logo
down
shadow

Full gradient descent in keras


Full gradient descent in keras

Content Index :

Full gradient descent in keras
Tag : python , By : mgz
Date : November 28 2020, 04:01 AM

around this issue This happens for two reasons:
First, when the data is not shuffled, the train/validation split is inappropriate. Second, full gradient descent performs a single update per epoch, so more training epochs might be required to converge. Why doesn't your model match the wave?
split_point = int(0.2*N)
x_val = x_train[-split_point:]
y_val = y_train[-split_point:]
x_train_ = x_train[:-split_point]
y_train_ = y_train[:-split_point]
plt.scatter(x_train_, y_train_, c='g')
plt.scatter(x_val, y_val, c='r')
plt.show()

Comments
No Comments Right Now !

Boards Message :
You Must Login Or Sign Up to Add Your Comments .

Share : facebook icon twitter icon

What is the difference between Gradient Descent and Newton's Gradient Descent?


Tag : machine-learning , By : nd27182
Date : March 29 2020, 07:55 AM
Any of those help At a local minimum (or maximum) x, the derivative of the target function f vanishes: f'(x) = 0 (assuming sufficient smoothness of f).
Gradient descent tries to find such a minimum x by using information from the first derivative of f: It simply follows the steepest descent from the current point. This is like rolling a ball down the graph of f until it comes to rest (while neglecting inertia).

Stochastic gradient descent from gradient descent implementation in R


Tag : r , By : jch
Date : March 29 2020, 07:55 AM
should help you out I have a working implementation of multivariable linear regression using gradient descent in R. I'd like to see if I can use what I have to run a stochastic gradient descent. I'm not sure if this is really inefficient or not. For example, for each value of α I want to perform 500 SGD iterations and be able to specify the number of randomly picked samples in each iteration. It would be nice to do this so I could see how the number of samples influences the results. I'm having trouble through with the mini-batching and I want to be able to easily plot the results. , Sticking with what you have now
## all of this is the same

download.file("https://raw.githubusercontent.com/dbouquin/IS_605/master/sgd_ex_data/ex3x.dat", "ex3x.dat", method="curl")
x <- read.table('ex3x.dat')
x <- scale(x)
download.file("https://raw.githubusercontent.com/dbouquin/IS_605/master/sgd_ex_data/ex3y.dat", "ex3y.dat", method="curl")
y <- read.table('ex3y.dat')
data3 <- cbind(x,y)
colnames(data3) <- c("area_sqft", "bedrooms","price")
x1 <- rep(1, length(data3$area_sqft))
x <- as.matrix(cbind(x1,x))
y <- as.matrix(y)
L <- length(y)
cost <- function(x,y,theta){
  gradient <- (1/L)* (t(x) %*% ((x%*%t(theta)) - y))
  return(t(gradient)) 
}
GD <- function(x, y, alpha){
  theta <- matrix(c(0,0,0), nrow=1)
  theta_r <- NULL
  for (i in 1:500) {
    theta <- theta - alpha*cost(x,y,theta)  
    theta_r <- rbind(theta_r,theta)    
  }
  return(theta_r)
}

myGoD <- function(x, y, alpha, n = nrow(x)) {
  idx <- sample(nrow(x), n)
  y <- y[idx, , drop = FALSE]
  x <- x[idx, , drop = FALSE]
  GD(x, y, alpha)
}
all.equal(GD(x, y, 0.001), myGoD(x, y, 0.001))
# [1] TRUE

set.seed(1)
head(myGoD(x, y, 0.001, n = 20), 2)
#          x1        V1       V2
# V1 147.5978  82.54083 29.26000
# V1 295.1282 165.00924 58.48424

set.seed(1)
head(myGoD(x, y, 0.001, n = 40), 2)
#          x1        V1        V2
# V1 290.6041  95.30257  59.66994
# V1 580.9537 190.49142 119.23446
alphas <- c(0.001,0.01,0.1,1.0)
ns <- c(47, 40, 30, 20, 10)

par(mfrow = n2mfrow(length(alphas)))
for(i in 1:length(alphas)) {

  # result <- myGoD(x, y, alphas[i]) ## original
  result <- myGoD(x, y, alphas[i], ns[i])

  # red = price 
  # blue = sq ft 
  # green = bedrooms
  plot(result[,1],ylim=c(min(result),max(result)),col="#CC6666",ylab="Value",lwd=0.35,
       xlab=paste("alpha=", alphas[i]),xaxt="n") #suppress auto x-axis title
  lines(result[,2],type="b",col="#0072B2",lwd=0.35)
  lines(result[,3],type="b",col="#66CC99",lwd=0.35)
}
GD <- function(x, y, alpha, n = nrow(x)){
  idx <- sample(nrow(x), n)
  y <- y[idx, , drop = FALSE]
  x <- x[idx, , drop = FALSE]
  theta <- matrix(c(0,0,0), nrow=1)
  theta_r <- NULL

  for (i in 1:500) {
    theta <- theta - alpha*cost(x,y,theta)  
    theta_r <- rbind(theta_r,theta)    
  }
  return(theta_r)
}

TensorFlow weights increasing when using the full dataset for the gradient descent


