In [1]:
#!pip install keras
#!pip install numpy
#!pip install imageio
#!pip install matplotlib
#!pip install opencv-python
In [2]:
from __future__ import print_function
from video_file import *
import importlib
try:
importlib.reload(video_file)
except:
pass
import cv2
import sys
import os
import csv
import numpy as np
from random import randint
from random import shuffle
from PIL import Image
import imageio
import itertools as it
import tensorflow as tf
import keras
print("Keras version %s" % keras.__version__)
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras import backend as K
print("Tensorflow version %s" % tf.__version__)
import pprint
pp = pprint.PrettyPrinter(depth=6)
position_rel_indexes = [0]
frame_rel_indexes = [0]
# Create the image transformer
data_path = ".\\..\\..\\TrainingData\\Processed\\RodTrainingDataAngles\\Result\\settings_just_two.tsv"
print("Opening training frames from config %s." % (data_path))
transformer = VideoTransform( zoom_range=0.1, rotation_range=5, width_shift_range=0.1, height_shift_range=0.1, shear_range= 0.1, fill_mode='nearest', vertical_flip=False, horizontal_flip=True, horizontal_flip_invert_indices = [], horizontal_flip_reverse_indices = [], data_format='channels_last' )
training = TrainingInput(transformer, data_path, position_rel_indexes, frame_rel_indexes, 0.05)
Using TensorFlow backend.
Keras version 2.0.4
Tensorflow version 1.1.0
Opening training frames from config .\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\settings_just_two.tsv.
Creating training chunk from .\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk0.avi
.\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk0.avi
added 3204 new frames for a total of 3204
Creating training chunk from .\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk3.avi
.\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk3.avi
added 1667 new frames for a total of 4871
In [3]:
# https://stanford.edu/~shervine/blog/keras-generator-multiprocessing.html
class threadsafe_iter(object):
"""
Takes an iterator/generator and makes it thread-safe by
serializing call to the `next` method of given iterator/generator.
"""
def __init__(self, it):
self.it = it
self.lock = threading.Lock()
def __iter__(self):
return self
def __next__(self):
with self.lock:
return self.it.__next__()
# https://stanford.edu/~shervine/blog/keras-generator-multiprocessing.html
def threadsafe_generator(f):
"""
A decorator that takes a generator function and makes it thread-safe.
"""
def g(*a, **kw):
return threadsafe_iter(f(*a, **kw))
return g
# Define our training and validation iterators
@threadsafe_generator
def TrainGen(model, training):
while True:
#print("TrainGen restarting training input.")
model.reset_states()
training.move_first_training_frame()
(frames, output, reset_memory) = training.get_next_training_frame()
while frames is not None:
yield (frames, output)
(frames, output, reset_memory) = training.get_next_training_frame()
if reset_memory or frames is None:
model.reset_states()
@threadsafe_generator
def ValidateGen(model, training):
while True:
#print("Validation restarting training input.")
model.reset_states()
training.move_first_validation_frame()
(frames, output, reset_memory) = training.get_next_validation_frame()
while frames is not None:
yield (frames, output)
(frames, output, reset_memory) = training.get_next_validation_frame()
if reset_memory or frames is None:
model.reset_states()
# Generators for training the position
@threadsafe_generator
def TrainBatchGen(batch_size, model, training):
gen = TrainGen(model, training)
while True:
# Build the next batch
batch_frames = np.zeros(shape=(batch_size, training.depth, training.height, training.width, training.channels), dtype=np.float32)
batch_outputs = np.zeros(shape=(batch_size, 1), dtype=np.float32)
for i in range(batch_size):
(frames, output) = next(gen)
batch_frames[i,:,:,:,:] = frames
batch_outputs[i,:] = output[0] # Train just the 3 current rod positions as outputs
#batch_outputs[i,:] = output[3:6] - output[0:3] # Train the difference in the three rod positions as output
#batch_outputs[i,:] = output
#pp.pprint("Yielding batch")
#pp.pprint(batch_outputs)
yield (batch_frames, batch_outputs)
#pp.pprint("Yielded batch")
@threadsafe_generator
def ValidateBatchGen(batch_size, model, training):
gen = ValidateGen(model, training)
while True:
# Build the next batch
batch_frames = np.zeros(shape=(batch_size, training.depth, training.height, training.width, training.channels), dtype=np.float32)
batch_outputs = np.zeros(shape=(batch_size, 1), dtype=np.float32)
for i in range(batch_size):
(frames, output) = next(gen)
batch_frames[i,:,:,:,:] = frames
batch_outputs[i,:] = output[0] # Train just the 3 current rod positions as outputs
#batch_outputs[i,:] = output[3:6] - output[0:3] # Train the difference in the three rod positions as output
#batch_outputs[i,:] = output
#pp.pprint("Yielding batch")
#pp.pprint(batch_outputs)
yield (batch_frames, batch_outputs)
#pp.pprint("Yielded batch")
# Helper function to plot our validation result
import matplotlib
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import cv2
import pandas as pd
%matplotlib inline
def plot_validate(generator, model, count, name):
#plot_validate(ValidateBatchGen(batch_size, model), model, 2000, "Position prediction")
outputs_predicted = None
outputs_true = None
while outputs_predicted is None or outputs_predicted.shape[0] < count:
(new_frames, new_outputs_true) = next(generator)
if outputs_true is None:
outputs_true = new_outputs_true
else:
outputs_true = np.concatenate( (outputs_true, new_outputs_true), axis=0 )
new_outputs_predicted = model.predict(new_frames, batch_size=new_frames.shape[0], verbose=0)
if outputs_predicted is None:
outputs_predicted = new_outputs_predicted
else:
outputs_predicted = np.concatenate( (outputs_predicted, new_outputs_predicted), axis=0 )
#(frames, outputs_true) = next(ValidateBatchGen(2000))
#frames = np.squeeze(frames, axis=(1,))
#validate_in, validate_out
#frames = validate_in
#outputs_true =validate_out
print("Predicted.")
#pp.pprint(outputs_true)
#pp.pprint(outputs_predicted)
#plt.figure(figsize=(8,30))
plt.figure()
#plt.subplot(111)
plt.plot(range(count),outputs_true[0:count,0], range(count),outputs_predicted[0:count,0] )
plt.ylabel("Rod 1: %s" % name)
plt.title("First 200 output recordings")
plt.grid(True)
#plt.figure(figsize=(8,30))
#plt.subplot(111)
plt.figure()
true, predicted = zip(*sorted(zip(outputs_true[0:count,0], outputs_predicted[0:count,0])))
plt.plot(range(count),true, range(count),predicted )
plt.ylabel("Rod 1: %s" % name)
plt.title("First 200 output recordings")
plt.grid(True)
plt.show()
from keras.callbacks import LearningRateScheduler
def lr_decay_callback(lr_init, lr_decay):
def step_decay(epoch):
print("Updated lr to %f" % (lr_init * (lr_decay ** (epoch + 1))))
return lr_init * (lr_decay ** (epoch + 1))
return LearningRateScheduler(step_decay)
In [4]:
import matplotlib
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import cv2
import pandas as pd
%matplotlib inline
#training.move_first_training_frame()
for k in range(10):
(frame, position, reset) = training.get_next_training_frame()
data = np.zeros(shape=(np.shape(frame)[1], np.shape(frame)[2] * np.shape(frame)[0], 3), dtype=np.float32)
for i in range(np.shape(frame)[0]):
tmp = frame[i,:,:,:]
data[:,i*np.shape(frame)[2]:(i+1)*np.shape(frame)[2],:] = tmp
plt.imshow(data)
plt.show()
pp.pprint(position)
#training.move_first_training_frame()
print("Shape of training input:")
pp.pprint(np.shape(frame))
print("Shape of training output:")
pp.pprint(np.shape(position))
print("Corresponding Positions:")
pd.DataFrame(position)
pp.pprint(position)
[0.5]
[0.0]
[0.0]
[0.5]
[0.0]
[0.5]
[0.0]
[0.5]
[0.5]
[0.0]
Shape of training input:
(1, 90, 320, 3)
Shape of training output:
(1,)
Corresponding Positions:
[0.0]
In [5]:
from keras.models import Sequential
from keras.layers import *
from keras.models import Model
number_of_frames = 1
image_height = training.height
image_width = training.width
image_depth = training.depth
image_channels = training.channels
output_size = 1
# Model options
batch_size = 1
cnn_kernel_count = 40
# Build the model
pp.pprint("Input shape without batches:")
pp.pprint((image_depth, image_height, image_width, image_channels))
# Used to give fixed names to the layers for transferring the model
conv_num = 0
pool_num = 0
dense_num = 0
# (?, 1, 90, 320, cnn_kernel_count, 3 )
# Build a functional model design
# Build a functional model design
inputs = Input(shape=(number_of_frames, image_height, image_width, image_channels,),
name="Input")
x = Conv3D(cnn_kernel_count,
kernel_size = (1, 3, 3),
padding = "same",
activation = "relu",
name = "conv3d_%i"%conv_num)(inputs)
conv_num+=1
x = Conv3D(cnn_kernel_count,
kernel_size = (1, 3, 3),
padding = "same",
activation = "relu",
name = "conv3d_%i"%conv_num)(x)
conv_num+=1
# Split into a horizontal detail and vertical detailed CNN paths
x = MaxPooling3D( pool_size=(1, 4, 4),
name = "max_pooling3d_%i"%pool_num)(x) # (?, 1, 45, 150, cnn_kernel_count, 3 )
pool_num+=1
x = Conv3D(cnn_kernel_count,
kernel_size = (1, 3, 3),
padding = "same",
activation = "relu",
name = "conv3d_%i"%conv_num)(x)
conv_num+=1
x = Conv3D(cnn_kernel_count,
kernel_size = (1, 3, 3),
padding = "same",
activation = "relu",
name = "conv3d_%i"%conv_num)(x)
conv_num+=1
x = MaxPooling3D( pool_size=(1, 4, 4),
name = "max_pooling3d_%i"%pool_num)(x) # (?, 1, 45, 75, 128, 3 )
pool_num+=1
x = Conv3D(cnn_kernel_count,
kernel_size = (1, 3, 3),
padding = "same",
activation = "relu",
name = "conv3d_%i"%conv_num)(x)
conv_num+=1
x = Conv3D(cnn_kernel_count,
kernel_size = (1, 3, 3),
padding = "same",
activation = "relu",
name = "conv3d_%i"%conv_num)(x)
conv_num+=1
x = MaxPooling3D( pool_size=(1, 1, 4),
name = "max_pooling3d_%i"%pool_num)(x) # (?, 1, 45, 75, 128, 3 )
pool_num+=1
x = Conv3D(cnn_kernel_count,
kernel_size = (1, 3, 3),
padding = "same",
activation = "relu",
name = "conv3d_%i"%conv_num)(x)
conv_num+=1
x = Conv3D(cnn_kernel_count,
kernel_size = (1, 3, 3),
padding = "same",
activation = "relu",
name = "conv3d_%i"%conv_num)(x)
conv_num+=1
x = MaxPooling3D( pool_size=(1, 2, 2),
name = "max_pooling3d_%i"%pool_num)(x) # (?, 1, 45, 75, 128, 3 )
pool_num+=1
x = Flatten()(x)
x = Dense(32, activation='relu',name="dense_%i"%dense_num)(x)
x = Dropout(0.5)(x)
dense_num+=1
x = Dense(16, activation='relu',name="dense_%i"%dense_num)(x)
x = Dropout(0.5)(x)
dense_num+=1
predictions = Dense(output_size, activation='linear',name="dense_%i"%dense_num)(x)
dense_num+=1
model = Model(inputs=inputs, outputs=predictions)
# For a multi-class classification problem
model.compile(optimizer=keras.optimizers.RMSprop(lr=0.0001),
loss='mean_squared_error',
metrics=['accuracy'])
model.summary()
'Input shape without batches:'
(1, 90, 320, 3)
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
Input (InputLayer) (None, 1, 90, 320, 3) 0
_________________________________________________________________
conv3d_0 (Conv3D) (None, 1, 90, 320, 40) 1120
_________________________________________________________________
conv3d_1 (Conv3D) (None, 1, 90, 320, 40) 14440
_________________________________________________________________
max_pooling3d_0 (MaxPooling3 (None, 1, 22, 80, 40) 0
_________________________________________________________________
conv3d_2 (Conv3D) (None, 1, 22, 80, 40) 14440
_________________________________________________________________
conv3d_3 (Conv3D) (None, 1, 22, 80, 40) 14440
_________________________________________________________________
max_pooling3d_1 (MaxPooling3 (None, 1, 5, 20, 40) 0
_________________________________________________________________
conv3d_4 (Conv3D) (None, 1, 5, 20, 40) 14440
_________________________________________________________________
conv3d_5 (Conv3D) (None, 1, 5, 20, 40) 14440
_________________________________________________________________
max_pooling3d_2 (MaxPooling3 (None, 1, 5, 5, 40) 0
_________________________________________________________________
conv3d_6 (Conv3D) (None, 1, 5, 5, 40) 14440
_________________________________________________________________
conv3d_7 (Conv3D) (None, 1, 5, 5, 40) 14440
_________________________________________________________________
max_pooling3d_3 (MaxPooling3 (None, 1, 2, 2, 40) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 160) 0
_________________________________________________________________
dense_0 (Dense) (None, 32) 5152
_________________________________________________________________
dropout_1 (Dropout) (None, 32) 0
_________________________________________________________________
dense_1 (Dense) (None, 16) 528
_________________________________________________________________
dropout_2 (Dropout) (None, 16) 0
_________________________________________________________________
dense_2 (Dense) (None, 1) 17
=================================================================
Total params: 107,897
Trainable params: 107,897
Non-trainable params: 0
_________________________________________________________________
In [6]:
def mse_wrap(y_true, y_pred):
