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Elvis Saravia omarsar

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Machine Learning & NLP Research • PhD • Building @dair-ai • Previously: Meta AI, Elastic

omarsar / py_scaling.py

Created August 13, 2018 07:12

	# scale units
	X_max, _ = torch.max(X, 0)
	xPredicted_max, _ = torch.max(xPredicted, 0)

	X = torch.div(X, X_max)
	xPredicted = torch.div(xPredicted, xPredicted_max)
	y = y / 100 # max test score is 100

omarsar / py_nn.py

Last active February 27, 2020 13:09

	class Neural_Network(nn.Module):
	def __init__(self, ):
	super(Neural_Network, self).__init__()
	# parameters
	# TODO: parameters can be parameterized instead of declaring them here
	self.inputSize = 2
	self.outputSize = 1
	self.hiddenSize = 3

	# weights

omarsar / py_train.py

Created August 13, 2018 07:23

	NN = Neural_Network()
	for i in range(1000): # trains the NN 1,000 times
	print ("#" + str(i) + " Loss: " + str(torch.mean((y - NN(X))**2).detach().item())) # mean sum squared loss
	NN.train(X, y)
	NN.saveWeights(NN)
	NN.predict()

omarsar / rnn_install.py

Created August 19, 2018 02:43

	import torch
	import torch.nn as nn
	import torch.nn.functional as F
	import os
	import numpy as np

omarsar / rnn_simple_rnn.py

Created August 19, 2018 02:47

	class SingleRNN(nn.Module):
	def __init__(self, n_inputs, n_neurons):
	super(SingleRNN, self).__init__()

	self.Wx = torch.randn(n_inputs, n_neurons) # 4 X 1
	self.Wy = torch.randn(n_neurons, n_neurons) # 1 X 1

	self.b = torch.zeros(1, n_neurons) # 1 X 4

	def forward(self, X0, X1):

omarsar / simple_rnn_data.py

Created August 19, 2018 02:50

omarsar / simple_rnn_output.py

Created August 19, 2018 02:52

omarsar / basic_rnn.py

Last active February 27, 2020 16:05

	class BasicRNN(nn.Module):
	def __init__(self, n_inputs, n_neurons):
	super(BasicRNN, self).__init__()

	self.Wx = torch.randn(n_inputs, n_neurons) # n_inputs X n_neurons
	self.Wy = torch.randn(n_neurons, n_neurons) # n_neurons X n_neurons

	self.b = torch.zeros(1, n_neurons) # 1 X n_neurons

	def forward(self, X0, X1):

omarsar / basic_rnn_input.py

Last active February 27, 2020 16:06

	N_INPUT = 3 # number of features in input
	N_NEURONS = 5 # number of units in layer

	X0_batch = torch.tensor([[0,1,2], [3,4,5],
	[6,7,8], [9,0,1]],
	dtype = torch.float) #t=0 => 4 X 3

	X1_batch = torch.tensor([[9,8,7], [0,0,0],
	[6,5,4], [3,2,1]],
	dtype = torch.float) #t=1 => 4 X 3

omarsar / basic_rnn_output.py

Created August 19, 2018 02:58