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Python layers.ELU属性代码示例

本文整理汇总了Python中keras.layers.ELU属性的典型用法代码示例。如果您正苦于以下问题:Python layers.ELU属性的具体用法?Python layers.ELU怎么用?Python layers.ELU使用的例子?那么恭喜您, 这里精选的属性代码示例或许可以为您提供帮助。您也可以进一步了解该属性所在keras.layers的用法示例。


在下文中一共展示了layers.ELU属性的14个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: get_model

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import ELU [as 别名]
def get_model(time_len=1):
  ch, row, col = 3, 160, 320  # camera format

  model = Sequential()
  model.add(Lambda(lambda x: x/127.5 - 1.,
            input_shape=(ch, row, col),
            output_shape=(ch, row, col)))
  model.add(Convolution2D(16, 8, 8, subsample=(4, 4), border_mode="same"))
  model.add(ELU())
  model.add(Convolution2D(32, 5, 5, subsample=(2, 2), border_mode="same"))
  model.add(ELU())
  model.add(Convolution2D(64, 5, 5, subsample=(2, 2), border_mode="same"))
  model.add(Flatten())
  model.add(Dropout(.2))
  model.add(ELU())
  model.add(Dense(512))
  model.add(Dropout(.5))
  model.add(ELU())
  model.add(Dense(1))

  model.compile(optimizer="adam", loss="mse")

  return model 
开发者ID:commaai,项目名称:research,代码行数:25,代码来源:train_steering_model.py

示例2: generate_dense_model

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import ELU [as 别名]
def generate_dense_model(input_shape, layers, nb_actions):
    model = Sequential()
    model.add(Flatten(input_shape=input_shape))
    model.add(Dropout(0.1))  # drop out the input to make model less sensitive to any 1 feature

    for layer in layers:
        model.add(Dense(layer))
        model.add(BatchNormalization())
        model.add(ELU(alpha=1.0))

    model.add(Dense(nb_actions))
    model.add(Activation('linear'))
    print(model.summary())

    return model 
开发者ID:endgameinc,项目名称:gym-malware,代码行数:17,代码来源:train_agent_kerasrl.py

示例3: Encoder

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import ELU [as 别名]
def Encoder(hidden_size, activation=None, return_sequences=True, bidirectional=False, use_gru=True):
    if activation is None:
        activation = ELU()
    if use_gru:
        def _encoder(x):
            if bidirectional:
                branch_1 = GRU(int(hidden_size/2), activation='linear',
                               return_sequences=return_sequences, go_backwards=False)(x)
                branch_2 = GRU(int(hidden_size/2), activation='linear',
                               return_sequences=return_sequences, go_backwards=True)(x)
                x = concatenate([branch_1, branch_2])
                x = activation(x)
                return x
            else:
                x = GRU(hidden_size, activation='linear',
                        return_sequences=return_sequences)(x)
                x = activation(x)
                return x
    else:
        def _encoder(x):
            if bidirectional:
                branch_1 = LSTM(int(hidden_size/2), activation='linear',
                                return_sequences=return_sequences, go_backwards=False)(x)
                branch_2 = LSTM(int(hidden_size/2), activation='linear',
                                return_sequences=return_sequences, go_backwards=True)(x)
                x = concatenate([branch_1, branch_2])
                x = activation(x)
                return x
            else:
                x = LSTM(hidden_size, activation='linear',
                         return_sequences=return_sequences)(x)
                x = activation(x)
                return x
    return _encoder 
开发者ID:saurabhmathur96,项目名称:Neural-Chatbot,代码行数:36,代码来源:sequence_blocks.py

示例4: AttentionDecoder

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import ELU [as 别名]
def AttentionDecoder(hidden_size, activation=None, return_sequences=True, bidirectional=False, use_gru=True):
    if activation is None:
        activation = ELU()
    if use_gru:
        def _decoder(x, attention):
            if bidirectional:
                branch_1 = AttentionWrapper(GRU(int(hidden_size/2), activation='linear', return_sequences=return_sequences,
                                                go_backwards=False), attention, single_attention_param=True)(x)
                branch_2 = AttentionWrapper(GRU(int(hidden_size/2), activation='linear', return_sequences=return_sequences,
                                                go_backwards=True), attention, single_attention_param=True)(x)
                x = concatenate([branch_1, branch_2])
                return activation(x)
            else:
                x = AttentionWrapper(GRU(hidden_size, activation='linear',
                                         return_sequences=return_sequences), attention, single_attention_param=True)(x)
                x = activation(x)
                return x
    else:
        def _decoder(x, attention):
            if bidirectional:
                branch_1 = AttentionWrapper(LSTM(int(hidden_size/2), activation='linear', return_sequences=return_sequences,
                                                 go_backwards=False), attention, single_attention_param=True)(x)
                branch_2 = AttentionWrapper(LSTM(hidden_size, activation='linear', return_sequences=return_sequences,
                                                go_backwards=True), attention, single_attention_param=True)(x)
                x = concatenate([branch_1, branch_2])
                x = activation(x)
                return x
            else:
                x = AttentionWrapper(LSTM(hidden_size, activation='linear', return_sequences=return_sequences),
                                     attention, single_attention_param=True)(x)
                x = activation(x)
                return x

