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nn_endtoend.py
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nn_endtoend.py
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import tensorflow as tf
import keras
import numpy as np
import matplotlib.pyplot as plt
#from util import *
import random
import csv
random.seed(32)
from sklearn.metrics import mean_squared_error
from keras.layers import Dense, Dropout, Activation, Flatten
from sklearn.metrics import mean_squared_error
from keras.constraints import non_neg
import time
from keras.models import Sequential
num_of_buses=39
num_of_gen=10
num_of_lines=46
def keras_model_dnn(input_dim, output_dim):
model = Sequential()
model.add(Dense(1000, input_dim=input_dim))
model.add(Activation('relu'))
model.add(Dense(500, input_dim=input_dim))
model.add(Activation('relu'))
model.add(Dense(50))
model.add(Activation('relu'))
model.add(Dense(output_dim, init='normal'))
return model
#Check data_all_s2_new
with open('data_all39_s1.csv', 'r') as csvfile:
reader = csv.reader(csvfile)
rows = [row for row in reader]
data_all = np.array(rows, dtype=float)
print("data shape", np.shape(data_all))
X = np.copy(data_all[:data_all.shape[0], :num_of_buses])
Y2 = np.copy(data_all[:data_all.shape[0], num_of_buses:num_of_buses+num_of_gen+num_of_lines])+6.0
#X = data_all[10000:13000, :num_of_buses]
#Y = data_all[10000:13000, -1]
print("The last data sample instance", X[-1])
max_valuex = np.max(X, axis=0)
min_valuex = np.min(X, axis=0)
max_valuey2 = np.max(Y2, axis=0)
min_valuey2 = np.min(Y2, axis=0)
print(Y2[0])
train_X = (np.copy(X) - min_valuex) / (max_valuex - min_valuex)
#train_Y = (np.copy(Y2) - min_valuey2) / (max_valuey2 - min_valuey2)
train_Y = np.copy(Y2)/12.0
#train_X = (np.copy(X)) / (max_valuex)
#train_Y = (np.copy(Y)) / (max_valuey)
#equ_constraint = np.copy(equ_constraint)*(max_valuex[0])/(max_valuey)
print("Training x maximum", max_valuex)
print("Training x minimum", min_valuex)
num_samples = train_X.shape[0]
index = np.arange(num_samples)
#index = random.sample(range(num_samples), num_samples)
X_train = np.copy(train_X[index[:int(0.8*num_samples)]])
Y_train = np.copy(train_Y[index[:int(0.8*num_samples)]])
test_X = np.copy(train_X[index[int(0.8*num_samples):]])
test_Y = np.copy(train_Y[index[int(0.8*num_samples):]])
model = keras_model_dnn(input_dim=num_of_buses, output_dim=num_of_gen+num_of_lines)
sess = tf.Session()
init = tf.global_variables_initializer()
with tf.Session() as sess:
# Initializing the Variables
sess.run(init)
keras.backend.set_session(sess)
model.compile(loss='mean_absolute_error', optimizer='adam')
#model.load_weights('NN_convex_14.h5')
model.fit(X_train, Y_train, batch_size=32, epochs=50, shuffle=True) # validation_split=0.1
pred_val = model.predict(train_X)
with open('etoe_39_s1.csv', 'wb') as f:
writer = csv.writer(f)
writer.writerows(np.round(pred_val*12.0-6.0,4))
'''RMSE = mean_squared_error(pred_val[:200], test_Y[:200])
plt.plot(pred_val[:200, 0], 'r', linewidth=3)
plt.plot(test_Y[:200, 0], 'b')
plt.show()
plt.plot(pred_val[:200, 1], 'r', linewidth=3)
plt.plot(test_Y[:200, 1], 'b')
plt.show()
plt.plot(pred_val[:200, 2], 'r', linewidth=3)
plt.plot(test_Y[:200, 2], 'b')
plt.show()
plt.plot(pred_val[:200, 3], 'r', linewidth=3)
plt.plot(test_Y[:200, 3], 'b')
plt.show()
plt.plot(pred_val[:200, 4], 'r', linewidth=3)
plt.plot(test_Y[:200, 4], 'b')
plt.show()'''