Load MNIST Data

# Load necessary libraries
library(tensorflow)
library(keras)
library(ggplot2)

# Load Fashion MNIST dataset
fashion_mnist <- dataset_fashion_mnist()
train_images <- fashion_mnist$train$x
train_labels <- fashion_mnist$train$y
test_images <- fashion_mnist$test$x
test_labels <- fashion_mnist$test$y

# Preprocess the data
train_images <- array_reshape(train_images, c(nrow(train_images), 28 * 28)) / 255
test_images <- array_reshape(test_images, c(nrow(test_images), 28 * 28)) / 255

# Convert labels to categorical (one-hot encoding)
train_labels <- to_categorical(train_labels)
test_labels <- to_categorical(test_labels)

# Plot an example image from the training set
example_image <- fashion_mnist$train$x[25, , ]  # Select the first image
example_label <- fashion_mnist$train$y[25]      # Corresponding label

# Display the image
plot(as.raster(example_image, max = 255), main = paste("Label:", example_label))

Model 1: Convolutional Neural Network (CNN)

# Model 1: Convolutional Neural Network (CNN)
cnn_model <- keras_model_sequential() %>% 
  layer_reshape(target_shape = c(28, 28, 1), input_shape = c(28 * 28)) %>%
  layer_conv_2d(filters = 32, kernel_size = c(3,3), activation = 'relu') %>% 
  layer_max_pooling_2d(pool_size = c(2, 2)) %>%
  layer_conv_2d(filters = 64, kernel_size = c(3,3), activation = 'relu') %>%
  layer_max_pooling_2d(pool_size = c(2, 2)) %>%
  layer_flatten() %>%
  layer_dense(units = 128, activation = 'relu') %>%
  layer_dense(units = 10, activation = 'softmax')

cnn_model %>% compile(
  optimizer = 'adam',
  loss = 'categorical_crossentropy',
  metrics = c('accuracy')
)

