deepviz | An R Package to Visualize Neural Network Architectures | Machine Learning library

You have too many Max-Pooling layers, the max pooling layer reduces the dimension of the inputted vector by factor of its parameter.

Try to reduce the pool_size parameters , or alternatively remove the last 2 max-pooling layers. A value you can try is pool_size=2 for all layers.

As for the parameters you should learn of the meaning of them: Here you can find an explanation of the convolution layer and max pooling layer parameters like filters , kernel size and pool size: Convolutional layer

The dropout layer is a regularization which maximize the effectiveness of the layer weights , every epoch it zeroes different percent (size of "rate" parameter) of the weights . the larger the rate - you have less overfitting but training time is longer. learn about it here: Dropout layer

The units is the size of the Fully Connected layer. Fully Connected layer

The input shape is a dimensions of your data, when the number of records does not count. In 1d vectors it is (N,C) when N is the vector length and C is number of channels you have, if you have 1 channel it is (N,1). In 2d vectors it is (Height,Width,Channels).