public LSTMModel(LSTMModelArgs args)
: base(args)
{
optimizer = tf.optimizers.Adam(args.LearningRate);
lstm = LSTM(args.NumUnits);
output = Dense(args.NumClasses);
}
public LSTMModel(LSTMModelArgs args)
: base(args)
{
optimizer = keras.optimizers.Adam(args.LearningRate);
var layers = keras.layers;
lstm = layers.LSTM(args.NumUnits);
output = layers.Dense(args.NumClasses);
}
void _minimize(GradientTape tape, OptimizerV2 optimizer, Tensor loss, List <IVariableV1> trainable_variables)
{
var gradients = tape.gradient(loss, trainable_variables);
gradients = optimizer._aggregate_gradients(zip(gradients, trainable_variables));
gradients = optimizer._clip_gradients(gradients);
optimizer.apply_gradients(zip(gradients, trainable_variables.Select(x => x as ResourceVariable)),
experimental_aggregate_gradients: false);
}
void run_optimization(OptimizerV2 optimizer, Tensor x, Tensor y, IVariableV1[] trainable_variables)
{
using var g = tf.GradientTape();
var pred = neural_net(x);
var loss = cross_entropy(pred, y);
// Compute gradients.
var gradients = g.gradient(loss, trainable_variables);
// Update W and b following gradients.
optimizer.apply_gradients(zip(gradients, trainable_variables.Select(x => x as ResourceVariable)));
}
void run_optimization(ConvNet conv_net, OptimizerV2 optimizer, Tensor x, Tensor y)
{
using var g = tf.GradientTape();
var pred = conv_net.Apply(x, training: true);
var loss = cross_entropy_loss(pred, y);
// Compute gradients.
var gradients = g.gradient(loss, conv_net.trainable_variables);
// Update W and b following gradients.
optimizer.apply_gradients(zip(gradients, conv_net.trainable_variables.Select(x => x as ResourceVariable)));
}
void run_optimization(OptimizerV2 optimizer, Tensor x, Tensor y)
{
using var g = tf.GradientTape();
var pred = conv_net(x);
var loss = cross_entropy(pred, y);
// Compute gradients.
var trainable_variables = new IVariableV1[] { wc1, wc2, wd1, wout, bc1, bc2, bd1, bout };
var gradients = g.gradient(loss, trainable_variables);
// Update W and b following gradients.
optimizer.apply_gradients(zip(gradients, trainable_variables.Select(x => x as ResourceVariable)));
}
// 运行优化器
void run_optimization(OptimizerV2 optimizer, Tensor x, Tensor y, IVariableV1[] trainable_variables)
{
using var g = tf.GradientTape();
var pred = neural_net(x);
var loss = cross_entropy(pred, y);
// 计算梯度
var gradients = g.gradient(loss, trainable_variables);
// 更新模型权重 w 和 b
var a = zip(gradients, trainable_variables.Select(x => x as ResourceVariable));
optimizer.apply_gradients(zip(gradients, trainable_variables.Select(x => x as ResourceVariable)));
}
public void compile(ILossFunc loss, OptimizerV2 optimizer, string[] metrics)
{
this.optimizer = optimizer;
compiled_loss = new LossesContainer(loss, output_names: output_names);
compiled_metrics = new MetricsContainer(metrics, output_names: output_names);
int experimental_steps_per_execution = 1;
_configure_steps_per_execution(experimental_steps_per_execution);
// Initialize cache attrs.
_reset_compile_cache();
_is_compiled = true;
this.loss = loss;
}
public void compile(OptimizerV2 optimizer = null,
ILossFunc loss = null,
string[] metrics = null)
{
this.optimizer = optimizer ?? new RMSprop(new RMSpropArgs
{
});
this.loss = loss ?? new MeanSquaredError();
compiled_loss = new LossesContainer(loss, output_names: output_names);
compiled_metrics = new MetricsContainer(metrics, output_names: output_names);
int experimental_steps_per_execution = 1;
_configure_steps_per_execution(experimental_steps_per_execution);
// Initialize cache attrs.
_reset_compile_cache();
_is_compiled = true;
}
