# How to use rate method in avocado

Best Python code snippet using avocado_python

learning_schedules.py

Source:learning_schedules.py

...24 """Helper function to return proper learning rate based on tf version."""25 if tf.executing_eagerly():26 return eager_decay_rate27 else:28 return eager_decay_rate()29def exponential_decay_with_burnin(global_step,30 learning_rate_base,31 learning_rate_decay_steps,32 learning_rate_decay_factor,33 burnin_learning_rate=0.0,34 burnin_steps=0,35 min_learning_rate=0.0,36 staircase=True):37 """Exponential decay schedule with burn-in period.38 In this schedule, learning rate is fixed at burnin_learning_rate39 for a fixed period, before transitioning to a regular exponential40 decay schedule.41 Args:42 global_step: int tensor representing global step.43 learning_rate_base: base learning rate.44 learning_rate_decay_steps: steps to take between decaying the learning rate.45 Note that this includes the number of burn-in steps.46 learning_rate_decay_factor: multiplicative factor by which to decay47 learning rate.48 burnin_learning_rate: initial learning rate during burn-in period. If49 0.0 (which is the default), then the burn-in learning rate is simply50 set to learning_rate_base.51 burnin_steps: number of steps to use burnin learning rate.52 min_learning_rate: the minimum learning rate.53 staircase: whether use staircase decay.54 Returns:55 If executing eagerly:56 returns a no-arg callable that outputs the (scalar)57 float tensor learning rate given the current value of global_step.58 If in a graph:59 immediately returns a (scalar) float tensor representing learning rate.60 """61 if burnin_learning_rate == 0:62 burnin_learning_rate = learning_rate_base63 def eager_decay_rate():64 """Callable to compute the learning rate."""65 post_burnin_learning_rate = tf.train.exponential_decay(66 learning_rate_base,67 global_step - burnin_steps,68 learning_rate_decay_steps,69 learning_rate_decay_factor,70 staircase=staircase)71 if callable(post_burnin_learning_rate):72 post_burnin_learning_rate = post_burnin_learning_rate()73 return tf.maximum(tf.where(74 tf.less(tf.cast(global_step, tf.int32), tf.constant(burnin_steps)),75 tf.constant(burnin_learning_rate),76 post_burnin_learning_rate), min_learning_rate, name='learning_rate')77 return _learning_rate_return_value(eager_decay_rate)78def exponential_decay_with_warmup(global_step,79 learning_rate_base,80 learning_rate_decay_steps,81 learning_rate_decay_factor,82 warmup_learning_rate=0.0,83 warmup_steps=0,84 min_learning_rate=0.0,85 staircase=True):86 """Exponential decay schedule with warm up period.87 Args:88 global_step: int tensor representing global step.89 learning_rate_base: base learning rate.90 learning_rate_decay_steps: steps to take between decaying the learning rate.91 Note that this includes the number of burn-in steps.92 learning_rate_decay_factor: multiplicative factor by which to decay learning93 rate.94 warmup_learning_rate: initial learning rate during warmup period.95 warmup_steps: number of steps to use warmup learning rate.96 min_learning_rate: the minimum learning rate.97 staircase: whether use staircase decay.98 Returns:99 If executing eagerly:100 returns a no-arg callable that outputs the (scalar)101 float tensor learning rate given the current value of global_step.102 If in a graph:103 immediately returns a (scalar) float tensor representing learning rate.104 """105 def eager_decay_rate():106 """Callable to compute the learning rate."""107 post_warmup_learning_rate = tf.train.exponential_decay(108 learning_rate_base,109 global_step - warmup_steps,110 learning_rate_decay_steps,111 learning_rate_decay_factor,112 staircase=staircase)113 if callable(post_warmup_learning_rate):114 post_warmup_learning_rate = post_warmup_learning_rate()115 if learning_rate_base < warmup_learning_rate:116 raise ValueError('learning_rate_base must be larger or equal to '117 'warmup_learning_rate.')118 slope = (learning_rate_base - warmup_learning_rate) / warmup_steps119 warmup_rate = slope * tf.cast(global_step,120 tf.float32) + warmup_learning_rate121 learning_rate = tf.where(122 tf.less(tf.cast(global_step, tf.int32), tf.constant(warmup_steps)),123 warmup_rate,124 tf.maximum(post_warmup_learning_rate, min_learning_rate),125 name='learning_rate')126 return learning_rate127 return _learning_rate_return_value(eager_decay_rate)128def cosine_decay_with_warmup(global_step,129 learning_rate_base,130 total_steps,131 warmup_learning_rate=0.0,132 warmup_steps=0,133 hold_base_rate_steps=0):134 """Cosine decay schedule with warm up period.135 Cosine annealing learning rate as described in:136 Loshchilov and Hutter, SGDR: Stochastic Gradient Descent with Warm Restarts.137 ICLR 2017. https://arxiv.org/abs/1608.03983138 In this schedule, the learning rate grows linearly from