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Updating models

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Sean
2024-10-26 12:20:02 -04:00
parent 4c05f955b3
commit f7011b5554
10 changed files with 88 additions and 298 deletions
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Models/__pycache__/bottleneck_residual.cpython-39.pyc
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Models/__pycache__/resnet50.cpython-39.pyc
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Models/bottleneck_residual.py
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@@ -2,11 +2,11 @@ from itertools import filterfalse
import sys
import os
from keras.layers.pooling import MaxPool2D
from numpy.lib.shape_base import expand_dims
#from keras.layers.pooling import MaxPool2D
#from numpy.lib.shape_base import expand_dims
sys.path.append('c:/git/keras')
sys.path.append('c:/git/absl')
#sys.path.append('c:/git/keras')
#sys.path.append('c:/git/absl')
# installed
# py -mpip install numpy
@@ -17,21 +17,21 @@ sys.path.append('c:/git/absl')
# c:\users\skess\appdata\local\programs\python\python39\lib\site-packages
import keras
from keras.optimizers import adam_v2
from tensorflow.keras.optimizers import Adam
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.utils import np_utils
from keras.layers import Dense
#from keras.optimizers import adam_v2
#from tensorflow.keras.optimizers import Adam
#from keras.datasets import cifar10
#from keras.preprocessing.image import ImageDataGenerator
#from keras.models import Sequential
#from keras.utils import np_utils
#from keras.layers import Dense
from keras.layers import Activation
from keras.layers import Flatten
from keras.layers import Dropout
#from keras.layers import Flatten
#from keras.layers import Dropout
from keras.layers import BatchNormalization
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.callbacks import ModelCheckpoint
from tensorflow.keras.callbacks import EarlyStopping
#from keras.layers import MaxPooling2D
#from keras.callbacks import ModelCheckpoint
#from tensorflow.keras.callbacks import EarlyStopping
from keras import regularizers
from keras import optimizers
from matplotlib import pyplot

32
Models/model_host.py
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@@ -35,7 +35,9 @@
# c:\users\skess\appdata\local\programs\python\python39\lib\site-packages
import keras
from keras.preprocessing.image import ImageDataGenerator
#from keras.preprocessing.image import ImageDataGenerator
from keras.src.legacy.preprocessing.image import ImageDataGenerator
from flask import Flask, render_template, send_file, request
import numpy as np
import tensorflow
@@ -124,6 +126,24 @@ def predict_resnet50():
response=str(predictions)+'-->'+str(threshold_output)
return response
# New model updated in October 2024
@app.route('/predict_resnet50_20241024_270', methods=['GET','POST'])
def predict_resnet50_20241024_270():
print('/predict_resnet50_20241024_270')
test_image=request.get_data()
test_image = PIL.Image.open(io.BytesIO(test_image))
test_image = test_image.convert('L')
test_array=keras.preprocessing.image.img_to_array(test_image)
batch_test_array=np.array([test_array])
predictions=resnet50_20241024_270_model.predict(batch_test_array)
if type(predictions) == list:
average_prediction = sum(predictions)/len(predictions)
threshold_output = np.where(average_prediction > 0.5, 1, 0)
else :
threshold_output = np.where(predictions > 0.5, 1, 0)
response=str(predictions)+'-->'+str(threshold_output)
return response
# This version expects the image to be of the form (x,x,3).
@app.route('/predict_resnet50B', methods=['GET','POST'])
def predict_resnet50B():
@@ -175,15 +195,23 @@ def process_image():
if __name__ == '__main__':
resnet50_model_name='../Weights/resnet50.h5'
resnet50_model = keras.models.load_model(resnet50_model_name)
print('Loading {model_name}'.format(model_name=resnet50_model_name))
resnet50_model = keras.models.load_model(resnet50_model_name)
resnet50_20241024_270_model_name='../Weights/resnet50_20241024_270.h5.keras'
print('Loading {model_name}'.format(model_name=resnet50_20241024_270_model_name))
resnet50_20241024_270_model = keras.models.load_model(resnet50_20241024_270_model_name)
resnet50b_model_name='../Weights/resnet50B.h5'
print('Loading {model_name}'.format(model_name=resnet50b_model_name))
resnet50b_model = keras.models.load_model(resnet50b_model_name)
vgg16_model_name='../Weights/vggnet16.h5'
print('Loading {model_name}'.format(model_name=vgg16_model_name))
vgg16_model=keras.models.load_model(vgg16_model_name)
lenet_model_name='../Weights/lenet5.h5'
print('Loading {model_name}'.format(model_name=lenet_model_name))
lenet_model=keras.models.load_model(lenet_model_name)
port = int(os.environ.get('PORT',5000))