# This is a rapped MSE function, since -1 is the same as 1 for rod rotation.
return K.square( K.min( K.abs( K.concatenate([y_pred - y_true, y_pred - y_true + 2, y_pred - y_true -2])), axis=1 ) )
def mse(y_true, y_pred):
return K.square(y_pred - y_true)
data_path = ".\\..\\..\\TrainingData\\Processed\\RodTrainingDataAngles\\Result\\settings_just_two.tsv"
transformer = VideoTransform( zoom_range=0.1, rotation_range=5, width_shift_range=0.1, height_shift_range=0.1, shear_range= 0.1, fill_mode='nearest', vertical_flip=False, horizontal_flip=True, horizontal_flip_invert_indices = [], horizontal_flip_reverse_indices = [], data_format='channels_last' )
training = TrainingInput(transformer, data_path, position_rel_indexes, frame_rel_indexes, 0.20)
model.compile(optimizer=keras.optimizers.RMSprop(lr=0.001),
loss=[mse],
metrics=[mse])
print("Updated learner.")
WEIGHTS_FNAME = '.\\RodAngle\\angle_weights_%i.hdf'
MODELS_FNAME = '.\\RodAngle\\angle_models_%i.h5'
batch_size = 1
batches_training_per_epoch = int(training.get_training_count() / batch_size)
batches_validation_per_epoch = int(training.get_validation_count() / batch_size)
print("Batch size %i: %i training batches, %i validation batches" % (batch_size, batches_training_per_epoch, batches_validation_per_epoch) )
epoch = 0
model.reset_states()
lr = 0.0002
lr_decay = lr_decay_callback(lr, 0.94)
print("Updated lr to %f" % lr)
model.compile(optimizer=keras.optimizers.RMSprop(lr=lr),
loss=[mse_wrap],
metrics=[mse])
start_epoch = epoch + 1
for epoch in range(start_epoch,10):
try:
model.fit_generator(TrainBatchGen(batch_size, model, training), batches_training_per_epoch, epochs=epoch+1, verbose=1, class_weight=None, max_q_size=10, workers=1, validation_data=ValidateBatchGen(batch_size, model, training), validation_steps = batches_validation_per_epoch, pickle_safe=False, initial_epoch=epoch, callbacks=[lr_decay])
model.save_weights(WEIGHTS_FNAME % epoch)
model.save(MODELS_FNAME % epoch)
print(("Wrote model to " + WEIGHTS_FNAME ) % epoch)
plot_validate(ValidateBatchGen(batch_size, model, training), model, 1000, "Angle prediction")
except KeyboardInterrupt:
print("\r\nUser stopped the training.")
break
# Plot the real versus predicted values for some of the validation data
plot_validate(ValidateBatchGen(batch_size, model, training), model, 1000, "Angle prediction")
data_path = ".\\..\\..\\TrainingData\\Processed\\RodTrainingDataAngles\\Result\\settings_full.tsv"
transformer = VideoTransform( zoom_range=0.1, rotation_range=5, width_shift_range=0.1, height_shift_range=0.1, shear_range= 0.1, fill_mode='nearest', vertical_flip=False, horizontal_flip=True, horizontal_flip_invert_indices = [], horizontal_flip_reverse_indices = [], data_format='channels_last' )
training = TrainingInput(transformer, data_path, position_rel_indexes, frame_rel_indexes, 0.20)
batch_size = 4
batches_training_per_epoch = int(training.get_training_count() / batch_size)
batches_validation_per_epoch = int(training.get_validation_count() / batch_size)
print("Batch size %i: %i training batches, %i validation batches" % (batch_size, batches_training_per_epoch, batches_validation_per_epoch) )
lr = 0.0002
lr_decay = lr_decay_callback(lr, 0.95)
print("Updated lr to %f" % lr)
model.compile(optimizer=keras.optimizers.RMSprop(lr=lr),
loss=[mse_wrap],
metrics=[mse])
epoch = 0
start_epoch = epoch + 1
for epoch in range(start_epoch,2000):
try:
model.fit_generator(TrainBatchGen(batch_size, model, training), batches_training_per_epoch, epochs=epoch+1, verbose=1, class_weight=None, max_q_size=10, workers=1, validation_data=ValidateBatchGen(batch_size, model, training), validation_steps = batches_validation_per_epoch, pickle_safe=False, initial_epoch=epoch, callbacks=[lr_decay])
model.save_weights(WEIGHTS_FNAME % epoch)
model.save(MODELS_FNAME % epoch)
print(("Wrote model to " + WEIGHTS_FNAME ) % epoch)
if epoch % 10 == 0:
plot_validate(ValidateBatchGen(batch_size, model, training), model, 2000, "Angle prediction")
except KeyboardInterrupt:
print("\r\nUser stopped the training.")
break
plot_validate(ValidateBatchGen(batch_size, model, training), model, 1000, "Angle prediction")
Creating training chunk from .\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk0.avi
.\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk0.avi
added 3204 new frames for a total of 3204
Creating training chunk from .\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk3.avi
.\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk3.avi
added 1667 new frames for a total of 4871
Updated learner.
Batch size 1: 3897 training batches, 972 validation batches
Updated lr to 0.000200
Updated lr to 0.000177
Epoch 2/2
3897/3897 [==============================] - 93s - loss: 0.2792 - mse: 0.7117 - val_loss: 0.0599 - val_mse: 0.0599
Wrote model to .\RodAngle\angle_weights_1.hdf
Updated lr to 0.000166
Epoch 3/3
3897/3897 [==============================] - 90s - loss: 0.0675 - mse: 0.0752 - val_loss: 0.0547 - val_mse: 0.0547
Wrote model to .\RodAngle\angle_weights_2.hdf
Predicted.
Updated lr to 0.000156
Epoch 4/4
3897/3897 [==============================] - 90s - loss: 0.0635 - mse: 0.0635 - val_loss: 0.0562 - val_mse: 0.0562
Wrote model to .\RodAngle\angle_weights_3.hdf
Updated lr to 0.000147
Epoch 5/5
3897/3897 [==============================] - 90s - loss: 0.0568 - mse: 0.0568 - val_loss: 0.0425 - val_mse: 0.0425
Wrote model to .\RodAngle\angle_weights_4.hdf
Predicted.
Updated lr to 0.000138
Epoch 6/6
3897/3897 [==============================] - 91s - loss: 0.0448 - mse: 0.0451 - val_loss: 0.0288 - val_mse: 0.0288
Wrote model to .\RodAngle\angle_weights_5.hdf
Updated lr to 0.000130
Epoch 7/7
3897/3897 [==============================] - 91s - loss: 0.0377 - mse: 0.0377 - val_loss: 0.0291 - val_mse: 0.0291
Wrote model to .\RodAngle\angle_weights_6.hdf
Predicted.
Updated lr to 0.000122
Epoch 8/8
3897/3897 [==============================] - 88s - loss: 0.0354 - mse: 0.0354 - val_loss: 0.0456 - val_mse: 0.0456
Wrote model to .\RodAngle\angle_weights_7.hdf
Updated lr to 0.000115
Epoch 9/9
3897/3897 [==============================] - 88s - loss: 0.0304 - mse: 0.0304 - val_loss: 0.0270 - val_mse: 0.0270
Wrote model to .\RodAngle\angle_weights_8.hdf
Predicted.
Updated lr to 0.000108
Epoch 10/10
3897/3897 [==============================] - 89s - loss: 0.0270 - mse: 0.0270 - val_loss: 0.0286 - val_mse: 0.0286
Wrote model to .\RodAngle\angle_weights_9.hdf
Predicted.
Creating training chunk from .\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk0.avi
.\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk0.avi
added 3204 new frames for a total of 3204
Creating training chunk from .\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk1.avi
.\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk1.avi
added 2763 new frames for a total of 5967
Creating training chunk from .\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk2.avi
.\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk2.avi
added 2355 new frames for a total of 8322
Creating training chunk from .\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk3.avi
.\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk3.avi
added 1667 new frames for a total of 9989
Creating training chunk from .\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk4.avi
.\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk4.avi
added 1172 new frames for a total of 11161
Creating training chunk from .\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk5.avi
.\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk5.avi
added 2190 new frames for a total of 13351
Creating training chunk from .\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk6.avi
.\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk6.avi
added 1644 new frames for a total of 14995
Batch size 4: 2999 training batches, 748 validation batches
Updated lr to 0.000200
Updated lr to 0.000180
Epoch 2/2
2999/2999 [==============================] - 269s - loss: 0.1423 - mse: 0.1552 - val_loss: 0.1275 - val_mse: 0.1480
Wrote model to .\RodAngle\angle_weights_1.hdf
Updated lr to 0.000171
Epoch 3/3
2999/2999 [==============================] - 266s - loss: 0.1138 - mse: 0.1346 - val_loss: 0.1226 - val_mse: 0.1589
Wrote model to .\RodAngle\angle_weights_2.hdf
Updated lr to 0.000163
Epoch 4/4
2999/2999 [==============================] - 266s - loss: 0.1064 - mse: 0.1334 - val_loss: 0.0802 - val_mse: 0.0940
Wrote model to .\RodAngle\angle_weights_3.hdf
Updated lr to 0.000155
Epoch 5/5
2999/2999 [==============================] - 274s - loss: 0.0991 - mse: 0.1306 - val_loss: 0.1315 - val_mse: 0.1777
Wrote model to .\RodAngle\angle_weights_4.hdf
Updated lr to 0.000147
Epoch 6/6
2999/2999 [==============================] - 266s - loss: 0.0948 - mse: 0.1254 - val_loss: 0.1292 - val_mse: 0.1789
Wrote model to .\RodAngle\angle_weights_5.hdf
Updated lr to 0.000140
Epoch 7/7
2999/2999 [==============================] - 266s - loss: 0.0910 - mse: 0.1258 - val_loss: 0.1293 - val_mse: 0.2102
Wrote model to .\RodAngle\angle_weights_6.hdf
Updated lr to 0.000133
Epoch 8/8
2999/2999 [==============================] - 266s - loss: 0.0883 - mse: 0.1269 - val_loss: 0.1060 - val_mse: 0.1698
Wrote model to .\RodAngle\angle_weights_7.hdf
Updated lr to 0.000126
Epoch 9/9
2999/2999 [==============================] - 267s - loss: 0.0853 - mse: 0.1248 - val_loss: 0.0965 - val_mse: 0.1603
Wrote model to .\RodAngle\angle_weights_8.hdf
Updated lr to 0.000120
Epoch 10/10
2999/2999 [==============================] - 267s - loss: 0.0878 - mse: 0.1288 - val_loss: 0.0663 - val_mse: 0.0877
Wrote model to .\RodAngle\angle_weights_9.hdf
Updated lr to 0.000114
Epoch 11/11
2999/2999 [==============================] - 266s - loss: 0.0845 - mse: 0.1260 - val_loss: 0.0862 - val_mse: 0.1286
Wrote model to .\RodAngle\angle_weights_10.hdf
Predicted.
