def trainAlgo(self):
self.model = Sequential()
self.model.add(
Conv2D(self.param['hidden_neuron'],
(self.param['hidden_kernel_size'],
self.param['hidden_kernel_size']),
input_shape=(32, 32, 3),
data_format='channels_last',
activation=self.param['hidden_activation'],
strides=(self.param['kernelSize_stride'],
self.param['kernelSize_stride'])))
self.model.add(
Conv2D(self.param['hidden_neuron'],
(self.param['hidden_kernel_size'],
self.param['hidden_kernel_size']),
data_format='channels_last',
activation=self.param['hidden_activation'],
strides=(self.param['kernelSize_stride'],
self.param['kernelSize_stride'])))
self.model.add(Flatten())
self.model.add(
Dense(self.outputData['Y'].shape[1], activation='softmax'))
self.model.compile(loss='categorical_crossentropy',
optimizer=self.param['optimizer'])
self.model.fit_generator(
XYdataGenerator(self.inputData['X'], self.outputData['Y'], 32, 32,
self.param['batch_size']),
steps_per_epoch=int(
ceil((len(self.inputData['X']) / self.param['batch_size']))),
epochs=self.param['epochs'])
def trainAlgo(self): self.model=Sequential() #first convolution and pooling layers self.model.add(Conv2D(self.param['hidden_neuron'],(self.param['hidden_kernel_size'],self.param['hidden_kernel_size']),padding="valid",input_shape=(self.param['pixel'],self.param['pixel'],3),data_format='channels_last',activation=self.param['hidden_activation'])) self.model.add(MaxPooling2D(pool_size=(3,3),strides=(2,2))) #second convolution and pooling layers self.model.add(Conv2D(256,(5,5),strides=(1,1),padding="same",activation="relu")) self.model.add(MaxPooling2D(pool_size=(3,3),strides=(2,2))) #three convolution and pooling layers self.model.add(Conv2D(384,(3,3),strides=(1,1),padding='same',activation='relu')) self.model.add(Conv2D(384,(3,3),strides=(1,1),padding='same',activation='relu')) self.model.add(Conv2D(256,(3,3),strides=(1,1),padding='same',activation='relu')) self.model.add(MaxPooling2D(pool_size=(3,3),strides=(2,2))) #fully connection self.model.add(Flatten()) self.model.add(Dense(1024,activation='relu')) self.model.add(Dropout(rate=0.5)) #classification self.model.add(Dense(self.outputData['Y'].shape[1],activation='softmax')) self.model.compile(loss='categorical_crossentropy',optimizer=self.param['optimizer']) self.model.fit_generator( XYdataGenerator(self.inputData['X'],self.outputData['Y'],self.param['pixel'],self.param['pixel'],self.param['batch_size']), steps_per_epoch=int(ceil((len(self.inputData['X'])/self.param['batch_size']))), epochs=self.param['epochs'] )
def trainAlgo(self):
self.model = Sequential()
self.model.add(
Conv2D(self.param['hidden_neuron'],
(self.param['hidden_kernel_size'],
self.param['hidden_kernel_size']),
input_shape=(32, 32, 3),
data_format='channels_last',
padding='same',
activation=self.param['hidden_activation']))
self.model.add(MaxPooling2D(pool_size=(2, 2)))
self.model.add(
Conv2D(self.param['hidden_neuron'] * 2,
(self.param['hidden_kernel_size'],
self.param['hidden_kernel_size']),
padding='same',
activation=self.param['hidden_activation']))
self.model.add(Dropout(self.param['dropout']))
self.model.add(MaxPooling2D(pool_size=(2, 2)))
self.model.add(
Conv2D(self.param['hidden_neuron'] * 4,
(self.param['hidden_kernel_size'],
self.param['hidden_kernel_size']),
padding='same',
activation=self.param['hidden_activation']))
self.model.add(Dropout(self.param['dropout']))
self.model.add(MaxPooling2D(pool_size=(2, 2)))
