session
def _call(self, inputs):
if not isinstance(inputs, (list, tuple)):
raise TypeError('`inputs` should be a list or tuple.')
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_tensor(value):
# Case: feeding symbolic tensor.
feed_symbols.append(tensor)
symbol_vals.append(value)
else:
feed_arrays.append(tensor)
# We need to do array conversion and type casting
# at this level, since
# `callable_fn` only supports exact matches.
array_vals.append(
np.asarray(value,
dtype=tf.as_dtype(tensor.dtype).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=tf.as_dtype(key.dtype).as_numpy_dtype))
# 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
session != self._session):
self._make_callable(feed_arrays,
feed_symbols,
symbol_vals,
session)
fetched = self._callable_fn(*array_vals)
return fetched[:len(self.outputs)]
def _legacy_call(self, inputs):
if not isinstance(inputs, (list, tuple)):
raise TypeError('`inputs` should be a list or tuple.')
feed_dict = self.feed_dict.copy()
for tensor, value in zip(self.inputs, inputs):
if is_sparse(tensor):
sparse_coo = value.tocoo()
indices = np.concatenate(
(np.expand_dims(sparse_coo.row, 1),
np.expand_dims(sparse_coo.col, 1)), 1)
value = (indices, sparse_coo.data, sparse_coo.shape)
feed_dict[tensor] = value
fetches = self.outputs + [self.updates_op] + self.fetches
session = get_session()
updated = session.run(fetches=fetches, feed_dict=feed_dict,
**self.session_kwargs)
return updated[:len(self.outputs)]
def __call__(self, inputs):
if hasattr(get_session(), '_make_callable_from_options'):
if py_any(is_sparse(x) for x in self.inputs):
if py_any(is_tensor(x) for x in inputs):
raise ValueError(
'Feeding from symbolic tensors is not '
'supported with sparse inputs.')
return self._legacy_call(inputs)
return self._call(inputs)
else:
if py_any(is_tensor(x) for x in inputs):
raise ValueError(
'In order to feed symbolic tensors to a Keras model '
'in TensorFlow, you need tensorflow 1.8 or higher.')
return self._legacy_call(inputs)
def function(inputs, outputs, updates=None, **kwargs):
"""Instantiates a Keras function.
# Arguments
inputs: List of placeholder tensors.
outputs: List of output tensors.
updates: List of update ops.
**kwargs: Passed to `tf.Session.run`.
# Returns
Output values as Numpy arrays.
# Raises
ValueError: if invalid kwargs are passed in.
"""
if kwargs:
for key in kwargs:
if not (has_arg(tf.Session.run, key, True) or has_arg(Function.__init__, key, True)):
msg = 'Invalid argument "%s" passed to K.function with TensorFlow backend' % key
raise ValueError(msg)
return Function(inputs, outputs, updates=updates, **kwargs)
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 tf.gradients(loss, variables, colocate_gradients_with_ops=True)
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 constant gradient with respect to any other variable.
"""
if isinstance(variables, (list, tuple)):
return map(tf.stop_gradient, variables)
else:
return tf.stop_gradient(variables)
# CONTROL FLOW
def rnn(step_function, inputs, initial_states,
go_backwards=False, mask=None, constants=None,
unroll=False, input_length=None):
"""Iterates over the time dimension of a tensor.
# Arguments
step_function:
Parameters:
inputs: Tensor with shape (samples, ...) (no time dimension),
representing input for the batch of samples at a certain
time step.
states: List of tensors.
Returns:
outputs: Tensor with shape (samples, ...) (no time dimension),
new_states: List of tensors, same length and shapes
as 'states'.
inputs: Tensor of temporal data of shape (samples, time, ...)
(at least 3D).
initial_states: Tensor with shape (samples, ...) (no time dimension),
containing the initial values for the states used in
the step function.
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),
with a zero for every element that is masked.
constants: A list of constant values passed at each step.
unroll: Whether to unroll the RNN or to use a symbolic loop
(`while_loop` or `scan` depending on backend).
input_length: Static number of timesteps in the input.
# 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).
"""
ndim = len(inputs.get_shape())
if ndim < 3:
raise ValueError('Input should be at least 3D.')
