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Make sure that changes to the global floatx are effectively taken into account by the backend. (#4739)

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This commit is contained in:
Julien Phalip 2016-12-16 16:59:01 -08:00 committed by François Chollet
parent 30fa61d457
commit 79406f111b
3 changed files with 117 additions and 34 deletions
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61
keras/backend/tensorflow_backend.py
View File

@ -12,7 +12,7 @@ import numpy as np
import os
import copy
import warnings
from .common import _FLOATX, _EPSILON, image_dim_ordering, reset_uids
from .common import floatx, _EPSILON, image_dim_ordering, reset_uids
py_all = all
# INTERNAL UTILS
@ -207,7 +207,7 @@ def to_dense(tensor):
return tensor
def variable(value, dtype=_FLOATX, name=None):
def variable(value, dtype=None, name=None):
'''Instantiates a variable and returns it.
# Arguments
@ -232,6 +232,8 @@ def variable(value, dtype=_FLOATX, name=None):
[ 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),
@ -271,7 +273,7 @@ def _initialize_variables():
sess.run(tf.initialize_variables(uninitialized_variables))
def placeholder(shape=None, ndim=None, dtype=_FLOATX, sparse=False, name=None):
def placeholder(shape=None, ndim=None, dtype=None, sparse=False, name=None):
'''Instantiates a placeholder tensor and returns it.
# Arguments
@ -296,6 +298,8 @@ def placeholder(shape=None, ndim=None, dtype=_FLOATX, sparse=False, name=None):
<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)])
@ -448,7 +452,7 @@ def eval(x):
return to_dense(x).eval(session=get_session())
def zeros(shape, dtype=_FLOATX, name=None):
def zeros(shape, dtype=None, name=None):
'''Instantiates an all-zeros variable and returns it.
# Arguments
@ -469,13 +473,15 @@ def zeros(shape, dtype=_FLOATX, name=None):
[ 0., 0., 0., 0.]], dtype=float32)
```
'''
if dtype is None:
dtype = floatx()
shape = tuple(map(int, shape))
tf_dtype = _convert_string_dtype(dtype)
return variable(tf.constant_initializer(0., dtype=tf_dtype)(shape),
dtype, name)
def ones(shape, dtype=_FLOATX, name=None):
def ones(shape, dtype=None, name=None):
'''Instantiates an all-ones tensor variable and returns it.
# Arguments
@ -498,13 +504,15 @@ def ones(shape, dtype=_FLOATX, name=None):
[ 1., 1., 1., 1.]], dtype=float32)
```
'''
if dtype is None:
dtype = floatx()
shape = tuple(map(int, shape))
tf_dtype = _convert_string_dtype(dtype)
return variable(tf.constant_initializer(1., dtype=tf_dtype)(shape),
dtype, name)
def eye(size, dtype=_FLOATX, name=None):
def eye(size, dtype=None, name=None):
'''Instantiate an identity matrix and returns it.
# Arguments
@ -577,7 +585,7 @@ def ones_like(x, name=None):
return tf.ones_like(x, name=name)
def random_uniform_variable(shape, low, high, dtype=_FLOATX,
def random_uniform_variable(shape, low, high, dtype=None,
name=None, seed=None):
'''Instantiates an Keras variable filled with
samples drawn from a uniform distribution and returns it.
@ -609,6 +617,8 @@ def random_uniform_variable(shape, low, high, dtype=_FLOATX,
[ 0.66137183, 0.00869417, 0.89220798]], dtype=float32)
```
'''
if dtype is None:
dtype = floatx()
shape = tuple(map(int, shape))
tf_dtype = _convert_string_dtype(dtype)
if seed is None:
@ -619,7 +629,7 @@ def random_uniform_variable(shape, low, high, dtype=_FLOATX,
return variable(value, dtype=dtype, name=name)
def random_normal_variable(shape, mean, scale, dtype=_FLOATX,
def random_normal_variable(shape, mean, scale, dtype=None,
name=None, seed=None):
'''Instantiates an Keras variable filled with
samples drawn from a normal distribution and returns it.
@ -651,6 +661,8 @@ def random_normal_variable(shape, mean, scale, dtype=_FLOATX,
