# CREATE MODEL
model = ResNet50(include_top=False,
input_shape=(None, None, 3),
weights='imagenet')
x = model.output
x = SpatialPyramidPooling([1, 2, 3, 6])(x)
predictions = Dense(classes, activation='softmax')(x)
model = Model(inputs=model.input,
outputs=predictions,
name='resnet50spp_pretrained')
return model
if __name__ == "__main__":
start = datetime.now()
model = make_model()
model.summary()
util = ModelUtils(epochs=100)
util.get_train_data()
util.get_val_data()
util.get_test_data()
util.mean_subtraction()
util.train(model)
util.evaluate()
util.save()
util.confusion_matrix()
util.plot_loss_accuracy()
time_elapsed = datetime.now() - start
print('Time elapsed (hh:mm:ss.ms) {}'.format(time_elapsed))
from datetime import datetime
from utils.model_utils import ModelUtils
from keras.layers import Dense, Dropout, Flatten, Activation, Conv2D, GlobalAveragePooling2D
from keras.models import Model
ACTIVATION='Mish'
if __name__ == "__main__":
start = datetime.now()
# CREATE MODEL
# # this is the model we will train
vgg = VGG19(input_shape=(224, 224, 3), classes=3, activation=ACTIVATION, include_top=False, weights='imagenet')
model = vgg.model()
# model = set_non_trainable(model)
x = model.output
x=Dense(4096,activation=ACTIVATION)(x)
x=Dense(4096,activation=ACTIVATION)(x)
x=Dense(3,activation='softmax')(x)
model = Model(model.input, x, name='vgg19')
model.summary()
util = ModelUtils(epochs=20)
util.get_train_data(resize=(224,224))
util.train(model, name=ACTIVATION)
util.evaluate()
util.save(name=ACTIVATION)
util.confusion_matrix(title=model.name)
util.plot_loss_accuracy(path=model.name+'.json', name=model.name)
time_elapsed = datetime.now() - start
print('Time elapsed (hh:mm:ss.ms) {}'.format(time_elapsed))