warmup_learning_rate139 to learning_rate_base for warmup_steps, then transitions to a cosine decay140 schedule.141 Args:142 global_step: int64 (scalar) tensor representing global step.143 learning_rate_base: base learning rate.144 total_steps: total number of training steps.145 warmup_learning_rate: initial learning rate for warm up.146 warmup_steps: number of warmup steps.147 hold_base_rate_steps: Optional number of steps to hold base learning rate148 before decaying.149 Returns:150 If executing eagerly:151 returns a no-arg callable that outputs the (scalar)152 float tensor learning rate given the current value of global_step.153 If in a graph:154 immediately returns a (scalar) float tensor representing learning rate.155 Raises:156 ValueError: if warmup_learning_rate is larger than learning_rate_base,157 or if warmup_steps is larger than total_steps.158 """159 if total_steps < warmup_steps:160 raise ValueError('total_steps must be larger or equal to '161 'warmup_steps.')162 def eager_decay_rate():163 """Callable to compute the learning rate."""164 learning_rate = 0.5 * learning_rate_base * (1 + tf.cos(165 np.pi *166 (tf.cast(global_step, tf.float32) - warmup_steps - hold_base_rate_steps167 ) / float(total_steps - warmup_steps - hold_base_rate_steps)))168 if hold_base_rate_steps > 0:169 learning_rate = tf.where(170 global_step > warmup_steps + hold_base_rate_steps,171 learning_rate, learning_rate_base)172 if warmup_steps > 0:173 if learning_rate_base < warmup_learning_rate:174 raise ValueError('learning_rate_base must be larger or equal to '175 'warmup_learning_rate.')176 slope = (learning_rate_base - warmup_learning_rate) / warmup_steps177 warmup_rate = slope * tf.cast(global_step,178 tf.float32) + warmup_learning_rate179 learning_rate = tf.where(global_step < warmup_steps, warmup_rate,180 learning_rate)181 return tf.where(global_step > total_steps, 0.0, learning_rate,182 name='learning_rate')183 return _learning_rate_return_value(eager_decay_rate)184def manual_stepping(global_step, boundaries, rates, warmup=False):185 """Manually stepped learning rate schedule.186 This function provides fine grained control over learning rates. One must187 specify a sequence of learning rates as well as a set of integer steps188 at which the current learning rate must transition to the next. For example,189 if boundaries = [5, 10] and rates = [.1, .01, .001], then the learning190 rate returned by this function is .1 for global_step=0,...,4, .01 for191 global_step=5...9, and .001 for global_step=10 and onward.192 Args:193 global_step: int64 (scalar) tensor representing global step.194 boundaries: a list of global steps at which to switch learning195 rates. This list is assumed to consist of increasing positive integers.196 rates: a list of (float) learning rates corresponding to intervals between197 the boundaries. The length of this list must be exactly198 len(boundaries) + 1.199 warmup: Whether to linearly interpolate learning rate for steps in200 [0, boundaries[0]].201 Returns:202 If executing eagerly:203 returns a no-arg callable that outputs the (scalar)204 float tensor learning rate given the current value of global_step.205 If in a graph:206 immediately returns a (scalar) float tensor representing learning rate.207 Raises:208 ValueError: if one of the following checks fails:209 1. boundaries is a strictly increasing list of positive integers210 2. len(rates) == len(boundaries) + 1211 3. boundaries[0] != 0212 """213 if any([b < 0 for b in boundaries]) or any(214 [not isinstance(b, int) for b in boundaries]):215 raise ValueError('boundaries must be a list of positive integers')216 if any([bnext <= b for bnext, b in zip(boundaries[1:], boundaries[:-1])]):217 raise ValueError('Entries in boundaries must be strictly increasing.')218 if any([not isinstance(r, float) for r in rates]):219 raise ValueError('Learning rates must be floats')220 if len(rates) != len(boundaries) + 1:221 raise ValueError('Number of provided learning rates must exceed '222 'number of boundary points by exactly 1.')223 if boundaries and boundaries[0] == 0:224 raise ValueError('First step cannot be zero.')225 if warmup and boundaries:226 slope = (rates[1] - rates[0]) * 1.0 / boundaries[0]227 warmup_steps = list(range(boundaries[0]))228 warmup_rates = [rates[0] + slope * step for step in warmup_steps]229 boundaries = warmup_steps + boundaries230 rates = warmup_rates + rates[1:]231 else:232 boundaries = [0] + boundaries233 num_boundaries = len(boundaries)234 def eager_decay_rate():235 """Callable to compute the learning rate."""236 rate_index = tf.reduce_max(tf.where(237 tf.greater_equal(global_step, boundaries),238 list(range(num_boundaries)),239 [0] * num_boundaries))240 return tf.reduce_sum(rates * tf.one_hot(rate_index, depth=num_boundaries),241 name='learning_rate')...

