Practical on Lecture 6 (R version)
25 January 2021
Contents
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In this practical we deal with a reduced version of the CIFAR10 dataset and train convolutional neural networks. We use a small subset of the dataset to be able to compute things in sensbile time within the tutorial.
The main focus is to see how “to assemble” different convolutional layers.
1 CIFAR10
1.1 Load the data
library(keras)CIFAR10 <- dataset_cifar10()x_train <- CIFAR10$train$x/255
x_test <- CIFAR10$test$x/255
y_train <- keras::to_categorical(CIFAR10$train$y, num_classes = 10)
y_test <- keras::to_categorical(CIFAR10$test$y, num_classes = 10)
label_name = c("flyer", "car", "bird", "cat", "deer", "dog", "frog ", "horse", "ship", "truck")
label_name [1] "flyer" "car" "bird" "cat" "deer" "dog" "frog " "horse" "ship"
[10] "truck"dim(x_train)[1] 50000 32 32 3dim(x_test)[1] 10000 32 32 3table(CIFAR10$train$y)
0 1 2 3 4 5 6 7 8 9
5000 5000 5000 5000 5000 5000 5000 5000 5000 5000 We consider only 3 classes - not all 10
We use only “airplane”, “cat” and “truck” to make the problem “easier and quicker” for the practical
1.2 Filter “airplane”, “cat” and “truck”
ind.select.train <- which(CIFAR10$train$y==0|CIFAR10$train$y==3|CIFAR10$train$y==9)
ind.select.test <- which(CIFAR10$test$y==0|CIFAR10$test$y==3|CIFAR10$test$y==9)
length(ind.select.train)[1] 15000length(ind.select.test)[1] 3000x_train.filter <- x_train[ind.select.train,,,]
x_test.filter <- x_test[ind.select.test,,,]
y_train.filter <- CIFAR10$train$y[ind.select.train,]
y_test.filter <- CIFAR10$test$y[ind.select.test,]
y_train.filter.cat <- y_train[ind.select.train,c(1,4,10)]
y_test.filter.cat <- y_test[ind.select.test,c(1,4,10)]We stay with a small training data size and validation data size - also due to time constraints. For testing we could use the full test set. We will also a batch size of 40.
Here I have chosen 8000 for train and 4000 validation but I have a “good” laptop. You migh try with a smaller data size such 2000 for the train set and 1000 for the validation set.
trainLength <- 8000
validateLength <- 4000
trainRange <- 1:trainLength
validateRange <- (trainLength+1):(trainLength+validateLength)
x_train.filter.small <- x_train.filter[1:trainLength,,,]
y_train.filter.small <- y_train.filter.cat[1:trainLength,]
x_val.filter.small <- x_train.filter[(trainLength+1):(trainLength+validateLength),,,]
y_val.filter.small <- y_train.filter.cat[(trainLength+1):(trainLength+validateLength),]1.3 A first CNN using Keras
img_width <- 32
img_height <- 32
model <- keras_model_sequential()
model %>%
layer_conv_2d(filter = 8, kernel_size = c(3,3), strides=c(1,1),padding="same", activation="relu",
input_shape = c(img_height, img_width, 3)) %>%
layer_batch_normalization() %>%
layer_max_pooling_2d(pool_size = c(2,2)) %>%
layer_conv_2d(filter = 4, kernel_size = c(3,3), strides=c(1,1),padding="valid", activation="relu") %>%
layer_batch_normalization() %>%
layer_max_pooling_2d(pool_size = c(2,2)) %>%
layer_flatten() %>%
layer_dense(units = 80, activation = "relu") %>%
layer_dropout(rate = 0.5) %>%
layer_dense(units = 40, activation = "relu") %>%
layer_dropout(rate = 0.5) %>%
layer_dense(units = 3, activation = "softmax")
summary(model) Model: "sequential"
________________________________________________________________________________
Layer (type) Output Shape Param #
================================================================================
conv2d_1 (Conv2D) (None, 32, 32, 8) 224
________________________________________________________________________________
batch_normalization_1 (BatchNormali (None, 32, 32, 8) 32
________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D) (None, 16, 16, 8) 0
________________________________________________________________________________
conv2d (Conv2D) (None, 14, 14, 4) 292
________________________________________________________________________________
batch_normalization (BatchNormaliza (None, 14, 14, 4) 16
________________________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 7, 7, 4) 0
________________________________________________________________________________
flatten (Flatten) (None, 196) 0
________________________________________________________________________________
dense_2 (Dense) (None, 80) 15760
________________________________________________________________________________
dropout_1 (Dropout) (None, 80) 0
________________________________________________________________________________
dense_1 (Dense) (None, 40) 3240
________________________________________________________________________________
dropout (Dropout) (None, 40) 0
________________________________________________________________________________
dense (Dense) (None, 3) 123
================================================================================
Total params: 19,687
Trainable params: 19,663
Non-trainable params: 24
________________________________________________________________________________1.3.1 TASK 0 : Try to understand the dimension of the layers and the number of parameters involved
1.4 Visualize this model
devtools::install_github("andrie/deepviz")library(deepviz)
model %>% plot_model()Warning: The `x` argument of `as_tibble.matrix()` must have unique column names if `.name_repair` is omitted as of tibble 2.0.0.
