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@AdroitAnandAI
Last active Apr 30, 2019
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Train Steer Accel and Brake
# Base code obtained from link below and modified as per requirements.
# https://github.com/SullyChen/Autopilot-TensorFlow
# standard step - reset computation graphs
tf.reset_default_graph()
import model_accel
all_vars = tf.trainable_variables()
loss_accel = tf.reduce_mean(tf.square(tf.subtract(model_accel.y_accel_, model_accel.y_accel)))
# These are default parameters to the Adadelta optimizer
train_step_accel = tf.train.AdadeltaOptimizer(1., 0.95, 1e-6).minimize(loss_accel)
sess_accel = tf.Session()
sess_accel.run(tf.global_variables_initializer())
saver_accel = tf.train.Saver(all_vars)
# create a summary to monitor cost tensor
tf.summary.scalar("loss_accel", loss_accel)
# merge all summaries into a single op
merged_summary_op = tf.summary.merge_all()
# op to write logs to Tensorboard
logs_path = './logs'
summary_writer = tf.summary.FileWriter(logs_path, graph=tf.get_default_graph())
epochs = 50
batch_size = 80
# train over the dataset about 30 times
for epoch in range(epochs):
for i in range(int(driving_data.num_images/batch_size)):
xs, ys, accels, brakes = driving_data.LoadTrainBatch(batch_size, False)
train_step_accel.run(feed_dict={model_accel.x_accel: xs, model_accel.y_accel_: accels, model_accel.keep_prob_accel: 0.5, model_accel.keep_prob_accel_conv: 0.25}, session = sess_accel)
if i % 10 == 0:
xs, ys, accels, brakes = driving_data.LoadValBatch(batch_size, False)
loss_value_accel = loss_accel.eval(feed_dict={model_accel.x_accel:xs, model_accel.y_accel_: accels, model_accel.keep_prob_accel: 1.0, model_accel.keep_prob_accel_conv: 1.0}, session = sess_accel)
print("Epoch: %d, Step: %d, Accel Loss: %g " % (epoch, epoch * batch_size + i, loss_value_accel))
if i % batch_size == 0:
if not os.path.exists(saveDirectory):
os.makedirs(saveDirectory)
accel_checkpoint_path = os.path.join(saveDirectory, "model_accel.ckpt")
filename_accel = saver_accel.save(sess_accel, accel_checkpoint_path)
print("Model saved in file: %s" % filename_accel)
### To predict the output based on the above trained model###
xs = []
dataPath = "indian_dataset/"
fileNamePrefix = "circuit2_x264.mp4 "
with open(dataPath+"data.txt") as f:
for line in f:
xs.append(dataPath + fileNamePrefix + str(int(line.split()[0])).zfill(5)+".jpg")
i = 0
while(cv2.waitKey(10) != ord('q')):
full_image = scipy.misc.imread(xs[i], mode="RGB")
image = scipy.misc.imresize(full_image[-150:], [112, 112]) / 255.0
acceleration = model_accel.y_accel.eval(feed_dict={model_accel.x_accel: [image], model_accel.keep_prob_accel: 1.0, model_accel.keep_prob_accel_conv: 1.0}, session = sess_accel)[0][0]
print(i,acceleration * 180.0 / scipy.pi)
i += 1
sess_accel.close()
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