tf.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: with tf.control_dependencies([tf.assert_greater_equal(x, y)]): output = tf.reduce_sum(x) Example of adding dependency to the tensor being checked: 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[

tf.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: with tf.control_dependencies([tf.assert_greater(x, y)]): output = tf.reduce_sum(x) Example of adding dependency to the tensor being checked: 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

tf.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: with tf.control_dependencies([tf.assert_equal(x, y)]): output = tf.reduce_sum(x) Example of adding dependency to the tensor being checked: 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,

tf.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.

tf.argmin(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. na

tf.argmax(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. nam

tf.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.

tf.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.

tf.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.

tf.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: # tensor 'a' is [[1, 2], [3, 4]] # tensor `b` is [[5, 0], [0, 6]] tf.accumulate_n([a, b, a]) ==> [[7, 4], [6, 14]] # Explicit