def test_gradient(self):
val = np.random.random((4, 2))
xth = KTH.variable(val)
xtf = KTF.variable(val)
expth = xth * KTH.exp(xth)
exptf = xtf * KTF.exp(xtf)
lossth = KTH.sum(expth)
losstf = KTF.sum(exptf)
zero_lossth = KTH.stop_gradient(lossth)
zero_losstf = KTF.stop_gradient(losstf)
gradth = KTH.gradients(lossth, [expth])
gradtf = KTF.gradients(losstf, [exptf])
zero_gradth = KTH.gradients(lossth + zero_lossth, [expth])
zero_gradtf = KTF.gradients(losstf + zero_losstf, [exptf])
zth = KTH.eval(gradth[0])
ztf = KTF.eval(gradtf[0])
zero_zth = KTH.eval(zero_gradth[0])
zero_ztf = KTF.eval(zero_gradtf[0])
assert zth.shape == ztf.shape
assert zero_zth.shape == zero_ztf.shape
assert_allclose(zth, ztf, atol=1e-05)
assert_allclose(zero_zth, zero_ztf, atol=1e-05)
assert_allclose(zero_zth, zth, atol=1e-05)
assert_allclose(zero_ztf, ztf, atol=1e-05)
def test_rnn(self):
# implement a simple RNN
input_dim = 8
output_dim = 4
timesteps = 5
input_val = np.random.random((32, timesteps, input_dim))
init_state_val = np.random.random((32, output_dim))
W_i_val = np.random.random((input_dim, output_dim))
W_o_val = np.random.random((output_dim, output_dim))
def rnn_step_fn(input_dim, output_dim, K):
W_i = K.variable(W_i_val)
W_o = K.variable(W_o_val)
def step_function(x, states):
assert len(states) == 1
prev_output = states[0]
output = K.dot(x, W_i) + K.dot(prev_output, W_o)
return output, [output]
return step_function
th_rnn_step_fn = rnn_step_fn(input_dim, output_dim, KTH)
inputs = KTH.variable(input_val)
initial_states = [KTH.variable(init_state_val)]
last_output, outputs, new_states = KTH.rnn(th_rnn_step_fn, inputs,
initial_states,
go_backwards=False,
masking=False)
th_last_output = KTH.eval(last_output)
th_outputs = KTH.eval(outputs)
assert len(new_states) == 1
th_state = KTH.eval(new_states[0])
tf_rnn_step_fn = rnn_step_fn(input_dim, output_dim, KTF)
inputs = KTF.variable(input_val)
initial_states = [KTF.variable(init_state_val)]
last_output, outputs, new_states = KTF.rnn(tf_rnn_step_fn, inputs,
initial_states,
go_backwards=False,
masking=False)
tf_last_output = KTF.eval(last_output)
tf_outputs = KTF.eval(outputs)
assert len(new_states) == 1
tf_state = KTF.eval(new_states[0])
assert_allclose(tf_last_output, th_last_output, atol=1e-04)
assert_allclose(tf_outputs, th_outputs, atol=1e-04)
assert_allclose(tf_state, th_state, atol=1e-04)
def test_repeat_elements(self):
reps = 3
for ndims in [1, 2, 3]:
shape = np.arange(2, 2 + ndims)
arr = np.arange(np.prod(shape)).reshape(shape)
arr_th = KTH.variable(arr)
arr_tf = KTF.variable(arr)
for rep_axis in range(ndims):
np_rep = np.repeat(arr, reps, axis=rep_axis)
th_z = KTH.repeat_elements(arr_th, reps, axis=rep_axis)
th_rep = KTH.eval(th_z)
tf_rep = KTF.eval(
KTF.repeat_elements(arr_tf, reps, axis=rep_axis))
assert th_rep.shape == np_rep.shape
assert tf_rep.shape == np_rep.shape
assert_allclose(np_rep, th_rep, atol=1e-05)
assert_allclose(np_rep, tf_rep, atol=1e-05)
if hasattr(th_z, '_keras_shape'):
assert th_z._keras_shape == th_rep.shape
# test theano shape inference when
# input shape has None entries
if K.backend() == 'theano':
shape = list(shape)
shape[rep_axis] = None
x = K.placeholder(shape=shape)
y = K.repeat_elements(x, reps, axis=rep_axis)
assert y._keras_shape == tuple(shape)
def test_gather(self):
shape = (10, 2, 3)
ref = np.arange(np.prod(shape)).reshape(shape)
ref_th = KTH.variable(ref)
ref_tf = KTF.variable(ref)
inds = [1, 3, 7, 9]
inds_th = KTH.variable(inds, dtype='int32')
inds_tf = KTF.variable(inds, dtype='int32')
th_z = KTH.gather(ref_th, inds_th)
th_result = KTH.eval(th_z)
tf_result = KTF.eval(KTF.gather(ref_tf, inds_tf))
assert_allclose(tf_result, th_result, atol=1e-05)
if hasattr(th_z, '_keras_shape'):
assert th_z._keras_shape == th_result.shape
# test theano shape inference when
# input shape has None entries
if K.backend() == 'theano':
x = K.placeholder(shape=(None, 3, 4))
indices = K.placeholder(shape=(5, 6), dtype='int32')
y = K.gather(x, indices)
assert y._keras_shape == (5, 6, 3, 4)
def test_shape_operations(self):
# concatenate
xval = np.random.random((4, 3))
xth = KTH.variable(xval)
xtf = KTF.variable(xval)
yval = np.random.random((4, 2))
yth = KTH.variable(yval)
ytf = KTF.variable(yval)
zth = KTH.eval(KTH.concatenate([xth, yth], axis=-1))
ztf = KTF.eval(KTF.concatenate([xtf, ytf], axis=-1))
assert zth.shape == ztf.shape
assert_allclose(zth, ztf, atol=1e-05)
check_single_tensor_operation('reshape', (4, 2), shape=(8, 1))
check_single_tensor_operation('permute_dimensions', (4, 2, 3),
pattern=(2, 0, 1))
check_single_tensor_operation('repeat', (4, 1), n=3)
check_single_tensor_operation('flatten', (4, 1))
