Deep Learning

Tensorflow



In [1]:

    
df <- read.csv('pesos-alturas.csv')
head(df)









    





Pesos Alturas

	74  1.73
	61  1.62
	61  1.63
	68  1.68
	70  1.68
	73  1.75



In [2]:

    
library(tensorflow)

Separando dados de treino e teste:



In [7]:

    
set.seed(42) 
indices <- sample.int(n = nrow(df), size = floor(.75*nrow(df)), replace = FALSE)
df_treino <- df[indices,]
df_teste  <- df[-indices,]



In [12]:

    
nrow(df_treino)



In [13]:

    
nrow(df_teste)

Definindo os coeficientes (aquilo que queremos calcular): y = ax + b



In [17]:

    
a <- tf$Variable(rnorm(1), name="ca")
b <- tf$Variable(rnorm(1), name="cl")

Definindo os placeholders (x e y):



In [20]:

    
x <- tf$placeholder("float")
y <- tf$placeholder("float")

A função que queremos usar:



In [21]:

    
y_hat <- tf$add(tf$multiply(x, a), b)

A função que queremos minimizar:



In [23]:

    
perda <- tf$reduce_sum(tf$pow(y_hat - y, 2)/2)

Learning rate:



In [24]:

    
l_rate <- 0.001



In [26]:

    
generator <- tf$train$GradientDescentOptimizer(learning_rate = l_rate)
optimizer <- generator$minimize(perda)



In [27]:

    
init <- tf$global_variables_initializer()



In [28]:

    
sess = tf$Session()
sess$run(init)

Roda até que o valor da perda estacione:



In [32]:

    
feed_dict <- dict(x = df_treino$Alturas, y = df_treino$Pesos)
epsilon <- .Machine$double.eps
last_cost <- Inf
while (TRUE) {
  sess$run(optimizer, feed_dict = feed_dict)
  current_cost <- sess$run(perda, feed_dict = feed_dict)
  if (last_cost - current_cost < epsilon) break
  last_cost <- current_cost
}

Mostra os coeficientes calculados:



In [38]:

    
tf_coef <- c(sess$run(b), sess$run(a))
tf_coef









    





	-95.8333740234375
	97.4041595458984



In [ ]: