x = model.output
x = SpatialPyramidPooling([1, 2, 3, 6])(x)
# x = Dense(1024, activation='relu')(x)
predictions = Dense(classes, activation='softmax')(x)
model = Model(inputs=model.input,
outputs=predictions,
name='inception_v3_spp')
return model
if __name__ == "__main__":
start = datetime.now()
model = InceptionV1(include_top=True,
input_shape=(224, 224, 3),
weights=None,
classes=2)
# model = make_model()
model.summary()
util = ModelUtils(epochs=120)
# util.get_train_data(resize=(224, 224))
# util.train(model)
# util.evaluate()
# util.save()
# util.confusion_matrix()
# util.plot_loss_accuracy()
util.plot_multiple_roc(model, (224, 224))
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
import tensorflow as tf
tf.set_random_seed(1000)
random.seed(1000)
np.random.seed(1000)
MODEL_SIZE=(227, 227)
if __name__ == "__main__":
start = datetime.now()
# CREATE MODEL
alexnet = AlexNet(input_shape=(227,227, 3), classes=2)
model = alexnet.model()
model.summary()
util = ModelUtils(epochs=120)
util.get_train_data()
# util.get_val_data(resize=(MODEL_SIZE))
# util.train(model)
# util.evaluate()
# util.save()
# util.confusion_matrix()
# util.plot_roc_curve()
# util.plot_loss_accuracy()
util.plot_multiple_roc(model)
time_elapsed = datetime.now() - start
print('Time elapsed (hh:mm:ss.ms) {}'.format(time_elapsed))