Python core.Dropout方法代碼示例

# 需要導入模塊: from keras.layers import core [as 別名]
# 或者: from keras.layers.core import Dropout [as 別名]
def deep_mlp(self):
        """
        Deep Multilayer Perceptrop.
        """
        if self._config.num_mlp_layers == 0:
            self.add(Dropout(0.5))
        else:
            for j in xrange(self._config.num_mlp_layers):
                self.add(Dense(self._config.mlp_hidden_dim))
                if self._config.mlp_activation == 'elu':
                    self.add(ELU())
                elif self._config.mlp_activation == 'leaky_relu':
                    self.add(LeakyReLU())
                elif self._config.mlp_activation == 'prelu':
                    self.add(PReLU())
                else:
                    self.add(Activation(self._config.mlp_activation))
                self.add(Dropout(0.5)) 

# 需要導入模塊: from keras.layers import core [as 別名]
# 或者: from keras.layers.core import Dropout [as 別名]
def create(self):
        self.textual_embedding(self, mask_zero=True)
        self.stacked_RNN(self)
        self.add(self._config.recurrent_encoder(
            self._config.hidden_state_dim, 
            return_sequences=False,
            go_backwards=self._config.go_backwards))
        self.add(Dropout(0.5))
        self.add(RepeatVector(self._config.max_output_time_steps))
        self.add(self._config.recurrent_decoder(
                self._config.hidden_state_dim, return_sequences=True))
        self.add(Dropout(0.5))
        self.add(TimeDistributedDense(self._config.output_dim))
        self.add(Activation('softmax'))


###
# Multimodal models
### 

# 需要導入模塊: from keras.layers import core [as 別名]
# 或者: from keras.layers.core import Dropout [as 別名]
def model_create(input_shape, num_classes):
        logging.debug('input_shape {}'.format(input_shape))

        model = Sequential()

        model.add(Conv2D(32, (3, 3), border_mode='same', input_shape=input_shape))
        model.add(Activation('relu'))

        model.add(Conv2D(32, (3, 3)))
        model.add(Activation('relu'))
        model.add(MaxPooling2D(pool_size=(2, 2)))
        model.add(Dropout(0.5))

        model.add(Flatten())
        model.add(Dense(128))
        model.add(Activation('relu'))
        model.add(Dropout(0.5))

        model.add(Dense(num_classes))
        model.add(Activation('softmax'))

        # use binary_crossentropy if has just 2 prediction yes or no
        model.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy'])

        return model 

# 需要導入模塊: from keras.layers import core [as 別名]
# 或者: from keras.layers.core import Dropout [as 別名]
def build_3dcnn_model(self, fusion_type, Fusion):
        if len(Fusion[0]) == 1: 
            input_shape = (32, 32, len(Fusion))
            model_in,model = self.cnn_2D(input_shape) 
        else:
            input_shape = (32, 32, 5, len(Fusion))
            model_in,model = self.cnn_3D(input_shape) 
        model = Dropout(0.5)(model)
        model = Dense(32, activation='relu', name = 'fc2')(model)
        model = Dense(self.config.classes, activation='softmax', name = 'fc3')(model) 
        model = Model(input=model_in,output=model)
        # 統計參數
        # model.summary()
        plot_model(model,to_file='experiments/img/' + str(Fusion) + fusion_type + r'_model.png',show_shapes=True)
        print('    Saving model  Architecture')
        
        adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-8)
        # model.compile(optimizer=adam, loss=self.mycrossentropy, metrics=['accuracy']) #有改善，但不穩定
        model.compile(optimizer=adam, loss='categorical_crossentropy', metrics=['accuracy']) 
        
        return model 

# 需要導入模塊: from keras.layers import core [as 別名]
# 或者: from keras.layers.core import Dropout [as 別名]
def cnn_2D(self, input_shape, modual=''):
        #建立Sequential模型    
        model_in = Input(input_shape) 
        model = Conv2D(
                filters = 6,
                kernel_size = (3, 3),
                input_shape = input_shape,
                activation='relu',
                kernel_initializer='he_normal',
                name = modual+'conv1'
            )(model_in)# now 30x30x6
        model = MaxPooling2D(pool_size=(2,2))(model)# now 15x15x6
        model = Conv2D(
                filters = 8,
                kernel_size = (4, 4),
                activation='relu',
                kernel_initializer='he_normal',
                name = modual+'conv2'
            )(model)# now 12x12x8
        model = MaxPooling2D(pool_size=(2,2))(model)# now 6x6x8
        model = Flatten()(model)
        model = Dropout(0.5)(model)
        model_out = Dense(100, activation='relu', name = modual+'fc1')(model)
      