    return _decoder 
开发者ID:saurabhmathur96,项目名称:Neural-Chatbot,代码行数:36,代码来源:sequence_blocks.py

示例5: Decoder

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import ELU [as 别名]
def Decoder(hidden_size, activation=None, return_sequences=True, bidirectional=False, use_gru=True):
    if activation is None:
        activation = ELU()
    if use_gru:
        def _decoder(x):
            if bidirectional:
                x = Bidirectional(
                    GRU(int(hidden_size/2), activation='linear', return_sequences=return_sequences))(x)
                x = activation(x)
                return x
            else:
                x = GRU(hidden_size, activation='linear',
                        return_sequences=return_sequences)(x)
                x = activation(x)
                return x
    else:
        def _decoder(x):
            if bidirectional:
                x = Bidirectional(
                    LSTM(int(hidden_size/2), activation='linear', return_sequences=return_sequences))(x)
                x = activation(x)
                return x
            else:
                x = LSTM(hidden_size, activation='linear',
                         return_sequences=return_sequences)(x)
                x = activation(x)
                return x
    return _decoder 
开发者ID:saurabhmathur96,项目名称:Neural-Chatbot,代码行数:30,代码来源:sequence_blocks.py

示例6: __init__

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import ELU [as 别名]
def __init__(self):
        super(ELUNet, self).__init__()
        self.elu = nn.ELU() 
开发者ID:gzuidhof,项目名称:nn-transfer,代码行数:5,代码来源:test_layers.py

示例7: test_elu

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import ELU [as 别名]
def test_elu(self):
        keras_model = Sequential()
        keras_model.add(ELU(input_shape=(3, 32, 32), name='elu'))
        keras_model.compile(loss=keras.losses.categorical_crossentropy,
                            optimizer=keras.optimizers.SGD())

        pytorch_model = ELUNet()

        self.transfer(keras_model, pytorch_model)
        self.assertEqualPrediction(keras_model, pytorch_model, self.test_data)

    # Tests activation function with learned parameters 
开发者ID:gzuidhof,项目名称:nn-transfer,代码行数:14,代码来源:test_layers.py

示例8: convresblock

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import ELU [as 别名]
def convresblock(x, nfeats=8, ksize=3, nskipped=2, elu=True):
    """The proposed residual block from [4].

    Running with elu=True will use ELU nonlinearity and running with
    elu=False will use BatchNorm + RELU nonlinearity.  While ELU's are fast
    due to the fact they do not suffer from BatchNorm overhead, they may
    overfit because they do not offer the stochastic element of the batch
    formation process of BatchNorm, which acts as a good regularizer.

    # Arguments
        x: 4D tensor, the tensor to feed through the block
        nfeats: Integer, number of feature maps for conv layers.
        ksize: Integer, width and height of conv kernels in first convolution.
        nskipped: Integer, number of conv layers for the residual function.
        elu: Boolean, whether to use ELU or BN+RELU.

    # Input shape
        4D tensor with shape:
        `(batch, channels, rows, cols)`

    # Output shape
        4D tensor with shape:
        `(batch, filters, rows, cols)`
    """
    y0 = Conv2D(nfeats, ksize, padding='same')(x)
    y = y0
    for i in range(nskipped):
        if elu:
            y = ELU()(y)
        else:
            y = BatchNormalization(axis=1)(y)
            y = Activation('relu')(y)
        y = Conv2D(nfeats, 1, padding='same')(y)
    return layers.add([y0, y]) 
开发者ID:hello-sea,项目名称:DeepLearning_Wavelet-LSTM,代码行数:36,代码来源:mnist_swwae.py

示例9: test_elu

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import ELU [as 别名]
def test_elu():
    for alpha in [0., .5, -1.]:
        layer_test(layers.ELU, kwargs={'alpha': alpha},
                   input_shape=(2, 3, 4)) 
开发者ID:hello-sea,项目名称:DeepLearning_Wavelet-LSTM,代码行数:6,代码来源:advanced_activations_test.py

示例10: create_model

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import ELU [as 别名]
def create_model(input_shape, hidden_layers=[1024, 512, 256], input_dropout=0.1, hidden_dropout=0.5):
    '''Define a simple multilayer perceptron.

