I am trying to implement a single-layer perceptron in python. Some of the code is what I have written and some of the code is from my professor who gave us a skeleton of basically how it should be implemented. Here is the necessary code to implement my problem
X = dataset_train # provided below
y = labels_train # provided below
w = np.zeros(19)
lr=0.5 # learning rate
epochs=100
my prediction definition
def perc(X):
""" Perceptron function.
Args:
X (numpy.ndarray): input vectors
Returns:
class labels of X
"""
# Note the input is a transposed array so X.shape[1] below is the num of observations
if len(X.shape)== 2:
# Initilizing an array for outputs
prod = np.empty(X.shape[1], dtype=int) #The original number of observations
res = np.empty(X.shape[1], dtype=int)
for i in range(X.shape[1]):
prod[i] = w.dot(X[:,i]) # cross product returns a scalar for each obs.
a = activation_func(prod[i]) # calls tanh
if a>=0.0: # zero is the threshold.
res[i] = 1
elif a< 0:
res[i] = -1
else:
prod = w.dot(X)
a = activation_func(prod)
res = 0
if a>=0.0:
res = 1
elif a< 0:
res= -1
return res
Note that my prediction method receives either a transposed 1d array or a 2d array as input. That is what I am checking for in the first if statement. Next is the activation function:
def activation_func(X): # Should receive a scalar from prediction
return ((np.exp(X)-np.exp(-X))/(np.exp(X)+np.exp(-X)))
the last bit of code is for basically updating the weights. This is more of what my professor wrote
for epoch in trange(epochs):
# predict all items from the dataset
predictions = perc(np.transpose(X, [1, 0]))
# compare with ground truth
predictions = y - predictions
if ((predictions == 0).all()):
print(f'No errors after {epoch} epochs. Training successful!')
else:
#sample one prediction at random
n = r.randint(0,99-1)
prediction_for_update = perc(X[n,:])
# update the weights of the perceptron from the random sample
w = w + lr * (y[n] - prediction_for_update)* X[n,:]
So for a 100 epochs it first takes the whole array and computes predictions with the definition perc(). He then specified that the weights should be updated by taking one random row and predicting with that observation. I added the code w = w + lr * (y[n] - prediction_for_update)* X[n,:]
From my understanding of the code, the first time around the weights are initialized with zeros. The 2darray that has the dimensions (9000, 16384) gets transposed and passed to prec(). The first initial predictions should just be an array of ones because 0 (weights) * observations = 0 and tanh of 0 is 0 and according to the threshold, anything greater or equal to zero is 1. Then after the if statement is evaluated one random row is passed to perc(). This should also return one but I see this warning as the code is running
RuntimeWarning: invalid value encountered in double_scalars
I check my numbers for the predictions and they are huge sometimes. (Sometimes they are normal). I don't know how this is possible since the activation function can only return numbers between -1 and 1. How can this be?
Here is the sample code you will have to change X back to an array. Below is a dictionary. It is a (99, 19) 2d array
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y is
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