The exact same model applied to both original MNIST and new fashion-mnist.
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) (None, 1, 28, 28) 0
_________________________________________________________________
batch_normalization_1 (Batch (None, 1, 28, 28) 112
_________________________________________________________________
conv2d_1 (Conv2D) (None, 64, 24, 24) 1664
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 64, 12, 12) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 512, 8, 8) 819712
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 512, 4, 4) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 8192) 0
_________________________________________________________________
dense_1 (Dense) (None, 128) 1048704
_________________________________________________________________
dropout_1 (Dropout) (None, 128) 0
_________________________________________________________________
dense_2 (Dense) (None, 64) 8256
_________________________________________________________________
dropout_2 (Dropout) (None, 64) 0
_________________________________________________________________
dense_3 (Dense) (None, 10) 650
=================================================================
Total params: 1,879,098
Trainable params: 1,879,042
Non-trainable params: 56
epochs = 30
batch_size = 256
opt = Adam(decay=0.001)
drop_out = 0.35
