def model_fn_diff_modes(features, labels, mode):
_, _ = features, labels
v = variables.Variable(21, name='some_var')
train_op = None
loss = constant_op.constant(104)
if mode == model_fn_lib.ModeKeys.TRAIN:
loss = constant_op.constant(105)
predictions = constant_op.constant([501])
train_op = control_flow_ops.group(
state_ops.assign_add(training.get_global_step(), 1),
state_ops.assign_add(v, 3))
elif mode == model_fn_lib.ModeKeys.EVAL:
loss = constant_op.constant(106)
predictions = constant_op.constant([502])
else:
loss = constant_op.constant(107)
predictions = constant_op.constant([503])
return model_fn_lib.EstimatorSpec(mode,
loss=loss,
train_op=train_op,
eval_metric_ops={
'abs_err':
metrics_lib.mean_absolute_error(
constant_op.constant(0),
predictions)
},
predictions=predictions)
def _model_fn(features, labels, mode):
predictions = layers.dense(
features['x'], 1, kernel_initializer=init_ops.zeros_initializer())
export_outputs = {
'predictions': export.RegressionOutput(predictions)
}
if mode == model_fn_lib.ModeKeys.PREDICT:
return model_fn_lib.EstimatorSpec(
mode, predictions=predictions, export_outputs=export_outputs)
loss = losses.mean_squared_error(labels, predictions)
train_op = training.GradientDescentOptimizer(learning_rate=0.5).minimize(
loss, training.get_global_step())
eval_metric_ops = {
'absolute_error': metrics_lib.mean_absolute_error(
labels, predictions)
}
return model_fn_lib.EstimatorSpec(
mode,
predictions=predictions,
loss=loss,
train_op=train_op,
eval_metric_ops=eval_metric_ops,
export_outputs=export_outputs)
def model_fn_diff_modes(features, labels, mode):
_, _ = features, labels
v = variables.Variable(21, name='some_var')
train_op = None
loss = constant_op.constant(104)
if mode == model_fn_lib.ModeKeys.TRAIN:
loss = constant_op.constant(105)
predictions = constant_op.constant([501])
train_op = control_flow_ops.group(
state_ops.assign_add(training.get_global_step(), 1),
state_ops.assign_add(v, 3))
elif mode == model_fn_lib.ModeKeys.EVAL:
loss = constant_op.constant(106)
predictions = constant_op.constant([502])
else:
loss = constant_op.constant(107)
predictions = constant_op.constant([503])
return model_fn_lib.EstimatorSpec(
mode,
loss=loss,
train_op=train_op,
eval_metric_ops={
'abs_err': metrics_lib.mean_absolute_error(
constant_op.constant(0), predictions)},
predictions=predictions)
def _model_fn(features, labels, mode):
predictions = layers.dense(
features['x'], 1, kernel_initializer=init_ops.zeros_initializer())
export_outputs = {
'predictions': export_output.RegressionOutput(predictions)
}
if mode == model_fn_lib.ModeKeys.PREDICT:
return model_fn_lib.EstimatorSpec(
mode, predictions=predictions, export_outputs=export_outputs)
loss = losses.mean_squared_error(labels, predictions)
train_op = training.GradientDescentOptimizer(learning_rate=0.5).minimize(
loss, training.get_global_step())
eval_metric_ops = {
'absolute_error': metrics_lib.mean_absolute_error(
labels, predictions)
}
return model_fn_lib.EstimatorSpec(
mode,
predictions=predictions,
loss=loss,
train_op=train_op,
eval_metric_ops=eval_metric_ops,
export_outputs=export_outputs)
def _model_fn(features, labels, mode, params):
if not self._export_mode:
# Always check batch size in params
self.assertEqual(batch_size_dict[mode], params['batch_size'])
else:
self.assertNotIn('batch_size', params)
# Check the input feeds correct shape for train and eval. When eval on CPU
# or predict, it is allowed to have dynamic shape. So, here only validates
# the fully known shape (which covers the TPU train).
if features['x'].shape.is_fully_defined():
self.assertEqual(batch_size_dict[mode], features['x'].shape[0])
predictions = layers.dense(
features['x'],
1,
kernel_initializer=init_ops.ones_initializer())
export_outputs = {
'predictions': export_output.RegressionOutput(predictions)
}
if mode == _PREDICT:
return _create_estimator_spec(
mode=mode,
predictions={'predictions': predictions},
export_outputs=export_outputs)
loss = losses.mean_squared_error(labels, predictions)
optimizer = tf.tpu.CrossShardOptimizer(
training.GradientDescentOptimizer(learning_rate=0.5))
train_op = optimizer.minimize(
loss, global_step=training.get_global_step())
eval_metrics = (
lambda labels, predictions: { # pylint: disable=g-long-lambda
'absolute_error':
metrics_lib.mean_absolute_error(labels, predictions)
},
[labels, predictions])
return _create_estimator_spec(
mode=mode,
loss=loss,
predictions={'predictions': predictions},
export_outputs=export_outputs,
train_op=train_op,
eval_metrics=eval_metrics)
def _metric_fn_on_cpu(self, labels, predictions):
return {
'mse': metrics_lib.mean_absolute_error(labels, predictions),
}
