def level1():
# Building the model using the pretrained model
conv_base1 = NASNetLarge(weights='imagenet', include_top=False, input_shape=(img_height, img_width, 3))
print("\n### LEVEL1 ###\npretrained network:")
conv_base1.summary()
model = models.Sequential()
model.add(conv_base1)
model.add(layers.GlobalAveragePooling2D())
model.add(layers.Dense(fcLayer1, activation='relu'))
model.add(layers.Dropout(dropout))
model.add(layers.Dense(classes, activation='softmax'))
# freezing the base network
print("trainable layers bevor freezing:", int(len(model.trainable_weights)/2)) # weights = weights + bias = 2 pro layer
conv_base1.trainable = False
print("trainable layers after freezing:", int(len(model.trainable_weights)/2))
print("\npretrained network + densely connected classifier")
model.summary()
# training the added layers only
model.compile(loss='categorical_crossentropy', optimizer=optimizers.RMSprop(lr=learning_rate, decay=lr_decay), metrics=['acc'])
callbacks_list_L1 = [ModelCheckpoint(filepath=weights_path, save_weights_only=True, monitor='val_acc', verbose=1, save_best_only=True),
ReduceLROnPlateau(monitor='val_acc', factor=factorL1, patience=patiencel1, verbose=1),
TensorBoard(log_dir=TensorBoardLogDir+'\\level1')]
print("\n### Level1 Training ... ")
# training the model
history = model.fit_generator(
train_generator,
steps_per_epoch=(nbrTrainImages * classes) // (batch * 5),
epochs=epochsL1,
callbacks=callbacks_list_L1,
validation_data=test_generator,
validation_steps=nbrTestImages,
verbose=verbose_train)
history_val1 = [history.history] # saving all results of the final test
plot(history_val1, "LEVEL1:", epochsL1)
print("\n### LEVEL1 Training finished successfully ###")
print("\nLoading trained weights from " + weights_path + " ...")
model.load_weights(weights_path)
model.compile(loss='categorical_crossentropy', optimizer=optimizers.RMSprop(lr=learning_rate), metrics=['acc'])
print("\n### Saving Level1 Model to ", model_path+'l1.h5', " ... ")
model.save(model_path+'l1.h5')
def Nas_Net(trainable=None, net="NASNetMobile"):
# Preprocessing the dataset into keras feedable format
train_datagen = ImageDataGenerator(rotation_range=rotation,
width_shift_range=width_shift,
height_shift_range=height_shift,
rescale=scale,
shear_range=shear,
zoom_range=zoom,
horizontal_flip=horizontal,
fill_mode=fill,
validation_split=validation)
test_datagen = ImageDataGenerator(rescale=scale, )
train_generator = train_datagen.flow_from_directory(
path,
target_size=target,
batch_size=batch,
class_mode='categorical',
subset='training',
)
validation_generator = train_datagen.flow_from_directory(
path,
target_size=target,
batch_size=batch,
class_mode='categorical',
subset='validation')
models_list = ['NASNetLarge', 'NASNetMobile']
# Loading the NasNet Model
if net == "NASNetLarge":
nasnet = NASNetLarge(include_top=False,
weights='imagenet',
input_shape=input_sh,
pooling=pooling_model)
if net == "NASNetMobile":
nasnet = NASNetMobile(include_top=False,
weights='imagenet',
input_shape=input_sh,
pooling=pooling_model)
if net not in models_list:
raise ValueError('Please provide the raise model ')
output = nasnet.layers[-1].output
if pooling_model is None:
output = keras.layers.Flatten()(output)
nasnet = Model(nasnet.input, output=output)
print(nasnet.summary())
print('\n\n\n')
# If you chose not for fine tuning
if trainable is None:
model = Sequential()
model.add(nasnet)
model.add(Dense(hidden, activation='relu', input_dim=input_sh))
model.add(Dropout(dropout_num))
model.add(Dense(hidden, activation='relu'))
model.add(Dropout(dropout_num))
if classes == 1:
model.add(Dense(classes, activation='sigmoid', name='Output'))
else:
model.add(Dense(classes, activation='softmax', name='Output'))
for layer in nasnet.layers:
layer.trainable = False
print("The model summary of Nasnet -->\n\n\n"
) # In this the Nasnet layers are not trainable
for i, layer in enumerate(nasnet.layers):
print(i, layer.name, layer.trainable)
model.compile(
loss=loss_param, # Change according to data
optimizer=optimizers.RMSprop(),
metrics=['accuracy'])
print("The summary of final Model \n\n\n")
print(model.summary())
print('\n\n\n')
fit_history = model.fit_generator(
train_generator,
steps_per_epoch=len(train_generator.filenames) // batch,
epochs=epoch,
shuffle=True,
validation_data=validation_generator,
validation_steps=len(train_generator.filenames) // batch,
class_weight=n,
callbacks=[
EarlyStopping(patience=patience_param,
restore_best_weights=True),
ReduceLROnPlateau(patience=patience_param)
])
os.chdir(output_path)
model.save("model.h5")
print(fit_history.history.keys())
plt.figure(1, figsize=(15, 8))
plt.subplot(221)
plt.plot(fit_history.history['accuracy'])
plt.plot(fit_history.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'valid'])
plt.subplot(222)
plt.plot(fit_history.history['loss'])
plt.plot(fit_history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'valid'])
plt.show()
if trainable is not None:
# Make last block of the conv_base trainable:
for layer in nasnet.layers[:trainable]:
layer.trainable = False
for layer in nasnet.layers[trainable:]:
layer.trainable = True
print('Last block of the conv_base is now trainable')
for i, layer in enumerate(nasnet.layers):
print(i, layer.name, layer.trainable)
model = Sequential()
model.add(nasnet)
model.add(Dense(hidden, activation='relu', input_dim=input_sh))
model.add(Dropout(dropout_num))
model.add(Dense(hidden, activation='relu'))
model.add(Dropout(dropout_num))
model.add(Dense(hidden, activation='relu'))
model.add(Dropout(dropout_num))
if classes == 1:
model.add(Dense(classes, activation='sigmoid', name='Output'))
else:
model.add(Dense(classes, activation='softmax', name='Output'))
for layer in nasnet.layers:
layer.trainable = False
print("The model summary of Nasnet -->\n\n\n"
) # In this the Nasnet layers are not trainable
model.compile(
loss=loss_param, # Change according to data
optimizer=optimizers.RMSprop(),
metrics=['accuracy'])
print("The summary of final Model \n\n\n")
print(model.summary())
print('\n\n\n')
fit_history = model.fit_generator(
train_generator,
steps_per_epoch=len(train_generator.filenames) // batch,
epochs=epoch,
shuffle=True,
validation_data=validation_generator,
validation_steps=len(train_generator.filenames) // batch,
class_weight=n,
callbacks=[
EarlyStopping(patience=patience_param,
restore_best_weights=True),
ReduceLROnPlateau(patience=patience_param)
])
os.chdir(output_path)
model.save("model.h5")
print(fit_history.history.keys())
plt.figure(1, figsize=(15, 8))
plt.subplot(221)
plt.plot(fit_history.history['accuracy'])
plt.plot(fit_history.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'valid'])
plt.subplot(222)
plt.plot(fit_history.history['loss'])
plt.plot(fit_history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'valid'])
plt.show()