check_single_tensor_operation('expand_dims', (4, 3), dim=-1)
check_single_tensor_operation('expand_dims', (4, 3, 2), dim=1)
check_single_tensor_operation('squeeze', (4, 3, 1), axis=2)
check_single_tensor_operation('squeeze', (4, 1, 1), axis=1)
check_composed_tensor_operations('reshape', {'shape': (4, 3, 1, 1)},
'squeeze', {'axis': 2},
(4, 3, 1, 1))
def test_nn_operations(self):
check_single_tensor_operation('relu', (4, 2), alpha=0.1, max_value=0.5)
check_single_tensor_operation('softmax', (4, 10))
check_single_tensor_operation('softplus', (4, 10))
check_single_tensor_operation('sigmoid', (4, 2))
check_single_tensor_operation('hard_sigmoid', (4, 2))
check_single_tensor_operation('tanh', (4, 2))
# dropout
val = np.random.random((100, 100))
xth = KTH.variable(val)
xtf = KTF.variable(val)
zth = KTH.eval(KTH.dropout(xth, level=0.2))
ztf = KTF.eval(KTF.dropout(xtf, level=0.2))
assert zth.shape == ztf.shape
# dropout patterns are different, only check mean
assert np.abs(zth.mean() - ztf.mean()) < 0.05
check_two_tensor_operation('binary_crossentropy', (4, 2), (4, 2), from_logits=True)
check_two_tensor_operation('categorical_crossentropy', (4, 2), (4, 2), from_logits=True)
check_two_tensor_operation('binary_crossentropy', (4, 2), (4, 2), from_logits=False)
check_two_tensor_operation('categorical_crossentropy', (4, 2), (4, 2), from_logits=False)
check_single_tensor_operation('l2_normalize', (4, 3), axis=-1)
check_single_tensor_operation('l2_normalize', (4, 3), axis=1)
def test_conv3d(self):
# TH input shape: (samples, input_depth, conv_dim1, conv_dim2, conv_dim3)
# TF input shape: (samples, conv_dim1, conv_dim2, conv_dim3, input_depth)
# TH kernel shape: (depth, input_depth, x, y, z)
# TF kernel shape: (x, y, z, input_depth, depth)
# test in dim_ordering = th
for input_shape in [(2, 3, 4, 5, 4), (2, 3, 5, 4, 6)]:
for kernel_shape in [(4, 3, 2, 2, 2), (4, 3, 3, 2, 4)]:
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))
kernel_tf = KTF.variable(kernel_val)
zth = KTH.eval(KTH.conv3d(xth, kernel_th))
ztf = KTF.eval(KTF.conv3d(xtf, kernel_tf))
assert zth.shape == ztf.shape
assert_allclose(zth, ztf, atol=1e-05)
# test in dim_ordering = tf
input_shape = (1, 2, 2, 2, 1)
kernel_shape = (2, 2, 2, 1, 1)
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.conv3d(xth, kernel_th, dim_ordering='tf'))
ztf = KTF.eval(KTF.conv3d(xtf, kernel_tf, dim_ordering='tf'))
assert zth.shape == ztf.shape
assert_allclose(zth, ztf, atol=1e-05)
def test_rnn_no_states(self):
# implement a simple RNN without states
input_dim = 8
output_dim = 4
timesteps = 5
input_val = np.random.random((32, timesteps, input_dim))
W_i_val = np.random.random((input_dim, output_dim))
def rnn_step_fn(input_dim, output_dim, K):
W_i = K.variable(W_i_val)
def step_function(x, states):
assert len(states) == 0
output = K.dot(x, W_i)
return output, []
return step_function
# test default setup
th_rnn_step_fn = rnn_step_fn(input_dim, output_dim, KTH)
th_inputs = KTH.variable(input_val)
th_initial_states = []
last_output, outputs, new_states = KTH.rnn(th_rnn_step_fn, th_inputs,
th_initial_states,
go_backwards=False,
mask=None)
th_last_output = KTH.eval(last_output)
th_outputs = KTH.eval(outputs)
assert len(new_states) == 0
tf_rnn_step_fn = rnn_step_fn(input_dim, output_dim, KTF)
tf_inputs = KTF.variable(input_val)
tf_initial_states = []
last_output, outputs, new_states = KTF.rnn(tf_rnn_step_fn, tf_inputs,
tf_initial_states,
go_backwards=False,
mask=None)
tf_last_output = KTF.eval(last_output)
tf_outputs = KTF.eval(outputs)
assert len(new_states) == 0
assert_allclose(tf_last_output, th_last_output, atol=1e-04)
assert_allclose(tf_outputs, th_outputs, atol=1e-04)
def check_single_tensor_operation(function_name, input_shape, **kwargs):
val = np.random.random(input_shape) - 0.5
xth = KTH.variable(val)
xtf = KTF.variable(val)
zth = KTH.eval(getattr(KTH, function_name)(xth, **kwargs))
ztf = KTF.eval(getattr(KTF, function_name)(xtf, **kwargs))
assert zth.shape == ztf.shape
assert_allclose(zth, ztf, atol=1e-05)
def test_tile(self):
shape = (3, 4)
arr = np.arange(np.prod(shape)).reshape(shape)
arr_th = KTH.variable(arr)
arr_tf = KTF.variable(arr)
n = (2, 1)
th_rep = KTH.eval(KTH.tile(arr_th, n))
tf_rep = KTF.eval(KTF.tile(arr_tf, n))
assert_allclose(tf_rep, th_rep, atol=1e-05)
def test_arange(self):
for test_value in (-20, 0, 1, 10):
t_a = KTF.arange(test_value)
a = KTF.eval(t_a)
assert np.array_equal(a, np.arange(test_value))
t_b = KTH.arange(test_value)
b = KTH.eval(t_b)
assert np.array_equal(b, np.arange(test_value))
assert np.array_equal(a, b)
assert KTF.dtype(t_a) == KTH.dtype(t_b)
for start, stop, step in ((0, 5, 1), (-5, 5, 2), (0, 1, 2)):