Using compatibility `.name_repair`.
This warning is displayed once every 8 hours.
Call `lifecycle::last_warnings()` to see where this warning was generated.1.5 Task 1:
Modify the model to have \((5\times 5\)) convolutions in the first layer and no max pooling layer after the second convolutional layer. No padding for the convolutional layers
You’ll need to update the number of neurons in the dense layers.
Call this model, model2.
Model: "sequential_1"
________________________________________________________________________________
Layer (type) Output Shape Param #
================================================================================
conv2d_3 (Conv2D) (None, 28, 28, 8) 608
________________________________________________________________________________
batch_normalization_3 (BatchNormali (None, 28, 28, 8) 32
________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D) (None, 14, 14, 8) 0
________________________________________________________________________________
conv2d_2 (Conv2D) (None, 12, 12, 4) 292
________________________________________________________________________________
batch_normalization_2 (BatchNormali (None, 12, 12, 4) 16
________________________________________________________________________________
flatten_1 (Flatten) (None, 576) 0
________________________________________________________________________________
dense_5 (Dense) (None, 80) 46160
________________________________________________________________________________
dropout_3 (Dropout) (None, 80) 0
________________________________________________________________________________
dense_4 (Dense) (None, 40) 3240
________________________________________________________________________________
dropout_2 (Dropout) (None, 40) 0
________________________________________________________________________________
dense_3 (Dense) (None, 3) 123
================================================================================
Total params: 50,471
Trainable params: 50,447
Non-trainable params: 24
________________________________________________________________________________1.6 Training your model
- first set up the compiler
model2 %>% compile(
loss = 'categorical_crossentropy',
metrics = 'accuracy',
optimizer = optimizer_adam(lr = 0.001)
)- Fit the model
epochs <- 20
batch.size <- 50
start.time <- Sys.time()
history.model2 <- model2 %>% fit(
x=x_train.filter.small, y=y_train.filter.small, validation_data = list(x_val.filter.small,y_val.filter.small),
epochs = epochs, batch_size = batch.size, verbose=FALSE
)
end.time <- Sys.time()
(running.time <- end.time - start.time)Time difference of 38.08836 secs1.7 Visualise the model 2
plot(history.model2 )`geom_smooth()` using formula 'y ~ x'
1.8 Evaluate Your Model
model.acc <- model2 %>% tensorflow::evaluate(x_test.filter,y_test.filter.cat)- Confusion matrix
class.name <- c("airplane","cat","truck")
pred<- model2 %>% keras::predict_classes(x_test.filter)
pred.class <- factor(pred,labels=class.name)
true.class <- factor(y_test.filter,labels=class.name)
table(pred.class,true.class) true.class
pred.class airplane cat truck
airplane 551 16 23
cat 251 934 132
truck 198 50 845(ACC <- sum(diag(table(pred.class,true.class)))/3000)[1] 0.77666671.9 Task 2
- Attempt to create a different model archiecture
- use two additional convolutional layer.
- Try to maximize the validation accuracy
and we’ll see in class who gets the best test accuracy (you can only check the test accuracy once).
Time difference of 1.812771 mins1.10 Visualise my new model
plot(history.model3 )`geom_smooth()` using formula 'y ~ x'
1.11 Evaluate my new model
(model.acc <- model3 %>% tensorflow::evaluate(x_test.filter,y_test.filter.cat)) loss accuracy
0.5492241 0.8166667 - Confusion matrix
class.name <- c("airplane","cat","truck")
pred<- model3 %>% keras::predict_classes(x_test.filter)
pred.class <- factor(pred,labels=class.name)
true.class <- factor(y_test.filter,labels=class.name)
table(pred.class,true.class) true.class
pred.class airplane cat truck
airplane 934 154 240
cat 38 822 66
truck 28 24 694(ACC <- sum(diag(table(pred.class,true.class)))/3000)[1] 0.8166667