[ 0.92629528, 0.28055015, 1.70484698]], dtype=float32)
```
'''
if dtype is None:
dtype = floatx()
shape = tuple(map(int, shape))
tf_dtype = _convert_string_dtype(dtype)
if seed is None:
@ -963,7 +975,7 @@ def var(x, axis=None, keepdims=False):
'''
axis = _normalize_axis(axis, ndim(x))
if x.dtype.base_dtype == tf.bool:
x = tf.cast(x, _FLOATX)
x = tf.cast(x, floatx())
m = tf.reduce_mean(x, reduction_indices=axis, keep_dims=True)
devs_squared = tf.square(x - m)
return tf.reduce_mean(devs_squared,
@ -982,7 +994,7 @@ def mean(x, axis=None, keepdims=False):
'''
axis = _normalize_axis(axis, ndim(x))
if x.dtype.base_dtype == tf.bool:
x = tf.cast(x, _FLOATX)
x = tf.cast(x, floatx())
return tf.reduce_mean(x, reduction_indices=axis, keep_dims=keepdims)
@ -2073,7 +2085,7 @@ def _preprocess_deconv_output_shape(shape, dim_ordering):
def _preprocess_conv2d_input(x, dim_ordering):
if _FLOATX == 'float64':
if dtype(x) == 'float64':
x = tf.cast(x, 'float32')
if dim_ordering == 'th':
# TF uses the last dimension as channel dimension,
@ -2085,7 +2097,7 @@ def _preprocess_conv2d_input(x, dim_ordering):
def _preprocess_conv3d_input(x, dim_ordering):
if _FLOATX == 'float64':
if dtype(x) == 'float64':
x = tf.cast(x, 'float32')
if dim_ordering == 'th':
# TF uses the last dimension as channel dimension,
@ -2097,7 +2109,7 @@ def _preprocess_conv3d_input(x, dim_ordering):
def _preprocess_conv2d_kernel(kernel, dim_ordering):
if _FLOATX == 'float64':
if dtype(kernel) == 'float64':
kernel = tf.cast(kernel, 'float32')
if dim_ordering == 'th':
# TF uses the last dimension as channel dimension,
@ -2109,7 +2121,7 @@ def _preprocess_conv2d_kernel(kernel, dim_ordering):
def _preprocess_conv3d_kernel(kernel, dim_ordering):
if _FLOATX == 'float64':
if dtype(kernel) == 'float64':
kernel = tf.cast(kernel, 'float32')
if dim_ordering == 'th':
# TF uses the last dimension as channel dimension,
@ -2134,7 +2146,7 @@ def _postprocess_conv2d_output(x, dim_ordering):
if dim_ordering == 'th':
x = tf.transpose(x, (0, 3, 1, 2))
if _FLOATX == 'float64':
if floatx() == 'float64':
x = tf.cast(x, 'float64')
return x
@ -2143,7 +2155,7 @@ def _postprocess_conv3d_output(x, dim_ordering):
if dim_ordering == 'th':
x = tf.transpose(x, (0, 4, 1, 2, 3))
if _FLOATX == 'float64':
if floatx() == 'float64':
x = tf.cast(x, 'float64')
return x
@ -2158,13 +2170,14 @@ def conv1d(x, kernel, stride=1, border_mode='valid',
border_mode: string, "same" or "valid".
'''
# pre-process dtype
if _FLOATX == 'float64':
x_dtype = dtype(x)
if x_dtype == 'float64':
x = tf.cast(x, 'float32')
kernel = tf.cast(kernel, 'float32')
padding = _preprocess_border_mode(border_mode)
x = tf.nn.conv1d(x, kernel, stride, padding=padding)
# post-process dtype
if _FLOATX == 'float64':
if x_dtype == 'float64':
x = tf.cast(x, 'float64')
return x
@ -2367,21 +2380,27 @@ def pool3d(x, pool_size, strides=(1, 1, 1), border_mode='valid',
# RANDOMNESS
def random_normal(shape, mean=0.0, std=1.0, dtype=_FLOATX, seed=None):
def random_normal(shape, mean=0.0, std=1.0, dtype=None, seed=None):
if dtype is None:
dtype = floatx()
if seed is None:
seed = np.random.randint(10e6)
return tf.random_normal(shape, mean=mean, stddev=std,
dtype=dtype, seed=seed)
def random_uniform(shape, low=0.0, high=1.0, dtype=_FLOATX, seed=None):
def random_uniform(shape, low=0.0, high=1.0, dtype=None, seed=None):
if dtype is None:
dtype = floatx()
if seed is None:
seed = np.random.randint(10e6)
return tf.random_uniform(shape, minval=low, maxval=high,
dtype=dtype, seed=seed)
def random_binomial(shape, p=0.0, dtype=_FLOATX, seed=None):
def random_binomial(shape, p=0.0, dtype=None, seed=None):
if dtype is None:
dtype = floatx()
if seed is None:
seed = np.random.randint(10e6)
return tf.select(tf.random_uniform(shape, dtype=dtype, seed=seed) <= p,

