using System;

using System.Collections.Generic;

using System.Text;

using Tensorflow.Operations;

using Tensorflow.Operations.Activation;

namespace Tensorflow

{

public static partial class tf

{

public static class nn

{

public static (Tensor, Tensor) moments(Tensor x,

int[] axes,

string name = null,

bool keep_dims = false) => nn_impl.moments(x,

axes,

name: name,

keep_dims: keep_dims);

public static Tensor embedding_lookup(RefVariable @params,

Tensor ids,

string partition_strategy = "mod",

string name = null) => embedding_ops._embedding_lookup_and_transform(@params,

ids,

partition_strategy: partition_strategy,

name: name);

public static Tensor embedding_lookup(Tensor @params,

Tensor ids,

string partition_strategy = "mod",

string name = null) => embedding_ops._embedding_lookup_and_transform(new Tensor[] { @params },

ids,

partition_strategy: partition_strategy,

name: name);

public static IActivation relu() => new relu();

public static Tensor relu(Tensor features, string name = null) => gen_nn_ops.relu(features, name);

public static Tensor[] fused_batch_norm(Tensor x,

RefVariable scale,

RefVariable offset,

Tensor mean = null,

Tensor variance = null,

float epsilon = 0.001f,

string data_format = "NHWC",

bool is_training = true,

string name = null) => nn_impl.fused_batch_norm(x, scale, offset, mean, variance,

epsilon: epsilon,

data_format: data_format,

is_training: is_training,

name: name);

public static IPoolFunction max_pool => new MaxPoolFunction();

public static Tensor[] top_k(Tensor input, int k = 1, bool sorted = true, string name = null)

=> gen_nn_ops.top_kv2(input, k: k, sorted: sorted, name: name);

public static Tensor bias_add(Tensor value, RefVariable bias, string data_format = null, string name = null)

{

return Python.with(ops.name_scope(name, "BiasAdd", new { value, bias }), scope =>

{

name = scope;

return gen_nn_ops.bias_add(value, bias, data_format: data_format, name: name);

});

}

public static Tensor softmax(Tensor logits, int axis = -1, string name = null)

=> gen_nn_ops.softmax(logits, name);

public static Tensor softmax_cross_entropy_with_logits_v2(Tensor labels, Tensor logits, int axis = -1, string name = null)

=> nn_ops.softmax_cross_entropy_with_logits_v2_helper(labels, logits, axis: axis, name: name);

}

}

}