how many neuron are there in my code? I have the following snippet
model=Sequential()
model.add(Dense(1000,input_dim=4,activation='relu'))
model.add(Dense(500,activation='relu'))
model.add(Dense(300,activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(3,activation='softmax'))
model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])

I wish to create a diagram from this code:
My understanding:

*

*there are 4 input layer (input_dim=4)

*there is 3 output layers (softmax)

part I'm not sure:
Are there 4 hidden layers (3 dense and 1 dropout)?
What does it mean the unit represent the output size? (i.e., first layer shows 1000, is it 1000 Nerons?!?)
https://machinelearningknowledge.ai/keras-dense-layer-explained-for-beginners/#1_Units


*

*Units The most basic parameter of all the parameters, it uses positive integer as it value and represents the output size of the
layer.


so my question is:
how many Hidden layers are there and what is the size (number of neurons per layer)



there are 4 input layer (input_dim=4)

Input dimension is 4, which means input layer has 4 neurons.

there is 3 output layers (softmax)

It's not three output layers. There is only one output layer and its dimension is three.
For the rest, I've commented after each line
model.add(Dense(1000,input_dim=4,activation='relu')) <-- Hidden Layer 1: 1000 neurons
model.add(Dense(500,activation='relu')) <-- Hidden Layer 2: 500 neurons
model.add(Dense(300,activation='relu')) <-- Hidden Layer 3: 300 neurons
model.add(Dropout(0.2)) <-- Dropout is typically not counted as a hidden layer
model.add(Dense(3,activation='softmax')) <-- Output Layer

So, there are three hidden layers, and one output layer. The dropout is not counted as a hidden layer.
