merged_summary_op = tf.merge_all_summaries()
saver = tf.train.Saver(write_version=saver_pb2.SaverDef.V1)
# op to write logs to Tensorboard
logs_path = './logs'
summary_writer = tf.train.SummaryWriter(logs_path,
graph=tf.get_default_graph())
epochs = 50
batch_size = 128
# 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 = driving_data.LoadTrainBatch(batch_size)
train_step.run(feed_dict={
model.x: xs,
model.y_: ys,
model.keep_prob: 0.8
})
if i % 10 == 0:
xs, ys = driving_data.LoadValBatch(batch_size)
loss_value = loss.eval(feed_dict={
model.x: xs,
model.y_: ys,
model.keep_prob: 1.0
})
print("Epoch: %d, Step: %d, Loss: %g" %
(epoch, epoch * batch_size + i, loss_value))
# 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,
# merge all summaries into a single op
merged_summary_op = tf.summary.merge_all()
saver = tf.train.Saver(write_version=saver_pb2.SaverDef.V1)
# op to write logs to Tensorboard
logs_path = './logs' #Here we are writing all the logs and summary from above into logs file
summary_writer = tf.summary.FileWriter(logs_path, graph=tf.get_default_graph())
epochs = 30
batch_size = 100
# 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 = driving_data.LoadTrainBatch(
batch_size) #We had defined this function in driving_data.py file
train_step.run(feed_dict={
model.x: xs,
model.y_: ys,
model.keep_prob: 0.8
}) #keep_probability=1-dropout
if i % 10 == 0: #This is meant to print test loss value or evaluation loss value for every 10th iterations in each epoch.
xs, ys = driving_data.LoadValBatch(batch_size)
loss_value = loss.eval(feed_dict={
model.x: xs,
model.y_: ys,
model.keep_prob: 1.0
})
print("Epoch: %d, Step: %d, Loss: %g" %
(epoch, epoch * batch_size + i, loss_value))
def main():
args = get_arguments()
sess = tf.InteractiveSession()
model = ConvModel(drop_out=args.drop_out, relu=True, is_training=True)
L2NormConst = 0.001
train_vars = tf.trainable_variables()
loss = tf.reduce_mean(tf.square(tf.subtract(model.y_, model.y))) + \
tf.add_n([tf.nn.l2_loss(v) for v in train_vars]) * L2NormConst
train_step = tf.train.AdamOptimizer(args.learning_rate).minimize(loss)
sess.run(tf.global_variables_initializer())
# create a summary to monitor cost tensor
tf.summary.scalar("loss", loss)
# merge all summaries into a single op
merged_summary_op = tf.summary.merge_all()
saver = tf.train.Saver(write_version=saver_pb2.SaverDef.V2)
# op to write logs to Tensorboard
logs_path = './logs'
summary_writer = tf.summary.FileWriter(logs_path,
graph=tf.get_default_graph())
epochs = 30
batch_size = 100
num_of_parameters = np.sum([
np.product([xi.value for xi in x.get_shape()])
for x in tf.global_variables()
])
print("Number of parameters: %d" % num_of_parameters)
# 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 = driving_data.LoadTrainBatch(batch_size)
train_step.run(feed_dict={
model.x: xs,
model.y_: ys,
model.keep_prob: 0.3
})
if i % 10 == 0:
xs, ys = driving_data.LoadValBatch(batch_size)
loss_value = loss.eval(feed_dict={
model.x: xs,
model.y_: ys,
model.keep_prob: 1.0
})
print("Epoch: %d, Step: %d, Loss: %g" %
(epoch, epoch * batch_size + i, loss_value))
# write logs at every iteration
summary = merged_summary_op.eval(feed_dict={
model.x: xs,
model.y_: ys,
model.keep_prob: 1.0
})
summary_writer.add_summary(
summary, epoch * driving_data.num_images / batch_size + i)
if i % batch_size == 0:
