vignettes/introduction.Rmd
introduction.RmdUsing the deepviz package you can plot the architecture of keras models.
Simple sequential model
require(keras)
# Sequential model with several different layers
model <- keras_model_sequential() %>%
layer_dense(10, input_shape = c(64, 64)) %>%
layer_conv_1d(filters = 16, kernel_size = 8) %>%
layer_max_pooling_1d() %>%
layer_flatten() %>%
layer_dense(25) %>%
layer_dense(25, activation = "relu") %>%
layer_dropout(0.25) %>%
layer_dense(2, activation = "sigmoid")
model
#> Model
#> ___________________________________________________________________________
#> Layer (type) Output Shape Param #
#> ===========================================================================
#> dense_1 (Dense) (None, 64, 10) 650
#> ___________________________________________________________________________
#> conv1d_1 (Conv1D) (None, 57, 16) 1296
#> ___________________________________________________________________________
#> max_pooling1d_1 (MaxPooling1D) (None, 28, 16) 0
#> ___________________________________________________________________________
#> flatten_1 (Flatten) (None, 448) 0
#> ___________________________________________________________________________
#> dense_2 (Dense) (None, 25) 11225
#> ___________________________________________________________________________
#> dense_3 (Dense) (None, 25) 650
#> ___________________________________________________________________________
#> dropout_1 (Dropout) (None, 25) 0
#> ___________________________________________________________________________
#> dense_4 (Dense) (None, 2) 52
#> ===========================================================================
#> Total params: 13,873
#> Trainable params: 13,873
#> Non-trainable params: 0
#> ___________________________________________________________________________
model %>% plot_model()# Model with several inputs and several outputs
# Example from https://keras.rstudio.com/articles/functional_api.html
model <- local({
main_input <- layer_input(shape = c(100), dtype = 'int32', name = 'main_input')
lstm_out <- main_input %>%
layer_embedding(input_dim = 10000, output_dim = 512, input_length = 100) %>%
layer_lstm(units = 32)
auxiliary_output <- lstm_out %>%
layer_dense(units = 1, activation = 'sigmoid', name = 'aux_output')
auxiliary_input <- layer_input(shape = c(5), name = 'aux_input')
main_output <- layer_concatenate(c(lstm_out, auxiliary_input)) %>%
layer_dense(units = 64, activation = 'relu') %>%
layer_dense(units = 64, activation = 'relu') %>%
layer_dense(units = 64, activation = 'relu') %>%
layer_dense(units = 1, activation = 'sigmoid', name = 'main_output')
keras_model(
inputs = c(main_input, auxiliary_input),
outputs = c(main_output, auxiliary_output)
)
})
model
#> Model
#> ___________________________________________________________________________
#> Layer (type) Output Shape Param # Connected to
#> ===========================================================================
#> main_input (InputLayer) (None, 100) 0
#> ___________________________________________________________________________
#> embedding_1 (Embedding) (None, 100, 512) 5120000 main_input[0][0]
#> ___________________________________________________________________________
#> lstm_1 (LSTM) (None, 32) 69760 embedding_1[0][0]
#> ___________________________________________________________________________
#> aux_input (InputLayer) (None, 5) 0
#> ___________________________________________________________________________
#> concatenate_1 (Concaten (None, 37) 0 lstm_1[0][0]
#> aux_input[0][0]
#> ___________________________________________________________________________
#> dense_5 (Dense) (None, 64) 2432 concatenate_1[0][0]
#> ___________________________________________________________________________
#> dense_6 (Dense) (None, 64) 4160 dense_5[0][0]
#> ___________________________________________________________________________
#> dense_7 (Dense) (None, 64) 4160 dense_6[0][0]
#> ___________________________________________________________________________
#> main_output (Dense) (None, 1) 65 dense_7[0][0]
#> ___________________________________________________________________________
#> aux_output (Dense) (None, 1) 33 lstm_1[0][0]
#> ===========================================================================
#> Total params: 5,200,610
#> Trainable params: 5,200,610
#> Non-trainable params: 0
#> ___________________________________________________________________________
model %>% plot_model()Examples of some famous architectures.