    Args:
        input_shape (tuple): input shape to the model. For this model, should be of shape (dim,)
        input_dropout (float): fraction of input features to drop out during training
        hidden_layers (tuple): a tuple/list with number of hidden units in each hidden layer

    Returns:
        keras.models.Sequential : a model to train
    '''
    model = Sequential()

    # dropout the input to prevent overfitting to any one feature
    # (a similar concept to randomization in random forests,
    #   but we choose less severe feature sampling  )
    model.add(Dropout(input_dropout, input_shape=input_shape))

    # set up hidden layers
    for n_hidden_units in hidden_layers:
        # the layer...activation will come later
        model.add(Dense(n_hidden_units))
        # dropout to prevent overfitting
        model.add(Dropout(hidden_dropout))
        # batchnormalization helps training
        model.add(BatchNormalization())
        # ...the activation!
        model.add(ELU())

    # the output layer
    model.add(Dense(1, activation='sigmoid'))

    # we'll optimize with plain old sgd
    model.compile(loss='binary_crossentropy',
                  optimizer='sgd', metrics=['accuracy'])

    return model 
开发者ID:endgameinc,项目名称:youarespecial,代码行数:39,代码来源:simple_multilayer.py

示例11: ResidualBlock1D_helper

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import ELU [as 别名]
def ResidualBlock1D_helper(layers, kernel_size, filters, final_stride=1):
    def f(_input):
        basic = _input
        for ln in range(layers):
            #basic = BatchNormalization()( basic ) # triggers known keras bug w/ TimeDistributed: https://github.com/fchollet/keras/issues/5221
            basic = ELU()(basic)  
            basic = Conv1D(filters, kernel_size, kernel_initializer='he_normal',
                           kernel_regularizer=l2(1.e-4), padding='same')(basic)

        # note that this strides without averaging
        return AveragePooling1D(pool_size=1, strides=final_stride)(Add()([_input, basic]))

    return f 
开发者ID:endgameinc,项目名称:youarespecial,代码行数:15,代码来源:malwaresnet.py

示例12: build_model

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import ELU [as 别名]
def build_model(output_size):
    channel_axis = 3
    freq_axis = 1
    padding = 37

    input_shape = (img_height, img_width, channels)
    print('Building model...')

    model = Sequential()
    #model.add(ZeroPadding2D(padding=(0, padding), data_format='channels_last', input_shape=input_shape))
    #model.add(BatchNormalization(axis=freq_axis, name='bn_0_freq'))

    #model.add(Conv2D(64, (3, 3), padding='same', name='conv1'))
    #model.add(BatchNormalization(axis=channel_axis, name='bn1'))
    #model.add(ELU())
    #model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name='pool1'))
    #model.add(Dropout(0.1, name='dropout1'))

    #model.add(Conv2D(128, (3, 3), padding='same', name='conv2'))
    #model.add(BatchNormalization(axis=channel_axis, name='bn2'))
    #model.add(ELU())
    #model.add(MaxPooling2D(pool_size=(3, 3), strides=(3, 3), name='pool2'))
    #model.add(Dropout(0.1, name='dropout2'))

    #model.add(Conv2D(128, (3, 3), padding='same', name='conv3'))
    #model.add(BatchNormalization(axis=channel_axis, name='bn3'))
    #model.add(ELU())
    #model.add(MaxPooling2D(pool_size=(4, 4), strides=(4, 4), name='pool3'))
    #model.add(Dropout(0.1, name='dropout3'))

    #model.add(Conv2D(128, (3, 3), padding='same', name='conv4'))
    #model.add(BatchNormalization(axis=channel_axis, name='bn4'))
    #model.add(ELU())
    #model.add(MaxPooling2D(pool_size=(4, 4), strides=(4, 4), name='pool4'))
    #model.add(Dropout(0.1, name='dropout4'))

    #model.add(Reshape(target_shape=(15, 128)))

    #model.add(GRU(32, return_sequences=True, name='gru1'))
    #model.add(GRU(32, return_sequences=False, name='gru2'))