65
Models/model_sk_resnet.py
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@@ -1,11 +1,24 @@
import sys
import os
from keras.layers.pooling import MaxPool2D
from keras.optimizer_v2 import gradient_descent
from keras.optimizers import SGD
from keras.optimizers import Adam
from tensorflow.keras.callbacks import TensorBoard
import keras
#from keras.models import Model
from keras.src.legacy.preprocessing.image import ImageDataGenerator
from keras.callbacks import ModelCheckpoint
#from tensorflow.keras.callbacks import ReduceLROnPlateau
from tensorflow.keras.callbacks import EarlyStopping
#from keras import regularizers
#from keras import optimizers
import numpy as np
import tensorflow
from resnet50 import *
import math
from time import time
sys.path.append('c:/git/keras')
sys.path.append('c:/git/absl')
# Notes: 10/23/2024 Achived 53.85%
# installed
# py -mpip install numpy
@@ -15,39 +28,14 @@ sys.path.append('c:/git/absl')
# py -mpip install matplotlib
# c:\users\skess\appdata\local\programs\python\python39\lib\site-packages
import keras
from keras.models import Model
#from keras.optimizers import *
#from keras.optimizers import adam_v2
from tensorflow.keras.optimizers import Adam
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.utils import np_utils
from keras.layers import Dense
from keras.layers import Activation
from keras.layers import Flatten
from keras.layers import Dropout
from keras.layers import BatchNormalization
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.callbacks import ModelCheckpoint
from tensorflow.keras.callbacks import ReduceLROnPlateau
from tensorflow.keras.callbacks import EarlyStopping
from keras import regularizers
from keras import optimizers
from matplotlib import pyplot
import numpy as np
import tensorflow
# from inception_module import *
from resnet50 import *
import math
# Create a TensorBoard instance with the path to the logs directory. Before training, in Terminal, run tensorboard --logdir=logs/
tensorboard = TensorBoard(log_dir='logs/{}'.format(time()))
# RESNET50 ARCHITECTURE
image_size = (128, 128)
#model_weights_file='model_resnet50.hdf5'
model_name='resnet50.h5'
model_name='resnet50_20241022.h5.keras'
train_ds = tensorflow.keras.preprocessing.image_dataset_from_directory(
'C:\\boneyard\\DeepLearning\\data',
@@ -82,7 +70,7 @@ metric='accuracy'
reduce_lr=tensorflow.keras.callbacks.ReduceLROnPlateau(monitor='val_loss',factor=np.sqrt(0.1),patience=5,min_lr=0.5e-6)
#Optimizer
optimizer=gradient_descent.SGD(learning_rate=initial_lrate, momentum=0.9, nesterov=False)
optimizer=SGD(learning_rate=initial_lrate, momentum=0.9, nesterov=False)
# Checkpointer
checkpointer=ModelCheckpoint(filepath=model_name,monitor=metric,verbose=1,save_best_only=True)
@@ -90,16 +78,15 @@ checkpointer=ModelCheckpoint(filepath=model_name,monitor=metric,verbose=1,save_b
#Early Stopping
early_stopping=EarlyStopping(monitor='val_loss',mode='min',verbose=1,patience=patience_on_early_stop)
# def resnet50(input_shape,classes)
model=resnet50(input_shape,1)
model.summary()
model.compile(loss='binary_crossentropy',optimizer=optimizer,metrics=['accuracy'])
history = model.fit(train_ds, validation_data=val_ds, epochs=epochs, batch_size=batch_size, callbacks=[reduce_lr,early_stopping,checkpointer])
history = model.fit(train_ds, validation_data=val_ds, epochs=epochs, batch_size=batch_size, callbacks=[tensorboard,reduce_lr,early_stopping,checkpointer])
# leave the following line commented out because the early stopping is in place
#model.save(model_name)
# plot the learning curves
pyplot.plot(history.history['accuracy'],label='train')
pyplot.plot(history.history['val_accuracy'],label='validation')
pyplot.legend()
pyplot.show()
# pyplot.plot(history.history['accuracy'],label='train')
# pyplot.plot(history.history['val_accuracy'],label='validation')
# pyplot.legend()
# pyplot.show()