# Transpose to time-major, i.e.
# from (batch, time, ...) to (time, batch, ...)
axes = [1, 0] + list(range(2, ndim))
inputs = tf.transpose(inputs, (axes))
if mask is not None:
if mask.dtype != tf.bool:
mask = tf.cast(mask, tf.bool)
if len(mask.get_shape()) == ndim - 1:
mask = expand_dims(mask)
mask = tf.transpose(mask, axes)
if constants is None:
constants = []
global uses_learning_phase
uses_learning_phase = False
if unroll:
if not inputs.get_shape()[0]:
raise ValueError('Unrolling requires a '
'fixed number of timesteps.')
states = initial_states
successive_states = []
successive_outputs = []
input_list = tf.unstack(inputs)
if go_backwards:
input_list.reverse()
if mask is not None:
mask_list = tf.unstack(mask)
if go_backwards:
mask_list.reverse()
for inp, mask_t in zip(input_list, mask_list):
output, new_states = step_function(inp, states + constants)
if getattr(output, '_uses_learning_phase', False):
uses_learning_phase = True
# 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 A and B are (nsamples, ndimensions).
# So we need to
# broadcast the mask to match the shape of A and B.
# That's what the tile call does,
# it just repeats the mask along its second dimension
# n times.
tiled_mask_t = tf.tile(mask_t,
tf.stack([1, tf.shape(output)[1]]))
if not successive_outputs:
prev_output = zeros_like(output)
else:
prev_output = successive_outputs[-1]
output = tf.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 = tf.tile(mask_t,
tf.stack([1, tf.shape(new_state)[1]]))
return_states.append(tf.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 = tf.stack(successive_outputs)
else:
for inp in input_list:
output, states = step_function(inp, states + constants)
if getattr(output, '_uses_learning_phase', False):
uses_learning_phase = True
successive_outputs.append(output)
successive_states.append(states)
last_output = successive_outputs[-1]
new_states = successive_states[-1]
outputs = tf.stack(successive_outputs)
else:
if go_backwards:
inputs = reverse(inputs, 0)
states = tuple(initial_states)
time_steps = tf.shape(inputs)[0]
outputs, _ = step_function(inputs[0], initial_states + constants)
output_ta = tensor_array_ops.TensorArray(
dtype=outputs.dtype,
size=time_steps,
tensor_array_name='output_ta')
input_ta = tensor_array_ops.TensorArray(
dtype=inputs.dtype,
size=time_steps,
tensor_array_name='input_ta')
input_ta = input_ta.unstack(inputs)
time = tf.constant(0, dtype='int32', name='time')
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=tf.bool,
size=time_steps,
tensor_array_name='mask_ta')
mask_ta = mask_ta.unstack(mask)
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 = input_ta.read(time)
mask_t = mask_ta.read(time)
output, new_states = step_function(current_input,
tuple(states) +
tuple(constants))
if getattr(output, '_uses_learning_phase', False):
global uses_learning_phase
uses_learning_phase = True
for state, new_state in zip(states, new_states):
new_state.set_shape(state.get_shape())
tiled_mask_t = tf.tile(mask_t,
tf.stack([1, tf.shape(output)[1]]))
output = tf.where(tiled_mask_t, output, states[0])
new_states = [
tf.where(tf.tile(mask_t, tf.stack([1, tf.shape(new_states[i])[1]])),
new_states[i], states[i]) for i in range(len(states))
]
output_ta_t = output_ta_t.write(time, output)
return (time + 1, output_ta_t) + tuple(new_states)
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 = input_ta.read(time)
output, new_states = step_function(current_input,
tuple(states) +
tuple(constants))
if getattr(output, '_uses_learning_phase', False):
global uses_learning_phase
uses_learning_phase = True
for state, new_state in zip(states, new_states):
new_state.set_shape(state.get_shape())
output_ta_t = output_ta_t.write(time, output)
return (time + 1, output_ta_t) + tuple(new_states)
final_outputs = control_flow_ops.while_loop(
cond=lambda time, *_: time < time_steps,
body=_step,
loop_vars=(time, output_ta) + states,
parallel_iterations=32,
swap_memory=True,
maximum_iterations=input_length)
last_time = final_outputs[0]
output_ta = final_outputs[1]
new_states = final_outputs[2:]
outputs = output_ta.stack()
last_output = output_ta.read(last_time - 1)
axes = [1, 0] + list(range(2, len(outputs.get_shape())))
outputs = tf.transpose(outputs, axes)
last_output._uses_learning_phase = uses_learning_phase
return last_output, outputs, new_states
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 != tf.bool:
condition = tf.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 = tf.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 = tf.concat([tf.shape(condition), [1] * ndim_diff], axis=0)
condition = tf.reshape(condition, cond_shape)
expr_shape = tf.shape(then_expression)
shape_diff = expr_shape - cond_shape
tile_shape = tf.where(shape_diff > 0, expr_shape, tf.ones_like(expr_shape))
condition = tf.tile(condition, tile_shape)
x = tf.where(condition, then_expression, else_expression)
return x
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()
uses_learning_phase = True
else:
uses_learning_phase = False
if training is 1 or training is True:
if callable(x):
return x()
else:
return x
elif training is 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)
if uses_learning_phase:
x._uses_learning_phase = True
return x
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
def relu(x, alpha=0., max_value=None):
"""Rectified linear unit.