a = KTF.eval(KTF.arange(start, stop, step))
assert np.array_equal(a, np.arange(start, stop, step))
b = KTH.eval(KTH.arange(start, stop, step))
assert np.array_equal(b, np.arange(start, stop, step))
assert np.array_equal(a, b)
for dtype in ('int32', 'int64', 'float32', 'float64'):
for backend in (KTF, KTH):
t = backend.arange(10, dtype=dtype)
assert backend.dtype(t) == dtype
def test_conv2d(self):
# TF kernel shape: (rows, cols, input_depth, depth)
# channels_first input shape: (n, input_depth, rows, cols)
for input_shape in [(2, 3, 4, 5), (2, 3, 5, 6)]:
for kernel_shape in [(2, 2, 3, 4), (4, 3, 3, 4)]:
for padding in ['valid', 'same']:
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))
kernel_tf = KTF.variable(kernel_val)
zth = KTH.eval(KTH.conv2d(xth, kernel_th, data_format='channels_first'))
ztf = KTF.eval(KTF.conv2d(xtf, kernel_tf, data_format='channels_first'))
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))
kernel_tf = KTF.variable(kernel_val)
zth = KTH.eval(KTH.conv2d(xth, kernel_th, data_format='channels_last'))
ztf = KTF.eval(KTF.conv2d(xtf, kernel_tf, data_format='channels_last'))
assert zth.shape == ztf.shape
assert_allclose(zth, ztf, atol=1e-05)
def test_random_normal(self):
mean = 0.
std = 1.
rand = KTF.eval(KTF.random_normal((1000, 1000), mean=mean, std=std))
assert(rand.shape == (1000, 1000))
assert(np.abs(np.mean(rand) - mean) < 0.01)
assert(np.abs(np.std(rand) - std) < 0.01)
rand = KTH.eval(KTH.random_normal((1000, 1000), mean=mean, std=std))
assert(rand.shape == (1000, 1000))
assert(np.abs(np.mean(rand) - mean) < 0.01)
assert(np.abs(np.std(rand) - std) < 0.01)
def test_conv2d(self):
# TH kernel shape: (depth, input_depth, rows, cols)
# TF kernel shape: (rows, cols, input_depth, depth)
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)
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))
kernel_tf = KTF.variable(kernel_val)
zth = KTH.eval(KTH.conv2d(xth, kernel_th))
ztf = KTF.eval(KTF.conv2d(xtf, kernel_tf))
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_tile(self):
shape = (3, 4)
arr = np.arange(np.prod(shape)).reshape(shape)
arr_th = KTH.variable(arr)
arr_tf = KTF.variable(arr)
n = (2, 1)
th_z = KTH.tile(arr_th, n)
th_rep = KTH.eval(th_z)
tf_rep = KTF.eval(KTF.tile(arr_tf, n))
assert_allclose(tf_rep, th_rep, atol=1e-05)
if hasattr(th_z, '_keras_shape'):
assert th_z._keras_shape == th_rep.shape
def test_random_binomial(self):
p = 0.5
rand = KTF.eval(KTF.random_binomial((1000, 1000), p))
assert(rand.shape == (1000, 1000))
assert(np.abs(np.mean(rand) - p) < 0.01)
assert(np.max(rand) == 1)
assert(np.min(rand) == 0)
rand = KTH.eval(KTH.random_binomial((1000, 1000), p))
assert(rand.shape == (1000, 1000))
assert(np.abs(np.mean(rand) - p) < 0.01)
assert(np.max(rand) == 1)
assert(np.min(rand) == 0)
def test_switch(self):
val = np.random.random()
xth = KTH.variable(val)
xth = KTH.switch(xth >= 0.5, xth * 0.1, xth * 0.2)
xtf = KTF.variable(val)
xtf = KTF.switch(xtf >= 0.5, xtf * 0.1, xtf * 0.2)
zth = KTH.eval(xth)
ztf = KTF.eval(xtf)
assert zth.shape == ztf.shape
assert_allclose(zth, ztf, atol=1e-05)
def test_in_top_k(self):
batch_size = 20
num_classes = 10
# Random prediction test case
predictions = np.random.random((batch_size, num_classes)).astype('float32')
targets = np.random.randint(num_classes, size=batch_size, dtype='int32')
predictions_th = KTH.variable(predictions, dtype='float32')
targets_th = KTH.variable(targets, dtype='int32')
predictions_tf = KTF.variable(predictions, dtype='float32')
targets_tf = KTF.variable(targets, dtype='int32')
for k in range(1, num_classes + 1):
res_th = KTH.eval(KTH.in_top_k(predictions_th, targets_th, k))
res_tf = KTF.eval(KTF.in_top_k(predictions_tf, targets_tf, k))
assert res_th.shape == res_tf.shape
assert_allclose(res_th, res_tf, atol=1e-05)
# Identical prediction test case:
# randomly set half of the predictions to an identical value
num_identical = num_classes // 2
for i in range(batch_size):
idx_identical = np.random.choice(num_classes, size=num_identical, replace=False)
predictions[i, idx_identical] = predictions[i, 0]
targets = np.zeros(batch_size, dtype='int32')
predictions_th = KTH.variable(predictions, dtype='float32')
targets_th = KTH.variable(targets, dtype='int32')
predictions_tf = KTF.variable(predictions, dtype='float32')
targets_tf = KTF.variable(targets, dtype='int32')
for k in range(1, num_classes + 1):
res_th = KTH.eval(KTH.in_top_k(predictions_th, targets_th, k))
res_tf = KTF.eval(KTF.in_top_k(predictions_tf, targets_tf, k))
assert res_th.shape == res_tf.shape
assert_allclose(res_th, res_tf, atol=1e-05)
def test_random_uniform(self):
min = -1.
