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"""Tests for tensorflow.learning.training_ops."""
import itertools
import tensorflow.python.platform
import numpy as np
from tensorflow.python.framework import types
from tensorflow.python.framework.test_util import TensorFlowTestCase
from tensorflow.python.ops import constant_op
from tensorflow.python.ops import variables
from tensorflow.python.platform import googletest
from tensorflow.python.training import training_ops
class TrainingOpsTest(TensorFlowTestCase):
def _toType(self, dtype):
if dtype == np.float32:
return types.float32
elif dtype == np.float64:
return types.float64
elif dtype == np.int32:
return types.int32
elif dtype == np.int64:
return types.int64
else:
assert False, (dtype)
def _testTypes(self, x, alpha, delta, use_gpu=None):
self.setUp()
with self.test_session(use_gpu=use_gpu):
var = variables.Variable(x)
variables.initialize_all_variables().run()
self.assertAllEqual(x, var.eval())
apply_sgd = training_ops.apply_gradient_descent(var, alpha, delta)
out = apply_sgd.eval()
self.assertShapeEqual(out, apply_sgd)
self.assertAllEqual(x - alpha * delta, out)
def testApplyGradientDescent(self):
for (dtype, use_gpu) in itertools.product(
[np.float32, np.float64], [False, True]):
x = np.arange(100).astype(dtype)
alpha = np.array(2.0).astype(dtype)
delta = np.arange(100).astype(dtype)
self._testTypes(x, alpha, delta, use_gpu)
def _testTypesForAdagrad(self, x, y, lr, grad, use_gpu=None):
self.setUp()
with self.test_session(use_gpu=use_gpu):
var = variables.Variable(x)
accum = variables.Variable(y)
variables.initialize_all_variables().run()
self.assertAllEqual(x, var.eval())
apply_adagrad = training_ops.apply_adagrad(var, accum, lr, grad)
out = apply_adagrad.eval()
self.assertShapeEqual(out, apply_adagrad)
self.assertAllClose(
x - lr * grad * (y + grad * grad) ** (-0.5), out)
self.assertAllEqual(y + grad * grad, accum.eval())
def testApplyAdagrad(self):
for (dtype, use_gpu) in itertools.product(
[np.float32, np.float64], [False, True]):
x = np.arange(100).astype(dtype)
y = np.arange(1, 101).astype(dtype)
lr = np.array(2.0).astype(dtype)
grad = np.arange(100).astype(dtype)
self._testTypesForAdagrad(x, y, lr, grad, use_gpu)
def _testTypesForSparseAdagrad(self, x, y, lr, grad, indices):
self.setUp()
with self.test_session(use_gpu=False):
var = variables.Variable(x)
accum = variables.Variable(y)
variables.initialize_all_variables().run()
self.assertAllEqual(x, var.eval())
sparse_apply_adagrad = training_ops.sparse_apply_adagrad(
var, accum, lr, grad,
constant_op.constant(indices, self._toType(indices.dtype)))
out = sparse_apply_adagrad.eval()
self.assertShapeEqual(out, sparse_apply_adagrad)
for (i, index) in enumerate(indices):
self.assertAllClose(
x[index] - lr * grad[i] * (y[index] + grad[i] * grad[i]) ** (-0.5),
var.eval()[index])
self.assertAllEqual(y[index] + grad[i] * grad[i], accum.eval()[index])
def testSparseApplyAdagrad(self):
for (dtype, index_type) in itertools.product(
[np.float32, np.float64], [np.int32, np.int64]):
x_val = [range(10), range(10, 20), range(20, 30)]
y_val = [range(1, 11), range(11, 21), range(21, 31)]
x = np.array(x_val).astype(dtype)
y = np.array(y_val).astype(dtype)
lr = np.array(2.0).astype(dtype)
grad_val = [range(10), range(10)]
grad = np.array(grad_val).astype(dtype)
indices = np.array([0, 2]).astype(index_type)
self._testTypesForSparseAdagrad(x, y, lr, grad, indices)
def testApplyAdam(self):
for dtype, use_gpu in itertools.product(
[np.float32, np.float64], [False, True]):
var = np.arange(100).astype(dtype)
m = np.arange(1, 101).astype(dtype)
v = np.arange(101, 201).astype(dtype)
grad = np.arange(100).astype(dtype)
self._testTypesForAdam(var, m, v, grad, use_gpu)
def _testTypesForAdam(self, var, m, v, grad, use_gpu):
self.setUp()
with self.test_session(use_gpu=use_gpu):
var_t = variables.Variable(var)
m_t = variables.Variable(m)
v_t = variables.Variable(v)
t = 1
beta1 = np.array(0.9, dtype=var.dtype)
beta2 = np.array(0.999, dtype=var.dtype)
beta1_power = beta1**t
beta2_power = beta2**t
lr = np.array(0.001, dtype=var.dtype)
epsilon = np.array(1e-8, dtype=var.dtype)
beta1_t = constant_op.constant(beta1, self._toType(var.dtype), [])
beta2_t = constant_op.constant(beta2, self._toType(var.dtype), [])
beta1_power_t = variables.Variable(beta1_power)
beta2_power_t = variables.Variable(beta2_power)
lr_t = constant_op.constant(lr, self._toType(var.dtype), [])
epsilon_t = constant_op.constant(epsilon, self._toType(var.dtype), [])
variables.initialize_all_variables().run()
self.assertAllEqual(var, var_t.eval())
new_var, _, _ = self._adamUpdateNumpy(var, grad, t, m, v,
lr, beta1, beta2, epsilon)
apply_adam = training_ops.apply_adam(var_t, m_t, v_t, beta1_power_t,
beta2_power_t, lr_t,
beta1_t, beta2_t, epsilon_t, grad)
out = apply_adam.eval()
self.assertShapeEqual(out, apply_adam)
self.assertAllClose(new_var, out)
def _adamUpdateNumpy(self, param, g_t, t, m, v, alpha, beta1,
beta2, epsilon):
alpha_t = alpha * np.sqrt(1 - beta2 ** t) / (1 - beta1 ** t)
m_t = beta1 * m + (1 - beta1) * g_t
v_t = beta2 * v + (1 - beta2) * g_t * g_t
param_t = param - alpha_t * m_t / (np.sqrt(v_t) + epsilon)
return param_t, m_t, v_t
if __name__ == '__main__':
googletest.main()
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