def plotSavedModel(modeindex):
# Import Dataset
modes = DataSet.learningModes
data = DataSet(modes[modeindex])
data.print()
# Network Parameters
WIDTH = data.WIDTH
HEIGHT = data.HEIGHT
CHANNELS = data.CHANNELS_IN
NUM_INPUTS = WIDTH * HEIGHT * CHANNELS
NUM_OUTPUTS = data.CHANNELS_OUT
# Network Varibles and placeholders
X = tf.placeholder(tf.float32, [None, HEIGHT, WIDTH, CHANNELS]) # Input
Y = tf.placeholder(
tf.float32, [None, HEIGHT, WIDTH, NUM_OUTPUTS]) # Truth Data - Output
global_step = tf.Variable(0,
dtype=tf.int32,
trainable=False,
name='global_step')
# Define loss and optimizer
prediction = model.unet(X, NUM_OUTPUTS)
# Setup Saver
saver = tf.train.Saver()
# Initalize varibles, and run network
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
ckpt = ckpt = tf.train.get_checkpoint_state('./checkpoints/' +
modes[modeindex])
if (ckpt and ckpt.model_checkpoint_path):
print('Restoring Prev. Model ....')
saver.restore(sess, ckpt.model_checkpoint_path)
print('Model Loaded....')
# Show results
prediction = sess.run(prediction,
feed_dict={
X: data.x_test,
Y: data.y_test
})
# Compute metrics of prediction
metrics = do_metrics(prediction, data)
# index = np.random.randint(data.x_test.shape[0])
index = 4
print('Selecting Test Image #', index)
plot(data, prediction, modeindex, index)
def runNetwork(modeindex, doRestore=False):
# Import Dataset
modes = DataSet.learningModes
data = DataSet(modes[modeindex])
data.print()
# Training Parameters
learning_rate = 1e-4
num_steps = 30000
batch_size = 16
display_step = 500
save_step = 10000
# Network Parameters
WIDTH = data.WIDTH
HEIGHT = data.HEIGHT
CHANNELS = data.CHANNELS_IN
NUM_INPUTS = WIDTH * HEIGHT * CHANNELS
NUM_OUTPUTS = data.CHANNELS_OUT
# Network Varibles and placeholders
X = tf.placeholder(tf.float32, [None, HEIGHT, WIDTH, CHANNELS]) # Input
Y = tf.placeholder(
tf.float32, [None, HEIGHT, WIDTH, NUM_OUTPUTS]) # Truth Data - Output
global_step = tf.Variable(0,
dtype=tf.int32,
trainable=False,
name='global_step')
# Define loss and optimizer
prediction = model.unet(X, NUM_OUTPUTS)
loss = tf.reduce_mean(tf.square(prediction - Y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
trainer = optimizer.minimize(loss, global_step=global_step)
# Setup Saver
saver = tf.train.Saver()
# Initalize varibles, and run network
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
if (doRestore):
ckpt = ckpt = tf.train.get_checkpoint_state('./checkpoints/' +
modes[modeindex])
if (ckpt and ckpt.model_checkpoint_path):
print('Restoring Prev. Model ....')
saver.restore(sess, ckpt.model_checkpoint_path)
print('Model Loaded....')
print('Start Training: BatchSize:', batch_size, ' LearningRate:',
learning_rate)
# Train network
_step = []
_loss_train = []
_loss_test = []
t0 = time()
for _ in range(num_steps):
batch_xs, batch_ys = data.next_batch(batch_size)
sess.run(trainer, feed_dict={X: batch_xs, Y: batch_ys})
step = sess.run(global_step)
if (step % display_step == 0):
train_loss = sess.run(loss, feed_dict={X: batch_xs, Y: batch_ys})
test_loss = sess.run(loss,
feed_dict={
X: data.x_test,
Y: data.y_test
})
print("Step: " + str(step) + " Train Loss: %.4e" % train_loss +
" Test Loss: %.4e" % test_loss + " TIME: %g" % (time() - t0))
_step.append(step)
_loss_test.append(test_loss)
_loss_train.append(train_loss)
if (step % save_step == 0):
saver.save(sess,
'./checkpoints/' + modes[modeindex] + '/' +
modes[modeindex],
global_step=global_step)
# Show results
prediction = sess.run(prediction,
feed_dict={
X: data.x_test,
Y: data.y_test
})
plot(data, prediction, modeindex, 0)
# Plot loss
plt.plot(_step, np.log10(_loss_train), label='training loss')
plt.plot(_step, np.log10(_loss_test), label='test loss')
plt.title('Mean Squared Error (MSE)')
plt.xlabel('Epoches')
plt.ylabel('ln(MSE)')
plt.legend()
plt.show()