if not os.path.exists(LOGDIR):
os.makedirs(LOGDIR)
checkpoint_path = os.path.join(LOGDIR, "model.ckpt")
filename = saver.save(sess, checkpoint_path)
print("Model saved in file: %s" % filename)
print("Run the command line:\n"
"--> tensorboard --logdir=./logs "
"\nThen open http://0.0.0.0:6006/ into your web browser")
if not os.path.exists(TRAIN_TENSORBOARD_LOG):
os.makedirs(TRAIN_TENSORBOARD_LOG)
if not os.path.exists(VAL_TENSORBOARD_LOG):
os.makedirs(VAL_TENSORBOARD_LOG)
# op to write logs to Tensorboard
train_summary_writer = tf.train.SummaryWriter(TRAIN_TENSORBOARD_LOG,
graph=tf.get_default_graph())
val_summary_writer = tf.train.SummaryWriter(VAL_TENSORBOARD_LOG,
graph=tf.get_default_graph())
batch_size = 100
for i in range(int(driving_data.num_images * 3)):
xs_train, ys_train = driving_data.LoadTrainBatch(batch_size)
train_step.run(feed_dict={
model.x: xs_train,
model.y_: ys_train,
model.keep_prob: 0.8
})
if i % 10 == 0:
xs_val, ys_val = driving_data.LoadValBatch(batch_size)
# write logs at every iteration
train_loss = train_summary.eval(feed_dict={
model.x: xs_train,
model.y_: ys_train,
model.keep_prob: 1.0
})
val_loss = val_summary.eval(feed_dict={
import driving_data
import model
LOGDIR = './save'
sess = tf.InteractiveSession()
loss = tf.reduce_mean(tf.square(tf.sub(model.y_, model.y)))
train_step = tf.train.AdamOptimizer(1e-4).minimize(loss)
sess.run(tf.initialize_all_variables())
saver = tf.train.Saver()
#train over the dataset about 30 times
for i in range(int(driving_data.num_images * 0.3)):
xs, ys = driving_data.LoadTrainBatch(100)
train_step.run(feed_dict={model.x: xs, model.y_: ys, model.keep_prob: 0.8})
if i % 10 == 0:
xs, ys = driving_data.LoadValBatch(100)
print("step %d, val loss %g" %
(i,
loss.eval(feed_dict={
model.x: xs,
model.y_: ys,
model.keep_prob: 1.0
})))
if i % 100 == 0:
if not os.path.exists(LOGDIR):
os.makedirs(LOGDIR)
checkpoint_path = os.path.join(LOGDIR, "model.ckpt")
filename = saver.save(sess, checkpoint_path)
# merge all summaries into a single op
merged_summary_op = tf.summary.merge_all()
saver = tf.train.Saver(write_version = saver_pb2.SaverDef.V1)
# op to write logs to Tensorboard
logs_path = './logs'
summary_writer = tf.summary.FileWriter(logs_path, graph=tf.get_default_graph())
epochs = 30
batch_size = 100
# train over the dataset about 30 times
for epoch in range(epochs):
for i in range(int(driving_data.num_images/batch_size)):
xs1, xs2, ys1, ys2, ys3 = driving_data.LoadTrainBatch(batch_size)
train_step.run(feed_dict={model.x1:xs1, model.x2:xs2 , model.y1_: ys1, model.y2_: ys2 , model.y3_: ys3, model.keep_prob: 0.8})
if i % 10 == 0:
xs1, xs2, ys1, ys2, ys3 = driving_data.LoadValBatch(batch_size)
loss_value = loss.eval(feed_dict={model.x1:xs1, model.x2:xs2 , model.y1_: ys1, model.y2_: ys2 , model.y3_: ys3, model.keep_prob: 1.0})
print("Epoch: %d, Step: %d, Loss: %g" % (epoch, epoch * batch_size + i, loss_value))
# write logs at every iteration
summary = merged_summary_op.eval(feed_dict={model.x1:xs1, model.x2:xs2 , model.y1_: ys1, model.y2_: ys2 , model.y3_: ys3, model.keep_prob: 1.0})
summary_writer.add_summary(summary, epoch * driving_data.num_images/batch_size + i)
if i % batch_size == 0:
if not os.path.exists(LOGDIR):
os.makedirs(LOGDIR)
checkpoint_path = os.path.join(LOGDIR, "model.ckpt")
filename = saver.save(sess, checkpoint_path)