def Network_config(class_num=4,
epoch=200,
initial_epoch=0,
batch_size=32,
train_data=None,
train_label=None,
test_data=None,
test_label=None,
fold=0):
adam = Adam(lr=0.005,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-08,
decay=0.0009)
sgd = SGD(lr=0.001, momentum=0.9, decay=0.0, nesterov=False)
input_tensor = Input(shape=(224, 224, 3))
#backbone
base_model = VGG16(input_tensor=input_tensor,
weights='imagenet',
include_top=False)
base_output = base_model.output
#self-attention
x = non_local.non_local_block(base_output,
intermediate_dim=None,
compression=2,
mode='embedded',
add_residual=False)
x = BatchNormalization()(x)
#channel-attention
y = channel_attention.squeeze_excitation_layer(base_output,
512,
ratio=4,
concate=False)
y = BatchNormalization()(y)
#concat
x = concatenate([base_output, x], axis=3)
x = concatenate([x, y], axis=3)
# spp
gap = GlobalAveragePooling2D()(x)
x = Flatten()(x)
x = concatenate([gap, x])
x = Dense(512, activation='relu')(x)
x = BatchNormalization()(x)
x = Dense(512, activation='relu')(x)
x = BatchNormalization()(x)
predict = Dense(class_num, activation='softmax')(x)
model = Model(inputs=input_tensor, outputs=predict)
for layer in (base_model.layers):
layer.trainable = False
for l in model.layers:
print(l.name)
model.compile(optimizer=adam,
loss='categorical_crossentropy',
metrics=[keras.metrics.categorical_accuracy])
model.summary()
tools.create_directory('./final/')
weights_file = './final/' + str(
fold
) + '-weights.{epoch:02d}-{categorical_accuracy:.4f}-{val_loss:.4f}-{val_categorical_accuracy:.4f}.h5'
csv_file = './final/record.csv'
lr_reducer = ReduceLROnPlateau(monitor='categorical_accuracy',
factor=0.2,
cooldown=0,
patience=2,
min_lr=0.5e-6)
early_stopper = EarlyStopping(monitor='val_categorical_accuracy',
min_delta=1e-4,
patience=30)
model_checkpoint = ModelCheckpoint(weights_file,
monitor='val_categorical_accuracy',
save_best_only=True,
verbose=1,
save_weights_only=True,
mode='max')
tensorboard = TensorBoard(log_dir='./logs/',
histogram_freq=0,
batch_size=8,
write_graph=True,
write_grads=True,
write_images=True,
embeddings_freq=0,
embeddings_layer_names=None,
embeddings_metadata=None)
CSV_record = CSVLogger(csv_file, separator=',', append=True)
callbacks = [
lr_reducer, early_stopper, model_checkpoint, tensorboard, CSV_record
]
gc.disable()
model.fit_generator(
generator=tools.batch_generator(np.array(train_data),
np.array(train_label), batch_size,
True, class_num, True),
steps_per_epoch=int(len(train_label) / batch_size) - 1,
max_q_size=20,
initial_epoch=initial_epoch,
epochs=epoch,
verbose=1,
callbacks=callbacks,
validation_data=tools.batch_generator(np.array(test_data),
np.array(test_label), batch_size,
True, class_num, False),
validation_steps=int(len(test_label) / batch_size) - 1,
class_weight='auto')
#confusion matrix
all_y_pred = []
all_y_true = []
for test_data_batch, test_label_batch in tools.batch_generator_confusion_matrix(
np.array(test_data), np.array(test_label), batch_size, True,
class_num):
y_pred = model.predict(test_data_batch, batch_size)
y_true = test_label_batch
for y_p in y_pred:
all_y_pred.append(np.where(y_p == max(y_p))[0][0])
for y_t in y_true:
all_y_true.append(np.where(y_t == max(y_t))[0][0])
