public void Compile(StringOrInstance optimizer, string[] loss, string[] metrics = null, float[] loss_weights = null,
string sample_weight_mode = "None", string[] weighted_metrics = null, NDarray[] target_tensors = null)
{
var args = new Dictionary <string, object>();
args["optimizer"] = optimizer;
args["loss"] = loss;
args["metrics"] = metrics;
args["loss_weights"] = loss_weights;
args["sample_weight_mode"] = sample_weight_mode;
args["weighted_metrics"] = weighted_metrics;
args["target_tensors"] = target_tensors;
InvokeMethod("compile", args);
}
public void Compile(StringOrInstance optimizer, string loss, string[] metrics = null, float[] loss_weights = null,
string sample_weight_mode = "None", string[] weighted_metrics = null, NDarray[] target_tensors = null)
{
var args = new Dictionary <string, object>();
args["optimizer"] = optimizer.PyObject;
args["loss"] = loss;
args["metrics"] = metrics;
args["loss_weights"] = loss_weights;
args["sample_weight_mode"] = sample_weight_mode;
args["weighted_metrics"] = weighted_metrics;
args["target_tensors"] = target_tensors;
InvokeMethod("compile", args);
//__self__.compile(optimizer: optimizer, loss: loss, metrics: metrics!=null ? metrics.ToList() : null, loss_weights: loss_weights, sample_weight_mode: sample_weight_mode,
// weighted_metrics: weighted_metrics, target_tensors: target_tensors);
}
/// <summary>
/// Initializes a new instance of the <see cref="Dense"/> class.
/// </summary>
/// <param name="units"> Positive integer, dimensionality of the output space.</param>
/// <param name="activation"> Activation function to use (see activations). If you don't specify anything, no activation is applied (ie. "linear" activation: a(x) = x).</param>
/// <param name="use_bias"> Boolean, whether the layer uses a bias vector.</param>
/// <param name="kernel_initializer"> Initializer for the kernel weights matrix (see initializers).</param>
/// <param name="bias_initializer"> Initializer for the bias vector (see initializers).</param>
/// <param name="kernel_regularizer"> Regularizer function applied to the kernel weights matrix (see regularizer).</param>
/// <param name="bias_regularizer"> Regularizer function applied to the bias vector (see regularizer).</param>
/// <param name="activity_regularizer"> Regularizer function applied to the output of the layer (its "activation"). (see regularizer).</param>
/// <param name="kernel_constraint"> Constraint function applied to the kernel weights matrix (see constraints).</param>
/// <param name="bias_constraint"> Constraint function applied to the bias vector (see constraints).</param>
/// <param name="input_shape">nD tensor with shape: (batch_size, ..., input_dim). The most common situation would be a 2D input with shape (batch_size, input_dim).</param>
public Dense(int units, int?input_dim = null, string activation = "", bool use_bias = true, StringOrInstance kernel_initializer = null,
string bias_initializer = "zeros", StringOrInstance kernel_regularizer = null, string bias_regularizer = "",
string activity_regularizer = "", string kernel_constraint = "", string bias_constraint = "", Shape input_shape = null)
{
this["units"] = units;
this["input_dim"] = input_dim;
this["activation"] = activation;
this["use_bias"] = use_bias;
this["kernel_initializer"] = kernel_initializer ?? "glorot_uniform";
this["bias_initializer"] = bias_initializer;
this["kernel_regularizer"] = kernel_regularizer;
this["bias_regularizer"] = bias_regularizer;
this["activity_regularizer"] = activity_regularizer;
this["kernel_constraint"] = kernel_constraint;
this["bias_constraint"] = bias_constraint;
Parameters["input_shape"] = input_shape;
PyInstance = Instance.keras.layers.Dense;
Init();
}
