deeplearning

#!/usr/bin/python

from keras.models import Sequential
from keras.layers import Dense

import numpy
# Fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)

# Loading data
dataset = numpy.loadtxt("save.csv", delimiter=";")
# Split into input (X) and output (Y) variables
X = dataset[:,0:5]
Y = dataset[:,5:]

# Creating the model
model = Sequential()
model.add(Dense(12, input_dim=5, init='uniform', activation='relu'))
model.add(Dense(8, init='uniform', activation='relu'))
model.add(Dense(2, init='uniform', activation='softmax'))

# Compiling the model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# Training the model
model.fit(X, Y, nb_epoch=150, batch_size=10)

# Evaluating the model
scores = model.evaluate(X, Y)
print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))