self.model.add(
Conv2D(self.param['hidden_neuron'] * 8,
(self.param['hidden_kernel_size'],
self.param['hidden_kernel_size']),
padding='same',
activation=self.param['hidden_activation']))
self.model.add(Dropout(self.param['dropout']))
self.model.add(MaxPooling2D(pool_size=(2, 2)))
self.model.add(Flatten())
self.model.add(
Dense(128, kernel_initializer='normal', activation='relu'))
self.model.add(Dropout(self.param['dropout']))
self.model.add(
Dense(self.outputData['Y'].shape[1],
kernel_initializer='normal',
activation='softmax'))
self.model.compile(loss='categorical_crossentropy',
optimizer=self.param['optimizer'],
metrics=['accuracy'])
self.model.fit_generator(
XYdataGenerator(self.inputData['X'], self.outputData['Y'], 32, 32,
self.param['batch_size']),
steps_per_epoch=int(
ceil((len(self.inputData['X']) / self.param['batch_size']))),
epochs=self.param['epochs'])
def trainAlgo(self):
self.model = Sequential()
a = int(self.param['hidden_neuron'])
b = int(self.param['DropOut'])
SZ = int(self.param['shape_size'])
self.model.add(
Conv2D(a // 8, (self.param['hidden_kernel_size'],
self.param['hidden_kernel_size']),
input_shape=(SZ, SZ, 3),
data_format='channels_last',
activation=self.param['hidden_activation'],
padding='same'))
self.model.add(
Conv2D(a // 4, (self.param['hidden_kernel_size'],
self.param['hidden_kernel_size']),
padding='same',
activation='relu'))
self.model.add(MaxPooling2D(pool_size=(2, 2)))
self.model.add(
Conv2D(a // 2, (self.param['hidden_kernel_size'],
self.param['hidden_kernel_size']),
padding='same',
activation='relu'))
self.model.add(
Conv2D(a, (self.param['hidden_kernel_size'],
self.param['hidden_kernel_size']),
padding='same',
activation='relu'))
self.model.add(MaxPooling2D(pool_size=(2, 2)))
self.model.add(Dropout(b // 2))
self.model.add(
Conv2D(a, (self.param['hidden_kernel_size'],
self.param['hidden_kernel_size']),
padding='same',
activation='relu'))
self.model.add(
Conv2D(a, (self.param['hidden_kernel_size'],
self.param['hidden_kernel_size']),
padding='same',
activation='relu'))
self.model.add(MaxPooling2D(pool_size=(2, 2)))
self.model.add(Flatten())
self.model.add(Dense(500, activation='relu'))
self.model.add(Dropout(b))
self.model.add(
Dense(self.outputData['Y'].shape[1], activation='softmax'))
self.model.compile(loss='categorical_crossentropy',
optimizer=self.param['optimizer'])
self.model.fit_generator(
XYdataGenerator(self.inputData['X'], self.outputData['Y'], SZ, SZ,
self.param['batch_size']),
steps_per_epoch=int(
ceil((len(self.inputData['X']) / self.param['batch_size']))),
epochs=self.param['epochs'])
def trainAlgo(self):
# 建立Keras 的 Sequential 模型
self.model = Sequential()
# 建立捲積層 1
self.model.add(
Conv2D(self.param['hidden_neuron'],
(self.param['hidden_kernel_size'],self.param['hidden_kernel_size'] ),
input_shape=(32,32,3),
activation=self.param['hidden_activation'],
padding='same'))
# 加入Dropout 避免 overfitting
self.model.add(Dropout( self.param['dropout'] ))
# 建立池化層 1
self.model.add(MaxPooling2D(pool_size=(2,2)))
# 建立捲積層 2
self.model.add(Conv2D(filters=64,kernel_size=(3,3),activation='relu',padding='same'))
# 加入Dropout 避免 overfitting
self.model.add(Dropout( self.param['dropout'] ))
# 建立池化層 2
self.model.add(MaxPooling2D(pool_size=(2,2)))
# 建立平坦層
self.model.add(Flatten())
# 加入Dropout 避免 overfitting
self.model.add(Dropout(self.param['dropout']))
# 建立隱藏層
self.model.add(Dense(1024,activation='relu'))
