public static Tensor DenseLayer(Tensor input, int outputDim, NeuralNetworkActivation activation, NeuralNetworkInitializer initializer, int seed, string name)
{
//if (input.shape.Rank != 1)
//{
// int newDim = input.Shape.Dimensions.Aggregate((d1, d2) => d1 * d2);
// input = CNTKLib.Reshape(input, new int[] { newDim });
//}
return(tf_with(tf.variable_scope(name), delegate
{
Tensor fullyConnected = FullyConnectedLinearLayer(input, outputDim, initializer, seed);
switch (activation)
{
case NeuralNetworkActivation.None: return fullyConnected;
case NeuralNetworkActivation.ReLU: return tf.nn.relu(fullyConnected, "ReLU");
case NeuralNetworkActivation.Sigmoid: return tf.nn.sigmoid(fullyConnected, "Sigmoid");
case NeuralNetworkActivation.Tanh: return tf.nn.tanh(fullyConnected, "Tanh");
default: throw new InvalidOperationException("Unexpected activation " + activation);
}
}));
}
public static Function DenseLayer(Variable input, int outputDim, DeviceDescriptor device, NeuralNetworkActivation activation, NeuralNetworkInitializer initializer, int seed, string name)
{
if (input.Shape.Rank != 1)
{
int newDim = input.Shape.Dimensions.Aggregate((d1, d2) => d1 * d2);
input = CNTKLib.Reshape(input, new int[] { newDim });
}
Function fullyConnected = FullyConnectedLinearLayer(input, outputDim, initializer, seed, device);
fullyConnected.SetName(name);
switch (activation)
{
case NeuralNetworkActivation.None: return(fullyConnected);
case NeuralNetworkActivation.ReLU: return(CNTKLib.ReLU(fullyConnected, name + "ReLU"));
case NeuralNetworkActivation.Sigmoid: return(CNTKLib.Sigmoid(fullyConnected, name + "Sigmoid"));
case NeuralNetworkActivation.Tanh: return(CNTKLib.Tanh(fullyConnected, name + "Tanh"));
default: throw new InvalidOperationException("Unexpected activation " + activation);
}
}