With default values, it returns element-wise `max(x, 0)`.
# Arguments
x: A tensor or variable.
alpha: A scalar, slope of negative section (default=`0.`).
max_value: Saturation threshold.
# Returns
A tensor.
"""
if alpha != 0.:
x = tf.nn.leaky_relu(x, alpha)
else:
x = tf.nn.relu(x)
if max_value is not None:
max_value = _to_tensor(max_value, x.dtype.base_dtype)
x = tf.minimum(x, max_value)
return x
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 = tf.nn.elu(x)
if alpha == 1:
return res
else:
return tf.where(x > 0, res, alpha * res)
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 tf.nn.softmax(x, axis=axis)
def softplus(x):
"""Softplus of a tensor.
# Arguments
x: A tensor or variable.
# Returns
A tensor.
"""
return tf.nn.softplus(x)
def softsign(x):
"""Softsign of a tensor.
# Arguments
x: A tensor or variable.
# Returns
A tensor.
"""
return tf.nn.softsign(x)
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`.
"""
output_dimensions = list(range(len(output.get_shape())))
if axis != -1 and axis not in output_dimensions:
raise ValueError(
'{}{}{}'.format(
'Unexpected channels axis {}. '.format(axis),
'Expected to be -1 or one of the axes of `output`, ',
'which has {} dimensions.'.format(len(output.get_shape()))))
# Note: tf.nn.softmax_cross_entropy_with_logits
# expects logits, Keras expects probabilities.
if not from_logits:
# scale preds so that the class probas of each sample sum to 1
output /= tf.reduce_sum(output, axis, True)
# manual computation of crossentropy
_epsilon = _to_tensor(epsilon(), output.dtype.base_dtype)
output = tf.clip_by_value(output, _epsilon, 1. - _epsilon)
return - tf.reduce_sum(target * tf.log(output), axis)
else:
return tf.nn.softmax_cross_entropy_with_logits(labels=target,
logits=output)
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`.
"""
output_dimensions = list(range(len(output.get_shape())))
if axis != -1 and axis not in output_dimensions:
raise ValueError(
'{}{}{}'.format(
'Unexpected channels axis {}. '.format(axis),
'Expected to be -1 or one of the axes of `output`, ',
'which has {} dimensions.'.format(len(output.get_shape()))))
# If the channels are not in the last axis, move them to be there:
if axis != -1 and axis != output_dimensions[-1]:
permutation = output_dimensions[:axis] + output_dimensions[axis + 1:]
permutation += [axis]
output = tf.transpose(output, perm=permutation)
# Note: tf.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 = tf.clip_by_value(output, _epsilon, 1 - _epsilon)
output = tf.log(output)
output_shape = output.get_shape()
targets = cast(flatten(target), 'int64')
logits = tf.reshape(output, [-1, int(output_shape[-1])])
res = tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=targets,
logits=logits)
if len(output_shape) >= 3:
# if our output includes timestep dimension
# or spatial dimensions we need to reshape
return tf.reshape(res, tf.shape(output)[:-1])
else:
return res
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: tf.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 = tf.clip_by_value(output, _epsilon, 1 - _epsilon)
output = tf.log(output / (1 - output))
return tf.nn.sigmoid_cross_entropy_with_logits(labels=target,
logits=output)
def sigmoid(x):
"""Element-wise sigmoid.
# Arguments
x: A tensor or variable.
# Returns
A tensor.
"""
return tf.nn.sigmoid(x)
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 = tf.clip_by_value(x, zero, one)
return x
def tanh(x):
"""Element-wise tanh.