self.model.add(Dropout(self.param['dropout']))
# 建立輸出層
self.model.add(Dense(self.outputData['Y'].shape[1],activation='softmax'))
#定義訓練方式
self.model.compile(loss='categorical_crossentropy',optimizer=self.param['optimizer'])
#開始訓練
self.model.fit_generator(
XYdataGenerator(self.inputData['X'],self.outputData['Y'],32,32,self.param['batch_size']),
steps_per_epoch=int(ceil((len(self.inputData['X'])/self.param['batch_size']))),
epochs=self.param['epochs'])
def trainAlgo(self):
self.model=Sequential()
self.model.add(Conv2D(self.param['hidden_neuron'],(self.param['hidden_kernel_size'],self.param['hidden_kernel_size']),padding="same",input_shape=(32,32,3),data_format='channels_last',activation=self.param['hidden_activation']))
self.model.add(Conv2D(64,(3,3),padding="same"))
self.model.add(Activation("relu"))
self.model.add(MaxPooling2D(pool_size=(2,2)))
self.model.add(Dropout(rate=0.25))
self.model.add(Conv2D(128,(3,3),padding="same"))
self.model.add(Activation("relu"))
self.model.add(Conv2D(128,(3,3),padding="same"))
self.model.add(Activation("relu"))
self.model.add(MaxPooling2D(pool_size=(2,2)))
self.model.add(Dropout(rate=0.25))
self.model.add(Conv2D(256,(3,3),padding="same"))
self.model.add(Activation("relu"))
self.model.add(Conv2D(256,(3,3),padding="same"))
self.model.add(Activation("relu"))
self.model.add(Conv2D(256,(3,3),padding="same"))
self.model.add(Activation("relu"))
self.model.add(MaxPooling2D(pool_size=(2,2)))
self.model.add(Dropout(rate=0.5))
self.model.add(Flatten())
self.model.add(Dense(4096,activation='relu'))
self.model.add(Dropout(rate=0.5))
self.model.add(Dense(4096,activation='relu'))
self.model.add(Dropout(rate=0.5))
self.model.add(Dense(1024))
self.model.add(Activation("relu"))
self.model.add(BatchNormalization())
self.model.add(Dropout(0.5))
self.model.add(Dense(self.outputData['Y'].shape[1],activation='softmax'))
self.model.compile(loss='categorical_crossentropy',optimizer=self.param['optimizer'])
self.model.fit_generator(
XYdataGenerator(self.inputData['X'],self.outputData['Y'],32,32,self.param['batch_size']),
steps_per_epoch=int(ceil((len(self.inputData['X'])/self.param['batch_size']))),
epochs=self.param['epochs']
)
def trainAlgo(self):
self.model = Sequential()
self.model.add(
Conv2D(filters=self.param['hidden_neuron'],
kernel_size=(self.param['hidden_kernel_size'],
self.param['hidden_kernel_size']),
padding="same",
input_shape=(28, 28, 3),
activation=self.param['hidden_activation']))
self.model.add(
MaxPool2D(pool_size=(self.param['pool_size'],
self.param['pool_size'])))
self.model.add(
Conv2D(filters=self.param['hidden_neuron'],
kernel_size=(self.param['hidden_kernel_size'],
self.param['hidden_kernel_size']),
padding="same",
input_shape=(28, 28, 1),
activation=self.param['hidden_activation']))
self.model.add(
MaxPool2D(pool_size=(self.param['pool_size'],
self.param['pool_size'])))
self.model.add(Dropout(self.param['dropout']))
self.model.add(Flatten())
self.model.add(Dense(128, activation='relu'))
self.model.add(Dropout(self.param['dropout_2']))
self.model.add(
Dense(self.outputData['Y'].shape[1], activation='softmax'))
self.model.compile(loss='categorical_crossentropy',
optimizer=self.param['optimizer'])
self.model.fit_generator(
XYdataGenerator(self.inputData['X'], self.outputData['Y'], 28, 28,
self.param['batch_size']),
steps_per_epoch=int(
ceil((len(self.inputData['X']) / self.param['batch_size']))),
epochs=self.param['epochs'],
verbose=1)