max = 1.
rand = KTF.eval(KTF.random_uniform((1000, 1000), min, max))
assert(rand.shape == (1000, 1000))
assert(np.abs(np.mean(rand)) < 0.01)
assert(np.max(rand) <= max)
assert(np.min(rand) >= min)
rand = KTH.eval(KTH.random_uniform((1000, 1000), min, max))
assert(rand.shape == (1000, 1000))
assert(np.abs(np.mean(rand)) < 0.01)
assert(np.max(rand) <= max)
assert(np.min(rand) >= min)
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)
zth = KTH.eval(getattr(KTH, function_name)(xth, yth, **kwargs))
ztf = KTF.eval(getattr(KTF, function_name)(xtf, ytf, **kwargs))
assert zth.shape == ztf.shape
assert_allclose(zth, ztf, atol=1e-05)
def test_random_uniform(self):
min_val = -1.
max_val = 1.
rand = KTF.eval(KTF.random_uniform((1000, 1000), min_val, max_val))
assert rand.shape == (1000, 1000)
assert np.abs(np.mean(rand)) < 0.01
assert np.max(rand) <= max_val
assert np.min(rand) >= min_val
rand = KTH.eval(KTH.random_uniform((1000, 1000), min_val, max_val))
assert rand.shape == (1000, 1000)
assert np.abs(np.mean(rand)) < 0.01
assert np.max(rand) <= max_val
assert np.min(rand) >= min_val
def test_ctc(self):
# simplified version of TensorFlow's test
label_lens = np.expand_dims(np.asarray([5, 4]), 1)
input_lens = np.expand_dims(np.asarray([5, 5]), 1) # number of timesteps
# the Theano and Tensorflow CTC code use different methods to ensure
# numerical stability. The Theano code subtracts out the max
# before the final log, so the results are different but scale
# identically and still train properly
loss_log_probs_tf = [3.34211, 5.42262]
loss_log_probs_th = [1.73308, 3.81351]
# dimensions are batch x time x categories
labels = np.asarray([[0, 1, 2, 1, 0], [0, 1, 1, 0, -1]])
inputs = np.asarray(
[
[
[0.633766, 0.221185, 0.0917319, 0.0129757, 0.0142857, 0.0260553],
[0.111121, 0.588392, 0.278779, 0.0055756, 0.00569609, 0.010436],
[0.0357786, 0.633813, 0.321418, 0.00249248, 0.00272882, 0.0037688],
[0.0663296, 0.643849, 0.280111, 0.00283995, 0.0035545, 0.00331533],
[0.458235, 0.396634, 0.123377, 0.00648837, 0.00903441, 0.00623107],
],
[
[0.30176, 0.28562, 0.0831517, 0.0862751, 0.0816851, 0.161508],
[0.24082, 0.397533, 0.0557226, 0.0546814, 0.0557528, 0.19549],
[0.230246, 0.450868, 0.0389607, 0.038309, 0.0391602, 0.202456],
[0.280884, 0.429522, 0.0326593, 0.0339046, 0.0326856, 0.190345],
[0.423286, 0.315517, 0.0338439, 0.0393744, 0.0339315, 0.154046],
],
],
dtype=np.float32,
)
labels_tf = KTF.variable(labels, dtype="int32")
inputs_tf = KTF.variable(inputs, dtype="float32")
input_lens_tf = KTF.variable(input_lens, dtype="int32")
label_lens_tf = KTF.variable(label_lens, dtype="int32")
res = KTF.eval(KTF.ctc_batch_cost(labels_tf, inputs_tf, input_lens_tf, label_lens_tf))
assert_allclose(res[:, 0], loss_log_probs_tf, atol=1e-05)
labels_th = KTH.variable(labels, dtype="int32")
inputs_th = KTH.variable(inputs, dtype="float32")
input_lens_th = KTH.variable(input_lens, dtype="int32")
label_lens_th = KTH.variable(label_lens, dtype="int32")
res = KTH.eval(KTH.ctc_batch_cost(labels_th, inputs_th, input_lens_th, label_lens_th))
assert_allclose(res[0, :], loss_log_probs_th, atol=1e-05)
def test_gather(self):
shape = (10, 2, 3)
ref = np.arange(np.prod(shape)).reshape(shape)
ref_th = KTH.variable(ref)
ref_tf = KTF.variable(ref)
inds = [1, 3, 7, 9]
inds_th = KTH.variable(inds, dtype='int32')
inds_tf = KTF.variable(inds, dtype='int32')
th_z = KTH.gather(ref_th, inds_th)
th_result = KTH.eval(th_z)
tf_result = KTF.eval(KTF.gather(ref_tf, inds_tf))
assert_allclose(tf_result, th_result, atol=1e-05)
if hasattr(th_z, '_keras_shape'):
assert th_z._keras_shape == th_result.shape
def test_repeat_elements(self):
reps = 3
for ndims in [1, 2, 3]:
shape = np.arange(2, 2 + ndims)
arr = np.arange(np.prod(shape)).reshape(shape)
arr_th = KTH.variable(arr)
arr_tf = KTF.variable(arr)
for rep_axis in range(ndims):
np_rep = np.repeat(arr, reps, axis=rep_axis)
th_rep = KTH.eval(KTH.repeat_elements(arr_th, reps, axis=rep_axis))
tf_rep = KTF.eval(KTF.repeat_elements(arr_tf, reps, axis=rep_axis))
assert th_rep.shape == np_rep.shape
assert tf_rep.shape == np_rep.shape
assert_allclose(np_rep, th_rep, atol=1e-05)
assert_allclose(np_rep, tf_rep, atol=1e-05)
def test_shape_operations(self):
# concatenate
xval = np.random.random((4, 3))
xth = KTH.variable(xval)
xtf = KTF.variable(xval)
yval = np.random.random((4, 2))
yth = KTH.variable(yval)
ytf = KTF.variable(yval)
zth = KTH.eval(KTH.concatenate([xth, yth], axis=-1))
ztf = KTF.eval(KTF.concatenate([xtf, ytf], axis=-1))
assert zth.shape == ztf.shape
assert_allclose(zth, ztf, atol=1e-05)
check_single_tensor_operation("reshape", (4, 2), shape=(8, 1))
check_single_tensor_operation("permute_dimensions", (4, 2, 3), pattern=(2, 0, 1))
check_single_tensor_operation("repeat", (4, 1), n=3)
