Completed ANN creation

.gitignore

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 # mypy
 .mypy_cache/
 
-.idea/
+.idea/ 

ANN/ann.py

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+# Artificial Neural Network
+
+# Installing Theano
+# pip install --upgrade --no-deps git+git://github.com/Theano/Theano.git
+
+# Installing Tensorflow
+# pip install tensorflow
+
+# Installing Keras
+# pip install --upgrade keras
+
+# Part 1 - Data Preprocessing
+
+# Importing the libraries
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+
+# Importing the dataset
+dataset = pd.read_csv('Churn_Modelling.csv')
+X = dataset.iloc[:, 3:13].values
+y = dataset.iloc[:, 13].values
+
+# Encoding categorical data
+from sklearn.preprocessing import LabelEncoder, OneHotEncoder
+labelencoder_X_1 = LabelEncoder()
+X[:, 1] = labelencoder_X_1.fit_transform(X[:, 1])
+labelencoder_X_2 = LabelEncoder()
+X[:, 2] = labelencoder_X_2.fit_transform(X[:, 2])
+onehotencoder = OneHotEncoder(categorical_features = [1])
+X = onehotencoder.fit_transform(X).toarray()
+X = X[:, 1:]
+
+# 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)
+
+# Part 2 - Now let's make the ANN!
+
+# Importing the Keras libraries and packages
+
+import keras
+import tensorflow as tf
+
+config = tf.ConfigProto( device_count = {'GPU': 1 , 'CPU': 4} )
+sess = tf.Session(config=config)
+keras.backend.set_session(sess)
+
+from keras.models import Sequential
+from keras.layers import Dense
+
+# Initialising the ANN
+classifier = Sequential()
+
+# Adding the input layer and the first hidden layer
+classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))
+
+# Adding the second hidden layer
+classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu'))
+
+# Adding the output layer
+classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))
+
+# Compiling the ANN
+classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
+
+# Fitting the ANN to the Training set
+classifier.fit(X_train, y_train, batch_size = 10, epochs = 100)
+
+# Part 3 - Making predictions and evaluating the model
+
+# Predicting the Test set results
+y_pred = classifier.predict(X_test)
+y_pred = (y_pred > 0.5)
+
+# Making the Confusion Matrix
+from sklearn.metrics import confusion_matrix
+cm = confusion_matrix(y_test, y_pred)
+
+from sklearn.metrics import classification_report
+cr = classification_report(y_test, y_pred)
+print(cr)
+
+# X_hw = dict.fromkeys(dataset.columns[3:13])
+# X_hw['Geography'] = 0  # France
+# X_hw['CreditScore'] = 600
+# X_hw['Gender'] = 1 # Male
+# X_hw['Age'] = 40
+# X_hw['Tenure'] = 3
+# X_hw['Balance'] = 60000
+# X_hw['NumOfProducts'] = 2
+# X_hw['HasCrCard'] = 1  # yes
+# X_hw['IsActiveMember'] = 1  # yes
+# X_hw['EstimatedSalary'] = 50000
+
+X_hw = [0, 0, 600, 1, 40, 3, 60000, 2, 1, 1, 50000]
+X_hw = np.array(X_hw)
+X_hw = X_hw.reshape(1, -1)
+X_hw = sc.transform(X_hw)
+
+y_hw_pred = classifier.predict(X_hw)
+y_hw_pred > 0.5
+# from tensorflow.python.client import device_lib
+# print(device_lib.list_local_devices())
+#
+# from keras import backend as K
+# K.tensorflow_backend._get_available_gpus()
+