Updated lr to 0.000108
Epoch 12/12
2999/2999 [==============================] - 266s - loss: 0.0818 - mse: 0.1228 - val_loss: 0.0717 - val_mse: 0.0952
Wrote model to .\RodAngle\angle_weights_11.hdf
Updated lr to 0.000103
Epoch 13/13
2999/2999 [==============================] - 266s - loss: 0.0826 - mse: 0.1280 - val_loss: 0.1016 - val_mse: 0.1627
Wrote model to .\RodAngle\angle_weights_12.hdf
Updated lr to 0.000098
Epoch 14/14
2999/2999 [==============================] - 266s - loss: 0.0814 - mse: 0.1229 - val_loss: 0.0625 - val_mse: 0.0945
Wrote model to .\RodAngle\angle_weights_13.hdf
Updated lr to 0.000093
Epoch 15/15
2999/2999 [==============================] - 265s - loss: 0.0812 - mse: 0.1266 - val_loss: 0.0900 - val_mse: 0.1560
Wrote model to .\RodAngle\angle_weights_14.hdf
Updated lr to 0.000088
Epoch 16/16
2999/2999 [==============================] - 266s - loss: 0.0803 - mse: 0.1240 - val_loss: 0.1066 - val_mse: 0.1600
Wrote model to .\RodAngle\angle_weights_15.hdf
Updated lr to 0.000084
Epoch 17/17
2999/2999 [==============================] - 266s - loss: 0.0791 - mse: 0.1227 - val_loss: 0.0589 - val_mse: 0.0828
Wrote model to .\RodAngle\angle_weights_16.hdf
Updated lr to 0.000079
Epoch 18/18
2999/2999 [==============================] - 265s - loss: 0.0810 - mse: 0.1312 - val_loss: 0.0812 - val_mse: 0.1069
Wrote model to .\RodAngle\angle_weights_17.hdf
Updated lr to 0.000075
Epoch 19/19
2999/2999 [==============================] - 265s - loss: 0.0800 - mse: 0.1257 - val_loss: 0.0655 - val_mse: 0.1140
Wrote model to .\RodAngle\angle_weights_18.hdf
Updated lr to 0.000072
Epoch 20/20
2999/2999 [==============================] - 266s - loss: 0.0780 - mse: 0.1240 - val_loss: 0.0581 - val_mse: 0.0827
Wrote model to .\RodAngle\angle_weights_19.hdf
Updated lr to 0.000068
Epoch 21/21
2999/2999 [==============================] - 266s - loss: 0.0781 - mse: 0.1282 - val_loss: 0.0683 - val_mse: 0.1169
Wrote model to .\RodAngle\angle_weights_20.hdf
Predicted.
Updated lr to 0.000065
Epoch 22/22
2999/2999 [==============================] - 265s - loss: 0.0772 - mse: 0.1258 - val_loss: 0.0679 - val_mse: 0.0913
Wrote model to .\RodAngle\angle_weights_21.hdf
Updated lr to 0.000061
Epoch 23/23
2999/2999 [==============================] - 265s - loss: 0.0766 - mse: 0.1225 - val_loss: 0.0805 - val_mse: 0.1290
Wrote model to .\RodAngle\angle_weights_22.hdf
Updated lr to 0.000058
Epoch 24/24
2999/2999 [==============================] - 266s - loss: 0.0775 - mse: 0.1224 - val_loss: 0.0738 - val_mse: 0.0963
Wrote model to .\RodAngle\angle_weights_23.hdf
Updated lr to 0.000055
Epoch 25/25
2999/2999 [==============================] - 265s - loss: 0.0777 - mse: 0.1258 - val_loss: 0.0884 - val_mse: 0.1405
Wrote model to .\RodAngle\angle_weights_24.hdf
Updated lr to 0.000053
Epoch 26/26
2999/2999 [==============================] - 264s - loss: 0.0756 - mse: 0.1197 - val_loss: 0.0946 - val_mse: 0.1716
Wrote model to .\RodAngle\angle_weights_25.hdf
Updated lr to 0.000050
Epoch 27/27
2999/2999 [==============================] - 265s - loss: 0.0783 - mse: 0.1247 - val_loss: 0.0817 - val_mse: 0.1529
Wrote model to .\RodAngle\angle_weights_26.hdf
Updated lr to 0.000048
Epoch 28/28
2999/2999 [==============================] - 266s - loss: 0.0766 - mse: 0.1227 - val_loss: 0.0926 - val_mse: 0.1597
Wrote model to .\RodAngle\angle_weights_27.hdf
Updated lr to 0.000045
Epoch 29/29
2999/2999 [==============================] - 265s - loss: 0.0763 - mse: 0.1261 - val_loss: 0.1041 - val_mse: 0.1530
Wrote model to .\RodAngle\angle_weights_28.hdf
Updated lr to 0.000043
Epoch 30/30
2999/2999 [==============================] - 265s - loss: 0.0773 - mse: 0.1264 - val_loss: 0.0663 - val_mse: 0.0902
Wrote model to .\RodAngle\angle_weights_29.hdf
Updated lr to 0.000041
Epoch 31/31
2999/2999 [==============================] - 266s - loss: 0.0762 - mse: 0.1211 - val_loss: 0.0858 - val_mse: 0.1340
Wrote model to .\RodAngle\angle_weights_30.hdf
Predicted.
Updated lr to 0.000039
Epoch 32/32
2999/2999 [==============================] - 265s - loss: 0.0761 - mse: 0.1255 - val_loss: 0.0736 - val_mse: 0.1244
Wrote model to .\RodAngle\angle_weights_31.hdf
Updated lr to 0.000037
Epoch 33/33
2999/2999 [==============================] - 265s - loss: 0.0758 - mse: 0.1241 - val_loss: 0.1036 - val_mse: 0.1545
Wrote model to .\RodAngle\angle_weights_32.hdf
Updated lr to 0.000035
Epoch 34/34
2999/2999 [==============================] - 265s - loss: 0.0754 - mse: 0.1185 - val_loss: 0.0802 - val_mse: 0.1490
Wrote model to .\RodAngle\angle_weights_33.hdf
Updated lr to 0.000033
Epoch 35/35
2999/2999 [==============================] - 265s - loss: 0.0752 - mse: 0.1214 - val_loss: 0.0781 - val_mse: 0.1261
Wrote model to .\RodAngle\angle_weights_34.hdf
Updated lr to 0.000032
Epoch 36/36
2999/2999 [==============================] - 265s - loss: 0.0748 - mse: 0.1195 - val_loss: 0.0755 - val_mse: 0.1231
Wrote model to .\RodAngle\angle_weights_35.hdf
Updated lr to 0.000030
Epoch 37/37
2999/2999 [==============================] - 265s - loss: 0.0762 - mse: 0.1241 - val_loss: 0.0685 - val_mse: 0.1415
Wrote model to .\RodAngle\angle_weights_36.hdf
Updated lr to 0.000028
Epoch 38/38
2999/2999 [==============================] - 267s - loss: 0.0766 - mse: 0.1289 - val_loss: 0.0686 - val_mse: 0.1086
Wrote model to .\RodAngle\angle_weights_37.hdf
Updated lr to 0.000027
Epoch 39/39
2999/2999 [==============================] - 266s - loss: 0.0760 - mse: 0.1208 - val_loss: 0.0818 - val_mse: 0.1077
Wrote model to .\RodAngle\angle_weights_38.hdf
Updated lr to 0.000026
Epoch 40/40
2999/2999 [==============================] - 266s - loss: 0.0755 - mse: 0.1213 - val_loss: 0.0974 - val_mse: 0.1447
Wrote model to .\RodAngle\angle_weights_39.hdf
Updated lr to 0.000024
Epoch 41/41
2999/2999 [==============================] - 265s - loss: 0.0735 - mse: 0.1228 - val_loss: 0.0764 - val_mse: 0.1252
Wrote model to .\RodAngle\angle_weights_40.hdf
Predicted.
Updated lr to 0.000023
Epoch 42/42
2999/2999 [==============================] - 266s - loss: 0.0754 - mse: 0.1253 - val_loss: 0.0781 - val_mse: 0.1194
Wrote model to .\RodAngle\angle_weights_41.hdf
Updated lr to 0.000022
Epoch 43/43
2999/2999 [==============================] - 267s - loss: 0.0749 - mse: 0.1233 - val_loss: 0.0729 - val_mse: 0.0964
Wrote model to .\RodAngle\angle_weights_42.hdf
Updated lr to 0.000021
Epoch 44/44
2999/2999 [==============================] - 268s - loss: 0.0752 - mse: 0.1183 - val_loss: 0.0821 - val_mse: 0.1318
Wrote model to .\RodAngle\angle_weights_43.hdf
Updated lr to 0.000020
Epoch 45/45
2999/2999 [==============================] - 265s - loss: 0.0734 - mse: 0.1224 - val_loss: 0.0981 - val_mse: 0.1610
Wrote model to .\RodAngle\angle_weights_44.hdf
Updated lr to 0.000019
Epoch 46/46
2999/2999 [==============================] - 265s - loss: 0.0771 - mse: 0.1252 - val_loss: 0.0973 - val_mse: 0.1703
Wrote model to .\RodAngle\angle_weights_45.hdf
Updated lr to 0.000018
Epoch 47/47
2999/2999 [==============================] - 265s - loss: 0.0763 - mse: 0.1225 - val_loss: 0.0444 - val_mse: 0.0683
Wrote model to .\RodAngle\angle_weights_46.hdf
Updated lr to 0.000017
Epoch 48/48
2999/2999 [==============================] - 265s - loss: 0.0764 - mse: 0.1242 - val_loss: 0.0585 - val_mse: 0.0816
Wrote model to .\RodAngle\angle_weights_47.hdf
Updated lr to 0.000016
Epoch 49/49
2999/2999 [==============================] - 265s - loss: 0.0766 - mse: 0.1239 - val_loss: 0.0549 - val_mse: 0.1011
Wrote model to .\RodAngle\angle_weights_48.hdf
Updated lr to 0.000015
Epoch 50/50
2999/2999 [==============================] - 265s - loss: 0.0737 - mse: 0.1232 - val_loss: 0.0904 - val_mse: 0.1418
Wrote model to .\RodAngle\angle_weights_49.hdf
Updated lr to 0.000015
Epoch 51/51
2999/2999 [==============================] - 265s - loss: 0.0740 - mse: 0.1211 - val_loss: 0.0968 - val_mse: 0.1475
Wrote model to .\RodAngle\angle_weights_50.hdf
Predicted.
Updated lr to 0.000014
Epoch 52/52
2999/2999 [==============================] - 265s - loss: 0.0760 - mse: 0.1233 - val_loss: 0.0847 - val_mse: 0.1328
Wrote model to .\RodAngle\angle_weights_51.hdf
Updated lr to 0.000013
Epoch 53/53
2999/2999 [==============================] - 265s - loss: 0.0754 - mse: 0.1252 - val_loss: 0.0811 - val_mse: 0.1196
Wrote model to .\RodAngle\angle_weights_52.hdf
Updated lr to 0.000013
Epoch 54/54
2999/2999 [==============================] - 265s - loss: 0.0758 - mse: 0.1222 - val_loss: 0.0856 - val_mse: 0.1592
Wrote model to .\RodAngle\angle_weights_53.hdf
Updated lr to 0.000012
Epoch 55/55
2999/2999 [==============================] - 266s - loss: 0.0758 - mse: 0.1246 - val_loss: 0.0810 - val_mse: 0.1518
Wrote model to .\RodAngle\angle_weights_54.hdf
Updated lr to 0.000011
Epoch 56/56
2999/2999 [==============================] - 265s - loss: 0.0734 - mse: 0.1183 - val_loss: 0.1097 - val_mse: 0.1838
Wrote model to .\RodAngle\angle_weights_55.hdf
Updated lr to 0.000011
Epoch 57/57
2999/2999 [==============================] - 265s - loss: 0.0743 - mse: 0.1208 - val_loss: 0.0656 - val_mse: 0.0999
Wrote model to .\RodAngle\angle_weights_56.hdf
Updated lr to 0.000010
Epoch 58/58
2999/2999 [==============================] - 265s - loss: 0.0741 - mse: 0.1243 - val_loss: 0.0523 - val_mse: 0.0758
Wrote model to .\RodAngle\angle_weights_57.hdf
Updated lr to 0.000010
Epoch 59/59
2999/2999 [==============================] - 266s - loss: 0.0754 - mse: 0.1243 - val_loss: 0.0872 - val_mse: 0.1584
Wrote model to .\RodAngle\angle_weights_58.hdf
Updated lr to 0.000009
Epoch 60/60
2999/2999 [==============================] - 265s - loss: 0.0747 - mse: 0.1277 - val_loss: 0.0515 - val_mse: 0.0766
Wrote model to .\RodAngle\angle_weights_59.hdf
Updated lr to 0.000009
Epoch 61/61
2999/2999 [==============================] - 265s - loss: 0.0760 - mse: 0.1278 - val_loss: 0.0871 - val_mse: 0.1629
Wrote model to .\RodAngle\angle_weights_60.hdf
Predicted.
