Creating the second fully connected layer with dropout
In this recipe, let's create the second fully connected layer along with dropout.
Getting ready
The following are the inputs to the function defined in the recipe Using functions to flatten the densely connected layer, create_fc_layer
:
Input
: This is the output of the first fully connected layer; that is,layer_fc1
Num_inputs
: This is the number of features in the output of the first fully connected layer,fc_size
Num_outputs
: This is the number of the fully connected neurons output (equal to the number of labels,num_classes
)Use_relu
: This is the binary flag set toFALSE
How to do it...
- Run the
create_fc_layer
function with the preceding input parameters:
layer_fc2 = create_fc_layer(input=layer_fc1_drop, num_inputs=fc_size, num_outputs=num_classes, use_relu=FALSE)
- Use TensorFlow's dropout function to handle the scaling and masking of neuron outputs:
layer_fc2_drop <- tf$nn$dropout(layer_fc2, keep_prob)
How it works...
In step 1, we create a...