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