confusion = confusion_matrix(y_true=all_y_true, y_pred=all_y_pred)
print(confusion)
f = open('confusion_matrix.txt', 'a+')
f.write(str(all_y_true) + "\n")
f.write(str(all_y_pred) + "\n")
f.write(str(confusion) + '\n')
f.close()
gc.enable()
def Network_config(class_num=4,
epoch=200,
initial_epoch=0,
batch_size=32,
train_data=None,
train_label=None,
test_data=None,
test_label=None,
fold=0):
adam = Adam(lr=0.005, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.000)
sgd = SGD(lr=0.001, momentum=0.9, decay=0.0, nesterov=False)
K.set_learning_phase(1)
base_model = VGG16(input_tensor=Input(shape=(224, 224, 3)),
weights='imagenet',
include_top=False)
x = base_model.output
x = Flatten()(x)
x = Dense(512, activation='relu')(x)
x = BatchNormalization()(x)
x = Dense(512, activation='relu')(x)
x = BatchNormalization()(x)
predictions = Dense(class_num, activation='softmax')(x)
# this is the model we will train
model = Model(inputs=base_model.input, outputs=predictions)
for layer in (base_model.layers):
layer.trainable = False
if layer.name.startswith('bn'):
layer.call(layer.input, training=False)
model.compile(optimizer=adam,
loss='categorical_crossentropy',
metrics=[keras.metrics.categorical_accuracy])
tools.create_directory('./tmpvgg/')
weights_file = './tmpvgg/' + str(
fold
) + '-weights.{epoch:02d}-{categorical_accuracy:.4f}-{val_loss:.4f}-{val_categorical_accuracy:.4f}.h5'
csv_file = './tmpvgg/record.csv'
lr_reducer = ReduceLROnPlateau(monitor='categorical_accuracy',
factor=0.5,
cooldown=0,
patience=5,
min_lr=0.5e-6)
early_stopper = EarlyStopping(monitor='val_categorical_accuracy',
min_delta=1e-4,
patience=50)
model_checkpoint = ModelCheckpoint(weights_file,
monitor='val_categorical_accuracy',
save_best_only=True,
verbose=2,
save_weights_only=True,
mode='max')
tensorboard = TensorBoard(log_dir='./logs/',
histogram_freq=0,
batch_size=8,
write_graph=True,
write_grads=True,
write_images=True,
embeddings_freq=0,
embeddings_layer_names=None,
embeddings_metadata=None)
CSV_record = CSVLogger(csv_file, separator=',', append=True)
callbacks = [
lr_reducer, early_stopper, model_checkpoint, tensorboard, CSV_record
]
gc.disable()
model.fit_generator(
generator=tools.batch_generator(np.array(train_data),
np.array(train_label), batch_size,
True, class_num),
steps_per_epoch=int(len(train_label) / batch_size) - 1,
max_q_size=50,
initial_epoch=initial_epoch,
epochs=epoch,
verbose=1,
callbacks=callbacks,
validation_data=tools.batch_generator(np.array(test_data),
np.array(test_label), batch_size,
True, class_num),
validation_steps=int(len(test_label) / batch_size) - 1,
class_weight='auto')
all_y_pred = []
all_y_true = []
for test_data_batch, test_label_batch in tools.batch_generator_confusion_matrix(
np.array(test_data), np.array(test_label), batch_size, True,
class_num):
y_pred = model.predict(test_data_batch, batch_size)
y_true = test_label_batch
for y_p in y_pred:
all_y_pred.append(np.where(y_p == max(y_p))[0][0])
for y_t in y_true:
all_y_true.append(np.where(y_t == max(y_t))[0][0])
confusion = confusion_matrix(y_true=all_y_true, y_pred=all_y_pred)
print(confusion)
f = open('confusion_matrix.txt', 'a+')
f.write(str(all_y_true) + "\n")
f.write(str(all_y_pred) + "\n")
f.write(str(confusion) + '\n')
f.close()
gc.enable()