184 lines
7.6 KiB
Python
184 lines
7.6 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 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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import math
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# INCEPTION ARCHITECTIURE
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image_size = (128, 128)
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#model_weights_file='inception.h5'
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model_name='inception.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=125
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initial_lrate=0.01
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batch_size=256
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patience_on_early_stop=10
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metric='auxilliary_output_2_accuracy'
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def decay(epoch,steps=100):
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initial_lrate=.01
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drop=.96
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epochs_drop=8
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lrate=initial_lrate*math.pow(drop,math.floor((1+epoch)/epochs_drop))
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return lrate
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lr_schedule=keras.callbacks.LearningRateScheduler(decay,verbose=1)
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#sgd=SGD(lr=initial_lrate,momentum=0.9,nesterov=False)
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optimizer=adam_v2.Adam(learning_rate=initial_lrate)
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optimizer=gradient_descent.SGD(learning_rate=initial_lrate, momentum=0.9, nesterov=False)
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#INPUT LAYER
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input_layer=keras.layers.Input(shape=input_shape)
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kernel_init=keras.initializers.glorot_uniform()
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bias_init=keras.initializers.Constant(value=.2)
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x=Conv2D(64, (7, 7), padding='same', strides=(2, 2), activation='relu', name='conv_1_7x7/2', kernel_initializer=kernel_init, bias_initializer=bias_init)(input_layer)
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x=MaxPool2D((3, 3), padding='same', strides=(2, 2), name='max_pool_1_3x3/2')(x)
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x=Conv2D(192, (3, 3), padding='same', strides=(1, 1), activation='relu', name='conv_2b_3x3/1')(x)
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x=MaxPool2D((3, 3), padding='same', strides=(2, 2), name='max_pool_2_3x3/2')(x)
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#BUILD THE INCEPTION MODULE AND MAX POOLING LAYERS (3a and 3b)
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x=inception_module(x,filters_1x1=64,filters_3x3_reduce=96,filters_3x3=128,filters_5x5_reduce=16,filters_5x5=32,filters_pool_proj=32,kernel_init=kernel_init,bias_init=bias_init,name='inception_3a')
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x=inception_module(x,filters_1x1=128,filters_3x3_reduce=128,filters_3x3=192,filters_5x5_reduce=32,filters_5x5=96,filters_pool_proj=64,kernel_init=kernel_init,bias_init=bias_init,name='inception_3b')
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x=MaxPool2D((3, 3), padding='same', strides=(2, 2), name='max_pool_3_3x3/2')(x)
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#BUILD THE INCEPTION MODULE AND MAX POOLING LAYERS (4a)
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x=inception_module(x,filters_1x1=192,filters_3x3_reduce=96,filters_3x3=208,filters_5x5_reduce=16,filters_5x5=48,filters_pool_proj=64,kernel_init=kernel_init,bias_init=bias_init,name='inception_4a')
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#BUILD THE CLASSIFIERS
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classifier_1 = keras.layers.AveragePooling2D((5, 5), strides=3)(x)
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classifier_1 = Conv2D(128, (1, 1), padding='same', activation='relu')(classifier_1)
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classifier_1 = Flatten()(classifier_1)
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classifier_1 = Dense(1024, activation='relu')(classifier_1)
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classifier_1 = Dropout(0.7)(classifier_1)
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classifier_1 = Dense(1, activation='sigmoid', name='auxilliary_output_1')(classifier_1)
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#BUILD THE INCEPTION MODULE AND MAX POOLING LAYERS (4b, 4c, 4d))
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x=inception_module(x,filters_1x1=160,filters_3x3_reduce=112,filters_3x3=224,filters_5x5_reduce=24,filters_5x5=64,filters_pool_proj=64,kernel_init=kernel_init,bias_init=bias_init,name='inception_4b')
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x=inception_module(x,filters_1x1=128,filters_3x3_reduce=128,filters_3x3=256,filters_5x5_reduce=24,filters_5x5=64,filters_pool_proj=64,kernel_init=kernel_init,bias_init=bias_init,name='inception_4c')
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x=inception_module(x,filters_1x1=112,filters_3x3_reduce=144,filters_3x3=288,filters_5x5_reduce=32,filters_5x5=64,filters_pool_proj=64,kernel_init=kernel_init,bias_init=bias_init,name='inception_4d')
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#BUILD THE CLASSIFIERS
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classifier_2 = keras.layers.AveragePooling2D((5, 5), strides=3)(x)
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classifier_2 = Conv2D(128, (1, 1), padding='same', activation='relu')(classifier_2)
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classifier_2 = Flatten()(classifier_2)
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classifier_2 = Dense(1024, activation='relu')(classifier_2)
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classifier_2 = Dropout(0.7)(classifier_2)
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classifier_2 = Dense(1, activation='sigmoid', name='auxilliary_output_2')(classifier_2)
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#BUILD THE INCEPTION MODULE AND MAX POOLING LAYERS (4e))
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x=inception_module(x,filters_1x1=256,filters_3x3_reduce=160,filters_3x3=320,filters_5x5_reduce=32,filters_5x5=128,filters_pool_proj=128,kernel_init=kernel_init,bias_init=bias_init,name='inception_4e')
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x=MaxPool2D((3,3),padding='same',strides=(2,2),name='max_pool_4_3x3/2')(x)
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#BUILD MODULES 5a and 5b
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x=inception_module(x,filters_1x1=256,filters_3x3_reduce=160,filters_3x3=320,filters_5x5_reduce=32,filters_5x5=128,filters_pool_proj=128,kernel_init=kernel_init,bias_init=bias_init,name='inception_5a')
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x=inception_module(x,filters_1x1=384,filters_3x3_reduce=192,filters_3x3=384,filters_5x5_reduce=48,filters_5x5=128,filters_pool_proj=128,kernel_init=kernel_init,bias_init=bias_init,name='inception_5b')
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#BUILD THE CLASSIFIER
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x=keras.layers.AveragePooling2D(pool_size=(2,2), strides=1, padding='valid',name='avg_pool_5_3x3/1')(x)
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x=Dropout(0.4)(x)
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x = Dense(1000, activation='relu', name='linear')(x)
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x=Flatten()(x)
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x=Dense(1,activation='sigmoid',name='output')(x)
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#model = Model(input_layer, [x], name='googlenet')
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#model.summary()
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#checkpointer=ModelCheckpoint(filepath=model_weights_file,verbose=1,save_best_only=True)
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#model.compile(loss='binary_crossentropy',optimizer=optimizer,metrics=['accuracy'])
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#history=model.fit(train_ds, epochs=epochs, validation_data=val_ds,batch_size=batch_size,verbose=2,callbacks=[lr_schedule])
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#history=model.fit(train_ds, epochs=epochs, validation_data=val_ds,batch_size=batch_size,verbose=2,callbacks=[checkpointer])
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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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model = Model(input_layer, [x, classifier_1, classifier_2], name='googlenet_complete_architecture')
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model.summary()
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model.compile(loss=['binary_crossentropy', 'binary_crossentropy', 'binary_crossentropy'], loss_weights=[1, 0.3, 0.3], optimizer=optimizer, metrics=['accuracy'])
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history = model.fit(train_ds, validation_data=val_ds, epochs=epochs, batch_size=batch_size, callbacks=[lr_schedule,checkpointer,early_stopping])
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#model.save(model_name)
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# plot the learning curves
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pyplot.plot(history.history['output_accuracy'],label='train')
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pyplot.plot(history.history['val_auxilliary_output_2_accuracy'],label='validation')
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pyplot.legend()
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pyplot.show()
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