deepctr.layers.utils module

Author:

Weichen Shen,wcshen1994@163.com

class deepctr.layers.utils.Add(**kwargs)

Bases: tensorflow.python.keras.engine.base_layer.Layer

build(input_shape)

Creates the variables of the layer.

call(inputs, **kwargs)

This is where the layer’s logic lives.

Arguments:

inputs: Input tensor, or list/tuple of input tensors. **kwargs: Additional keyword arguments.

Returns:

A tensor or list/tuple of tensors.

class deepctr.layers.utils.Hash(num_buckets, mask_zero=False, **kwargs)

Bases: tensorflow.python.keras.engine.base_layer.Layer

hash the input to [0,num_buckets) if mask_zero = True,0 or 0.0 will be set to 0,other value will be set in range[1,num_buckets)

build(input_shape)

Creates the variables of the layer.

call(x, mask=None, **kwargs)

This is where the layer’s logic lives.

Arguments:

inputs: Input tensor, or list/tuple of input tensors. **kwargs: Additional keyword arguments.

Returns:

A tensor or list/tuple of tensors.

compute_mask(inputs, mask)

Computes an output mask tensor.

Arguments:

inputs: Tensor or list of tensors. mask: Tensor or list of tensors.

Returns:

None or a tensor (or list of tensors,

one per output tensor of the layer).

get_config()

Returns the config of the layer.

A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.

The config of a layer does not include connectivity information, nor the layer class name. These are handled by Network (one layer of abstraction above).

Returns:

Python dictionary.

class deepctr.layers.utils.Linear(l2_reg=0.0, mode=0, use_bias=False, **kwargs)

Bases: tensorflow.python.keras.engine.base_layer.Layer

build(input_shape)

Creates the variables of the layer.

call(inputs, **kwargs)

This is where the layer’s logic lives.

Arguments:

inputs: Input tensor, or list/tuple of input tensors. **kwargs: Additional keyword arguments.

Returns:

A tensor or list/tuple of tensors.

compute_mask(inputs, mask)

Computes an output mask tensor.

Arguments:

inputs: Tensor or list of tensors. mask: Tensor or list of tensors.

Returns:

None or a tensor (or list of tensors,

one per output tensor of the layer).

compute_output_shape(input_shape)

Computes the output shape of the layer.

Assumes that the layer will be built to match that input shape provided.

Arguments:

input_shape: Shape tuple (tuple of integers)

or list of shape tuples (one per output tensor of the layer). Shape tuples can include None for free dimensions, instead of an integer.

Returns:

An input shape tuple.

get_config()

Returns the config of the layer.

A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.

The config of a layer does not include connectivity information, nor the layer class name. These are handled by Network (one layer of abstraction above).

Returns:

Python dictionary.

class deepctr.layers.utils.NoMask(**kwargs)

Bases: tensorflow.python.keras.engine.base_layer.Layer

build(input_shape)

Creates the variables of the layer.

call(x, mask=None, **kwargs)

This is where the layer’s logic lives.

Arguments:

inputs: Input tensor, or list/tuple of input tensors. **kwargs: Additional keyword arguments.

Returns:

A tensor or list/tuple of tensors.

compute_mask(inputs, mask)

Computes an output mask tensor.

Arguments:

inputs: Tensor or list of tensors. mask: Tensor or list of tensors.

Returns:

None or a tensor (or list of tensors,

one per output tensor of the layer).

deepctr.layers.utils.add_func(inputs)

deepctr.layers.utils.combined_dnn_input(sparse_embedding_list, dense_value_list)

deepctr.layers.utils.concat_func(inputs, axis=-1, mask=False)

deepctr.layers.utils.div(x, y, name=None)

deepctr.layers.utils.reduce_max(input_tensor, axis=None, keep_dims=False, name=None, reduction_indices=None)

deepctr.layers.utils.reduce_mean(input_tensor, axis=None, keep_dims=False, name=None, reduction_indices=None)

deepctr.layers.utils.reduce_sum(input_tensor, axis=None, keep_dims=False, name=None, reduction_indices=None)

deepctr.layers.utils.softmax(logits, dim=-1, name=None)