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"""Tests for Adam."""
import tensorflow.python.platform
import numpy as np
import tensorflow as tf
def adam_update_numpy(param, g_t, t, m, v, alpha=0.001, beta1=0.9, beta2=0.999,
epsilon=1e-8):
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
class AdamOptimizerTest(tf.test.TestCase):
def testSparse(self):
with self.test_session():
# Initialize variables for numpy implementation.
m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0
var0_np = np.array([1.0, 2.0], dtype=np.float32)
grads0_np = np.array([0.1, 0.1], dtype=np.float32)
var1_np = np.array([3.0, 4.0], dtype=np.float32)
grads1_np = np.array([0.01, 0.01], dtype=np.float32)
var0 = tf.Variable(var0_np)
var1 = tf.Variable(var1_np)
grads0_np_indices = np.array([0, 1], dtype=np.int32)
grads0 = tf.IndexedSlices(tf.constant(grads0_np),
tf.constant(grads0_np_indices),
tf.constant([2]))
grads1_np_indices = np.array([0, 1], dtype=np.int32)
grads1 = tf.IndexedSlices(tf.constant(grads1_np),
tf.constant(grads1_np_indices),
tf.constant([2]))
opt = tf.train.AdamOptimizer()
update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
tf.initialize_all_variables().run()
# Fetch params to validate initial values
self.assertAllClose([1.0, 2.0], var0.eval())
self.assertAllClose([3.0, 4.0], var1.eval())
beta1_power, beta2_power = opt._get_beta_accumulators()
# Run 3 steps of Adam
for t in range(1, 4):
self.assertAllClose(0.9 ** t, beta1_power.eval())
self.assertAllClose(0.999 ** t, beta2_power.eval())
update.run()
var0_np, m0, v0 = adam_update_numpy(var0_np, grads0_np, t, m0, v0)
var1_np, m1, v1 = adam_update_numpy(var1_np, grads1_np, t, m1, v1)
# Validate updated params
self.assertAllClose(var0_np, var0.eval())
self.assertAllClose(var1_np, var1.eval())
def testBasic(self):
with self.test_session():
# Initialize variables for numpy implementation.
m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0
var0_np = np.array([1.0, 2.0], dtype=np.float32)
grads0_np = np.array([0.1, 0.1], dtype=np.float32)
var1_np = np.array([3.0, 4.0], dtype=np.float32)
grads1_np = np.array([0.01, 0.01], dtype=np.float32)
var0 = tf.Variable(var0_np)
var1 = tf.Variable(var1_np)
grads0 = tf.constant(grads0_np)
grads1 = tf.constant(grads1_np)
opt = tf.train.AdamOptimizer()
update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
tf.initialize_all_variables().run()
# Fetch params to validate initial values
self.assertAllClose([1.0, 2.0], var0.eval())
self.assertAllClose([3.0, 4.0], var1.eval())
beta1_power, beta2_power = opt._get_beta_accumulators()
# Run 3 steps of Adam
for t in range(1, 4):
self.assertAllClose(0.9 ** t, beta1_power.eval())
self.assertAllClose(0.999 ** t, beta2_power.eval())
update.run()
var0_np, m0, v0 = adam_update_numpy(var0_np, grads0_np, t, m0, v0)
var1_np, m1, v1 = adam_update_numpy(var1_np, grads1_np, t, m1, v1)
# Validate updated params
self.assertAllClose(var0_np, var0.eval())
self.assertAllClose(var1_np, var1.eval())
def testFloat64(self):
with self.test_session():
opt = tf.train.AdamOptimizer()
# compute_gradients.
values = [1.0, 3.0]
good_vars = [tf.Variable([v]) for v in values]
bad_loss = tf.constant(2.0, tf.float64, name="bad_loss")
self.assertRaisesRegexp(
ValueError, r"Invalid type.*float64.*bad_loss.*expected.*float32",
opt.compute_gradients, bad_loss, good_vars)
bad_vars = [
tf.Variable(np.array([v], np.float64), name="bad_var")
for v in values]
self.assertRaisesRegexp(
ValueError, r"Invalid type.*float64.*bad_var.*expected.*float32",
opt.compute_gradients, tf.cast(bad_vars[0] + bad_vars[1], tf.float32),
bad_vars)
opt.compute_gradients(good_vars[0] + good_vars[1], good_vars)
# apply_gradients.
bad_grads = [
tf.constant([0.1], dtype=np.float64, name="bad_grad"),
tf.constant([0.01])]
self.assertRaisesRegexp(
ValueError, r"Invalid type.*float64.*bad_grad.*expected.*float32",
opt.apply_gradients, zip(bad_grads, good_vars))
good_grads = [tf.constant([0.01]), tf.constant([0.02])]
self.assertRaisesRegexp(
ValueError, r"Invalid type.*float64.*bad_var.*expected.*float32",
opt.apply_gradients, zip(good_grads, bad_vars))
opt.apply_gradients(zip(good_grads, good_vars))
def testSharing(self):
with self.test_session():
# Initialize variables for numpy implementation.
m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0
var0_np = np.array([1.0, 2.0], dtype=np.float32)
grads0_np = np.array([0.1, 0.1], dtype=np.float32)
var1_np = np.array([3.0, 4.0], dtype=np.float32)
grads1_np = np.array([0.01, 0.01], dtype=np.float32)
var0 = tf.Variable(var0_np)
var1 = tf.Variable(var1_np)
grads0 = tf.constant(grads0_np)
grads1 = tf.constant(grads1_np)
opt = tf.train.AdamOptimizer()
update1 = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
update2 = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
tf.initialize_all_variables().run()
beta1_power, beta2_power = opt._get_beta_accumulators()
# Fetch params to validate initial values
self.assertAllClose([1.0, 2.0], var0.eval())
self.assertAllClose([3.0, 4.0], var1.eval())
# Run 3 steps of intertwined Adam1 and Adam2.
for t in range(1, 4):
self.assertAllClose(0.9 ** t, beta1_power.eval())
self.assertAllClose(0.999 ** t, beta2_power.eval())
if t % 2 == 0:
update1.run()
else:
update2.run()
var0_np, m0, v0 = adam_update_numpy(var0_np, grads0_np, t, m0, v0)
var1_np, m1, v1 = adam_update_numpy(var1_np, grads1_np, t, m1, v1)
# Validate updated params
self.assertAllClose(var0_np, var0.eval())
self.assertAllClose(var1_np, var1.eval())
if __name__ == "__main__":
tf.test.main()
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