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Add TF/TH kernel conversion util

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This commit is contained in:
Francois Chollet 2016-04-15 22:37:41 -07:00
parent 2eea3a4c5d
commit ed0cd2c60d
2 changed files with 71 additions and 30 deletions
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24
keras/utils/np_utils.py
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@ -50,3 +50,27 @@ def probas_to_classes(y_pred):
def categorical_probas_to_classes(p):
return np.argmax(p, axis=1)
def convert_kernel(kernel, dim_ordering='th'):
'''Converts a kernel matrix (numpy array)
from Theano format to TensorFlow format
(or reciprocally, since the transformation
is its own inverse).
'''
new_kernel = np.copy(kernel)
if dim_ordering == 'th':
w = kernel.shape[2]
h = kernel.shape[3]
for i in range(w):
for j in range(h):
new_kernel[:, :, i, j] = kernel[:, :, w - i - 1, h - j - 1]
elif dim_ordering == 'tf':
w = kernel.shape[0]
h = kernel.shape[1]
for i in range(w):
for j in range(h):
new_kernel[i, j, :, :] = kernel[w - i - 1, h - j - 1, :, :]
else:
raise Exception('Invalid dim_ordering: ' + str(dim_ordering))
return new_kernel

77
tests/keras/backend/test_backends.py
View File

@ -5,6 +5,7 @@ import numpy as np
from keras.backend import theano_backend as KTH
from keras.backend import tensorflow_backend as KTF
from keras.utils.np_utils import convert_kernel
def check_single_tensor_operation(function_name, input_shape, **kwargs):
@ -22,10 +23,12 @@ def check_single_tensor_operation(function_name, input_shape, **kwargs):
def check_two_tensor_operation(function_name, x_input_shape,
y_input_shape, **kwargs):
xval = np.random.random(x_input_shape) - 0.5
xth = KTH.variable(xval)
xtf = KTF.variable(xval)
yval = np.random.random(y_input_shape) - 0.5
yth = KTH.variable(yval)
ytf = KTF.variable(yval)
@ -369,42 +372,56 @@ class TestBackend(object):
check_single_tensor_operation('l2_normalize', (4, 3), axis=-1)
check_single_tensor_operation('l2_normalize', (4, 3), axis=1)
# def test_conv2d(self):
# '''conv2d works "properly" with Theano and TF but outputs different
# values in each case. Cause unclear (input / kernel shape format?)
# '''
# # TH kernel shape: (depth, input_depth, rows, cols)
# check_two_tensor_operation('conv2d', (5, 3, 10, 12), (4, 3, 2, 2),
# strides=(1, 1), border_mode='valid')
# check_two_tensor_operation('conv2d', (5, 3, 10, 12), (4, 3, 2, 2),
# strides=(1, 1), border_mode='same')
def test_conv2d(self):
# TH kernel shape: (depth, input_depth, rows, cols)
# TF kernel shape: (rows, cols, input_depth, depth)
# # TF kernel shape: (rows, cols, input_depth, depth)
# check_two_tensor_operation('conv2d', (5, 10, 12, 3), (2, 2, 3, 4),
# strides=(1, 1), border_mode='valid', dim_ordering='tf')
# check_two_tensor_operation('conv2d', (5, 10, 12, 3), (2, 2, 3, 4),
# strides=(1, 1), border_mode='same', dim_ordering='tf')
for input_shape in [(2, 3, 4, 5), (2, 3, 5, 6)]:
for kernel_shape in [(4, 3, 2, 2), (4, 3, 3, 4)]:
xval = np.random.random(input_shape)
# check_two_tensor_operation('conv2d', (5, 3, 10, 12), (4, 3, 3, 3),
# strides=(1, 1), border_mode='valid')
# check_two_tensor_operation('conv2d', (5, 3, 10, 12), (4, 3, 3, 3),
# strides=(1, 1), border_mode='same')
xth = KTH.variable(xval)
xtf = KTF.variable(xval)
# check_two_tensor_operation('conv2d', (5, 3, 10, 12), (4, 3, 3, 3),
# strides=(2, 2), border_mode='valid')
kernel_val = np.random.random(kernel_shape) - 0.5
# def test_pool2d(self):
# '''pool2d works "properly" with Theano and TF but outputs different
# values in each case. Cause unclear (input shape format?)
# '''
# check_single_tensor_operation('pool2d', (5, 3, 10, 12), pool_size=(2, 2),
# strides=(1, 1), border_mode='valid')
kernel_th = KTH.variable(convert_kernel(kernel_val))
kernel_tf = KTF.variable(kernel_val)
# check_single_tensor_operation('pool2d', (5, 3, 9, 11), pool_size=(2, 2),
# strides=(1, 1), border_mode='valid')
zth = KTH.eval(KTH.conv2d(xth, kernel_th))
ztf = KTF.eval(KTF.conv2d(xtf, kernel_tf))
# check_single_tensor_operation('pool2d', (5, 3, 9, 11), pool_size=(2, 3),
# strides=(1, 1), border_mode='valid')
assert zth.shape == ztf.shape
assert_allclose(zth, ztf, atol=1e-05)
input_shape = (1, 6, 5, 3)
kernel_shape = (3, 3, 3, 2)
xval = np.random.random(input_shape)
xth = KTH.variable(xval)
xtf = KTF.variable(xval)
kernel_val = np.random.random(kernel_shape) - 0.5
kernel_th = KTH.variable(convert_kernel(kernel_val, dim_ordering='tf'))
kernel_tf = KTF.variable(kernel_val)
zth = KTH.eval(KTH.conv2d(xth, kernel_th, dim_ordering='tf'))
ztf = KTF.eval(KTF.conv2d(xtf, kernel_tf, dim_ordering='tf'))
assert zth.shape == ztf.shape
assert_allclose(zth, ztf, atol=1e-05)
def test_pool2d(self):
check_single_tensor_operation('pool2d', (5, 3, 10, 12), pool_size=(2, 2),
strides=(1, 1), border_mode='valid')
check_single_tensor_operation('pool2d', (5, 3, 9, 11), pool_size=(2, 2),
strides=(1, 1), border_mode='valid')
check_single_tensor_operation('pool2d', (5, 3, 9, 11), pool_size=(2, 3),
strides=(1, 1), border_mode='valid')
def test_random_normal(self):
mean = 0.