THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.Assert, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.Assert

Return

Applicative

Origial documentation for Builder.Assert

def Assert(builder):

THIS METHOD IS AUTOMATICALLY GENERATED

@immutable

This method is a lifted version the function tf.Assert to work with tensorbuilder.core.builders.Builders. Instead of taking a Tensor as its first argument it takes a builder, the rest of the arguments are exactly the same.

Original Documentation for tf.Assert

def Assert(condition, data, summarize=None, name=None)

Asserts that the given condition is true.

If condition evaluates to false, print the list of tensors in data. summarize determines how many entries of the tensors to print.

NOTE: To ensure that Assert executes, one usually attaches a dependency:

python # Ensure maximum element of x is smaller or equal to 1 assert_op = tf.Assert(tf.less_equal(tf.reduce_max(x), 1.), [x]) x = tf.with_dependencies([assert_op], x)

Args: condition: The condition to evaluate. data: The tensors to print out when condition is false. summarize: Print this many entries of each tensor. name: A name for this operation (optional).

Returns: assert_op: An Operation that, when executed, raises a tf.errors.InvalidArgumentError if condition is not true.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.Assert_layer, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.Assert_layer

Return

Applicative

Origial documentation for Builder.Assert_layer

def Assert_layer(builder, size):

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .fully_connected(size, activation_fn = tf.Assert, ...)

Arguments

• size: the size of the resulting layer
• All other *args and **kwargs are forwarded to tf.contrib.layers.fully_connected

Return

Builder

Origial documentation for tf.Assert

def Assert(condition, data, summarize=None, name=None):

Asserts that the given condition is true.

If condition evaluates to false, print the list of tensors in data. summarize determines how many entries of the tensors to print.

NOTE: To ensure that Assert executes, one usually attaches a dependency:

python # Ensure maximum element of x is smaller or equal to 1 assert_op = tf.Assert(tf.less_equal(tf.reduce_max(x), 1.), [x]) x = tf.with_dependencies([assert_op], x)

Args: condition: The condition to evaluate. data: The tensors to print out when condition is false. summarize: Print this many entries of each tensor. name: A name for this operation (optional).

Returns: assert_op: An Operation that, when executed, raises a tf.errors.InvalidArgumentError if condition is not true.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.BuilderTree, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.BuilderTree

Return

Applicative

Origial documentation for Builder.BuilderTree

def BuilderTree(self, builder_iterable):

None

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.NoGradient_layer, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.NoGradient_layer

Return

Applicative

Origial documentation for Builder.NoGradient_layer

def NoGradient_layer(builder, size):

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .fully_connected(size, activation_fn = tf.NoGradient, ...)

Arguments

• size: the size of the resulting layer
• All other *args and **kwargs are forwarded to tf.contrib.layers.fully_connected

Return

Builder

Origial documentation for tf.NoGradient

def NotDifferentiable(op_type):

Specifies that ops of type op_type is not differentiable.

This function should not be used for operations that have a well-defined gradient that is not yet implemented.

This function is only used when defining a new op type. It may be used for ops such as tf.size() that are not differentiable. For example:

python tf.NotDifferentiable("Size")

The gradient computed for 'op_type' will then propagate zeros.

For ops that have a well-defined gradient but are not yet implemented, no declaration should be made, and an error must be thrown if an attempt to request its gradient is made.

Args: op_type: The string type of an operation. This corresponds to the OpDef.name field for the proto that defines the operation.

Raises: TypeError: If op_type is not a string.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.NotDifferentiable, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.NotDifferentiable

Return

Applicative

Origial documentation for Builder.NotDifferentiable

def NotDifferentiable(builder):

THIS METHOD IS AUTOMATICALLY GENERATED

@immutable

This method is a lifted version the function tf.NotDifferentiable to work with tensorbuilder.core.builders.Builders. Instead of taking a Tensor as its first argument it takes a builder, the rest of the arguments are exactly the same.

Original Documentation for tf.NotDifferentiable

def NotDifferentiable(op_type)

Specifies that ops of type op_type is not differentiable.

This function should not be used for operations that have a well-defined gradient that is not yet implemented.

This function is only used when defining a new op type. It may be used for ops such as tf.size() that are not differentiable. For example:

python tf.NotDifferentiable("Size")

The gradient computed for 'op_type' will then propagate zeros.

For ops that have a well-defined gradient but are not yet implemented, no declaration should be made, and an error must be thrown if an attempt to request its gradient is made.

Args: op_type: The string type of an operation. This corresponds to the OpDef.name field for the proto that defines the operation.

Raises: TypeError: If op_type is not a string.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.NotDifferentiable_layer, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.NotDifferentiable_layer

Return

Applicative

Origial documentation for Builder.NotDifferentiable_layer

def NotDifferentiable_layer(builder, size):

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .fully_connected(size, activation_fn = tf.NotDifferentiable, ...)

Arguments

• size: the size of the resulting layer
• All other *args and **kwargs are forwarded to tf.contrib.layers.fully_connected

Return

Builder

Origial documentation for tf.NotDifferentiable

def NotDifferentiable(op_type):

Specifies that ops of type op_type is not differentiable.

This function should not be used for operations that have a well-defined gradient that is not yet implemented.

This function is only used when defining a new op type. It may be used for ops such as tf.size() that are not differentiable. For example:

python tf.NotDifferentiable("Size")

The gradient computed for 'op_type' will then propagate zeros.

For ops that have a well-defined gradient but are not yet implemented, no declaration should be made, and an error must be thrown if an attempt to request its gradient is made.

Args: op_type: The string type of an operation. This corresponds to the OpDef.name field for the proto that defines the operation.

Raises: TypeError: If op_type is not a string.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.Print, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.Print

Return

Applicative

Origial documentation for Builder.Print

def Print(builder):

THIS METHOD IS AUTOMATICALLY GENERATED

@immutable

This method is a lifted version the function tf.Print to work with tensorbuilder.core.builders.Builders. Instead of taking a Tensor as its first argument it takes a builder, the rest of the arguments are exactly the same.

Original Documentation for tf.Print

def Print(input_, data, message=None, first_n=None, summarize=None, name=None)

Prints a list of tensors.

This is an identity op with the side effect of printing data when evaluating.

Args: input_: A tensor passed through this op. data: A list of tensors to print out when op is evaluated. message: A string, prefix of the error message. first_n: Only log first_n number of times. Negative numbers log always; this is the default. summarize: Only print this many entries of each tensor. If None, then a maximum of 3 elements are printed per input tensor. name: A name for the operation (optional).

Returns: Same tensor as input_.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.Print_layer, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.Print_layer

Return

Applicative

Origial documentation for Builder.Print_layer

def Print_layer(builder, size):

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .fully_connected(size, activation_fn = tf.Print, ...)

Arguments

• size: the size of the resulting layer
• All other *args and **kwargs are forwarded to tf.contrib.layers.fully_connected

Return

Builder

Origial documentation for tf.Print

def Print(input_, data, message=None, first_n=None, summarize=None, name=None):

Prints a list of tensors.

This is an identity op with the side effect of printing data when evaluating.

Args: input_: A tensor passed through this op. data: A list of tensors to print out when op is evaluated. message: A string, prefix of the error message. first_n: Only log first_n number of times. Negative numbers log always; this is the default. summarize: Only print this many entries of each tensor. If None, then a maximum of 3 elements are printed per input tensor. name: A name for the operation (optional).

Returns: Same tensor as input_.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.abs, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.abs

Return

Applicative

Origial documentation for Builder.abs

def abs(builder):

THIS METHOD IS AUTOMATICALLY GENERATED

@immutable

This method is a lifted version the function tf.abs to work with tensorbuilder.core.builders.Builders. Instead of taking a Tensor as its first argument it takes a builder, the rest of the arguments are exactly the same.

Original Documentation for tf.abs

def abs(x, name=None)

Computes the absolute value of a tensor.

Given a tensor of real numbers x, this operation returns a tensor containing the absolute value of each element in x. For example, if x is an input element and y is an output element, this operation computes \(y = |x|\).

See tf.complex_abs() to compute the absolute value of a complex number.

Args: x: A Tensor or SparseTensor of type float32, float64, int32, or int64. name: A name for the operation (optional).

Returns: A Tensor or SparseTensor the same size and type as x with absolute values.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.abs_layer, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.abs_layer

Return

Applicative

Origial documentation for Builder.abs_layer

def abs_layer(builder, size):

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .fully_connected(size, activation_fn = tf.abs, ...)

Arguments

• size: the size of the resulting layer
• All other *args and **kwargs are forwarded to tf.contrib.layers.fully_connected

Return

Builder

Origial documentation for tf.abs

def abs(x, name=None):

Computes the absolute value of a tensor.

Given a tensor of real numbers x, this operation returns a tensor containing the absolute value of each element in x. For example, if x is an input element and y is an output element, this operation computes \(y = |x|\).

See tf.complex_abs() to compute the absolute value of a complex number.

Args: x: A Tensor or SparseTensor of type float32, float64, int32, or int64. name: A name for the operation (optional).

Returns: A Tensor or SparseTensor the same size and type as x with absolute values.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.accumulate_n, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.accumulate_n

Return

Applicative

Origial documentation for Builder.accumulate_n

def accumulate_n(builder):

THIS METHOD IS AUTOMATICALLY GENERATED

@immutable

This method is a lifted version the function tf.accumulate_n to work with tensorbuilder.core.builders.Builders. Instead of taking a Tensor as its first argument it takes a builder, the rest of the arguments are exactly the same.

Original Documentation for tf.accumulate_n

def accumulate_n(inputs, shape=None, tensor_dtype=None, name=None)

Returns the element-wise sum of a list of tensors.

Optionally, pass shape and tensor_dtype for shape and type checking, otherwise, these are inferred.

NOTE: This operation is not differentiable and cannot be used if inputs depend on trainable variables. Please use tf.add_n for such cases.

For example:

```python

tensor 'a' is [[1, 2], [3, 4]]

tensor b is [[5, 0], [0, 6]]

tf.accumulate_n([a, b, a]) ==> [[7, 4], [6, 14]]

Explicitly pass shape and type

tf.accumulate_n([a, b, a], shape=[2, 2], tensor_dtype=tf.int32) ==> [[7, 4], [6, 14]] ```

Args: inputs: A list of Tensor objects, each with same shape and type. shape: Shape of elements of inputs. tensor_dtype: The type of inputs. name: A name for the operation (optional).

Returns: A Tensor of same shape and type as the elements of inputs.

Raises: ValueError: If inputs don't all have same shape and dtype or the shape cannot be inferred.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.accumulate_n_layer, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.accumulate_n_layer

Return

Applicative

Origial documentation for Builder.accumulate_n_layer

def accumulate_n_layer(builder, size):

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .fully_connected(size, activation_fn = tf.accumulate_n, ...)

Arguments

• size: the size of the resulting layer
• All other *args and **kwargs are forwarded to tf.contrib.layers.fully_connected

Return

Builder

Origial documentation for tf.accumulate_n

def accumulate_n(inputs, shape=None, tensor_dtype=None, name=None):

Returns the element-wise sum of a list of tensors.

Optionally, pass shape and tensor_dtype for shape and type checking, otherwise, these are inferred.

NOTE: This operation is not differentiable and cannot be used if inputs depend on trainable variables. Please use tf.add_n for such cases.

For example:

```python

tensor 'a' is [[1, 2], [3, 4]]

tensor b is [[5, 0], [0, 6]]

tf.accumulate_n([a, b, a]) ==> [[7, 4], [6, 14]]

Explicitly pass shape and type

tf.accumulate_n([a, b, a], shape=[2, 2], tensor_dtype=tf.int32) ==> [[7, 4], [6, 14]] ```

Args: inputs: A list of Tensor objects, each with same shape and type. shape: Shape of elements of inputs. tensor_dtype: The type of inputs. name: A name for the operation (optional).

Returns: A Tensor of same shape and type as the elements of inputs.

Raises: ValueError: If inputs don't all have same shape and dtype or the shape cannot be inferred.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.acos, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.acos

Return

Applicative

Origial documentation for Builder.acos

def acos(builder):

THIS METHOD IS AUTOMATICALLY GENERATED

@immutable

This method is a lifted version the function tf.acos to work with tensorbuilder.core.builders.Builders. Instead of taking a Tensor as its first argument it takes a builder, the rest of the arguments are exactly the same.

Original Documentation for tf.acos

def acos(x, name=None)

Computes acos of x element-wise.

Args: x: A Tensor. Must be one of the following types: half, float32, float64, int32, int64, complex64, complex128. name: A name for the operation (optional).

Returns: A Tensor. Has the same type as x.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.acos_layer, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.acos_layer

Return

Applicative

Origial documentation for Builder.acos_layer

def acos_layer(builder, size):

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .fully_connected(size, activation_fn = tf.acos, ...)

Arguments

• size: the size of the resulting layer
• All other *args and **kwargs are forwarded to tf.contrib.layers.fully_connected

Return

Builder

Origial documentation for tf.acos

def acos(x, name=None):

Computes acos of x element-wise.

Args: x: A Tensor. Must be one of the following types: half, float32, float64, int32, int64, complex64, complex128. name: A name for the operation (optional).

Returns: A Tensor. Has the same type as x.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.add, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.add

Return

Applicative

Origial documentation for Builder.add

def add(builder):

THIS METHOD IS AUTOMATICALLY GENERATED

@immutable

This method is a lifted version the function tf.add to work with tensorbuilder.core.builders.Builders. Instead of taking a Tensor as its first argument it takes a builder, the rest of the arguments are exactly the same.

Original Documentation for tf.add

def add(x, y, name=None)

Returns x + y element-wise.

NOTE: Add supports broadcasting. AddN does not. More about broadcasting here

Args: x: A Tensor. Must be one of the following types: half, float32, float64, uint8, int8, int16, int32, int64, complex64, complex128, string. y: A Tensor. Must have the same type as x. name: A name for the operation (optional).

Returns: A Tensor. Has the same type as x.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.add_check_numerics_ops, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.add_check_numerics_ops

Return

Applicative

Origial documentation for Builder.add_check_numerics_ops

def add_check_numerics_ops(builder):

THIS METHOD IS AUTOMATICALLY GENERATED

@immutable

This method is a lifted version the function tf.add_check_numerics_ops to work with tensorbuilder.core.builders.Builders. Instead of taking a Tensor as its first argument it takes a builder, the rest of the arguments are exactly the same.

Original Documentation for tf.add_check_numerics_ops

def add_check_numerics_ops()

Connect a check_numerics to every floating point tensor.

check_numerics operations themselves are added for each half, float, or double tensor in the graph. For all ops in the graph, the check_numerics op for all of its (half, float, or double) inputs is guaranteed to run before the check_numerics op on any of its outputs.

Returns: A group op depending on all check_numerics ops added.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.add_check_numerics_ops_layer, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.add_check_numerics_ops_layer

Return

Applicative

Origial documentation for Builder.add_check_numerics_ops_layer

def add_check_numerics_ops_layer(builder, size):

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .fully_connected(size, activation_fn = tf.add_check_numerics_ops, ...)

Arguments

• size: the size of the resulting layer
• All other *args and **kwargs are forwarded to tf.contrib.layers.fully_connected

Return

Builder

Origial documentation for tf.add_check_numerics_ops

def add_check_numerics_ops():

Connect a check_numerics to every floating point tensor.

check_numerics operations themselves are added for each half, float, or double tensor in the graph. For all ops in the graph, the check_numerics op for all of its (half, float, or double) inputs is guaranteed to run before the check_numerics op on any of its outputs.

Returns: A group op depending on all check_numerics ops added.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.add_layer, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.add_layer

Return

Applicative

Origial documentation for Builder.add_layer

def add_layer(builder, size):

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .fully_connected(size, activation_fn = tf.add, ...)

Arguments

• size: the size of the resulting layer
• All other *args and **kwargs are forwarded to tf.contrib.layers.fully_connected

Return

Builder

Origial documentation for tf.add

def add(x, y, name=None):

Returns x + y element-wise.

NOTE: Add supports broadcasting. AddN does not. More about broadcasting here

Args: x: A Tensor. Must be one of the following types: half, float32, float64, uint8, int8, int16, int32, int64, complex64, complex128, string. y: A Tensor. Must have the same type as x. name: A name for the operation (optional).

Returns: A Tensor. Has the same type as x.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.add_n, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.add_n

Return

Applicative

Origial documentation for Builder.add_n

def add_n(builder):

THIS METHOD IS AUTOMATICALLY GENERATED

@immutable

This method is a lifted version the function tf.add_n to work with tensorbuilder.core.builders.Builders. Instead of taking a Tensor as its first argument it takes a builder, the rest of the arguments are exactly the same.

Original Documentation for tf.add_n

def add_n(inputs, name=None)

Adds all input tensors element-wise.

Args: inputs: A list of Tensor objects, each with same shape and type. name: A name for the operation (optional).

Returns: A Tensor of same shape and type as the elements of inputs.

Raises: ValueError: If inputs don't all have same shape and dtype or the shape cannot be inferred.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.add_n_layer, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.add_n_layer

Return

Applicative

Origial documentation for Builder.add_n_layer

def add_n_layer(builder, size):

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .fully_connected(size, activation_fn = tf.add_n, ...)

Arguments

• size: the size of the resulting layer
• All other *args and **kwargs are forwarded to tf.contrib.layers.fully_connected

Return

Builder

Origial documentation for tf.add_n

def add_n(inputs, name=None):

Adds all input tensors element-wise.

Args: inputs: A list of Tensor objects, each with same shape and type. name: A name for the operation (optional).

Returns: A Tensor of same shape and type as the elements of inputs.

Raises: ValueError: If inputs don't all have same shape and dtype or the shape cannot be inferred.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.add_regularization_loss, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.add_regularization_loss

Return

Applicative

Origial documentation for Builder.add_regularization_loss

def add_regularization_loss(builder):

THIS METHOD IS AUTOMATICALLY GENERATED

@immutable

This method is a lifted version the function tensorbuilder.add_regularization_loss to work with tensorbuilder.core.builders.Builders. Instead of taking a Tensor as its first argument it takes a builder, the rest of the arguments are exactly the same.

Original Documentation for tensorbuilder.add_regularization_loss

def add_regularization_loss(tensor, graph=None, scope="add_regularization_loss")

None

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.add_to_collection, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.add_to_collection

Return

Applicative

Origial documentation for Builder.add_to_collection

def add_to_collection(builder):

THIS METHOD IS AUTOMATICALLY GENERATED

@immutable

This method is a lifted version the function tf.add_to_collection to work with tensorbuilder.core.builders.Builders. Instead of taking a Tensor as its first argument it takes a builder, the rest of the arguments are exactly the same.

Original Documentation for tf.add_to_collection

def add_to_collection(name, value)

Wrapper for Graph.add_to_collection() using the default graph.

See Graph.add_to_collection() for more details.

Args: name: The key for the collection. For example, the GraphKeys class contains many standard names for collections. value: The value to add to the collection.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.add_to_collection_layer, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.add_to_collection_layer

Return

Applicative

Origial documentation for Builder.add_to_collection_layer

def add_to_collection_layer(builder, size):

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .fully_connected(size, activation_fn = tf.add_to_collection, ...)

Arguments

• size: the size of the resulting layer
• All other *args and **kwargs are forwarded to tf.contrib.layers.fully_connected

Return

Builder

Origial documentation for tf.add_to_collection

def add_to_collection(name, value):

Wrapper for Graph.add_to_collection() using the default graph.

See Graph.add_to_collection() for more details.