def trainAlgo(self):
self.model = Sequential()
a = int(self.param['hidden_neuron'])
self.model.add(
Conv2D((a // 8), (self.param['hidden_kernel_size'],
self.param['hidden_kernel_size']),
input_shape=(32, 32, 3),
data_format='channels_last',
activation=self.param['hidden_activation'],
padding='same'))
self.model.add(
Conv2D((a // 8), (self.param['hidden_kernel_size'],
self.param['hidden_kernel_size']),
activation=self.param['hidden_activation'],
padding='same'))
self.model.add(
MaxPooling2D(pool_size=(self.param['pool_size'],
self.param['pool_size']),
strides=(2, 2)))
self.model.add(
Conv2D((a // 4), (self.param['hidden_kernel_size'],
self.param['hidden_kernel_size']),
activation=self.param['hidden_activation'],
padding='same'))
self.model.add(
Conv2D((a // 4), (self.param['hidden_kernel_size'],
self.param['hidden_kernel_size']),
activation=self.param['hidden_activation'],
padding='same'))
self.model.add(
MaxPooling2D(pool_size=(self.param['pool_size'],
self.param['pool_size']),
strides=(2, 2)))
self.model.add(
Conv2D((a // 2), (self.param['hidden_kernel_size'],
self.param['hidden_kernel_size']),
activation=self.param['hidden_activation'],
padding='same'))
self.model.add(
Conv2D((a // 2), (self.param['hidden_kernel_size'],
self.param['hidden_kernel_size']),
activation=self.param['hidden_activation'],
padding='same'))
self.model.add(
Conv2D((a // 2), (self.param['hidden_kernel_size'],
self.param['hidden_kernel_size']),
activation=self.param['hidden_activation'],
padding='same'))
self.model.add(
MaxPooling2D(pool_size=(self.param['pool_size'],
self.param['pool_size']),
strides=(2, 2)))
if (self.param['vgg16_CCCP']):
self.model.add(
Conv2D(self.param['hidden_neuron'],
(self.param['hidden_kernel_size'],
self.param['hidden_kernel_size']),
activation=self.param['hidden_activation'],
padding='same'))
self.model.add(
Conv2D(self.param['hidden_neuron'],
(self.param['hidden_kernel_size'],
self.param['hidden_kernel_size']),
activation=self.param['hidden_activation'],
padding='same'))
self.model.add(
Conv2D(self.param['hidden_neuron'],
(self.param['hidden_kernel_size'],
self.param['hidden_kernel_size']),
activation=self.param['hidden_activation'],
padding='same'))
self.model.add(
MaxPooling2D(pool_size=(self.param['pool_size'],
self.param['pool_size']),
strides=(2, 2)))
if (self.param['vgg16_CCCP2']):
self.model.add(
Conv2D(self.param['hidden_neuron'],
(self.param['hidden_kernel_size'],
self.param['hidden_kernel_size']),
activation=self.param['hidden_activation'],
padding='same'))
self.model.add(
Conv2D(self.param['hidden_neuron'],
(self.param['hidden_kernel_size'],
self.param['hidden_kernel_size']),
activation=self.param['hidden_activation'],
padding='same'))
self.model.add(
Conv2D(self.param['hidden_neuron'],
(self.param['hidden_kernel_size'],
self.param['hidden_kernel_size']),
activation=self.param['hidden_activation'],
padding='same'))
self.model.add(
MaxPooling2D(pool_size=(self.param['pool_size'],
self.param['pool_size']),
strides=(2, 2)))
self.model.add(Flatten())
if (self.param['vgg16_fc']):
self.model.add(Dropout(self.param['dropout_value']))
self.model.add(Dense(500, activation='softmax'))
if (self.param['vgg16_fc2']):
self.model.add(Dropout(self.param['dropout_value']))
self.model.add(Dense(10, activation='softmax'))
self.model.add(Dropout(self.param['dropout_value']))
self.model.add(
Dense(self.outputData['Y'].shape[1], activation='softmax'))