Updated lr to 0.000008
Epoch 62/62
2999/2999 [==============================] - 264s - loss: 0.0758 - mse: 0.1230 - val_loss: 0.0902 - val_mse: 0.1221
Wrote model to .\RodAngle\angle_weights_61.hdf
Updated lr to 0.000008
Epoch 63/63
2999/2999 [==============================] - 265s - loss: 0.0751 - mse: 0.1228 - val_loss: 0.0760 - val_mse: 0.1264
Wrote model to .\RodAngle\angle_weights_62.hdf
Updated lr to 0.000008
Epoch 64/64
2999/2999 [==============================] - 265s - loss: 0.0755 - mse: 0.1254 - val_loss: 0.1131 - val_mse: 0.1859
Wrote model to .\RodAngle\angle_weights_63.hdf
Updated lr to 0.000007
Epoch 65/65
2999/2999 [==============================] - 265s - loss: 0.0753 - mse: 0.1257 - val_loss: 0.0840 - val_mse: 0.1321
Wrote model to .\RodAngle\angle_weights_64.hdf
Updated lr to 0.000007
Epoch 66/66
2999/2999 [==============================] - 265s - loss: 0.0745 - mse: 0.1206 - val_loss: 0.0531 - val_mse: 0.0776
Wrote model to .\RodAngle\angle_weights_65.hdf
Updated lr to 0.000006
Epoch 67/67
2999/2999 [==============================] - 264s - loss: 0.0752 - mse: 0.1241 - val_loss: 0.1037 - val_mse: 0.1741
Wrote model to .\RodAngle\angle_weights_66.hdf
Updated lr to 0.000006
Epoch 68/68
2999/2999 [==============================] - 265s - loss: 0.0727 - mse: 0.1182 - val_loss: 0.0838 - val_mse: 0.1535
Wrote model to .\RodAngle\angle_weights_67.hdf
Updated lr to 0.000006
Epoch 69/69
2999/2999 [==============================] - 267s - loss: 0.0733 - mse: 0.1203 - val_loss: 0.1013 - val_mse: 0.1725
Wrote model to .\RodAngle\angle_weights_68.hdf
Updated lr to 0.000006
Epoch 70/70
2999/2999 [==============================] - 265s - loss: 0.0740 - mse: 0.1192 - val_loss: 0.1120 - val_mse: 0.1695
Wrote model to .\RodAngle\angle_weights_69.hdf
Updated lr to 0.000005
Epoch 71/71
2999/2999 [==============================] - 266s - loss: 0.0746 - mse: 0.1233 - val_loss: 0.0830 - val_mse: 0.1325
Wrote model to .\RodAngle\angle_weights_70.hdf
Predicted.
Updated lr to 0.000005
Epoch 72/72
2999/2999 [==============================] - 265s - loss: 0.0744 - mse: 0.1238 - val_loss: 0.0978 - val_mse: 0.1590
Wrote model to .\RodAngle\angle_weights_71.hdf
Updated lr to 0.000005
Epoch 73/73
2999/2999 [==============================] - 265s - loss: 0.0757 - mse: 0.1231 - val_loss: 0.0817 - val_mse: 0.1341
Wrote model to .\RodAngle\angle_weights_72.hdf
Updated lr to 0.000004
Epoch 74/74
2999/2999 [==============================] - 265s - loss: 0.0742 - mse: 0.1241 - val_loss: 0.1152 - val_mse: 0.2140
Wrote model to .\RodAngle\angle_weights_73.hdf
Updated lr to 0.000004
Epoch 75/75
2999/2999 [==============================] - 264s - loss: 0.0729 - mse: 0.1209 - val_loss: 0.0801 - val_mse: 0.1203
Wrote model to .\RodAngle\angle_weights_74.hdf
Updated lr to 0.000004
Epoch 76/76
2999/2999 [==============================] - 265s - loss: 0.0713 - mse: 0.1224 - val_loss: 0.0941 - val_mse: 0.1449
Wrote model to .\RodAngle\angle_weights_75.hdf
Updated lr to 0.000004
Epoch 77/77
2999/2999 [==============================] - 265s - loss: 0.0740 - mse: 0.1197 - val_loss: 0.0924 - val_mse: 0.1670
Wrote model to .\RodAngle\angle_weights_76.hdf
Updated lr to 0.000004
Epoch 78/78
2999/2999 [==============================] - 265s - loss: 0.0739 - mse: 0.1188 - val_loss: 0.0919 - val_mse: 0.1664
Wrote model to .\RodAngle\angle_weights_77.hdf
Updated lr to 0.000003
Epoch 79/79
2999/2999 [==============================] - 265s - loss: 0.0747 - mse: 0.1229 - val_loss: 0.0710 - val_mse: 0.1098
Wrote model to .\RodAngle\angle_weights_78.hdf
Updated lr to 0.000003
Epoch 80/80
2999/2999 [==============================] - 266s - loss: 0.0747 - mse: 0.1220 - val_loss: 0.0895 - val_mse: 0.1645
Wrote model to .\RodAngle\angle_weights_79.hdf
Updated lr to 0.000003
Epoch 81/81
2999/2999 [==============================] - 265s - loss: 0.0752 - mse: 0.1258 - val_loss: 0.0987 - val_mse: 0.1588
Wrote model to .\RodAngle\angle_weights_80.hdf
Predicted.
Updated lr to 0.000003
Epoch 82/82
2999/2999 [==============================] - 267s - loss: 0.0735 - mse: 0.1227 - val_loss: 0.0790 - val_mse: 0.1271
Wrote model to .\RodAngle\angle_weights_81.hdf
Updated lr to 0.000003
Epoch 83/83
2999/2999 [==============================] - 265s - loss: 0.0728 - mse: 0.1205 - val_loss: 0.0656 - val_mse: 0.0922
Wrote model to .\RodAngle\angle_weights_82.hdf
Updated lr to 0.000003
Epoch 84/84
2999/2999 [==============================] - 265s - loss: 0.0736 - mse: 0.1205 - val_loss: 0.0875 - val_mse: 0.1357
Wrote model to .\RodAngle\angle_weights_83.hdf
Updated lr to 0.000003
Epoch 85/85
2999/2999 [==============================] - 265s - loss: 0.0729 - mse: 0.1187 - val_loss: 0.0760 - val_mse: 0.1254
Wrote model to .\RodAngle\angle_weights_84.hdf
Updated lr to 0.000002
Epoch 86/86
2999/2999 [==============================] - 266s - loss: 0.0756 - mse: 0.1221 - val_loss: 0.0867 - val_mse: 0.1491
Wrote model to .\RodAngle\angle_weights_85.hdf
Updated lr to 0.000002
Epoch 87/87
2999/2999 [==============================] - 265s - loss: 0.0739 - mse: 0.1189 - val_loss: 0.0626 - val_mse: 0.1122
Wrote model to .\RodAngle\angle_weights_86.hdf
Updated lr to 0.000002
Epoch 88/88
2999/2999 [==============================] - 268s - loss: 0.0743 - mse: 0.1236 - val_loss: 0.0543 - val_mse: 0.0791
Wrote model to .\RodAngle\angle_weights_87.hdf
Updated lr to 0.000002
Epoch 89/89
2999/2999 [==============================] - 265s - loss: 0.0745 - mse: 0.1209 - val_loss: 0.0736 - val_mse: 0.1202
Wrote model to .\RodAngle\angle_weights_88.hdf
Updated lr to 0.000002
Epoch 90/90
2999/2999 [==============================] - 265s - loss: 0.0721 - mse: 0.1226 - val_loss: 0.0970 - val_mse: 0.1682
Wrote model to .\RodAngle\angle_weights_89.hdf
Updated lr to 0.000002
Epoch 91/91
2999/2999 [==============================] - 265s - loss: 0.0743 - mse: 0.1215 - val_loss: 0.0762 - val_mse: 0.1251
Wrote model to .\RodAngle\angle_weights_90.hdf
Predicted.
Updated lr to 0.000002
Epoch 92/92
2999/2999 [==============================] - 266s - loss: 0.0741 - mse: 0.1180 - val_loss: 0.0760 - val_mse: 0.1269
Wrote model to .\RodAngle\angle_weights_91.hdf
Updated lr to 0.000002
Epoch 93/93
2999/2999 [==============================] - 264s - loss: 0.0731 - mse: 0.1234 - val_loss: 0.0900 - val_mse: 0.1642
Wrote model to .\RodAngle\angle_weights_92.hdf
Updated lr to 0.000002
Epoch 94/94
2999/2999 [==============================] - 265s - loss: 0.0747 - mse: 0.1228 - val_loss: 0.0849 - val_mse: 0.1578
Wrote model to .\RodAngle\angle_weights_93.hdf
Updated lr to 0.000002
Epoch 95/95
2999/2999 [==============================] - 265s - loss: 0.0709 - mse: 0.1163 - val_loss: 0.0967 - val_mse: 0.1955
Wrote model to .\RodAngle\angle_weights_94.hdf
Updated lr to 0.000001
Epoch 96/96
2999/2999 [==============================] - 265s - loss: 0.0749 - mse: 0.1230 - val_loss: 0.0765 - val_mse: 0.1257
Wrote model to .\RodAngle\angle_weights_95.hdf
Updated lr to 0.000001
Epoch 97/97
2999/2999 [==============================] - 265s - loss: 0.0762 - mse: 0.1229 - val_loss: 0.0850 - val_mse: 0.1572
Wrote model to .\RodAngle\angle_weights_96.hdf
Updated lr to 0.000001
Epoch 98/98
2999/2999 [==============================] - 265s - loss: 0.0751 - mse: 0.1243 - val_loss: 0.0653 - val_mse: 0.1033
Wrote model to .\RodAngle\angle_weights_97.hdf
Updated lr to 0.000001
Epoch 99/99
2999/2999 [==============================] - 266s - loss: 0.0728 - mse: 0.1187 - val_loss: 0.0908 - val_mse: 0.1389
Wrote model to .\RodAngle\angle_weights_98.hdf
Updated lr to 0.000001
Epoch 100/100
2999/2999 [==============================] - 265s - loss: 0.0732 - mse: 0.1192 - val_loss: 0.1002 - val_mse: 0.1747
Wrote model to .\RodAngle\angle_weights_99.hdf
Updated lr to 0.000001
Epoch 101/101
2999/2999 [==============================] - 265s - loss: 0.0760 - mse: 0.1239 - val_loss: 0.0761 - val_mse: 0.1491
Wrote model to .\RodAngle\angle_weights_100.hdf
Predicted.
Updated lr to 0.000001
Epoch 102/102
2999/2999 [==============================] - 266s - loss: 0.0733 - mse: 0.1255 - val_loss: 0.0594 - val_mse: 0.1088
Wrote model to .\RodAngle\angle_weights_101.hdf
Updated lr to 0.000001
Epoch 103/103
2999/2999 [==============================] - 266s - loss: 0.0756 - mse: 0.1235 - val_loss: 0.0711 - val_mse: 0.1202
Wrote model to .\RodAngle\angle_weights_102.hdf
Updated lr to 0.000001
Epoch 104/104
2999/2999 [==============================] - 266s - loss: 0.0728 - mse: 0.1205 - val_loss: 0.0978 - val_mse: 0.1716
Wrote model to .\RodAngle\angle_weights_103.hdf
Updated lr to 0.000001
Epoch 105/105
2999/2999 [==============================] - 266s - loss: 0.0739 - mse: 0.1217 - val_loss: 0.0587 - val_mse: 0.0840
Wrote model to .\RodAngle\angle_weights_104.hdf
Updated lr to 0.000001
Epoch 106/106
2999/2999 [==============================] - 266s - loss: 0.0753 - mse: 0.1271 - val_loss: 0.0417 - val_mse: 0.0656
Wrote model to .\RodAngle\angle_weights_105.hdf
Updated lr to 0.000001
Epoch 107/107
2999/2999 [==============================] - 266s - loss: 0.0722 - mse: 0.1189 - val_loss: 0.0959 - val_mse: 0.1681
Wrote model to .\RodAngle\angle_weights_106.hdf
Updated lr to 0.000001
Epoch 108/108
2999/2999 [==============================] - 266s - loss: 0.0753 - mse: 0.1220 - val_loss: 0.0707 - val_mse: 0.1103
Wrote model to .\RodAngle\angle_weights_107.hdf
Updated lr to 0.000001
Epoch 109/109
2999/2999 [==============================] - 269s - loss: 0.0729 - mse: 0.1210 - val_loss: 0.0597 - val_mse: 0.0841
Wrote model to .\RodAngle\angle_weights_108.hdf
Updated lr to 0.000001
Epoch 110/110
2999/2999 [==============================] - 268s - loss: 0.0754 - mse: 0.1264 - val_loss: 0.0908 - val_mse: 0.1634
Wrote model to .\RodAngle\angle_weights_109.hdf
Updated lr to 0.000001
Epoch 111/111
2999/2999 [==============================] - 266s - loss: 0.0746 - mse: 0.1207 - val_loss: 0.0230 - val_mse: 0.0230
Wrote model to .\RodAngle\angle_weights_110.hdf
Predicted.
