@@ -86,35 +86,30 @@ public BatchNormalization BatchNormalization(int axis = -1,
8686 /// <returns>A tensor of rank 3 representing activation(conv1d(inputs, kernel) + bias).</returns>
8787 public Conv1D Conv1D ( int filters ,
8888 Shape kernel_size ,
89- int ? strides = null ,
89+ int strides = 1 ,
9090 string padding = "valid" ,
91- string data_format = null ,
92- int ? dilation_rate = null ,
91+ string data_format = "channels_last" ,
92+ int dilation_rate = 1 ,
9393 int groups = 1 ,
9494 string activation = null ,
9595 bool use_bias = true ,
9696 string kernel_initializer = "glorot_uniform" ,
9797 string bias_initializer = "zeros" )
98- {
99- // Special case: Conv1D will be implemented as Conv2D with H=1, so we need to add a 1-sized dimension to the kernel.
100- // Lower-level logic handles the stride and dilation_rate, but the kernel_size needs to be set properly here.
101-
102- return new Conv1D ( new Conv1DArgs
98+ => new Conv1D ( new Conv1DArgs
10399 {
104100 Rank = 1 ,
105101 Filters = filters ,
106102 KernelSize = kernel_size ?? new Shape ( 1 , 5 ) ,
107- Strides = strides == null ? 1 : strides ,
103+ Strides = strides ,
108104 Padding = padding ,
109105 DataFormat = data_format ,
110- DilationRate = dilation_rate == null ? 1 : dilation_rate ,
106+ DilationRate = dilation_rate ,
111107 Groups = groups ,
112108 UseBias = use_bias ,
113109 Activation = GetActivationByName ( activation ) ,
114110 KernelInitializer = GetInitializerByName ( kernel_initializer ) ,
115111 BiasInitializer = GetInitializerByName ( bias_initializer )
116112 } ) ;
117- }
118113
119114 /// <summary>
120115 /// 2D convolution layer (e.g. spatial convolution over images).
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