40
keras/backend/theano_backend.py
View File

@ -14,7 +14,7 @@ except ImportError:
from theano.sandbox.softsign import softsign as T_softsign
import inspect
import numpy as np
from .common import _FLOATX, _EPSILON, image_dim_ordering
from .common import _FLOATX, floatx, _EPSILON, image_dim_ordering
py_all = all
@ -56,9 +56,11 @@ def to_dense(tensor):
return tensor
def variable(value, dtype=_FLOATX, name=None):
def variable(value, dtype=None, name=None):
'''Instantiates a variable.
'''
if dtype is None:
dtype = floatx()
if hasattr(value, 'tocoo'):
_assert_sparse_module()
return th_sparse_module.as_sparse_variable(value)
@ -67,9 +69,11 @@ def variable(value, dtype=_FLOATX, name=None):
return theano.shared(value=value, name=name, strict=False)
def placeholder(shape=None, ndim=None, dtype=_FLOATX, sparse=False, name=None):
def placeholder(shape=None, ndim=None, dtype=None, sparse=False, name=None):
'''Instantiate an input data placeholder variable.
'''
if dtype is None:
dtype = floatx()
if shape is None and ndim is None:
raise ValueError('Specify either a shape or ndim value.')
if shape is not None:
@ -111,21 +115,27 @@ def eval(x):
return to_dense(x).eval()
def zeros(shape, dtype=_FLOATX, name=None):
def zeros(shape, dtype=None, name=None):
'''Instantiates an all-zeros variable.
'''
if dtype is None:
dtype = floatx()
return variable(np.zeros(shape), dtype, name)
def ones(shape, dtype=_FLOATX, name=None):
def ones(shape, dtype=None, name=None):
'''Instantiates an all-ones variable.
'''
if dtype is None:
dtype = floatx()
return variable(np.ones(shape), dtype, name)
def eye(size, dtype=_FLOATX, name=None):
def eye(size, dtype=None, name=None):
'''Instantiates an identity matrix.
'''
if dtype is None:
dtype = floatx()
return variable(np.eye(size), dtype, name)
@ -137,12 +147,12 @@ def zeros_like(x, name=None):
return T.zeros_like(x)
def random_uniform_variable(shape, low, high, dtype=_FLOATX, name=None):
def random_uniform_variable(shape, low, high, dtype=None, name=None):
return variable(np.random.uniform(low=low, high=high, size=shape),
dtype=dtype, name=name)
def random_normal_variable(shape, mean, scale, dtype=_FLOATX, name=None):
def random_normal_variable(shape, mean, scale, dtype=None, name=None):
return variable(np.random.normal(loc=0.0, scale=scale, size=shape),
dtype=dtype, name=name)
@ -284,7 +294,7 @@ def mean(x, axis=None, keepdims=False):
dtype = None
# bool is available since theano v0.9dev
if 'int' in x.dtype or x.dtype == 'bool':
dtype = _FLOATX
dtype = floatx()
return T.mean(x, axis=axis, keepdims=keepdims, dtype=dtype)
@ -1799,21 +1809,27 @@ def _old_theano_pool3d(x, pool_size, strides=(1, 1, 1), border_mode='valid',
# RANDOMNESS
def random_normal(shape, mean=0.0, std=1.0, dtype=_FLOATX, seed=None):
def random_normal(shape, mean=0.0, std=1.0, dtype=None, seed=None):
if dtype is None:
dtype = floatx()
if seed is None:
seed = np.random.randint(1, 10e6)
rng = RandomStreams(seed=seed)
return rng.normal(size=shape, avg=mean, std=std, dtype=dtype)
def random_uniform(shape, low=0.0, high=1.0, dtype=_FLOATX, seed=None):
def random_uniform(shape, low=0.0, high=1.0, dtype=None, seed=None):
if dtype is None:
dtype = floatx()
if seed is None:
seed = np.random.randint(1, 10e6)
rng = RandomStreams(seed=seed)
return rng.uniform(shape, low=low, high=high, dtype=dtype)
def random_binomial(shape, p=0.0, dtype=_FLOATX, seed=None):
def random_binomial(shape, p=0.0, dtype=None, seed=None):
if dtype is None:
dtype = floatx()
if seed is None:
seed = np.random.randint(1, 10e6)
rng = RandomStreams(seed=seed)

50
tests/keras/backend/test_backends.py
View File

@ -3,11 +3,19 @@ from numpy.testing import assert_allclose
import numpy as np
import scipy.sparse as sparse
from keras.backend import theano_backend as KTH
from keras import backend as K
from keras.backend import theano_backend as KTH, floatx, set_floatx, variable
from keras.backend import tensorflow_backend as KTF
from keras.utils.np_utils import convert_kernel
def check_dtype(var, dtype):
if K._BACKEND == 'theano':
assert var.dtype == dtype
else:
assert var.dtype.name == '%s_ref' % dtype
def check_single_tensor_operation(function_name, input_shape, **kwargs):
val = np.random.random(input_shape) - 0.5
xth = KTH.variable(val)
@ -930,6 +938,46 @@ class TestBackend(object):
t = backend.arange(10, dtype=dtype)
assert backend.dtype(t) == dtype
def test_setfloatx_incorrect_values(self):
# Keep track of the old value
old_floatx = floatx()
# Try some incorrect values
initial = floatx()
for value in ['', 'beerfloat', 123]:
with pytest.raises(Exception):
set_floatx(value)
assert floatx() == initial
# Restore old value
set_floatx(old_floatx)
def test_setfloatx_correct_values(self):
# Keep track of the old value
old_floatx = floatx()
# Check correct values
for value in ['float16', 'float32', 'float64']:
set_floatx(value)
assert floatx() == value
# Restore old value
set_floatx(old_floatx)
def test_set_floatx(self):
"""
Make sure that changes to the global floatx are effectively
taken into account by the backend.
"""
# Keep track of the old value
old_floatx = floatx()
set_floatx('float16')
var = variable([10])
check_dtype(var, 'float16')
set_floatx('float64')
var = variable([10])
check_dtype(var, 'float64')
# Restore old value
set_floatx(old_floatx)
if __name__ == '__main__':
pytest.main([__file__])