check_single_tensor_operation("flatten", (4, 1))
check_single_tensor_operation("expand_dims", (4, 3), dim=-1)
check_single_tensor_operation("expand_dims", (4, 3, 2), dim=1)
check_single_tensor_operation("squeeze", (4, 3, 1), axis=2)
def check_composed_tensor_operations(first_function_name, first_function_args,
second_function_name, second_function_args,
input_shape):
''' Creates a random tensor t0 with shape input_shape and compute
t1 = first_function_name(t0, **first_function_args)
t2 = second_function_name(t1, **second_function_args)
with both Theano and TensorFlow backends and ensures the answers match.
'''
val = np.random.random(input_shape) - 0.5
xth = KTH.variable(val)
xtf = KTF.variable(val)
yth = getattr(KTH, first_function_name)(xth, **first_function_args)
ytf = getattr(KTF, first_function_name)(xtf, **first_function_args)
zth = KTH.eval(getattr(KTH, second_function_name)(yth, **second_function_args))
ztf = KTF.eval(getattr(KTF, second_function_name)(ytf, **second_function_args))
assert zth.shape == ztf.shape
assert_allclose(zth, ztf, atol=1e-05)
def test_repeat_elements(self):
reps = 3
for ndims in [1, 2, 3]:
shape = np.arange(2, 2 + ndims)
arr = np.arange(np.prod(shape)).reshape(shape)
arr_th = KTH.variable(arr)
arr_tf = KTF.variable(arr)
for rep_axis in range(ndims):
np_rep = np.repeat(arr, reps, axis=rep_axis)
th_z = KTH.repeat_elements(arr_th, reps, axis=rep_axis)
th_rep = KTH.eval(th_z)
tf_rep = KTF.eval(
KTF.repeat_elements(arr_tf, reps, axis=rep_axis))
assert th_rep.shape == np_rep.shape
assert tf_rep.shape == np_rep.shape
assert_allclose(np_rep, th_rep, atol=1e-05)
assert_allclose(np_rep, tf_rep, atol=1e-05)
if hasattr(th_z, '_keras_shape'):
assert th_z._keras_shape == th_rep.shape
def test_nn_operations(self):
check_single_tensor_operation('relu', (4, 2), alpha=0.1, max_value=0.5)
check_single_tensor_operation('softmax', (4, 10))
# check_single_tensor_operation('softplus', (4, 10))
check_single_tensor_operation('elu', (4, 10), alpha=0.5)
check_single_tensor_operation('sigmoid', (4, 2))
# check_single_tensor_operation('hard_sigmoid', (4, 2))
check_single_tensor_operation('tanh', (4, 2))
# dropout
val = np.random.random((100, 100))
xth = KTH.variable(val)
xtf = KTF.variable(val)
zth = KTH.eval(KTH.dropout(xth, level=0.2))
ztf = KTF.eval(KTF.dropout(xtf, level=0.2))
assert zth.shape == ztf.shape
# dropout patterns are different, only check mean
assert np.abs(zth.mean() - ztf.mean()) < 0.05
'''
def test_shape_operations(self):
# concatenate
xval = np.random.random((4, 3))
xth = KTH.variable(xval)
xtf = KTF.variable(xval)
yval = np.random.random((4, 2))
yth = KTH.variable(yval)
ytf = KTF.variable(yval)
zth = KTH.eval(KTH.concatenate([xth, yth], axis=-1))
ztf = KTF.eval(KTF.concatenate([xtf, ytf], axis=-1))
assert zth.shape == ztf.shape
assert_allclose(zth, ztf, atol=1e-05)
check_single_tensor_operation('reshape', (4, 2), shape=(8, 1))
check_single_tensor_operation('permute_dimensions', (4, 2, 3),
pattern=(2, 0, 1))
check_single_tensor_operation('repeat', (4, 1), n=3)
check_single_tensor_operation('flatten', (4, 1))
check_single_tensor_operation('expand_dims', (4, 3), dim=-1)
check_single_tensor_operation('expand_dims', (4, 3, 2), dim=1)
check_single_tensor_operation('squeeze', (4, 3, 1), axis=2)
def test_switch(self):
val = np.random.random()
xth = KTH.variable(val)
xth = KTH.switch(xth >= 0.5, xth * 0.1, xth * 0.2)
xtf = KTF.variable(val)
xtf = KTF.switch(xtf >= 0.5, xtf * 0.1, xtf * 0.2)
zth = KTH.eval(xth)
ztf = KTF.eval(xtf)
assert zth.shape == ztf.shape
assert_allclose(zth, ztf, atol=1e-05)
xth1 = KTH.variable(0.7)
xth2 = KTH.variable([1, 0.2])
with pytest.raises(ValueError):
xth = KTH.switch(xth1 >= 0.5, xth2 * 0.1, xth2 * 0.2)
xth = KTH.switch(xth2 >= 0.5, xth1 * 0.1, xth1 * 0.2)
assert_allclose(xth, KTH.variable([0.1, 0.2 * 0.2]), atol=1e-05)
def test_extract2(self, input_shape, kernel_shape):
xval = np.random.random(input_shape)
kernel = [kernel_shape, kernel_shape]
strides = [kernel_shape, kernel_shape]
xth = KTH.variable(xval)
xtf = KTF.variable(xval)
ztf = KTF.eval(
KCTF.extract_image_patches(xtf,
kernel,
strides,
data_format="channels_last",
padding="same"))
zth = KTH.eval(
KCTH.extract_image_patches(xth,
kernel,
strides,
data_format="channels_last",
padding="same"))
assert zth.shape == ztf.shape
assert_allclose(zth, ztf, atol=1e-02)
def check_composed_tensor_operations(first_function_name, first_function_args,
second_function_name,
second_function_args, input_shape):
''' Creates a random tensor t0 with shape input_shape and compute
t1 = first_function_name(t0, **first_function_args)
t2 = second_function_name(t1, **second_function_args)
with both Theano and TensorFlow backends and ensures the answers match.
