def from_tensor(self, *, tensor, batched):
# Check whether underspecified
if self.is_underspecified():
raise TensorforceError.unexpected()
# Check whether TensorFlow tensor
if not isinstance(tensor, tf.Tensor):
raise TensorforceError.type(
name='TensorSpec.from_tensor', argument='tensor', dtype=type(tensor)
)
# Check whether tensor type and shape match
if tf_util.dtype(x=tensor) != self.type:
raise TensorforceError.value(
name='TensorSpec.from_tensor', argument='tensor.dtype', value=tensor
)
if tf_util.shape(x=tensor)[int(batched):] != self.shape:
raise TensorforceError.value(
name='TensorSpec.from_tensor', argument='tensor.shape', value=tensor
)
# Convert tensor value to Numpy array
value = tensor.numpy()
# Check for nan or inf
if np.isnan(value).any() or np.isinf(value).any():
raise TensorforceError.value(
name='TensorSpec.from_tensor', argument='tensor', value=value
)
# Check num_values
if self.type == 'int' and self.num_values is not None:
if (value < 0).any() or (value >= self.num_values).any():
raise TensorforceError.value(
name='TensorSpec.from_tensor', argument='tensor', value=value
)
# Check min/max_value
elif self.type == 'int' or self.type == 'float':
if self.min_value is not None:
if (value < self.min_value).any():
raise TensorforceError.value(
name='TensorSpec.from_tensor', argument='tensor', value=value
)
if self.max_value is not None:
if (value > self.max_value).any():
raise TensorforceError.value(
name='TensorSpec.from_tensor', argument='tensor', value=value
)
# If singleton shape, return Python object instead of Numpy array
if self.shape == () and not batched:
value = value.item()
return value
def tf_assert(self, *, x, batch_size=None, include_type_shape=False, message=None):
if not isinstance(x, (tf.Tensor, tf.Variable)):
raise TensorforceError.type(name='TensorSpec.tf_assert', argument='x', dtype=type(x))
if batch_size is None:
pass
elif not isinstance(batch_size, tf.Tensor):
raise TensorforceError.type(
name='TensorSpec.tf_assert', argument='batch_size', dtype=type(batch_size)
)
elif tf_util.dtype(x=batch_size) != 'int' or tf_util.shape(x=batch_size) != ():
raise TensorforceError.value(
name='TensorSpec.tf_assert', argument='batch_size', value=batch_size
)
assertions = list()
if message is not None and '{name}' in message:
message = message.format(name='', issue='{issue}')
# Type
tf.debugging.assert_type(
tensor=x, tf_type=self.tf_type(),
message=(None if message is None else message.format(issue='type'))
)
# Shape
shape = tf_util.constant(value=self.shape, dtype='int')
if batch_size is not None:
shape = tf.concat(values=(tf.expand_dims(input=batch_size, axis=0), shape), axis=0)
assertions.append(
tf.debugging.assert_equal(
x=tf_util.cast(x=tf.shape(input=x), dtype='int'), y=shape,
message=(None if message is None else message.format(issue='shape'))
)
)
if self.type == 'float':
assertions.append(tf.debugging.assert_all_finite(
x=x, message=('' if message is None else message.format(issue='inf/nan value'))
))
# Min/max value (includes num_values)
if self.type != 'bool' and self.min_value is not None:
assertions.append(tf.debugging.assert_greater_equal(
x=x, y=tf_util.constant(value=self.min_value, dtype=self.type),
message=(None if message is None else message.format(issue='min value'))
))
if self.type != 'bool' and self.max_value is not None:
assertions.append(tf.debugging.assert_less_equal(
x=x, y=tf_util.constant(value=self.max_value, dtype=self.type),
message=(None if message is None else message.format(issue='max value'))
))
return assertions