        return model_in, model_out 

# 需要導入模塊: from keras.layers import core [as 別名]
# 或者: from keras.layers.core import Dropout [as 別名]
def cnn_3D(self, input_shape, modual=''):
        #建立Sequential模型
        model_in = Input(input_shape)    
        model = Convolution3D(
                filters = 6,
                kernel_size = (3, 3, 3),
                input_shape = input_shape,
                activation='relu',
                kernel_initializer='he_normal',
                name = modual+'conv1'
            )(model_in)# now 30x30x3x6
        model = MaxPooling3D(pool_size=(2,2,1))(model)# now 15x15x3x6
        model = Convolution3D(
                filters = 8,
                kernel_size = (4, 4, 3),
                activation='relu',
                kernel_initializer='he_normal',
                name = modual+'conv2'
            )(model)# now 12x12x1x8
        model = MaxPooling3D(pool_size=(2,2,1))(model)# now 6x6x1x8
        model = Flatten()(model)
        model = Dropout(0.5)(model)
        model_out = Dense(100, activation='relu', name = modual+'fc1')(model)
      
        return model_in, model_out 

# 需要導入模塊: from keras.layers import core [as 別名]
# 或者: from keras.layers.core import Dropout [as 別名]
def build_model(layers):
    """
    模型定義
    """
    model = Sequential()

    model.add(LSTM(units=layers[1], input_shape=(layers[1], layers[0]), return_sequences=True))
    model.add(Dropout(0.2))

    model.add(LSTM(layers[2], return_sequences=False))
    model.add(Dropout(0.2))

    model.add(Dense(units=layers[3]))
    model.add(Activation("tanh"))

    start = time.time()
    model.compile(loss="mse", optimizer="rmsprop")
    print("> Compilation Time : ", time.time() - start)
    return model 

# 需要導入模塊: from keras.layers import core [as 別名]
# 或者: from keras.layers.core import Dropout [as 別名]
def build_model():
    """
    定義模型
    """
    model = Sequential()

    model.add(LSTM(units=Conf.LAYERS[1], input_shape=(Conf.LAYERS[1], Conf.LAYERS[0]), return_sequences=True))
    model.add(Dropout(0.2))

    model.add(LSTM(Conf.LAYERS[2], return_sequences=False))
    model.add(Dropout(0.2))

    model.add(Dense(units=Conf.LAYERS[3]))
    # model.add(BatchNormalization(weights=None, epsilon=1e-06, momentum=0.9))
    model.add(Activation("tanh"))
    # act = PReLU(alpha_initializer='zeros', weights=None)
    # act = LeakyReLU(alpha=0.3)
    # model.add(act)

    start = time.time()
    model.compile(loss="mse", optimizer="rmsprop")
    print("> Compilation Time : ", time.time() - start)
    return model 

# 需要導入模塊: from keras.layers import core [as 別名]
# 或者: from keras.layers.core import Dropout [as 別名]
def build_model(layers):
    model = Sequential()

    model.add(LSTM(
        input_dim=layers[0],
        output_dim=layers[1],
        return_sequences=True))
    model.add(Dropout(0.2))

    model.add(LSTM(
        layers[2],
        return_sequences=False))
    model.add(Dropout(0.2))

    model.add(Dense(
        output_dim=layers[2]))
    model.add(Activation("linear"))

    start = time.time()
    model.compile(loss="mse", optimizer="rmsprop", metrics=['accuracy'])
    print("Compilation Time : ", time.time() - start)
    return model 

# 需要導入模塊: from keras.layers import core [as 別名]
# 或者: from keras.layers.core import Dropout [as 別名]
def build_model2(layers):
    d = 0.2
    model = Sequential()
    model.add(LSTM(128, input_shape=(
        layers[1], layers[0]), return_sequences=True))
    model.add(Dropout(d))
    model.add(LSTM(64, input_shape=(
        layers[1], layers[0]), return_sequences=False))
    model.add(Dropout(d))
    model.add(Dense(16, init='uniform', activation='relu'))
    model.add(Dense(1, init='uniform', activation='relu'))
    model.compile(loss='mse', optimizer='adam', metrics=['accuracy'])
    return model