109
Models/model_sk_resnet_v2.py
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@@ -1,109 +0,0 @@
import sys
import os
from keras.layers.pooling import MaxPool2D
from keras.optimizer_v2 import gradient_descent
sys.path.append('c:/git/keras')
sys.path.append('c:/git/absl')
# installed
# py -mpip install numpy
# py -mpip show numpy
# py -mpip install tensorflow
# py -mpip show tensorflow
# py -mpip install matplotlib
# c:\users\skess\appdata\local\programs\python\python39\lib\site-packages
import keras
from keras.models import Model
#from keras.optimizers import *
#from keras.optimizers import adam_v2
from tensorflow.keras.optimizers import Adam
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.utils import np_utils
from keras.layers import Dense
from keras.layers import Activation
from keras.layers import Flatten
from keras.layers import Dropout
from keras.layers import BatchNormalization
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.callbacks import ModelCheckpoint
from tensorflow.keras.callbacks import ReduceLROnPlateau
from tensorflow.keras.callbacks import EarlyStopping
from keras import regularizers
from keras import optimizers
from matplotlib import pyplot
import numpy as np
import tensorflow
from resnet50 import *
import math
# RESNET50 ARCHITECTIURE
image_size = (128, 128)
# November 11, 2022 : This model scored 78.95 in Total Accuracy Weights=../Weights/resnet50.h5.v2
model_name='resnet50.h5'
train_ds = tensorflow.keras.preprocessing.image_dataset_from_directory(
'C:\\boneyard\\DeepLearning\\data',
label_mode="binary",
subset="training",
validation_split=.20,
image_size=image_size,
color_mode='grayscale',
batch_size=32,
seed=50
)
val_ds = tensorflow.keras.preprocessing.image_dataset_from_directory(
'C:\\boneyard\\DeepLearning\\data',
label_mode="binary",
subset="validation",
validation_split=.20,
image_size=image_size,
color_mode='grayscale',
batch_size=32,
seed=50
)
input_shape=(128,128,1)
epochs=200
initial_lrate=0.01
batch_size=128
patience_on_early_stop=10
reduce_factor=np.sqrt(0.1)
metric='val_loss'
mode='min'
#ReduceLROnPlateau
reduce_lr=tensorflow.keras.callbacks.ReduceLROnPlateau(monitor=metric,factor=np.sqrt(0.1),patience=5,min_lr=0.5e-6,verbose=True)
#Optimizer
optimizer=gradient_descent.SGD(learning_rate=initial_lrate, momentum=0.9, nesterov=False)
# Checkpointer
checkpointer=ModelCheckpoint(filepath=model_name,mode=mode,monitor=metric,verbose=1,save_best_only=True)
#Early Stopping
early_stopping=EarlyStopping(monitor=metric,mode=mode,verbose=1,patience=patience_on_early_stop)
# def resnet50(input_shape,classes)
model=resnet50(input_shape,1)
model.summary()
#model.compile(loss='binary_crossentropy',optimizer=optimizer,metrics=['accuracy','mse'])
model.compile(loss='binary_crossentropy',optimizer=optimizer,metrics=['accuracy','mse'])
history = model.fit(train_ds, validation_data=val_ds, epochs=epochs, batch_size=batch_size, callbacks=[early_stopping,checkpointer])
#model.save(model_name)
# plot the learning curves
pyplot.plot(history.history['accuracy'],label='train')
pyplot.plot(history.history['val_accuracy'],label='validation')
pyplot.plot(history.history['mse'],label='mse')
pyplot.plot(history.history[metric],label=metric)
pyplot.legend()
pyplot.show()