'''
val = np.random.random(input_shape) - 0.5
xth = KTH.variable(val)
xtf = KTF.variable(val)
yth = getattr(KTH, first_function_name)(xth, **first_function_args)
ytf = getattr(KTF, first_function_name)(xtf, **first_function_args)
zth = KTH.eval(
getattr(KTH, second_function_name)(yth, **second_function_args))
ztf = KTF.eval(
getattr(KTF, second_function_name)(ytf, **second_function_args))
assert zth.shape == ztf.shape
assert_allclose(zth, ztf, atol=1e-05)
def test_tile(self):
shape = (3, 4)
arr = np.arange(np.prod(shape)).reshape(shape)
arr_th = KTH.variable(arr)
arr_tf = KTF.variable(arr)
n = (2, 1)
th_z = KTH.tile(arr_th, n)
th_rep = KTH.eval(th_z)
tf_rep = KTF.eval(KTF.tile(arr_tf, n))
assert_allclose(tf_rep, th_rep, atol=1e-05)
if hasattr(th_z, '_keras_shape'):
assert th_z._keras_shape == th_rep.shape
# test theano shape inference when
# input shape has None entries
if K.backend() == 'theano':
x = K.placeholder(shape=(None, 4))
n = 2
y = KTH.tile(x, n)
assert y._keras_shape == (None, 8)
n = (4, 3)
y = K.tile(x, n)
assert y._keras_shape == (None, 12)
def test_nn_operations(self):
check_single_tensor_operation('relu', (4, 2), alpha=0.1, max_value=0.5)
check_single_tensor_operation('softmax', (4, 10))
check_single_tensor_operation('softplus', (4, 10))
check_single_tensor_operation('sigmoid', (4, 2))
check_single_tensor_operation('hard_sigmoid', (4, 2))
check_single_tensor_operation('tanh', (4, 2))
# dropout
val = np.random.random((20, 20))
xth = KTH.variable(val)
xtf = KTF.variable(val)
zth = KTH.eval(KTH.dropout(xth, level=0.2))
ztf = KTF.eval(KTF.dropout(xtf, level=0.2))
assert zth.shape == ztf.shape
# dropout patterns are different, only check mean
assert np.abs(zth.mean() - ztf.mean()) < 0.05
check_two_tensor_operation('binary_crossentropy', (4, 2), (4, 2), from_logits=True)
check_two_tensor_operation('categorical_crossentropy', (4, 2), (4, 2), from_logits=True)
check_two_tensor_operation('binary_crossentropy', (4, 2), (4, 2), from_logits=False)
check_two_tensor_operation('categorical_crossentropy', (4, 2), (4, 2), from_logits=False)
def test_tile(self):
shape = (3, 4)
arr = np.arange(np.prod(shape)).reshape(shape)
arr_th = KTH.variable(arr)
arr_tf = KTF.variable(arr)
n = (2, 1)
th_z = KTH.tile(arr_th, n)
th_rep = KTH.eval(th_z)
tf_rep = KTF.eval(KTF.tile(arr_tf, n))
assert_allclose(tf_rep, th_rep, atol=1e-05)
if hasattr(th_z, '_keras_shape'):
assert th_z._keras_shape == th_rep.shape
# test theano shape inference when
# input shape has None entries
if K.backend() == 'theano':
x = K.placeholder(shape=(None, 4))
n = 2
y = KTH.tile(x, n)
assert y._keras_shape == (None, 8)
n = (4, 3)
y = K.tile(x, n)
assert y._keras_shape == (None, 12)
def test_rnn(self):
# implement a simple RNN
input_dim = 8
output_dim = 4
timesteps = 5
input_val = np.random.random((32, timesteps, input_dim))
init_state_val = np.random.random((32, output_dim))
W_i_val = np.random.random((input_dim, output_dim))
W_o_val = np.random.random((output_dim, output_dim))
def rnn_step_fn(input_dim, output_dim, K):
W_i = K.variable(W_i_val)
W_o = K.variable(W_o_val)
def step_function(x, states):
assert len(states) == 1
prev_output = states[0]
output = K.dot(x, W_i) + K.dot(prev_output, W_o)
return output, [output]
return step_function
# test default setup
th_rnn_step_fn = rnn_step_fn(input_dim, output_dim, KTH)
th_inputs = KTH.variable(input_val)
th_initial_states = [KTH.variable(init_state_val)]
last_output, outputs, new_states = KTH.rnn(th_rnn_step_fn,
th_inputs,
th_initial_states,
go_backwards=False,
mask=None)
th_last_output = KTH.eval(last_output)
th_outputs = KTH.eval(outputs)
assert len(new_states) == 1
th_state = KTH.eval(new_states[0])
tf_rnn_step_fn = rnn_step_fn(input_dim, output_dim, KTF)
tf_inputs = KTF.variable(input_val)
tf_initial_states = [KTF.variable(init_state_val)]
last_output, outputs, new_states = KTF.rnn(tf_rnn_step_fn,
tf_inputs,
tf_initial_states,
go_backwards=False,
mask=None)
tf_last_output = KTF.eval(last_output)
tf_outputs = KTF.eval(outputs)