optimizer_builder.py

Source:optimizer_builder.py

...39 optimizer = None40 summary_vars = []41 if optimizer_type == 'rms_prop_optimizer':42 config = optimizer_config.rms_prop_optimizer43 learning_rate = _create_learning_rate(config.learning_rate,44 global_step=global_step)45 summary_vars.append(learning_rate)46 optimizer = tf.train.RMSPropOptimizer(47 learning_rate,48 decay=config.decay,49 momentum=config.momentum_optimizer_value,50 epsilon=config.epsilon)51 if optimizer_type == 'momentum_optimizer':52 config = optimizer_config.momentum_optimizer53 learning_rate = _create_learning_rate(config.learning_rate,54 global_step=global_step)55 summary_vars.append(learning_rate)56 optimizer = tf.train.MomentumOptimizer(57 learning_rate,58 momentum=config.momentum_optimizer_value)59 if optimizer_type == 'adam_optimizer':60 config = optimizer_config.adam_optimizer61 learning_rate = _create_learning_rate(config.learning_rate,62 global_step=global_step)63 summary_vars.append(learning_rate)64 optimizer = tf.train.AdamOptimizer(learning_rate, epsilon=config.epsilon)65 if optimizer is None:66 raise ValueError('Optimizer %s not supported.' % optimizer_type)67 if optimizer_config.use_moving_average:68 optimizer = tf_opt.MovingAverageOptimizer(69 optimizer, average_decay=optimizer_config.moving_average_decay)70 return optimizer, summary_vars71def build_optimizers_tf_v2(optimizer_config, global_step=None):72 """Create a TF v2 compatible optimizer based on config.73 Args:74 optimizer_config: A Optimizer proto message.75 global_step: A variable representing the current step.76 If None, defaults to tf.train.get_or_create_global_step()77 Returns:78 An optimizer and a list of variables for summary.79 Raises:80 ValueError: when using an unsupported input data type.81 """82 optimizer_type = optimizer_config.WhichOneof('optimizer')83 optimizer = None84 summary_vars = []85 if optimizer_type == 'rms_prop_optimizer':86 config = optimizer_config.rms_prop_optimizer87 learning_rate = _create_learning_rate(config.learning_rate,88 global_step=global_step)89 summary_vars.append(learning_rate)90 optimizer = tf.keras.optimizers.RMSprop(91 learning_rate,92 decay=config.decay,93 momentum=config.momentum_optimizer_value,94 epsilon=config.epsilon)95 if optimizer_type == 'momentum_optimizer':96 config = optimizer_config.momentum_optimizer97 learning_rate = _create_learning_rate(config.learning_rate,98 global_step=global_step)99 summary_vars.append(learning_rate)100 optimizer = tf.keras.optimizers.SGD(101 learning_rate,102 momentum=config.momentum_optimizer_value)103 if optimizer_type == 'adam_optimizer':104 config = optimizer_config.adam_optimizer105 learning_rate = _create_learning_rate(config.learning_rate,106 global_step=global_step)107 summary_vars.append(learning_rate)108 optimizer = tf.keras.optimizers.Adam(learning_rate, epsilon=config.epsilon)109 if optimizer is None:110 raise ValueError('Optimizer %s not supported.' % optimizer_type)111 if optimizer_config.use_moving_average:112 optimizer = ema_optimizer.ExponentialMovingAverage(113 optimizer=optimizer,114 average_decay=optimizer_config.moving_average_decay)115 return optimizer, summary_vars116def build(config, global_step=None):117 if tf.executing_eagerly():118 return build_optimizers_tf_v2(config, global_step)119 else:120 return build_optimizers_tf_v1(config, global_step)121def _create_learning_rate(learning_rate_config, global_step=None):122 """Create optimizer learning rate based on config.123 Args:124 learning_rate_config: A LearningRate proto message.125 global_step: A variable representing the current step.126 If None, defaults to tf.train.get_or_create_global_step()127 Returns:128 A learning rate.129 Raises:130 ValueError: when using an unsupported input data type.131 """132 if global_step is None:133 global_step = tf.train.get_or_create_global_step()134 learning_rate = None135 learning_rate_type = learning_rate_config.WhichOneof('learning_rate')...

