def Multimodel(cnn_weights_path=None,
all_weights_path=None,
class_num=119,
cnn_no_vary=False):
input_layer = Input(shape=(200, 200, 3))
incptionResnet = InceptionResNetV2(include_top=False,
weights=None,
input_tensor=input_layer,
input_shape=(224, 224, 3))
xception = Xception(include_top=False,
weights=None,
input_tensor=input_layer,
input_shape=(224, 224, 3))
if cnn_no_vary:
for i, layer in enumerate(incptionResnet.layers):
incptionResnet.layers[i].trainable = False
for i, layer in enumerate(xception.layers):
xception.layers[i].trainable = False
if cnn_weights_path != None:
incptionResnet.load_weights(cnn_weights_path[0])
xception.load_weights(cnn_weights_path[1])
print(incptionResnet.output.shape, xception.output.shape)
model1 = GlobalMaxPool2D(data_format='channels_last')(
incptionResnet.output)
model2 = GlobalMaxPool2D(data_format='channels_last')(xception.output)
print(model1.shape, model2.shape)
# 把top1_model和top2_model连接起来
x = keras.layers.Concatenate(axis=1)([model1, model2])
# x = keras.layers.Add()([model1, model2])
# 全连接层
x = Dense(units=256 * 3, activation="relu")(x)
x = Dense(units=256, activation="relu")(x)
# x = Dropout(0.5)(x)
x = Dense(units=class_num, activation="softmax")(x)
model = Model(inputs=input_layer, outputs=x)
# 加载全部的参数
if all_weights_path:
model.load_weights(all_weights_path)
return model
def build_model(input_shapes, n_classes=None):
#### DEFINING INPUT AND BASE ARCHITECTURE
# You need to modify the name and shape of the "image_input"
# according to the preprocessing and name of your
# initial feature.
# This feature should to be preprocessed as an "Image", with a
# custom preprocessing.
image_shape = (197, 197, 3)
image_input_name = "name_of_your_image_input_preprocessed"
image_input = Input(shape=image_shape, name=image_input_name)
base_model = Xception(include_top=False,
weights=None,
input_tensor=image_input)
#### LOADING WEIGHTS OF PRE TRAINED MODEL
# To leverage this architecture, it is better to use weights
# computed on a previous training on a large dataset (Imagenet).
# To do so, you need to download the file containing the weights
# and load them into your model.
# You can do it by using the macro "Download pre-trained model"
# of the "Deep Learning image" plugin (CPU or GPU version depending
# on your setup) available in the plugin store. For this architecture,
# you need to select:
# "Xception trained on Imagenet"
# This will download the weights and put them into a managed folder
folder = dataiku.Folder("name_of_folder_containing_xception_weights")
weights_path = "xception_imagenet_weights_notop.h5"
base_model.load_weights(os.path.join(folder.get_path(), weights_path),
by_name=True,
skip_mismatch=True)
#### ADDING FULLY CONNECTED CLASSIFICATION LAYER
x = base_model.layers[-1].output
x = Flatten()(x)
predictions = Dense(n_classes, activation="softmax")(x)
model = Model(input=base_model.input, output=predictions)
return model
validation_data=[X_val, y_val_matrix],
epochs=EPOCHS,
steps_per_epoch=STEPS_PER_EPOCH,
verbose=1,
callbacks=[checkpoint, lr_scheduler, csv_logger])
###################################### end version basic and balance ###################################################
####################################### run on test set ################################################################
## only run for testing by adding parameter 'test' when running script
elif modus == 'test':
y_test_matrix = to_categorical(y_test, len(labeltonumber))
if worker == 'single':
print(model.metrics_names)
#model.load_weights(save_modeldirectory + '/Xception_genus_pad_version1.1/Xception.109.0.964.hdf5')
model.load_weights(save_modeldirectory + '/{}'.format(weightfile))
accuracy = model.evaluate(x=X_test, y=y_test_matrix)
## get predicted labels for test set
y_prob = model.predict(X_test)
y_pred = y_prob.argmax(axis=-1)
elif worker == 'parallel':
print(parallel_model.metrics_names)
parallel_model.load_weights(save_modeldirectory +
'/{}'.format(weightfile))
accuracy = parallel_model.evaluate(x=X_test, y=y_test_matrix)
## get predicted labels for test set
y_prob = parallel_model.predict(X_test)
y_pred = y_prob.argmax(axis=-1)
print('loss: {}, accuracy: {}'.format(accuracy[0], accuracy[1]))
## get precision, recall, f1-score and support for each class predicted on test set
X_train, X_val, y_train, y_val = train_test_split(X_train,
y_train,
test_size=0.25,
random_state=42,
stratify=y_train)
# print(X_train)
# print(y_train)
# print(X_test)
# print(y_test)
## define the model (preset weights)
print('[INFO] defining model...')
## create the pre-trained base model
base_model = Xception(include_top=True, weights=None, classes=40)
base_model.load_weights(
'frogsumimodels/Xception_species_distort_TF000/Xception.101.0.952.hdf5')
base_model.layers.pop()
base_model_layers = base_model.output
predictions = Dense(len(labeltonumber),
activation='softmax')(base_model_layers)
## create the model to train with the correct number of output
model = Model(inputs=base_model.input, outputs=predictions)
## print a summary of the model
print(model.summary())
## values from Olafenwa and Olafenva - 2018 and
## https://machinelearningmastery.com/evaluate-performance-deep-learning-models-keras/
EPOCHS = 200
print('epochs: {}'.format(EPOCHS))
