# Read Checkpoint File Gaussian

Density Gaussian. Options. Current. Use the density matrix for the current method. This is the default when no option is given to Density. All. Use all available densities. This is allowed for population analysis but not for electrostatics or density evaluation. Note that this option does not produce densities for all of the excited states in a CI Singles calculation, only the density for the state of interest see the examples below for a method of doing the former. FileExplorer_Outputs.png' alt='Read Checkpoint File Gaussian' title='Read Checkpoint File Gaussian' />Die PCFAQ enthlt Antworten zu vielen Fragen rund um den PC, sowie Erklrungen der hufigsten Computerbegriffe und ein Wrterbuch. SCFUse the SCF density. HF is a synonym for SCF. MP2. Use the generalized density corresponding to the second order energy. TransitionN or N,MUse the CIS transition density between state M and state N. M defaults to 0, which corresponds to the ground state. All. Transition. Use all available CIS transition densities. CIUse the generalized density corresponding to the CI energy. CCUse the generalized density corresponding to the QCI or coupled cluster energy. QCI is a synonym for CC. Rho. CIUse the one particle density computed using the CI wavefunction for state N. Bus Lines'>Bus Lines. This is not the same as the CI density Wiberg. Chapter 9 of Exploring Chemistry with Electronic Structure Methods discusses this issue Foresman. Rho. 2Use the density correct to second order in Mller Plesset theory. This is not the same as the MP2 density, and its use is discouraged Wiberg. CISNUse the total unrelaxed CIS density for state N. Note that this is not the same as the density resulting from CISRootN, DensityCurrent, which is to be preferred Wiberg. Checkpoint. Recover the density from the checkpoint file for analysis. Implies GuessOnly. Chk. Basis the calculation does not recompute new integrals, SCF, and so on, and retrieves the basis set from the checkpoint file. Read Checkpoint File Gaussian CurveRegression Tutorial with the Keras Deep Learning Library in Python. Keras is a deep learning library that wraps the efficient numerical libraries Theano and Tensor. Flow. In this post you will discover how to develop and evaluate neural network models using Keras for a regression problem. Read Checkpoint File Gaussian SurfaceAfter completing this step by step tutorial, you will know How to load a CSV dataset and make it available to Keras. How to create a neural network model with Keras for a regression problem. How to use scikit learn with Keras to evaluate models using cross validation. How to perform data preparation in order to improve skill with Keras models. How to tune the network topology of models with Keras. Lets get started. Update Mar2. 01. Gaussian. GaussViewGaussian. Gaussian sometimes gives error messages, that merely transport the information that something went terribly wrong. Of course, there is practically no hint on what. QMMM Study Tutorial using GaussView, Gaussian, and TAO package Peng Tao and H. Bernhard Schlegel Department of Chemistry, Wayne State University, 5101 Cass Ave. Updated example for Keras 2. Tensor. Flow 1. 0. Theano 0. 9. 0. Regression Tutorial with Keras Deep Learning Library in Python. Photo by Salim Fadhley, some rights reserved. Problem Description. The problem that we will look at in this tutorial is the Boston house price dataset. You can download this dataset and save it to your current working directly with the file name housing. The dataset describes 1. Boston suburbs and is concerned with modeling the price of houses in those suburbs in thousands of dollars. As such, this is a regression predictive modeling problem. Input attributes include things like crime rate, proportion of nonretail business acres, chemical concentrations and more. This is a well studied problem in machine learning. It is convenient to work with because all of the input and output attributes are numerical and there are 5. We can now load our dataset from a file in the local directory. The dataset is in fact not in CSV format in the UCI Machine Learning Repository, the attributes are. Download the free trial version below to get started. Doubleclick the downloaded file to install the software. Reasonable performance for models evaluated using Mean Squared Error MSE are around 2. This is a nice target to aim for with our neural network model. Need help with Deep Learning in Python Take my free 2 week email course and discover MLPs, CNNs and LSTMs with sample code. Click to sign up now and also get a free PDF Ebook version of the course. Start Your FREE Mini Course Now Develop a Baseline Neural Network Model. In this section we will create a baseline neural network model for the regression problem. Lets start off by including all of the functions and objects we will need for this tutorial. Sequential. from keras. Dense. from keras. Keras. Regressor. KFold. from sklearn. Standard. Scaler. Pipelineimport