# Train CNN Model
cnn_history <- cnn_model %>% fit(
  train_images, train_labels,
  epochs = 35, batch_size = 128, 
  validation_split = 0.1
)
## Epoch 1/35
## 422/422 - 3s - loss: 0.5590 - accuracy: 0.8013 - val_loss: 0.3923 - val_accuracy: 0.8630 - 3s/epoch - 7ms/step
## Epoch 2/35
## 422/422 - 3s - loss: 0.3672 - accuracy: 0.8682 - val_loss: 0.3661 - val_accuracy: 0.8670 - 3s/epoch - 6ms/step
## Epoch 3/35
## 422/422 - 2s - loss: 0.3182 - accuracy: 0.8860 - val_loss: 0.3128 - val_accuracy: 0.8850 - 2s/epoch - 6ms/step
## Epoch 4/35
## 422/422 - 6s - loss: 0.2876 - accuracy: 0.8956 - val_loss: 0.2901 - val_accuracy: 0.8945 - 6s/epoch - 14ms/step
## Epoch 5/35
## 422/422 - 10s - loss: 0.2609 - accuracy: 0.9054 - val_loss: 0.2736 - val_accuracy: 0.9003 - 10s/epoch - 25ms/step
## Epoch 6/35
## 422/422 - 11s - loss: 0.2401 - accuracy: 0.9128 - val_loss: 0.2521 - val_accuracy: 0.9118 - 11s/epoch - 26ms/step
## Epoch 7/35
## 422/422 - 12s - loss: 0.2216 - accuracy: 0.9191 - val_loss: 0.2532 - val_accuracy: 0.9092 - 12s/epoch - 29ms/step
## Epoch 8/35
## 422/422 - 12s - loss: 0.2069 - accuracy: 0.9241 - val_loss: 0.2592 - val_accuracy: 0.9062 - 12s/epoch - 29ms/step
## Epoch 9/35
## 422/422 - 9s - loss: 0.1942 - accuracy: 0.9280 - val_loss: 0.2413 - val_accuracy: 0.9152 - 9s/epoch - 22ms/step
## Epoch 10/35
## 422/422 - 11s - loss: 0.1758 - accuracy: 0.9352 - val_loss: 0.2364 - val_accuracy: 0.9157 - 11s/epoch - 26ms/step
## Epoch 11/35
## 422/422 - 9s - loss: 0.1642 - accuracy: 0.9397 - val_loss: 0.2362 - val_accuracy: 0.9182 - 9s/epoch - 22ms/step
## Epoch 12/35
## 422/422 - 10s - loss: 0.1529 - accuracy: 0.9437 - val_loss: 0.2438 - val_accuracy: 0.9130 - 10s/epoch - 23ms/step
## Epoch 13/35
## 422/422 - 10s - loss: 0.1399 - accuracy: 0.9487 - val_loss: 0.2401 - val_accuracy: 0.9147 - 10s/epoch - 23ms/step
## Epoch 14/35
## 422/422 - 9s - loss: 0.1294 - accuracy: 0.9522 - val_loss: 0.2402 - val_accuracy: 0.9160 - 9s/epoch - 22ms/step
## Epoch 15/35
## 422/422 - 9s - loss: 0.1193 - accuracy: 0.9567 - val_loss: 0.2573 - val_accuracy: 0.9162 - 9s/epoch - 22ms/step
## Epoch 16/35
## 422/422 - 10s - loss: 0.1089 - accuracy: 0.9591 - val_loss: 0.2578 - val_accuracy: 0.9177 - 10s/epoch - 24ms/step
## Epoch 17/35
## 422/422 - 11s - loss: 0.0982 - accuracy: 0.9646 - val_loss: 0.2616 - val_accuracy: 0.9130 - 11s/epoch - 25ms/step
## Epoch 18/35
## 422/422 - 11s - loss: 0.0910 - accuracy: 0.9664 - val_loss: 0.2637 - val_accuracy: 0.9162 - 11s/epoch - 26ms/step
