top_model = Flatten()(top_model)
top_model = Dense(600)(top_model)
top_model = BatchNormalization()(top_model)
top_model = Activation('relu', name='relu_conv9')(top_model)
# top_model = Dense(600)(top_model)
# top_model = Activation('relu', name='relu_conv10')(top_model)
top_model = Dense(10)(top_model)
top_model = BatchNormalization()(top_model)
top_model = Activation('softmax', name='loss')(top_model)
top_model = Model(model.input, top_model, name='top_net')
top_model.load_weights(top_model_weights_path)
#設定model參數
top_model.compile(loss=keras.losses.categorical_crossentropy,
optimizer='adam',
metrics=['accuracy'])
#設定訓練紀錄
history = LossHistory(model, x_test, y_test)
#開始訓練
top_model.fit_generator(
generator=train_flow,
steps_per_epoch=30000 // 20,
verbose=2,
validation_data=train_flow,
validation_steps=5000 // 32,
epochs=30,
callbacks=[history]
)
#pre=model.predict(test_data)
#print(np.argmax(pre, axis=1))
rotation_range=35,
width_shift_range=0.22,
height_shift_range=0.22)
inp = Input(shape=(28, 28, 1), dtype='float32')
model = Conv2D(16, (3, 3), padding='same', activation='relu')(inp)
model = MaxPooling2D((2, 2))(model)
model = Dropout(0.3)(model)
model = Conv2D(32, (3, 3), padding='same', activation='relu')(model)
model = MaxPooling2D((2, 2))(model)
model = Conv2D(64, (3, 3), padding='same', activation='relu')(model)
model = MaxPooling2D((2, 2))(model)
model = Flatten()(model)
model = Dropout(0.2)(model)
output = Dense(10, activation='softmax')(model)
model = Model(inputs=inp, outputs=output)
model.summary()
train_generator = train_gen.flow(train[:40000], labels[:40000], batch_size=128)
model.compile(loss='categorical_crossentropy',
optimizer='Adam',
metrics=['accuracy'])
checkpt = ModelCheckpoint(
filepath=r'./models/model.{epoch:02d}-{val_loss:.2f}.hdf5',
monitor='val_loss',
save_best_only=True)
csv_logger = CSVLogger('history.log')
model.fit_generator(train_generator,
steps_per_epoch=int(40000 / 128),
epochs=3000,
callbacks=[csv_logger, checkpt],
validation_data=(train[40000:], labels[40000:]))
class MultiClassDecisionDriverLayer(BaseDriverLayer):
def build_model(self):
inp = Input(shape=(200, 200, 3))
self.model = Conv2D(32, (3, 3), padding='same')(inp)
self.model = Activation('relu')(self.model)
self.model = Conv2D(32, (3, 3))(self.model)
self.model = Activation('relu')(self.model)
self.model = MaxPooling2D(pool_size=(2, 2))(self.model)
self.model = Dropout(0.25)(self.model)
self.model = Conv2D(64, (3, 3), padding='same')(self.model)
self.model = Activation('relu')(self.model)
self.model = Conv2D(64, (3, 3))(self.model)
self.model = Activation('relu')(self.model)
self.model = MaxPooling2D(pool_size=(2, 2))(self.model)
self.model = Dropout(0.25)(self.model)
self.model = Flatten()(self.model)
self.model = Dense(512)(self.model)
self.model = Activation('relu')(self.model)
self.model = Dropout(0.5)(self.model)
output1 = Dense(1, activation='sigmoid')(self.model)
output2 = Dense(1, activation='sigmoid')(self.model)
output3 = Dense(1, activation='sigmoid')(self.model)
model = Model(inp, [output1, output2, output3])
model.compile(optimizers.rmsprop(lr=0.0001, decay=1e-6),
loss=[
"binary_crossentropy",
"binary_crossentropy",
"binary_crossentropy",
],
metrics=["accuracy"])
return model
def train(self):
train_datagen = ImageDataGenerator(rescale=1. / 255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
valid_datagen = ImageDataGenerator(rescale=1. / 255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1. / 255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
train_generator = train_datagen.flow_from_directory(
directory=os.path.join(self.data_path, 'train/'),
target_size=(200, 200),
color_mode="rgb",
batch_size=32,
class_mode='categorical',
shuffle=True,
seed=42)
valid_generator = valid_datagen.flow_from_directory(
directory=os.path.join(self.data_path, 'valid/'),
target_size=(200, 200),
color_mode="rgb",
batch_size=32,
class_mode="categorical",
shuffle=True,
seed=42)
test_generator = test_datagen.flow_from_directory(
directory=os.path.join(self.data_path, 'test/'),
target_size=(200, 200),
color_mode="rgb",
batch_size=32,
class_mode='categorical')
STEP_SIZE_TRAIN = train_generator.n // train_generator.batch_size
STEP_SIZE_VALID = valid_generator.n // valid_generator.batch_size
self.model.fit_generator(generator=train_generator,
steps_per_epoch=STEP_SIZE_TRAIN,
validation_data=valid_generator,
validation_steps=STEP_SIZE_VALID,
epochs=10)
self.model.evaluate_generator(generator=valid_generator,
steps=STEP_SIZE_VALID)
STEP_SIZE_TEST = test_generator.n // test_generator.batch_size
test_generator.reset()
pred = self.model.predict_generator(test_generator,
steps=STEP_SIZE_TEST,
verbose=1)
predicted_class_indices = np.argmax(pred, axis=1)
labels = train_generator.class_indices
self.labels = dict((v, k) for k, v in labels.items())
predictions = [self.labels[k] for k in predicted_class_indices]
print(predictions)
def predict(self, image_data):
image_array = image.img_to_array(image_data)
image_array = np.expand_dims(image_array, axis=0)
result = self.model.predict_classes(image_array)
return self.labels.get(result[0])
vgg = cnn_algo()
# vgg.summary()
for layers in vgg.layers: # we are keeping the weights constant, it is already trained by the thousands of weights that is in imagenet
layers.trainable = False # we are not training all the layers
x = Flatten()(
vgg.output
) # need to add last layer, after flattning we can add the last layer
folders = glob.glob(
cwd + '/train/*') # check number of folders inside training folder
print(folders)
prediction = Dense(len(folders), activation='softmax')(
x) # its the sigmoid activation function
model = Dropout(0.2)
model = input_model()
model.summary() # view the structure of model
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
training_set = aggumentation_training()
test_set = aggumentation_training()
r = model.fit_generator(training_set,
epochs=3,
steps_per_epoch=len(training_set),
validation_steps=len(test_set))
model.save('My_face_features_model.h5')
