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tf.layers.cs
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152 lines (142 loc) · 5.83 KB
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using System;
using System.Collections.Generic;
using System.Text;
using Tensorflow.Keras.Layers;
using Tensorflow.Operations.Activation;
namespace Tensorflow
{
public static partial class tf
{
public static class layers
{
public static Tensor conv2d(Tensor inputs,
int filters,
int[] kernel_size,
int[] strides = null,
string padding = "valid",
string data_format= "channels_last",
int[] dilation_rate = null,
bool use_bias = true,
IActivation activation = null,
IInitializer kernel_initializer = null,
IInitializer bias_initializer = null,
bool trainable = true,
string name = null)
{
if (strides == null)
strides = new int[] { 1, 1 };
if (dilation_rate == null)
dilation_rate = new int[] { 1, 1 };
if (bias_initializer == null)
bias_initializer = tf.zeros_initializer;
var layer = new Conv2D(filters,
kernel_size: kernel_size,
strides: strides,
padding: padding,
data_format: data_format,
dilation_rate: dilation_rate,
activation: activation,
use_bias: use_bias,
kernel_initializer: kernel_initializer,
bias_initializer: bias_initializer,
trainable: trainable,
name: name);
return layer.apply(inputs);
}
/// <summary>
/// Functional interface for the batch normalization layer.
/// http://arxiv.org/abs/1502.03167
/// </summary>
/// <param name="inputs"></param>
/// <param name="axis"></param>
/// <param name="momentum"></param>
/// <param name="epsilon"></param>
/// <param name="center"></param>
/// <param name="scale"></param>
/// <param name="beta_initializer"></param>
/// <param name="gamma_initializer"></param>
/// <param name="moving_mean_initializer"></param>
/// <param name="moving_variance_initializer"></param>
/// <param name="training"></param>
/// <param name="trainable"></param>
/// <param name="name"></param>
/// <param name="renorm"></param>
/// <param name="renorm_momentum"></param>
/// <returns></returns>
public static Tensor batch_normalization(Tensor inputs,
int axis = -1,
float momentum = 0.99f,
float epsilon = 0.001f,
bool center = true,
bool scale = true,
IInitializer beta_initializer = null,
IInitializer gamma_initializer = null,
IInitializer moving_mean_initializer = null,
IInitializer moving_variance_initializer = null,
Tensor training = null,
bool trainable = true,
string name = null,
bool renorm = false,
float renorm_momentum = 0.99f)
{
var layer = new BatchNormalization(
axis: axis,
momentum: momentum,
epsilon: epsilon,
center: center,
scale: scale,
beta_initializer: beta_initializer,
gamma_initializer: gamma_initializer,
moving_mean_initializer: moving_mean_initializer,
moving_variance_initializer: moving_variance_initializer,
renorm: renorm,
renorm_momentum: renorm_momentum,
trainable: trainable,
name: name);
return layer.apply(inputs, training: training);
}
/// <summary>
/// Max pooling layer for 2D inputs (e.g. images).
/// </summary>
/// <param name="inputs">The tensor over which to pool. Must have rank 4.</param>
/// <param name="pool_size"></param>
/// <param name="strides"></param>
/// <param name="padding"></param>
/// <param name="data_format"></param>
/// <param name="name"></param>
/// <returns></returns>
public static Tensor max_pooling2d(Tensor inputs,
int[] pool_size,
int[] strides,
string padding = "valid",
string data_format = "channels_last",
string name = null)
{
var layer = new MaxPooling2D(pool_size: pool_size,
strides: strides,
padding: padding,
data_format: data_format,
name: name);
return layer.apply(inputs);
}
public static Tensor dense(Tensor inputs,
int units,
IActivation activation = null,
bool use_bias = true,
IInitializer kernel_initializer = null,
IInitializer bias_initializer = null,
bool trainable = true,
string name = null,
bool? reuse = null)
{
if (bias_initializer == null)
bias_initializer = tf.zeros_initializer;
var layer = new Dense(units, activation,
use_bias: use_bias,
bias_initializer: bias_initializer,
kernel_initializer: kernel_initializer);
return layer.apply(inputs);
}
}
}
}