assert len(new_states) == 1
tf_state = KTF.eval(new_states[0])
assert_allclose(tf_last_output, th_last_output, atol=1e-04)
assert_allclose(tf_outputs, th_outputs, atol=1e-04)
assert_allclose(tf_state, th_state, atol=1e-04)
# test unroll
unrolled_last_output, unrolled_outputs, unrolled_new_states = KTH.rnn(
th_rnn_step_fn,
th_inputs,
th_initial_states,
go_backwards=False,
mask=None,
unroll=True,
input_length=timesteps)
unrolled_th_last_output = KTH.eval(unrolled_last_output)
unrolled_th_outputs = KTH.eval(unrolled_outputs)
assert len(unrolled_new_states) == 1
unrolled_th_state = KTH.eval(unrolled_new_states[0])
assert_allclose(th_last_output, unrolled_th_last_output, atol=1e-04)
assert_allclose(th_outputs, unrolled_th_outputs, atol=1e-04)
assert_allclose(th_state, unrolled_th_state, atol=1e-04)
# test go_backwards
th_rnn_step_fn = rnn_step_fn(input_dim, output_dim, KTH)
th_inputs = KTH.variable(input_val)
th_initial_states = [KTH.variable(init_state_val)]
last_output, outputs, new_states = KTH.rnn(th_rnn_step_fn,
th_inputs,
th_initial_states,
go_backwards=True,
mask=None)
th_last_output = KTH.eval(last_output)
th_outputs = KTH.eval(outputs)
assert len(new_states) == 1
th_state = KTH.eval(new_states[0])
tf_rnn_step_fn = rnn_step_fn(input_dim, output_dim, KTF)
tf_inputs = KTF.variable(input_val)
tf_initial_states = [KTF.variable(init_state_val)]
last_output, outputs, new_states = KTF.rnn(tf_rnn_step_fn,
tf_inputs,
tf_initial_states,
go_backwards=True,
mask=None)
tf_last_output = KTF.eval(last_output)
tf_outputs = KTF.eval(outputs)
assert len(new_states) == 1
tf_state = KTF.eval(new_states[0])
assert_allclose(tf_last_output, th_last_output, atol=1e-04)
assert_allclose(tf_outputs, th_outputs, atol=1e-04)
assert_allclose(tf_state, th_state, atol=1e-04)
# test unroll with backwards = True
bwd_last_output, bwd_outputs, bwd_new_states = KTH.rnn(
th_rnn_step_fn,
th_inputs,
th_initial_states,
go_backwards=True,
mask=None)
bwd_th_last_output = KTH.eval(bwd_last_output)
bwd_th_outputs = KTH.eval(bwd_outputs)
assert len(bwd_new_states) == 1
bwd_th_state = KTH.eval(bwd_new_states[0])
bwd_unrolled_last_output, bwd_unrolled_outputs, bwd_unrolled_new_states = KTH.rnn(
th_rnn_step_fn,
th_inputs,
th_initial_states,
go_backwards=True,
mask=None,
unroll=True,
input_length=timesteps)
bwd_unrolled_th_last_output = KTH.eval(bwd_unrolled_last_output)
bwd_unrolled_th_outputs = KTH.eval(bwd_unrolled_outputs)
assert len(bwd_unrolled_new_states) == 1
bwd_unrolled_th_state = KTH.eval(bwd_unrolled_new_states[0])
assert_allclose(bwd_th_last_output,
bwd_unrolled_th_last_output,
atol=1e-04)
assert_allclose(bwd_th_outputs, bwd_unrolled_th_outputs, atol=1e-04)
assert_allclose(bwd_th_state, bwd_unrolled_th_state, atol=1e-04)
# test unroll with masking
np_mask = np.random.randint(2, size=(32, timesteps))
th_mask = KTH.variable(np_mask)
masked_last_output, masked_outputs, masked_new_states = KTH.rnn(
th_rnn_step_fn,
th_inputs,
th_initial_states,
go_backwards=False,
mask=th_mask)
masked_th_last_output = KTH.eval(masked_last_output)
masked_th_outputs = KTH.eval(masked_outputs)
assert len(masked_new_states) == 1
masked_th_state = KTH.eval(masked_new_states[0])
unrolled_masked_last_output, unrolled_masked_outputs, unrolled_masked_new_states = KTH.rnn(
th_rnn_step_fn,
th_inputs,
th_initial_states,
go_backwards=False,
mask=th_mask,
unroll=True,
input_length=timesteps)
unrolled_masked_th_last_output = KTH.eval(unrolled_masked_last_output)
unrolled_masked_th_outputs = KTH.eval(unrolled_masked_outputs)
assert len(unrolled_masked_new_states) == 1
unrolled_masked_th_state = KTH.eval(unrolled_masked_new_states[0])
assert_allclose(unrolled_masked_th_last_output,
masked_th_last_output,
atol=1e-04)
assert_allclose(unrolled_masked_th_outputs,
masked_th_outputs,
atol=1e-04)
assert_allclose(unrolled_masked_th_state, masked_th_state, atol=1e-04)
def test_rnn(self):
# implement a simple RNN
input_dim = 8
output_dim = 4
timesteps = 5
input_val = np.random.random((32, timesteps, input_dim))
