def main():
pl_train, pl_labels = get_dataset('./Pan_Licence/')
pl_labels = to_categorical(pl_labels, num_classes=36)
x_train, x_val, y_train, y_val = train_test_split(pl_train,
pl_labels,
test_size=0.2,
random_state=2064)
tb = TensorBoard(log_dir='./logs/Squeezenet', write_graph=True)
model = SqueezeNet()
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
print(model.summary())
history = model.fit(x_train,
y_train,
batch_size=32,
epochs=5,
validation_split=0.1,
shuffle=True,
callbacks=[tb])
## Save Model
json_model = model.to_json()
with open('model_squeezenet.json', 'w') as f:
f.write(json_model)
model.save_weights('model_squeezenet.h5')
print('Model Saved')
print('Evaluating Model')
predict = model.evaluate(x=x_val, y=y_val, batch_size=1)
print('Score', predict[1] * 100.00)
print('Loss', predict[0])
Y_train = to_categorical(y_train, nb_classes)
Y_test = to_categorical(y_test, nb_classes)
# classes = to_categorical(classes, nb_classes=nr_classes)
print('Loading model..')
model = SqueezeNet(nb_classes, input_shape=input_shape)
model.compile(loss="categorical_crossentropy",
optimizer='adam',
metrics=['accuracy'])
if os.path.isfile(weights_file):
print('Loading weights: %s' % weights_file)
model.load_weights(weights_file, by_name=True)
print('Fitting model')
model.fit(X_train,
Y_train,
batch_size=batch_size,
nb_epoch=nb_epoch,
verbose=1,
validation_split=0.2,
initial_epoch=0)
print("Finished fitting model")
print('Saving weights')
model.save_weights(weights_file, overwrite=True)
print('Evaluating model')
score = model.evaluate(X_test, Y_test, verbose=1)
print('result: %s' % score)
random.shuffle(imgpaths_classes)
images, classes = zip(*imgpaths_classes)
classes = to_categorical(classes, nb_classes=nb_classes)
images = np.asarray(images)
print('Loading model..')
model = SqueezeNet(nb_classes, input_shape=(227, 227, 3))
model.compile(loss="categorical_crossentropy",
optimizer='adam',
metrics=['accuracy', 'categorical_crossentropy'])
if os.path.isfile(weights_file):
print('Loading weights: %s' % weights_file)
model.load_weights(weights_file, by_name=True)
print('Fitting model')
model.fit(images,
classes,
batch_size=batch_size,
nb_epoch=nb_epoch,
verbose=1,
validation_split=0.2,
initial_epoch=0)
print("Finished fitting model")
print('Saving weights')
model.save_weights(weights_file, overwrite=True)
print('Evaluating model')
score = model.evaluate(images, classes, verbose=1)
print('result: %s' % score)