def doTestSparse(self, use_resource=False):
# need to use a larger value of epsilon here so that
# np.sqrt(v_t) + epsilon doesn't get rounded to 0 when
# the dtype is half and np.sqrt(v_t) = 0, as is the case
# when the gradient is 0
sparse_epsilon = 1e-7
for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
with self.cached_session():
# Initialize variables for numpy implementation.
m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0
var0_np = np.array([1.0, 1.0, 2.0], dtype=dtype.as_numpy_dtype)
grads0_np = np.array([0.1, 0, 0.1], dtype=dtype.as_numpy_dtype)
var1_np = np.array([3.0, 3.0, 4.0], dtype=dtype.as_numpy_dtype)
grads1_np = np.array([0.01, 0, 0.01],
dtype=dtype.as_numpy_dtype)
if use_resource:
var0 = resource_variable_ops.ResourceVariable(var0_np)
var1 = resource_variable_ops.ResourceVariable(var1_np)
else:
var0 = variables.Variable(var0_np)
var1 = variables.Variable(var1_np)
grads0_np_indices = np.array([0, 2], dtype=np.int32)
grads0 = ops.IndexedSlices(
constant_op.constant(grads0_np[grads0_np_indices]),
constant_op.constant(grads0_np_indices),
constant_op.constant([3]))
grads1_np_indices = np.array([0, 2], dtype=np.int32)
grads1 = ops.IndexedSlices(
constant_op.constant(grads1_np[grads1_np_indices]),
constant_op.constant(grads1_np_indices),
constant_op.constant([3]))
opt = nadam_optimizer.NadamOptimizer(epsilon=sparse_epsilon)
update = opt.apply_gradients(
zip([grads0, grads1], [var0, var1]))
variables.global_variables_initializer().run()
# Fetch params to validate initial values
self.assertAllClose([1.0, 1.0, 2.0], var0.eval())
self.assertAllClose([3.0, 3.0, 4.0], var1.eval())
beta1_power, beta2_power = opt._get_beta_accumulators()
# Run 3 steps of Nadam
for t in range(1, 4):
self.assertAllCloseAccordingToType(0.9**t,
beta1_power.eval())
self.assertAllCloseAccordingToType(0.999**t,
beta2_power.eval())
update.run()
var0_np, m0, v0 = nadam_update_numpy(
var0_np, grads0_np, t, m0, v0, epsilon=sparse_epsilon)
var1_np, m1, v1 = nadam_update_numpy(
var1_np, grads1_np, t, m1, v1, epsilon=sparse_epsilon)
# Validate updated params
self.assertAllCloseAccordingToType(var0_np, var0.eval())
self.assertAllCloseAccordingToType(var1_np, var1.eval())
def doTestSparse(self, use_resource=False):
for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
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=dtype.as_numpy_dtype)
grads0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype)
var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype)
grads1_np = np.array([0.01, 0.01], dtype=dtype.as_numpy_dtype)
if use_resource:
var0 = resource_variable_ops.ResourceVariable(var0_np)
var1 = resource_variable_ops.ResourceVariable(var1_np)
else:
var0 = variables.Variable(var0_np)
var1 = variables.Variable(var1_np)
grads0_np_indices = np.array([0, 1], dtype=np.int32)
grads0 = ops.IndexedSlices(
constant_op.constant(grads0_np),
constant_op.constant(grads0_np_indices),
constant_op.constant([2]))
grads1_np_indices = np.array([0, 1], dtype=np.int32)
grads1 = ops.IndexedSlices(
constant_op.constant(grads1_np),
constant_op.constant(grads1_np_indices),
constant_op.constant([2]))
opt = nadam_optimizer.NadamOptimizer()
update = opt.apply_gradients(
zip([grads0, grads1], [var0, var1]))
variables.global_variables_initializer().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 Nadam
for t in range(1, 4):
self.assertAllCloseAccordingToType(0.9**t,
beta1_power.eval())
self.assertAllCloseAccordingToType(0.999**t,
beta2_power.eval())
update.run()
var0_np, m0, v0 = nadam_update_numpy(
var0_np, grads0_np, t, m0, v0)
var1_np, m1, v1 = nadam_update_numpy(
var1_np, grads1_np, t, m1, v1)
# Validate updated params
self.assertAllCloseAccordingToType(var0_np, var0.eval())
self.assertAllCloseAccordingToType(var1_np, var1.eval())
return out_layer
plt.title('Optimizer: Nadam')
plt.xlabel('training_epochs')
plt.ylabel('loss')
# Construct model
pred = multilayer_perceptron(x, weights, biases)
# Define loss and optimizer
with tf.name_scope('cost'):
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
# optimizer setting
with tf.name_scope('optimizer'):
optimizer = nadam_optimizer.NadamOptimizer(learning_rate=learning_rate).minimize(cost)
# Initializing the variables
init = tf.global_variables_initializer()
all_loss = []
all_step = []
# Launch the graph
with tf.Session() as sess:
sess.run(init)
# Training cycle
for epoch in range(training_epochs):
avg_cost = 0.