| @@ -85,7 +85,7 @@ namespace Tensorflow.Keras.Layers | |||||
| /// <param name="bias_initializer">Initializer for the bias vector (see keras.initializers).</param> | /// <param name="bias_initializer">Initializer for the bias vector (see keras.initializers).</param> | ||||
| /// <returns>A tensor of rank 3 representing activation(conv1d(inputs, kernel) + bias).</returns> | /// <returns>A tensor of rank 3 representing activation(conv1d(inputs, kernel) + bias).</returns> | ||||
| public Conv1D Conv1D(int filters, | public Conv1D Conv1D(int filters, | ||||
| Shape? kernel_size = null, | |||||
| Shape kernel_size, | |||||
| int? strides = null, | int? strides = null, | ||||
| string padding = "valid", | string padding = "valid", | ||||
| string data_format = null, | string data_format = null, | ||||
| @@ -14,14 +14,12 @@ namespace TensorFlowNET.Keras.UnitTest | |||||
| { | { | ||||
| var filters = 8; | var filters = 8; | ||||
| var conv = keras.layers.Conv1D(filters, activation: "linear"); | |||||
| var conv = keras.layers.Conv1D(filters, kernel_size: 3, activation: "linear"); | |||||
| var x = np.arange(256.0f).reshape((8, 8, 4)); | var x = np.arange(256.0f).reshape((8, 8, 4)); | ||||
| var y = conv.Apply(x); | var y = conv.Apply(x); | ||||
| Assert.AreEqual(3, y.shape.ndim); | |||||
| Assert.AreEqual(x.dims[0], y.shape[0]); | |||||
| Assert.AreEqual(x.dims[1] - 4, y.shape[1]); | |||||
| Assert.AreEqual(y.shape, (8, 6, 8)); | |||||
| Assert.AreEqual(filters, y.shape[2]); | Assert.AreEqual(filters, y.shape[2]); | ||||
| } | } | ||||