DeepLearning Lesson4: Image Classification with CNN

1. Follow the instruction below and then report how the performance changed.(apply all at once)

•Convolutional input layer, 32 feature maps with a size of 3×3 and a rectifier activation function.

•Dropout layer at 20%.

•Convolutional layer, 32 feature maps with a size of 3×3 and a rectifier activation function.

•Max Pool layer with size 2×2.

•Convolutional layer, 64 feature maps with a size of 3×3 and a rectifier activation function.

•Dropout layer at 20%.

•Convolutional layer, 64 feature maps with a size of 3×3 and a rectifier activation function.

•Max Pool layer with size 2×2.

•Convolutional layer, 128 feature maps with a size of 3×3 and a rectifier activation function.

•Dropout layer at 20%.

•Convolutional layer,128 feature maps with a size of 3×3 and a rectifier activation function.

•Max Pool layer with size 2×2.

•Flatten layer.

•Dropout layer at 20%.

•Fully connected layer with 1024 units and a rectifier activation function.

•Dropout layer at 20%.

•Fully connected layer with 512 units and a rectifier activation function.

•Dropout layer at 20%.

•Fully connected output layer with 10 units and a Softmax activation function

Did the performance change?

2. predict the first 4 image of the test data. Then, print the actual label for those 4 images (label means the probability associated with them) to check if the model predicted correctly or not

3. Visualize Loss and Accuracy using the history object

Bonus Question

Program the question 2 using the saved model (.h5 model)