Args: name: The key for the collection. For example, the GraphKeys class contains many standard names for collections. value: The value to add to the collection.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.all_candidate_sampler, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.all_candidate_sampler

Return

Applicative

Origial documentation for Builder.all_candidate_sampler

def all_candidate_sampler(builder):

THIS METHOD IS AUTOMATICALLY GENERATED

@immutable

This method is a lifted version the function tf.nn.all_candidate_sampler to work with tensorbuilder.core.builders.Builders. Instead of taking a Tensor as its first argument it takes a builder, the rest of the arguments are exactly the same.

Original Documentation for tf.nn.all_candidate_sampler

def all_candidate_sampler(true_classes, num_true, num_sampled, unique, seed=None, name=None)

Generate the set of all classes.

Deterministically generates and returns the set of all possible classes. For testing purposes. There is no need to use this, since you might as well use full softmax or full logistic regression.

Args: true_classes: A Tensor of type int64 and shape [batch_size, num_true]. The target classes. num_true: An int. The number of target classes per training example. num_sampled: An int. The number of possible classes. unique: A bool. Ignored. unique. seed: An int. An operation-specific seed. Default is 0. name: A name for the operation (optional).

Returns: sampled_candidates: A tensor of type int64 and shape [num_sampled]. This operation deterministically returns the entire range [0, num_sampled]. true_expected_count: A tensor of type float. Same shape as true_classes. The expected counts under the sampling distribution of each of true_classes. All returned values are 1.0. sampled_expected_count: A tensor of type float. Same shape as sampled_candidates. The expected counts under the sampling distribution of each of sampled_candidates. All returned values are 1.0.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.all_candidate_sampler_layer, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.all_candidate_sampler_layer

Return

Applicative

Origial documentation for Builder.all_candidate_sampler_layer

def all_candidate_sampler_layer(builder, size):

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .fully_connected(size, activation_fn = tf.nn.all_candidate_sampler, ...)

Arguments

• size: the size of the resulting layer
• All other *args and **kwargs are forwarded to tf.contrib.layers.fully_connected

Return

Builder

Origial documentation for tf.nn.all_candidate_sampler

def all_candidate_sampler(true_classes, num_true, num_sampled, unique, seed=None, name=None):

Generate the set of all classes.

Deterministically generates and returns the set of all possible classes. For testing purposes. There is no need to use this, since you might as well use full softmax or full logistic regression.

Args: true_classes: A Tensor of type int64 and shape [batch_size, num_true]. The target classes. num_true: An int. The number of target classes per training example. num_sampled: An int. The number of possible classes. unique: A bool. Ignored. unique. seed: An int. An operation-specific seed. Default is 0. name: A name for the operation (optional).

Returns: sampled_candidates: A tensor of type int64 and shape [num_sampled]. This operation deterministically returns the entire range [0, num_sampled]. true_expected_count: A tensor of type float. Same shape as true_classes. The expected counts under the sampling distribution of each of true_classes. All returned values are 1.0. sampled_expected_count: A tensor of type float. Same shape as sampled_candidates. The expected counts under the sampling distribution of each of sampled_candidates. All returned values are 1.0.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.all_variables, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.all_variables

Return

Applicative

Origial documentation for Builder.all_variables

def all_variables(builder):

THIS METHOD IS AUTOMATICALLY GENERATED

@immutable

This method is a lifted version the function tf.all_variables to work with tensorbuilder.core.builders.Builders. Instead of taking a Tensor as its first argument it takes a builder, the rest of the arguments are exactly the same.

Original Documentation for tf.all_variables

def all_variables()

Returns all variables that must be saved/restored.

The Variable() constructor automatically adds new variables to the graph collection GraphKeys.VARIABLES. This convenience function returns the contents of that collection.

Returns: A list of Variable objects.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.all_variables_layer, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.all_variables_layer

Return

Applicative

Origial documentation for Builder.all_variables_layer

def all_variables_layer(builder, size):

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .fully_connected(size, activation_fn = tf.all_variables, ...)

Arguments

• size: the size of the resulting layer
• All other *args and **kwargs are forwarded to tf.contrib.layers.fully_connected

Return

Builder

Origial documentation for tf.all_variables

def all_variables():

Returns all variables that must be saved/restored.

The Variable() constructor automatically adds new variables to the graph collection GraphKeys.VARIABLES. This convenience function returns the contents of that collection.

Returns: A list of Variable objects.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.arg_max, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.arg_max

Return

Applicative

Origial documentation for Builder.arg_max

def arg_max(builder):

THIS METHOD IS AUTOMATICALLY GENERATED

@immutable

This method is a lifted version the function tf.arg_max to work with tensorbuilder.core.builders.Builders. Instead of taking a Tensor as its first argument it takes a builder, the rest of the arguments are exactly the same.

Original Documentation for tf.arg_max

def arg_max(input, dimension, name=None)

Returns the index with the largest value across dimensions of a tensor.

Args: input: A Tensor. Must be one of the following types: float32, float64, int64, int32, uint8, uint16, int16, int8, complex64, complex128, qint8, quint8, qint32, half. dimension: A Tensor. Must be one of the following types: int32, int64. int32, 0 <= dimension < rank(input). Describes which dimension of the input Tensor to reduce across. For vectors, use dimension = 0. name: A name for the operation (optional).

Returns: A Tensor of type int64.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.arg_max_layer, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.arg_max_layer

Return

Applicative

Origial documentation for Builder.arg_max_layer

def arg_max_layer(builder, size):

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .fully_connected(size, activation_fn = tf.arg_max, ...)

Arguments

• size: the size of the resulting layer
• All other *args and **kwargs are forwarded to tf.contrib.layers.fully_connected

Return

Builder

Origial documentation for tf.arg_max

def arg_max(input, dimension, name=None):

Returns the index with the largest value across dimensions of a tensor.

Args: input: A Tensor. Must be one of the following types: float32, float64, int64, int32, uint8, uint16, int16, int8, complex64, complex128, qint8, quint8, qint32, half. dimension: A Tensor. Must be one of the following types: int32, int64. int32, 0 <= dimension < rank(input). Describes which dimension of the input Tensor to reduce across. For vectors, use dimension = 0. name: A name for the operation (optional).

Returns: A Tensor of type int64.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.arg_min, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.arg_min

Return

Applicative

Origial documentation for Builder.arg_min

def arg_min(builder):

THIS METHOD IS AUTOMATICALLY GENERATED

@immutable

This method is a lifted version the function tf.arg_min to work with tensorbuilder.core.builders.Builders. Instead of taking a Tensor as its first argument it takes a builder, the rest of the arguments are exactly the same.

Original Documentation for tf.arg_min

def arg_min(input, dimension, name=None)

Returns the index with the smallest value across dimensions of a tensor.

Args: input: A Tensor. Must be one of the following types: float32, float64, int64, int32, uint8, uint16, int16, int8, complex64, complex128, qint8, quint8, qint32, half. dimension: A Tensor. Must be one of the following types: int32, int64. int32, 0 <= dimension < rank(input). Describes which dimension of the input Tensor to reduce across. For vectors, use dimension = 0. name: A name for the operation (optional).

Returns: A Tensor of type int64.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.arg_min_layer, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.arg_min_layer

Return

Applicative

Origial documentation for Builder.arg_min_layer

def arg_min_layer(builder, size):

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .fully_connected(size, activation_fn = tf.arg_min, ...)

Arguments

• size: the size of the resulting layer
• All other *args and **kwargs are forwarded to tf.contrib.layers.fully_connected

Return

Builder

Origial documentation for tf.arg_min

def arg_min(input, dimension, name=None):

Returns the index with the smallest value across dimensions of a tensor.

Args: input: A Tensor. Must be one of the following types: float32, float64, int64, int32, uint8, uint16, int16, int8, complex64, complex128, qint8, quint8, qint32, half. dimension: A Tensor. Must be one of the following types: int32, int64. int32, 0 <= dimension < rank(input). Describes which dimension of the input Tensor to reduce across. For vectors, use dimension = 0. name: A name for the operation (optional).

Returns: A Tensor of type int64.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.argmax_layer, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.argmax_layer

Return

Applicative

Origial documentation for Builder.argmax_layer

def argmax_layer(builder, size):

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .fully_connected(size, activation_fn = tf.argmax, ...)

Arguments

• size: the size of the resulting layer
• All other *args and **kwargs are forwarded to tf.contrib.layers.fully_connected

Return

Builder

Origial documentation for tf.argmax

def arg_max(input, dimension, name=None):

Returns the index with the largest value across dimensions of a tensor.

Args: input: A Tensor. Must be one of the following types: float32, float64, int64, int32, uint8, uint16, int16, int8, complex64, complex128, qint8, quint8, qint32, half. dimension: A Tensor. Must be one of the following types: int32, int64. int32, 0 <= dimension < rank(input). Describes which dimension of the input Tensor to reduce across. For vectors, use dimension = 0. name: A name for the operation (optional).

Returns: A Tensor of type int64.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.argmin_layer, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.argmin_layer

Return

Applicative

Origial documentation for Builder.argmin_layer

def argmin_layer(builder, size):

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .fully_connected(size, activation_fn = tf.argmin, ...)

Arguments

• size: the size of the resulting layer
• All other *args and **kwargs are forwarded to tf.contrib.layers.fully_connected

Return

Builder

Origial documentation for tf.argmin

def arg_min(input, dimension, name=None):

Returns the index with the smallest value across dimensions of a tensor.

Args: input: A Tensor. Must be one of the following types: float32, float64, int64, int32, uint8, uint16, int16, int8, complex64, complex128, qint8, quint8, qint32, half. dimension: A Tensor. Must be one of the following types: int32, int64. int32, 0 <= dimension < rank(input). Describes which dimension of the input Tensor to reduce across. For vectors, use dimension = 0. name: A name for the operation (optional).