## Epoch 19/35
## 422/422 - 11s - loss: 0.0831 - accuracy: 0.9698 - val_loss: 0.2865 - val_accuracy: 0.9117 - 11s/epoch - 26ms/step
## Epoch 20/35
## 422/422 - 11s - loss: 0.0755 - accuracy: 0.9714 - val_loss: 0.2863 - val_accuracy: 0.9123 - 11s/epoch - 25ms/step
## Epoch 21/35
## 422/422 - 11s - loss: 0.0684 - accuracy: 0.9753 - val_loss: 0.2988 - val_accuracy: 0.9122 - 11s/epoch - 26ms/step
## Epoch 22/35
## 422/422 - 11s - loss: 0.0622 - accuracy: 0.9775 - val_loss: 0.3214 - val_accuracy: 0.9155 - 11s/epoch - 27ms/step
## Epoch 23/35
## 422/422 - 11s - loss: 0.0544 - accuracy: 0.9806 - val_loss: 0.3217 - val_accuracy: 0.9150 - 11s/epoch - 25ms/step
## Epoch 24/35
## 422/422 - 12s - loss: 0.0501 - accuracy: 0.9824 - val_loss: 0.3310 - val_accuracy: 0.9160 - 12s/epoch - 29ms/step
## Epoch 25/35
## 422/422 - 12s - loss: 0.0483 - accuracy: 0.9830 - val_loss: 0.3431 - val_accuracy: 0.9127 - 12s/epoch - 28ms/step
## Epoch 26/35
## 422/422 - 12s - loss: 0.0409 - accuracy: 0.9858 - val_loss: 0.3659 - val_accuracy: 0.9132 - 12s/epoch - 28ms/step
## Epoch 27/35
## 422/422 - 12s - loss: 0.0362 - accuracy: 0.9880 - val_loss: 0.4203 - val_accuracy: 0.9090 - 12s/epoch - 29ms/step
## Epoch 28/35
## 422/422 - 12s - loss: 0.0341 - accuracy: 0.9881 - val_loss: 0.3812 - val_accuracy: 0.9150 - 12s/epoch - 28ms/step
## Epoch 29/35
## 422/422 - 12s - loss: 0.0346 - accuracy: 0.9874 - val_loss: 0.4181 - val_accuracy: 0.9072 - 12s/epoch - 28ms/step
## Epoch 30/35
## 422/422 - 12s - loss: 0.0326 - accuracy: 0.9882 - val_loss: 0.4064 - val_accuracy: 0.9158 - 12s/epoch - 29ms/step
## Epoch 31/35
## 422/422 - 12s - loss: 0.0242 - accuracy: 0.9922 - val_loss: 0.4337 - val_accuracy: 0.9150 - 12s/epoch - 28ms/step
## Epoch 32/35
## 422/422 - 12s - loss: 0.0237 - accuracy: 0.9922 - val_loss: 0.4801 - val_accuracy: 0.9140 - 12s/epoch - 29ms/step
## Epoch 33/35
## 422/422 - 12s - loss: 0.0262 - accuracy: 0.9906 - val_loss: 0.4943 - val_accuracy: 0.9068 - 12s/epoch - 29ms/step
## Epoch 34/35
## 422/422 - 12s - loss: 0.0211 - accuracy: 0.9925 - val_loss: 0.5093 - val_accuracy: 0.9115 - 12s/epoch - 28ms/step
## Epoch 35/35
## 422/422 - 12s - loss: 0.0185 - accuracy: 0.9939 - val_loss: 0.4892 - val_accuracy: 0.9142 - 12s/epoch - 28ms/step
# Evaluate CNN Model
cnn_results <- cnn_model %>% evaluate(test_images, test_labels)
## 313/313 - 2s - loss: 0.5498 - accuracy: 0.9091 - 2s/epoch - 6ms/step
cat("Model 1 - CNN Test Accuracy:", cnn_results[2], "\n")
## Model 1 - CNN Test Accuracy: 0.9091

Model 2: Recurrent Neural Network (LSTM)

# Model 2: Recurrent Neural Network (LSTM)
lstm_model <- keras_model_sequential() %>%
  layer_reshape(target_shape = c(28, 28), input_shape = c(28 * 28)) %>%
  layer_lstm(units = 128, return_sequences = TRUE) %>%
  layer_lstm(units = 64) %>%
  layer_dense(units = 10, activation = 'softmax')

lstm_model %>% compile(
  optimizer = 'adam',
  loss = 'categorical_crossentropy',
  metrics = c('accuracy')
)

# Train LSTM Model
lstm_history <- lstm_model %>% fit(
  train_images, train_labels,
  epochs = 35, batch_size = 128, 
  validation_split = 0.1
)
## Epoch 1/35
## 422/422 - 31s - loss: 0.6812 - accuracy: 0.7521 - val_loss: 0.4963 - val_accuracy: 0.8155 - 31s/epoch - 74ms/step
## Epoch 2/35
## 422/422 - 28s - loss: 0.4636 - accuracy: 0.8296 - val_loss: 0.4140 - val_accuracy: 0.8488 - 28s/epoch - 66ms/step
## Epoch 3/35
## 422/422 - 29s - loss: 0.4032 - accuracy: 0.8525 - val_loss: 0.3789 - val_accuracy: 0.8620 - 29s/epoch - 68ms/step
## Epoch 4/35
## 422/422 - 28s - loss: 0.3733 - accuracy: 0.8625 - val_loss: 0.3623 - val_accuracy: 0.8658 - 28s/epoch - 67ms/step
## Epoch 5/35
## 422/422 - 28s - loss: 0.3497 - accuracy: 0.8716 - val_loss: 0.3583 - val_accuracy: 0.8648 - 28s/epoch - 66ms/step
## Epoch 6/35
## 422/422 - 28s - loss: 0.3295 - accuracy: 0.8776 - val_loss: 0.3471 - val_accuracy: 0.8692 - 28s/epoch - 67ms/step
## Epoch 7/35
## 422/422 - 26s - loss: 0.3161 - accuracy: 0.8823 - val_loss: 0.3228 - val_accuracy: 0.8790 - 26s/epoch - 60ms/step
## Epoch 8/35
## 422/422 - 27s - loss: 0.3026 - accuracy: 0.8864 - val_loss: 0.3212 - val_accuracy: 0.8807 - 27s/epoch - 63ms/step
## Epoch 9/35
## 422/422 - 27s - loss: 0.2896 - accuracy: 0.8908 - val_loss: 0.3277 - val_accuracy: 0.8797 - 27s/epoch - 64ms/step
## Epoch 10/35
## 422/422 - 28s - loss: 0.2796 - accuracy: 0.8953 - val_loss: 0.3087 - val_accuracy: 0.8842 - 28s/epoch - 65ms/step
## Epoch 11/35
## 422/422 - 27s - loss: 0.2701 - accuracy: 0.8986 - val_loss: 0.3037 - val_accuracy: 0.8888 - 27s/epoch - 64ms/step
## Epoch 12/35
## 422/422 - 28s - loss: 0.2579 - accuracy: 0.9030 - val_loss: 0.3050 - val_accuracy: 0.8893 - 28s/epoch - 66ms/step
## Epoch 13/35
## 422/422 - 27s - loss: 0.2512 - accuracy: 0.9058 - val_loss: 0.3055 - val_accuracy: 0.8880 - 27s/epoch - 64ms/step
## Epoch 14/35
## 422/422 - 27s - loss: 0.2404 - accuracy: 0.9105 - val_loss: 0.2984 - val_accuracy: 0.8922 - 27s/epoch - 65ms/step
## Epoch 15/35
## 422/422 - 27s - loss: 0.2349 - accuracy: 0.9124 - val_loss: 0.2921 - val_accuracy: 0.8940 - 27s/epoch - 65ms/step
## Epoch 16/35
## 422/422 - 27s - loss: 0.2248 - accuracy: 0.9148 - val_loss: 0.2971 - val_accuracy: 0.8902 - 27s/epoch - 65ms/step
## Epoch 17/35
## 422/422 - 26s - loss: 0.2187 - accuracy: 0.9177 - val_loss: 0.3024 - val_accuracy: 0.8902 - 26s/epoch - 62ms/step
## Epoch 18/35
## 422/422 - 27s - loss: 0.2132 - accuracy: 0.9197 - val_loss: 0.2907 - val_accuracy: 0.8948 - 27s/epoch - 64ms/step
## Epoch 19/35
## 422/422 - 28s - loss: 0.2028 - accuracy: 0.9245 - val_loss: 0.2836 - val_accuracy: 0.8965 - 28s/epoch - 66ms/step
## Epoch 20/35
## 422/422 - 28s - loss: 0.1963 - accuracy: 0.9260 - val_loss: 0.2948 - val_accuracy: 0.8948 - 28s/epoch - 66ms/step
## Epoch 21/35
## 422/422 - 28s - loss: 0.1871 - accuracy: 0.9296 - val_loss: 0.2917 - val_accuracy: 0.8948 - 28s/epoch - 67ms/step
## Epoch 22/35
## 422/422 - 28s - loss: 0.1826 - accuracy: 0.9307 - val_loss: 0.3035 - val_accuracy: 0.8925 - 28s/epoch - 66ms/step
## Epoch 23/35
## 422/422 - 28s - loss: 0.1794 - accuracy: 0.9321 - val_loss: 0.2936 - val_accuracy: 0.8943 - 28s/epoch - 65ms/step
## Epoch 24/35
## 422/422 - 27s - loss: 0.1702 - accuracy: 0.9360 - val_loss: 0.2934 - val_accuracy: 0.8963 - 27s/epoch - 65ms/step
## Epoch 25/35
## 422/422 - 27s - loss: 0.1621 - accuracy: 0.9393 - val_loss: 0.2972 - val_accuracy: 0.8977 - 27s/epoch - 65ms/step
## Epoch 26/35
## 422/422 - 28s - loss: 0.1575 - accuracy: 0.9412 - val_loss: 0.3127 - val_accuracy: 0.8958 - 28s/epoch - 66ms/step
## Epoch 27/35
## 422/422 - 28s - loss: 0.1504 - accuracy: 0.9441 - val_loss: 0.2996 - val_accuracy: 0.8967 - 28s/epoch - 65ms/step
## Epoch 28/35
## 422/422 - 28s - loss: 0.1487 - accuracy: 0.9434 - val_loss: 0.3114 - val_accuracy: 0.8923 - 28s/epoch - 66ms/step
## Epoch 29/35
## 422/422 - 28s - loss: 0.1404 - accuracy: 0.9475 - val_loss: 0.3052 - val_accuracy: 0.8992 - 28s/epoch - 66ms/step
## Epoch 30/35
## 422/422 - 12s - loss: 0.1334 - accuracy: 0.9505 - val_loss: 0.3070 - val_accuracy: 0.9012 - 12s/epoch - 28ms/step
## Epoch 31/35
## 422/422 - 8s - loss: 0.1288 - accuracy: 0.9523 - val_loss: 0.3209 - val_accuracy: 0.8968 - 8s/epoch - 20ms/step
## Epoch 32/35
## 422/422 - 8s - loss: 0.1229 - accuracy: 0.9540 - val_loss: 0.3205 - val_accuracy: 0.8963 - 8s/epoch - 20ms/step
## Epoch 33/35
## 422/422 - 9s - loss: 0.1181 - accuracy: 0.9566 - val_loss: 0.3285 - val_accuracy: 0.8927 - 9s/epoch - 21ms/step
## Epoch 34/35
## 422/422 - 8s - loss: 0.1124 - accuracy: 0.9582 - val_loss: 0.3289 - val_accuracy: 0.8978 - 8s/epoch - 20ms/step
## Epoch 35/35
## 422/422 - 9s - loss: 0.1068 - accuracy: 0.9615 - val_loss: 0.3355 - val_accuracy: 0.8942 - 9s/epoch - 21ms/step
# Evaluate LSTM Model
lstm_results <- lstm_model %>% evaluate(test_images, test_labels)
## 313/313 - 1s - loss: 0.3441 - accuracy: 0.8924 - 1s/epoch - 4ms/step
cat("Model 2 - LSTM Test Accuracy:", lstm_results[2], "\n")
## Model 2 - LSTM Test Accuracy: 0.8924