# Arguments
x: A tensor or variable.
# Returns
A tensor.
"""
return tf.nn.tanh(x)
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 tf.nn.dropout(x * 1., retain_prob, noise_shape, seed=seed)
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 tf.nn.l2_normalize(x, axis=axis)
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 tf.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.
"""
if dtype(x) == 'float64':
x = tf.cast(x, 'float32')
tf_data_format = 'NWC' # to pass TF Conv2dNative operations
if data_format == 'channels_first':
if not _has_nchw_support():
x = tf.transpose(x, (0, 2, 1)) # NCW -> NWC
else:
tf_data_format = 'NCW'
return x, tf_data_format
def _preprocess_conv2d_input(x, data_format):
"""Transpose and cast the input before the conv2d.
# Arguments
x: input tensor.
data_format: string, `"channels_last"` or `"channels_first"`.
# Returns
A tensor.
"""
if dtype(x) == 'float64':
x = tf.cast(x, 'float32')
tf_data_format = 'NHWC'
if data_format == 'channels_first':
if not _has_nchw_support():
x = tf.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.
"""
if dtype(x) == 'float64':
x = tf.cast(x, 'float32')
tf_data_format = 'NDHWC'
if data_format == 'channels_first':
if not _has_nchw_support():
x = tf.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, `"same"` or `"valid"`.
# Returns
a string, `"SAME"` or `"VALID"`.
# Raises
ValueError: if `padding` is invalid.
"""
if padding == 'same':
padding = 'SAME'
elif padding == 'valid':
padding = 'VALID'
else:
raise ValueError('Invalid padding: ' + str(padding))
return padding
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, `"channels_last"` or `"channels_first"`.
dilation_rate: integer dilate rate.
# Returns
A tensor, result of 1D convolution.
# Raises
ValueError: If `data_format` is neither
`"channels_last"` nor `"channels_first"`.
"""
data_format = normalize_data_format(data_format)
kernel_shape = kernel.get_shape().as_list()
if padding == 'causal':
if data_format != 'channels_last':
raise ValueError('When using causal padding in `conv1d`, '
'`data_format` must be "channels_last" '
'(temporal data).')
# 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 = tf.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 = tf.transpose(x, (0, 2, 1)) # NWC -> NCW
return x
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: 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 2D convolution.
# Raises
ValueError: If `data_format` is neither
`"channels_last"` nor `"channels_first"`.
"""
data_format = normalize_data_format(data_format)
x, tf_data_format = _preprocess_conv2d_input(x, data_format)
padding = _preprocess_padding(padding)
x = tf.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 = tf.transpose(x, (0, 3, 1, 2)) # NHWC -> NCHW
return x
def conv2d_transpose(x, kernel, output_shape, strides=(1, 1),
padding='valid', data_format=None):
"""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.
# Returns
A tensor, result of transposed 2D convolution.
# Raises
ValueError: If `data_format` is neither
`"channels_last"` nor `"channels_first"`.
"""
data_format = normalize_data_format(data_format)
if isinstance(output_shape, (tuple, list)):
output_shape = tf.stack(output_shape)
x, tf_data_format = _preprocess_conv2d_input(x, data_format)
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 = (tf.shape(x)[0],) + tuple(output_shape[1:])
output_shape = tf.stack(list(output_shape))
padding = _preprocess_padding(padding)
if tf_data_format == 'NHWC':
strides = (1,) + strides + (1,)
else:
strides = (1, 1) + strides
x = tf.nn.conv2d_transpose(x, kernel, output_shape, strides,
padding=padding,
data_format=tf_data_format)
if data_format == 'channels_first' and tf_data_format == 'NHWC':
x = tf.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"` nor `"channels_first"`.
"""
data_format = normalize_data_format(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)
if tf_data_format == 'NWC':
tf_data_format = 'NHWC'
else:
tf_data_format = 'NCHW'
padding = _preprocess_padding(padding)
if tf_data_format == 'NHWC':
spatial_start_dim = 1
strides = (1,) + strides * 2 + (1,)
else:
spatial_start_dim = 2
strides = (1, 1) + strides * 2
x = tf.expand_dims(x, spatial_start_dim)
depthwise_kernel = tf.expand_dims(depthwise_kernel, 0)
pointwise_kernel = tf.expand_dims(pointwise_kernel, 0)
dilation_rate = (1,) + dilation_rate
x = tf.nn.separable_conv2d(x, depthwise_kernel, pointwise_kernel,
strides=strides,
padding=padding,
rate=dilation_rate,
data_format=tf_data_format)
x = tf.squeeze(x, [spatial_start_dim])
if data_format == 'channels_first' and tf_data_format == 'NHWC':
x = tf.transpose(x, (0, 2, 1)) # NWC -> NCW
return x
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"` nor `"channels_first"`.