Returns: A Tensor of type int64.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.as_dtype, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.as_dtype

Return

Applicative

Origial documentation for Builder.as_dtype

def as_dtype(builder):

THIS METHOD IS AUTOMATICALLY GENERATED

@immutable

This method is a lifted version the function tf.as_dtype to work with tensorbuilder.core.builders.Builders. Instead of taking a Tensor as its first argument it takes a builder, the rest of the arguments are exactly the same.

Original Documentation for tf.as_dtype

def as_dtype(type_value)

Converts the given type_value to a DType.

Args: type_value: A value that can be converted to a tf.DType object. This may currently be a tf.DType object, a DataType enum, a string type name, or a numpy.dtype.

Returns: A DType corresponding to type_value.

Raises: TypeError: If type_value cannot be converted to a DType.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.as_dtype_layer, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.as_dtype_layer

Return

Applicative

Origial documentation for Builder.as_dtype_layer

def as_dtype_layer(builder, size):

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .fully_connected(size, activation_fn = tf.as_dtype, ...)

Arguments

• size: the size of the resulting layer
• All other *args and **kwargs are forwarded to tf.contrib.layers.fully_connected

Return

Builder

Origial documentation for tf.as_dtype

def as_dtype(type_value):

Converts the given type_value to a DType.

Args: type_value: A value that can be converted to a tf.DType object. This may currently be a tf.DType object, a DataType enum, a string type name, or a numpy.dtype.

Returns: A DType corresponding to type_value.

Raises: TypeError: If type_value cannot be converted to a DType.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.as_string, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.as_string

Return

Applicative

Origial documentation for Builder.as_string

def as_string(builder):

THIS METHOD IS AUTOMATICALLY GENERATED

@immutable

This method is a lifted version the function tf.as_string to work with tensorbuilder.core.builders.Builders. Instead of taking a Tensor as its first argument it takes a builder, the rest of the arguments are exactly the same.

Original Documentation for tf.as_string

def as_string(input, precision=None, scientific=None, shortest=None, width=None, fill=None, name=None)

Converts each entry in the given tensor to strings. Supports many numeric

types and boolean.

Args: input: A Tensor. Must be one of the following types: int32, int64, complex64, float32, float64, bool, int8. precision: An optional int. Defaults to -1. The post-decimal precision to use for floating point numbers. Only used if precision > -1. scientific: An optional bool. Defaults to False. Use scientific notation for floating point numbers. shortest: An optional bool. Defaults to False. Use shortest representation (either scientific or standard) for floating point numbers. width: An optional int. Defaults to -1. Pad pre-decimal numbers to this width. Applies to both floating point and integer numbers. Only used if width > -1. fill: An optional string. Defaults to "". The value to pad if width > -1. If empty, pads with spaces. Another typical value is '0'. String cannot be longer than 1 character. name: A name for the operation (optional).

Returns: A Tensor of type string.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.as_string_layer, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.as_string_layer

Return

Applicative

Origial documentation for Builder.as_string_layer

def as_string_layer(builder, size):

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .fully_connected(size, activation_fn = tf.as_string, ...)

Arguments

• size: the size of the resulting layer
• All other *args and **kwargs are forwarded to tf.contrib.layers.fully_connected

Return

Builder

Origial documentation for tf.as_string

def as_string(input, precision=None, scientific=None, shortest=None, width=None, fill=None, name=None):

Converts each entry in the given tensor to strings. Supports many numeric

types and boolean.

Args: input: A Tensor. Must be one of the following types: int32, int64, complex64, float32, float64, bool, int8. precision: An optional int. Defaults to -1. The post-decimal precision to use for floating point numbers. Only used if precision > -1. scientific: An optional bool. Defaults to False. Use scientific notation for floating point numbers. shortest: An optional bool. Defaults to False. Use shortest representation (either scientific or standard) for floating point numbers. width: An optional int. Defaults to -1. Pad pre-decimal numbers to this width. Applies to both floating point and integer numbers. Only used if width > -1. fill: An optional string. Defaults to "". The value to pad if width > -1. If empty, pads with spaces. Another typical value is '0'. String cannot be longer than 1 character. name: A name for the operation (optional).

Returns: A Tensor of type string.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.asin, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.asin

Return

Applicative

Origial documentation for Builder.asin

def asin(builder):

THIS METHOD IS AUTOMATICALLY GENERATED

@immutable

This method is a lifted version the function tf.asin to work with tensorbuilder.core.builders.Builders. Instead of taking a Tensor as its first argument it takes a builder, the rest of the arguments are exactly the same.

Original Documentation for tf.asin

def asin(x, name=None)

Computes asin of x element-wise.

Args: x: A Tensor. Must be one of the following types: half, float32, float64, int32, int64, complex64, complex128. name: A name for the operation (optional).

Returns: A Tensor. Has the same type as x.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.asin_layer, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.asin_layer

Return

Applicative

Origial documentation for Builder.asin_layer

def asin_layer(builder, size):

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .fully_connected(size, activation_fn = tf.asin, ...)

Arguments

• size: the size of the resulting layer
• All other *args and **kwargs are forwarded to tf.contrib.layers.fully_connected

Return

Builder

Origial documentation for tf.asin

def asin(x, name=None):

Computes asin of x element-wise.

Args: x: A Tensor. Must be one of the following types: half, float32, float64, int32, int64, complex64, complex128. name: A name for the operation (optional).

Returns: A Tensor. Has the same type as x.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.assert_equal, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.assert_equal

Return

Applicative

Origial documentation for Builder.assert_equal

def assert_equal(builder):

THIS METHOD IS AUTOMATICALLY GENERATED

@immutable

This method is a lifted version the function tf.assert_equal to work with tensorbuilder.core.builders.Builders. Instead of taking a Tensor as its first argument it takes a builder, the rest of the arguments are exactly the same.

Original Documentation for tf.assert_equal

def assert_equal(x, y, data=None, summarize=None, message=None, name=None)

Assert the condition x == y holds element-wise.

Example of adding a dependency to an operation:

python with tf.control_dependencies([tf.assert_equal(x, y)]): output = tf.reduce_sum(x)

Example of adding dependency to the tensor being checked:

python x = tf.with_dependencies([tf.assert_equal(x, y)], x)

This condition holds if for every pair of (possibly broadcast) elements x[i], y[i], we have x[i] == y[i]. If both x and y are empty, this is trivially satisfied.

Args: x: Numeric Tensor. y: Numeric Tensor, same dtype as and broadcastable to x. data: The tensors to print out if the condition is False. Defaults to error message and first few entries of x, y. summarize: Print this many entries of each tensor. message: A string to prefix to the default message. name: A name for this operation (optional). Defaults to "assert_equal".

Returns: Op that raises InvalidArgumentError if x == y is False.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.assert_equal_layer, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.assert_equal_layer

Return

Applicative

Origial documentation for Builder.assert_equal_layer

def assert_equal_layer(builder, size):

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .fully_connected(size, activation_fn = tf.assert_equal, ...)

Arguments

• size: the size of the resulting layer
• All other *args and **kwargs are forwarded to tf.contrib.layers.fully_connected

Return

Builder

Origial documentation for tf.assert_equal

def assert_equal(x, y, data=None, summarize=None, message=None, name=None):

Assert the condition x == y holds element-wise.

Example of adding a dependency to an operation:

python with tf.control_dependencies([tf.assert_equal(x, y)]): output = tf.reduce_sum(x)

Example of adding dependency to the tensor being checked:

python x = tf.with_dependencies([tf.assert_equal(x, y)], x)

This condition holds if for every pair of (possibly broadcast) elements x[i], y[i], we have x[i] == y[i]. If both x and y are empty, this is trivially satisfied.

Args: x: Numeric Tensor. y: Numeric Tensor, same dtype as and broadcastable to x. data: The tensors to print out if the condition is False. Defaults to error message and first few entries of x, y. summarize: Print this many entries of each tensor. message: A string to prefix to the default message. name: A name for this operation (optional). Defaults to "assert_equal".

Returns: Op that raises InvalidArgumentError if x == y is False.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.assert_greater, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.assert_greater

Return

Applicative

Origial documentation for Builder.assert_greater

def assert_greater(builder):

THIS METHOD IS AUTOMATICALLY GENERATED

@immutable

This method is a lifted version the function tf.assert_greater to work with tensorbuilder.core.builders.Builders. Instead of taking a Tensor as its first argument it takes a builder, the rest of the arguments are exactly the same.

Original Documentation for tf.assert_greater

def assert_greater(x, y, data=None, summarize=None, message=None, name=None)

Assert the condition x > y holds element-wise.

Example of adding a dependency to an operation:

python with tf.control_dependencies([tf.assert_greater(x, y)]): output = tf.reduce_sum(x)

Example of adding dependency to the tensor being checked:

python x = tf.with_dependencies([tf.assert_greater(x, y)], x)

This condition holds if for every pair of (possibly broadcast) elements x[i], y[i], we have x[i] > y[i]. If both x and y are empty, this is trivially satisfied.

Args: x: Numeric Tensor. y: Numeric Tensor, same dtype as and broadcastable to x. data: The tensors to print out if the condition is False. Defaults to error message and first few entries of x, y. summarize: Print this many entries of each tensor. message: A string to prefix to the default message. name: A name for this operation (optional). Defaults to "assert_greater".

Returns: Op that raises InvalidArgumentError if x > y is False.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.assert_greater_equal, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.assert_greater_equal

Return

Applicative

Origial documentation for Builder.assert_greater_equal

def assert_greater_equal(builder):

THIS METHOD IS AUTOMATICALLY GENERATED

@immutable

This method is a lifted version the function tf.assert_greater_equal to work with tensorbuilder.core.builders.Builders. Instead of taking a Tensor as its first argument it takes a builder, the rest of the arguments are exactly the same.

Original Documentation for tf.assert_greater_equal

def assert_greater_equal(x, y, data=None, summarize=None, message=None, name=None)

Assert the condition x >= y holds element-wise.