init_state_val = np.random.random((32, output_dim))
W_i_val = np.random.random((input_dim, output_dim))
W_o_val = np.random.random((output_dim, output_dim))
def rnn_step_fn(input_dim, output_dim, K):
W_i = K.variable(W_i_val)
W_o = K.variable(W_o_val)
def step_function(x, states):
assert len(states) == 1
prev_output = states[0]
output = K.dot(x, W_i) + K.dot(prev_output, W_o)
return output, [output]
return step_function
th_rnn_step_fn = rnn_step_fn(input_dim, output_dim, KTH)
th_inputs = KTH.variable(input_val)
th_initial_states = [KTH.variable(init_state_val)]
last_output, outputs, new_states = KTH.rnn(th_rnn_step_fn, th_inputs,
th_initial_states,
go_backwards=False,
mask=None)
th_last_output = KTH.eval(last_output)
th_outputs = KTH.eval(outputs)
assert len(new_states) == 1
th_state = KTH.eval(new_states[0])
tf_rnn_step_fn = rnn_step_fn(input_dim, output_dim, KTF)
tf_inputs = KTF.variable(input_val)
tf_initial_states = [KTF.variable(init_state_val)]
last_output, outputs, new_states = KTF.rnn(tf_rnn_step_fn, tf_inputs,
tf_initial_states,
go_backwards=False,
mask=None)
tf_last_output = KTF.eval(last_output)
tf_outputs = KTF.eval(outputs)
assert len(new_states) == 1
tf_state = KTF.eval(new_states[0])
assert_allclose(tf_last_output, th_last_output, atol=1e-04)
assert_allclose(tf_outputs, th_outputs, atol=1e-04)
assert_allclose(tf_state, th_state, atol=1e-04)
# test unroll
unrolled_last_output, unrolled_outputs, unrolled_new_states = KTH.rnn(
th_rnn_step_fn, th_inputs,
th_initial_states,
go_backwards=False,
mask=None,
unroll=True,
input_length=timesteps)
unrolled_th_last_output = KTH.eval(unrolled_last_output)
unrolled_th_outputs = KTH.eval(unrolled_outputs)
assert len(unrolled_new_states) == 1
unrolled_th_state = KTH.eval(unrolled_new_states[0])
assert_allclose(th_last_output, unrolled_th_last_output, atol=1e-04)
assert_allclose(th_outputs, unrolled_th_outputs, atol=1e-04)
assert_allclose(th_state, unrolled_th_state, atol=1e-04)
# test unroll with backwards = True
bwd_last_output, bwd_outputs, bwd_new_states = KTH.rnn(
th_rnn_step_fn, th_inputs,
th_initial_states,
go_backwards=True,
mask=None)
bwd_th_last_output = KTH.eval(bwd_last_output)
bwd_th_outputs = KTH.eval(bwd_outputs)
assert len(bwd_new_states) == 1
bwd_th_state = KTH.eval(bwd_new_states[0])
bwd_unrolled_last_output, bwd_unrolled_outputs, bwd_unrolled_new_states = KTH.rnn(
th_rnn_step_fn, th_inputs,
th_initial_states,
go_backwards=True,
mask=None,
unroll=True,
input_length=timesteps)
bwd_unrolled_th_last_output = KTH.eval(bwd_unrolled_last_output)
bwd_unrolled_th_outputs = KTH.eval(bwd_unrolled_outputs)
assert len(bwd_unrolled_new_states) == 1
bwd_unrolled_th_state = KTH.eval(bwd_unrolled_new_states[0])
assert_allclose(bwd_th_last_output, bwd_unrolled_th_last_output, atol=1e-04)
assert_allclose(bwd_th_outputs, bwd_unrolled_th_outputs, atol=1e-04)
assert_allclose(bwd_th_state, bwd_unrolled_th_state, atol=1e-04)
# test unroll with masking
np_mask = np.random.randint(2, size=(32, timesteps))
th_mask = KTH.variable(np_mask)
masked_last_output, masked_outputs, masked_new_states = KTH.rnn(
th_rnn_step_fn, th_inputs,
th_initial_states,
go_backwards=False,
mask=th_mask)
masked_th_last_output = KTH.eval(masked_last_output)
masked_th_outputs = KTH.eval(masked_outputs)
assert len(masked_new_states) == 1
masked_th_state = KTH.eval(masked_new_states[0])
unrolled_masked_last_output, unrolled_masked_outputs, unrolled_masked_new_states = KTH.rnn(
th_rnn_step_fn, th_inputs,
th_initial_states,
go_backwards=False,
mask=th_mask,
unroll=True,
input_length=timesteps)
unrolled_masked_th_last_output = KTH.eval(unrolled_masked_last_output)
unrolled_masked_th_outputs = KTH.eval(unrolled_masked_outputs)
assert len(unrolled_masked_new_states) == 1
unrolled_masked_th_state = KTH.eval(unrolled_masked_new_states[0])
assert_allclose(unrolled_masked_th_last_output, masked_th_last_output, atol=1e-04)
assert_allclose(unrolled_masked_th_outputs, masked_th_outputs, atol=1e-04)
assert_allclose(unrolled_masked_th_state, masked_th_state, atol=1e-04)