self.model.compile(loss='categorical_crossentropy',
optimizer=self.param['optimizer'])
self.model.fit_generator(
XYdataGenerator(self.inputData['X'], self.outputData['Y'], 32, 32,
self.param['batch_size']),
steps_per_epoch=int(
ceil((len(self.inputData['X']) / self.param['batch_size']))),
epochs=self.param['epochs'])
def trainAlgo(self):
self.model = Sequential()
self.model.add(
Conv2D(self.param['hidden_neuron'],
(self.param['hidden_kernel_size'],
self.param['hidden_kernel_size']),
input_shape=(32, 32, 3),
activation=self.param['hidden_activation'],
padding='same'))
self.model.add(
Conv2D(32, (3, 3), input_shape=(32, 32, 3), padding='same'))
self.model.add(Activation(self.param['hidden_activation']))
self.model.add(
Conv2D(32, (3, 3), input_shape=(32, 32, 3), padding='same'))
self.model.add(Activation(self.param['hidden_activation']))
self.model.add(
Conv2D(32, (3, 3), input_shape=(32, 32, 3), padding='same'))
self.model.add(Activation(self.param['hidden_activation']))
self.model.add(
Conv2D(48, (3, 3), input_shape=(32, 32, 3), padding='same'))
self.model.add(Activation(self.param['hidden_activation']))
self.model.add(
Conv2D(48, (3, 3), input_shape=(32, 32, 3), padding='same'))
self.model.add(Activation(self.param['hidden_activation']))
self.model.add(MaxPooling2D(pool_size=(2, 2)))
self.model.add(Dropout(self.param['dropout']))
self.model.add(
Conv2D(80, (3, 3), input_shape=(32, 32, 3), padding='same'))
self.model.add(Activation(self.param['hidden_activation']))
self.model.add(
Conv2D(80, (3, 3), input_shape=(32, 32, 3), padding='same'))
self.model.add(Activation(self.param['hidden_activation']))
self.model.add(
Conv2D(80, (3, 3), input_shape=(32, 32, 3), padding='same'))
self.model.add(Activation(self.param['hidden_activation']))
self.model.add(
Conv2D(80, (3, 3), input_shape=(32, 32, 3), padding='same'))
self.model.add(Activation(self.param['hidden_activation']))
self.model.add(
Conv2D(80, (3, 3), input_shape=(32, 32, 3), padding='same'))
self.model.add(Activation(self.param['hidden_activation']))
self.model.add(MaxPooling2D(pool_size=(2, 2)))
self.model.add(Dropout(self.param['dropout']))
self.model.add(
Conv2D(128, (3, 3), input_shape=(32, 32, 3), padding='same'))
self.model.add(Activation(self.param['hidden_activation']))
self.model.add(
Conv2D(128, (3, 3), input_shape=(32, 32, 3), padding='same'))
self.model.add(Activation(self.param['hidden_activation']))
self.model.add(
Conv2D(128, (3, 3), input_shape=(32, 32, 3), padding='same'))
self.model.add(Activation(self.param['hidden_activation']))
self.model.add(
Conv2D(128, (3, 3), input_shape=(32, 32, 3), padding='same'))
self.model.add(Activation(self.param['hidden_activation']))
self.model.add(
Conv2D(128, (3, 3), input_shape=(32, 32, 3), padding='same'))
self.model.add(Activation(self.param['hidden_activation']))
self.model.add(GlobalMaxPooling2D())
self.model.add(Dropout(self.param['dropout']))
self.model.add(Dense(500, activation='relu'))
self.model.add(Dropout(self.param['dropout2']))
self.model.add(
Dense(self.outputData['Y'].shape[1], activation='softmax'))
self.model.compile(loss='categorical_crossentropy',
optimizer=self.param['optimizer'],
metrics=['accuracy'])
self.model.fit_generator(
XYdataGenerator(self.inputData['X'], self.outputData['Y'], 32, 32,
self.param['batch_size']),
steps_per_epoch=int(
ceil((len(self.inputData['X']) / self.param['batch_size']))),
epochs=self.param['epochs'])