# In[10]: 

# 需要導入模塊: from keras.layers import core [as 別名]
# 或者: from keras.layers.core import Dropout [as 別名]
def inception_pseudo(self,dim=224,freeze_layers=30,full_freeze='N'):
		model = InceptionV3(weights='imagenet',include_top=False)
		x = model.output
		x = GlobalAveragePooling2D()(x)
		x = Dense(512, activation='relu')(x)
		x = Dropout(0.5)(x)
		x = Dense(512, activation='relu')(x)
		x = Dropout(0.5)(x)
		out = Dense(5,activation='softmax')(x)
		model_final = Model(input = model.input,outputs=out)
		if full_freeze != 'N':
			for layer in model.layers[0:freeze_layers]:
				layer.trainable = False
		return model_final

	# ResNet50 Model for transfer Learning 

# 需要導入模塊: from keras.layers import core [as 別名]
# 或者: from keras.layers.core import Dropout [as 別名]
def resnet_pseudo(self,dim=224,freeze_layers=10,full_freeze='N'):
		model = ResNet50(weights='imagenet',include_top=False)
		x = model.output
		x = GlobalAveragePooling2D()(x)
		x = Dense(512, activation='relu')(x)
		x = Dropout(0.5)(x)
		x = Dense(512, activation='relu')(x)
		x = Dropout(0.5)(x)
		out = Dense(5,activation='softmax')(x)
		model_final = Model(input = model.input,outputs=out)
		if full_freeze != 'N':
			for layer in model.layers[0:freeze_layers]:
				layer.trainable = False
		return model_final

	# VGG16 Model for transfer Learning 

# 需要導入模塊: from keras.layers import core [as 別名]
# 或者: from keras.layers.core import Dropout [as 別名]
def inception_pseudo(self,dim=224,freeze_layers=30,full_freeze='N'):
		model = InceptionV3(weights='imagenet',include_top=False)
		x = model.output
		x = GlobalAveragePooling2D()(x)
		x = Dense(512, activation='relu')(x)
		x = Dropout(0.5)(x)
		x = Dense(512, activation='relu')(x)
		x = Dropout(0.5)(x)
		out = Dense(1)(x)
		model_final = Model(input = model.input,outputs=out)
		if full_freeze != 'N':
			for layer in model.layers[0:freeze_layers]:
				layer.trainable = False
		return model_final

	# ResNet50 Model for transfer Learning 

# 需要導入模塊: from keras.layers import core [as 別名]
# 或者: from keras.layers.core import Dropout [as 別名]
def resnet_pseudo(self,dim=224,freeze_layers=10,full_freeze='N'):
		model = ResNet50(weights='imagenet',include_top=False)
		x = model.output
		x = GlobalAveragePooling2D()(x)
		x = Dense(512, activation='relu')(x)
		x = Dropout(0.5)(x)
		x = Dense(512, activation='relu')(x)
		x = Dropout(0.5)(x)
		out = Dense(1)(x)
		model_final = Model(input = model.input,outputs=out)
		if full_freeze != 'N':
			for layer in model.layers[0:freeze_layers]:
				layer.trainable = False
		return model_final

	# VGG16 Model for transfer Learning 

# 需要導入模塊: from keras.layers import core [as 別名]
# 或者: from keras.layers.core import Dropout [as 別名]
def inception_pseudo(self,dim=224,freeze_layers=10,full_freeze='N'):
        model = InceptionV3(weights='imagenet',include_top=False)
        x = model.output
        x = GlobalAveragePooling2D()(x)
        x = Dense(512, activation='relu')(x)
        x = Dropout(0.5)(x)
        x = Dense(512, activation='relu')(x)
        x = Dropout(0.5)(x)
        out = Dense(5,activation='softmax')(x)
        model_final = Model(input = model.input,outputs=out)
        if full_freeze != 'N':
            for layer in model.layers[0:freeze_layers]:
                layer.trainable = False
        return model_final

# ResNet50 Model for transfer Learning