optimizer_builder_tf1_test.py

Source:optimizer_builder_tf1_test.py

...37 }38 """39 learning_rate_proto = optimizer_pb2.LearningRate()40 text_format.Merge(learning_rate_text_proto, learning_rate_proto)41 learning_rate = optimizer_builder._create_learning_rate(42 learning_rate_proto)43 self.assertTrue(44 six.ensure_str(learning_rate.op.name).endswith('learning_rate'))45 with self.test_session():46 learning_rate_out = learning_rate.eval()47 self.assertAlmostEqual(learning_rate_out, 0.004)48 def testBuildExponentialDecayLearningRate(self):49 learning_rate_text_proto = """50 exponential_decay_learning_rate {51 initial_learning_rate: 0.00452 decay_steps: 9999953 decay_factor: 0.8554 staircase: false55 }56 """57 learning_rate_proto = optimizer_pb2.LearningRate()58 text_format.Merge(learning_rate_text_proto, learning_rate_proto)59 learning_rate = optimizer_builder._create_learning_rate(60 learning_rate_proto)61 self.assertTrue(62 six.ensure_str(learning_rate.op.name).endswith('learning_rate'))63 self.assertIsInstance(learning_rate, tf.Tensor)64 def testBuildManualStepLearningRate(self):65 learning_rate_text_proto = """66 manual_step_learning_rate {67 initial_learning_rate: 0.00268 schedule {69 step: 10070 learning_rate: 0.00671 }72 schedule {73 step: 9000074 learning_rate: 0.0000675 }76 warmup: true77 }78 """79 learning_rate_proto = optimizer_pb2.LearningRate()80 text_format.Merge(learning_rate_text_proto, learning_rate_proto)81 learning_rate = optimizer_builder._create_learning_rate(82 learning_rate_proto)83 self.assertIsInstance(learning_rate, tf.Tensor)84 def testBuildCosineDecayLearningRate(self):85 learning_rate_text_proto = """86 cosine_decay_learning_rate {87 learning_rate_base: 0.00288 total_steps: 2000089 warmup_learning_rate: 0.000190 warmup_steps: 100091 hold_base_rate_steps: 2000092 }93 """94 learning_rate_proto = optimizer_pb2.LearningRate()95 text_format.Merge(learning_rate_text_proto, learning_rate_proto)96 learning_rate = optimizer_builder._create_learning_rate(97 learning_rate_proto)98 self.assertIsInstance(learning_rate, tf.Tensor)99 def testRaiseErrorOnEmptyLearningRate(self):100 learning_rate_text_proto = """101 """102 learning_rate_proto = optimizer_pb2.LearningRate()103 text_format.Merge(learning_rate_text_proto, learning_rate_proto)104 with self.assertRaises(ValueError):105 optimizer_builder._create_learning_rate(learning_rate_proto)106@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only test.')107class OptimizerBuilderTest(tf.test.TestCase):108 def testBuildRMSPropOptimizer(self):109 optimizer_text_proto = """110 rms_prop_optimizer: {111 learning_rate: {112 exponential_decay_learning_rate {113 initial_learning_rate: 0.004114 decay_steps: 800720115 decay_factor: 0.95116 }117 }118 momentum_optimizer_value: 0.9119 decay: 0.9...

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