def network1(
input_shape=INPUT_SHAPE
): #before proceding further please analyse the network.py,pipeline.py in folder pipeline(it's an order not a request!!)
return (NeuralNetwork().input(input_shape).conv(
[5, 5, 6]) #filters in conv. layer
.max_pool().relu().flatten().dense(120) #neurons in dense layer
.relu().dense(N_CLASSES))
def make_network2(input_shape=INPUT_SHAPE):
return (NeuralNetwork()
.input(input_shape)
.conv([5, 5, 12]) # <== doubled
.max_pool()
.relu()
.conv([5, 5, 32]) # <== doubled
.max_pool()
.relu()
.flatten()
.dense(240) # <== doubled
.relu()
.dense(N_CLASSES))
def make_network5(input_shape=INPUT_SHAPE):
return (NeuralNetwork().input(input_shape).conv(
[5, 5, 24]).max_pool().elu() # <== ELU
.conv([5, 5, 64]).max_pool().elu() # <== ELU
.flatten().dense(480).elu() # <== ELU
.dense(N_CLASSES))
def make_network7(input_shape=INPUT_SHAPE):
return (NeuralNetwork().input(input_shape).conv(
[5, 5, 24]).max_pool().relu().conv(
[5, 5, 64]).max_pool().relu().flatten().dense(480).relu().dense(
240) # <== one more dense layer
.relu().dense(N_CLASSES))
def make_network9(input_shape=INPUT_SHAPE):
return (NeuralNetwork().input(input_shape).conv(
[5, 5, 24]).max_pool().relu().conv([5, 5, 64]).max_pool().relu().conv(
[3, 3, 64]) # <= smaller kernel here (the image is small by here)
.max_pool().relu().flatten().dense(480).relu().dense(N_CLASSES))