"""
data_format = normalize_data_format(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 = tf.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 = tf.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"` nor `"channels_first"`.
"""
data_format = normalize_data_format(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 = tf.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 = tf.transpose(x, (0, 3, 1, 2)) # NHWC -> NCHW
return x
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"` nor `"channels_first"`.
"""
data_format = normalize_data_format(data_format)
x, tf_data_format = _preprocess_conv3d_input(x, data_format)
padding = _preprocess_padding(padding)
x = tf.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 = tf.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"` nor `"channels_first"`.
"""
data_format = normalize_data_format(data_format)
if isinstance(output_shape, (tuple, list)):
output_shape = tf.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 = (tf.shape(x)[0],) + tuple(output_shape[1:])
output_shape = tf.stack(list(output_shape))
padding = _preprocess_padding(padding)
if tf_data_format == 'NDHWC':
strides = (1,) + strides + (1,)
else:
strides = (1, 1) + strides
x = tf.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 = tf.transpose(x, (0, 4, 1, 2, 3))
return x
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"`.
"""
data_format = normalize_data_format(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,)
pool_size = (1,) + pool_size + (1,)
else:
strides = (1, 1) + strides
pool_size = (1, 1) + pool_size
if pool_mode == 'max':
x = tf.nn.max_pool(x, pool_size, strides,
padding=padding,
data_format=tf_data_format)
elif pool_mode == 'avg':
x = tf.nn.avg_pool(x, pool_size, strides,
padding=padding,
data_format=tf_data_format)
else:
raise ValueError('Invalid pool_mode: ' + str(pool_mode))
if data_format == 'channels_first' and tf_data_format == 'NHWC':
x = tf.transpose(x, (0, 3, 1, 2)) # NHWC -> NCHW
return x
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"`.
"""
data_format = normalize_data_format(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 = tf.nn.max_pool3d(x, pool_size, strides,
padding=padding,
data_format=tf_data_format)
elif pool_mode == 'avg':
x = tf.nn.avg_pool3d(x, pool_size, strides,
padding=padding,
data_format=tf_data_format)
else:
raise ValueError('Invalid pool_mode: ' + str(pool_mode))
if data_format == 'channels_first' and tf_data_format == 'NDHWC':
x = tf.transpose(x, (0, 4, 1, 2, 3))
return x
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
"""
data_format = normalize_data_format(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)))
if ndim(x) == 5:
if data_format == 'channels_first':
if len(bias_shape) == 1:
x += reshape(bias, (1, bias_shape[0], 1, 1, 1))
else:
x += reshape(bias, (1, bias_shape[3]) + bias_shape[:3])
elif data_format == 'channels_last':
if len(bias_shape) == 1:
x += reshape(bias, (1, 1, 1, bias_shape[0]))
else:
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 = tf.nn.bias_add(x, bias,
data_format='NCHW')
else:
x += reshape(bias, (1, bias_shape[0], 1, 1))
else:
x += reshape(bias, (1, bias_shape[2]) + bias_shape[:2])
elif data_format == 'channels_last':
if len(bias_shape) == 1:
x = tf.nn.bias_add(x, bias,
data_format='NHWC')
else:
x += reshape(bias, (1,) + bias_shape)
elif ndim(x) == 3:
if data_format == 'channels_first':
if len(bias_shape) == 1:
x += reshape(bias, (1, bias_shape[0], 1))
else:
x += reshape(bias, (1, bias_shape[1], bias_shape[0]))
elif data_format == 'channels_last':
if len(bias_shape) == 1:
x += reshape(bias, (1, 1, bias_shape[0]))
else:
x += reshape(bias, (1, ) + bias_shape)
else:
x = tf.nn.bias_add(x, bias)
return x
# RANDOMNESS
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 tf.random_normal(shape, mean=mean, stddev=stddev,
dtype=dtype, seed=seed)
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 tf.random_uniform(shape, minval=minval, maxval=maxval,
dtype=dtype, seed=seed)
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 tf.where(tf.random_uniform(shape, dtype=dtype, seed=seed) <= p,
