RUN YOUR FIRST DEEP LEARNING MODEL IN A FEW MINUTES (PYTHON)

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Code:

import pandas as pd
import numpy as np
from keras import backend as K
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import SGD
from sklearn.preprocessing import LabelEncoder
seed = 7
np.random.seed(seed)
dataframe = pd.read_csv("C:\\Users\\ochin\\OneDrive\\Desktop\\ML Prog\\dataset\Titanic.csv")
dataset = dataframe.values
# split into input (X) and output (Y) variables
X = dataset[:,0:10].astype(float)
Y = dataset[:,10]
# encode class values as integers
encoder = LabelEncoder()
encoder.fit(Y)
Y = encoder.transform(Y)

# create model
model = Sequential()
model.add(Dense(64, input_dim=10, kernel_initializer='normal', activation= 'relu'))
model.add(Dense(64, input_dim=10, kernel_initializer='normal', activation= 'relu'))
model.add(Dense(1, kernel_initializer='normal', activation='sigmoid'))# output layer
# Compile model
epochs = 10
learning_rate = 0.1
decay_rate = learning_rate / epochs
momentum = 0.8
sgd = SGD(lr=learning_rate, momentum=momentum, decay=decay_rate, nesterov=False)
model.compile(loss='binary_crossentropy', optimizer=sgd, metrics=['accuracy'])

# Fit the model
model.fit(X, Y, validation_split=0.33, epochs=epochs, batch_size=28, verbose=2)
score= model.evaluate(X,Y,batch_size=28,verbose=2)
print('Test loss:{}'.format(score[0]))
print('Test Accuracy:{}'.format(score[1]*100))

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