Updated lr to 0.000001
Epoch 112/112
2999/2999 [==============================] - 266s - loss: 0.0740 - mse: 0.1188 - val_loss: 0.0665 - val_mse: 0.1178
Wrote model to .\RodAngle\angle_weights_111.hdf
Updated lr to 0.000001
Epoch 113/113
2999/2999 [==============================] - 268s - loss: 0.0731 - mse: 0.1210 - val_loss: 0.1060 - val_mse: 0.1794
Wrote model to .\RodAngle\angle_weights_112.hdf
Updated lr to 0.000001
Epoch 114/114
2999/2999 [==============================] - 266s - loss: 0.0741 - mse: 0.1240 - val_loss: 0.1028 - val_mse: 0.1758
Wrote model to .\RodAngle\angle_weights_113.hdf
Updated lr to 0.000001
Epoch 115/115
2999/2999 [==============================] - 265s - loss: 0.0751 - mse: 0.1229 - val_loss: 0.0916 - val_mse: 0.1415
Wrote model to .\RodAngle\angle_weights_114.hdf
Updated lr to 0.000001
Epoch 116/116
2999/2999 [==============================] - 266s - loss: 0.0727 - mse: 0.1217 - val_loss: 0.0516 - val_mse: 0.0877
Wrote model to .\RodAngle\angle_weights_115.hdf
Updated lr to 0.000000
Epoch 117/117
2999/2999 [==============================] - 266s - loss: 0.0723 - mse: 0.1178 - val_loss: 0.0834 - val_mse: 0.1598
Wrote model to .\RodAngle\angle_weights_116.hdf
Updated lr to 0.000000
Epoch 118/118
2999/2999 [==============================] - 268s - loss: 0.0727 - mse: 0.1181 - val_loss: 0.0942 - val_mse: 0.1625
Wrote model to .\RodAngle\angle_weights_117.hdf
Updated lr to 0.000000
Epoch 119/119
1197/2999 [==========>...................] - ETA: 146s - loss: 0.0742 - mse: 0.1232
User stopped the training.
Predicted.
In [ ]:
data_path = ".\\..\\..\\TrainingData\\Processed\\RodTrainingDataAngles\\Result\\settings_full.tsv"
transformer = VideoTransform( zoom_range=0.1, rotation_range=5, width_shift_range=0.1, height_shift_range=0.1, shear_range= 0.1, fill_mode='nearest', vertical_flip=False, horizontal_flip=True, horizontal_flip_invert_indices = [], horizontal_flip_reverse_indices = [], data_format='channels_last' )
training = TrainingInput(transformer, data_path, position_rel_indexes, frame_rel_indexes, 0.20)
batch_size = 4
batches_training_per_epoch = int(training.get_training_count() / batch_size)
batches_validation_per_epoch = int(training.get_validation_count() / batch_size)
print("Batch size %i: %i training batches, %i validation batches" % (batch_size, batches_training_per_epoch, batches_validation_per_epoch) )
lr = 0.0002
lr_decay = lr_decay_callback(lr, 0.97)
print("Updated lr to %f" % lr)
model.compile(optimizer=keras.optimizers.RMSprop(lr=lr),
loss=[mse_wrap],
metrics=[mse])
epoch = 0
start_epoch = epoch + 1
for epoch in range(start_epoch,2000):
try:
model.fit_generator(TrainBatchGen(batch_size, model, training), batches_training_per_epoch, epochs=epoch+1, verbose=1, class_weight=None, max_q_size=10, workers=1, validation_data=ValidateBatchGen(batch_size, model, training), validation_steps = batches_validation_per_epoch, pickle_safe=False, initial_epoch=epoch, callbacks=[lr_decay])
model.save_weights(WEIGHTS_FNAME % epoch)
model.save(MODELS_FNAME % epoch)
print(("Wrote model to " + WEIGHTS_FNAME ) % epoch)
if epoch % 10 == 0:
plot_validate(ValidateBatchGen(batch_size, model, training), model, 2000, "Angle prediction")
except KeyboardInterrupt:
print("\r\nUser stopped the training.")
break
plot_validate(ValidateBatchGen(batch_size, model, training), model, 2000, "Angle prediction")
Creating training chunk from .\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk0.avi
.\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk0.avi
added 3204 new frames for a total of 3204
Creating training chunk from .\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk1.avi
.\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk1.avi
added 2763 new frames for a total of 5967
Creating training chunk from .\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk2.avi
.\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk2.avi
added 2355 new frames for a total of 8322
Creating training chunk from .\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk3.avi
.\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk3.avi
added 1667 new frames for a total of 9989
Creating training chunk from .\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk4.avi
.\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk4.avi
added 1172 new frames for a total of 11161
Creating training chunk from .\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk5.avi
.\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk5.avi
added 2190 new frames for a total of 13351
Creating training chunk from .\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk6.avi
.\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk6.avi
added 1644 new frames for a total of 14995
Batch size 4: 2999 training batches, 748 validation batches
Updated lr to 0.000200
Updated lr to 0.000188
Epoch 2/2
2999/2999 [==============================] - 270s - loss: 0.1691 - mse: 0.2496 - val_loss: 0.0805 - val_mse: 0.1117
Wrote model to .\RodAngle\angle_weights_1.hdf
Updated lr to 0.000183
Epoch 3/3
2999/2999 [==============================] - 269s - loss: 0.1009 - mse: 0.1431 - val_loss: 0.1224 - val_mse: 0.1706
Wrote model to .\RodAngle\angle_weights_2.hdf
Updated lr to 0.000177
Epoch 4/4
2999/2999 [==============================] - 272s - loss: 0.1084 - mse: 0.1548 - val_loss: 0.0893 - val_mse: 0.1049
Wrote model to .\RodAngle\angle_weights_3.hdf
Updated lr to 0.000172
Epoch 5/5
2999/2999 [==============================] - 269s - loss: 0.0936 - mse: 0.1331 - val_loss: 0.0789 - val_mse: 0.1206
Wrote model to .\RodAngle\angle_weights_4.hdf
Updated lr to 0.000167
Epoch 6/6
2999/2999 [==============================] - 269s - loss: 0.0943 - mse: 0.1401 - val_loss: 0.0857 - val_mse: 0.1325
Wrote model to .\RodAngle\angle_weights_5.hdf
Updated lr to 0.000162
Epoch 7/7
2999/2999 [==============================] - 269s - loss: 0.0871 - mse: 0.1226 - val_loss: 0.0797 - val_mse: 0.1253
Wrote model to .\RodAngle\angle_weights_6.hdf
Updated lr to 0.000157
Epoch 8/8
2999/2999 [==============================] - 269s - loss: 0.0882 - mse: 0.1277 - val_loss: 0.1232 - val_mse: 0.1965
Wrote model to .\RodAngle\angle_weights_7.hdf
Updated lr to 0.000152
Epoch 9/9
2999/2999 [==============================] - 268s - loss: 0.0910 - mse: 0.1358 - val_loss: 0.0865 - val_mse: 0.1390
Wrote model to .\RodAngle\angle_weights_8.hdf
Updated lr to 0.000147
Epoch 10/10
2999/2999 [==============================] - 269s - loss: 0.1173 - mse: 0.1744 - val_loss: 0.1201 - val_mse: 0.1966
Wrote model to .\RodAngle\angle_weights_9.hdf
Updated lr to 0.000143
Epoch 11/11
2999/2999 [==============================] - 270s - loss: 0.0869 - mse: 0.1233 - val_loss: 0.1005 - val_mse: 0.1674
Wrote model to .\RodAngle\angle_weights_10.hdf
Predicted.
Updated lr to 0.000139
Epoch 12/12
2999/2999 [==============================] - 266s - loss: 0.0854 - mse: 0.1241 - val_loss: 0.0937 - val_mse: 0.1464
Wrote model to .\RodAngle\angle_weights_11.hdf
Updated lr to 0.000135
Epoch 13/13
2999/2999 [==============================] - 266s - loss: 0.0889 - mse: 0.1350 - val_loss: 0.1066 - val_mse: 0.1754
Wrote model to .\RodAngle\angle_weights_12.hdf
Updated lr to 0.000131
Epoch 14/14
2999/2999 [==============================] - 266s - loss: 0.0862 - mse: 0.1280 - val_loss: 0.0958 - val_mse: 0.1449
Wrote model to .\RodAngle\angle_weights_13.hdf
Updated lr to 0.000127
Epoch 15/15
2999/2999 [==============================] - 267s - loss: 0.0912 - mse: 0.1426 - val_loss: 0.1179 - val_mse: 0.2124
Wrote model to .\RodAngle\angle_weights_14.hdf
Updated lr to 0.000123
Epoch 16/16
2999/2999 [==============================] - 266s - loss: 0.0867 - mse: 0.1321 - val_loss: 0.0670 - val_mse: 0.1151
Wrote model to .\RodAngle\angle_weights_15.hdf
Updated lr to 0.000119
Epoch 17/17
2999/2999 [==============================] - 266s - loss: 0.0849 - mse: 0.1228 - val_loss: 0.0976 - val_mse: 0.1423
Wrote model to .\RodAngle\angle_weights_16.hdf
Updated lr to 0.000116
Epoch 18/18
2999/2999 [==============================] - 266s - loss: 0.0875 - mse: 0.1310 - val_loss: 0.1233 - val_mse: 0.1955
Wrote model to .\RodAngle\angle_weights_17.hdf
Updated lr to 0.000112
Epoch 19/19
2999/2999 [==============================] - 266s - loss: 0.0859 - mse: 0.1315 - val_loss: 0.0664 - val_mse: 0.1079
Wrote model to .\RodAngle\angle_weights_18.hdf
Updated lr to 0.000109
Epoch 20/20
2999/2999 [==============================] - 266s - loss: 0.0827 - mse: 0.1225 - val_loss: 0.0850 - val_mse: 0.1547
Wrote model to .\RodAngle\angle_weights_19.hdf
Updated lr to 0.000105
Epoch 21/21
2999/2999 [==============================] - 266s - loss: 0.0865 - mse: 0.1301 - val_loss: 0.0748 - val_mse: 0.1265
Wrote model to .\RodAngle\angle_weights_20.hdf
Predicted.
Updated lr to 0.000102
Epoch 22/22
2999/2999 [==============================] - 268s - loss: 0.0833 - mse: 0.1249 - val_loss: 0.0627 - val_mse: 0.0638
Wrote model to .\RodAngle\angle_weights_21.hdf
Updated lr to 0.000099
Epoch 23/23
2999/2999 [==============================] - 268s - loss: 0.0862 - mse: 0.1242 - val_loss: 0.1012 - val_mse: 0.1378
Wrote model to .\RodAngle\angle_weights_22.hdf
Updated lr to 0.000096
Epoch 24/24
2999/2999 [==============================] - 265s - loss: 0.0838 - mse: 0.1292 - val_loss: 0.0879 - val_mse: 0.1571
Wrote model to .\RodAngle\angle_weights_23.hdf
Updated lr to 0.000093
Epoch 25/25
2999/2999 [==============================] - 266s - loss: 0.0820 - mse: 0.1276 - val_loss: 0.0750 - val_mse: 0.1189
Wrote model to .\RodAngle\angle_weights_24.hdf
Updated lr to 0.000091
Epoch 26/26
2999/2999 [==============================] - 266s - loss: 0.0832 - mse: 0.1244 - val_loss: 0.0945 - val_mse: 0.1393
Wrote model to .\RodAngle\angle_weights_25.hdf
Updated lr to 0.000088
Epoch 27/27
2999/2999 [==============================] - 266s - loss: 0.0822 - mse: 0.1223 - val_loss: 0.0590 - val_mse: 0.0960
Wrote model to .\RodAngle\angle_weights_26.hdf
Updated lr to 0.000085
Epoch 28/28
2999/2999 [==============================] - 267s - loss: 0.0845 - mse: 0.1312 - val_loss: 0.0827 - val_mse: 0.1305
Wrote model to .\RodAngle\angle_weights_27.hdf
Updated lr to 0.000083
Epoch 29/29
2999/2999 [==============================] - 266s - loss: 0.0843 - mse: 0.1249 - val_loss: 0.0524 - val_mse: 0.0764
Wrote model to .\RodAngle\angle_weights_28.hdf
Updated lr to 0.000080
Epoch 30/30
2999/2999 [==============================] - 267s - loss: 0.0827 - mse: 0.1278 - val_loss: 0.0641 - val_mse: 0.1099
Wrote model to .\RodAngle\angle_weights_29.hdf
Updated lr to 0.000078
Epoch 31/31
2999/2999 [==============================] - 266s - loss: 0.0831 - mse: 0.1213 - val_loss: 0.1123 - val_mse: 0.1510
Wrote model to .\RodAngle\angle_weights_30.hdf
Predicted.