120
Models/model_sk_resnet_v3.py
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@@ -1,120 +0,0 @@
import sys
import os
from keras.layers.pooling import MaxPool2D
from keras.optimizer_v2 import gradient_descent
sys.path.append('c:/git/keras')
sys.path.append('c:/git/absl')
# installed
# py -mpip install numpy
# py -mpip show numpy
# py -mpip install tensorflow
# py -mpip show tensorflow
# py -mpip install matplotlib
# c:\users\skess\appdata\local\programs\python\python39\lib\site-packages
import keras
from keras.models import Model
#from keras.optimizers import *
#from keras.optimizers import adam_v2
from tensorflow.keras.optimizers import Adam
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.utils import np_utils
from keras.layers import Dense
from keras.layers import Activation
from keras.layers import Flatten
from keras.layers import Dropout
from keras.layers import BatchNormalization
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.callbacks import ModelCheckpoint
from tensorflow.keras.callbacks import ReduceLROnPlateau
from tensorflow.keras.callbacks import EarlyStopping
from keras import regularizers
from keras import optimizers
from matplotlib import pyplot
import numpy as np
import tensorflow
# from inception_module import *
from resnet50 import *
import math
# RESNET50 ARCHITECTIURE
image_size = (128, 128)
#model_weights_file='model_resnet50.hdf5'
model_name='resnet50.h5'
train_ds = tensorflow.keras.preprocessing.image_dataset_from_directory(
'C:\\boneyard\\DeepLearning\\data',
label_mode="binary",
subset="training",
validation_split=.20,
image_size=image_size,
color_mode='grayscale',
batch_size=32,
seed=50
)
val_ds = tensorflow.keras.preprocessing.image_dataset_from_directory(
'C:\\boneyard\\DeepLearning\\data',
label_mode="binary",
subset="validation",
validation_split=.20,
image_size=image_size,
color_mode='grayscale',
batch_size=32,
seed=50
)
input_shape=(128,128,1)
epochs=200
#initial_lrate=0.01
initial_lrate=0.01
batch_size=128
#patience_on_early_stop=15
patience_on_early_stop=epochs*.10
#metric='val_mse'
#metric='val_loss'
#reduce_factor=.25
#reduce_factor=np.sqrt(0.1)
reduce_factor=9.9e-4
metric='val_loss'
mode='min'
#ReduceLROnPlateau
#reduce_lr=tensorflow.keras.callbacks.ReduceLROnPlateau(monitor=metric,factor=np.sqrt(0.1),patience=5,min_lr=0.5e-6,verbose=True)
#reduce_lr=tensorflow.keras.callbacks.ReduceLROnPlateau(monitor='val_mse',factor=reduce_factor,min_lr=0.5e-6,patience=5,verbose=True)
reduce_lr=tensorflow.keras.callbacks.ReduceLROnPlateau(monitor=metric,factor=reduce_factor,min_lr=0.1e-3,patience=5,verbose=True)
#Optimizer
optimizer=gradient_descent.SGD(learning_rate=initial_lrate, momentum=0.9, nesterov=False)
#optimizer=adam_v2.Adam(learning_rate=initial_lrate)
# Checkpointer
checkpointer=ModelCheckpoint(filepath=model_name,mode=mode,monitor=metric,verbose=1,save_best_only=True)
#Early Stopping
early_stopping=EarlyStopping(monitor=metric,mode=mode,verbose=1,patience=patience_on_early_stop)
# def resnet50(input_shape,classes)
model=resnet50(input_shape,1)
model.summary()
#model.compile(loss='binary_crossentropy',optimizer=optimizer,metrics=['accuracy','mse'])
model.compile(loss='binary_crossentropy',optimizer=optimizer,metrics=['accuracy'])
#history = model.fit(train_ds, validation_data=val_ds, epochs=epochs, batch_size=batch_size, callbacks=[reduce_lr,early_stopping,checkpointer])
history = model.fit(train_ds, validation_data=val_ds, epochs=epochs, batch_size=batch_size, callbacks=[early_stopping,checkpointer])
#model.save(model_name)
# plot the learning curves
pyplot.plot(history.history['accuracy'],label='train')
pyplot.plot(history.history['val_accuracy'],label='validation')
#pyplot.plot(history.history['mse'],label='mse')
#pyplot.plot(history.history['mse'],label='mse')
pyplot.plot(history.history[metric],label=metric)
pyplot.legend()
pyplot.show()

24
Models/resnet50.py
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@@ -3,10 +3,12 @@ from itertools import filterfalse
import sys
import os
from keras.layers.pooling import AveragePooling2D, MaxPool2D
#from keras.layers.pooling import AveragePooling2D, MaxPool2D
from keras.layers import AveragePooling2D, MaxPooling2D
sys.path.append('c:/git/keras')
sys.path.append('c:/git/absl')
#sys.path.append('c:/git/keras')
#sys.path.append('c:/git/absl')
# installed
# py -mpip install numpy
@@ -17,21 +19,21 @@ sys.path.append('c:/git/absl')
# c:\users\skess\appdata\local\programs\python\python39\lib\site-packages
import keras
from keras.optimizers import adam_v2
from tensorflow.keras.optimizers import Adam
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
#from keras.optimizers import adam_v2
#from tensorflow.keras.optimizers import Adam
#from keras.datasets import cifar10
#from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.utils import np_utils
#from keras.utils import np_utils
from keras.layers import Dense
from keras.layers import Activation
from keras.layers import Flatten
from keras.layers import Dropout
#from keras.layers import Dropout
from keras.layers import BatchNormalization
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.callbacks import ModelCheckpoint
from tensorflow.keras.callbacks import EarlyStopping
#from keras.callbacks import ModelCheckpoint
#from tensorflow.keras.callbacks import EarlyStopping
from keras import regularizers
from keras import optimizers
from matplotlib import pyplot

4
Models/run_host.bat
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@@ -1 +1,3 @@
py model_host.py
call ..\.venv\scripts\activate
py model_host.py