Example of adding a dependency to an operation:

python with tf.control_dependencies([tf.assert_greater_equal(x, y)]): output = tf.reduce_sum(x)

Example of adding dependency to the tensor being checked:

python x = tf.with_dependencies([tf.assert_greater_equal(x, y)], x)

This condition holds if for every pair of (possibly broadcast) elements x[i], y[i], we have x[i] >= y[i]. If both x and y are empty, this is trivially satisfied.

Args: x: Numeric Tensor. y: Numeric Tensor, same dtype as and broadcastable to x. data: The tensors to print out if the condition is False. Defaults to error message and first few entries of x, y. summarize: Print this many entries of each tensor. message: A string to prefix to the default message. name: A name for this operation (optional). Defaults to "assert_greater_equal"

Returns: Op that raises InvalidArgumentError if x >= y is False.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.assert_greater_equal_layer, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.assert_greater_equal_layer

Return

Applicative

Origial documentation for Builder.assert_greater_equal_layer

def assert_greater_equal_layer(builder, size):

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .fully_connected(size, activation_fn = tf.assert_greater_equal, ...)

Arguments

• size: the size of the resulting layer
• All other *args and **kwargs are forwarded to tf.contrib.layers.fully_connected

Return

Builder

Origial documentation for tf.assert_greater_equal

def assert_greater_equal(x, y, data=None, summarize=None, message=None, name=None):

Assert the condition x >= y holds element-wise.

Example of adding a dependency to an operation:

python with tf.control_dependencies([tf.assert_greater_equal(x, y)]): output = tf.reduce_sum(x)

Example of adding dependency to the tensor being checked:

python x = tf.with_dependencies([tf.assert_greater_equal(x, y)], x)

This condition holds if for every pair of (possibly broadcast) elements x[i], y[i], we have x[i] >= y[i]. If both x and y are empty, this is trivially satisfied.

Args: x: Numeric Tensor. y: Numeric Tensor, same dtype as and broadcastable to x. data: The tensors to print out if the condition is False. Defaults to error message and first few entries of x, y. summarize: Print this many entries of each tensor. message: A string to prefix to the default message. name: A name for this operation (optional). Defaults to "assert_greater_equal"

Returns: Op that raises InvalidArgumentError if x >= y is False.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.assert_greater_layer, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.assert_greater_layer

Return

Applicative

Origial documentation for Builder.assert_greater_layer

def assert_greater_layer(builder, size):

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .fully_connected(size, activation_fn = tf.assert_greater, ...)

Arguments

• size: the size of the resulting layer
• All other *args and **kwargs are forwarded to tf.contrib.layers.fully_connected

Return

Builder

Origial documentation for tf.assert_greater

def assert_greater(x, y, data=None, summarize=None, message=None, name=None):

Assert the condition x > y holds element-wise.

Example of adding a dependency to an operation:

python with tf.control_dependencies([tf.assert_greater(x, y)]): output = tf.reduce_sum(x)

Example of adding dependency to the tensor being checked:

python x = tf.with_dependencies([tf.assert_greater(x, y)], x)

This condition holds if for every pair of (possibly broadcast) elements x[i], y[i], we have x[i] > y[i]. If both x and y are empty, this is trivially satisfied.

Args: x: Numeric Tensor. y: Numeric Tensor, same dtype as and broadcastable to x. data: The tensors to print out if the condition is False. Defaults to error message and first few entries of x, y. summarize: Print this many entries of each tensor. message: A string to prefix to the default message. name: A name for this operation (optional). Defaults to "assert_greater".

Returns: Op that raises InvalidArgumentError if x > y is False.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.assert_integer, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.assert_integer

Return

Applicative

Origial documentation for Builder.assert_integer

def assert_integer(builder):

THIS METHOD IS AUTOMATICALLY GENERATED

@immutable

This method is a lifted version the function tf.assert_integer to work with tensorbuilder.core.builders.Builders. Instead of taking a Tensor as its first argument it takes a builder, the rest of the arguments are exactly the same.

Original Documentation for tf.assert_integer

def assert_integer(x, message=None, name=None)

Assert that x is of integer dtype.

Example of adding a dependency to an operation:

python with tf.control_dependencies([tf.assert_integer(x)]): output = tf.reduce_sum(x)

Example of adding dependency to the tensor being checked:

python x = tf.with_dependencies([tf.assert_integer(x)], x)

Args: x: Tensor whose basetype is integer and is not quantized. message: A string to prefix to the default message. name: A name for this operation (optional). Defaults to "assert_integer".

Raises: TypeError: If x.dtype is anything other than non-quantized integer.

Returns: A no_op that does nothing. Type can be determined statically.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.assert_integer_layer, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.assert_integer_layer

Return

Applicative

Origial documentation for Builder.assert_integer_layer

def assert_integer_layer(builder, size):

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .fully_connected(size, activation_fn = tf.assert_integer, ...)

Arguments

• size: the size of the resulting layer
• All other *args and **kwargs are forwarded to tf.contrib.layers.fully_connected

Return

Builder

Origial documentation for tf.assert_integer

def assert_integer(x, message=None, name=None):

Assert that x is of integer dtype.

Example of adding a dependency to an operation:

python with tf.control_dependencies([tf.assert_integer(x)]): output = tf.reduce_sum(x)

Example of adding dependency to the tensor being checked:

python x = tf.with_dependencies([tf.assert_integer(x)], x)

Args: x: Tensor whose basetype is integer and is not quantized. message: A string to prefix to the default message. name: A name for this operation (optional). Defaults to "assert_integer".

Raises: TypeError: If x.dtype is anything other than non-quantized integer.

Returns: A no_op that does nothing. Type can be determined statically.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.assert_less, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.assert_less

Return

Applicative

Origial documentation for Builder.assert_less

def assert_less(builder):

THIS METHOD IS AUTOMATICALLY GENERATED

@immutable

This method is a lifted version the function tf.assert_less to work with tensorbuilder.core.builders.Builders. Instead of taking a Tensor as its first argument it takes a builder, the rest of the arguments are exactly the same.

Original Documentation for tf.assert_less

def assert_less(x, y, data=None, summarize=None, message=None, name=None)

Assert the condition x < y holds element-wise.

Example of adding a dependency to an operation:

python with tf.control_dependencies([tf.assert_less(x, y)]): output = tf.reduce_sum(x)

Example of adding dependency to the tensor being checked:

python x = tf.with_dependencies([tf.assert_less(x, y)], x)

This condition holds if for every pair of (possibly broadcast) elements x[i], y[i], we have x[i] < y[i]. If both x and y are empty, this is trivially satisfied.

Args: x: Numeric Tensor. y: Numeric Tensor, same dtype as and broadcastable to x. data: The tensors to print out if the condition is False. Defaults to error message and first few entries of x, y. summarize: Print this many entries of each tensor. message: A string to prefix to the default message. name: A name for this operation (optional). Defaults to "assert_less".

Returns: Op that raises InvalidArgumentError if x < y is False.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.assert_less_equal, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.assert_less_equal

Return

Applicative

Origial documentation for Builder.assert_less_equal

def assert_less_equal(builder):

THIS METHOD IS AUTOMATICALLY GENERATED

@immutable

This method is a lifted version the function tf.assert_less_equal to work with tensorbuilder.core.builders.Builders. Instead of taking a Tensor as its first argument it takes a builder, the rest of the arguments are exactly the same.

Original Documentation for tf.assert_less_equal

def assert_less_equal(x, y, data=None, summarize=None, message=None, name=None)

Assert the condition x <= y holds element-wise.

Example of adding a dependency to an operation:

python with tf.control_dependencies([tf.assert_less_equal(x, y)]): output = tf.reduce_sum(x)

Example of adding dependency to the tensor being checked:

python x = tf.with_dependencies([tf.assert_less_equal(x, y)], x)

This condition holds if for every pair of (possibly broadcast) elements x[i], y[i], we have x[i] <= y[i]. If both x and y are empty, this is trivially satisfied.

Args: x: Numeric Tensor. y: Numeric Tensor, same dtype as and broadcastable to x. data: The tensors to print out if the condition is False. Defaults to error message and first few entries of x, y. summarize: Print this many entries of each tensor. message: A string to prefix to the default message. name: A name for this operation (optional). Defaults to "assert_less_equal"

Returns: Op that raises InvalidArgumentError if x <= y is False.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.assert_less_equal_layer, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.assert_less_equal_layer

Return

Applicative

Origial documentation for Builder.assert_less_equal_layer

def assert_less_equal_layer(builder, size):

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .fully_connected(size, activation_fn = tf.assert_less_equal, ...)

Arguments

• size: the size of the resulting layer
• All other *args and **kwargs are forwarded to tf.contrib.layers.fully_connected

Return

Builder

Origial documentation for tf.assert_less_equal

def assert_less_equal(x, y, data=None, summarize=None, message=None, name=None):

Assert the condition x <= y holds element-wise.

Example of adding a dependency to an operation:

python with tf.control_dependencies([tf.assert_less_equal(x, y)]): output = tf.reduce_sum(x)

Example of adding dependency to the tensor being checked:

python x = tf.with_dependencies([tf.assert_less_equal(x, y)], x)

This condition holds if for every pair of (possibly broadcast) elements x[i], y[i], we have x[i] <= y[i]. If both x and y are empty, this is trivially satisfied.

Args: x: Numeric Tensor. y: Numeric Tensor, same dtype as and broadcastable to x. data: The tensors to print out if the condition is False. Defaults to error message and first few entries of x, y. summarize: Print this many entries of each tensor. message: A string to prefix to the default message. name: A name for this operation (optional). Defaults to "assert_less_equal"

Returns: Op that raises InvalidArgumentError if x <= y is False.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.assert_less_layer, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.assert_less_layer

Return

Applicative

Origial documentation for Builder.assert_less_layer

def assert_less_layer(builder, size):

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .fully_connected(size, activation_fn = tf.assert_less, ...)