Updated lr to 0.000075
Epoch 32/32
2999/2999 [==============================] - 266s - loss: 0.0820 - mse: 0.1236 - val_loss: 0.0986 - val_mse: 0.1703
Wrote model to .\RodAngle\angle_weights_31.hdf
Updated lr to 0.000073
Epoch 33/33
2999/2999 [==============================] - 266s - loss: 0.0838 - mse: 0.1264 - val_loss: 0.0730 - val_mse: 0.1222
Wrote model to .\RodAngle\angle_weights_32.hdf
Updated lr to 0.000071
Epoch 34/34
2999/2999 [==============================] - 266s - loss: 0.0809 - mse: 0.1199 - val_loss: 0.0939 - val_mse: 0.1344
Wrote model to .\RodAngle\angle_weights_33.hdf
Updated lr to 0.000069
Epoch 35/35
2999/2999 [==============================] - 268s - loss: 0.0823 - mse: 0.1201 - val_loss: 0.0771 - val_mse: 0.1197
Wrote model to .\RodAngle\angle_weights_34.hdf
Updated lr to 0.000067
Epoch 36/36
2999/2999 [==============================] - 266s - loss: 0.0807 - mse: 0.1225 - val_loss: 0.0758 - val_mse: 0.1121
Wrote model to .\RodAngle\angle_weights_35.hdf
Updated lr to 0.000065
Epoch 37/37
2999/2999 [==============================] - 267s - loss: 0.0837 - mse: 0.1294 - val_loss: 0.1235 - val_mse: 0.1944
Wrote model to .\RodAngle\angle_weights_36.hdf
Updated lr to 0.000063
Epoch 38/38
2999/2999 [==============================] - 266s - loss: 0.1001 - mse: 0.1464 - val_loss: 0.1129 - val_mse: 0.1759
Wrote model to .\RodAngle\angle_weights_37.hdf
Updated lr to 0.000061
Epoch 39/39
2999/2999 [==============================] - 265s - loss: 0.0815 - mse: 0.1216 - val_loss: 0.0792 - val_mse: 0.1202
Wrote model to .\RodAngle\angle_weights_38.hdf
Updated lr to 0.000059
Epoch 40/40
2999/2999 [==============================] - 268s - loss: 0.0831 - mse: 0.1287 - val_loss: 0.0800 - val_mse: 0.1260
Wrote model to .\RodAngle\angle_weights_39.hdf
Updated lr to 0.000057
Epoch 41/41
2999/2999 [==============================] - 267s - loss: 0.0838 - mse: 0.1265 - val_loss: 0.0957 - val_mse: 0.1347
Wrote model to .\RodAngle\angle_weights_40.hdf
Predicted.
Updated lr to 0.000056
Epoch 42/42
2999/2999 [==============================] - 266s - loss: 0.0817 - mse: 0.1262 - val_loss: 0.0890 - val_mse: 0.1616
Wrote model to .\RodAngle\angle_weights_41.hdf
Updated lr to 0.000054
Epoch 43/43
2999/2999 [==============================] - 266s - loss: 0.0839 - mse: 0.1303 - val_loss: 0.0992 - val_mse: 0.1645
Wrote model to .\RodAngle\angle_weights_42.hdf
Updated lr to 0.000052
Epoch 44/44
2999/2999 [==============================] - 266s - loss: 0.0795 - mse: 0.1180 - val_loss: 0.0828 - val_mse: 0.1317
Wrote model to .\RodAngle\angle_weights_43.hdf
Updated lr to 0.000051
Epoch 45/45
2999/2999 [==============================] - 266s - loss: 0.0805 - mse: 0.1215 - val_loss: 0.0921 - val_mse: 0.1599
Wrote model to .\RodAngle\angle_weights_44.hdf
Updated lr to 0.000049
Epoch 46/46
2999/2999 [==============================] - 266s - loss: 0.0816 - mse: 0.1221 - val_loss: 0.0854 - val_mse: 0.1334
Wrote model to .\RodAngle\angle_weights_45.hdf
Updated lr to 0.000048
Epoch 47/47
2999/2999 [==============================] - 267s - loss: 0.0836 - mse: 0.1271 - val_loss: 0.0653 - val_mse: 0.1112
Wrote model to .\RodAngle\angle_weights_46.hdf
Updated lr to 0.000046
Epoch 48/48
2999/2999 [==============================] - 267s - loss: 0.0816 - mse: 0.1260 - val_loss: 0.0781 - val_mse: 0.1093
Wrote model to .\RodAngle\angle_weights_47.hdf
Updated lr to 0.000045
Epoch 49/49
2999/2999 [==============================] - 267s - loss: 0.0801 - mse: 0.1183 - val_loss: 0.0689 - val_mse: 0.1131
Wrote model to .\RodAngle\angle_weights_48.hdf
Updated lr to 0.000044
Epoch 50/50
2999/2999 [==============================] - 266s - loss: 0.0809 - mse: 0.1232 - val_loss: 0.1204 - val_mse: 0.1875
Wrote model to .\RodAngle\angle_weights_49.hdf
Updated lr to 0.000042
Epoch 51/51
2999/2999 [==============================] - 266s - loss: 0.0812 - mse: 0.1263 - val_loss: 0.0749 - val_mse: 0.0984
Wrote model to .\RodAngle\angle_weights_50.hdf
Predicted.
Updated lr to 0.000041
Epoch 52/52
2999/2999 [==============================] - 268s - loss: 0.0803 - mse: 0.1220 - val_loss: 0.0983 - val_mse: 0.1421
Wrote model to .\RodAngle\angle_weights_51.hdf
Updated lr to 0.000040
Epoch 53/53
2999/2999 [==============================] - 266s - loss: 0.0803 - mse: 0.1251 - val_loss: 0.0842 - val_mse: 0.1303
Wrote model to .\RodAngle\angle_weights_52.hdf
Updated lr to 0.000039
Epoch 54/54
2999/2999 [==============================] - 267s - loss: 0.0813 - mse: 0.1213 - val_loss: 0.1027 - val_mse: 0.1525
Wrote model to .\RodAngle\angle_weights_53.hdf
Updated lr to 0.000037
Epoch 55/55
2999/2999 [==============================] - 266s - loss: 0.0808 - mse: 0.1234 - val_loss: 0.1121 - val_mse: 0.1627
Wrote model to .\RodAngle\angle_weights_54.hdf
Updated lr to 0.000036
Epoch 56/56
2999/2999 [==============================] - 266s - loss: 0.0801 - mse: 0.1225 - val_loss: 0.0668 - val_mse: 0.1140
Wrote model to .\RodAngle\angle_weights_55.hdf
Updated lr to 0.000035
Epoch 57/57
2999/2999 [==============================] - 266s - loss: 0.0804 - mse: 0.1191 - val_loss: 0.0559 - val_mse: 0.0764
Wrote model to .\RodAngle\angle_weights_56.hdf
Updated lr to 0.000034
Epoch 58/58
2999/2999 [==============================] - 266s - loss: 0.0819 - mse: 0.1251 - val_loss: 0.1264 - val_mse: 0.2012
Wrote model to .\RodAngle\angle_weights_57.hdf
Updated lr to 0.000033
Epoch 59/59
2999/2999 [==============================] - 266s - loss: 0.0793 - mse: 0.1214 - val_loss: 0.0685 - val_mse: 0.1132
Wrote model to .\RodAngle\angle_weights_58.hdf
Updated lr to 0.000032
Epoch 60/60
2999/2999 [==============================] - 266s - loss: 0.0793 - mse: 0.1173 - val_loss: 0.0575 - val_mse: 0.0806
Wrote model to .\RodAngle\angle_weights_59.hdf
Updated lr to 0.000031
Epoch 61/61
2999/2999 [==============================] - 267s - loss: 0.0805 - mse: 0.1213 - val_loss: 0.0790 - val_mse: 0.1419
Wrote model to .\RodAngle\angle_weights_60.hdf
Predicted.
Updated lr to 0.000030
Epoch 62/62
2999/2999 [==============================] - 265s - loss: 0.0830 - mse: 0.1287 - val_loss: 0.0730 - val_mse: 0.1194
Wrote model to .\RodAngle\angle_weights_61.hdf
Updated lr to 0.000029
Epoch 63/63
2999/2999 [==============================] - 266s - loss: 0.0790 - mse: 0.1258 - val_loss: 0.0636 - val_mse: 0.1096
Wrote model to .\RodAngle\angle_weights_62.hdf
Updated lr to 0.000028
Epoch 64/64
2999/2999 [==============================] - 266s - loss: 0.0774 - mse: 0.1151 - val_loss: 0.0659 - val_mse: 0.0892
Wrote model to .\RodAngle\angle_weights_63.hdf
Updated lr to 0.000028
Epoch 65/65
2999/2999 [==============================] - 265s - loss: 0.0793 - mse: 0.1225 - val_loss: 0.0905 - val_mse: 0.1361
Wrote model to .\RodAngle\angle_weights_64.hdf
Updated lr to 0.000027
Epoch 66/66
2999/2999 [==============================] - 266s - loss: 0.0769 - mse: 0.1179 - val_loss: 0.0929 - val_mse: 0.1379
Wrote model to .\RodAngle\angle_weights_65.hdf
Updated lr to 0.000026
Epoch 67/67
2999/2999 [==============================] - 265s - loss: 0.0767 - mse: 0.1180 - val_loss: 0.1120 - val_mse: 0.2061
Wrote model to .\RodAngle\angle_weights_66.hdf
Updated lr to 0.000025
Epoch 68/68
2999/2999 [==============================] - 266s - loss: 0.0821 - mse: 0.1302 - val_loss: 0.0840 - val_mse: 0.1071
Wrote model to .\RodAngle\angle_weights_67.hdf
Updated lr to 0.000024
Epoch 69/69
2999/2999 [==============================] - 266s - loss: 0.0778 - mse: 0.1186 - val_loss: 0.0502 - val_mse: 0.0728
Wrote model to .\RodAngle\angle_weights_68.hdf
Updated lr to 0.000024
Epoch 70/70
2999/2999 [==============================] - 266s - loss: 0.0777 - mse: 0.1208 - val_loss: 0.0677 - val_mse: 0.1152
Wrote model to .\RodAngle\angle_weights_69.hdf
Updated lr to 0.000023
Epoch 71/71
2999/2999 [==============================] - 267s - loss: 0.0802 - mse: 0.1257 - val_loss: 0.0773 - val_mse: 0.1245
Wrote model to .\RodAngle\angle_weights_70.hdf
Predicted.
