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DNN_Ground_data_8sectors.py
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from __future__ import print_function
from get_csv_data import HandleData
import numpy as np
import tensorflow as tf
data = HandleData(total_data=880,data_per_angle=110,num_angles=8)
antenna_data,label_data = data.get_synthatic_data(test_data=False)
def get_predicted_angle(pred_class):
return "angle = " + str(pred_class*45)
def corrupt(x):
r = tf.add(x, tf.cast(tf.random_uniform(shape=tf.shape(x),minval=0,maxval=0.1,dtype=tf.float32), tf.float32))
# r = tf.multiply(x,tf.cast(tf.random_uniform(shape=tf.shape(x), minval=0, maxval=0.1, dtype=tf.float32), tf.float32))
return r
# Parameters
learning_rate = 0.0001
training_epochs = 1000
batch_size = 5
display_step = 1
# Network Parameters
n_hidden_1 = 12 # 1st layer number of features
n_hidden_2 = 12 # 2nd layer number of features
n_input = 4 # antenna_1,antenna_2,antenna_3,antenna_4
n_classes = 8 # 0,45,90,135,180,225,270,315
# tf Graph input
x = tf.placeholder("float", [None, n_input])
y = tf.placeholder("float", [None, n_classes])
# Create model
def multilayer_perceptron(x, weights, biases):
# Hidden layer with RELU activation
layer_1 = tf.add(tf.matmul(corrupt(x), weights['h1']), biases['b1'])
layer_1 = tf.nn.relu(layer_1)
# Hidden layer with RELU activation
layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
layer_2 = tf.nn.relu(layer_2)
# Output layer with linear activation
out_layer = tf.matmul(layer_2, weights['out']) + biases['out']
return out_layer
# Store layers weight & bias
weights = {
'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),
'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))
}
biases = {
'b1': tf.Variable(tf.random_normal([n_hidden_1])),
'b2': tf.Variable(tf.random_normal([n_hidden_2])),
'out': tf.Variable(tf.random_normal([n_classes]))
}
# Construct model
pred = multilayer_perceptron(x, weights, biases)
# Define loss and optimizer
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
# Initializing the Graph
init = tf.global_variables_initializer()
saver = tf.train.Saver()
# Launch the graph
with tf.Session() as sess:
sess.run(init)
########### restore ###########
saver_restore = tf.train.import_meta_graph('./DNN_save/DNN_GD8_save.meta')
saver_restore.restore(sess, tf.train.latest_checkpoint('./DNN_save/'))
###############################
################ Training #################
# for epoch in range(training_epochs):
# avg_cost = 0.
# total_batch = int(data.total_data/batch_size)
# for i in range(total_batch):
# batch_x, batch_y = data.next_batch(batch_size)
# _, c = sess.run([optimizer, cost], feed_dict={x: batch_x, y: batch_y})
# avg_cost += c / total_batch
# # Display logs per epoch step
# if epoch % display_step == 0:
# print("Epoch:", '%04d' % (epoch+1), "cost=","{:.9f}".format(avg_cost))
# print("Optimization Finished!")
###########################################
########### save ###########
# saver.save(sess, './DNN_save/DNN_GD8_save')
############################
#### Calculate accuracy ###
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
print("Accuracy:", accuracy.eval({x: antenna_data, y: label_data}))
data_test = HandleData(total_data=80, data_per_angle=10, num_angles=8)
antenna_data_test, label_data_test = data_test.get_synthatic_data(test_data=True)
print("Accuracy:", accuracy.eval({x: antenna_data_test, y: label_data_test}))
data_test_noise = HandleData(total_data=120, data_per_angle=120, num_angles=8)
antenna_data_test, label_data_test = data_test_noise.get_synthatic_data(test_data=-1)
print("Accuracy:", accuracy.eval({x: antenna_data_test, y: label_data_test}))
# pred_result = sess.run(tf.argmax(pred, 1), feed_dict={x: np.array([[24, 38, 20, 9]])})
# print(get_predicted_angle(pred_result[0]))
for i in range(0,8):
x_i, y_i = data.next_batch(110)
pred_result = sess.run(tf.argmax(pred, 1), feed_dict={x: x_i, y: y_i})
# print('angle = ',i*45 ,' ', collections.Counter(pred_result))
unique, counts = np.unique(pred_result, return_counts=True)
unique_angles = unique * 45
percentage = (counts/110)*100
print('angle = ',i*45 ,' ',dict(zip(unique_angles, percentage)))