198 lines
6.8 KiB
Python
198 lines
6.8 KiB
Python
import sys
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import os
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from keras.optimizers import SGD
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from keras.optimizers import Adam
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from tensorflow.keras.callbacks import TensorBoard
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import keras
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from keras.src.legacy.preprocessing.image import ImageDataGenerator
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from keras.callbacks import ModelCheckpoint
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from tensorflow.keras.callbacks import EarlyStopping
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from keras.layers import Flatten
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from keras.layers import Dense
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from keras.layers import MaxPool2D
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from keras.layers import Dropout
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from keras.models import Model
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from keras.layers import AveragePooling2D
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from keras.models import Sequential
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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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import math
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from time import time
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# VGG 16 ARCHITECTIURE
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image_size = (128, 128)
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model_weights_file='vggnet16.h5.keras'
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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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# base_hidden_units=32
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weight_decay=.005
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model=Sequential()
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input_shape=(128,128,1)
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base_filters=32
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learning_rate=.00001
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batch_size=256
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epochs=200
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patience_on_early_stop=5
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# Create a TensorBoard instance with the path to the logs directory. Before training, in Terminal, run tensorboard --logdir=logs/
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tensorboard = TensorBoard(log_dir='logs/{}'.format(time()))
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# Build the network based on AlexNet but using 6 convolutional and 1 Fully Connected
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# Inspired by VGGNet we will add a pooling layer after every two convolutional layers
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#CONV1
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model.add(Conv2D(filters=base_filters,kernel_size=(3,3),padding='same',kernel_regularizer=regularizers.l2(weight_decay), input_shape=input_shape,activation='relu'))
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model.add(BatchNormalization())
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#CONV2
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model.add(Conv2D(filters=base_filters,kernel_size=(3,3),padding='same',kernel_regularizer=regularizers.l2(weight_decay),activation='relu'))
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model.add(BatchNormalization())
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#POOL + Dropout
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model.add(AveragePooling2D(pool_size=(2,2),strides=(2,2),padding='same'))
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model.add(Dropout(0.2))
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#CONV3
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model.add(Conv2D(filters=base_filters*2,kernel_size=(3,3),padding='same',kernel_regularizer=regularizers.l2(weight_decay),activation='relu'))
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model.add(BatchNormalization())
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#CONV4
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model.add(Conv2D(filters=base_filters*2,kernel_size=3,padding='same',kernel_regularizer=regularizers.l2(weight_decay),activation='relu'))
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model.add(BatchNormalization())
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#POOL + Dropout
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model.add(AveragePooling2D(pool_size=(2,2),strides=(2,2),padding='same'))
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#CONV5
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model.add(Conv2D(filters=base_filters*4,kernel_size=(3,3),padding='same',kernel_regularizer=regularizers.l2(weight_decay),activation='relu'))
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model.add(BatchNormalization())
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#CONV6
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model.add(Conv2D(filters=base_filters*4,kernel_size=(3,3),padding='same',kernel_regularizer=regularizers.l2(weight_decay),activation='relu'))
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model.add(BatchNormalization())
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#CONV7
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model.add(Conv2D(filters=base_filters*4,kernel_size=(3,3),padding='same',kernel_regularizer=regularizers.l2(weight_decay),activation='relu'))
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model.add(BatchNormalization())
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#POOL + Dropout
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model.add(AveragePooling2D(pool_size=(2,2),strides=(2,2),padding='same'))
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#CONV8
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model.add(Conv2D(filters=base_filters*8,kernel_size=(3,3),padding='same',kernel_regularizer=regularizers.l2(weight_decay),activation='relu'))
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model.add(BatchNormalization())
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#CONV9
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model.add(Conv2D(filters=base_filters*8,kernel_size=(3,3),padding='same',kernel_regularizer=regularizers.l2(weight_decay),activation='relu'))
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model.add(BatchNormalization())
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#CONV10
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model.add(Conv2D(filters=base_filters*8,kernel_size=(3,3),padding='same',kernel_regularizer=regularizers.l2(weight_decay),activation='relu'))
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model.add(BatchNormalization())
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#POOL + Dropout
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model.add(AveragePooling2D(pool_size=(2,2),strides=(2,2),padding='same'))
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#CONV11
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model.add(Conv2D(filters=base_filters*8,kernel_size=(3,3),padding='same',kernel_regularizer=regularizers.l2(weight_decay),activation='relu'))
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model.add(BatchNormalization())
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#CONV12
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model.add(Conv2D(filters=base_filters*8,kernel_size=(3,3),padding='same',kernel_regularizer=regularizers.l2(weight_decay),activation='relu'))
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model.add(BatchNormalization())
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#CONV13
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model.add(Conv2D(filters=base_filters*8,kernel_size=(3,3),padding='same',kernel_regularizer=regularizers.l2(weight_decay),activation='relu'))
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model.add(BatchNormalization())
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#POOL + Dropout
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model.add(AveragePooling2D(pool_size=(2,2),strides=(2,2),padding='same'))
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#Flatten
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model.add(Flatten())
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#FC14
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model.add(Dense(4096,activation='relu'))
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#DROPOUT
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model.add(Dropout(.50))
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#FC15
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model.add(Dense(4096,activation='relu'))
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#DROPOUT
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model.add(Dropout(.50))
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#SIGMOID
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model.add(Dense(1,activation='sigmoid'))
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model.summary()
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# File path to save the file. Only save the weights if there is an improvement.
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checkpointer=ModelCheckpoint(filepath=model_weights_file,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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# Adam optimizer with learning rate .0001
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#Optimizer
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optimizer = keras.optimizers.Adam(learning_rate=learning_rate, beta_1=0.9, beta_2=0.999,epsilon=1e-8)
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# Cross entropy loss function
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model.compile(loss='binary_crossentropy', optimizer=optimizer,metrics=['accuracy'])
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# Allows to do real-time data augmentation on images on cpu in parallel to training your model on gpu.
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# The callback to checkpointer saves the model wights. Other callback can be added...like a stopping function
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# history=model.fit_generator(dataGen.flow(x_train,y_train,batch_size=batch_size),callbacks=[checkpointer],steps_per_epoch=x_train.shape[0] //
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# batch_size,epochs=epochs,verbose=2,validation_data=(x_valid,y_valid))
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history=model.fit(train_ds, epochs=epochs, validation_data=val_ds,batch_size=batch_size,verbose=2,callbacks=[early_stopping,tensorboard,checkpointer])
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# Here is the evaluation part
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# score=model.evaluate(x=train_ds,verbose=1)
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# print('\n', 'Test Accuracy:', score[1])
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# model.save(model_name)
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# This loads the best weights we saved from the fitting excercise and displays the accuracy of the model
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# model.load_weights(model_weights_file)
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# score=model.evaluate(train_ds,verbose=1)
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# print('\n', 'Test Accuracy:', score[1])
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# scores=model.evaluate(x_test,y_test,batch_size=128,verbose=1)
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# print('\nTest result: %.3f loss: %.3f' % (scores[1]*100,scores[0]))
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# predictions=model.predict(batch_test_array)
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# print(predictions)
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# threshold_output = np.where(predictions > 0.5, 1, 0)
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# print(threshold_output)
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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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