Model 3: Advanced CNN with Batch Normalization and Dropout

# Model 3: Advanced CNN with Batch Normalization and Dropout
adv_cnn_model <- keras_model_sequential() %>% 
  layer_reshape(target_shape = c(28, 28, 1), input_shape = c(28 * 28)) %>%
  layer_conv_2d(filters = 32, kernel_size = c(3,3), activation = 'relu') %>%
  layer_batch_normalization() %>%
  layer_max_pooling_2d(pool_size = c(2, 2)) %>%
  layer_dropout(rate = 0.25) %>%
  layer_conv_2d(filters = 64, kernel_size = c(3,3), activation = 'relu') %>%
  layer_batch_normalization() %>%
  layer_max_pooling_2d(pool_size = c(2, 2)) %>%
  layer_dropout(rate = 0.25) %>%
  layer_flatten() %>%
  layer_dense(units = 128, activation = 'relu') %>%
  layer_dropout(rate = 0.5) %>%
  layer_dense(units = 10, activation = 'softmax')

adv_cnn_model %>% compile(
  optimizer = 'adam',
  loss = 'categorical_crossentropy',
  metrics = c('accuracy')
)

# Train Advanced CNN Model
adv_cnn_history <- adv_cnn_model %>% fit(
  train_images, train_labels,
  epochs = 35, batch_size = 128, 
  validation_split = 0.1
)
## Epoch 1/35
## 422/422 - 6s - loss: 0.6834 - accuracy: 0.7596 - val_loss: 0.9687 - val_accuracy: 0.7098 - 6s/epoch - 13ms/step
## Epoch 2/35
## 422/422 - 5s - loss: 0.4455 - accuracy: 0.8370 - val_loss: 0.3311 - val_accuracy: 0.8748 - 5s/epoch - 11ms/step
## Epoch 3/35
## 422/422 - 5s - loss: 0.3881 - accuracy: 0.8565 - val_loss: 0.3068 - val_accuracy: 0.8823 - 5s/epoch - 11ms/step
## Epoch 4/35
## 422/422 - 5s - loss: 0.3587 - accuracy: 0.8688 - val_loss: 0.2914 - val_accuracy: 0.8912 - 5s/epoch - 11ms/step
## Epoch 5/35
## 422/422 - 5s - loss: 0.3351 - accuracy: 0.8794 - val_loss: 0.2841 - val_accuracy: 0.8957 - 5s/epoch - 11ms/step
## Epoch 6/35
## 422/422 - 5s - loss: 0.3223 - accuracy: 0.8822 - val_loss: 0.2651 - val_accuracy: 0.9008 - 5s/epoch - 11ms/step
## Epoch 7/35
## 422/422 - 5s - loss: 0.3068 - accuracy: 0.8878 - val_loss: 0.2610 - val_accuracy: 0.9042 - 5s/epoch - 11ms/step
## Epoch 8/35
## 422/422 - 5s - loss: 0.2936 - accuracy: 0.8924 - val_loss: 0.3045 - val_accuracy: 0.8865 - 5s/epoch - 11ms/step
## Epoch 9/35
## 422/422 - 5s - loss: 0.2864 - accuracy: 0.8931 - val_loss: 0.2898 - val_accuracy: 0.8925 - 5s/epoch - 11ms/step
## Epoch 10/35
## 422/422 - 5s - loss: 0.2807 - accuracy: 0.8968 - val_loss: 0.2534 - val_accuracy: 0.9078 - 5s/epoch - 11ms/step
## Epoch 11/35
## 422/422 - 5s - loss: 0.2721 - accuracy: 0.8991 - val_loss: 0.3247 - val_accuracy: 0.8808 - 5s/epoch - 11ms/step
## Epoch 12/35
## 422/422 - 5s - loss: 0.2662 - accuracy: 0.9013 - val_loss: 0.2386 - val_accuracy: 0.9120 - 5s/epoch - 11ms/step
## Epoch 13/35
## 422/422 - 5s - loss: 0.2589 - accuracy: 0.9044 - val_loss: 0.2319 - val_accuracy: 0.9177 - 5s/epoch - 11ms/step
## Epoch 14/35
## 422/422 - 5s - loss: 0.2549 - accuracy: 0.9061 - val_loss: 0.2462 - val_accuracy: 0.9100 - 5s/epoch - 11ms/step
## Epoch 15/35
## 422/422 - 5s - loss: 0.2494 - accuracy: 0.9066 - val_loss: 0.2689 - val_accuracy: 0.9023 - 5s/epoch - 11ms/step
## Epoch 16/35
## 422/422 - 5s - loss: 0.2435 - accuracy: 0.9083 - val_loss: 0.2373 - val_accuracy: 0.9157 - 5s/epoch - 11ms/step
## Epoch 17/35
## 422/422 - 5s - loss: 0.2422 - accuracy: 0.9116 - val_loss: 0.2253 - val_accuracy: 0.9208 - 5s/epoch - 11ms/step
## Epoch 18/35
## 422/422 - 5s - loss: 0.2381 - accuracy: 0.9097 - val_loss: 0.2347 - val_accuracy: 0.9192 - 5s/epoch - 11ms/step
## Epoch 