CopyTypeError: The added layer must be an instance of class Layer

You have created model using keras.models.Model while you are adding a layer from tensorflow.keras.layers.
Note that keras and tensorflow.keras is different. Make sure that you stick with one of them.

You have imported layers from tensorflow.keras while other functions you have imported from keras. You can either import your layers from keras or try importing other functions from tensorflow.keras which might work.

You return four variables s1,s2,s3,s4 and receive them using a single variable obj. This is what is called a tuple, obj is associated with 4 values, the values of s1,s2,s3,s4. So, use index as you use in a list to get the value you want, in order.

Copyobj=list_benefits()
print obj[0] + " is a benefit of functions!"
print obj[1] + " is a benefit of functions!"
print obj[2] + " is a benefit of functions!"
print obj[3] + " is a benefit of functions!"

Version conflict of tensorflow and keras. Solved by changing it to tf 1.14.

I have FOUND the ANSWER

infc = arcpy.GetParameterAsText(0)  
repnum = arcpy.GetParameterAsText(1)  

gRows = arcpy.da.SearchCursor(infc, "Shape@")  
for row in gRows:  
    origpoly = row  
del gRows  

aRows = arcpy.da.InsertCursor(infc, "Shape@")  

for x in xrange(0, int(repnum)):  
    aRows.insertRow(origpoly)  
del aRows  

p1=(input("Name: "),input("Age: "))

As of right now, you're making a 2-tuple, not a Person. This should be

p1 = Person(input("Name: "), input("Age: "))

Note that the model input should be a tensor, so running a model would be like:

gan = vanillaGAN(noise_dims=40, input_dims=784)

noise = sample(32,40)

# define the tensors
noise_tensor = tf.placeholder(tf.float32, shape=[32,40])
gen_output = gan.gen_forward(noise_tensor)
dis_output = gan.dis_forward(noise_tensor)

# define the initializer
# Ref: https://stackoverflow.com/questions/45139423/tensorflow-error-failedpeconditionerror-attempting-to-use-uninitialized-variab
init = tf.global_variables_initializer() 

# run the graph
with tf.Session() as sess:
    sess.run(init)

    gen_output = sess.run(gen_output, feed_dict = {noise_tensor:noise})
    dis_output = sess.run(dis_output, feed_dict = {noise_tensor:noise})

    print(gen_output.shape)
    print(dis_output.shape)

The error message is saying that numpy array doesn't have the attribute xxx.shape.ndims.

Experiment:

1. Access xxx.shape.ndims of a numpy array by noise.shape.ndims:

Traceback (most recent call last):

File "", line 1, in noise.shape.ndims

AttributeError: 'tuple' object has no attribute 'ndims'

2. Access xxx.shape.ndims of a tensor by noise_tensor.shape.ndims:

2