Updated lr to 0.000022
Epoch 72/72
2999/2999 [==============================] - 265s - loss: 0.0776 - mse: 0.1203 - val_loss: 0.0998 - val_mse: 0.1406
Wrote model to .\RodAngle\angle_weights_71.hdf
Updated lr to 0.000022
Epoch 73/73
2999/2999 [==============================] - 266s - loss: 0.0761 - mse: 0.1144 - val_loss: 0.0860 - val_mse: 0.1326
Wrote model to .\RodAngle\angle_weights_72.hdf
Updated lr to 0.000021
Epoch 74/74
2999/2999 [==============================] - 267s - loss: 0.0785 - mse: 0.1215 - val_loss: 0.0892 - val_mse: 0.1356
Wrote model to .\RodAngle\angle_weights_73.hdf
Updated lr to 0.000020
Epoch 75/75
2999/2999 [==============================] - 266s - loss: 0.0781 - mse: 0.1189 - val_loss: 0.1163 - val_mse: 0.2083
Wrote model to .\RodAngle\angle_weights_74.hdf
Updated lr to 0.000020
Epoch 76/76
2999/2999 [==============================] - 266s - loss: 0.0788 - mse: 0.1214 - val_loss: 0.0912 - val_mse: 0.1481
Wrote model to .\RodAngle\angle_weights_75.hdf
Updated lr to 0.000019
Epoch 77/77
2999/2999 [==============================] - 265s - loss: 0.0781 - mse: 0.1228 - val_loss: 0.0500 - val_mse: 0.0735
Wrote model to .\RodAngle\angle_weights_76.hdf
Updated lr to 0.000019
Epoch 78/78
2999/2999 [==============================] - 267s - loss: 0.0955 - mse: 0.1424 - val_loss: 0.0736 - val_mse: 0.0988
Wrote model to .\RodAngle\angle_weights_77.hdf
Updated lr to 0.000018
Epoch 79/79
2999/2999 [==============================] - 266s - loss: 0.0771 - mse: 0.1171 - val_loss: 0.0680 - val_mse: 0.0908
Wrote model to .\RodAngle\angle_weights_78.hdf
Updated lr to 0.000017
Epoch 80/80
2999/2999 [==============================] - 265s - loss: 0.0758 - mse: 0.1193 - val_loss: 0.0885 - val_mse: 0.1470
Wrote model to .\RodAngle\angle_weights_79.hdf
Updated lr to 0.000017
Epoch 81/81
2999/2999 [==============================] - 266s - loss: 0.0817 - mse: 0.1294 - val_loss: 0.0966 - val_mse: 0.1456
Wrote model to .\RodAngle\angle_weights_80.hdf
Predicted.
Updated lr to 0.000016
Epoch 82/82
2999/2999 [==============================] - 266s - loss: 0.0785 - mse: 0.1222 - val_loss: 0.0848 - val_mse: 0.1339
Wrote model to .\RodAngle\angle_weights_81.hdf
Updated lr to 0.000016
Epoch 83/83
2999/2999 [==============================] - 265s - loss: 0.0809 - mse: 0.1290 - val_loss: 0.0579 - val_mse: 0.1022
Wrote model to .\RodAngle\angle_weights_82.hdf
Updated lr to 0.000015
Epoch 84/84
2999/2999 [==============================] - 265s - loss: 0.0845 - mse: 0.1364 - val_loss: 0.0678 - val_mse: 0.1124
Wrote model to .\RodAngle\angle_weights_83.hdf
Updated lr to 0.000015
Epoch 85/85
2999/2999 [==============================] - 266s - loss: 0.0780 - mse: 0.1195 - val_loss: 0.0819 - val_mse: 0.1395
Wrote model to .\RodAngle\angle_weights_84.hdf
Updated lr to 0.000015
Epoch 86/86
2999/2999 [==============================] - 266s - loss: 0.0782 - mse: 0.1201 - val_loss: 0.0669 - val_mse: 0.1150
Wrote model to .\RodAngle\angle_weights_85.hdf
Updated lr to 0.000014
Epoch 87/87
2999/2999 [==============================] - 265s - loss: 0.0959 - mse: 0.1487 - val_loss: 0.0854 - val_mse: 0.1296
Wrote model to .\RodAngle\angle_weights_86.hdf
Updated lr to 0.000014
Epoch 88/88
2999/2999 [==============================] - 268s - loss: 0.0902 - mse: 0.1376 - val_loss: 0.0895 - val_mse: 0.1368
Wrote model to .\RodAngle\angle_weights_87.hdf
Updated lr to 0.000013
Epoch 89/89
2999/2999 [==============================] - 267s - loss: 0.0823 - mse: 0.1288 - val_loss: 0.0637 - val_mse: 0.1124
Wrote model to .\RodAngle\angle_weights_88.hdf
Updated lr to 0.000013
Epoch 90/90
2999/2999 [==============================] - 266s - loss: 0.0775 - mse: 0.1210 - val_loss: 0.1072 - val_mse: 0.1657
Wrote model to .\RodAngle\angle_weights_89.hdf
Updated lr to 0.000013
Epoch 91/91
2999/2999 [==============================] - 267s - loss: 0.0795 - mse: 0.1295 - val_loss: 0.0650 - val_mse: 0.0966
Wrote model to .\RodAngle\angle_weights_90.hdf
Predicted.
Updated lr to 0.000012
Epoch 92/92
2999/2999 [==============================] - 266s - loss: 0.0812 - mse: 0.1288 - val_loss: 0.0978 - val_mse: 0.1647
Wrote model to .\RodAngle\angle_weights_91.hdf
Updated lr to 0.000012
Epoch 93/93
2999/2999 [==============================] - 266s - loss: 0.0951 - mse: 0.1421 - val_loss: 0.0716 - val_mse: 0.1132
Wrote model to .\RodAngle\angle_weights_92.hdf
Updated lr to 0.000011
Epoch 94/94
2999/2999 [==============================] - 265s - loss: 0.0757 - mse: 0.1166 - val_loss: 0.0639 - val_mse: 0.1109
Wrote model to .\RodAngle\angle_weights_93.hdf
Updated lr to 0.000011
Epoch 95/95
2999/2999 [==============================] - 266s - loss: 0.0789 - mse: 0.1251 - val_loss: 0.0513 - val_mse: 0.0738
Wrote model to .\RodAngle\angle_weights_94.hdf
Updated lr to 0.000011
Epoch 96/96
2999/2999 [==============================] - 266s - loss: 0.0764 - mse: 0.1207 - val_loss: 0.0884 - val_mse: 0.1509
Wrote model to .\RodAngle\angle_weights_95.hdf
Updated lr to 0.000010
Epoch 97/97
2999/2999 [==============================] - 266s - loss: 0.0788 - mse: 0.1258 - val_loss: 0.0620 - val_mse: 0.0863
Wrote model to .\RodAngle\angle_weights_96.hdf
Updated lr to 0.000010
Epoch 98/98
2999/2999 [==============================] - 266s - loss: 0.0764 - mse: 0.1180 - val_loss: 0.0807 - val_mse: 0.1289
Wrote model to .\RodAngle\angle_weights_97.hdf
Updated lr to 0.000010
Epoch 99/99
2999/2999 [==============================] - 266s - loss: 0.0756 - mse: 0.1161 - val_loss: 0.0693 - val_mse: 0.1156
Wrote model to .\RodAngle\angle_weights_98.hdf
Updated lr to 0.000010
Epoch 100/100
2999/2999 [==============================] - 269s - loss: 0.0757 - mse: 0.1197 - val_loss: 0.0787 - val_mse: 0.1250
Wrote model to .\RodAngle\angle_weights_99.hdf
Updated lr to 0.000009
Epoch 101/101
2999/2999 [==============================] - 266s - loss: 0.0767 - mse: 0.1217 - val_loss: 0.1019 - val_mse: 0.1700
Wrote model to .\RodAngle\angle_weights_100.hdf
Predicted.
Updated lr to 0.000009
Epoch 102/102
2999/2999 [==============================] - 265s - loss: 0.0761 - mse: 0.1180 - val_loss: 0.0832 - val_mse: 0.1525
Wrote model to .\RodAngle\angle_weights_101.hdf
Updated lr to 0.000009
Epoch 103/103
2999/2999 [==============================] - 266s - loss: 0.0771 - mse: 0.1228 - val_loss: 0.0911 - val_mse: 0.1355
Wrote model to .\RodAngle\angle_weights_102.hdf
Updated lr to 0.000008
Epoch 104/104
2999/2999 [==============================] - 267s - loss: 0.0770 - mse: 0.1173 - val_loss: 0.0839 - val_mse: 0.1506
Wrote model to .\RodAngle\angle_weights_103.hdf
Updated lr to 0.000008
Epoch 105/105
2999/2999 [==============================] - 266s - loss: 0.0760 - mse: 0.1242 - val_loss: 0.0752 - val_mse: 0.1247
Wrote model to .\RodAngle\angle_weights_104.hdf
Updated lr to 0.000008
Epoch 106/106
2999/2999 [==============================] - 266s - loss: 0.0746 - mse: 0.1136 - val_loss: 0.0710 - val_mse: 0.0934
Wrote model to .\RodAngle\angle_weights_105.hdf
Updated lr to 0.000008
Epoch 107/107
2999/2999 [==============================] - 267s - loss: 0.0761 - mse: 0.1215 - val_loss: 0.0747 - val_mse: 0.1427
Wrote model to .\RodAngle\angle_weights_106.hdf
Updated lr to 0.000007
Epoch 108/108
2999/2999 [==============================] - 266s - loss: 0.0767 - mse: 0.1234 - val_loss: 0.0870 - val_mse: 0.1344
Wrote model to .\RodAngle\angle_weights_107.hdf
Updated lr to 0.000007
Epoch 109/109
2999/2999 [==============================] - 266s - loss: 0.0785 - mse: 0.1255 - val_loss: 0.1009 - val_mse: 0.1584
Wrote model to .\RodAngle\angle_weights_108.hdf
Updated lr to 0.000007
Epoch 110/110
2999/2999 [==============================] - 266s - loss: 0.0760 - mse: 0.1167 - val_loss: 0.0697 - val_mse: 0.1190
Wrote model to .\RodAngle\angle_weights_109.hdf
Updated lr to 0.000007
Epoch 111/111
2999/2999 [==============================] - 266s - loss: 0.0766 - mse: 0.1195 - val_loss: 0.0924 - val_mse: 0.1510
Wrote model to .\RodAngle\angle_weights_110.hdf
Predicted.
Updated lr to 0.000007
Epoch 112/112
2999/2999 [==============================] - 269s - loss: 0.0864 - mse: 0.1351 - val_loss: 0.0711 - val_mse: 0.1166
Wrote model to .\RodAngle\angle_weights_111.hdf
Updated lr to 0.000006
Epoch 113/113
2999/2999 [==============================] - 265s - loss: 0.0749 - mse: 0.1191 - val_loss: 0.1076 - val_mse: 0.1778
Wrote model to .\RodAngle\angle_weights_112.hdf
Updated lr to 0.000006
Epoch 114/114
2999/2999 [==============================] - 269s - loss: 0.0753 - mse: 0.1160 - val_loss: 0.0588 - val_mse: 0.0818
Wrote model to .\RodAngle\angle_weights_113.hdf
Updated lr to 0.000006
Epoch 115/115
2999/2999 [==============================] - 268s - loss: 0.0772 - mse: 0.1226 - val_loss: 0.0593 - val_mse: 0.0933
Wrote model to .\RodAngle\angle_weights_114.hdf
Updated lr to 0.000006
Epoch 116/116
2999/2999 [==============================] - 266s - loss: 0.0754 - mse: 0.1175 - val_loss: 0.0582 - val_mse: 0.0850
Wrote model to .\RodAngle\angle_weights_115.hdf
Updated lr to 0.000006
Epoch 117/117
2999/2999 [==============================] - 265s - loss: 0.0775 - mse: 0.1203 - val_loss: 0.0630 - val_mse: 0.0924
Wrote model to .\RodAngle\angle_weights_116.hdf
Updated lr to 0.000005
Epoch 118/118
2999/2999 [==============================] - 266s - loss: 0.0830 - mse: 0.1303 - val_loss: 0.0835 - val_mse: 0.1301
Wrote model to .\RodAngle\angle_weights_117.hdf
Updated lr to 0.000005
Epoch 119/119
2999/2999 [==============================] - 267s - loss: 0.0775 - mse: 0.1202 - val_loss: 0.0852 - val_mse: 0.1328
Wrote model to .\RodAngle\angle_weights_118.hdf
Updated lr to 0.000005
Epoch 120/120
2999/2999 [==============================] - 266s - loss: 0.0760 - mse: 0.1211 - val_loss: 0.1002 - val_mse: 0.1803
Wrote model to .\RodAngle\angle_weights_119.hdf
Updated lr to 0.000005
Epoch 121/121
2999/2999 [==============================] - 266s - loss: 0.0782 - mse: 0.1260 - val_loss: 0.1007 - val_mse: 0.1688
Wrote model to .\RodAngle\angle_weights_120.hdf
Predicted.