tf.ones(shape, dtype=dtype),
tf.zeros(shape, dtype=dtype))
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 tf.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
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 = tf.shape(labels)
num_batches_tns = tf.stack([label_shape[0]])
max_num_labels_tns = tf.stack([label_shape[1]])
def range_less_than(_, current_input):
return tf.expand_dims(tf.range(label_shape[1]), 0) < tf.fill(
max_num_labels_tns, current_input)
init = tf.cast(tf.fill([1, label_shape[1]], 0), tf.bool)
dense_mask = functional_ops.scan(range_less_than, label_lengths,
initializer=init, parallel_iterations=1)
dense_mask = dense_mask[:, 0, :]
label_array = tf.reshape(tf.tile(tf.range(label_shape[1]), num_batches_tns),
label_shape)
label_ind = tf.boolean_mask(label_array, dense_mask)
batch_array = tf.transpose(tf.reshape(tf.tile(tf.range(label_shape[0]),
max_num_labels_tns), reverse(label_shape, 0)))
batch_ind = tf.boolean_mask(batch_array, dense_mask)
indices = tf.transpose(tf.reshape(concatenate([batch_ind, label_ind], axis=0), [2, -1]))
vals_sparse = tf.gather_nd(labels, indices)
return tf.SparseTensor(tf.to_int64(indices), vals_sparse, tf.to_int64(label_shape))
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 = tf.to_int32(tf.squeeze(label_length, axis=-1))
input_length = tf.to_int32(tf.squeeze(input_length, axis=-1))
sparse_labels = tf.to_int32(ctc_label_dense_to_sparse(y_true, label_length))
y_pred = tf.log(tf.transpose(y_pred, perm=[1, 0, 2]) + epsilon())
return tf.expand_dims(ctc.ctc_loss(inputs=y_pred,
labels=sparse_labels,
sequence_length=input_length), 1)
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 = tf.log(tf.transpose(y_pred, perm=[1, 0, 2]) + epsilon())
input_length = tf.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 = [tf.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
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 tf.map_fn(fn, elems, name=name, dtype=dtype)
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 tf.foldl(fn, elems, initializer=initializer, name=name)
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
Tensor with same type and shape as `initializer`.
"""
return tf.foldr(fn, elems, initializer=initializer, name=name)
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)
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
the tensor after 1d conv with un-shared weights, with shape (batch_size, output_length, filters)
# Raises
ValueError: If `data_format` is neither
`"channels_last"` nor `"channels_first"`.
"""
data_format = normalize_data_format(data_format)
stride = strides[0]
kernel_shape = int_shape(kernel)
output_length, feature_dim, filters = kernel_shape
xs = []
for i in range(output_length):
slice_length = py_slice(i * stride,
i * stride + kernel_size[0])
xs.append(reshape(inputs[:, slice_length, :],
(1, -1, feature_dim)))
x_aggregate = concatenate(xs, axis=0)
# Shape: `(output_length, batch_size, filters)`.
output = batch_dot(x_aggregate, kernel)
return permute_dimensions(output, (1, 0, 2))
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'.
# Raises
ValueError: if `data_format` is neither
`channels_last` or `channels_first`.
"""
data_format = normalize_data_format(data_format)
stride_row, stride_col = strides
output_row, output_col = output_shape
kernel_shape = int_shape(kernel)
_, feature_dim, filters = kernel_shape
xs = []
for i in range(output_row):
for j in range(output_col):
slice_row = py_slice(i * stride_row,
i * stride_row + kernel_size[0])
slice_col = py_slice(j * stride_col,
j * stride_col + kernel_size[1])
if data_format == 'channels_first':
xs.append(reshape(inputs[:, :, slice_row, slice_col],
(1, -1, feature_dim)))
else:
xs.append(reshape(inputs[:, slice_row, slice_col, :],
(1, -1, feature_dim)))
x_aggregate = concatenate(xs, axis=0)
output = batch_dot(x_aggregate, kernel)
output = reshape(output,
(output_row, output_col, -1, filters))
if data_format == 'channels_first':
output = permute_dimensions(output, (2, 3, 0, 1))
else:
output = permute_dimensions(output, (2, 0, 1, 3))
return output
