GGPLOT AXIS LIMITS AND SCALES This article describes R functions for changing ggplot axis limits (or scales). We’ll describe how to specify the minimum and the maximum values of axes. Among the different functions available in ggplot2 for setting the axis range, the coord_cartesian() function is the most preferred, because it zoom the plot without clipping the data. In …
Visualize Loss History Preliminaries /* Load libraries */ import numpy as np from keras.datasets import imdb from keras.preprocessing.text import Tokenizer from keras import models from keras import layers import matplotlib.pyplot as plt /* Set random seed */ np.random.seed(0) Using TensorFlow backend. Load Movie Review Data /* Set the number of features we want */ number_of_features …
Preprocessing Data For Neural Networks Typically, a neural network’s parameters are initialized (i.e. created) as small random numbers. Neural networks often behave poorly when the feature values much larger than parameter values. Furthermore, since an observation’s feature values will are combined as they pass through individual units, it is important that all features have the …
Neural Network Weight Regularization Preliminaries /* Load libraries */ import numpy as np from keras.datasets import imdb from keras.preprocessing.text import Tokenizer from keras import models from keras import layers from keras import regularizers /* Set random seed */ np.random.seed(0) Using TensorFlow backend. Load Movie Review Text Data /* Set the number of features we want …
Neural Network Early Stopping Preliminaries /* Load libraries */ import numpy as np from keras.datasets import imdb from keras.preprocessing.text import Tokenizer from keras import models from keras import layers from keras.callbacks import EarlyStopping, ModelCheckpoint /* Set random seed */ np.random.seed(0) Using TensorFlow backend. Load Movie Review Text Data /* Set the number of features we …
LSTM Recurrent Neural Network Oftentimes we have text data that we want to classify. While it is possible to use a type of convolutional network, we are going to focus on a more popular option: the recurrent neural network. The key feature of recurrent neural networks is that information loops back in the network. This …
Feed forward Neural Networks For Regression Preliminaries /* Load libraries */ import numpy as np from keras.preprocessing.text import Tokenizer from keras import models from keras import layers from sklearn.datasets import make_regression from sklearn.model_selection import train_test_split from sklearn import preprocessing /* Set random seed */ np.random.seed(0) Using TensorFlow backend. Generate Training Data /* Generate features matrix …
Feedforward Neural Network For Multiclass Classification Preliminaries /* Load libraries */ import numpy as np from keras.datasets import reuters from keras.utils.np_utils import to_categorical from keras.preprocessing.text import Tokenizer from keras import models from keras import layers /* Set random seed */ np.random.seed(0) Using TensorFlow backend. Load Movie Review Data /* Set the number of features we …
Feedforward Neural Network For Binary Classification Preliminaries /* Load libraries */ import numpy as np from keras.datasets import imdb from keras.preprocessing.text import Tokenizer from keras import models from keras import layers /* Set random seed */ np.random.seed(0) Using TensorFlow backend. Load Movie Review Data /* Set the number of features we want */ number_of_features = …
Adding Dropout Preliminaries /* Load libraries */ import numpy as np from keras.datasets import imdb from keras.preprocessing.text import Tokenizer from keras import models from keras import layers /* Set random seed */ np.random.seed(0) Using TensorFlow backend. Load IMDB Movie Review Data /* Set the number of features we want */ number_of_features = 1000 /* Load …