- Run the create_conv_layer function with the preceding input parameters:
# Convolutional Layer 1
conv1 <- create_conv_layer(input=x_image,
num_input_channels=num_channels,
filter_size=filter_size1,
num_filters=num_filters1,
use_pooling=TRUE)
- Extract the layers of the first convolution layer:
layer_conv1 <- conv1$layer
conv1_images <- conv1$layer$eval(feed_dict = dict(x = train_data$images, y_true = train_data$labels))
- Extract the final weights of the first convolution layer:
weights_conv1 <- conv1$weights
weights_conv1 <- weights_conv1$eval(session=sess)
- Generate the first convolution layer plots:
drawImage_conv(sample(1:50000, size=1), images.bw = conv1_images, images.lab=images.lab.train)
- Generate the first convolution layer weight plots:
drawImage_conv_weights(weights_conv1)
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