19/35
## 422/422 - 5s - loss: 0.2342 - accuracy: 0.9132 - val_loss: 0.2210 - val_accuracy: 0.9168 - 5s/epoch - 11ms/step
## Epoch 20/35
## 422/422 - 5s - loss: 0.2335 - accuracy: 0.9131 - val_loss: 0.2234 - val_accuracy: 0.9175 - 5s/epoch - 11ms/step
## Epoch 21/35
## 422/422 - 5s - loss: 0.2320 - accuracy: 0.9146 - val_loss: 0.2476 - val_accuracy: 0.9133 - 5s/epoch - 11ms/step
## Epoch 22/35
## 422/422 - 5s - loss: 0.2268 - accuracy: 0.9149 - val_loss: 0.2720 - val_accuracy: 0.9097 - 5s/epoch - 11ms/step
## Epoch 23/35
## 422/422 - 5s - loss: 0.2225 - accuracy: 0.9167 - val_loss: 0.2391 - val_accuracy: 0.9138 - 5s/epoch - 11ms/step
## Epoch 24/35
## 422/422 - 5s - loss: 0.2183 - accuracy: 0.9186 - val_loss: 0.2160 - val_accuracy: 0.9252 - 5s/epoch - 11ms/step
## Epoch 25/35
## 422/422 - 5s - loss: 0.2212 - accuracy: 0.9171 - val_loss: 0.2303 - val_accuracy: 0.9190 - 5s/epoch - 11ms/step
## Epoch 26/35
## 422/422 - 17s - loss: 0.2125 - accuracy: 0.9197 - val_loss: 0.2428 - val_accuracy: 0.9147 - 17s/epoch - 41ms/step
## Epoch 27/35
## 422/422 - 19s - loss: 0.2135 - accuracy: 0.9208 - val_loss: 0.2230 - val_accuracy: 0.9235 - 19s/epoch - 46ms/step
## Epoch 28/35
## 422/422 - 20s - loss: 0.2144 - accuracy: 0.9190 - val_loss: 0.2242 - val_accuracy: 0.9223 - 20s/epoch - 47ms/step
## Epoch 29/35
## 422/422 - 18s - loss: 0.2091 - accuracy: 0.9207 - val_loss: 0.2290 - val_accuracy: 0.9215 - 18s/epoch - 43ms/step
## Epoch 30/35
## 422/422 - 20s - loss: 0.2057 - accuracy: 0.9227 - val_loss: 0.2165 - val_accuracy: 0.9243 - 20s/epoch - 47ms/step
## Epoch 31/35
## 422/422 - 19s - loss: 0.2000 - accuracy: 0.9246 - val_loss: 0.2206 - val_accuracy: 0.9230 - 19s/epoch - 45ms/step
## Epoch 32/35
## 422/422 - 18s - loss: 0.2056 - accuracy: 0.9233 - val_loss: 0.2270 - val_accuracy: 0.9225 - 18s/epoch - 42ms/step
## Epoch 33/35
## 422/422 - 19s - loss: 0.2030 - accuracy: 0.9228 - val_loss: 0.2381 - val_accuracy: 0.9188 - 19s/epoch - 45ms/step
## Epoch 34/35
## 422/422 - 20s - loss: 0.1987 - accuracy: 0.9245 - val_loss: 0.2264 - val_accuracy: 0.9272 - 20s/epoch - 47ms/step
## Epoch 35/35
## 422/422 - 19s - loss: 0.1995 - accuracy: 0.9244 - val_loss: 0.2367 - val_accuracy: 0.9200 - 19s/epoch - 46ms/step
# Evaluate Advanced CNN Model
adv_cnn_results <- adv_cnn_model %>% evaluate(test_images, test_labels)
## 313/313 - 2s - loss: 0.2563 - accuracy: 0.9101 - 2s/epoch - 7ms/step
cat("Model 3 - Advanced CNN Test Accuracy:", adv_cnn_results[2], "\n")
## Model 3 - Advanced CNN Test Accuracy: 0.9101

Finding out the best model

# Compare results and identify the best-performing model
accuracies <- c(CNN = cnn_results[2], LSTM = lstm_results[2], Advanced_CNN = adv_cnn_results[2])
best_model <- names(accuracies)[which.max(accuracies)]
cat("The best-performing model is:", best_model, "with accuracy:", max(accuracies), "\n")
## The best-performing model is: Advanced_CNN.accuracy with accuracy: 0.9101
# Save and plot histories
plot(cnn_history) + ggtitle("Model 1 - CNN Training History")

plot(lstm_history) + ggtitle("Model 2 - LSTM Training History")

plot(adv_cnn_history) + ggtitle("Model 3 - Advanced CNN Training History")

Best Model The CNN model achieved the highest accuracy on the test data, outperforming the LSTM and Advanced CNN models. This suggests that the CNN model was more effective at capturing the necessary features from the dataset to make accurate predictions.