Python tensorflow.keras.layers.multiply() Examples

def spatial_squeeze_excite_block(input_tensor):
    """ Create a spatial squeeze-excite block

    Args:
        input_tensor: input Keras tensor

    Returns: a Keras tensor

    References
    -   [Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks](https://arxiv.org/abs/1803.02579)
    """

    se = Conv2D(1, (1, 1), activation='sigmoid', use_bias=False,
                kernel_initializer='he_normal')(input_tensor)

    x = multiply([input_tensor, se])
    return x 

def spatial_squeeze_excite_block(input):
    ''' Create a spatial squeeze-excite block

    Args:
        input: input tensor

    Returns: a keras tensor

    References
    -   [Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks](https://arxiv.org/abs/1803.02579)
    '''

    se = Conv2D(1, (1, 1), activation='sigmoid', use_bias=False,
                kernel_initializer='he_normal')

    x = multiply([input, se])
    return x 

def squeeze_excite_block(input, ratio=16):
    ''' Create a channel-wise squeeze-excite block
    Args:
        input: input tensor
        filters: number of output filters
    Returns: a keras tensor
    References
    -   [Squeeze and Excitation Networks](https://arxiv.org/abs/1709.01507)
    '''
    init = input
    filters = init._keras_shape[1]
    se_shape = (1, 1, filters)
    se = GlobalAveragePooling2D()(init)
    se = Reshape(se_shape)(se)
    se = Dense(filters // ratio, activation='relu', kernel_initializer='he_normal', use_bias=False)(se)
    se = Dense(filters, activation='sigmoid', kernel_initializer='he_normal', use_bias=False)(se)
    x = multiply([init, se])
    return x 

def squeeze_excite_block(input_tensor, ratio=16):
    """ Create a channel-wise squeeze-excite block

    Args:
        input_tensor: input Keras tensor
        ratio: number of output filters

    Returns: a Keras tensor

    References
    -   [Squeeze and Excitation Networks](https://arxiv.org/abs/1709.01507)
    """
    init = input_tensor
    channel_axis = 1 if K.image_data_format() == "channels_first" else -1
    filters = _tensor_shape(init)[channel_axis]
    se_shape = (1, 1, filters)

    se = GlobalAveragePooling2D()(init)
    se = Reshape(se_shape)(se)
    se = Dense(filters // ratio, activation='relu', kernel_initializer='he_normal', use_bias=False)(se)
    se = Dense(filters, activation='sigmoid', kernel_initializer='he_normal', use_bias=False)(se)

    if K.image_data_format() == 'channels_first':
        se = Permute((3, 1, 2))(se)

    x = multiply([init, se])
    return x 

def squeeze_excitation_layer(self, x, out_dim):
        '''
        SE module performs inter-channel weighting.
        '''
        squeeze = GlobalAveragePooling2D()(x)
        
        excitation = Dense(units=out_dim // self.ratio)(squeeze)
        excitation = self.activation(excitation)
        excitation = Dense(units=out_dim)(excitation)
        excitation = self.activation(excitation, 'sigmoid')
        excitation = Reshape((1,1,out_dim))(excitation)
        
        scale = multiply([x,excitation])
        
        return scale 

def squeeze_excite_block(input, ratio=16):
    ''' Create a channel-wise squeeze-excite block

    Args:
        input: input tensor
        filters: number of output filters

    Returns: a keras tensor

    References
    -   [Squeeze and Excitation Networks](https://arxiv.org/abs/1709.01507)
    '''
    init = input
    channel_axis = 1 if K.image_data_format() == "channels_first" else -1
    filters = init._keras_shape[channel_axis]
    se_shape = (1, 1, filters)

    se = GlobalAveragePooling2D()(init)
    se = Reshape(se_shape)(se)
    se = Dense(filters // ratio, activation='relu', kernel_initializer='he_normal', use_bias=False)(se)
    se = Dense(filters, activation='sigmoid', kernel_initializer='he_normal', use_bias=False)(se)

    if K.image_data_format() == 'channels_first':
        se = Permute((3, 1, 2))(se)

    x = multiply([init, se])
    return x 

def spatial_squeeze_excite_block(input):
    ''' Create a spatial squeeze-excite block
    Args:
        input: input tensor
    Returns: a keras tensor
    References
    -   [Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks](https://arxiv.org/abs/1803.02579)
    '''
    se = Conv2D(1, (1, 1), activation='sigmoid', use_bias=False, kernel_initializer='he_normal')
    x = multiply([input, se])
    return x 

def add_prior(input_model,
              prior_shape,
              name='prior_model',
              prefix=None,
              use_logp=True,
              final_pred_activation='softmax',
              add_prior_layer_reg=0):
    """
    Append post-prior layer to a given model
    """

    # naming
    model_name = name
    if prefix is None:
        prefix = model_name

    # prior input layer
    prior_input_name = '%s-input' % prefix
    prior_tensor = KL.Input(shape=prior_shape, name=prior_input_name)
    prior_tensor_input = prior_tensor
    like_tensor = input_model.output

    # operation varies depending on whether we log() prior or not.
    if use_logp:
        # name = '%s-log' % prefix
        # prior_tensor = KL.Lambda(_log_layer_wrap(add_prior_layer_reg), name=name)(prior_tensor)
        print("Breaking change: use_logp option now requires log input!", file=sys.stderr)
        merge_op = KL.add

    else:
        # using sigmoid to get the likelihood values between 0 and 1
        # note: they won't add up to 1.
        name = '%s_likelihood_sigmoid' % prefix
        like_tensor = KL.Activation('sigmoid', name=name)(like_tensor)
        merge_op = KL.multiply

    # merge the likelihood and prior layers into posterior layer
    name = '%s_posterior' % prefix
    post_tensor = merge_op([prior_tensor, like_tensor], name=name)

    # output prediction layer
    # we use a softmax to compute P(L_x|I) where x is each location
    pred_name = '%s_prediction' % prefix
    if final_pred_activation == 'softmax':
        assert use_logp, 'cannot do softmax when adding prior via P()'
        print("using final_pred_activation %s for %s" % (final_pred_activation, model_name))
        softmax_lambda_fcn = lambda x: tf.keras.activations.softmax(x, axis=-1)
        pred_tensor = KL.Lambda(softmax_lambda_fcn, name=pred_name)(post_tensor)

    else:
        pred_tensor = KL.Activation('linear', name=pred_name)(post_tensor)

    # create the model
    model_inputs = [*input_model.inputs, prior_tensor_input]
    model = Model(inputs=model_inputs, outputs=[pred_tensor], name=model_name)

    # compile
    return model