コード例 #1
0
ファイル: Lambda.cs プロジェクト: quakemaster/MxNet.Sharp
        public Shape ComputeOutputShape(Shape input_shape)
        {
            int num_samples;

            if (this._output_shape == null)
            {
                var x = K.Placeholder(shape: input_shape);
                x = this.Invoke(new KerasSymbol[] { x }, null)[0];
                return(x.Shape);
            }
            else
            {
                num_samples = input_shape[0];
                var shape = _output_shape;
                shape.Insert(0, num_samples);
                return(shape);
            }
        }
コード例 #2
0
ファイル: InputLayer.cs プロジェクト: quakemaster/MxNet.Sharp
        public InputLayer(
            Shape input_shape        = null,
            int?batch_size           = null,
            Shape batch_input_shape  = null,
            DType dtype              = null,
            KerasSymbol input_tensor = null,
            bool sparse              = false,
            string name              = null)
        {
            if (name != null)
            {
                var prefix = "input";
                name = prefix + "_" + K.GetUid(prefix).ToString();
            }

            this.trainable        = false;
            this.built            = true;
            this.sparse           = sparse;
            this.supports_masking = true;
            if (input_shape != null && batch_input_shape != null)
            {
                throw new Exception("Only provide the input_shape OR batch_input_shape argument to InputLayer, not both at the same time.");
            }

            if (input_tensor != null && batch_input_shape == null)
            {
                // If input_tensor is set, and batch_input_shape is not set:
                // Attempt automatic input shape inference.
                try
                {
                    batch_input_shape = input_tensor.Shape;
                }
                catch (Exception)
                {
                    if (input_shape == null && batch_input_shape == null)
                    {
                        throw new Exception("InputLayer was provided an input_tensor argument, but its input shape cannot be automatically inferred. You should pass an input_shape or batch_input_shape argument.");
                    }
                }
            }
            if (batch_input_shape == null)
            {
                if (input_shape == null)
                {
                    throw new Exception("An Input layer should be passed either a `batch_input_shape` or an `input_shape`.");
                }
                else
                {
                    var batchShapeData = input_shape.Data.ToList();
                    batchShapeData.Insert(0, batch_size.Value);
                    batch_input_shape = new Shape(batchShapeData);
                }
            }
            else
            {
                batch_input_shape = new Shape(batch_input_shape);
            }

            if (dtype == null)
            {
                if (input_tensor == null)
                {
                    dtype = K.FloatX();
                }
                else
                {
                    dtype = K.DataType(input_tensor);
                }
            }
            this.batch_input_shape = batch_input_shape;
            this.dtype             = dtype;
            if (input_tensor == null)
            {
                this.is_placeholder = true;
                input_tensor        = K.Placeholder(shape: batch_input_shape, dtype: dtype, sparse: this.sparse, name: this.name);
            }
            else
            {
                this.is_placeholder       = false;
                input_tensor._keras_shape = batch_input_shape;
            }
            // Create an input node to add to this.outbound_node
            // and set output_tensors' _keras_history.
            input_tensor._uses_learning_phase = false;
            input_tensor._keras_history       = (this, 0, 0);

            var node = new Node(this,
                                inbound_layers: new Layer[0],
                                node_indices: new int[0],
                                tensor_indices: new int[0],
                                input_tensors: new KerasSymbol[] { input_tensor },
                                output_tensors: new KerasSymbol[] { input_tensor },
                                input_masks: new KerasSymbol[] { null },
                                output_masks: new KerasSymbol[] { null },
                                input_shapes: new Shape[] { batch_input_shape },
                                output_shapes: new Shape[] { batch_input_shape }
                                );
        }