Tag : python , By : dyarborough
Date : March 29 2020, 07:55 AM
wish helps you The problem isn't the Optimizer, it's your loss. It should return the mean loss, not the sum. If you're doing an L2 regression, for instance, it should look like this:
l_value = tf.pow(tf.abs(ground_truth - predict), 2) # distance for each individual position of the output matrix of shape = (n_examples, example_data_size)
regression_loss = tf.reduce_sum(l_value, axis=1) # distance per example, shape = (n_examples, 1)
total_regression_loss = tf.reduce_mean(regression_loss) # mean distance of all examples, shape = (1)

Keras MNIST Gradient Descent Stuck / Learning Very Slowly


Tag : python , By : Vasiliy
Date : March 29 2020, 07:55 AM
I wish this help you You are using a ReLU activation which basically cuts off the activations below 0, and using a default random_normal initialisation which has the parameters keras.initializers.RandomUniform(minval=-0.05, maxval=0.05, seed=None) by default. As you can see, the initialisation values are very close to 0 and half of them (-.05 to 0) don't get activated at all. And the ones that do get activated (0 to 0.05) propagate the gradients very very slowly.
My guess is to change the initialisation to be around 0 and n (which is the operating range for ReLUs) and your model should converge quickly.

What is gradient descent.does gradient descent can give better result than sklearn linear regression algorithm


Tag : python , By : Tim
Date : March 29 2020, 07:55 AM
I think the issue was by ths following , https://scikit-learn.org/stable/modules/sgd.html
if you want to use Gradient Descent approach, you should consider using SDRClassifier in SKlearn because SKlearn gives two Approaches to using Linear Regression. The first is LinearRegression class and is using Ordinary Least Squares solver from scipy the other one is SDRClassifier class which is an Implementation of the Gradient Descent Algorithm. So to answer your Question if you are using SDRClassifier in SKlearn then you are using an Implementation of Gradient Descent Algorithm behind the Scene.
Related Posts Related QUESTIONS :
  • about backpropagation deep neural network in tensorflow
  • Sort strings in pandas
  • How do access my flask app hosted in docker?
  • Replace the sentence include some text with Python regex
  • Counting the most common element in a 2D List in Python
  • logout a user from the system using a function in python
  • mp4 metadata not found but exists
  • Django: QuerySet with ExpressionWrapper
  • Pandas string search in list of dicts
  • Decryption from RSA encrypted string from sqlite is not the same
  • need of maximum value in int
  • a list of several tuples, how to extract the same of the first two elements in the small tuple in the large tuple
  • Display image of 2D Sinewaves in 3D
  • how to prevent a for loop from overwriting a dictionary?
  • How To Fix: RuntimeError: size mismatch in pyTorch
  • Concatenating two Pandas DataFrames while maintaining index order
  • Why does this not run into an infinite loop?
  • Python Multithreading no current event loop
  • Element Tree - Seaching for specific element value without looping
  • Ignore Nulls in pandas map dictionary
  • How do I get scrap data from web pages using beautifulsoup in python
  • Variable used, golobal or local?
  • I have a regex statement to pull all numbers out of a text file, but it only finds 77 out of the 81 numbers in the file
  • How do I create a dataframe of jobs and companies that includes hyperlinks?
  • Detect if user has clicked the 'maximized' button
  • Does flask_login automatically set the "next" argument?
  • Indents in python 3
  • How to create a pool of threads
  • Pandas giving IndexError on one dataframe but not on another similar dataframe
  • Django Rest Framework - Testing client.login doesn't login user, ret anonymous user
  • Running dag without dag file in airflow
  • Filling across a specified dimension of a numpy array
  • Python populating dataframe in pandas from text files
  • How to interpolate a single ("non-piecewise") cubic spline from a set of data points?
  • Divide 2 integers (leetcode 29) - recursion issue
  • Can someone explain why do I get this output in Python?
  • How do I scrape pdf and html from search results without obvious url
  • Is there a way to automatically make a "collage" of plots with matplotlib?
  • How to combine multiple rows in pandas with shared column values
  • How do I get LOAD_CLASSDEREF instruction after dis.dis?
  • Django - How to add items to Bootstrap dropdown?
  • Linear Regression - Does the below implementation of ridge regression finding coefficient term using gradient method is
  • How to drop all rows in pandas dataframe with negative values?
  • Most Efficient Way to Find Closest Date Between 2 Dataframes
  • Execution error when Passing arguments to a python script using os.system. The script takes sys.argv arguments
  • Looping through a function
  • Create a plot for each unique ID
  • a thread python with 'while' got another thread never start
  • Solution from SciPy solve_ivp contains oscillations for a system of first-order ODEs
  • trigger python events driven by selenium controlled browser
  • Passing line-edits to a contextmanager to set validators
  • Python: globals().items() iterations try to change a dict
  • Is it possible to specify starting values for each parameter (instead of bounds) for scipy's differential evolution?
  • why datetime.now() and constructed datetime using all fields(like year,month...) of now has big timedelta?
  • MySQL multiple table UPDATE query using sqlalchemy core?
  • find if a semantic version is superset of of another version python
  • Type checking against dynamically created objects
  • Struggling with simple reverse function
  • Is there a function for finding the midpoint of n points on sklearn.neighbors.NearestNeighbors?
  • How to set max number of tweets to fetch
  • shadow
    Privacy Policy - Terms - Contact Us © scrbit.com