Arguments

• size: the size of the resulting layer
• All other *args and **kwargs are forwarded to tf.contrib.layers.fully_connected

Return

Builder

Origial documentation for tf.assert_less

def assert_less(x, y, data=None, summarize=None, message=None, name=None):

Assert the condition x < y holds element-wise.

Example of adding a dependency to an operation:

python with tf.control_dependencies([tf.assert_less(x, y)]): output = tf.reduce_sum(x)

Example of adding dependency to the tensor being checked:

python x = tf.with_dependencies([tf.assert_less(x, y)], x)

This condition holds if for every pair of (possibly broadcast) elements x[i], y[i], we have x[i] < y[i]. If both x and y are empty, this is trivially satisfied.

Args: x: Numeric Tensor. y: Numeric Tensor, same dtype as and broadcastable to x. data: The tensors to print out if the condition is False. Defaults to error message and first few entries of x, y. summarize: Print this many entries of each tensor. message: A string to prefix to the default message. name: A name for this operation (optional). Defaults to "assert_less".

Returns: Op that raises InvalidArgumentError if x < y is False.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.assert_negative, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.assert_negative

Return

Applicative

Origial documentation for Builder.assert_negative

def assert_negative(builder):

THIS METHOD IS AUTOMATICALLY GENERATED

@immutable

This method is a lifted version the function tf.assert_negative to work with tensorbuilder.core.builders.Builders. Instead of taking a Tensor as its first argument it takes a builder, the rest of the arguments are exactly the same.

Original Documentation for tf.assert_negative

def assert_negative(x, data=None, summarize=None, message=None, name=None)

Assert the condition x < 0 holds element-wise.

Example of adding a dependency to an operation:

python with tf.control_dependencies([tf.assert_negative(x)]): output = tf.reduce_sum(x)

Example of adding dependency to the tensor being checked:

python x = tf.with_dependencies([tf.assert_negative(x)], x)

Negative means, for every element x[i] of x, we have x[i] < 0. If x is empty this is trivially satisfied.

Args: x: Numeric Tensor. data: The tensors to print out if the condition is False. Defaults to error message and first few entries of x. summarize: Print this many entries of each tensor. message: A string to prefix to the default message. name: A name for this operation (optional). Defaults to "assert_negative".

Returns: Op raising InvalidArgumentError unless x is all negative.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.assert_negative_layer, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.assert_negative_layer

Return

Applicative

Origial documentation for Builder.assert_negative_layer

def assert_negative_layer(builder, size):

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .fully_connected(size, activation_fn = tf.assert_negative, ...)

Arguments

• size: the size of the resulting layer
• All other *args and **kwargs are forwarded to tf.contrib.layers.fully_connected

Return

Builder

Origial documentation for tf.assert_negative

def assert_negative(x, data=None, summarize=None, message=None, name=None):

Assert the condition x < 0 holds element-wise.

Example of adding a dependency to an operation:

python with tf.control_dependencies([tf.assert_negative(x)]): output = tf.reduce_sum(x)

Example of adding dependency to the tensor being checked:

python x = tf.with_dependencies([tf.assert_negative(x)], x)

Negative means, for every element x[i] of x, we have x[i] < 0. If x is empty this is trivially satisfied.

Args: x: Numeric Tensor. data: The tensors to print out if the condition is False. Defaults to error message and first few entries of x. summarize: Print this many entries of each tensor. message: A string to prefix to the default message. name: A name for this operation (optional). Defaults to "assert_negative".

Returns: Op raising InvalidArgumentError unless x is all negative.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.assert_non_negative, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.assert_non_negative

Return

Applicative

Origial documentation for Builder.assert_non_negative

def assert_non_negative(builder):

THIS METHOD IS AUTOMATICALLY GENERATED

@immutable

This method is a lifted version the function tf.assert_non_negative to work with tensorbuilder.core.builders.Builders. Instead of taking a Tensor as its first argument it takes a builder, the rest of the arguments are exactly the same.

Original Documentation for tf.assert_non_negative

def assert_non_negative(x, data=None, summarize=None, message=None, name=None)

Assert the condition x >= 0 holds element-wise.

Example of adding a dependency to an operation:

python with tf.control_dependencies([tf.assert_non_negative(x)]): output = tf.reduce_sum(x)

Example of adding dependency to the tensor being checked:

python x = tf.with_dependencies([tf.assert_non_negative(x)], x)

Non-negative means, for every element x[i] of x, we have x[i] >= 0. If x is empty this is trivially satisfied.

Args: x: Numeric Tensor. data: The tensors to print out if the condition is False. Defaults to error message and first few entries of x. summarize: Print this many entries of each tensor. message: A string to prefix to the default message. name: A name for this operation (optional). Defaults to "assert_non_negative".

Returns: Op raising InvalidArgumentError unless x is all non-negative.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.assert_non_negative_layer, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.assert_non_negative_layer

Return

Applicative

Origial documentation for Builder.assert_non_negative_layer

def assert_non_negative_layer(builder, size):

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .fully_connected(size, activation_fn = tf.assert_non_negative, ...)

Arguments

• size: the size of the resulting layer
• All other *args and **kwargs are forwarded to tf.contrib.layers.fully_connected

Return

Builder

Origial documentation for tf.assert_non_negative

def assert_non_negative(x, data=None, summarize=None, message=None, name=None):

Assert the condition x >= 0 holds element-wise.

Example of adding a dependency to an operation:

python with tf.control_dependencies([tf.assert_non_negative(x)]): output = tf.reduce_sum(x)

Example of adding dependency to the tensor being checked:

python x = tf.with_dependencies([tf.assert_non_negative(x)], x)

Non-negative means, for every element x[i] of x, we have x[i] >= 0. If x is empty this is trivially satisfied.

Args: x: Numeric Tensor. data: The tensors to print out if the condition is False. Defaults to error message and first few entries of x. summarize: Print this many entries of each tensor. message: A string to prefix to the default message. name: A name for this operation (optional). Defaults to "assert_non_negative".

Returns: Op raising InvalidArgumentError unless x is all non-negative.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.assert_non_positive, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.assert_non_positive

Return

Applicative

Origial documentation for Builder.assert_non_positive

def assert_non_positive(builder):

THIS METHOD IS AUTOMATICALLY GENERATED

@immutable

This method is a lifted version the function tf.assert_non_positive to work with tensorbuilder.core.builders.Builders. Instead of taking a Tensor as its first argument it takes a builder, the rest of the arguments are exactly the same.

Original Documentation for tf.assert_non_positive

def assert_non_positive(x, data=None, summarize=None, message=None, name=None)

Assert the condition x <= 0 holds element-wise.

Example of adding a dependency to an operation:

python with tf.control_dependencies([tf.assert_non_positive(x)]): output = tf.reduce_sum(x)

Example of adding dependency to the tensor being checked:

python x = tf.with_dependencies([tf.assert_non_positive(x)], x)

Non-positive means, for every element x[i] of x, we have x[i] <= 0. If x is empty this is trivially satisfied.

Args: x: Numeric Tensor. data: The tensors to print out if the condition is False. Defaults to error message and first few entries of x. summarize: Print this many entries of each tensor. message: A string to prefix to the default message. name: A name for this operation (optional). Defaults to "assert_non_positive".

Returns: Op raising InvalidArgumentError unless x is all non-positive.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.assert_non_positive_layer, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.assert_non_positive_layer

Return

Applicative

Origial documentation for Builder.assert_non_positive_layer

def assert_non_positive_layer(builder, size):

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .fully_connected(size, activation_fn = tf.assert_non_positive, ...)

Arguments

• size: the size of the resulting layer
• All other *args and **kwargs are forwarded to tf.contrib.layers.fully_connected

Return

Builder

Origial documentation for tf.assert_non_positive

def assert_non_positive(x, data=None, summarize=None, message=None, name=None):

Assert the condition x <= 0 holds element-wise.

Example of adding a dependency to an operation:

python with tf.control_dependencies([tf.assert_non_positive(x)]): output = tf.reduce_sum(x)

Example of adding dependency to the tensor being checked:

python x = tf.with_dependencies([tf.assert_non_positive(x)], x)

Non-positive means, for every element x[i] of x, we have x[i] <= 0. If x is empty this is trivially satisfied.

Args: x: Numeric Tensor. data: The tensors to print out if the condition is False. Defaults to error message and first few entries of x. summarize: Print this many entries of each tensor. message: A string to prefix to the default message. name: A name for this operation (optional). Defaults to "assert_non_positive".

Returns: Op raising InvalidArgumentError unless x is all non-positive.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.assert_positive, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.assert_positive

Return

Applicative

Origial documentation for Builder.assert_positive

def assert_positive(builder):

THIS METHOD IS AUTOMATICALLY GENERATED

@immutable

This method is a lifted version the function tf.assert_positive to work with tensorbuilder.core.builders.Builders. Instead of taking a Tensor as its first argument it takes a builder, the rest of the arguments are exactly the same.

Original Documentation for tf.assert_positive

def assert_positive(x, data=None, summarize=None, message=None, name=None)

Assert the condition x > 0 holds element-wise.

Example of adding a dependency to an operation:

python with tf.control_dependencies([tf.assert_positive(x)]): output = tf.reduce_sum(x)

Example of adding dependency to the tensor being checked:

python x = tf.with_dependencies([tf.assert_positive(x)], x)

Positive means, for every element x[i] of x, we have x[i] > 0. If x is empty this is trivially satisfied.

Args: x: Numeric Tensor. data: The tensors to print out if the condition is False. Defaults to error message and first few entries of x. summarize: Print this many entries of each tensor. message: A string to prefix to the default message. name: A name for this operation (optional). Defaults to "assert_positive".

Returns: Op raising InvalidArgumentError unless x is all positive.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.assert_positive_layer, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.assert_positive_layer

Return

Applicative

Origial documentation for Builder.assert_positive_layer

def assert_positive_layer(builder, size):

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .fully_connected(size, activation_fn = tf.assert_positive, ...)

Arguments

• size: the size of the resulting layer
• All other *args and **kwargs are forwarded to tf.contrib.layers.fully_connected

Return

Builder

Origial documentation for tf.assert_positive

def assert_positive(x, data=None, summarize=None, message=None, name=None):

Assert the condition x > 0 holds element-wise.

Example of adding a dependency to an operation:

python with tf.control_dependencies([tf.assert_positive(x)]): output = tf.reduce_sum(x)

Example of adding dependency to the tensor being checked:

python x = tf.with_dependencies([tf.assert_positive(x)], x)

Positive means, for every element x[i] of x, we have x[i] > 0. If x is empty this is trivially satisfied.

Args: x: Numeric Tensor. data: The tensors to print out if the condition is False. Defaults to error message and first few entries of x. summarize: Print this many entries of each tensor. message: A string to prefix to the default message. name: A name for this operation (optional). Defaults to "assert_positive".

Returns: Op raising InvalidArgumentError unless x is all positive.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.assert_proper_iterable, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.assert_proper_iterable

Return

Applicative

Origial documentation for Builder.assert_proper_iterable

def assert_proper_iterable(builder):

THIS METHOD IS AUTOMATICALLY GENERATED

@immutable

This method is a lifted version the function tf.assert_proper_iterable to work with tensorbuilder.core.builders.Builders. Instead of taking a Tensor as its first argument it takes a builder, the rest of the arguments are exactly the same.

Original Documentation for tf.assert_proper_iterable

def assert_proper_iterable(values)

Static assert that values is a "proper" iterable.

Ops that expect iterables of Tensor can call this to validate input. Useful since Tensor, ndarray, byte/text type are all iterables themselves.

Args: values: Object to be checked.

Raises: TypeError: If values is not iterable or is one of Tensor, SparseTensor, np.array, tf.compat.bytes_or_text_types.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.assert_proper_iterable_layer, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.assert_proper_iterable_layer

Return

Applicative

Origial documentation for Builder.assert_proper_iterable_layer

def assert_proper_iterable_layer(builder, size):

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .fully_connected(size, activation_fn = tf.assert_proper_iterable, ...)

Arguments

• size: the size of the resulting layer
• All other *args and **kwargs are forwarded to tf.contrib.layers.fully_connected

Return

Builder

Origial documentation for tf.assert_proper_iterable

def assert_proper_iterable(values):

Static assert that values is a "proper" iterable.

Ops that expect iterables of Tensor can call this to validate input. Useful since Tensor, ndarray, byte/text type are all iterables themselves.

Args: values: Object to be checked.

Raises: TypeError: If values is not iterable or is one of Tensor, SparseTensor, np.array, tf.compat.bytes_or_text_types.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.assert_rank, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.assert_rank

Return

Applicative

Origial documentation for Builder.assert_rank

def assert_rank(builder):

THIS METHOD IS AUTOMATICALLY GENERATED

@immutable

This method is a lifted version the function tf.assert_rank to work with tensorbuilder.core.builders.Builders. Instead of taking a Tensor as its first argument it takes a builder, the rest of the arguments are exactly the same.

Original Documentation for tf.assert_rank

def assert_rank(x, rank, data=None, summarize=None, message=None, name=None)

Assert x has rank equal to rank.

Example of adding a dependency to an operation:

python with tf.control_dependencies([tf.assert_rank(x, 2)]): output = tf.reduce_sum(x)

Example of adding dependency to the tensor being checked:

python x = tf.with_dependencies([tf.assert_rank(x, 2)], x)

Args: x: Numeric Tensor. rank: Scalar integer Tensor. data: The tensors to print out if the condition is False. Defaults to error message and first few entries of x. summarize: Print this many entries of each tensor. message: A string to prefix to the default message. name: A name for this operation (optional). Defaults to "assert_rank".

Returns: Op raising InvalidArgumentError unless x has specified rank. If static checks determine x has correct rank, a no_op is returned.

Raises: ValueError: If static checks determine x has wrong rank.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.assert_rank_at_least, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.assert_rank_at_least

Return

Applicative

Origial documentation for Builder.assert_rank_at_least

def assert_rank_at_least(builder):

THIS METHOD IS AUTOMATICALLY GENERATED

@immutable

This method is a lifted version the function tf.assert_rank_at_least to work with tensorbuilder.core.builders.Builders. Instead of taking a Tensor as its first argument it takes a builder, the rest of the arguments are exactly the same.

Original Documentation for tf.assert_rank_at_least

def assert_rank_at_least(x, rank, data=None, summarize=None, message=None, name=None)

Assert x has rank equal to rank or higher.

Example of adding a dependency to an operation:

python with tf.control_dependencies([tf.assert_rank_at_least(x, 2)]): output = tf.reduce_sum(x)

Example of adding dependency to the tensor being checked:

python x = tf.with_dependencies([tf.assert_rank_at_least(x, 2)], x)

Args: x: Numeric Tensor. rank: Scalar Tensor. data: The tensors to print out if the condition is False. Defaults to error message and first few entries of x. summarize: Print this many entries of each tensor. message: A string to prefix to the default message. name: A name for this operation (optional). Defaults to "assert_rank_at_least".

Returns: Op raising InvalidArgumentError unless x has specified rank or higher. If static checks determine x has correct rank, a no_op is returned.

Raises: ValueError: If static checks determine x has wrong rank.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.assert_rank_at_least_layer, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.assert_rank_at_least_layer

Return

Applicative

Origial documentation for Builder.assert_rank_at_least_layer

def assert_rank_at_least_layer(builder, size):

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .fully_connected(size, activation_fn = tf.assert_rank_at_least, ...)

Arguments

• size: the size of the resulting layer
• All other *args and **kwargs are forwarded to tf.contrib.layers.fully_connected

Return

Builder

Origial documentation for tf.assert_rank_at_least

def assert_rank_at_least(x, rank, data=None, summarize=None, message=None, name=None):

Assert x has rank equal to rank or higher.

Example of adding a dependency to an operation:

python with tf.control_dependencies([tf.assert_rank_at_least(x, 2)]): output = tf.reduce_sum(x)

Example of adding dependency to the tensor being checked:

python x = tf.with_dependencies([tf.assert_rank_at_least(x, 2)], x)

Args: x: Numeric Tensor. rank: Scalar Tensor. data: The tensors to print out if the condition is False. Defaults to error message and first few entries of x. summarize: Print this many entries of each tensor. message: A string to prefix to the default message. name: A name for this operation (optional). Defaults to "assert_rank_at_least".

Returns: Op raising InvalidArgumentError unless x has specified rank or higher. If static checks determine x has correct rank, a no_op is returned.

Raises: ValueError: If static checks determine x has wrong rank.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.assert_rank_layer, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.assert_rank_layer

Return

Applicative

Origial documentation for Builder.assert_rank_layer

def assert_rank_layer(builder, size):

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .fully_connected(size, activation_fn = tf.assert_rank, ...)

Arguments

• size: the size of the resulting layer
• All other *args and **kwargs are forwarded to tf.contrib.layers.fully_connected

Return

Builder

Origial documentation for tf.assert_rank

def assert_rank(x, rank, data=None, summarize=None, message=None, name=None):

Assert x has rank equal to rank.

Example of adding a dependency to an operation:

python with tf.control_dependencies([tf.assert_rank(x, 2)]): output = tf.reduce_sum(x)

Example of adding dependency to the tensor being checked:

python x = tf.with_dependencies([tf.assert_rank(x, 2)], x)

Args: x: Numeric Tensor. rank: Scalar integer Tensor. data: The tensors to print out if the condition is False. Defaults to error message and first few entries of x. summarize: Print this many entries of each tensor. message: A string to prefix to the default message. name: A name for this operation (optional). Defaults to "assert_rank".

Returns: Op raising InvalidArgumentError unless x has specified rank. If static checks determine x has correct rank, a no_op is returned.

Raises: ValueError: If static checks determine x has wrong rank.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.assert_type, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.assert_type

Return

Applicative

Origial documentation for Builder.assert_type

def assert_type(builder):

THIS METHOD IS AUTOMATICALLY GENERATED

@immutable

This method is a lifted version the function tf.assert_type to work with tensorbuilder.core.builders.Builders. Instead of taking a Tensor as its first argument it takes a builder, the rest of the arguments are exactly the same.

Original Documentation for tf.assert_type

def assert_type(tensor, tf_type, message=None, name=None)

Statically asserts that the given Tensor is of the specified type.

Args: tensor: A tensorflow Tensor. tf_type: A tensorflow type (dtypes.float32, tf.int64, dtypes.bool, etc). message: A string to prefix to the default message. name: A name to give this Op. Defaults to "assert_type"

Raises: TypeError: If the tensors data type doesn't match tf_type.

Returns: A no_op that does nothing. Type can be determined statically.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.assert_type_layer, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.assert_type_layer

Return

Applicative

Origial documentation for Builder.assert_type_layer

def assert_type_layer(builder, size):

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .fully_connected(size, activation_fn = tf.assert_type, ...)

Arguments

• size: the size of the resulting layer
• All other *args and **kwargs are forwarded to tf.contrib.layers.fully_connected

Return

Builder

Origial documentation for tf.assert_type

def assert_type(tensor, tf_type, message=None, name=None):

Statically asserts that the given Tensor is of the specified type.

Args: tensor: A tensorflow Tensor. tf_type: A tensorflow type (dtypes.float32, tf.int64, dtypes.bool, etc). message: A string to prefix to the default message. name: A name to give this Op. Defaults to "assert_type"

Raises: TypeError: If the tensors data type doesn't match tf_type.

