public SatoshiAlphaModel(
Prediction.TrainArgs args,
Prediction.Structure training_structure = null,
int gcn_layer_count = 2,
int intention_count = 10
) : base(args, training_structure)
{
self.gcn_layer_count = gcn_layer_count;
self.intention_count = intention_count;
// GCN layers
self.pos_embadding = keras.layers.Dense(64, activation: tf.nn.tanh);
self.adj_dense = keras.layers.Dense(self.args.obs_frames, activation: tf.nn.tanh);
foreach (var count in range(self.gcn_layer_count))
{
self.gcn_layers[string.Format("{0}", count)] = GraphConv_layer(64, activation: count < self.gcn_layer_count - 1 ? tf.nn.relu : tf.nn.tanh);
}
self.gcn_dropout = keras.layers.Dropout(self.args.dropout);
self.gcn_bn = keras.layers.BatchNormalization();
self.adj_dense2 = keras.layers.Dense(self.intention_count, activation: tf.nn.tanh);
self.gcn_transfer = GraphConv_layer(64, tf.nn.tanh);
// context feature
self.average_pooling = keras.layers.MaxPooling2D((5, 5));
self.flatten = keras.layers.Flatten();
self.context_dense1 = keras.layers.Dense(self.intention_count * 64, activation: tf.nn.tanh);
// decoder
self.concat = keras.layers.Concatenate();
self.decoder = keras.layers.Dense(2);
}
public LinearModel(
Prediction.TrainArgs args,
Prediction.Structure training_structure = null
) : base(args, training_structure)
{
Tensor P;
var diff_weights = this.args.diff_weights;
if (diff_weights == 0)
{
P = tf.diag(tf.ones(this.args.obs_frames));
}
else
{
P = tf.diag(tf.nn.softmax(
tf.pow(tf.cast(tf.range(1, 1 + this.args.obs_frames), tf.float32), diff_weights)
));
}
this.x = tf.cast(np.arange(this.args.obs_frames), tf.float32);
this.x_p = tf.cast(np.arange(this.args.pred_frames) + this.args.obs_frames, tf.float32);
var A = tf.transpose(tf.stack(new Tensor[] {
tf.ones((this.args.obs_frames), dtype: tf.float32),
this.x
}));
this.A_p = tf.transpose(tf.stack(new Tensor[] {
tf.ones((this.args.pred_frames), dtype: tf.float32),
this.x_p
}));
this.W = tf.matmul(tf.matmul(ndarray_inv((tf.matmul(tf.matmul(tf.transpose(A), P), A)).numpy()).astype(np.float32), tf.transpose(A)), P);
}
public Model(TrainArgs args, Structure training_structure) : base((BaseArgs)args)
{
this._train_args = args;
this._training_structure = training_structure;
}