# Creates a network that illustrates depthwise separable convolution
depthwise_separable <- local({
input <-
layer_input(
shape = c(3, 64, 64),
dtype = 'float32',
name = 'input'
)
conv_1x1 <- input %>%
layer_conv_2d(8, kernel_size = c(1, 1), name = "1x1_convolution")
conv_1 <- conv_1x1 %>%
layer_conv_2d(8, kernel_size = c(3, 3), name = "3x3_convolution_1")
conv_2 <- conv_1x1 %>%
layer_conv_2d(8, kernel_size = c(3, 3), name = "3x3_convolution_2")
conv_3 <- conv_1x1 %>%
layer_conv_2d(8, kernel_size = c(3, 3), name = "3x3_convolution_3")
output <- layer_concatenate(
c(conv_1, conv_2, conv_3),
name = "concat"
)
keras_model(
inputs = c(input),
outputs = c(output)
)
})
depthwise_separable
#> Model
#> ___________________________________________________________________________
#> Layer (type) Output Shape Param # Connected to
#> ===========================================================================
#> input (InputLayer) (None, 3, 64, 64 0
#> ___________________________________________________________________________
#> 1x1_convolution (Conv2D (None, 3, 64, 8) 520 input[0][0]
#> ___________________________________________________________________________
#> 3x3_convolution_1 (Conv (None, 1, 62, 8) 584 1x1_convolution[0][0]
#> ___________________________________________________________________________
#> 3x3_convolution_2 (Conv (None, 1, 62, 8) 584 1x1_convolution[0][0]
#> ___________________________________________________________________________
#> 3x3_convolution_3 (Conv (None, 1, 62, 8) 584 1x1_convolution[0][0]
#> ___________________________________________________________________________
#> concat (Concatenate) (None, 1, 62, 24 0 3x3_convolution_1[0][0]
#> 3x3_convolution_2[0][0]
#> 3x3_convolution_3[0][0]
#> ===========================================================================
#> Total params: 2,272
#> Trainable params: 2,272
#> Non-trainable params: 0
#> ___________________________________________________________________________
depthwise_separable %>% plot_model()# Creates a network that illustrates a module of the resnet network
# references: https://arxiv.org/pdf/1610.02357.pdf
resnet <- local({
input <- layer_input(shape = c(3, 64, 64), dtype = 'float32')
stream_1 <- input %>%
layer_conv_2d(1, kernel_size = c(3, 3), padding = "same", activation = "relu") %>%
layer_conv_2d(1, kernel_size = c(3, 3), padding = "same", activation = "relu")
output <- layer_add(c(input, stream_1))
keras_model(inputs = c(input),
outputs = c(output))
})
resnet
#> Model
#> ___________________________________________________________________________
#> Layer (type) Output Shape Param # Connected to
#> ===========================================================================
#> input_1 (InputLayer) (None, 3, 64, 64 0
#> ___________________________________________________________________________
#> conv2d_1 (Conv2D) (None, 3, 64, 1) 577 input_1[0][0]
#> ___________________________________________________________________________
#> conv2d_2 (Conv2D) (None, 3, 64, 1) 10 conv2d_1[0][0]
#> ___________________________________________________________________________
#> add_1 (Add) (None, 3, 64, 64 0 input_1[0][0]
#> conv2d_2[0][0]
#> ===========================================================================
#> Total params: 587
#> Trainable params: 587
#> Non-trainable params: 0
#> ___________________________________________________________________________
resnet %>% plot_model()# Creates a network that illustrates the inception v3 network
# references: https://arxiv.org/pdf/1610.02357.pdf
inception_v3 <- local({
input <- layer_input(shape = c(3, 64, 64), dtype = 'float32')
stream_1 <- input %>%
layer_conv_2d(1, kernel_size = c(1, 1), filters = 3)
stream_2 <- input %>%
layer_conv_2d(1, kernel_size = c(1, 1)) %>%
layer_conv_2d(1, kernel_size = c(3, 3), padding = "same")
stream_3 <- input %>%
layer_average_pooling_2d(pool_size = c(1, 1)) %>%
layer_conv_2d(8, kernel_size = c(3, 3), padding = "same")
stream_4 <- input %>%
layer_conv_2d(8, kernel_size = c(1, 1)) %>%
layer_conv_2d(8, kernel_size = c(3, 3), padding = "same") %>%
layer_conv_2d(8, kernel_size = c(3, 3), padding = "same")
output <- layer_concatenate(
c(stream_1, stream_2, stream_3, stream_4),
name = "concat"
)
keras_model(inputs = c(input),
outputs = c(output))
})
inception_v3
#> Model
#> ___________________________________________________________________________
#> Layer (type) Output Shape Param # Connected to
#> ===========================================================================
#> input_2 (InputLayer) (None, 3, 64, 64 0
#> ___________________________________________________________________________
#> conv2d_7 (Conv2D) (None, 3, 64, 8) 520 input_2[0][0]
#> ___________________________________________________________________________
#> conv2d_4 (Conv2D) (None, 3, 64, 1) 65 input_2[0][0]
#> ___________________________________________________________________________
#> average_pooling2d_1 (Av (None, 3, 64, 64 0 input_2[0][0]
#> ___________________________________________________________________________
#> conv2d_8 (Conv2D) (None, 3, 64, 8) 584 conv2d_7[0][0]
#> ___________________________________________________________________________
#> conv2d_3 (Conv2D) (None, 3, 64, 3) 195 input_2[0][0]
#> ___________________________________________________________________________
#> conv2d_5 (Conv2D) (None, 3, 64, 1) 10 conv2d_4[0][0]
#> ___________________________________________________________________________
#> conv2d_6 (Conv2D) (None, 3, 64, 8) 4616 average_pooling2d_1[0][0]
#> ___________________________________________________________________________
#> conv2d_9 (Conv2D) (None, 3, 64, 8) 584 conv2d_8[0][0]
#> ___________________________________________________________________________
#> concat (Concatenate) (None, 3, 64, 20 0 conv2d_3[0][0]
#> conv2d_5[0][0]
#> conv2d_6[0][0]
#> conv2d_9[0][0]
#> ===========================================================================
#> Total params: 6,574
#> Trainable params: 6,574
#> Non-trainable params: 0
#> ___________________________________________________________________________
inception_v3 %>% plot_model()