Returns: A no_op that does nothing. Type can be determined statically.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.assert_variables_initialized, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.assert_variables_initialized

Return

Applicative

Origial documentation for Builder.assert_variables_initialized

def assert_variables_initialized(builder):

THIS METHOD IS AUTOMATICALLY GENERATED

@immutable

This method is a lifted version the function tf.assert_variables_initialized to work with tensorbuilder.core.builders.Builders. Instead of taking a Tensor as its first argument it takes a builder, the rest of the arguments are exactly the same.

Original Documentation for tf.assert_variables_initialized

def assert_variables_initialized(var_list=None)

Returns an Op to check if variables are initialized.

NOTE: This function is obsolete and will be removed in 6 months. Please change your implementation to use report_uninitialized_variables().

When run, the returned Op will raise the exception FailedPreconditionError if any of the variables has not yet been initialized.

Note: This function is implemented by trying to fetch the values of the variables. If one of the variables is not initialized a message may be logged by the C++ runtime. This is expected.

Args: var_list: List of Variable objects to check. Defaults to the value of all_variables().

Returns: An Op, or None if there are no variables.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.assert_variables_initialized_layer, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.assert_variables_initialized_layer

Return

Applicative

Origial documentation for Builder.assert_variables_initialized_layer

def assert_variables_initialized_layer(builder, size):

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .fully_connected(size, activation_fn = tf.assert_variables_initialized, ...)

Arguments

• size: the size of the resulting layer
• All other *args and **kwargs are forwarded to tf.contrib.layers.fully_connected

Return

Builder

Origial documentation for tf.assert_variables_initialized

def assert_variables_initialized(var_list=None):

Returns an Op to check if variables are initialized.

NOTE: This function is obsolete and will be removed in 6 months. Please change your implementation to use report_uninitialized_variables().

When run, the returned Op will raise the exception FailedPreconditionError if any of the variables has not yet been initialized.

Note: This function is implemented by trying to fetch the values of the variables. If one of the variables is not initialized a message may be logged by the C++ runtime. This is expected.

Args: var_list: List of Variable objects to check. Defaults to the value of all_variables().

Returns: An Op, or None if there are no variables.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.assign, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.assign

Return

Applicative

Origial documentation for Builder.assign

def assign(builder):

THIS METHOD IS AUTOMATICALLY GENERATED

@immutable

This method is a lifted version the function tf.assign to work with tensorbuilder.core.builders.Builders. Instead of taking a Tensor as its first argument it takes a builder, the rest of the arguments are exactly the same.

Original Documentation for tf.assign

def assign(ref, value, validate_shape=None, use_locking=None, name=None)

Update 'ref' by assigning 'value' to it.

This operation outputs "ref" after the assignment is done. This makes it easier to chain operations that need to use the reset value.

Args: ref: A mutable Tensor. Should be from a Variable node. May be uninitialized. value: A Tensor. Must have the same type as ref. The value to be assigned to the variable. validate_shape: An optional bool. Defaults to True. If true, the operation will validate that the shape of 'value' matches the shape of the Tensor being assigned to. If false, 'ref' will take on the shape of 'value'. use_locking: An optional bool. Defaults to True. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional).

Returns: Same as "ref". Returned as a convenience for operations that want to use the new value after the variable has been reset.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.assign_add, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.assign_add

Return

Applicative

Origial documentation for Builder.assign_add

def assign_add(builder):

THIS METHOD IS AUTOMATICALLY GENERATED

@immutable

This method is a lifted version the function tf.assign_add to work with tensorbuilder.core.builders.Builders. Instead of taking a Tensor as its first argument it takes a builder, the rest of the arguments are exactly the same.

Original Documentation for tf.assign_add

def assign_add(ref, value, use_locking=None, name=None)

Update 'ref' by adding 'value' to it.

This operation outputs "ref" after the update is done. This makes it easier to chain operations that need to use the reset value.

Args: ref: A mutable Tensor. Must be one of the following types: float32, float64, int64, int32, uint8, uint16, int16, int8, complex64, complex128, qint8, quint8, qint32, half. Should be from a Variable node. value: A Tensor. Must have the same type as ref. The value to be added to the variable. use_locking: An optional bool. Defaults to False. If True, the addition will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional).

Returns: Same as "ref". Returned as a convenience for operations that want to use the new value after the variable has been updated.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.assign_add_layer, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.assign_add_layer

Return

Applicative

Origial documentation for Builder.assign_add_layer

def assign_add_layer(builder, size):

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .fully_connected(size, activation_fn = tf.assign_add, ...)

Arguments

• size: the size of the resulting layer
• All other *args and **kwargs are forwarded to tf.contrib.layers.fully_connected

Return

Builder

Origial documentation for tf.assign_add

def assign_add(ref, value, use_locking=None, name=None):

Update 'ref' by adding 'value' to it.

This operation outputs "ref" after the update is done. This makes it easier to chain operations that need to use the reset value.

Args: ref: A mutable Tensor. Must be one of the following types: float32, float64, int64, int32, uint8, uint16, int16, int8, complex64, complex128, qint8, quint8, qint32, half. Should be from a Variable node. value: A Tensor. Must have the same type as ref. The value to be added to the variable. use_locking: An optional bool. Defaults to False. If True, the addition will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional).

Returns: Same as "ref". Returned as a convenience for operations that want to use the new value after the variable has been updated.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.assign_layer, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.assign_layer

Return

Applicative

Origial documentation for Builder.assign_layer

def assign_layer(builder, size):

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .fully_connected(size, activation_fn = tf.assign, ...)

Arguments

• size: the size of the resulting layer
• All other *args and **kwargs are forwarded to tf.contrib.layers.fully_connected

Return

Builder

Origial documentation for tf.assign

def assign(ref, value, validate_shape=None, use_locking=None, name=None):

Update 'ref' by assigning 'value' to it.

This operation outputs "ref" after the assignment is done. This makes it easier to chain operations that need to use the reset value.

Args: ref: A mutable Tensor. Should be from a Variable node. May be uninitialized. value: A Tensor. Must have the same type as ref. The value to be assigned to the variable. validate_shape: An optional bool. Defaults to True. If true, the operation will validate that the shape of 'value' matches the shape of the Tensor being assigned to. If false, 'ref' will take on the shape of 'value'. use_locking: An optional bool. Defaults to True. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional).

Returns: Same as "ref". Returned as a convenience for operations that want to use the new value after the variable has been reset.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.assign_sub, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.assign_sub

Return

Applicative

Origial documentation for Builder.assign_sub

def assign_sub(builder):

THIS METHOD IS AUTOMATICALLY GENERATED

@immutable

This method is a lifted version the function tf.assign_sub to work with tensorbuilder.core.builders.Builders. Instead of taking a Tensor as its first argument it takes a builder, the rest of the arguments are exactly the same.

Original Documentation for tf.assign_sub

def assign_sub(ref, value, use_locking=None, name=None)

Update 'ref' by subtracting 'value' from it.

This operation outputs "ref" after the update is done. This makes it easier to chain operations that need to use the reset value.

Args: ref: A mutable Tensor. Must be one of the following types: float32, float64, int64, int32, uint8, uint16, int16, int8, complex64, complex128, qint8, quint8, qint32, half. Should be from a Variable node. value: A Tensor. Must have the same type as ref. The value to be subtracted to the variable. use_locking: An optional bool. Defaults to False. If True, the subtraction will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional).

Returns: Same as "ref". Returned as a convenience for operations that want to use the new value after the variable has been updated.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.assign_sub_layer, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.assign_sub_layer

Return

Applicative

Origial documentation for Builder.assign_sub_layer

def assign_sub_layer(builder, size):

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .fully_connected(size, activation_fn = tf.assign_sub, ...)

Arguments

• size: the size of the resulting layer
• All other *args and **kwargs are forwarded to tf.contrib.layers.fully_connected

Return

Builder

Origial documentation for tf.assign_sub

def assign_sub(ref, value, use_locking=None, name=None):

Update 'ref' by subtracting 'value' from it.

This operation outputs "ref" after the update is done. This makes it easier to chain operations that need to use the reset value.

Args: ref: A mutable Tensor. Must be one of the following types: float32, float64, int64, int32, uint8, uint16, int16, int8, complex64, complex128, qint8, quint8, qint32, half. Should be from a Variable node. value: A Tensor. Must have the same type as ref. The value to be subtracted to the variable. use_locking: An optional bool. Defaults to False. If True, the subtraction will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional).

Returns: Same as "ref". Returned as a convenience for operations that want to use the new value after the variable has been updated.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.atan, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.atan

Return

Applicative

Origial documentation for Builder.atan

def atan(builder):

THIS METHOD IS AUTOMATICALLY GENERATED

@immutable

This method is a lifted version the function tf.atan to work with tensorbuilder.core.builders.Builders. Instead of taking a Tensor as its first argument it takes a builder, the rest of the arguments are exactly the same.

Original Documentation for tf.atan

def atan(x, name=None)

Computes atan of x element-wise.

Args: x: A Tensor. Must be one of the following types: half, float32, float64, int32, int64, complex64, complex128. name: A name for the operation (optional).

Returns: A Tensor. Has the same type as x.

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .compose(Builder.atan_layer, ...)

Arguments

• All other *args and **kwargs are forwarded to Builder.atan_layer

Return

Applicative

Origial documentation for Builder.atan_layer

def atan_layer(builder, size):

THIS METHOD IS AUTOMATICALLY GENERATED

Alias for .fully_connected(size, activation_fn = tf.atan, ...)

Arguments

• size: the size of the resulting layer
• All other *args and **kwargs are forwarded to tf.contrib.layers.fully_connected

Return

Builder

Origial documentation for tf.atan

def atan(x, name=None):

Computes atan of x element-wise.

Args: x: A Tensor. Must be one of the following types: half, float32, float64, int32, int64, complex64, complex128. name: A name for the operation (optional).

Returns: A Tensor. Has the same type as x.