    #model.add(Dropout(0.3, name='dropout_final'))

    model.add(Reshape(target_shape=(img_height * img_width,), input_shape=input_shape))
    model.add(Dense(output_size, activation='softmax', name='output', input_shape=input_shape))

    return model 
开发者ID:kristijanbartol,项目名称:Deep-Music-Tagger,代码行数:49,代码来源:train.py

示例13: build_model

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import ELU [as 别名]
def build_model(output_size):
    channel_axis = 3
    freq_axis = 1
    padding = 37

    input_shape = (img_height, img_width, channels)
    print('Building model...')

    model = Sequential()
    model.add(ZeroPadding2D(padding=(0, padding), data_format='channels_last', input_shape=input_shape))
    model.add(BatchNormalization(axis=freq_axis, name='bn_0_freq'))

    model.add(Conv2D(64, (3, 3), padding='same', name='conv1'))
    model.add(BatchNormalization(axis=channel_axis, name='bn1'))
    model.add(ELU())
    model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name='pool1'))
    model.add(Dropout(0.1, name='dropout1'))

    model.add(Conv2D(128, (3, 3), padding='same', name='conv2'))
    model.add(BatchNormalization(axis=channel_axis, name='bn2'))
    model.add(ELU())
    model.add(MaxPooling2D(pool_size=(3, 3), strides=(3, 3), name='pool2'))
    model.add(Dropout(0.1, name='dropout2'))

    model.add(Conv2D(128, (3, 3), padding='same', name='conv3'))
    model.add(BatchNormalization(axis=channel_axis, name='bn3'))
    model.add(ELU())
    model.add(MaxPooling2D(pool_size=(4, 4), strides=(4, 4), name='pool3'))
    model.add(Dropout(0.1, name='dropout3'))

    model.add(Conv2D(128, (3, 3), padding='same', name='conv4'))
    model.add(BatchNormalization(axis=channel_axis, name='bn4'))
    model.add(ELU())
    model.add(MaxPooling2D(pool_size=(4, 4), strides=(4, 4), name='pool4'))
    model.add(Dropout(0.1, name='dropout4'))

    model.add(Reshape(target_shape=(15, 128)))

    model.add(GRU(32, return_sequences=True, name='gru1'))
    model.add(GRU(32, return_sequences=False, name='gru2'))

    model.add(Dropout(0.3, name='dropout_final'))

    model.add(Dense(output_size, activation='softmax', name='output'))

    return model 
开发者ID:kristijanbartol,项目名称:Deep-Music-Tagger,代码行数:48,代码来源:train.py

示例14: buildModel

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import ELU [as 别名]
def buildModel(cameraFormat=(3, 480, 640)):
  """
  Build and return a CNN; details in the comments.
  The intent is a scaled down version of the model from "End to End Learning
  for Self-Driving Cars": https://arxiv.org/abs/1604.07316.

  Args:
    cameraFormat: (3-tuple) Ints to specify the input dimensions (color
        channels, rows, columns).
  Returns:
    A compiled Keras model.
  """
  print "Building model..."
  ch, row, col = cameraFormat

  model = Sequential()

  # Use a lambda layer to normalize the input data
  model.add(Lambda(
      lambda x: x/127.5 - 1.,
      input_shape=(ch, row, col),
      output_shape=(ch, row, col))
  )

  # Several convolutional layers, each followed by ELU activation
  # 8x8 convolution (kernel) with 4x4 stride over 16 output filters
  model.add(Convolution2D(16, 8, 8, subsample=(4, 4), border_mode="same"))
  model.add(ELU())
  # 5x5 convolution (kernel) with 2x2 stride over 32 output filters
  model.add(Convolution2D(32, 5, 5, subsample=(2, 2), border_mode="same"))
  model.add(ELU())
  # 5x5 convolution (kernel) with 2x2 stride over 64 output filters
  model.add(Convolution2D(64, 5, 5, subsample=(2, 2), border_mode="same"))
  # Flatten the input to the next layer
  model.add(Flatten())
  # Apply dropout to reduce overfitting
  model.add(Dropout(.2))
  model.add(ELU())
  # Fully connected layer
  model.add(Dense(512))
  # More dropout
  model.add(Dropout(.5))
  model.add(ELU())
  # Fully connected layer with one output dimension (representing the speed).
  model.add(Dense(1))

  # Adam optimizer is a standard, efficient SGD optimization method
  # Loss function is mean squared error, standard for regression problems
  model.compile(optimizer="adam", loss="mse")

  return model 
开发者ID:BoltzmannBrain,项目名称:self-driving,代码行数:53,代码来源:cnn_prediction.py


注:本文中的keras.layers.ELU属性示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版权归原作者所有,传播和使用请参考对应项目的License;未经允许,请勿转载。