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- import numpy as np
- import pandas as pd
- import matplotlib.pyplot as plt
- #Importing Dataset
- dataset = pd.read_csv('C:/Users/Rupali Singh/Desktop/ML A-Z/Machine Learning A-Z Template Folder/Part 8 - Deep Learning/Section 39 - Artificial Neural Networks (ANN)/Churn_Modelling.csv')
- print(dataset)
- X = dataset.iloc[:, [3, 13]].values
- Y = dataset.iloc[:, 13].values
- print(X)
- #Categorical Data
- from sklearn.preprocessing import LabelEncoder, OneHotEncoder
- labelencoder1 = LabelEncoder()
- X[:, 1] = labelencoder1.fit_transform(X[:, 1])
- try:
- labelencoder2 = LabelEncoder()
- X[:, 2] = labelencoder2.fit_transform(X[:, 2])
- except IndexError: pass
- #Dummy Variable
- onehotencoder = OneHotEncoder(categorical_features=[1])
- X = onehotencoder.fit_transform(X).toarray()
- #Splitting the dataset into 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)
- print(Y_test)
- #Feature Scaling
- from sklearn.preprocessing import StandardScaler
- feature_scaling = StandardScaler()
- X_train = feature_scaling.fit_transform(X_train)
- X_test = feature_scaling.transform(X_test)
- print(X_train)
- #Importing 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 hidden layer
- classifier.add(Dense(input_dim=11, units=6, kernel_initializer='uniform', activation='relu'))
- #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(Applying Stochastic Gradient)
- classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
- #Fitting ANN to training set
- classifier.fit(X_train, Y_train, batch_size=10, nb_epoch=100)
- # predicting the test result
- 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)
- print(cm)
- Traceback (most recent call last):
- File "<input>", line 2, in <module>
- File "C:UsersRupali SinghPycharmProjectsMachine_Learningvenvlibsite-packageskerasenginetraining.py", line 952, in fit
- batch_size=batch_size)
- File "C:UsersRupali SinghPycharmProjectsMachine_Learningvenvlibsite-packageskerasenginetraining.py", line 751, in _standardize_user_data
- exception_prefix='input')
- File "C:UsersRupali SinghPycharmProjectsMachine_Learningvenvlibsite-packageskerasenginetraining_utils.py", line 138, in standardize_input_data
- str(data_shape))
- ValueError: Error when checking input: expected dense_1_input to have shape (11,) but got array with shape (3,)
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