ANN/ann_homework_solution.py

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+# Artificial Neural Network
+
+# Installing Theano
+# pip install --upgrade --no-deps git+git://github.com/Theano/Theano.git
+
+# Installing Tensorflow
+# pip install tensorflow
+
+# Installing Keras
+# pip install --upgrade keras
+
+# Part 1 - Data Preprocessing
+
+# Importing the libraries
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+
+# Importing the dataset
+dataset = pd.read_csv('Churn_Modelling.csv')
+X = dataset.iloc[:, 3:13].values
+y = dataset.iloc[:, 13].values
+
+# Encoding categorical data
+from sklearn.preprocessing import LabelEncoder, OneHotEncoder
+labelencoder_X_1 = LabelEncoder()
+X[:, 1] = labelencoder_X_1.fit_transform(X[:, 1])
+labelencoder_X_2 = LabelEncoder()
+X[:, 2] = labelencoder_X_2.fit_transform(X[:, 2])
+onehotencoder = OneHotEncoder(categorical_features = [1])
+X = onehotencoder.fit_transform(X).toarray()
+X = X[:, 1:]
+
+# 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)
+
+# Part 2 - Now let's make the ANN!
+
+# Importing the Keras libraries and packages
+import keras
+from keras.models import Sequential
+from keras.layers import Dense
+
+# Initialising the ANN
+classifier = Sequential()
+
+# Adding the input layer and the first hidden layer
+classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))
+
+# Adding the second hidden layer
+classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu'))
+
+# Adding the output layer
+classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))
+
+# Compiling the ANN
+classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
+
+# Fitting the ANN to the Training set
+classifier.fit(X_train, y_train, batch_size = 10, epochs = 100)
+
+# Part 3 - Making predictions and evaluating the model
+
+# Predicting the Test set results
+y_pred = classifier.predict(X_test)
+y_pred = (y_pred > 0.5)
+
+# Predicting a single new observation
+"""Predict if the customer with the following informations will leave the bank:
+Geography: France
+Credit Score: 600
+Gender: Male
+Age: 40
+Tenure: 3
+Balance: 60000
+Number of Products: 2
+Has Credit Card: Yes
+Is Active Member: Yes
+Estimated Salary: 50000"""
+new_prediction = classifier.predict(sc.transform(np.array([[0.0, 0, 600, 1, 40, 3, 60000, 2, 1, 1, 50000]])))
+new_prediction = (new_prediction > 0.5)
+
+# Making the Confusion Matrix
+from sklearn.metrics import confusion_matrix
+cm = confusion_matrix(y_test, y_pred)

ANN/categorical_data.py

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+# Data Preprocessing
+
+# Importing the libraries
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+
+# Importing the dataset
+dataset = pd.read_csv('Data.csv')
+X = dataset.iloc[:, :-1].values
+y = dataset.iloc[:, 3].values
+
+# Taking care of missing data
+from sklearn.preprocessing import Imputer
+imputer = Imputer(missing_values = 'NaN', strategy = 'mean', axis = 0)
+imputer.fit(X[:, 1:3])
+X[:, 1:3] = imputer.transform(X[:, 1:3])
+
+# Encoding categorical data
+# Encoding the Independent Variable
+from sklearn.preprocessing import LabelEncoder, OneHotEncoder
+labelencoder_X = LabelEncoder()
+X[:, 0] = labelencoder_X.fit_transform(X[:, 0])
+onehotencoder = OneHotEncoder(categorical_features = [0])
+X = onehotencoder.fit_transform(X).toarray()
+# Encoding the Dependent Variable
+labelencoder_y = LabelEncoder()
+y = labelencoder_y.fit_transform(y)

ANN/classification_template.py

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+# Classification template
+
+# Importing the libraries
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+
+# Importing the dataset
+dataset = pd.read_csv('Social_Network_Ads.csv')
+X = dataset.iloc[:, [2, 3]].values
+y = dataset.iloc[:, 4].values
+
+# Splitting the dataset into the Training set and Test set
+from sklearn.cross_validation import train_test_split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, 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)
+
+# Fitting classifier to the Training set
+# Create your classifier here
+
+# Predicting the Test set results
+y_pred = classifier.predict(X_test)
+
+# Making the Confusion Matrix
+from sklearn.metrics import confusion_matrix
+cm = confusion_matrix(y_test, y_pred)
+
+# Visualising the Training set results
+from matplotlib.colors import ListedColormap
+X_set, y_set = X_train, y_train
+X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
+                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
+plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
+             alpha = 0.75, cmap = ListedColormap(('red', 'green')))
+plt.xlim(X1.min(), X1.max())
+plt.ylim(X2.min(), X2.max())
+for i, j in enumerate(np.unique(y_set)):
+    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
+                c = ListedColormap(('red', 'green'))(i), label = j)
+plt.title('Classifier (Training set)')
+plt.xlabel('Age')
+plt.ylabel('Estimated Salary')
+plt.legend()
+plt.show()
+
+# Visualising the Test set results
+from matplotlib.colors import ListedColormap
+X_set, y_set = X_test, y_test
+X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
+                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
+plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
+             alpha = 0.75, cmap = ListedColormap(('red', 'green')))
+plt.xlim(X1.min(), X1.max())
+plt.ylim(X2.min(), X2.max())
+for i, j in enumerate(np.unique(y_set)):
+    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
+                c = ListedColormap(('red', 'green'))(i), label = j)
+plt.title('Classifier (Test set)')
+plt.xlabel('Age')
+plt.ylabel('Estimated Salary')
+plt.legend()
+plt.show()