Just to answer the questions more specifically, here’s a definition of epoch and loss: Epoch: A full pass over all of your training data. For example, in your view above, you have 1213 observations. So an epoch concludes when it has finished a training pass over all 1213 of your observations. Loss: A scalar value … Read more
A simple example which encodes an array using LabelEncoder, OneHotEncoder, LabelBinarizer is shown below. I see that OneHotEncoder needs data in integer encoded form first to convert into its respective encoding which is not required in the case of LabelBinarizer. from numpy import array from sklearn.preprocessing import LabelEncoder from sklearn.preprocessing import OneHotEncoder from sklearn.preprocessing import … Read more
You first need to split the data into training and test set (validation set could be useful too). Don’t forget that testing data points represent real-world data. Feature normalization (or data standardization) of the explanatory (or predictor) variables is a technique used to center and normalise the data by subtracting the mean and dividing by … Read more
In stratKFolds, each test set should not overlap, even when shuffle is included. With stratKFolds and shuffle=True, the data is shuffled once at the start, and then divided into the number of desired splits. The test data is always one of the splits, the train data is the rest. In ShuffleSplit, the data is shuffled … Read more
In general, the error ValueError: Wrong number of items passed 3, placement implies 1 suggests that you are attempting to put too many pigeons in too few pigeonholes. In this case, the value on the right of the equation results[‘predictedY’] = predictedY is trying to put 3 “things” into a container that allows only one. … Read more
The Problem: The pipeline is assuming LabelBinarizer’s fit_transform method is defined to take three positional arguments: def fit_transform(self, x, y) …rest of the code while it is defined to take only two: def fit_transform(self, x): …rest of the code Possible Solution: This can be solved by making a custom transformer that can handle 3 positional … Read more
If you stored the complete model, not only the weights, in the HDF5 file, then it is as simple as from keras.models import load_model model = load_model(‘model.h5’)
This is likely due to your system’s overcommit handling mode. In the default mode, 0, Heuristic overcommit handling. Obvious overcommits of address space are refused. Used for a typical system. It ensures a seriously wild allocation fails while allowing overcommit to reduce swap usage. The root is allowed to allocate slightly more memory in this … Read more
Just to answer this question in a little more detail, and as Pavel said, Batch Normalization is just another layer, so you can use it as such to create your desired network architecture. The general use case is to use BN between the linear and non-linear layers in your network, because it normalizes the input … Read more
I was faced with the same issue in windows 10, Updating the environment variable following steps, it’s working fine. I know It is a lengthy answer for the simple environment setups, I thought it’s may be useful for the new window 10 users. 1) Open Anaconda Prompt: 2) Check Conda Installed Location. where conda 3) … Read more