Crafting Tomorrow's Solutions Today: Mastering ANNs with Python and R.

An ANN is based on a collection of connected nodes called neurons, modelling the neurons in a biological brain. Each connection, like the synapses in a biological brain, transmits a signal to other neurons. A neuron receiving a signal then processes it and transmits signal to neurons connected to it. The "signal" at a connection is a real number, and the output of each neuron is computed by some non-linear function of the sum of its inputs. The connections are also called edges. Neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Neurons may have a threshold such that a signal is sent only if the aggregate signal crosses that threshold. Neurons are aggregated into layers which perform different transformations on their inputs. Signals travel from the input layer to the output layer through number of hidden layers.

Artificial Neural Network in Python - Classification case.

        
#Importing the libraries
import numpy as np
import pandas as pd
import tensorflow as tf
#Checking the tensorflow version
tf.__version__

#Importing the dataset
dataset = pd.read_csv('my_dataset.csv')
X = dataset.iloc[:, 3:-1].values
y = dataset.iloc[:, -1].values

#Encoding categorical data
#Label Encoding for certain column
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
X[:, 2] = le.fit_transform(X[:, 2])
#One Hot Encoding for certain column
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [1])], remainder='passthrough')
X = np.array(ct.fit_transform(X))


#Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)

#Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

#Initializing the Artificial Neural Networks
ann = tf.keras.models.Sequential()

#Adding the input layer and the first hidden layer
ann.add(tf.keras.layers.Dense(units=6, activation='relu'))

#Adding the second hidden layer
ann.add(tf.keras.layers.Dense(units=6, activation='relu'))

#Adding the output layer
ann.add(tf.keras.layers.Dense(units=1, activation='sigmoid'))

#Compiling the Artificial Neural Network
ann.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])

#Training the Artificial Neural Network on the Training set
ann.fit(X_train, y_train, batch_size = 32, epochs = 100)

#Predicting the Test set results
y_pred = ann.predict(X_test)
y_pred = (y_pred > 0.5)
print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))

#Making the Confusion Matrix
from sklearn.metrics import confusion_matrix, accuracy_score
cm = confusion_matrix(y_test, y_pred)
print(cm)
accuracy_score(y_test, y_pred)

Artificial Neural Network in Python - Regression case.

#Importing the libraries
import numpy as np
import pandas as pd
import tensorflow as tf
#Checking the tensorflow version
tf.__version__

#Importing the dataset
dataset = pd.read_excel('my_dataset.xlsx')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, -1].values

#Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)

#Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

#Initializing the Artificial Neural Networks
ann = tf.keras.models.Sequential()

#Adding the input layer and the first hidden layer
ann.add(tf.keras.layers.Dense(units=6, activation='relu'))

#Adding the second hidden layer
ann.add(tf.keras.layers.Dense(units=6, activation='relu'))

#Adding the output layer
ann.add(tf.keras.layers.Dense(units=1))

#Compiling the Artificial Neural Network
ann.compile(optimizer = 'adam', loss = 'mean_squared_error')

#Training the Artificial Neural Network on the Training set
ann.fit(X_train, y_train, batch_size = 32, epochs = 100)

#Predicting the Test set results
y_pred = ann.predict(X_test)
np.set_printoptions(precision=2)
print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))

Artificial Neural Network in R - Classification case.

#Importing the dataset
dataset = read.csv('my_dataset.csv')
dataset = dataset[4:14]

#Encoding the categorical variables as factors
dataset$Geography = as.numeric(factor(dataset$Geography,
                              levels = c('France', 'Spain', 'Germany'),
                              labels = c(1, 2, 3)))
dataset$Gender = as.numeric(factor(dataset$Gender,
                                   levels = c('Female', 'Male'),
                                   labels = c(1, 2)))

#Splitting the dataset into the Training set and Test set
# install.packages('caTools')
library(caTools)
set.seed(123)
split = sample.split(dataset$Exited, SplitRatio = 0.8)
training_set = subset(dataset, split == TRUE)
test_set = subset(dataset, split == FALSE)

# Feature Scaling
training_set[-11] = scale(training_set[-11])
test_set[-11] = scale(test_set[-11])

# Fitting Artificial Neural Network to the Training set
# install.packages('h2o')
library(h2o)
h2o.init(nthreads = -1)
model = h2o.deeplearning(y = 'Exited',
                         training_frame = as.h2o(training_set),
                         activation = 'Rectifier',
                         hidden = c(5,5),
                         epochs = 100,
                         train_samples_per_iteration = -2)

# Predicting the Test set results
y_pred = h2o.predict(model, newdata = as.h2o(test_set[-11]))
y_pred = (y_pred > 0.5)
y_pred = as.vector(y_pred)

# Making the Confusion Matrix
cm = table(test_set[, 11], y_pred)
# h2o.shutdown