We will start by using a not-so-deep convolutional neural network to develop an image classification model. We will use the following code for this:
# Model architecturemodel <- keras_model_sequential()model %>% layer_conv_2d(filters = 32, kernel_size = c(3,3), activation = 'relu', input_shape = c(224,224,3)) %>% layer_conv_2d(filters = 32, kernel_size = c(3,3), activation = 'relu') %>% layer_max_pooling_2d(pool_size = c(2,2)) %>% layer_dropout(rate = 0.25) %>% layer_flatten() %>% layer_dense(units = 256, activation = 'relu') %>% layer_dropout(rate = 0.25) %>% layer_dense(units = 10, activation = 'softmax') summary(model)_________________________________________________________________________Layer (type) Output Shape Param ...