Updated lr to 0.000005
Epoch 122/122
2999/2999 [==============================] - 266s - loss: 0.0762 - mse: 0.1186 - val_loss: 0.0482 - val_mse: 0.0726
Wrote model to .\RodAngle\angle_weights_121.hdf
Updated lr to 0.000005
Epoch 123/123
2999/2999 [==============================] - 267s - loss: 0.0773 - mse: 0.1235 - val_loss: 0.0962 - val_mse: 0.1749
Wrote model to .\RodAngle\angle_weights_122.hdf
Updated lr to 0.000005
Epoch 124/124
2999/2999 [==============================] - 266s - loss: 0.0761 - mse: 0.1182 - val_loss: 0.0915 - val_mse: 0.1374
Wrote model to .\RodAngle\angle_weights_123.hdf
Updated lr to 0.000004
Epoch 125/125
2999/2999 [==============================] - 266s - loss: 0.0762 - mse: 0.1180 - val_loss: 0.0761 - val_mse: 0.1216
Wrote model to .\RodAngle\angle_weights_124.hdf
Updated lr to 0.000004
Epoch 126/126
2999/2999 [==============================] - 267s - loss: 0.0815 - mse: 0.1316 - val_loss: 0.0761 - val_mse: 0.1226
Wrote model to .\RodAngle\angle_weights_125.hdf
Updated lr to 0.000004
Epoch 127/127
2999/2999 [==============================] - 266s - loss: 0.0752 - mse: 0.1144 - val_loss: 0.0719 - val_mse: 0.1205
Wrote model to .\RodAngle\angle_weights_126.hdf
Updated lr to 0.000004
Epoch 128/128
2999/2999 [==============================] - 266s - loss: 0.0751 - mse: 0.1181 - val_loss: 0.1037 - val_mse: 0.1597
Wrote model to .\RodAngle\angle_weights_127.hdf
Updated lr to 0.000004
Epoch 129/129
2999/2999 [==============================] - 267s - loss: 0.0777 - mse: 0.1248 - val_loss: 0.1132 - val_mse: 0.2012
Wrote model to .\RodAngle\angle_weights_128.hdf
Updated lr to 0.000004
Epoch 130/130
2999/2999 [==============================] - 267s - loss: 0.0770 - mse: 0.1222 - val_loss: 0.0884 - val_mse: 0.1480
Wrote model to .\RodAngle\angle_weights_129.hdf
Updated lr to 0.000004
Epoch 131/131
2999/2999 [==============================] - 267s - loss: 0.0751 - mse: 0.1133 - val_loss: 0.1198 - val_mse: 0.2114
Wrote model to .\RodAngle\angle_weights_130.hdf
Predicted.
Updated lr to 0.000004
Epoch 132/132
2999/2999 [==============================] - 301s - loss: 0.0787 - mse: 0.1219 - val_loss: 0.0633 - val_mse: 0.0856
Wrote model to .\RodAngle\angle_weights_131.hdf
Updated lr to 0.000003
Epoch 133/133
2999/2999 [==============================] - 263s - loss: 0.0760 - mse: 0.1215 - val_loss: 0.0904 - val_mse: 0.1405
Wrote model to .\RodAngle\angle_weights_132.hdf
Updated lr to 0.000003
Epoch 134/134
2999/2999 [==============================] - 264s - loss: 0.0761 - mse: 0.1185 - val_loss: 0.1025 - val_mse: 0.1710
Wrote model to .\RodAngle\angle_weights_133.hdf
Updated lr to 0.000003
Epoch 135/135
2999/2999 [==============================] - 268s - loss: 0.0756 - mse: 0.1191 - val_loss: 0.0600 - val_mse: 0.0708
Wrote model to .\RodAngle\angle_weights_134.hdf
Updated lr to 0.000003
Epoch 136/136
2999/2999 [==============================] - 262s - loss: 0.1421 - mse: 0.1947 - val_loss: 0.0719 - val_mse: 0.1152
Wrote model to .\RodAngle\angle_weights_135.hdf
Updated lr to 0.000003
Epoch 137/137
2999/2999 [==============================] - 263s - loss: 0.0750 - mse: 0.1144 - val_loss: 0.0793 - val_mse: 0.1484
Wrote model to .\RodAngle\angle_weights_136.hdf
Updated lr to 0.000003
Epoch 138/138
2999/2999 [==============================] - 262s - loss: 0.0774 - mse: 0.1232 - val_loss: 0.0901 - val_mse: 0.1407
Wrote model to .\RodAngle\angle_weights_137.hdf
Updated lr to 0.000003
Epoch 139/139
2999/2999 [==============================] - 263s - loss: 0.0769 - mse: 0.1221 - val_loss: 0.0770 - val_mse: 0.1270
Wrote model to .\RodAngle\angle_weights_138.hdf
Updated lr to 0.000003
Epoch 140/140
2999/2999 [==============================] - 264s - loss: 0.0762 - mse: 0.1174 - val_loss: 0.0816 - val_mse: 0.1350
Wrote model to .\RodAngle\angle_weights_139.hdf
Updated lr to 0.000003
Epoch 141/141
2999/2999 [==============================] - 263s - loss: 0.0772 - mse: 0.1216 - val_loss: 0.0867 - val_mse: 0.1275
Wrote model to .\RodAngle\angle_weights_140.hdf
Predicted.
Updated lr to 0.000003
Epoch 142/142
2999/2999 [==============================] - 263s - loss: 0.0745 - mse: 0.1185 - val_loss: 0.0862 - val_mse: 0.1355
Wrote model to .\RodAngle\angle_weights_141.hdf
Updated lr to 0.000003
Epoch 143/143
2999/2999 [==============================] - 263s - loss: 0.0793 - mse: 0.1291 - val_loss: 0.1119 - val_mse: 0.1843
Wrote model to .\RodAngle\angle_weights_142.hdf
Updated lr to 0.000002
Epoch 144/144
2999/2999 [==============================] - 263s - loss: 0.0753 - mse: 0.1179 - val_loss: 0.0830 - val_mse: 0.1253
Wrote model to .\RodAngle\angle_weights_143.hdf
Updated lr to 0.000002
Epoch 145/145
2999/2999 [==============================] - 263s - loss: 0.0763 - mse: 0.1231 - val_loss: 0.0843 - val_mse: 0.1306
Wrote model to .\RodAngle\angle_weights_144.hdf
Updated lr to 0.000002
Epoch 146/146
2999/2999 [==============================] - 262s - loss: 0.0965 - mse: 0.1514 - val_loss: 0.0714 - val_mse: 0.0959
Wrote model to .\RodAngle\angle_weights_145.hdf
Updated lr to 0.000002
Epoch 147/147
2999/2999 [==============================] - 262s - loss: 0.0768 - mse: 0.1214 - val_loss: 0.0708 - val_mse: 0.0954
Wrote model to .\RodAngle\angle_weights_146.hdf
Updated lr to 0.000002
Epoch 148/148
2999/2999 [==============================] - 263s - loss: 0.0759 - mse: 0.1195 - val_loss: 0.0693 - val_mse: 0.1179
Wrote model to .\RodAngle\angle_weights_147.hdf
Updated lr to 0.000002
Epoch 149/149
2999/2999 [==============================] - 262s - loss: 0.0760 - mse: 0.1202 - val_loss: 0.0762 - val_mse: 0.1373
Wrote model to .\RodAngle\angle_weights_148.hdf
Updated lr to 0.000002
Epoch 150/150
2999/2999 [==============================] - 263s - loss: 0.0749 - mse: 0.1187 - val_loss: 0.0696 - val_mse: 0.1117
Wrote model to .\RodAngle\angle_weights_149.hdf
Updated lr to 0.000002
Epoch 151/151
2999/2999 [==============================] - 262s - loss: 0.0821 - mse: 0.1302 - val_loss: 0.0741 - val_mse: 0.1189
Wrote model to .\RodAngle\angle_weights_150.hdf
Predicted.
Updated lr to 0.000002
Epoch 152/152
2226/2999 [=====================>........] - ETA: 55s - loss: 0.0759 - mse: 0.1184
User stopped the training.
In [ ]:
data_path = ".\\..\\..\\TrainingData\\Processed\\RodTrainingDataAngles\\Result\\settings_full.tsv"
transformer = VideoTransform( zoom_range=0.1, rotation_range=5, width_shift_range=0.1, height_shift_range=0.1, shear_range= 0.1, fill_mode='nearest', vertical_flip=False, horizontal_flip=True, horizontal_flip_invert_indices = [], horizontal_flip_reverse_indices = [], data_format='channels_last' )
training = TrainingInput(transformer, data_path, position_rel_indexes, frame_rel_indexes, 0.05)
lr = 0.00003
print("Updated lr to %f" % lr)
model.compile(optimizer=keras.optimizers.RMSprop(lr=lr),
loss=[mse_wrap],
metrics=[mse])
start_epoch = epoch + 1
for epoch in range(start_epoch,200):
try:
model.fit_generator(TrainBatchGen(batch_size, model, training), batches_training_per_epoch, epochs=epoch+1, verbose=1, callbacks=None, class_weight=None, max_q_size=10, workers=1, validation_data=ValidateBatchGen(batch_size, model, training), validation_steps = batches_validation_per_epoch, pickle_safe=False, initial_epoch=epoch)
model.save_weights(WEIGHTS_FNAME % epoch)
model.save(MODELS_FNAME % epoch)
print(("Wrote model to " + WEIGHTS_FNAME ) % epoch)
if epoch % 4 == 0:
plot_validate(ValidateBatchGen(batch_size, model, training), model, 1000, "Angle prediction")
except KeyboardInterrupt:
print("\r\nUser stopped the training.")
assert(False)
break
plot_validate(ValidateBatchGen(batch_size, model, training), model, 1000, "Angle prediction")
Creating training chunk from .\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk0.avi
.\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk0.avi
added 3204 new frames for a total of 3204
Creating training chunk from .\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk1.avi
.\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk1.avi
added 2763 new frames for a total of 5967
Creating training chunk from .\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk2.avi
.\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk2.avi
added 2355 new frames for a total of 8322
Creating training chunk from .\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk3.avi
.\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk3.avi
added 1667 new frames for a total of 9989
Creating training chunk from .\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk4.avi
.\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk4.avi
added 1172 new frames for a total of 11161
Creating training chunk from .\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk5.avi
.\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk5.avi
added 2190 new frames for a total of 13351
Creating training chunk from .\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk6.avi
.\..\..\TrainingData\Processed\RodTrainingDataAngles\Result\chunk6.avi
added 1644 new frames for a total of 14995
Updated lr to 0.000030
Epoch 62/62
1157/1157 [==============================] - 89s - loss: 0.0518 - mse: 0.1601 - val_loss: 0.0091 - val_mse: 0.0091
Wrote model to .\RodAngle\angle_weights_61.hdf
Epoch 63/63
1157/1157 [==============================] - 90s - loss: 0.0490 - mse: 0.1474 - val_loss: 0.0091 - val_mse: 0.0091
Wrote model to .\RodAngle\angle_weights_62.hdf
Epoch 64/64
1157/1157 [==============================] - 92s - loss: 0.0487 - mse: 0.1520 - val_loss: 0.0114 - val_mse: 0.0114
Wrote model to .\RodAngle\angle_weights_63.hdf
Epoch 65/65
1157/1157 [==============================] - 91s - loss: 0.0515 - mse: 0.1555 - val_loss: 0.0153 - val_mse: 0.0153
Wrote model to .\RodAngle\angle_weights_64.hdf
Predicted.
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Epoch 66/66
1157/1157 [==============================] - 93s - loss: 0.0502 - mse: 0.1375 - val_loss: 0.0144 - val_mse: 0.0144
Wrote model to .\RodAngle\angle_weights_65.hdf
Epoch 67/67
1157/1157 [==============================] - 96s - loss: 0.0486 - mse: 0.1435 - val_loss: 0.0165 - val_mse: 0.0165
Wrote model to .\RodAngle\angle_weights_66.hdf
Epoch 68/68
1157/1157 [==============================] - 97s - loss: 0.0500 - mse: 0.1362 - val_loss: 0.0100 - val_mse: 0.0100
Wrote model to .\RodAngle\angle_weights_67.hdf
Epoch 69/69
1157/1157 [==============================] - 89s - loss: 0.0511 - mse: 0.1480 - val_loss: 0.0160 - val_mse: 0.0808
Wrote model to .\RodAngle\angle_weights_68.hdf
Predicted.
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Epoch 70/70
790/1157 [===================>..........] - ETA: 26s - loss: 0.0498 - mse: 0.1516
Content source: glmcdona/FoosAI
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