numpyimport pandasfrom keras. Sequentialfrom keras. Densefrom keras. wrappers. Keras. Regressorfrom sklearn. KFoldfrom sklearn. Standard. Scalerfrom sklearn. Pipeline. We can now load our dataset from a file in the local directory. The dataset is in fact not in CSV format in the UCI Machine Learning Repository, the attributes are instead separated by whitespace. We can load this easily using the pandas library. We can then split the input X and output Y attributes so that they are easier to model with Keras and scikit learn. True, headerNone. X and output Y variables. X dataset ,0 1. Y dataset ,1. True,headerNonedatasetdataframe. X and output Y variables. Xdataset ,0 1. Ydataset ,1. We can create Keras models and evaluate them with scikit learn by using handy wrapper objects provided by the Keras library. This is desirable, because scikit learn excels at evaluating models and will allow us to use powerful data preparation and model evaluation schemes with very few lines of code. The Keras wrappers require a function as an argument. This function that we must define is responsible for creating the neural network model to be evaluated. Sd Host Controller Driver Windows 8 on this page. Below we define the function to create the baseline model to be evaluated. It is a simple model that has a single fully connected hidden layer with the same number of neurons as input attributes 1. The network uses good practices such as the rectifier activation function for the hidden layer. No activation function is used for the output layer because it is a regression problem and we are interested in predicting numerical values directly without transform. The efficient ADAM optimization algorithm is used and a mean squared error loss function is optimized. This will be the same metric that we will use to evaluate the performance of the model. It is a desirable metric because by taking the square root gives us an error value we can directly understand in the context of the problem thousands of dollars. Sequential. model. Dense1. 3, inputdim1. Dense1, kernelinitializernormal. Compile model. model. Sequentialmodel. Dense1. Dense1,kernelinitializernormal Compile modelmodel. The Keras wrapper object for use in scikit learn as a regression estimator is called Keras. Regressor. We create an instance and pass it both the name of the function to create the neural network model as well as some parameters to pass along to the fit function of the model later, such as the number of epochs and batch size. Both of these are set to sensible defaults. We also initialize the random number generator with a constant random seed, a process we will repeat for each model evaluated in this tutorial. This is an attempt to ensure we compare models consistently. Keras. Regressorbuildfnbaselinemodel, nbepoch1. Keras. Regressorbuildfnbaselinemodel,nbepoch1. The final step is to evaluate this baseline model. We will use 1. 0 fold cross validation to evaluate the model. KFoldnsplits1. X, Y, cvkfold. Results. MSE results. KFoldnsplits1. X,Y,cvkfoldprintResults. MSEresults. mean,results. Running this code gives us an estimate of the models performance on the problem for unseen data. The result reports the mean squared error including the average and standard deviation average variance across all 1. Baseline 3. 1. 6. MSE1. Baseline 3. MSE3. Modeling The Standardized Dataset. An important concern with the Boston house price dataset is that the input attributes all vary in their scales because they measure different quantities. It is almost always good practice to prepare your data before modeling it using a neural network model. Continuing on from the above baseline model, we can re evaluate the same model using a standardized version of the input dataset. We can use scikit learns Pipeline framework to perform the standardization during the model evaluation process, within each fold of the cross validation. This ensures that there is no data leakage from each testset cross validation fold into the training data. The code below creates a scikit learn Pipeline that first standardizes the dataset then creates and evaluate the baseline neural network model. Standard. Scaler. Keras. Regressorbuildfnbaselinemodel, epochs5. Pipelineestimators. KFoldnsplits1. X, Y, cvkfold. Standardized. MSE results. Standard. Scalerestimators. Keras. Regressorbuildfnbaselinemodel,epochs5. PipelineestimatorskfoldKFoldnsplits1. X,Y,cvkfoldprintStandardized. MSEresults. mean,results. Running the example provides an improved performance over the baseline model without standardized data, dropping the error. Standardized 2. 9. MSE1. Standardized 2. MSEA further extension of this section would be to similarly apply a rescaling to the output variable such as normalizing it to the range of 0 1 and use a Sigmoid or similar activation function on the output layer to narrow output predictions to the same range. Tune The Neural Network Topology. There are many concerns that can be optimized for a neural network model.