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Update 10_dl_cnn.Rmd #172

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16 changes: 8 additions & 8 deletions 10_dl_cnn.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -115,7 +115,7 @@ library(keras)
simple_cnn_model <- keras_model_sequential() %>%
layer_embedding(input_dim = max_words + 1, output_dim = 16,
input_length = max_length) %>%
layer_conv_1d(filter = 32, kernel_size = 5, activation = "relu") %>%
layer_conv_1d(filters = 32, kernel_size = 5, activation = "relu") %>%
layer_global_max_pooling_1d() %>%
layer_dense(units = 64, activation = "relu") %>%
layer_dense(units = 1, activation = "sigmoid")
Expand Down Expand Up @@ -217,7 +217,7 @@ We can think of the convolutional layers as doing preprocessing\index{preprocess
cnn_double_dense <- keras_model_sequential() %>%
layer_embedding(input_dim = max_words + 1, output_dim = 16,
input_length = max_length) %>%
layer_conv_1d(filter = 32, kernel_size = 5, activation = "relu") %>%
layer_conv_1d(filters = 32, kernel_size = 5, activation = "relu") %>%
layer_global_max_pooling_1d() %>%
layer_dense(units = 64, activation = "relu") %>%
layer_dense(units = 64, activation = "relu") %>%
Expand Down Expand Up @@ -266,9 +266,9 @@ We can also change the number of convolutional layers, by adding more such layer
cnn_double_conv <- keras_model_sequential() %>%
layer_embedding(input_dim = max_words + 1, output_dim = 16,
input_length = max_length) %>%
layer_conv_1d(filter = 32, kernel_size = 5, activation = "relu") %>%
layer_conv_1d(filters = 32, kernel_size = 5, activation = "relu") %>%
layer_max_pooling_1d(pool_size = 2) %>%
layer_conv_1d(filter = 64, kernel_size = 3, activation = "relu") %>%
layer_conv_1d(filters = 64, kernel_size = 3, activation = "relu") %>%
layer_global_max_pooling_1d() %>%
layer_dense(units = 64, activation = "relu") %>%
layer_dense(units = 1, activation = "sigmoid")
Expand Down Expand Up @@ -445,7 +445,7 @@ Our model will be very similar to the baseline CNN model from Section \@ref(firs
cnn_bpe <- keras_model_sequential() %>%
layer_embedding(input_dim = max_words + 1, output_dim = 16,
input_length = bpe_max_length) %>%
layer_conv_1d(filter = 32, kernel_size = 7, activation = "relu") %>%
layer_conv_1d(filters = 32, kernel_size = 7, activation = "relu") %>%
layer_global_max_pooling_1d() %>%
layer_dense(units = 64, activation = "relu") %>%
layer_dense(units = 1, activation = "sigmoid")
Expand Down Expand Up @@ -708,7 +708,7 @@ Next, we specify the Keras model we want to run.
model <- keras_model_sequential() %>%
layer_embedding(input_dim = max_words + 1, output_dim = 16,
input_length = max_length) %>%
layer_conv_1d(filter = 32,
layer_conv_1d(filters = 32,
kernel_size = FLAGS$kernel_size1,
strides = FLAGS$strides1,
activation = "relu") %>%
Expand Down Expand Up @@ -828,7 +828,7 @@ fit_split <- function(split, prepped_rec) {
mod <- keras_model_sequential() %>%
layer_embedding(input_dim = max_words + 1, output_dim = 16,
input_length = max_length) %>%
layer_conv_1d(filter = 32, kernel_size = 5, activation = "relu") %>%
layer_conv_1d(filters = 32, kernel_size = 5, activation = "relu") %>%
layer_global_max_pooling_1d() %>%
layer_dense(units = 64, activation = "relu") %>%
layer_dense(units = 1, activation = "sigmoid") %>%
Expand Down Expand Up @@ -924,7 +924,7 @@ Instead of using specific validation data that we can then compute performance m
final_mod <- keras_model_sequential() %>%
layer_embedding(input_dim = max_words + 1, output_dim = 16,
input_length = max_length) %>%
layer_conv_1d(filter = 32, kernel_size = 7,
layer_conv_1d(filters = 32, kernel_size = 7,
strides = 1, activation = "relu") %>%
layer_global_max_pooling_1d() %>%
layer_dense(units = 64, activation = "relu") %>%
Expand Down